Hindawi Publishing CorporationScienticaVolume 2012 Article ID 283821 14 pageshttpdxdoiorg1060642012283821

Review ArticleDiabetes Technology Markers Monitoring Assessment andControl of Blood Glucose Fluctuations in Diabetes

Boris P Kovatchev

Department of Psychiatry and Neurobehavioral Sciences Department of Systems and Information EngineeringCenter for Diabetes Technology and University of Virginia Health System University of Virginia PO Box 400888Charlottesville VA 22908 USA

Correspondence should be addressed to Boris P Kovatchev borisvirginiaedu

Received 29 August 2012 Accepted 2 October 2012

Academic Editors G Da Silva Xavier E Hajduch M Hickey R Laybutt and A Pileggi

Copyright copy 2012 Boris P Kovatchev is is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

People with diabetes face a life-long optimization problem to maintain strict glycemic control without increasing their risk forhypoglycemia Since the discovery of insulin in 1921 the external regulation of diabetes by engineering means has became ahallmark of this optimization Diabetes technology has progressed remarkably over the past 50 yearsmdasha progress that includes thedevelopment of markers for diabetes control sophisticated monitoring techniques mathematical models assessment proceduresand control algorithms Continuous glucosemonitoring (CGM)was introduced in 1999 and has evolved frommeans for retroactivereview of blood glucose proles to versatile reliable devices which monitor the course of glucose uctuations in real time andprovide interactive feedback to the patient Technology integrating CGM with insulin pumps is now available opening the eldfor automated closed-loop control known as the articial pancreas Following a number of in-clinic trials the quest for a wearableambulatory articial pancreas is under way with a rst prototype tested in outpatient setting during the past year is paperdiscusses key milestones of diabetes technology development focusing on the progress in the past 10 years and on the articialpancreasmdashstill not a cure but arguably the most promising treatment of diabetes to date

1 Introduction

In health glucose metabolism is tightly controlled by ahormonal network including the gut the liver the pancreasand the brain to ensure stable fasting blood glucose (BG)levels and transient postprandial glucose uctuations Indiabetes this network control is disrupted by deciency orabsence of insulin secretion andor insulin resistance whichhas to be compensated by technological means Generallypeople with diabetes are classied into type 1 and themuch more prevalent type 2 diabetes accounting for 90ndash95of all cases Type 1 diabetes is characterized by absolutedeciency of insulin secretion resulting from autoimmuneresponse targeting the 120573120573-cells of the pancreas type 2 diabetesis triggered by a combination of resistance to insulin andinsufficient 120573120573-cell function [1]

For the 1900 years following the clinical introduction ofthe term diabetes (Aretaeus the Cappadocian 1st CenturyAD) diet was the only albeit unsuccessful treatment In the19th century it was understood that diabetes is a complex of

disorders characterized by a common nal element of hyper-glycemia (elevated blood sugar levels) With the discoveryof insulin in 1921 by Frederick Banting at the University ofToronto diabetes particularly type 1 was no longer a deathsentence For this breakthrough Banting and John Macleodwere awarded the Nobel Prize in physiology or medicinein 1923 To recognize the contributions of their colleaguesBanting shared his prize with Charles Best and Macleodshared his with J B Collip Insulin injections became thestandard treatment for type 1 diabetes and for many peoplewith type 2 diabetes e eld of diabetes technology wasborn

Forty years aer the discovery of insulinmdashin 1963mdashaninsulin pump delivering insulin and glucagon (to counteracthypoglycemia) was designed by Kadish [2] In 1969 therst portable blood glucose metermdashthe Ames Reectancemetermdashwas manufactured e rst commercial subcuta-neous insulin pumpmdashthe AutoSyringemdashwas introduced byDean Kamen in the 1970s and by the end of the decade the

2 Scientica

rst trials of continuous subcutaneous insulin infusion (CSII)were reported by Pickup et al in England [3] and Tamborlaneet al in theUSA [4] showing the feasibility of thisminimally-invasive mode of insulin replacement e next logical stepwas automated insulin delivery controlled by a mathematicalalgorithm mdasha method that became known as closed-loopcontrol or the ldquoarticial pancreasrdquo e articial pancreasidea can be traced back to the 1970s when the possibilityfor external blood glucose regulation was established bystudies using intravenous (iv) glucose measurement andiv infusion of glucose and insulin Five teams reported ivclosed-loop control results between 1974 and 1978 Albisseret al [5] Pfeiffer et al [6] Mirouze et al [7] Kraegenet al [8] and Shichiri et al [9] In 1977 one of these designs[6] resulted in the rst commercial devicemdashthe Biostator[10]mdasha large (refrigerator-sized) device that has been usedextensively for glucose-control research (Figure 1) Systemssuch as the Biostator have been employed in hospital settingsto maintain normoglycemia using negative (via insulin)and positive (via glucose or glucagon) control [11ndash13] Areview of methods for iv glucose control can be found in[14]

In 1979 another key element of the closed-loop con-trolmdashthe Minimal Model of Glucose Kineticsmdashwas intro-duced by Bergman and Cobelli [15] is and subsequentmathematical models serves as the ldquobrainrdquo behind themajor-ity of control algorithms used in contemporary articialpancreas systems Detailed description of the major earlyalgorithm designs can be found in [16ndash19] More workfollowed spanning a range of control techniques powered byphysiologic modeling and computer simulation [20ndash23]

e nal critical technology leap enabling minimallyinvasive closed-loop designs was made at the turn of the cen-tury with the introduction of continuous glucose monitoring(CGM [24ndash26])mdashan event that started the ongoing questfor wearable articial pancreas Figure 1 depicts the timelineof these events and the acceleration of diabetes technologydevelopment in the past two decades

2 Markers of Average Glycemia and BloodGlucose (BG) Fluctuations

21 Hemoglobin A1c (HbA1c) In the early 1990s the land-mark Diabetes Control and Complications Trial (DCCT[27ndash29]) and the StockholmDiabetes Intervention study [30]clearly indicated that intensive insulin treatment can reducethe long-term complications of type 1 diabetes In 1998the UK Prospective Diabetes Study group established thatintensive treatment with insulin or with oral medications tomaintain nearly normal levels of glycemia markedly reduceschronic complications in type 2 diabetes as well [31] HbA1cwas identied as the primary marker of long-term averageglucose control [32 33] and still remains the gold-standardassay reecting average glycemia widely accepted in researchas a primary outcome for virtually all studies of diabetestreatment and in the clinical practice as primary feedbackto the patient and the physician and a base for treatmentoptimization

22 Risk for Hypoglycemia However the DCCT also showedthat intensive treatment of diabetes can also increase therisk for severe hypoglycemia (low blood glucose that couldresult in stupor unconsciousness and even death [34]Indeed HbA1c has repeatedly been proven to be an inef-fective assessment of patientsrsquo risk for hypoglycemia eDCCT concluded that only about 8 of severe hypoglycemicepisodes could be predicted from known variables includingHbA1c [34] later this prediction was improved to 18 bya structural equation model using history of severe hypo-glycemia awareness and autonomic symptom score [35]In subsequent studies HbA1c has never been signicantlyassociated with severe hypoglycemia [36ndash39] Neverthelessthe physiological mechanisms of hypoglycemia were wellestablished by a number of studies that have investigatedthe relationships between intensive therapy hypoglycemiaunawareness and impaired counterregulation [40ndash43] andconcluded that recurrent hypoglycemia spirals into a ldquoviciouscyclerdquo known as hypoglycemia-associated autonomic failure(HAAF [44]) are observed primarily in type 1 and alsoin type 2 diabetes [45] e acute risk for hypoglycemiawas attributed to impairments in the systemic reactionto falling BG levels in health falling BG concentrationtriggers a sequence of responses beginning with attenuationof endogenous insulin production followed by increase inglucagon and epinephrine and if BG concentration fallsfurther resulting in autonomic symptoms andor neurogly-copenia in type 1 diabetes and to some extent in type 2diabetes these defense mechanisms are impaired [46ndash48]As a result hypoglycemia was identied as the primarybarrier to optimal diabetes control [49 50] e clinicaloptimization problem of diabetes was therefore clearly for-mulated reduce average glycemia and exposure to highblood glucose levels (thereby HbA1c) while preventinghypoglycemia

23 Glucose Variability (GV) Blood glucose variability isthe primary challenge to the success of this optimizationand is typically at the root of cliniciansrsquo inability to safelyachieve near-normal average glycemia as reected byHbA1c[51] Indeed in addition to establishing HbA1c as the goldstandard for average glycemic control the DCCT concludedthat ldquoHbA1c is not the most complete expression of thedegree of glycemia Other features of diabetic glucose controlwhich are not reected by HbA1c may add to or modify therisk of complications For example the risk of complicationsmay be highly dependent on the extent of postprandialglycemic excursionsrdquo [28] As noted above while targetHbA1c values of 7 or less result in decreased risk ofvascular complications [27 29 31 33] the risk for severehypoglycemia increases with tightening glycemic control [3448] At the high end of the BG scale a number of studies haveimplicated postprandial BG uctuation as an independentrisk factor for diabetes complications [52 53] particularlycardiovascular disease [54ndash57]us a strategy for achievingoptimal glucose control can only be successful if it reducesGV is is because bringing average glycemia down is onlypossible if GV is constrained otherwise BG uctuations

Scientica 3

First use of CSII Pickup et al Br Med J 1978 Tamborlaneet al NEJM 1979

The auto syringe (Dean Kamen)

Subcutaneous continuous glucose monitoring

MinimedCGMS1999

Backpack insulin and glucagon pump

Kadish 1964The Biostator

The minimal model of glucose kinetics

Bergman and Cobelli AJP 1979

Insulin discoveredFrederick Banting

Ames reflectance

meter

1960s 1970s 1990s1920s 1980s 2000s

Blood glucose meters and insulin pumps becoming smaller

Models of diabetes becoming larger and more complex

F 1 e Diabetes technology timeline from the discovery of insulin to the introduction of continuous glucose monitoring

would inevitably enter the range of hypoglycemia (see [51Figure 4])

24 Behavioral Triggers of Glucose Variability Formulatedfrom an engineering point of view the control of diabetesis driven by routine self-treatment behaviors which mayoccasionally evolve into hypo- or hyperglycemia-triggeringevents for example insulin mistiming bolusbasal imbal-ance missed meal or excessive exercise A formal math-ematical description of this process and its potential todestabilize the system was given by the Stochastic Modelof Self-Regulation Behavior which provided a probabilisticinterpretation of the event sequence internal condition rarrperceptionawareness rarr appraisal rarr self-regulation deci-sion [58ndash61] e parameters of this process are individualcontingent on behavioral interpretation for example ona personrsquos ability to control hisher BG within optimallimits e effect of this process is mediated by the specicsof a personrsquos metabolic system such as rate of glucoseappearance in the blood or insulin sensitivity e netresult from this biobehavioral interplay is a certain degreeof glucose variability which in turn could provide feedbackto the person regarding the effectiveness of hisher glycemiccontrol and could prompt corrective actions if neededfor example adjustment of insulin timing or bolusbasalratio

25 Physiological Mechanisms of GV Once triggered theprogression and the extent of glycemic excursions depend onindividual parameters of insulin transport insulin sensitivityand counterregulatory response Technologies utilizing sub-cutaneous insulin injection (eg CSII articial pancreas) relyon the transport of sc-injected insulin into the circulatione duration of this transport varies from person to personand is a major mechanism of postprandial GV because ofthe introduced delaysmdasha postprandial excursion has time todevelop due to insulin deciency (relative to health) in itsinitial stages even if a meal bolus is given on time us themodeling and the formal description of sc insulin transportis important for the effectiveness of modern diabetes controlstrategies [62 63] Aer entering the circulation the actionof insulin is determined by the dynamics of insulin-mediatedglucose utilizationmdasha process that has been mathematicallycharacterized by Bergman and Cobellirsquos classic MinimalModel which introduced the mathematical formulation ofinsulin sensitivity [15] a key metabolic parameter that hasbeen the subject of investigation of a number of subsequentstudies [64ndash70] It is nowwell known that insulin sensitivity isenhanced by exercise [71ndash74] methods exist for quantitativeassessment of insulin sensitivity in laboratory [64] and inoutpatient settings [67] including methods for assessmentduring physical activity [75] e processes of gastric emp-tying and glucose appearance in the blood are similarly well-quantied [76 77] It is apparent that a major source of GV

4 Scientica

is rapid onset of hyperglycemia due to consumption of ldquohighglycemic indexrdquo foods especially in large quantity For exam-ple foods with simple carbohydrate and high fat (classicallypizza) present challenges to technology and optimal therapyby resulting in sustained postprandial hyperglycemia

3 Monitoring of BG Fluctuations in Diabetes

31e Frequency of BGObservation Intuitively the aggres-siveness of glucose control in diabetes would depend on thefrequency of glucose measurement For example if only theaverage glycemic state of a patient is available once every fewmonths (as it would be with measurement of HbA1c alone)then control strategies could only target adjustment of long-term average glycemia but would not be able to respondto daily or hourly variation in glucose level Rapid BGchanges would remain largely unnoticed unless they led toacute complications such as severe hypoglycemia or diabeticketoacidosis us the frequency of glucose measurementdetermines to a large extent the aggressiveness of possibletreatments Table 1 presents the frequency and the temporalresolution of commonly used glucose assessment techniquesGenerally HbA1c reects long-term (over 2-3months) bloodglucose average thus the temporal resolution of HbA1c islimited to reect slow changes in average glycemia Self-monitoring of blood glucose (SMBG) is a standard practiceincluding several (eg 2ndash5) BG readings per day us thetemporal resolution of SMBG allows for assessment of dailyBG proles or weekly trends ith the advent of CGM itis now well accepted that BG uctuations are a process intime which has two principal components risk associatedwith the amplitude (variability) of BG changes and timeindicating the rate of event progression Contemporary CGMdevices are capable of producing BG determinations every5ndash10 minutes which provides vast amounts of data withhigh temporal resolution and allows for detailed monitoringof glucose uctuations on a temporal scale of minutesmdashafrequency that enables closed-loop control

32 Self-Monitoring of Blood Glucose Contemporary homeBG meters offer convenient means for frequent and accurateBG determinations through self-monitoring Most devicesare capable of storing BG readings (typically over 150readings) and have interfaces to download these readingsinto a computer e meters are usually accompanied bysoware that has capabilities for basic data analyses (egcalculation of mean BG estimates of the average BG overthe previous two weeks percentages in target hypoglycemicand hyperglycemic zones etc) and log of the data andgraphical representation (eg histograms pie charts) [78ndash81] Analytical methods based on SMBG data are discussedin the next section

33 Continuous Glucose Monitoring Since the advent ofCGM technology 10 years ago [25ndash27] signicant progresshas been made towards versatile and reliable CGM devicesthat not only monitor the entire course of BG day and nightbut also provide feedback to the patient such as alarms when

BG reaches preset low or high levels A number of studieshave documented the benets of CGM [82ndash85] and chartedguidelines for clinical use and its future as a precursor toclosed-loop control [86ndash89] However while CGM has thepotential to revolutionize the control of diabetes it also gen-erates data streams that are both voluminous and complexe utilization of such data requires an understanding ofthe physical biochemical and mathematical principles andproperties involved in this new technology It is important toknow that CGM devices measure glucose concentration in adifferent compartmentmdashthe interstitium Interstitial glucose(IG) uctuations are related to BG presumably via diffusionprocess [90ndash92] To account for the gradient between BGand IG CGM devices are calibrated with capillary glucosewhich brings the typically lower IG concentration to BGlevels Successful calibration would adjust the amplitude ofIG uctuations with respect to BG but would not eliminatethe possible time lag due to BG-to-IG glucose transport andthe sensor processing time (instrument delay) Because sucha time lag could greatly inuence the accuracy of CGM anumber of studies were dedicated to its investigation yieldingvarious results [93ndash96] For example it was hypothesized thatif a glucose fall is due to peripheral glucose consumptionthe physiologic time lag would be negative that is fall in IGwould precede fall in BG [90 97] In most studies IG laggedbehind BG (most of the time) by 4ndash10 minutes regardlessof the direction of BG change [92 93] e formulation ofthe push-pull phenomenon offered reconciliation of theseresults and provided arguments for a more complex BG-IG relationship than a simple constant or directional timelag [96 98] In addition errors from calibration loss ofsensitivity and random noise confound CGM data [99]Nevertheless the accuracy of CGM is increasing and maybe reaching a physiological limit for subcutaneous glucosemonitoring [100ndash103]

In addition to presenting frequent data (eg every 5ndash10minutes) CGM devices typically display directional trendsand BG rate of change and are capable of alerting the patientof upcoming hypo- or hyperglycemia ese features arebased on methods which predict blood glucose and generatealarms and warning messages In the past several yearsthese methods have rapidly evolved from a concept [104] toimplementation in CGM devices such as the Guardian RTand the MiniMed Paradigm REAL-Time System (MedtronicNorhtridge CA USA) [105] and the Freestyle Navigator(Abbott Diabetes Care Alameda CA USA) [106] Alarmsfor particularly rapid rates of BG change (eg greater than2mgdLmin) are available as well (Guardian RT and Dex-com Seven Plus Dexcom San Diego CA USA) Discussionof the methods for testing of the accuracy and the utility ofsuch alarms has been initiated [106ndash108] and the next logicalstepmdashprevention of hypoglycemia via shutoff of the insulinpumpmdashhas been undertaken [109]

4 Assessment of BG Fluctuations in Diabetes

As presented in Table 1 different frequencies of BGmonitor-ing provide data for different types of analytical techniques

Scientica 5

T 1 Frequency of available glucose monitoring technologies

Measure Temporal resolution Reects Methods typically used to present and analyze the data

HbA1c Monthsyears Slow changes in average BG Direct assay and review of values group comparisonswhen treatments are evaluated

Self-monitoring of bloodglucose (SMBG) Daysweeks Daily variation

weekly trends

Mean and standard deviation (SD) coefficient ofvariation (CV) Interquartile range (IQR)119872119872-value(1965) MAGE (1970)lability index (2004)lowhigh BG (risk) Indices (1998)average daily risk range (ADRR 2006)

Continuous glucosemonitoring (CGM) Minuteshours System dynamics

uctuation and periodicity

CONGA (2005)glucose rate of Change amp CGM versions of lowhighBG (risk) indices (2005)time seriesdynamical system analysis

assessing the glycemic state or the BG dynamics of aperson with diabetes A brief account of analytical methodsapplicable to SMBG andor CGM data is given below Someof these methods such as the Risk Analysis of BG data haveentered the design of closed-loop control systems preventinghypoglycemia [110]

41 SMBG-Based Analytical Methods e computation ofmean glucose values from SMBG data is typically used as adescriptor of overall glycemic control Computing pre- andpostmeal averages and their difference can serve as an indica-tion of the effectiveness of premeal bolus timing and amountSimilarly the percentages of SMBG readings within belowor above preset target limits would serve as indication of thegeneral behavior of BG uctuations e suggested limits are70 and 180mgdL (39ndash10mmoll) which create three sug-gested by the DCCT and commonly accepted bands hypo-glycemia (BG le 70mgdL) normoglycemia (70mgdL ltBG le 180mgdL) hyperglycemia (BG gt 180mgdL) [1]Percentage of time within additional bands can be computedas well to emphasize the frequency of extreme glucoseexcursions Computing standard deviation (SD) as ameasureof glucose variability is not recommended because the BGmeasurement scale is highly asymmetric the hypoglycemicrange is numerically narrower than the hyperglycemic rangeand the distribution of the glucose values of an individualis typically quite skewed [111] erefore SD would bepredominantly inuenced by hyperglycemic excursions andwould not be sensitive to hypoglycemia It is also possiblefor condence intervals based on SD to assume unrealisticnegative valuesus as a standard measure of GV we wouldsuggest reporting interquartile range (IQR) which is suitablefor asymmetric distributions Several diabetes-specic met-rics are also available to serve the analysis of SMBG dataincluding the mean amplitude of glucose excursions (MAGE[112]) the 119872119872-value [113] the lability index [114] and thelow and high blood glucose indices (LBGI HBGI) whichreect the risks associated with hypo- and hyperglycemiarespectively [37 115] In a series of studies we have shownthat specic risk analysis of SMBG data could also capturelong-term trends towards increased risk for hypoglycemia

[36ndash38] and could identify 24-hour periods of increased riskfor hypoglycemia [39 116]

42 Risk Analysis of BG Data To provide a avor for theanalytical techniques used for SMBG CGM and closed-loopcontrol data we will present a bit more detail on the conceptfor risk analysis of BG data [117] e risk analysis steps areas follows

421 Symmetrization of the BG Scale A nonlinear transfor-mation is applied to the BG measurements scale to map theentire BG range (20 to 600mgdL or 11 to 333mmoll) to asymmetric intervalis is needed because the distribution ofBG values of a person with diabetes is asymmetric typicallyskewed towards hyperglycemiae BG value of 1125mgdL(625mmoll) is mapped to zero corresponding to zero riskfor hypo- or hyperglycemia (we should note that this is nota normoglycemic or fasting value which in health wouldbe lt100mgdL it is zero-risk value pertinent to diabetes)e analytical form of this transformation is 119891119891119891BG) = 120574120574 120574119891ln 119891BG)120572120572 minus 120573120573) where the parameters are estimated as 120572120572 =1084 120573120573 = 12057312057381 and 120574120574 = 11205730120574 if BG is measured in mgdLand 120572120572 = 10120572120572 120573120573 = 181205721 and 120574120574 = 11205741205744 if BG is measuredin mmoll [111]

422 Assignment of a Risk Value to Each BG Reading Aquadratic risk function is dened as by the formula 119903119903119891BG) =10 120574 119891119891119891BG)120572 e function 119903119903119891BG) ranges from 0 to 100Its minimum value is achieved at BG = 1125mgdL asafe euglycemic BG reading while its maximum is reachedat the extreme ends of the BG scale us 119903119903119891BG) can beinterpreted as a measure of the risk associated with a certainBG level e le branch of this parabola identies the riskof hypoglycemia while the right branch identies the risk ofhyperglycemia

423 Computing Measures of Risk for Hypoglycemia Hyper-glycemia and Glucose Variability Let 1199091199091 119909119909120572hellip 119909119909119899119899 be aseries of 119899119899 BG readings and let 119903119903119903119903119891BG) = 119903119903119891BG) if 119891119891119891BG) lt 0and 0 otherwise 119903119903119903119891BG) = 119903119903119891BG) if 119891119891119891BG) gt 0 and 0

6 Scientica

otherwise en the low and high blood glucose indices arecomputed as follows

LBGI = 1119899119899

11989911989910055761005576119894119894=111990311990311990311990310076491007649119909119909119894119894100766510076652 HBGI = 1

119899119899

11989911989910055761005576119894119894=11199031199031199031007649100764911990911990911989411989410076651007665

2 (1)

us the LBGI is a nonnegative quantity that increaseswhen the number andor extent of low BG readings increasesand the HBGI is nonnegative quantity that increases whenthe number andor extent of high BG readings increasesBased on this same technique we also dene the average dailyrisk range (ADRR) which is a measure of risks associatedwith overall glycemic variability [118] In studies the LBGItypically accounted for 40ndash55 of the variance of futuresignicant hypoglycemia in the subsequent 3ndash6 months [36ndash38] which made it a potent predictor of hypoglycemia basedon SMBGeADRR has been shown superior to traditionalglucose variability measures in terms of risk assessment andprediction of extreme glycemic excursions [118] Specicallyit has been demonstrated that classication of risk for hypo-glycemia based on four ADRR categories low risk ADRR lt20 low-moderate risk 20 le ADRR lt 30 moderate-high risk30 le ADRR lt 40 and high risk ADRR gt 40 resulted inmore than a sixfold increase in risk for hypoglycemia fromthe lowest to the highest risk category [118] In addition thelow and high BG indices have been adapted to continuousmonitoring data [119] and can be used in the same way aswith SMBG to assess the risk for hypo- or hyperglycemia

43 CGM-Based Analytical Methods While traditional risk[119] and variability [120] analyses are still applied to CGMdata the high temporal resolution of CGM brought aboutthe possibility for use of sophisticated analytical methodsassessing system (person) dynamics on the time scale of min-utes is necessitated the development of new technologiesfor data analysis and visualization that are not available forSMBG data Analysis of the BG rate of change (measuredin mgdLmin) is a way to evaluate the dynamics of BGuctuations on the time scale of minutes e BG rate ofchange at 119905119905119894119894mdashis computed as the ratio (BG(119905119905119894119894)minusBG(119905119905119894119894minus1))(119905119905119894119894minus119905119905119894119894minus1) where BG(119905119905119894119894) and BG(119905119905119894119894minus1) are CGM readings taken attimes 119905119905119894119894 and 119905119905119894119894minus1 for example minutes apart Investigationof the frequency of glucose uctuations showed that optimalevaluation of the BG rate of change would be achieved overtime periods of 15minutes [121] for exampleΔ119905119905 = 119905119905119894119894 minus119905119905119894119894minus1 =15 A large variation of the BG rate of change indicates rapidand more pronounced BG uctuations and therefore a lessstable system us the standard deviation of the BG rateof change is a measure of stability of glucose uctuation (weshould note that as opposed to the distribution of BG levelsthe distribution of the BG rate of change is symmetric andtherefore using SD is statistically accurate [122]) e SDof BG rate of change has been introduced as a measure ofstability computed fromCGMdata and is known asCONGAof order 1 In general CONGA122 of order 119899119899 is computedas the standard deviation of CGM readings that are 119899119899 hoursapart reecting glucose stability over these time intervals[123]

Most important to the development of the articial pan-creas algorithms is a class of methods allowing the predictionof BG values ahead in time ese methods typically basedon time-series techniques have been applied successfully ina number of studies [124ndash129] In addition to time seriesneural networks have been used for the prediction of glucoselevels from CGM [130 131] Detailed reviews of CGM dataanalysis methods are presented in [122] including severalgraphs that could be used for the visualization of the rathercomplex CGM data sets and in [132] where a broad reviewof modeling analytical and control techniques for diabetesis provided

5 Control of BG Fluctuations in Diabetes

51 Intraperitoneal Insulin Delivery As detailed in the Intro-duction the articial pancreas idea can be traced back to theearly 70s when external BG regulation in people with dia-betes was achieved by iv glucose measurement and iv infu-sion of glucose and insulin However the intravenous routeof closed-loop control remains cumbersome and unsuitedfor outpatient use An alternative has been presented byimplantable intraperitoneal (ip) systems employing iv sam-pling and ip insulin delivery [133ndash136] e ip infusionroute has several desirable characteristics reproducibility ofinsulin absorption quick time to peak and return to baselineof insulin action near-physiological peripheral insulin levelsand restoration of glucagon response to hypoglycemia andexercise [133 137ndash139] However while ip systems haveachieved excellent BG control their implementation stillrequires considerable surgery and is associated with signi-cant cost Nevertheless the development of less invasive andcheaper implantable ports (eg DiaPort Roche DiagnosticsMannheim Germany) may contribute to the future prolifer-ation of ip insulin delivery [140ndash142]

52e Subcutaneous Route to Closed-Loop Control Follow-ing the progress of minimally invasive subcutaneous CGMthe next logical step was the development of sc closed-loopglucose control which links a CGM device with CSII insulinpump A key element of this combination was a controlalgorithm which monitors BG uctuations and the actionsof the insulin pump and computes insulin delivery rate everyfew minutes [143] Figure 2 presents key milestones in thetimeline of this development

Following the pioneering work of Hovorka et al [144145] and Steil et al [146] in 2006 the Juvenile DiabetesResearch Foundation (JDRF) initiated the Articial PancreasProject and funded several centers in the USA and Europeto carry closed-loop control research [147] In 2008 theUSA National Institutes of Health launched an articialpancreas initiative and in 2010 the European APHomeconsortium was established By the end of the rst decade ofthis century the articial pancreas became a global researchtopic engaging physicians and engineers in unprecedentedcollaboration [148 149]

Scientica 7

2006 2008 2010

The JDRF artificial pancreas consortium

is launched (Kowalski)

Studies of hybrid closed-loop control (Weinzimer

and Tamborlane)

First human trials begin using a system designed entirely in silico UVA

Italy and France(Kovatchev Cobelli Renard)

NIH funds artificial pancreas

EU launches the APHome

artificial pancreas initiative

JDRF multicenter trialof modular control-to-

range

2004 the ADICOLproject

(Hovorka)

First studies of automated sc

closed loop (Steil)

FDA accepts the UVAPadova metabolic simulator as a substitute

to animal trials(Kovatchev Cobelli Dalla

Man and Breton)

Modular control-to-rangeintroduced trials at UVA

Italy and France(Kovatchev

Cobelli and Renard)

The APS introduced(Dassau Doyle

First studies of outpatient closed-loop

control (CobelliRenard Zisser and

Kovatchev )

2012

DiAs first portableAP platform

(Keith-HynesKovatchev)

studies Zisser)

30

2012

F 2 Timeline of the articial pancreas developments in the last decademdashtheoretical work and a number of in-clinic studies leading tothe rst trials of wearable articial pancreas device

53 In Silico Models of the Human Metabolic System Acritical step towards accelerated clinical progress of thearticial pancreas was the development of sophisticatedcomputer simulator of the human metabolic system allowingrapid in silico testing of closed-loop control algorithmsis simulation environment was based on the previouslyintroduced Meal Model of glucose-insulin dynamics [76 77]and was equipped with a ldquopopulationrdquo of in silico imagesof 119873119873 119873 119873119873119873 ldquosubjectsrdquo with type 1 diabetes separated inthree age groups 119873119873 119873 119873119873119873 simulated ldquochildrenrdquo below theage of 11 119873119873 119873 119873119873119873 ldquoadolescentsrdquo 12ndash18 years old and119873119873 119873 119873119873119873 ldquoadultsrdquo e characteristics of these ldquosubjectsrdquo(eg weight daily insulin dose carbohydrate ratio etc)were tailored to span a wide range of intersubject variabilityapproximating the variability observed in people in vivo[150] Simulation experiments allow any CGM device anyinsulin pump and any control algorithm to be linked in aclosed-loop system in silico prior to their use in clinical trialsWith this technology any meal and insulin delivery scenariocan be pilot-tested very efficientlymdasha 24-hour period ofclosed-loop control is simulated in under 2 secondsWe needto emphasize however that good in silico performance of acontrol algorithmdoes not guarantee in vivo performancemdashitonly helps test extreme situations and the stability of thealgorithm and rule out inefficient scenarios us computersimulation is only a prerequisite to but not a substitute forclinical trials

In January 2008 in an unprecedented decision theUSA Food and Drug Administration accepted this computer

simulator as a substitute to animal trials for the testing ofclosed-loop control strategies is opened the eld for effi-cient and cost-effective in silico experiments leading directlyto human studies Only three months later in April 2008 therst human trials began at the University of irginia (USA)Montpellier (France) and Padova (Italy) using a controlsystem designed entirely in silico [151]

54 Control System Designs e rst studies of ovorkaet al [144 145] and Steil et al [146] outlined the twomajor types of closed-loop control algorithms now in use inarticial pancreas systemsmdashmodel-predictive control (MPC[145]) and proportional-integral-derivative (PID [146])respectively By 2007 the blueprints of the contemporarycontrollers were in place including run-to-run control[152ndash154] and linear MPC [155] To date the trials ofsubcutaneous closed-loop control systems have been usingeither PID [146 156] or MPC [157ndash160] but MPC becamethe approach of choice targeted by recent research erewere two important reasons making MPC preferable (i)PID is purely reactive responding to changes in glucoselevel while a properly tuned MPC allows for prediction ofglucose dynamics and as a result for mitigation of the timedelays inherent with subcutaneous glucose monitoring andsubcutaneous insulin infusion [62 63] (ii) MPC allows forrelatively straightforward personalizing of the control usingpatient-specic model parameters In addition MPC couldhave ldquolearningrdquo capabilitiesmdashit has been shown that a class

8 Scientica

of algorithms (known as run-to-run control) can ldquolearnrdquospecics of patientsrsquo daily routine (eg timing of meals) andthen optimize the response to a subsequent meal using thisinformation or account for circadian uctuation in insulinresistance (eg dawn phenomenon observed in some people)[149]

In 2008 a universal research platformmdashthe APSmdashwasintroduced enabling automated communication betweenseveral CGM devices insulin pumps and control algorithms[161] e APS was very instrumental for a number ofinpatient trials of closed-loop control A year later a mod-ular architecture was introduced proposing standardizationsequential testing and clinical deployment of articial pan-creas components [162]

55 Inpatient Clinical Trials Between 2008 and 2011 prom-ising results were reported by several groups [156ndash160 163ndash167] Most of these studies pointed out the superiority ofclosed-loop control over standard CSII therapy in termsof (i) increased time within target glucose range (typically39ndash10mmoll) (ii) reduced incidence of hypoglycemia and(iii) better overnight control Two of these studies [159166] had state-of-the-art randomized cross-over design butlacked automated data transfermdashall CGM readings weretransferred to the controllermanually by the study personneland all insulin pump commands were entered manually aswell To distinguish the various degrees of automation inclosed-loop studies the notion of fully-integrated closed-loop control emerged dened as having all of the followingthree components (i) automated data transfer from theCGM to the controller (ii) real-time control action and (iii)automated command of the insulin pump e rst (andthe largest to date) randomized cross-over study of fully-integrated closed-loop control was published in 2012 [168]However even this contemporary trial of fully automatedCLC which enrolled 38 patients with T1D at three centersand tested two different control algorithms achieving note-worthy glycemic control and prevention of hypoglycemia didnot leave the clinical setting e technology used by thisstudy was still based on a laptop computer wired to a CGMand an insulin pump a system limiting the free movementof the study subjects and too cumbersome to be used beyondhospital connes

5 earale tpatient rticial ancreas e transitionof closed-loop control to ambulatory use began in 2011 withthe development of the Diabetes Assistant (DiAs)mdashthe rstwearable articial pancreas platform based on a smart phonee design characteristics of DiAs included the following

(i) based on readily available inexpensive wearablehardware platform

(ii) computationally capable of running advanced closed-loop control algorithms

(iii) wirelessly connectable to CGM devices and insulinpumps

(iv) capable of broadband communication with a centrallocation for remote monitoring and safety supervi-sion of the participants in outpatient clinical trials

In ctober 2011 the rst two pilot trials of wearableoutpatient articial pancreas were performed simultaneouslyin Padova (Italy) and Montpellier (France) [169] ese 2-day trials allowed the renement of a wearable system andenabled a subsequentmultisite feasibility study of ambulatoryarticial pancreas which was completed recently at theUniversities of Virginia Padova and Montpellier and at theSansumDiabetes Research institute Santa Barbara CAUSAResults from this study are forthcoming

6 Conclusions

Solving the optimization problem of diabetes requiresreplacement of insulin action through insulin injections ororal medications (applicable primarily to type 2 diabetes)which until fully automated closed-loop control becomesavailable would remain a process largely controlled bypatient behavior In engineering terms BG uctuations indiabetes result from the activity of a complex metabolicsystem perturbed by behavioral challenges e frequencyand extent of these challenges and the ability of the personrsquosmetabolic system to absorb them determines the qualityof glycemic control Along with HbA1c the magnitudeand speed of BG uctuations is the primary measurablemarker of glucose control in diabetes ese same quanti-tiesmdashHbA1c and glucose variabilitymdashare also the principalfeedback available to patients to assist with optimization oftheir diabetes control

In the past 30 years the technology for monitoring ofblood glucose levels in diabetes has progressed from assess-ment of average glycemia via HbA1c once in several monthsthrough daily SMBG to minutely continuous glucose mon-itoring e increasing temporal resolution of the moni-toring technology enabled increasingly intensive diabetestreatment from daily insulin injections or oral medicationthrough insulin pump therapy to the articial pancreasis progress is accompanied by increasingly sophisticatedanalytical methods for retrieval of blood glucose data rangingfrom subjective interpretation of glucose values and straight-forward summary statistics through risk and variabilityanalysis to real-time closed-loop control algorithms based oncomplex models of the human metabolism

It is therefore evident that the development of diabetestechnology is accelerating exponentially A primary cata-lyst of this acceleration is unprecedented interdisciplinarycollaboration between physicians chemists engineers andmathematicians As a result a wearable articial pancreassuitable for outpatient use is now within reach

e primary engineering challenges to the widespreadadoption of closed-loop control as a viable therapeutic optionfor diabetes include system connectivity the accuracy ofsubcutaneous glucose sensing and the speed of action ofsubcutaneously injected insulin ese challenges are well

Scientica 9

understood by those working in the eld wireless commu-nication between CGM devices insulin pumps and closed-loop controllers are under development and testing newgenerations of CGM device demonstrate superior accuracyand reliability and new insulin analogs and methods forinsulin delivery are being engineered to approximate asclose as possible the action prole of endogenous insulinIt should be noted however that the signals available toa contemporary closed-loop control system are generallylimited to CGM and insulin delivery data user input aboutcarbohydrate intake and physical activity could be availableas well In contrast the endocrine pancreas receives directand rapid control inputs from other nutrients (eg lipids andamino acids) adjacent cells (somatostatin from the delta cellsand glucagon from alpha cells) incretins and neural signalsus while articial closed-loop control is expected to bevastly superior to the diabetes control methods employed inthe clinical practice today it will continue to be imperfectwhen compared to the natural endocrine regulation of bloodglucose

Acknowledgments

is work was made possible by the JDRF Articial PancreasProject the National Institutes of HealthNIDDK GrantsRO1 DK 51562 and RO1 DK 085623 and by the generoussupport of PBM Science Charlottesville Virginia CA USAand the Frederick Banting Foundation Richmond VirginiaCA USA e author thanks his colleagues at the Universityof Virginia Center forDiabetes Technology for their relentlesswork on articial pancreas development

References

[1] American Diabetes Association ldquoDiagnosis and classicationof diabetes mellitusrdquo Diabetes Care vol 27 pp s5ndashs10 2004

[2] A H Kadish ldquoAutomation control of blood sugarmdashI A ser-vomechanism for glucose monitoring and controlrdquo AmericanJournal of Medical Electronics vol 39 pp 82ndash86 1964

[3] J C Pickup H Keen J A Parsons and K G M M AlbertildquoContinuous subcutaneous insulin infusion an approach toachieving normoglycaemiardquo British Medical Journal vol 1 no6107 pp 204ndash207 1978

[4] W V Tamborlane R S Sherwin M Genel and P FeligldquoReduction to normal of plasma glucose in juvenile diabetes bysubcutaneous administration of insulinwith a portable infusionpumprdquo New England Journal of Medicine vol 300 no 11 pp573ndash578 1979

[5] A M Albisser B S Leibel and T G Ewart ldquoAn articialendocrine pancreasrdquoDiabetes vol 23 no 5 pp 389ndash396 1974

[6] E F Pfeier um Ch and A H Clemens ldquoe articialbeta cell a continuous control of blood sugar by external regu-lation of insulin infusion (glucose controlled insulin infusionsystem)rdquo Hormone and Metabolic Research vol 6 no 5 pp339ndash342 1974

[7] J Mirouze J L Selam T C Pham and D Cavadore ldquoEvalua-tion of exogenous insulin homoeostasis by the artical pancreasin insulin dependent diabetesrdquo Diabetologia vol 13 no 3 pp273ndash278 1977

[8] E W Kraegen L V Campbell and Y O Chia ldquoControlof blood glucose in diabetics using an articial pancreasrdquoAustralian and New Zealand Journal of Medicine vol 7 no 3pp 280ndash286 1977

[9] M Shichiri R Kawamori Y Yamasaki M Inoue Y Shigetaand H Abe ldquoComputer algorithm for the articial pancreaticbeta cellrdquo Articial rgans vol 2 supplement pp 247ndash2501978

[10] A H Clemens P H Chang and R W Myers ldquoe devel-opment of Biostator a Glucose Controlled Insulin InfusionSystem (GCIIS)rdquo Hormone and Metabolic Research vol 7 pp23ndash33 1977

[11] E B Marliss F T Murray and E F Stokes ldquoNormalization ofglycemia in diabetics during meals with insulin and glucagondelivery by the articial pancreasrdquo Diabetes vol 26 no 7 pp663ndash672 1977

[12] J V Santiago A H Clemens W L Clarke and D M KipnisldquoClosed-loop and open-loop devices for blood glucose controlin normal and diabetic subjectsrdquo Diabetes vol 28 no 1 pp71ndash84 1979

[13] U Fischer E Jutzi E J Freyse and E Salzsieder ldquoDerivationand experimental proof of a new algorithm for the articial B-cell based on the individual analysis of the physiological insulin-glucose relationshiprdquo Endokrinologie vol 71 no 1 pp 65ndash751978

[14] R S Parker F J Doyle and N A Peppas ldquoe intravenousroute to blood glucose control a review of control algorithmsfor noninvasive monitoring and regulation in type I diabeticpatientsrdquo IEEE Engineering in Medicine and Biology Magazinevol 20 no 1 pp 65ndash73 2001

[15] R N Bergman Y Z Ider C R Bowden and C CobellildquoQuantitative estimation of insulin sensitivityrdquo e AmericanJournal of Physiology vol 236 no 6 pp E667ndashE677 1979

[16] H M Broekhuyse J D Nelson B Zinman and A M AlbisserldquoComparison of algorithms for the closed-loop control of bloodglucose using the articial beta cellrdquo IEEE Transactions onBiomedical Engineering vol 28 no 10 pp 678ndash687 1981

[17] A H Clemens ldquoFeedback control dynamics for glucosecontrolled insulin infusion systemrdquo Medical Progress throughTechnology vol 6 no 3 pp 91ndash98 1979

[18] C Cobelli and A Ruggeri ldquoEvaluation of portalperipheralroute and of algorithms for insulin delivery in the closed-loop control of glucose in diabetes a modeling studyrdquo IEEETransactions on Biomedical Engineering vol 30 no 2 pp93ndash103 1983

[19] E Salzsieder G Albrecht U Fischer and E J Freyse ldquoKineticmodeling of the glucoregulatory system to improve insulintherapyrdquo IEEE Transactions on Biomedical Engineering vol 32no 10 pp 846ndash855 1985

[20] P Brunetti C Cobelli P Cruciani et al ldquoA simulation study ona self-tuning portable controller of blood glucoserdquo InternationalJournal of Articial rgans vol 16 no 1 pp 51ndash57 1993

[21] U Fischer W Schenk E Salzsieder G Albrecht P Abel andE J Freyse ldquoDoes physiological blood glucose control requirean adaptive control strategyrdquo IEEE Transactions on BiomedicalEngineering vol 34 no 8 pp 575ndash582 1987

[22] J T Sorensen A physiologic model of glucose metabolism inman and its use to design and assess improved insulin therapiesfor diabetes [PhD dissertation] Department of Chemical Engi-neering MIT 1985

[23] R S Parker F J Doyle and N A Peppas ldquoA model-basedalgorithm for blood glucose control in type I diabetic patientsrdquo

10 Scientica

IEEE Transactions on Biomedical Engineering vol 46 no 2 pp148ndash157 1999

[24] J J Mastrototaro ldquoe MiniMed continuous glucose monitor-ing Systemrdquo Diabetes Technology anderapeutics vol 2 no 1pp S13ndashS18 2000

[25] B W Bode ldquoClinical utility of the continuous glucose monitor-ing systemrdquo Diabetes Technology anderapeutics vol 2 no 1supplement pp S35ndashS41 2000

[26] B Feldman R Brazg S Schwartz and R Weinstein ldquoAcontinuous glucose sensor based on wired enzyme technol-ogymdashresults from a 3-day trial in patients with type 1 diabetesrdquoDiabetes Technology anderapeutics vol 5 no 5 pp 769ndash7792003

[27] eDiabetes Control and Complications Trial Research Groupldquoe effect of intensive treatment of diabetes on the develop-ment and progression of long-term complications of insulin-dependent diabetes mellitusrdquoNew England Journal of Medicinevol 329 pp 978ndash986 1993

[28] eDiabetes Control and Complications Trial Research Groupldquoe relationship of glycemic exposure (HbA1c) to the riskof development and progression of retinopathy in the Dia-betes Control and Complications Trialrdquo Diabetes vol 44 pp968ndash983 1995

[29] J M Lachin S Genuth D M Nathan B Zinman and B NRutledge ldquoEffect of glycemic exposure on the risk of microvas-cular complications in the diabetes control and complicationstrial-revisitedrdquo Diabetes vol 57 no 4 pp 995ndash1001 2008

[30] P Reichard and M Pihl ldquoMortality and treatment side-effectsduring long-term intensied conventional insulin treatment inthe Stockholm Diabetes Intervention Studyrdquo Diabetes vol 43no 2 pp 313ndash317 1994

[31] UK Prospective Diabetes Study Group (UKPDS) ldquoIntensiveblood-glucose control with sulphonylureas or insulin comparedwith conventional treatment and risk of complications inpatients with type 2 diabetes (UKPDS 33)rdquo Lancet vol 352 no9131 pp 837ndash853 1998

[32] P A Svendsen T Lauritzen U Soegaard and J NerupldquoGlycosylated haemoglobin and steady-state mean blood glu-cose concentration in type 1 (insulin-dependent) diabetesrdquoDiabetologia vol 23 no 5 pp 403ndash405 1982

[33] J V Santiago ldquoLessons from the diabetes control and compli-cations trialrdquo Diabetes vol 42 no 11 pp 1549ndash1554 1993

[34] eDiabetes Control and Complications Trial Research GroupldquoHypoglycemia in the diabetes control and complications trialrdquoDiabetes vol 46 pp 271ndash286 1997

[35] A E Gold BM Frier KMMacLeod and I J Deary ldquoA struc-tural equation model for predictors of severe hypoglycaemiain patients with insulin-dependent diabetes mellitusrdquo DiabeticMedicine vol 14 pp 309ndash315 1997

[36] D J Cox B P Kovatchev D M Julian et al ldquoFrequency ofsevere hypoglycemia in insulin-dependent diabetesmellitus canbe predicted from self-monitoring blood glucose datardquo Journalof Clinical Endocrinology and Metabolism vol 79 no 6 pp1659ndash1662 1994

[37] B P Kovatchev D J Cox L A Gonder-Frederick D Young-Hyman D Schlundt and W Clarke ldquoAssessment of risk forsevere hypoglycemia among adults with IDDM validation ofthe low blood glucose indexrdquo Diabetes Care vol 21 no 11 pp1870ndash1875 1998

[38] B P Kovatchev D J Cox A Kumar L Gonder-Frederick andW L Clarke ldquoAlgorithmic evaluation of metabolic control and

risk of severe hypoglycemia in type 1 and type 2 diabetes usingself-monitoring blood glucose datardquo Diabetes Technology anderapeutics vol 5 no 5 pp 817ndash828 2003

[39] D J Cox L Gonder-Frederick L Ritterband W Clarke andB P Kovatchev ldquoPrediction of severe hypoglycemiardquo DiabetesCare vol 30 no 6 pp 1370ndash1373 2007

[40] S A Amiel R S Sherwin D C Simonson and W VTamborlane ldquoEffect of intensive insulin therapy on glycemicthresholds for counterregulatory hormone releaserdquo Diabetesvol 37 no 7 pp 901ndash907 1988

[41] S A Amiel W V Tamborlane D C Simonson and RS Sherwin ldquoDefective glucose counterregulation aer strictglycemic control of insulin-dependent diabetes mellitusrdquo NewEngland Journal of Medicine vol 316 no 22 pp 1376ndash13831987

[42] P E Cryer and J E Gerich ldquoGlucose counterregulation hypo-glycemia and intensive insulin therapy in diabetes mellitusrdquoNew England Journal of Medicine vol 313 no 4 pp 232ndash2411985

[43] N H White D A Skor and P E Cryer ldquoIdentication of typeI diabetic patients at increased risk for hypoglycemia duringintensive therapyrdquo New England Journal of Medicine vol 308no 9 pp 485ndash491 1983

[44] P E Cryer ldquoIatrogenic hypoglycemia as a cause ofhypoglycemia-associated autonomic failure in IDDM avicious cyclerdquo Diabetes vol 41 no 3 pp 255ndash260 1992

[45] J N Henderson K V Allen I J Deary and B M FrierldquoHypoglycaemia in insulin-treated Type 2 diabetes frequencysymptoms and impaired awarenessrdquo Diabetic Medicine vol 20no 12 pp 1016ndash1021 2003

[46] P E Cryer Hypoglycemia Pathophysiology Diagnosis andTreatment Oxford University Press New York NY USA 1997

[47] P E Cryer S N Davis and H Shamoon ldquoHypoglycemia indiabetesrdquo Diabetes Care vol 26 no 6 pp 1902ndash1912 2003

[48] B P Childs N G Clark and D J Cox ldquoDening and reportinghypoglycemia in diabetes a report from the American diabetesassociation workgroup on hypoglycemiardquo Diabetes Care vol28 no 5 pp 1245ndash1249 2005

[49] P E Cryer ldquoHypoglycaemia the limiting factor in the glycaemicmanagement of Type I and Type II diabetesrdquo Diabetologia vol45 no 7 pp 937ndash948 2002

[50] P E Cryer ldquoHypoglycemia the limiting factor in the manage-ment of IDDMrdquo Diabetes vol 43 no 11 pp 1378ndash1389 1994

[51] A L McCall and B P Kovatchev ldquoe median is not the onlymessage a clinicianrsquos perspective on mathematical analysis ofglycemic variability and modeling in diabetes mellitusrdquo Journalof Diabetes Science and Technology vol 3 pp 3ndash11 2009

[52] M Brownlee and I B Hirsch ldquoGlycemic variability ahemoglobin A1c-independent risk factor for diabetic compli-cationsrdquo Journal of the American Medical Association vol 295no 14 pp 1707ndash1708 2006

[53] I B Hirsch and M Brownlee ldquoShould minimal blood glucosevariability become the gold standard of glycemic controlrdquoJournal of Diabetes and Its Complications vol 19 no 3 pp178ndash181 2005

[54] K Esposito D Giugliano F Nappo and R Marfella ldquoRegres-sion of carotid atherosclerosis by control of postprandial hyper-glycemia in type 2 diabetes mellitusrdquo Circulation vol 110 no2 pp 214ndash219 2004

[55] S Haffner ldquoe importance of postprandial hyperglycaemia indevelopment of cardiovascular disease in people with diabetes

Scientica 11

pointrdquo International Journal of Clinical Practice no 123 pp24ndash26 2001

[56] LMonnier EMas C Ginet et al ldquoActivation of oxidative stressby acute glucose uctuations compared with sustained chronichyperglycemia in patients with type 2 diabetesrdquo Journal of theAmerican Medical Association vol 295 no 14 pp 1681ndash16872006

[57] T S Temelkova-Kurktschiev C Koehler E Henkel W Leon-hardt K Fuecker and M Hanefeld ldquoPostchallenge plasmaglucose and glycemic spikes are more strongly associated withatherosclerosis than fasting glucose or HbA(1c) levelrdquo DiabetesCare vol 23 no 12 pp 1830ndash1834 2000

[58] W L Clarke D J Cox L Gonder-Frederick D JulianB Kovatchev and D Young-Hyman ldquoBiopsychobehavioralmodel of risk of severe hypoglycemia self-management behav-iorsrdquo Diabetes Care vol 22 no 4 pp 580ndash584 1999

[59] D J Cox L A Gonder-Frederick B P Kovatchev et alldquoBiopsychobehavioral model of severe hypoglycemia II under-standing the risk of severe hypoglycemiardquo Diabetes Care vol22 no 12 pp 2018ndash2025 1999

[60] L Gonder-Frederick D Cox B Kovatchev D Schlundt andW Clarke ldquoA biopsychobehavioral model of risk of severehypoglycemiardquoDiabetes Care vol 20 no 4 pp 661ndash669 1997

[61] B Kovatchev D Cox L Gonder-Frederick D Schlundtand W Clarke ldquoStochastic model of self-regulation decisionmaking exemplied by decisions concerning hypoglycemiardquoHealth Psychology vol 17 no 3 pp 277ndash284 1998

[62] G Nucci and C Cobelli ldquoModels of subcutaneous insulinkinetics A critical reviewrdquo Computer Methods and Programs inBiomedicine vol 62 no 3 pp 249ndash257 2000

[63] M E Wilinska L J Chassin H C Schaller L Schaupp T RPieber and R Hovorka ldquoInsulin kinetics in type-1 diabetescontinuous and bolus delivery of rapid acting insulinrdquo IEEETransactions on Biomedical Engineering vol 52 no 1 pp 3ndash122005

[64] R N Bergman D T Finegood and M Ader ldquoAssessmentof insulin sensitivity in vivordquo Endocrine Reviews vol 236 ppE667ndashE677 1985

[65] R N Bergman ldquoe minimal model of glucose regulation abiographyrdquoAdvances in ExperimentalMedicine and Biology vol537 pp 1ndash19 2003

[66] R N Bergman D J Zaccaro R M Watanabe et al ldquoMinimalmodel-based insulin sensitivity has greater heritability and adifferent genetic basis than homeostasis model assessment orfasting insulinrdquo Diabetes vol 52 no 8 pp 2168ndash2174 2003

[67] A Caumo R N Bergman and C Cobelli ldquoInsulin sensitivityfrom meal tolerance tests in normal subjects a minimal modelindexrdquo Journal of Clinical Endocrinology and Metabolism vol85 no 11 pp 4396ndash4402 2000

[68] J O Clausen K Borch-Johnsen H Ibsen et al ldquoInsulin sen-sitivity index acute insulin response and glucose effectivenessin a population-based sample of 380 young healthy Caucasiansanalysis of the impact of gender body fat physical tness andlife-style factorsrdquo Journal of Clinical Investigation vol 98 no 5pp 1195ndash1209 1996

[69] T C Ni M Ader and R N Bergman ldquoReassessment of glu-cose effectiveness and insulin sensitivity from minimal modelanalysis a theoretical evaluation of the single-compartmentglucose distribution assumptionrdquo Diabetes vol 46 no 11 pp1813ndash1821 1997

[70] S Welch S S P Gebhart R N Bergman and L S PhillipsldquoMinimal model analysis of intravenous glucose tolerance

test-derived insulin sensitivity in diabetic subjectsrdquo Journalof Clinical Endocrinology and Metabolism vol 71 no 6 pp1508ndash1518 1990

[71] D Dawson M A Vincent E J Barrett et al ldquoCapillary recruit-ment in skeletal muscle in response to exercise and hyperin-sulinemia assessed with contrast-enhanced ultrasoundrdquo Amer-ican Journal of Physiology vol 282 no 3 pp E714ndashE720 2002

[72] K J Mikines B Sonne P A Farrell B Tronier and H GalboldquoEffect of physical exercise on sensitivity and responsiveness toinsulin in humansrdquoAmerican Journal of Physiology vol 254 no3 pp E248ndashE259 1988

[73] E A Richter ldquoGlucose utilizationrdquo in Handbook of PhysiologyL B Rowell and J T Shepherd Eds pp 912ndash951 OxfordUniversity Press New York NY USA 1996

[74] D H Wasserman R J Geer D E Rice et al ldquoInteraction ofexercise and insulin action in humansrdquo American Journal ofPhysiology vol 260 no 1 pp E37ndashE45 1991

[75] G Pillonetto A Caumo G Sparacino and C Cobelli ldquoAnew dynamic index of insulin sensitivityrdquo IEEE Transactions onBiomedical Engineering vol 53 no 3 pp 369ndash379 2006

[76] C Dalla Man D M Raimondo R A Rizza and C CobellildquoGIM Simulation soware of meal glucose-insulin modelrdquoJournal of Diabetes Science and Technology vol 1 pp 323ndash3302007

[77] C Dalla Man R A Rizza and C Cobelli ldquoMeal simulationmodel of the glucose-insulin systemrdquo IEEE Transactions onBiomedical Engineering vol 54 no 10 pp 1740ndash1749 2007

[78] W L Clarke D Cox L A Gonder-Frederick W Carter andS L Pohl ldquoEvaluating clinical accuracy of systems for self-monitoring of blood glucoserdquo Diabetes Care vol 10 no 5 pp622ndash628 1987

[79] e Diabetes Research In Children Network (Direcnet) StudyGroup ldquoA multicenter study of the accuracy of the one touchultra home glucose meter in children with type 1 diabetesrdquoDiabetes Technology anderapeutics vol 5 no 6 pp 933ndash9412003

[80] I B Hirsch B W Bode B P Childs et al ldquoSelf-monitoring ofblood glucose (SMBG) in insulin- and non-insulin-using adultswith diabetes consensus recommendations for improvingSMBG accuracy utilization and researchrdquo Diabetes Technologyanderapeutics vol 10 no 6 pp 419ndash439 2008

[81] G Freckmann A Baumstark N Jendrike et al ldquoSystemaccuracy evaluation of 27 blood glucose monitoring systemsaccording to DIN en ISO 15197rdquo Diabetes Technology anderapeutics vol 12 no 3 pp 221ndash231 2010

[82] D Deiss J Bolinder J P Riveline et al ldquoImproved glycemiccontrol in poorly controlled patients with type 1 diabetes usingreal-time continuous glucose monitoringrdquo Diabetes Care vol29 no 12 pp 2730ndash2732 2006

[83] S Garg H Zisser S Schwartz et al ldquoImprovement in glycemicexcursions with a transcutaneous real-time continuous glucosesensor a randomized controlled trialrdquo Diabetes Care vol 29no 1 pp 44ndash50 2006

[84] B P Kovatchev and W L Clarke ldquoContinuous glucose mon-itoring reduces risks for hypo- and hyperglycemia and glucosevariability in diabetesrdquo Diabetes vol 56 1 Article ID 0086OR2007

[85] e Juvenile Diabetes Research Foundation Continuous Glu-cose Monitoring Study Group ldquoContinuous glucose monitor-ing and intensive treatment of type 1 diabetesrdquo New EnglandJournal of Medicine vol 359 pp 1464ndash1476 2008

12 Scientica

[86] D C Klonoff ldquoContinuous glucose monitoring roadmap for21st century diabetes therapyrdquo Diabetes Care vol 28 no 5 pp1231ndash1239 2005

[87] R Hovorka ldquoContinuous glucose monitoring and closed-loopsystemsrdquo Diabetic Medicine vol 23 no 1 pp 1ndash12 2006

[88] D C Klonoff ldquoe articial pancreas how sweet engineeringwill solve bitter problemsrdquo Journal of Diabetes Science andTechnology vol 1 pp 72ndash81 2007

[89] I B Hirsch D Armstrong R M Bergenstal et al ldquoClin-ical application of emerging sensor technologies in diabetesmanagement consensus guidelines for continuous glucosemonitoring (CGM)rdquoDiabetes Technology anderapeutics vol10 no 4 pp 232ndash246 2008

[90] K Rebrin G M Steil W P Van Antwerp and J J Mastro-totaro ldquoSubcutaneous glucose predicts plasma glucose inde-pendent of insulin implications for continuous monitoringrdquoAmerican Journal of Physiology vol 277 no 3 pp E561ndashE5711999

[91] K Rebrin and G M Steil ldquoCan interstitial glucose assessmentreplace blood glucosemeasurementsrdquoDiabetes Technology anderapeutics vol 2 no 3 pp 461ndash472 2000

[92] G M Steil K Rebrin F Hariri et al ldquoInterstitial uid glucosedynamics during insulin-induced hypoglycaemiardquo Diabetolo-gia vol 48 no 9 pp 1833ndash1840 2005

[93] M S Boyne D M Silver J Kaplan and C D Saudek ldquoTimingof changes in interstitial and venous blood glucose measuredwith a continuous subcutaneous glucose sensorrdquo Diabetes vol52 no 11 pp 2790ndash2794 2003

[94] E Kulcu J A Tamada G Reach R O Potts and M JLesho ldquoPhysiological differences between interstitial glucoseand blood glucose measured in human subjectsrdquoDiabetes Carevol 26 no 8 pp 2405ndash2409 2003

[95] P J Stout J R Racchini andM E Hilgers ldquoA novel approach tomitigating the physiological lag between blood and interstitialuid glucose measurementsrdquo Diabetes Technology and era-peutics vol 6 no 5 pp 635ndash644 2004

[96] I M E Wentholt A A M Hart J B L Hoekstra and J HDevries ldquoRelationship between interstitial and blood glucose intype 1 diabetes patients delay and the push-pull phenomenonrevisitedrdquo Diabetes Technology and erapeutics vol 9 no 2pp 169ndash175 2007

[97] K J C Wientjes and A J M Schoonen ldquoDetermination oftime delay between blood and interstitial adipose tissue glucoseconcentration change by microdialysis in healthy volunteersrdquonternational Journal of Articial rgans vol 24 no 12 pp884ndash889 2001

[98] B Aussedat M Dupire-Angel R Gifford J C Klein G SWilson and G Reach ldquoInterstitial glucose concentration andglycemia implications for continuous subcutaneous glucosemonitoringrdquo American Journal of Physiology vol 278 no 4 ppE716ndashE728 2000

[99] B P Kovatchev and W L Clarke ldquoPeculiarities of the con-tinuous glucose monitoring data stream and their impact ondeveloping closed-loop control technologyrdquo Journal of DiabetesScience and Technology vol 2 pp 158ndash163 2008

[100] W L Clarke and B P Kovatchev ldquoContinuous glucose sen-sorsmdashcontinuing questions about clinical accuracyrdquo Journal ofDiabetes Science and Technology vol 1 pp 164ndash170 2007

[101] e Diabetes Research in Children Network (DirecNet) StudyGroup ldquoe accuracy of the guardian RT continuous glucosemonitor in children with type 1 diabetesrdquo Diabetes Technologyanderapeutics vol 10 pp 266ndash272 2008

[102] B Kovatchev S Anderson L Heinemann and W ClarkeldquoComparison of the numerical and clinical accuracy of fourcontinuous glucose monitorsrdquo Diabetes Care vol 31 no 6 pp1160ndash1164 2008

[103] S K Garg J Smith C Beatson B Lopez-Baca M Voelmleand P A Gottlieb ldquoComparison of accuracy and safety ofthe SEVEN and the navigator continuous glucose monitoringsystemsrdquo Diabetes Technology and erapeutics vol 11 no 2pp 65ndash72 2009

[104] T Heise T Koschinsky L Heinemann and V Lodwig ldquoHypo-glycemia warning signal and glucose sensors requirements andconceptsrdquo Diabetes Technology and erapeutics vol 5 no 4pp 563ndash571 2003

[105] B Bode K Gross N Rikalo et al ldquoAlarms based on real-timesensor glucose values alert patients to hypo- and hyperglycemiathe Guardian continuous monitoring systemrdquo Diabetes Tech-nology anderapeutics vol 6 no 2 pp 105ndash113 2004

[106] G McGarraugh and R Bergenstal ldquoDetection of hypoglycemiawith continuous interstitial and traditional blood glucosemonitoring using the FreeStyle navigator continuous glucosemonitoring systemrdquo Diabetes Technology and erapeutics vol11 no 3 pp 145ndash150 2009

[107] S E Noujaim D Horwitz M Sharma and J Marhoul ldquoAccu-racy requirements for a hypoglycemia detector an analyticalmodel to evaluate the effects of bias precision and rate ofglucose changerdquo Journal of Diabetes Science and Technology vol1 pp 653ndash668 2007

[108] W K Ward ldquoe role of new technology in the early detectionof hypoglycemiardquo Diabetes Technology and erapeutics vol 6no 2 pp 115ndash117 2004

[109] B Buckingham E Cobry P Clinton et al ldquoPreventing hypo-glycemia using predictive alarm algorithms and insulin pumpsuspensionrdquo Diabetes Technology and erapeutics vol 11 no2 pp 93ndash97 2009

[110] C S Hughes S D Patek M D Breton and B P KovatchevldquoHypoglycemia prevention via pump attenuation and red-yellow-green ldquotrafficrdquo lights using continuous glucose moni-toring and insulin pump datardquo Journal of Diabetes Science andTechnology vol 4 no 5 pp 1146ndash1155 2010

[111] B P Kovatchev D J Cox L A Gonder-Frederick and WClarke ldquoSymmetrization of the blood glucose measurementscale and its applicationsrdquo Diabetes Care vol 20 no 11 pp1655ndash1658 1997

[112] F J Service G D Molnar J W Rosevear E Ackerman LC Gatewood and W F Taylor ldquoMean amplitude of glycemicexcursions a measure of diabetic instabilityrdquo Diabetes vol 19no 9 pp 644ndash655 1970

[113] J Schlichtkrull O Munck and M Jersild ldquoe M-value anindex of blood glucose control in diabeticsrdquo Acta MedicaScandinavica vol 177 pp 95ndash102 1965

[114] E A Ryan T Shandro K Green et al ldquoAssessment of theseverity of hypoglycemia and glycemic lambility in type 1diabetic subjects undergoing islet in transplantationrdquo Diabetesvol 53 no 4 pp 955ndash962 2004

[115] B P Kovatchev D J Cox L Gonder-Frederick and WL Clarke ldquoMethods for quantifying self-monitoring bloodglucose proles exemplied by an examination of blood glucosepatterns in patients with type 1 and type 2 diabetesrdquo DiabetesTechnology anderapeutics vol 4 no 3 pp 295ndash303 2002

[116] B P Kovatchev D J Cox L S Farhy M Straume L Gonder-Frederick and W L Clarke ldquoEpisodes of severe hypoglycemia

Scientica 13

in type 1 diabetes are preceded and followed within 48 hours bymeasurable disturbances in blood glucoserdquo Journal of ClinicalEndocrinology and Metabolism vol 85 no 11 pp 4287ndash42922000

[117] B P Kovatchev M Straume D J Cox and L S FarhyldquoRisk analysis of blood glucose data a quantitative approach tooptimizing the control of Insulin Dependent Diabetesrdquo Journalof eoretical Medicine vol 3 no 1 pp 1ndash10 2000

[118] B P Kovatchev E Otto D Cox L Gonder-Frederick andW Clarke ldquoEvaluation of a new measure of blood glucosevariability in diabetesrdquo Diabetes Care vol 29 no 11 pp2433ndash2438 2006

[119] B P Kovatchev W L Clarke M Breton K Brayman and AMcCall ldquoQuantifying temporal glucose variability in diabetesvia continuous glucose monitoring mathematical methods andclinical applicationrdquo Diabetes Technology and erapeutics vol7 no 6 pp 849ndash862 2005

[120] D Rodbard ldquoNew and improved methods to characterizeglycemic variability using continuous glucosemonitoringrdquoDia-betes Technology and erapeutics vol 11 no 9 pp 551ndash5652009

[121] M Miller and P Strange ldquoUse of Fourier models for analysisand interpretation of continuous glucose monitoring glucoseprolesrdquo Journal of Diabetes Science and Technology vol 1 pp630ndash638 2007

[122] W Clarke and B Kovatchev ldquoStatistical tools to analyzecontinuous glucose monitor datardquo Diabetes Technology anderapeutics vol 11 pp S45ndashS54 2009

[123] C M Mcdonnell S M Donath S I Vidmar G A Wertherand F J Cameron ldquoA novel approach to continuous glucoseanalysis utilizing glycemic variationrdquo Diabetes Technology anderapeutics vol 7 no 2 pp 253ndash263 2005

[124] G Sparacino F Zanderigo A Maran and C Cobelli ldquoContin-uous glucosemonitoring andhypohyperglycaemia predictionrdquoDiabetes Research and Clinical Practice vol 74 no 2 supple-ment pp S160ndashS163 2006

[125] G Sparacino F Zanderigo G Corazza A Maran AFacchinetti and C Cobelli ldquoGlucose concentration can bepredicted ahead in time from continuous glucose monitoringsensor time-seriesrdquo IEEE Transactions on Biomedical Engineer-ing vol 54 pp 931ndash937 2007

[126] J Reifman S Rajaraman A Gribok and W K Ward ldquoPredic-tive monitoring for improved management of glucose levelsrdquoJournal of Diabetes Science and Technology vol 1 pp 478ndash4862007

[127] F Zanderigo G Sparacino B Kovatchev and C CobellildquoGlucose prediction algorithms from continuous monitoringassessment of accuracy via continuous glucose-error grid anal-ysisrdquo Journal of Diabetes Science and Technology vol 1 pp645ndash651 2007

[128] F Cameron G Niemayer K Gundy-Burlet and B Bucking-ham ldquoStatistical hypoglycemia predictionrdquo Journal of DiabetesScience and Technology vol 2 pp 612ndash621 2008

[129] A Gani A V Gribok S Rajaraman W K Ward and JReifman ldquoPredicting subcutaneous glucose concentration inhumans data-driven glucose modelingrdquo IEEE Transactions onBiomedical Engineering vol 56 no 2 pp 246ndash254 2009

[130] M Eren-Oruklu A Cinar L Quinn andD Smith ldquoEstimationof future glucose concentrations with subject-specic recursivelinear modelsrdquo Diabetes Technology and erapeutics vol 11no 4 pp 243ndash253 2009

[131] S M Pappada B D Cameron and P M Rosman ldquoDevelop-ment of a neural network for prediction of glucose concentra-tion in type 1 diabetes patientsrdquo Journal of Diabetes Science andTechnology vol 2 pp 792ndash801 2008

[132] C Cobelli C Dalla Man G Sparacino L Magni G Nicolaoand B P Kovatchev ldquoDiabetes models signals and controlrdquoIEEE Reviews in Biomedical Engineering vol 2 pp 54ndash96 2009

[133] H LeBlanc D Chauvet P Lombrail and J J Robert ldquoGlycemiccontrol with closed-loop intraperitoneal insulin in type Idiabetesrdquo Diabetes Care vol 9 no 2 pp 124ndash128 1986

[134] C D Saudek J L Selman H A Pitt et al ldquoA preliminary trialof the programmable implantablemedication system for insulindeliveryrdquo New England Journal of Medicine vol 321 no 9 pp574ndash579 1989

[135] C Broussolle N Jeandidier and H Hanaire-Broutin ldquoFrenchmulticentre experience of implantable insulin pumpsrdquo Lancetvol 343 no 8896 pp 514ndash515 1994

[136] H Hanaire-Broutin C Broussolle N Jeandidier et al ldquoFea-sibility of intraperitoneal insulin therapy with programmableimplantable pumps in IDDM a multicenter studyrdquo DiabetesCare vol 18 no 3 pp 388ndash392 1995

[137] P R Oskarsson P E Lins H W Henriksson and U CAdamson ldquoMetabolic and hormonal responses to exercisein type 1 diabetic patients during continuous subcutaneousas compared to continuous intraperitoneal insulin infusionrdquoDiabetes and Metabolism vol 25 no 6 pp 491ndash497 1999

[138] P R Oskarsson P E Lins L Backman and U C AdamsonldquoContinuous intraperitoneal insulin infusion partly restores theglucagon response to hypoglycaemia in type 1 diabetic patientsrdquoDiabetes and Metabolism vol 26 no 2 pp 118ndash124 2000

[139] B Catargi L Meyer V Melki E Renard and N JeandidierldquoComparison of blood glucose stability and HbA1C betweenimplantable insulin pumps using U400 hoe 21pH insulin andexternal pumps using lispro in type 1 diabetic patients a pilotstudyrdquo Diabetes and Metabolism vol 28 no 2 pp 133ndash1372002

[140] E Renard ldquoImplantable closed-loop glucose-sensing andinsulin delivery the future for insulin pump therapyrdquo CurrentOpinion in Pharmacology vol 2 no 6 pp 708ndash716 2002

[141] E Renard G Costalat H Chevassus and J Bringer ldquoArticial120573120573-cell clinical experience toward an implantable closed-loopinsulin delivery systemrdquo Diabetes and Metabolism vol 32 no5 pp 497ndash502 2006

[142] E Renard J Place M Cantwell H Chevassus and C CPalerm ldquoClosed-loop insulin delivery using a subcutaneousglucose sensor and intraperitoneal insulin delivery feasibilitystudy testing a new model for the articial pancreasrdquo DiabetesCare vol 33 no 1 pp 121ndash127 2010

[143] R Bellazzi G Nucci and C Cobelli ldquoe subcutaneousroute to insulin-dependent diabetes therapy closed-loop andpartially closed-loop control strategies for insulin deliveryand measuring glucose concentrationrdquo IEEE Engineering inMedicine and Biology Magazine vol 20 no 1 pp 54ndash64 2001

[144] R Hovorka L J Chassin M E Wilinska et al ldquoClosingthe loop the adicol experiencerdquo Diabetes Technology anderapeutics vol 6 no 3 pp 307ndash318 2004

[145] R Hovorka V Canonico L J Chassin et al ldquoNonlinear modelpredictive control of glucose concentration in subjects withtype 1 diabetesrdquo Physiological Measurement vol 25 no 4 pp905ndash920 2004

14 Scientica

[146] G M Steil K Rebrin C Darwin F Hariri and M F SaadldquoFeasibility of automating insulin delivery for the treatment oftype 1 diabetesrdquo Diabetes vol 55 no 12 pp 3344ndash3350 2006

[147] e JDRF e-Newsletter Emerging Technologies in DiabetesResearch 2006

[148] W L Clarke and B Kovatchev ldquoe articial pancreas howclose are we to closing the looprdquo Pediatric EndocrinologyReviews vol 4 no 4 pp 314ndash316 2007

[149] C Cobelli E Renard and B P Kovatchev ldquoPerspectives indiabetes articial pancreas past present futurerdquo Diabetes vol60 pp 2672ndash2682 2011

[150] B P Kovatchev M D Breton C Dalla Man and C Cobelli ldquoInsilico preclinical trials a proof of concept in closed-loop controlof type 1 diabetesrdquo Journal of Diabetes Science and Technologyvol 3 pp 44ndash55 2009

[151] B Kovatchev C Cobelli E Renard et al ldquoMultinational studyof subcutaneous model-predictive closed-loop control in type1 diabetes mellitus summary of the resultsrdquo Journal of DiabetesScience and Technology vol 4 no 6 pp 1374ndash1381 2010

[152] H Zisser L Jovanovic F Doyle P Ospina and C OwensldquoRun-to-run control of meal-related insulin dosingrdquo DiabetesTechnology anderapeutics vol 7 no 1 pp 48ndash57 2005

[153] C Owens H Zisser L Jovanovic B Srinivasan D Bonvinand F J Doyle III ldquoRun-to-run control of blood glucoseconcentrations for people with type 1 diabetes mellitusrdquo IEEETransactions on Biomedical Engineering vol 53 no 6 pp996ndash1005 2006

[154] C C Palerm H Zisser W C Bevier L Jovanovič and F JDoyle III ldquoPrandial insulin dosing using run-to-run controlapplication of clinical data and medical expertise to dene asuitable performance metricrdquo Diabetes Care vol 30 no 5 pp1131ndash1136 2007

[155] LMagni F Raimondo L Bossi et al ldquoModel predictive controlof type 1 diabetes an in silico trialrdquo Journal of Diabetes Scienceand Technology vol 1 pp 804ndash812 2007

[156] S A Weinzimer G M Steil K L Swan J Dziura N Kurtzand W V Tamborlane ldquoFully automated closed-loop insulindelivery versus semiautomated hybrid control in pediatricpatients with type 1 diabetes using an articial pancreasrdquoDiabetes Care vol 31 no 5 pp 934ndash939 2008

[157] W L Clarke S Anderson M Breton S Patek L Kashmerand B Kovatchev ldquoClosed-loop articial pancreas using sub-cutaneous glucose sensing and insulin delivery and a modelpredictive control algorithm the Virginia experiencerdquo Journalof Diabetes Science and Technology vol 3 no 5 pp 1031ndash10382009

[158] D Bruttomesso A Farret S Costa et al ldquoClosed-loop arti-cial pancreas using subcutaneous glucose sensing and insulindelivery and a model predictive control algorithm preliminarystudies in Padova and Montpellierrdquo Journal of Diabetes Scienceand Technology vol 3 no 5 pp 1014ndash1021 2009

[159] R Hovorka J M Allen D Elleri et al ldquoManual closed-loop insulin delivery in children and adolescents with type 1diabetes a phase 2 randomised crossover trialrdquoe Lancet vol375 no 9716 pp 743ndash751 2010

[160] F H El-khatib S J Russell D M Nathan R G Sutherlin andE R Damiano ldquoA bihormonal closed-loop articial pancreasfor type 1 diabetesrdquo Science Translational Medicine vol 2 no27 Article ID 27ra27 2010

[161] E Dassau H Zisser C C Palerm B A Buckingham LJovanovič and F J Doye III ldquoModular articial 120573120573-cell system a

prototype for clinical researchrdquo Journal of Diabetes Science andTechnology vol 2 pp 863ndash872 2008

[162] B Kovatchev S Patek E Dassau et al ldquoControl to range fordiabetes functionality and modular architecturerdquo Journal ofDiabetes Science and Rechnology vol 3 no 5 pp 1058ndash10652009

[163] E Atlas R Nimri S Miller E A Grunberg and M PhillipldquoMD-logic articial pancreas system a pilot study in adults withtype 1 diabetesrdquo Diabetes Care vol 33 no 5 pp 1072ndash10762010

[164] E Dassau H Zisser M W Percival B Grosman L Jovanovičand F J Doyle III ldquoClinical results of automated articialpancreatic 120573120573-cell system with unannounced meal using multi-parametric MPC and insulin-on-boardrdquo in Proceedings of the70th American Diabetes Association Meeting Diabetes vol 59supplement 1 A94 Orlando Fla USA 2010

[165] E M Renard A Farret J Place C Cobelli B P Kovatchev andMD Breton ldquoClosed-loop insulin delivery using subcutaneousinfusion and glucose sensing and equipped with a dedicatedsafety supervision algorithm improves safety of glucose controlin type 1 diabetesrdquo Diabetologia vol 53 supplement 1 p S252010

[166] R Hovorka K Kumareswaran J Harris et al ldquoOvernightclosed loop insulin delivery articial pancreas in adults withtype 1 diabetes crossover randomized controlled studiesrdquoBritish Medical Journal vol 342 no 7803 Article ID d18552011

[167] H Zisser E Dassau W Bevier et al ldquoInitial evaluation ofa fully automated articial pancreasrdquo in Proceedings of the71st American Diabetes Association Meeting Diabetes vol 60supplement 1 A41 San Diego Calif USA 2011

[168] M D Breton A Farret and D Bruttomesso ldquoFully-integratedarticial pancreas in type 1 diabetes modular closed-loopglucose control maintains near-normoglycemiardquo Diabetes vol61 pp 2230ndash2237 2012

[169] C Cobelli E Renard and B P Kovatchev ldquoPilot studies ofwearable articial pancreas in type 1 diabetesrdquo Diabetes Carevol 35 no 9 pp e65ndashe67 2012

Submit your manuscripts athttpwwwhindawicom

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Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

2 Scientica

rst trials of continuous subcutaneous insulin infusion (CSII)were reported by Pickup et al in England [3] and Tamborlaneet al in theUSA [4] showing the feasibility of thisminimally-invasive mode of insulin replacement e next logical stepwas automated insulin delivery controlled by a mathematicalalgorithm mdasha method that became known as closed-loopcontrol or the ldquoarticial pancreasrdquo e articial pancreasidea can be traced back to the 1970s when the possibilityfor external blood glucose regulation was established bystudies using intravenous (iv) glucose measurement andiv infusion of glucose and insulin Five teams reported ivclosed-loop control results between 1974 and 1978 Albisseret al [5] Pfeiffer et al [6] Mirouze et al [7] Kraegenet al [8] and Shichiri et al [9] In 1977 one of these designs[6] resulted in the rst commercial devicemdashthe Biostator[10]mdasha large (refrigerator-sized) device that has been usedextensively for glucose-control research (Figure 1) Systemssuch as the Biostator have been employed in hospital settingsto maintain normoglycemia using negative (via insulin)and positive (via glucose or glucagon) control [11ndash13] Areview of methods for iv glucose control can be found in[14]

In 1979 another key element of the closed-loop con-trolmdashthe Minimal Model of Glucose Kineticsmdashwas intro-duced by Bergman and Cobelli [15] is and subsequentmathematical models serves as the ldquobrainrdquo behind themajor-ity of control algorithms used in contemporary articialpancreas systems Detailed description of the major earlyalgorithm designs can be found in [16ndash19] More workfollowed spanning a range of control techniques powered byphysiologic modeling and computer simulation [20ndash23]

e nal critical technology leap enabling minimallyinvasive closed-loop designs was made at the turn of the cen-tury with the introduction of continuous glucose monitoring(CGM [24ndash26])mdashan event that started the ongoing questfor wearable articial pancreas Figure 1 depicts the timelineof these events and the acceleration of diabetes technologydevelopment in the past two decades

2 Markers of Average Glycemia and BloodGlucose (BG) Fluctuations

21 Hemoglobin A1c (HbA1c) In the early 1990s the land-mark Diabetes Control and Complications Trial (DCCT[27ndash29]) and the StockholmDiabetes Intervention study [30]clearly indicated that intensive insulin treatment can reducethe long-term complications of type 1 diabetes In 1998the UK Prospective Diabetes Study group established thatintensive treatment with insulin or with oral medications tomaintain nearly normal levels of glycemia markedly reduceschronic complications in type 2 diabetes as well [31] HbA1cwas identied as the primary marker of long-term averageglucose control [32 33] and still remains the gold-standardassay reecting average glycemia widely accepted in researchas a primary outcome for virtually all studies of diabetestreatment and in the clinical practice as primary feedbackto the patient and the physician and a base for treatmentoptimization

22 Risk for Hypoglycemia However the DCCT also showedthat intensive treatment of diabetes can also increase therisk for severe hypoglycemia (low blood glucose that couldresult in stupor unconsciousness and even death [34]Indeed HbA1c has repeatedly been proven to be an inef-fective assessment of patientsrsquo risk for hypoglycemia eDCCT concluded that only about 8 of severe hypoglycemicepisodes could be predicted from known variables includingHbA1c [34] later this prediction was improved to 18 bya structural equation model using history of severe hypo-glycemia awareness and autonomic symptom score [35]In subsequent studies HbA1c has never been signicantlyassociated with severe hypoglycemia [36ndash39] Neverthelessthe physiological mechanisms of hypoglycemia were wellestablished by a number of studies that have investigatedthe relationships between intensive therapy hypoglycemiaunawareness and impaired counterregulation [40ndash43] andconcluded that recurrent hypoglycemia spirals into a ldquoviciouscyclerdquo known as hypoglycemia-associated autonomic failure(HAAF [44]) are observed primarily in type 1 and alsoin type 2 diabetes [45] e acute risk for hypoglycemiawas attributed to impairments in the systemic reactionto falling BG levels in health falling BG concentrationtriggers a sequence of responses beginning with attenuationof endogenous insulin production followed by increase inglucagon and epinephrine and if BG concentration fallsfurther resulting in autonomic symptoms andor neurogly-copenia in type 1 diabetes and to some extent in type 2diabetes these defense mechanisms are impaired [46ndash48]As a result hypoglycemia was identied as the primarybarrier to optimal diabetes control [49 50] e clinicaloptimization problem of diabetes was therefore clearly for-mulated reduce average glycemia and exposure to highblood glucose levels (thereby HbA1c) while preventinghypoglycemia

23 Glucose Variability (GV) Blood glucose variability isthe primary challenge to the success of this optimizationand is typically at the root of cliniciansrsquo inability to safelyachieve near-normal average glycemia as reected byHbA1c[51] Indeed in addition to establishing HbA1c as the goldstandard for average glycemic control the DCCT concludedthat ldquoHbA1c is not the most complete expression of thedegree of glycemia Other features of diabetic glucose controlwhich are not reected by HbA1c may add to or modify therisk of complications For example the risk of complicationsmay be highly dependent on the extent of postprandialglycemic excursionsrdquo [28] As noted above while targetHbA1c values of 7 or less result in decreased risk ofvascular complications [27 29 31 33] the risk for severehypoglycemia increases with tightening glycemic control [3448] At the high end of the BG scale a number of studies haveimplicated postprandial BG uctuation as an independentrisk factor for diabetes complications [52 53] particularlycardiovascular disease [54ndash57]us a strategy for achievingoptimal glucose control can only be successful if it reducesGV is is because bringing average glycemia down is onlypossible if GV is constrained otherwise BG uctuations

Scientica 3

First use of CSII Pickup et al Br Med J 1978 Tamborlaneet al NEJM 1979

The auto syringe (Dean Kamen)

Subcutaneous continuous glucose monitoring

MinimedCGMS1999

Backpack insulin and glucagon pump

Kadish 1964The Biostator

The minimal model of glucose kinetics

Bergman and Cobelli AJP 1979

Insulin discoveredFrederick Banting

Ames reflectance

meter

1960s 1970s 1990s1920s 1980s 2000s

Blood glucose meters and insulin pumps becoming smaller

Models of diabetes becoming larger and more complex

F 1 e Diabetes technology timeline from the discovery of insulin to the introduction of continuous glucose monitoring

would inevitably enter the range of hypoglycemia (see [51Figure 4])

24 Behavioral Triggers of Glucose Variability Formulatedfrom an engineering point of view the control of diabetesis driven by routine self-treatment behaviors which mayoccasionally evolve into hypo- or hyperglycemia-triggeringevents for example insulin mistiming bolusbasal imbal-ance missed meal or excessive exercise A formal math-ematical description of this process and its potential todestabilize the system was given by the Stochastic Modelof Self-Regulation Behavior which provided a probabilisticinterpretation of the event sequence internal condition rarrperceptionawareness rarr appraisal rarr self-regulation deci-sion [58ndash61] e parameters of this process are individualcontingent on behavioral interpretation for example ona personrsquos ability to control hisher BG within optimallimits e effect of this process is mediated by the specicsof a personrsquos metabolic system such as rate of glucoseappearance in the blood or insulin sensitivity e netresult from this biobehavioral interplay is a certain degreeof glucose variability which in turn could provide feedbackto the person regarding the effectiveness of hisher glycemiccontrol and could prompt corrective actions if neededfor example adjustment of insulin timing or bolusbasalratio

25 Physiological Mechanisms of GV Once triggered theprogression and the extent of glycemic excursions depend onindividual parameters of insulin transport insulin sensitivityand counterregulatory response Technologies utilizing sub-cutaneous insulin injection (eg CSII articial pancreas) relyon the transport of sc-injected insulin into the circulatione duration of this transport varies from person to personand is a major mechanism of postprandial GV because ofthe introduced delaysmdasha postprandial excursion has time todevelop due to insulin deciency (relative to health) in itsinitial stages even if a meal bolus is given on time us themodeling and the formal description of sc insulin transportis important for the effectiveness of modern diabetes controlstrategies [62 63] Aer entering the circulation the actionof insulin is determined by the dynamics of insulin-mediatedglucose utilizationmdasha process that has been mathematicallycharacterized by Bergman and Cobellirsquos classic MinimalModel which introduced the mathematical formulation ofinsulin sensitivity [15] a key metabolic parameter that hasbeen the subject of investigation of a number of subsequentstudies [64ndash70] It is nowwell known that insulin sensitivity isenhanced by exercise [71ndash74] methods exist for quantitativeassessment of insulin sensitivity in laboratory [64] and inoutpatient settings [67] including methods for assessmentduring physical activity [75] e processes of gastric emp-tying and glucose appearance in the blood are similarly well-quantied [76 77] It is apparent that a major source of GV

4 Scientica

is rapid onset of hyperglycemia due to consumption of ldquohighglycemic indexrdquo foods especially in large quantity For exam-ple foods with simple carbohydrate and high fat (classicallypizza) present challenges to technology and optimal therapyby resulting in sustained postprandial hyperglycemia

3 Monitoring of BG Fluctuations in Diabetes

31e Frequency of BGObservation Intuitively the aggres-siveness of glucose control in diabetes would depend on thefrequency of glucose measurement For example if only theaverage glycemic state of a patient is available once every fewmonths (as it would be with measurement of HbA1c alone)then control strategies could only target adjustment of long-term average glycemia but would not be able to respondto daily or hourly variation in glucose level Rapid BGchanges would remain largely unnoticed unless they led toacute complications such as severe hypoglycemia or diabeticketoacidosis us the frequency of glucose measurementdetermines to a large extent the aggressiveness of possibletreatments Table 1 presents the frequency and the temporalresolution of commonly used glucose assessment techniquesGenerally HbA1c reects long-term (over 2-3months) bloodglucose average thus the temporal resolution of HbA1c islimited to reect slow changes in average glycemia Self-monitoring of blood glucose (SMBG) is a standard practiceincluding several (eg 2ndash5) BG readings per day us thetemporal resolution of SMBG allows for assessment of dailyBG proles or weekly trends ith the advent of CGM itis now well accepted that BG uctuations are a process intime which has two principal components risk associatedwith the amplitude (variability) of BG changes and timeindicating the rate of event progression Contemporary CGMdevices are capable of producing BG determinations every5ndash10 minutes which provides vast amounts of data withhigh temporal resolution and allows for detailed monitoringof glucose uctuations on a temporal scale of minutesmdashafrequency that enables closed-loop control

32 Self-Monitoring of Blood Glucose Contemporary homeBG meters offer convenient means for frequent and accurateBG determinations through self-monitoring Most devicesare capable of storing BG readings (typically over 150readings) and have interfaces to download these readingsinto a computer e meters are usually accompanied bysoware that has capabilities for basic data analyses (egcalculation of mean BG estimates of the average BG overthe previous two weeks percentages in target hypoglycemicand hyperglycemic zones etc) and log of the data andgraphical representation (eg histograms pie charts) [78ndash81] Analytical methods based on SMBG data are discussedin the next section

33 Continuous Glucose Monitoring Since the advent ofCGM technology 10 years ago [25ndash27] signicant progresshas been made towards versatile and reliable CGM devicesthat not only monitor the entire course of BG day and nightbut also provide feedback to the patient such as alarms when

BG reaches preset low or high levels A number of studieshave documented the benets of CGM [82ndash85] and chartedguidelines for clinical use and its future as a precursor toclosed-loop control [86ndash89] However while CGM has thepotential to revolutionize the control of diabetes it also gen-erates data streams that are both voluminous and complexe utilization of such data requires an understanding ofthe physical biochemical and mathematical principles andproperties involved in this new technology It is important toknow that CGM devices measure glucose concentration in adifferent compartmentmdashthe interstitium Interstitial glucose(IG) uctuations are related to BG presumably via diffusionprocess [90ndash92] To account for the gradient between BGand IG CGM devices are calibrated with capillary glucosewhich brings the typically lower IG concentration to BGlevels Successful calibration would adjust the amplitude ofIG uctuations with respect to BG but would not eliminatethe possible time lag due to BG-to-IG glucose transport andthe sensor processing time (instrument delay) Because sucha time lag could greatly inuence the accuracy of CGM anumber of studies were dedicated to its investigation yieldingvarious results [93ndash96] For example it was hypothesized thatif a glucose fall is due to peripheral glucose consumptionthe physiologic time lag would be negative that is fall in IGwould precede fall in BG [90 97] In most studies IG laggedbehind BG (most of the time) by 4ndash10 minutes regardlessof the direction of BG change [92 93] e formulation ofthe push-pull phenomenon offered reconciliation of theseresults and provided arguments for a more complex BG-IG relationship than a simple constant or directional timelag [96 98] In addition errors from calibration loss ofsensitivity and random noise confound CGM data [99]Nevertheless the accuracy of CGM is increasing and maybe reaching a physiological limit for subcutaneous glucosemonitoring [100ndash103]

In addition to presenting frequent data (eg every 5ndash10minutes) CGM devices typically display directional trendsand BG rate of change and are capable of alerting the patientof upcoming hypo- or hyperglycemia ese features arebased on methods which predict blood glucose and generatealarms and warning messages In the past several yearsthese methods have rapidly evolved from a concept [104] toimplementation in CGM devices such as the Guardian RTand the MiniMed Paradigm REAL-Time System (MedtronicNorhtridge CA USA) [105] and the Freestyle Navigator(Abbott Diabetes Care Alameda CA USA) [106] Alarmsfor particularly rapid rates of BG change (eg greater than2mgdLmin) are available as well (Guardian RT and Dex-com Seven Plus Dexcom San Diego CA USA) Discussionof the methods for testing of the accuracy and the utility ofsuch alarms has been initiated [106ndash108] and the next logicalstepmdashprevention of hypoglycemia via shutoff of the insulinpumpmdashhas been undertaken [109]

4 Assessment of BG Fluctuations in Diabetes

As presented in Table 1 different frequencies of BGmonitor-ing provide data for different types of analytical techniques

Scientica 5

T 1 Frequency of available glucose monitoring technologies

Measure Temporal resolution Reects Methods typically used to present and analyze the data

HbA1c Monthsyears Slow changes in average BG Direct assay and review of values group comparisonswhen treatments are evaluated

Self-monitoring of bloodglucose (SMBG) Daysweeks Daily variation

weekly trends

Mean and standard deviation (SD) coefficient ofvariation (CV) Interquartile range (IQR)119872119872-value(1965) MAGE (1970)lability index (2004)lowhigh BG (risk) Indices (1998)average daily risk range (ADRR 2006)

Continuous glucosemonitoring (CGM) Minuteshours System dynamics

uctuation and periodicity

CONGA (2005)glucose rate of Change amp CGM versions of lowhighBG (risk) indices (2005)time seriesdynamical system analysis

assessing the glycemic state or the BG dynamics of aperson with diabetes A brief account of analytical methodsapplicable to SMBG andor CGM data is given below Someof these methods such as the Risk Analysis of BG data haveentered the design of closed-loop control systems preventinghypoglycemia [110]

41 SMBG-Based Analytical Methods e computation ofmean glucose values from SMBG data is typically used as adescriptor of overall glycemic control Computing pre- andpostmeal averages and their difference can serve as an indica-tion of the effectiveness of premeal bolus timing and amountSimilarly the percentages of SMBG readings within belowor above preset target limits would serve as indication of thegeneral behavior of BG uctuations e suggested limits are70 and 180mgdL (39ndash10mmoll) which create three sug-gested by the DCCT and commonly accepted bands hypo-glycemia (BG le 70mgdL) normoglycemia (70mgdL ltBG le 180mgdL) hyperglycemia (BG gt 180mgdL) [1]Percentage of time within additional bands can be computedas well to emphasize the frequency of extreme glucoseexcursions Computing standard deviation (SD) as ameasureof glucose variability is not recommended because the BGmeasurement scale is highly asymmetric the hypoglycemicrange is numerically narrower than the hyperglycemic rangeand the distribution of the glucose values of an individualis typically quite skewed [111] erefore SD would bepredominantly inuenced by hyperglycemic excursions andwould not be sensitive to hypoglycemia It is also possiblefor condence intervals based on SD to assume unrealisticnegative valuesus as a standard measure of GV we wouldsuggest reporting interquartile range (IQR) which is suitablefor asymmetric distributions Several diabetes-specic met-rics are also available to serve the analysis of SMBG dataincluding the mean amplitude of glucose excursions (MAGE[112]) the 119872119872-value [113] the lability index [114] and thelow and high blood glucose indices (LBGI HBGI) whichreect the risks associated with hypo- and hyperglycemiarespectively [37 115] In a series of studies we have shownthat specic risk analysis of SMBG data could also capturelong-term trends towards increased risk for hypoglycemia

[36ndash38] and could identify 24-hour periods of increased riskfor hypoglycemia [39 116]

42 Risk Analysis of BG Data To provide a avor for theanalytical techniques used for SMBG CGM and closed-loopcontrol data we will present a bit more detail on the conceptfor risk analysis of BG data [117] e risk analysis steps areas follows

421 Symmetrization of the BG Scale A nonlinear transfor-mation is applied to the BG measurements scale to map theentire BG range (20 to 600mgdL or 11 to 333mmoll) to asymmetric intervalis is needed because the distribution ofBG values of a person with diabetes is asymmetric typicallyskewed towards hyperglycemiae BG value of 1125mgdL(625mmoll) is mapped to zero corresponding to zero riskfor hypo- or hyperglycemia (we should note that this is nota normoglycemic or fasting value which in health wouldbe lt100mgdL it is zero-risk value pertinent to diabetes)e analytical form of this transformation is 119891119891119891BG) = 120574120574 120574119891ln 119891BG)120572120572 minus 120573120573) where the parameters are estimated as 120572120572 =1084 120573120573 = 12057312057381 and 120574120574 = 11205730120574 if BG is measured in mgdLand 120572120572 = 10120572120572 120573120573 = 181205721 and 120574120574 = 11205741205744 if BG is measuredin mmoll [111]

422 Assignment of a Risk Value to Each BG Reading Aquadratic risk function is dened as by the formula 119903119903119891BG) =10 120574 119891119891119891BG)120572 e function 119903119903119891BG) ranges from 0 to 100Its minimum value is achieved at BG = 1125mgdL asafe euglycemic BG reading while its maximum is reachedat the extreme ends of the BG scale us 119903119903119891BG) can beinterpreted as a measure of the risk associated with a certainBG level e le branch of this parabola identies the riskof hypoglycemia while the right branch identies the risk ofhyperglycemia

423 Computing Measures of Risk for Hypoglycemia Hyper-glycemia and Glucose Variability Let 1199091199091 119909119909120572hellip 119909119909119899119899 be aseries of 119899119899 BG readings and let 119903119903119903119903119891BG) = 119903119903119891BG) if 119891119891119891BG) lt 0and 0 otherwise 119903119903119903119891BG) = 119903119903119891BG) if 119891119891119891BG) gt 0 and 0

6 Scientica

otherwise en the low and high blood glucose indices arecomputed as follows

LBGI = 1119899119899

11989911989910055761005576119894119894=111990311990311990311990310076491007649119909119909119894119894100766510076652 HBGI = 1

119899119899

11989911989910055761005576119894119894=11199031199031199031007649100764911990911990911989411989410076651007665

2 (1)

us the LBGI is a nonnegative quantity that increaseswhen the number andor extent of low BG readings increasesand the HBGI is nonnegative quantity that increases whenthe number andor extent of high BG readings increasesBased on this same technique we also dene the average dailyrisk range (ADRR) which is a measure of risks associatedwith overall glycemic variability [118] In studies the LBGItypically accounted for 40ndash55 of the variance of futuresignicant hypoglycemia in the subsequent 3ndash6 months [36ndash38] which made it a potent predictor of hypoglycemia basedon SMBGeADRR has been shown superior to traditionalglucose variability measures in terms of risk assessment andprediction of extreme glycemic excursions [118] Specicallyit has been demonstrated that classication of risk for hypo-glycemia based on four ADRR categories low risk ADRR lt20 low-moderate risk 20 le ADRR lt 30 moderate-high risk30 le ADRR lt 40 and high risk ADRR gt 40 resulted inmore than a sixfold increase in risk for hypoglycemia fromthe lowest to the highest risk category [118] In addition thelow and high BG indices have been adapted to continuousmonitoring data [119] and can be used in the same way aswith SMBG to assess the risk for hypo- or hyperglycemia

43 CGM-Based Analytical Methods While traditional risk[119] and variability [120] analyses are still applied to CGMdata the high temporal resolution of CGM brought aboutthe possibility for use of sophisticated analytical methodsassessing system (person) dynamics on the time scale of min-utes is necessitated the development of new technologiesfor data analysis and visualization that are not available forSMBG data Analysis of the BG rate of change (measuredin mgdLmin) is a way to evaluate the dynamics of BGuctuations on the time scale of minutes e BG rate ofchange at 119905119905119894119894mdashis computed as the ratio (BG(119905119905119894119894)minusBG(119905119905119894119894minus1))(119905119905119894119894minus119905119905119894119894minus1) where BG(119905119905119894119894) and BG(119905119905119894119894minus1) are CGM readings taken attimes 119905119905119894119894 and 119905119905119894119894minus1 for example minutes apart Investigationof the frequency of glucose uctuations showed that optimalevaluation of the BG rate of change would be achieved overtime periods of 15minutes [121] for exampleΔ119905119905 = 119905119905119894119894 minus119905119905119894119894minus1 =15 A large variation of the BG rate of change indicates rapidand more pronounced BG uctuations and therefore a lessstable system us the standard deviation of the BG rateof change is a measure of stability of glucose uctuation (weshould note that as opposed to the distribution of BG levelsthe distribution of the BG rate of change is symmetric andtherefore using SD is statistically accurate [122]) e SDof BG rate of change has been introduced as a measure ofstability computed fromCGMdata and is known asCONGAof order 1 In general CONGA122 of order 119899119899 is computedas the standard deviation of CGM readings that are 119899119899 hoursapart reecting glucose stability over these time intervals[123]

Most important to the development of the articial pan-creas algorithms is a class of methods allowing the predictionof BG values ahead in time ese methods typically basedon time-series techniques have been applied successfully ina number of studies [124ndash129] In addition to time seriesneural networks have been used for the prediction of glucoselevels from CGM [130 131] Detailed reviews of CGM dataanalysis methods are presented in [122] including severalgraphs that could be used for the visualization of the rathercomplex CGM data sets and in [132] where a broad reviewof modeling analytical and control techniques for diabetesis provided

5 Control of BG Fluctuations in Diabetes

51 Intraperitoneal Insulin Delivery As detailed in the Intro-duction the articial pancreas idea can be traced back to theearly 70s when external BG regulation in people with dia-betes was achieved by iv glucose measurement and iv infu-sion of glucose and insulin However the intravenous routeof closed-loop control remains cumbersome and unsuitedfor outpatient use An alternative has been presented byimplantable intraperitoneal (ip) systems employing iv sam-pling and ip insulin delivery [133ndash136] e ip infusionroute has several desirable characteristics reproducibility ofinsulin absorption quick time to peak and return to baselineof insulin action near-physiological peripheral insulin levelsand restoration of glucagon response to hypoglycemia andexercise [133 137ndash139] However while ip systems haveachieved excellent BG control their implementation stillrequires considerable surgery and is associated with signi-cant cost Nevertheless the development of less invasive andcheaper implantable ports (eg DiaPort Roche DiagnosticsMannheim Germany) may contribute to the future prolifer-ation of ip insulin delivery [140ndash142]

52e Subcutaneous Route to Closed-Loop Control Follow-ing the progress of minimally invasive subcutaneous CGMthe next logical step was the development of sc closed-loopglucose control which links a CGM device with CSII insulinpump A key element of this combination was a controlalgorithm which monitors BG uctuations and the actionsof the insulin pump and computes insulin delivery rate everyfew minutes [143] Figure 2 presents key milestones in thetimeline of this development

Following the pioneering work of Hovorka et al [144145] and Steil et al [146] in 2006 the Juvenile DiabetesResearch Foundation (JDRF) initiated the Articial PancreasProject and funded several centers in the USA and Europeto carry closed-loop control research [147] In 2008 theUSA National Institutes of Health launched an articialpancreas initiative and in 2010 the European APHomeconsortium was established By the end of the rst decade ofthis century the articial pancreas became a global researchtopic engaging physicians and engineers in unprecedentedcollaboration [148 149]

Scientica 7

2006 2008 2010

The JDRF artificial pancreas consortium

is launched (Kowalski)

Studies of hybrid closed-loop control (Weinzimer

and Tamborlane)

First human trials begin using a system designed entirely in silico UVA

Italy and France(Kovatchev Cobelli Renard)

NIH funds artificial pancreas

EU launches the APHome

artificial pancreas initiative

JDRF multicenter trialof modular control-to-

range

2004 the ADICOLproject

(Hovorka)

First studies of automated sc

closed loop (Steil)

FDA accepts the UVAPadova metabolic simulator as a substitute

to animal trials(Kovatchev Cobelli Dalla

Man and Breton)

Modular control-to-rangeintroduced trials at UVA

Italy and France(Kovatchev

Cobelli and Renard)

The APS introduced(Dassau Doyle

First studies of outpatient closed-loop

control (CobelliRenard Zisser and

Kovatchev )

2012

DiAs first portableAP platform

(Keith-HynesKovatchev)

studies Zisser)

30

2012

F 2 Timeline of the articial pancreas developments in the last decademdashtheoretical work and a number of in-clinic studies leading tothe rst trials of wearable articial pancreas device

53 In Silico Models of the Human Metabolic System Acritical step towards accelerated clinical progress of thearticial pancreas was the development of sophisticatedcomputer simulator of the human metabolic system allowingrapid in silico testing of closed-loop control algorithmsis simulation environment was based on the previouslyintroduced Meal Model of glucose-insulin dynamics [76 77]and was equipped with a ldquopopulationrdquo of in silico imagesof 119873119873 119873 119873119873119873 ldquosubjectsrdquo with type 1 diabetes separated inthree age groups 119873119873 119873 119873119873119873 simulated ldquochildrenrdquo below theage of 11 119873119873 119873 119873119873119873 ldquoadolescentsrdquo 12ndash18 years old and119873119873 119873 119873119873119873 ldquoadultsrdquo e characteristics of these ldquosubjectsrdquo(eg weight daily insulin dose carbohydrate ratio etc)were tailored to span a wide range of intersubject variabilityapproximating the variability observed in people in vivo[150] Simulation experiments allow any CGM device anyinsulin pump and any control algorithm to be linked in aclosed-loop system in silico prior to their use in clinical trialsWith this technology any meal and insulin delivery scenariocan be pilot-tested very efficientlymdasha 24-hour period ofclosed-loop control is simulated in under 2 secondsWe needto emphasize however that good in silico performance of acontrol algorithmdoes not guarantee in vivo performancemdashitonly helps test extreme situations and the stability of thealgorithm and rule out inefficient scenarios us computersimulation is only a prerequisite to but not a substitute forclinical trials

In January 2008 in an unprecedented decision theUSA Food and Drug Administration accepted this computer

simulator as a substitute to animal trials for the testing ofclosed-loop control strategies is opened the eld for effi-cient and cost-effective in silico experiments leading directlyto human studies Only three months later in April 2008 therst human trials began at the University of irginia (USA)Montpellier (France) and Padova (Italy) using a controlsystem designed entirely in silico [151]

54 Control System Designs e rst studies of ovorkaet al [144 145] and Steil et al [146] outlined the twomajor types of closed-loop control algorithms now in use inarticial pancreas systemsmdashmodel-predictive control (MPC[145]) and proportional-integral-derivative (PID [146])respectively By 2007 the blueprints of the contemporarycontrollers were in place including run-to-run control[152ndash154] and linear MPC [155] To date the trials ofsubcutaneous closed-loop control systems have been usingeither PID [146 156] or MPC [157ndash160] but MPC becamethe approach of choice targeted by recent research erewere two important reasons making MPC preferable (i)PID is purely reactive responding to changes in glucoselevel while a properly tuned MPC allows for prediction ofglucose dynamics and as a result for mitigation of the timedelays inherent with subcutaneous glucose monitoring andsubcutaneous insulin infusion [62 63] (ii) MPC allows forrelatively straightforward personalizing of the control usingpatient-specic model parameters In addition MPC couldhave ldquolearningrdquo capabilitiesmdashit has been shown that a class

8 Scientica

of algorithms (known as run-to-run control) can ldquolearnrdquospecics of patientsrsquo daily routine (eg timing of meals) andthen optimize the response to a subsequent meal using thisinformation or account for circadian uctuation in insulinresistance (eg dawn phenomenon observed in some people)[149]

In 2008 a universal research platformmdashthe APSmdashwasintroduced enabling automated communication betweenseveral CGM devices insulin pumps and control algorithms[161] e APS was very instrumental for a number ofinpatient trials of closed-loop control A year later a mod-ular architecture was introduced proposing standardizationsequential testing and clinical deployment of articial pan-creas components [162]

55 Inpatient Clinical Trials Between 2008 and 2011 prom-ising results were reported by several groups [156ndash160 163ndash167] Most of these studies pointed out the superiority ofclosed-loop control over standard CSII therapy in termsof (i) increased time within target glucose range (typically39ndash10mmoll) (ii) reduced incidence of hypoglycemia and(iii) better overnight control Two of these studies [159166] had state-of-the-art randomized cross-over design butlacked automated data transfermdashall CGM readings weretransferred to the controllermanually by the study personneland all insulin pump commands were entered manually aswell To distinguish the various degrees of automation inclosed-loop studies the notion of fully-integrated closed-loop control emerged dened as having all of the followingthree components (i) automated data transfer from theCGM to the controller (ii) real-time control action and (iii)automated command of the insulin pump e rst (andthe largest to date) randomized cross-over study of fully-integrated closed-loop control was published in 2012 [168]However even this contemporary trial of fully automatedCLC which enrolled 38 patients with T1D at three centersand tested two different control algorithms achieving note-worthy glycemic control and prevention of hypoglycemia didnot leave the clinical setting e technology used by thisstudy was still based on a laptop computer wired to a CGMand an insulin pump a system limiting the free movementof the study subjects and too cumbersome to be used beyondhospital connes

5 earale tpatient rticial ancreas e transitionof closed-loop control to ambulatory use began in 2011 withthe development of the Diabetes Assistant (DiAs)mdashthe rstwearable articial pancreas platform based on a smart phonee design characteristics of DiAs included the following

(i) based on readily available inexpensive wearablehardware platform

(ii) computationally capable of running advanced closed-loop control algorithms

(iii) wirelessly connectable to CGM devices and insulinpumps

(iv) capable of broadband communication with a centrallocation for remote monitoring and safety supervi-sion of the participants in outpatient clinical trials

In ctober 2011 the rst two pilot trials of wearableoutpatient articial pancreas were performed simultaneouslyin Padova (Italy) and Montpellier (France) [169] ese 2-day trials allowed the renement of a wearable system andenabled a subsequentmultisite feasibility study of ambulatoryarticial pancreas which was completed recently at theUniversities of Virginia Padova and Montpellier and at theSansumDiabetes Research institute Santa Barbara CAUSAResults from this study are forthcoming

6 Conclusions

Solving the optimization problem of diabetes requiresreplacement of insulin action through insulin injections ororal medications (applicable primarily to type 2 diabetes)which until fully automated closed-loop control becomesavailable would remain a process largely controlled bypatient behavior In engineering terms BG uctuations indiabetes result from the activity of a complex metabolicsystem perturbed by behavioral challenges e frequencyand extent of these challenges and the ability of the personrsquosmetabolic system to absorb them determines the qualityof glycemic control Along with HbA1c the magnitudeand speed of BG uctuations is the primary measurablemarker of glucose control in diabetes ese same quanti-tiesmdashHbA1c and glucose variabilitymdashare also the principalfeedback available to patients to assist with optimization oftheir diabetes control

In the past 30 years the technology for monitoring ofblood glucose levels in diabetes has progressed from assess-ment of average glycemia via HbA1c once in several monthsthrough daily SMBG to minutely continuous glucose mon-itoring e increasing temporal resolution of the moni-toring technology enabled increasingly intensive diabetestreatment from daily insulin injections or oral medicationthrough insulin pump therapy to the articial pancreasis progress is accompanied by increasingly sophisticatedanalytical methods for retrieval of blood glucose data rangingfrom subjective interpretation of glucose values and straight-forward summary statistics through risk and variabilityanalysis to real-time closed-loop control algorithms based oncomplex models of the human metabolism

It is therefore evident that the development of diabetestechnology is accelerating exponentially A primary cata-lyst of this acceleration is unprecedented interdisciplinarycollaboration between physicians chemists engineers andmathematicians As a result a wearable articial pancreassuitable for outpatient use is now within reach

e primary engineering challenges to the widespreadadoption of closed-loop control as a viable therapeutic optionfor diabetes include system connectivity the accuracy ofsubcutaneous glucose sensing and the speed of action ofsubcutaneously injected insulin ese challenges are well

Scientica 9

understood by those working in the eld wireless commu-nication between CGM devices insulin pumps and closed-loop controllers are under development and testing newgenerations of CGM device demonstrate superior accuracyand reliability and new insulin analogs and methods forinsulin delivery are being engineered to approximate asclose as possible the action prole of endogenous insulinIt should be noted however that the signals available toa contemporary closed-loop control system are generallylimited to CGM and insulin delivery data user input aboutcarbohydrate intake and physical activity could be availableas well In contrast the endocrine pancreas receives directand rapid control inputs from other nutrients (eg lipids andamino acids) adjacent cells (somatostatin from the delta cellsand glucagon from alpha cells) incretins and neural signalsus while articial closed-loop control is expected to bevastly superior to the diabetes control methods employed inthe clinical practice today it will continue to be imperfectwhen compared to the natural endocrine regulation of bloodglucose

Acknowledgments

is work was made possible by the JDRF Articial PancreasProject the National Institutes of HealthNIDDK GrantsRO1 DK 51562 and RO1 DK 085623 and by the generoussupport of PBM Science Charlottesville Virginia CA USAand the Frederick Banting Foundation Richmond VirginiaCA USA e author thanks his colleagues at the Universityof Virginia Center forDiabetes Technology for their relentlesswork on articial pancreas development

References

[1] American Diabetes Association ldquoDiagnosis and classicationof diabetes mellitusrdquo Diabetes Care vol 27 pp s5ndashs10 2004

[2] A H Kadish ldquoAutomation control of blood sugarmdashI A ser-vomechanism for glucose monitoring and controlrdquo AmericanJournal of Medical Electronics vol 39 pp 82ndash86 1964

[3] J C Pickup H Keen J A Parsons and K G M M AlbertildquoContinuous subcutaneous insulin infusion an approach toachieving normoglycaemiardquo British Medical Journal vol 1 no6107 pp 204ndash207 1978

[4] W V Tamborlane R S Sherwin M Genel and P FeligldquoReduction to normal of plasma glucose in juvenile diabetes bysubcutaneous administration of insulinwith a portable infusionpumprdquo New England Journal of Medicine vol 300 no 11 pp573ndash578 1979

[5] A M Albisser B S Leibel and T G Ewart ldquoAn articialendocrine pancreasrdquoDiabetes vol 23 no 5 pp 389ndash396 1974

[6] E F Pfeier um Ch and A H Clemens ldquoe articialbeta cell a continuous control of blood sugar by external regu-lation of insulin infusion (glucose controlled insulin infusionsystem)rdquo Hormone and Metabolic Research vol 6 no 5 pp339ndash342 1974

[7] J Mirouze J L Selam T C Pham and D Cavadore ldquoEvalua-tion of exogenous insulin homoeostasis by the artical pancreasin insulin dependent diabetesrdquo Diabetologia vol 13 no 3 pp273ndash278 1977

[8] E W Kraegen L V Campbell and Y O Chia ldquoControlof blood glucose in diabetics using an articial pancreasrdquoAustralian and New Zealand Journal of Medicine vol 7 no 3pp 280ndash286 1977

[9] M Shichiri R Kawamori Y Yamasaki M Inoue Y Shigetaand H Abe ldquoComputer algorithm for the articial pancreaticbeta cellrdquo Articial rgans vol 2 supplement pp 247ndash2501978

[10] A H Clemens P H Chang and R W Myers ldquoe devel-opment of Biostator a Glucose Controlled Insulin InfusionSystem (GCIIS)rdquo Hormone and Metabolic Research vol 7 pp23ndash33 1977

[11] E B Marliss F T Murray and E F Stokes ldquoNormalization ofglycemia in diabetics during meals with insulin and glucagondelivery by the articial pancreasrdquo Diabetes vol 26 no 7 pp663ndash672 1977

[12] J V Santiago A H Clemens W L Clarke and D M KipnisldquoClosed-loop and open-loop devices for blood glucose controlin normal and diabetic subjectsrdquo Diabetes vol 28 no 1 pp71ndash84 1979

[13] U Fischer E Jutzi E J Freyse and E Salzsieder ldquoDerivationand experimental proof of a new algorithm for the articial B-cell based on the individual analysis of the physiological insulin-glucose relationshiprdquo Endokrinologie vol 71 no 1 pp 65ndash751978

[14] R S Parker F J Doyle and N A Peppas ldquoe intravenousroute to blood glucose control a review of control algorithmsfor noninvasive monitoring and regulation in type I diabeticpatientsrdquo IEEE Engineering in Medicine and Biology Magazinevol 20 no 1 pp 65ndash73 2001

[15] R N Bergman Y Z Ider C R Bowden and C CobellildquoQuantitative estimation of insulin sensitivityrdquo e AmericanJournal of Physiology vol 236 no 6 pp E667ndashE677 1979

[16] H M Broekhuyse J D Nelson B Zinman and A M AlbisserldquoComparison of algorithms for the closed-loop control of bloodglucose using the articial beta cellrdquo IEEE Transactions onBiomedical Engineering vol 28 no 10 pp 678ndash687 1981

[17] A H Clemens ldquoFeedback control dynamics for glucosecontrolled insulin infusion systemrdquo Medical Progress throughTechnology vol 6 no 3 pp 91ndash98 1979

[18] C Cobelli and A Ruggeri ldquoEvaluation of portalperipheralroute and of algorithms for insulin delivery in the closed-loop control of glucose in diabetes a modeling studyrdquo IEEETransactions on Biomedical Engineering vol 30 no 2 pp93ndash103 1983

[19] E Salzsieder G Albrecht U Fischer and E J Freyse ldquoKineticmodeling of the glucoregulatory system to improve insulintherapyrdquo IEEE Transactions on Biomedical Engineering vol 32no 10 pp 846ndash855 1985

[20] P Brunetti C Cobelli P Cruciani et al ldquoA simulation study ona self-tuning portable controller of blood glucoserdquo InternationalJournal of Articial rgans vol 16 no 1 pp 51ndash57 1993

[21] U Fischer W Schenk E Salzsieder G Albrecht P Abel andE J Freyse ldquoDoes physiological blood glucose control requirean adaptive control strategyrdquo IEEE Transactions on BiomedicalEngineering vol 34 no 8 pp 575ndash582 1987

[22] J T Sorensen A physiologic model of glucose metabolism inman and its use to design and assess improved insulin therapiesfor diabetes [PhD dissertation] Department of Chemical Engi-neering MIT 1985

[23] R S Parker F J Doyle and N A Peppas ldquoA model-basedalgorithm for blood glucose control in type I diabetic patientsrdquo

10 Scientica

IEEE Transactions on Biomedical Engineering vol 46 no 2 pp148ndash157 1999

[24] J J Mastrototaro ldquoe MiniMed continuous glucose monitor-ing Systemrdquo Diabetes Technology anderapeutics vol 2 no 1pp S13ndashS18 2000

[25] B W Bode ldquoClinical utility of the continuous glucose monitor-ing systemrdquo Diabetes Technology anderapeutics vol 2 no 1supplement pp S35ndashS41 2000

[26] B Feldman R Brazg S Schwartz and R Weinstein ldquoAcontinuous glucose sensor based on wired enzyme technol-ogymdashresults from a 3-day trial in patients with type 1 diabetesrdquoDiabetes Technology anderapeutics vol 5 no 5 pp 769ndash7792003

[27] eDiabetes Control and Complications Trial Research Groupldquoe effect of intensive treatment of diabetes on the develop-ment and progression of long-term complications of insulin-dependent diabetes mellitusrdquoNew England Journal of Medicinevol 329 pp 978ndash986 1993

[28] eDiabetes Control and Complications Trial Research Groupldquoe relationship of glycemic exposure (HbA1c) to the riskof development and progression of retinopathy in the Dia-betes Control and Complications Trialrdquo Diabetes vol 44 pp968ndash983 1995

[29] J M Lachin S Genuth D M Nathan B Zinman and B NRutledge ldquoEffect of glycemic exposure on the risk of microvas-cular complications in the diabetes control and complicationstrial-revisitedrdquo Diabetes vol 57 no 4 pp 995ndash1001 2008

[30] P Reichard and M Pihl ldquoMortality and treatment side-effectsduring long-term intensied conventional insulin treatment inthe Stockholm Diabetes Intervention Studyrdquo Diabetes vol 43no 2 pp 313ndash317 1994

[31] UK Prospective Diabetes Study Group (UKPDS) ldquoIntensiveblood-glucose control with sulphonylureas or insulin comparedwith conventional treatment and risk of complications inpatients with type 2 diabetes (UKPDS 33)rdquo Lancet vol 352 no9131 pp 837ndash853 1998

[32] P A Svendsen T Lauritzen U Soegaard and J NerupldquoGlycosylated haemoglobin and steady-state mean blood glu-cose concentration in type 1 (insulin-dependent) diabetesrdquoDiabetologia vol 23 no 5 pp 403ndash405 1982

[33] J V Santiago ldquoLessons from the diabetes control and compli-cations trialrdquo Diabetes vol 42 no 11 pp 1549ndash1554 1993

[34] eDiabetes Control and Complications Trial Research GroupldquoHypoglycemia in the diabetes control and complications trialrdquoDiabetes vol 46 pp 271ndash286 1997

[35] A E Gold BM Frier KMMacLeod and I J Deary ldquoA struc-tural equation model for predictors of severe hypoglycaemiain patients with insulin-dependent diabetes mellitusrdquo DiabeticMedicine vol 14 pp 309ndash315 1997

[36] D J Cox B P Kovatchev D M Julian et al ldquoFrequency ofsevere hypoglycemia in insulin-dependent diabetesmellitus canbe predicted from self-monitoring blood glucose datardquo Journalof Clinical Endocrinology and Metabolism vol 79 no 6 pp1659ndash1662 1994

[37] B P Kovatchev D J Cox L A Gonder-Frederick D Young-Hyman D Schlundt and W Clarke ldquoAssessment of risk forsevere hypoglycemia among adults with IDDM validation ofthe low blood glucose indexrdquo Diabetes Care vol 21 no 11 pp1870ndash1875 1998

[38] B P Kovatchev D J Cox A Kumar L Gonder-Frederick andW L Clarke ldquoAlgorithmic evaluation of metabolic control and

risk of severe hypoglycemia in type 1 and type 2 diabetes usingself-monitoring blood glucose datardquo Diabetes Technology anderapeutics vol 5 no 5 pp 817ndash828 2003

[39] D J Cox L Gonder-Frederick L Ritterband W Clarke andB P Kovatchev ldquoPrediction of severe hypoglycemiardquo DiabetesCare vol 30 no 6 pp 1370ndash1373 2007

[40] S A Amiel R S Sherwin D C Simonson and W VTamborlane ldquoEffect of intensive insulin therapy on glycemicthresholds for counterregulatory hormone releaserdquo Diabetesvol 37 no 7 pp 901ndash907 1988

[41] S A Amiel W V Tamborlane D C Simonson and RS Sherwin ldquoDefective glucose counterregulation aer strictglycemic control of insulin-dependent diabetes mellitusrdquo NewEngland Journal of Medicine vol 316 no 22 pp 1376ndash13831987

[42] P E Cryer and J E Gerich ldquoGlucose counterregulation hypo-glycemia and intensive insulin therapy in diabetes mellitusrdquoNew England Journal of Medicine vol 313 no 4 pp 232ndash2411985

[43] N H White D A Skor and P E Cryer ldquoIdentication of typeI diabetic patients at increased risk for hypoglycemia duringintensive therapyrdquo New England Journal of Medicine vol 308no 9 pp 485ndash491 1983

[44] P E Cryer ldquoIatrogenic hypoglycemia as a cause ofhypoglycemia-associated autonomic failure in IDDM avicious cyclerdquo Diabetes vol 41 no 3 pp 255ndash260 1992

[45] J N Henderson K V Allen I J Deary and B M FrierldquoHypoglycaemia in insulin-treated Type 2 diabetes frequencysymptoms and impaired awarenessrdquo Diabetic Medicine vol 20no 12 pp 1016ndash1021 2003

[46] P E Cryer Hypoglycemia Pathophysiology Diagnosis andTreatment Oxford University Press New York NY USA 1997

[47] P E Cryer S N Davis and H Shamoon ldquoHypoglycemia indiabetesrdquo Diabetes Care vol 26 no 6 pp 1902ndash1912 2003

[48] B P Childs N G Clark and D J Cox ldquoDening and reportinghypoglycemia in diabetes a report from the American diabetesassociation workgroup on hypoglycemiardquo Diabetes Care vol28 no 5 pp 1245ndash1249 2005

[49] P E Cryer ldquoHypoglycaemia the limiting factor in the glycaemicmanagement of Type I and Type II diabetesrdquo Diabetologia vol45 no 7 pp 937ndash948 2002

[50] P E Cryer ldquoHypoglycemia the limiting factor in the manage-ment of IDDMrdquo Diabetes vol 43 no 11 pp 1378ndash1389 1994

[51] A L McCall and B P Kovatchev ldquoe median is not the onlymessage a clinicianrsquos perspective on mathematical analysis ofglycemic variability and modeling in diabetes mellitusrdquo Journalof Diabetes Science and Technology vol 3 pp 3ndash11 2009

[52] M Brownlee and I B Hirsch ldquoGlycemic variability ahemoglobin A1c-independent risk factor for diabetic compli-cationsrdquo Journal of the American Medical Association vol 295no 14 pp 1707ndash1708 2006

[53] I B Hirsch and M Brownlee ldquoShould minimal blood glucosevariability become the gold standard of glycemic controlrdquoJournal of Diabetes and Its Complications vol 19 no 3 pp178ndash181 2005

[54] K Esposito D Giugliano F Nappo and R Marfella ldquoRegres-sion of carotid atherosclerosis by control of postprandial hyper-glycemia in type 2 diabetes mellitusrdquo Circulation vol 110 no2 pp 214ndash219 2004

[55] S Haffner ldquoe importance of postprandial hyperglycaemia indevelopment of cardiovascular disease in people with diabetes

Scientica 11

pointrdquo International Journal of Clinical Practice no 123 pp24ndash26 2001

[56] LMonnier EMas C Ginet et al ldquoActivation of oxidative stressby acute glucose uctuations compared with sustained chronichyperglycemia in patients with type 2 diabetesrdquo Journal of theAmerican Medical Association vol 295 no 14 pp 1681ndash16872006

[57] T S Temelkova-Kurktschiev C Koehler E Henkel W Leon-hardt K Fuecker and M Hanefeld ldquoPostchallenge plasmaglucose and glycemic spikes are more strongly associated withatherosclerosis than fasting glucose or HbA(1c) levelrdquo DiabetesCare vol 23 no 12 pp 1830ndash1834 2000

[58] W L Clarke D J Cox L Gonder-Frederick D JulianB Kovatchev and D Young-Hyman ldquoBiopsychobehavioralmodel of risk of severe hypoglycemia self-management behav-iorsrdquo Diabetes Care vol 22 no 4 pp 580ndash584 1999

[59] D J Cox L A Gonder-Frederick B P Kovatchev et alldquoBiopsychobehavioral model of severe hypoglycemia II under-standing the risk of severe hypoglycemiardquo Diabetes Care vol22 no 12 pp 2018ndash2025 1999

[60] L Gonder-Frederick D Cox B Kovatchev D Schlundt andW Clarke ldquoA biopsychobehavioral model of risk of severehypoglycemiardquoDiabetes Care vol 20 no 4 pp 661ndash669 1997

[61] B Kovatchev D Cox L Gonder-Frederick D Schlundtand W Clarke ldquoStochastic model of self-regulation decisionmaking exemplied by decisions concerning hypoglycemiardquoHealth Psychology vol 17 no 3 pp 277ndash284 1998

[62] G Nucci and C Cobelli ldquoModels of subcutaneous insulinkinetics A critical reviewrdquo Computer Methods and Programs inBiomedicine vol 62 no 3 pp 249ndash257 2000

[63] M E Wilinska L J Chassin H C Schaller L Schaupp T RPieber and R Hovorka ldquoInsulin kinetics in type-1 diabetescontinuous and bolus delivery of rapid acting insulinrdquo IEEETransactions on Biomedical Engineering vol 52 no 1 pp 3ndash122005

[64] R N Bergman D T Finegood and M Ader ldquoAssessmentof insulin sensitivity in vivordquo Endocrine Reviews vol 236 ppE667ndashE677 1985

[65] R N Bergman ldquoe minimal model of glucose regulation abiographyrdquoAdvances in ExperimentalMedicine and Biology vol537 pp 1ndash19 2003

[66] R N Bergman D J Zaccaro R M Watanabe et al ldquoMinimalmodel-based insulin sensitivity has greater heritability and adifferent genetic basis than homeostasis model assessment orfasting insulinrdquo Diabetes vol 52 no 8 pp 2168ndash2174 2003

[67] A Caumo R N Bergman and C Cobelli ldquoInsulin sensitivityfrom meal tolerance tests in normal subjects a minimal modelindexrdquo Journal of Clinical Endocrinology and Metabolism vol85 no 11 pp 4396ndash4402 2000

[68] J O Clausen K Borch-Johnsen H Ibsen et al ldquoInsulin sen-sitivity index acute insulin response and glucose effectivenessin a population-based sample of 380 young healthy Caucasiansanalysis of the impact of gender body fat physical tness andlife-style factorsrdquo Journal of Clinical Investigation vol 98 no 5pp 1195ndash1209 1996

[69] T C Ni M Ader and R N Bergman ldquoReassessment of glu-cose effectiveness and insulin sensitivity from minimal modelanalysis a theoretical evaluation of the single-compartmentglucose distribution assumptionrdquo Diabetes vol 46 no 11 pp1813ndash1821 1997

[70] S Welch S S P Gebhart R N Bergman and L S PhillipsldquoMinimal model analysis of intravenous glucose tolerance

test-derived insulin sensitivity in diabetic subjectsrdquo Journalof Clinical Endocrinology and Metabolism vol 71 no 6 pp1508ndash1518 1990

[71] D Dawson M A Vincent E J Barrett et al ldquoCapillary recruit-ment in skeletal muscle in response to exercise and hyperin-sulinemia assessed with contrast-enhanced ultrasoundrdquo Amer-ican Journal of Physiology vol 282 no 3 pp E714ndashE720 2002

[72] K J Mikines B Sonne P A Farrell B Tronier and H GalboldquoEffect of physical exercise on sensitivity and responsiveness toinsulin in humansrdquoAmerican Journal of Physiology vol 254 no3 pp E248ndashE259 1988

[73] E A Richter ldquoGlucose utilizationrdquo in Handbook of PhysiologyL B Rowell and J T Shepherd Eds pp 912ndash951 OxfordUniversity Press New York NY USA 1996

[74] D H Wasserman R J Geer D E Rice et al ldquoInteraction ofexercise and insulin action in humansrdquo American Journal ofPhysiology vol 260 no 1 pp E37ndashE45 1991

[75] G Pillonetto A Caumo G Sparacino and C Cobelli ldquoAnew dynamic index of insulin sensitivityrdquo IEEE Transactions onBiomedical Engineering vol 53 no 3 pp 369ndash379 2006

[76] C Dalla Man D M Raimondo R A Rizza and C CobellildquoGIM Simulation soware of meal glucose-insulin modelrdquoJournal of Diabetes Science and Technology vol 1 pp 323ndash3302007

[77] C Dalla Man R A Rizza and C Cobelli ldquoMeal simulationmodel of the glucose-insulin systemrdquo IEEE Transactions onBiomedical Engineering vol 54 no 10 pp 1740ndash1749 2007

[78] W L Clarke D Cox L A Gonder-Frederick W Carter andS L Pohl ldquoEvaluating clinical accuracy of systems for self-monitoring of blood glucoserdquo Diabetes Care vol 10 no 5 pp622ndash628 1987

[79] e Diabetes Research In Children Network (Direcnet) StudyGroup ldquoA multicenter study of the accuracy of the one touchultra home glucose meter in children with type 1 diabetesrdquoDiabetes Technology anderapeutics vol 5 no 6 pp 933ndash9412003

[80] I B Hirsch B W Bode B P Childs et al ldquoSelf-monitoring ofblood glucose (SMBG) in insulin- and non-insulin-using adultswith diabetes consensus recommendations for improvingSMBG accuracy utilization and researchrdquo Diabetes Technologyanderapeutics vol 10 no 6 pp 419ndash439 2008

[81] G Freckmann A Baumstark N Jendrike et al ldquoSystemaccuracy evaluation of 27 blood glucose monitoring systemsaccording to DIN en ISO 15197rdquo Diabetes Technology anderapeutics vol 12 no 3 pp 221ndash231 2010

[82] D Deiss J Bolinder J P Riveline et al ldquoImproved glycemiccontrol in poorly controlled patients with type 1 diabetes usingreal-time continuous glucose monitoringrdquo Diabetes Care vol29 no 12 pp 2730ndash2732 2006

[83] S Garg H Zisser S Schwartz et al ldquoImprovement in glycemicexcursions with a transcutaneous real-time continuous glucosesensor a randomized controlled trialrdquo Diabetes Care vol 29no 1 pp 44ndash50 2006

[84] B P Kovatchev and W L Clarke ldquoContinuous glucose mon-itoring reduces risks for hypo- and hyperglycemia and glucosevariability in diabetesrdquo Diabetes vol 56 1 Article ID 0086OR2007

[85] e Juvenile Diabetes Research Foundation Continuous Glu-cose Monitoring Study Group ldquoContinuous glucose monitor-ing and intensive treatment of type 1 diabetesrdquo New EnglandJournal of Medicine vol 359 pp 1464ndash1476 2008

12 Scientica

[86] D C Klonoff ldquoContinuous glucose monitoring roadmap for21st century diabetes therapyrdquo Diabetes Care vol 28 no 5 pp1231ndash1239 2005

[87] R Hovorka ldquoContinuous glucose monitoring and closed-loopsystemsrdquo Diabetic Medicine vol 23 no 1 pp 1ndash12 2006

[88] D C Klonoff ldquoe articial pancreas how sweet engineeringwill solve bitter problemsrdquo Journal of Diabetes Science andTechnology vol 1 pp 72ndash81 2007

[89] I B Hirsch D Armstrong R M Bergenstal et al ldquoClin-ical application of emerging sensor technologies in diabetesmanagement consensus guidelines for continuous glucosemonitoring (CGM)rdquoDiabetes Technology anderapeutics vol10 no 4 pp 232ndash246 2008

[90] K Rebrin G M Steil W P Van Antwerp and J J Mastro-totaro ldquoSubcutaneous glucose predicts plasma glucose inde-pendent of insulin implications for continuous monitoringrdquoAmerican Journal of Physiology vol 277 no 3 pp E561ndashE5711999

[91] K Rebrin and G M Steil ldquoCan interstitial glucose assessmentreplace blood glucosemeasurementsrdquoDiabetes Technology anderapeutics vol 2 no 3 pp 461ndash472 2000

[92] G M Steil K Rebrin F Hariri et al ldquoInterstitial uid glucosedynamics during insulin-induced hypoglycaemiardquo Diabetolo-gia vol 48 no 9 pp 1833ndash1840 2005

[93] M S Boyne D M Silver J Kaplan and C D Saudek ldquoTimingof changes in interstitial and venous blood glucose measuredwith a continuous subcutaneous glucose sensorrdquo Diabetes vol52 no 11 pp 2790ndash2794 2003

[94] E Kulcu J A Tamada G Reach R O Potts and M JLesho ldquoPhysiological differences between interstitial glucoseand blood glucose measured in human subjectsrdquoDiabetes Carevol 26 no 8 pp 2405ndash2409 2003

[95] P J Stout J R Racchini andM E Hilgers ldquoA novel approach tomitigating the physiological lag between blood and interstitialuid glucose measurementsrdquo Diabetes Technology and era-peutics vol 6 no 5 pp 635ndash644 2004

[96] I M E Wentholt A A M Hart J B L Hoekstra and J HDevries ldquoRelationship between interstitial and blood glucose intype 1 diabetes patients delay and the push-pull phenomenonrevisitedrdquo Diabetes Technology and erapeutics vol 9 no 2pp 169ndash175 2007

[97] K J C Wientjes and A J M Schoonen ldquoDetermination oftime delay between blood and interstitial adipose tissue glucoseconcentration change by microdialysis in healthy volunteersrdquonternational Journal of Articial rgans vol 24 no 12 pp884ndash889 2001

[98] B Aussedat M Dupire-Angel R Gifford J C Klein G SWilson and G Reach ldquoInterstitial glucose concentration andglycemia implications for continuous subcutaneous glucosemonitoringrdquo American Journal of Physiology vol 278 no 4 ppE716ndashE728 2000

[99] B P Kovatchev and W L Clarke ldquoPeculiarities of the con-tinuous glucose monitoring data stream and their impact ondeveloping closed-loop control technologyrdquo Journal of DiabetesScience and Technology vol 2 pp 158ndash163 2008

[100] W L Clarke and B P Kovatchev ldquoContinuous glucose sen-sorsmdashcontinuing questions about clinical accuracyrdquo Journal ofDiabetes Science and Technology vol 1 pp 164ndash170 2007

[101] e Diabetes Research in Children Network (DirecNet) StudyGroup ldquoe accuracy of the guardian RT continuous glucosemonitor in children with type 1 diabetesrdquo Diabetes Technologyanderapeutics vol 10 pp 266ndash272 2008

[102] B Kovatchev S Anderson L Heinemann and W ClarkeldquoComparison of the numerical and clinical accuracy of fourcontinuous glucose monitorsrdquo Diabetes Care vol 31 no 6 pp1160ndash1164 2008

[103] S K Garg J Smith C Beatson B Lopez-Baca M Voelmleand P A Gottlieb ldquoComparison of accuracy and safety ofthe SEVEN and the navigator continuous glucose monitoringsystemsrdquo Diabetes Technology and erapeutics vol 11 no 2pp 65ndash72 2009

[104] T Heise T Koschinsky L Heinemann and V Lodwig ldquoHypo-glycemia warning signal and glucose sensors requirements andconceptsrdquo Diabetes Technology and erapeutics vol 5 no 4pp 563ndash571 2003

[105] B Bode K Gross N Rikalo et al ldquoAlarms based on real-timesensor glucose values alert patients to hypo- and hyperglycemiathe Guardian continuous monitoring systemrdquo Diabetes Tech-nology anderapeutics vol 6 no 2 pp 105ndash113 2004

[106] G McGarraugh and R Bergenstal ldquoDetection of hypoglycemiawith continuous interstitial and traditional blood glucosemonitoring using the FreeStyle navigator continuous glucosemonitoring systemrdquo Diabetes Technology and erapeutics vol11 no 3 pp 145ndash150 2009

[107] S E Noujaim D Horwitz M Sharma and J Marhoul ldquoAccu-racy requirements for a hypoglycemia detector an analyticalmodel to evaluate the effects of bias precision and rate ofglucose changerdquo Journal of Diabetes Science and Technology vol1 pp 653ndash668 2007

[108] W K Ward ldquoe role of new technology in the early detectionof hypoglycemiardquo Diabetes Technology and erapeutics vol 6no 2 pp 115ndash117 2004

[109] B Buckingham E Cobry P Clinton et al ldquoPreventing hypo-glycemia using predictive alarm algorithms and insulin pumpsuspensionrdquo Diabetes Technology and erapeutics vol 11 no2 pp 93ndash97 2009

[110] C S Hughes S D Patek M D Breton and B P KovatchevldquoHypoglycemia prevention via pump attenuation and red-yellow-green ldquotrafficrdquo lights using continuous glucose moni-toring and insulin pump datardquo Journal of Diabetes Science andTechnology vol 4 no 5 pp 1146ndash1155 2010

[111] B P Kovatchev D J Cox L A Gonder-Frederick and WClarke ldquoSymmetrization of the blood glucose measurementscale and its applicationsrdquo Diabetes Care vol 20 no 11 pp1655ndash1658 1997

[112] F J Service G D Molnar J W Rosevear E Ackerman LC Gatewood and W F Taylor ldquoMean amplitude of glycemicexcursions a measure of diabetic instabilityrdquo Diabetes vol 19no 9 pp 644ndash655 1970

[113] J Schlichtkrull O Munck and M Jersild ldquoe M-value anindex of blood glucose control in diabeticsrdquo Acta MedicaScandinavica vol 177 pp 95ndash102 1965

[114] E A Ryan T Shandro K Green et al ldquoAssessment of theseverity of hypoglycemia and glycemic lambility in type 1diabetic subjects undergoing islet in transplantationrdquo Diabetesvol 53 no 4 pp 955ndash962 2004

[115] B P Kovatchev D J Cox L Gonder-Frederick and WL Clarke ldquoMethods for quantifying self-monitoring bloodglucose proles exemplied by an examination of blood glucosepatterns in patients with type 1 and type 2 diabetesrdquo DiabetesTechnology anderapeutics vol 4 no 3 pp 295ndash303 2002

[116] B P Kovatchev D J Cox L S Farhy M Straume L Gonder-Frederick and W L Clarke ldquoEpisodes of severe hypoglycemia

Scientica 13

in type 1 diabetes are preceded and followed within 48 hours bymeasurable disturbances in blood glucoserdquo Journal of ClinicalEndocrinology and Metabolism vol 85 no 11 pp 4287ndash42922000

[117] B P Kovatchev M Straume D J Cox and L S FarhyldquoRisk analysis of blood glucose data a quantitative approach tooptimizing the control of Insulin Dependent Diabetesrdquo Journalof eoretical Medicine vol 3 no 1 pp 1ndash10 2000

[118] B P Kovatchev E Otto D Cox L Gonder-Frederick andW Clarke ldquoEvaluation of a new measure of blood glucosevariability in diabetesrdquo Diabetes Care vol 29 no 11 pp2433ndash2438 2006

[119] B P Kovatchev W L Clarke M Breton K Brayman and AMcCall ldquoQuantifying temporal glucose variability in diabetesvia continuous glucose monitoring mathematical methods andclinical applicationrdquo Diabetes Technology and erapeutics vol7 no 6 pp 849ndash862 2005

[120] D Rodbard ldquoNew and improved methods to characterizeglycemic variability using continuous glucosemonitoringrdquoDia-betes Technology and erapeutics vol 11 no 9 pp 551ndash5652009

[121] M Miller and P Strange ldquoUse of Fourier models for analysisand interpretation of continuous glucose monitoring glucoseprolesrdquo Journal of Diabetes Science and Technology vol 1 pp630ndash638 2007

[122] W Clarke and B Kovatchev ldquoStatistical tools to analyzecontinuous glucose monitor datardquo Diabetes Technology anderapeutics vol 11 pp S45ndashS54 2009

[123] C M Mcdonnell S M Donath S I Vidmar G A Wertherand F J Cameron ldquoA novel approach to continuous glucoseanalysis utilizing glycemic variationrdquo Diabetes Technology anderapeutics vol 7 no 2 pp 253ndash263 2005

[124] G Sparacino F Zanderigo A Maran and C Cobelli ldquoContin-uous glucosemonitoring andhypohyperglycaemia predictionrdquoDiabetes Research and Clinical Practice vol 74 no 2 supple-ment pp S160ndashS163 2006

[125] G Sparacino F Zanderigo G Corazza A Maran AFacchinetti and C Cobelli ldquoGlucose concentration can bepredicted ahead in time from continuous glucose monitoringsensor time-seriesrdquo IEEE Transactions on Biomedical Engineer-ing vol 54 pp 931ndash937 2007

[126] J Reifman S Rajaraman A Gribok and W K Ward ldquoPredic-tive monitoring for improved management of glucose levelsrdquoJournal of Diabetes Science and Technology vol 1 pp 478ndash4862007

[127] F Zanderigo G Sparacino B Kovatchev and C CobellildquoGlucose prediction algorithms from continuous monitoringassessment of accuracy via continuous glucose-error grid anal-ysisrdquo Journal of Diabetes Science and Technology vol 1 pp645ndash651 2007

[128] F Cameron G Niemayer K Gundy-Burlet and B Bucking-ham ldquoStatistical hypoglycemia predictionrdquo Journal of DiabetesScience and Technology vol 2 pp 612ndash621 2008

[129] A Gani A V Gribok S Rajaraman W K Ward and JReifman ldquoPredicting subcutaneous glucose concentration inhumans data-driven glucose modelingrdquo IEEE Transactions onBiomedical Engineering vol 56 no 2 pp 246ndash254 2009

[130] M Eren-Oruklu A Cinar L Quinn andD Smith ldquoEstimationof future glucose concentrations with subject-specic recursivelinear modelsrdquo Diabetes Technology and erapeutics vol 11no 4 pp 243ndash253 2009

[131] S M Pappada B D Cameron and P M Rosman ldquoDevelop-ment of a neural network for prediction of glucose concentra-tion in type 1 diabetes patientsrdquo Journal of Diabetes Science andTechnology vol 2 pp 792ndash801 2008

[132] C Cobelli C Dalla Man G Sparacino L Magni G Nicolaoand B P Kovatchev ldquoDiabetes models signals and controlrdquoIEEE Reviews in Biomedical Engineering vol 2 pp 54ndash96 2009

[133] H LeBlanc D Chauvet P Lombrail and J J Robert ldquoGlycemiccontrol with closed-loop intraperitoneal insulin in type Idiabetesrdquo Diabetes Care vol 9 no 2 pp 124ndash128 1986

[134] C D Saudek J L Selman H A Pitt et al ldquoA preliminary trialof the programmable implantablemedication system for insulindeliveryrdquo New England Journal of Medicine vol 321 no 9 pp574ndash579 1989

[135] C Broussolle N Jeandidier and H Hanaire-Broutin ldquoFrenchmulticentre experience of implantable insulin pumpsrdquo Lancetvol 343 no 8896 pp 514ndash515 1994

[136] H Hanaire-Broutin C Broussolle N Jeandidier et al ldquoFea-sibility of intraperitoneal insulin therapy with programmableimplantable pumps in IDDM a multicenter studyrdquo DiabetesCare vol 18 no 3 pp 388ndash392 1995

[137] P R Oskarsson P E Lins H W Henriksson and U CAdamson ldquoMetabolic and hormonal responses to exercisein type 1 diabetic patients during continuous subcutaneousas compared to continuous intraperitoneal insulin infusionrdquoDiabetes and Metabolism vol 25 no 6 pp 491ndash497 1999

[138] P R Oskarsson P E Lins L Backman and U C AdamsonldquoContinuous intraperitoneal insulin infusion partly restores theglucagon response to hypoglycaemia in type 1 diabetic patientsrdquoDiabetes and Metabolism vol 26 no 2 pp 118ndash124 2000

[139] B Catargi L Meyer V Melki E Renard and N JeandidierldquoComparison of blood glucose stability and HbA1C betweenimplantable insulin pumps using U400 hoe 21pH insulin andexternal pumps using lispro in type 1 diabetic patients a pilotstudyrdquo Diabetes and Metabolism vol 28 no 2 pp 133ndash1372002

[140] E Renard ldquoImplantable closed-loop glucose-sensing andinsulin delivery the future for insulin pump therapyrdquo CurrentOpinion in Pharmacology vol 2 no 6 pp 708ndash716 2002

[141] E Renard G Costalat H Chevassus and J Bringer ldquoArticial120573120573-cell clinical experience toward an implantable closed-loopinsulin delivery systemrdquo Diabetes and Metabolism vol 32 no5 pp 497ndash502 2006

[142] E Renard J Place M Cantwell H Chevassus and C CPalerm ldquoClosed-loop insulin delivery using a subcutaneousglucose sensor and intraperitoneal insulin delivery feasibilitystudy testing a new model for the articial pancreasrdquo DiabetesCare vol 33 no 1 pp 121ndash127 2010

[143] R Bellazzi G Nucci and C Cobelli ldquoe subcutaneousroute to insulin-dependent diabetes therapy closed-loop andpartially closed-loop control strategies for insulin deliveryand measuring glucose concentrationrdquo IEEE Engineering inMedicine and Biology Magazine vol 20 no 1 pp 54ndash64 2001

[144] R Hovorka L J Chassin M E Wilinska et al ldquoClosingthe loop the adicol experiencerdquo Diabetes Technology anderapeutics vol 6 no 3 pp 307ndash318 2004

[145] R Hovorka V Canonico L J Chassin et al ldquoNonlinear modelpredictive control of glucose concentration in subjects withtype 1 diabetesrdquo Physiological Measurement vol 25 no 4 pp905ndash920 2004

14 Scientica

[146] G M Steil K Rebrin C Darwin F Hariri and M F SaadldquoFeasibility of automating insulin delivery for the treatment oftype 1 diabetesrdquo Diabetes vol 55 no 12 pp 3344ndash3350 2006

[147] e JDRF e-Newsletter Emerging Technologies in DiabetesResearch 2006

[148] W L Clarke and B Kovatchev ldquoe articial pancreas howclose are we to closing the looprdquo Pediatric EndocrinologyReviews vol 4 no 4 pp 314ndash316 2007

[149] C Cobelli E Renard and B P Kovatchev ldquoPerspectives indiabetes articial pancreas past present futurerdquo Diabetes vol60 pp 2672ndash2682 2011

[150] B P Kovatchev M D Breton C Dalla Man and C Cobelli ldquoInsilico preclinical trials a proof of concept in closed-loop controlof type 1 diabetesrdquo Journal of Diabetes Science and Technologyvol 3 pp 44ndash55 2009

[151] B Kovatchev C Cobelli E Renard et al ldquoMultinational studyof subcutaneous model-predictive closed-loop control in type1 diabetes mellitus summary of the resultsrdquo Journal of DiabetesScience and Technology vol 4 no 6 pp 1374ndash1381 2010

[152] H Zisser L Jovanovic F Doyle P Ospina and C OwensldquoRun-to-run control of meal-related insulin dosingrdquo DiabetesTechnology anderapeutics vol 7 no 1 pp 48ndash57 2005

[153] C Owens H Zisser L Jovanovic B Srinivasan D Bonvinand F J Doyle III ldquoRun-to-run control of blood glucoseconcentrations for people with type 1 diabetes mellitusrdquo IEEETransactions on Biomedical Engineering vol 53 no 6 pp996ndash1005 2006

[154] C C Palerm H Zisser W C Bevier L Jovanovič and F JDoyle III ldquoPrandial insulin dosing using run-to-run controlapplication of clinical data and medical expertise to dene asuitable performance metricrdquo Diabetes Care vol 30 no 5 pp1131ndash1136 2007

[155] LMagni F Raimondo L Bossi et al ldquoModel predictive controlof type 1 diabetes an in silico trialrdquo Journal of Diabetes Scienceand Technology vol 1 pp 804ndash812 2007

[156] S A Weinzimer G M Steil K L Swan J Dziura N Kurtzand W V Tamborlane ldquoFully automated closed-loop insulindelivery versus semiautomated hybrid control in pediatricpatients with type 1 diabetes using an articial pancreasrdquoDiabetes Care vol 31 no 5 pp 934ndash939 2008

[157] W L Clarke S Anderson M Breton S Patek L Kashmerand B Kovatchev ldquoClosed-loop articial pancreas using sub-cutaneous glucose sensing and insulin delivery and a modelpredictive control algorithm the Virginia experiencerdquo Journalof Diabetes Science and Technology vol 3 no 5 pp 1031ndash10382009

[158] D Bruttomesso A Farret S Costa et al ldquoClosed-loop arti-cial pancreas using subcutaneous glucose sensing and insulindelivery and a model predictive control algorithm preliminarystudies in Padova and Montpellierrdquo Journal of Diabetes Scienceand Technology vol 3 no 5 pp 1014ndash1021 2009

[159] R Hovorka J M Allen D Elleri et al ldquoManual closed-loop insulin delivery in children and adolescents with type 1diabetes a phase 2 randomised crossover trialrdquoe Lancet vol375 no 9716 pp 743ndash751 2010

[160] F H El-khatib S J Russell D M Nathan R G Sutherlin andE R Damiano ldquoA bihormonal closed-loop articial pancreasfor type 1 diabetesrdquo Science Translational Medicine vol 2 no27 Article ID 27ra27 2010

[161] E Dassau H Zisser C C Palerm B A Buckingham LJovanovič and F J Doye III ldquoModular articial 120573120573-cell system a

prototype for clinical researchrdquo Journal of Diabetes Science andTechnology vol 2 pp 863ndash872 2008

[162] B Kovatchev S Patek E Dassau et al ldquoControl to range fordiabetes functionality and modular architecturerdquo Journal ofDiabetes Science and Rechnology vol 3 no 5 pp 1058ndash10652009

[163] E Atlas R Nimri S Miller E A Grunberg and M PhillipldquoMD-logic articial pancreas system a pilot study in adults withtype 1 diabetesrdquo Diabetes Care vol 33 no 5 pp 1072ndash10762010

[164] E Dassau H Zisser M W Percival B Grosman L Jovanovičand F J Doyle III ldquoClinical results of automated articialpancreatic 120573120573-cell system with unannounced meal using multi-parametric MPC and insulin-on-boardrdquo in Proceedings of the70th American Diabetes Association Meeting Diabetes vol 59supplement 1 A94 Orlando Fla USA 2010

[165] E M Renard A Farret J Place C Cobelli B P Kovatchev andMD Breton ldquoClosed-loop insulin delivery using subcutaneousinfusion and glucose sensing and equipped with a dedicatedsafety supervision algorithm improves safety of glucose controlin type 1 diabetesrdquo Diabetologia vol 53 supplement 1 p S252010

[166] R Hovorka K Kumareswaran J Harris et al ldquoOvernightclosed loop insulin delivery articial pancreas in adults withtype 1 diabetes crossover randomized controlled studiesrdquoBritish Medical Journal vol 342 no 7803 Article ID d18552011

[167] H Zisser E Dassau W Bevier et al ldquoInitial evaluation ofa fully automated articial pancreasrdquo in Proceedings of the71st American Diabetes Association Meeting Diabetes vol 60supplement 1 A41 San Diego Calif USA 2011

[168] M D Breton A Farret and D Bruttomesso ldquoFully-integratedarticial pancreas in type 1 diabetes modular closed-loopglucose control maintains near-normoglycemiardquo Diabetes vol61 pp 2230ndash2237 2012

[169] C Cobelli E Renard and B P Kovatchev ldquoPilot studies ofwearable articial pancreas in type 1 diabetesrdquo Diabetes Carevol 35 no 9 pp e65ndashe67 2012

Submit your manuscripts athttpwwwhindawicom

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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MEDIATORSINFLAMMATION

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Behavioural Neurology

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Disease Markers

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OncologyJournal of

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Oxidative Medicine and Cellular Longevity

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PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Scientica 3

First use of CSII Pickup et al Br Med J 1978 Tamborlaneet al NEJM 1979

The auto syringe (Dean Kamen)

Subcutaneous continuous glucose monitoring

MinimedCGMS1999

Backpack insulin and glucagon pump

Kadish 1964The Biostator

The minimal model of glucose kinetics

Bergman and Cobelli AJP 1979

Insulin discoveredFrederick Banting

Ames reflectance

meter

1960s 1970s 1990s1920s 1980s 2000s

Blood glucose meters and insulin pumps becoming smaller

Models of diabetes becoming larger and more complex

F 1 e Diabetes technology timeline from the discovery of insulin to the introduction of continuous glucose monitoring

would inevitably enter the range of hypoglycemia (see [51Figure 4])

24 Behavioral Triggers of Glucose Variability Formulatedfrom an engineering point of view the control of diabetesis driven by routine self-treatment behaviors which mayoccasionally evolve into hypo- or hyperglycemia-triggeringevents for example insulin mistiming bolusbasal imbal-ance missed meal or excessive exercise A formal math-ematical description of this process and its potential todestabilize the system was given by the Stochastic Modelof Self-Regulation Behavior which provided a probabilisticinterpretation of the event sequence internal condition rarrperceptionawareness rarr appraisal rarr self-regulation deci-sion [58ndash61] e parameters of this process are individualcontingent on behavioral interpretation for example ona personrsquos ability to control hisher BG within optimallimits e effect of this process is mediated by the specicsof a personrsquos metabolic system such as rate of glucoseappearance in the blood or insulin sensitivity e netresult from this biobehavioral interplay is a certain degreeof glucose variability which in turn could provide feedbackto the person regarding the effectiveness of hisher glycemiccontrol and could prompt corrective actions if neededfor example adjustment of insulin timing or bolusbasalratio

25 Physiological Mechanisms of GV Once triggered theprogression and the extent of glycemic excursions depend onindividual parameters of insulin transport insulin sensitivityand counterregulatory response Technologies utilizing sub-cutaneous insulin injection (eg CSII articial pancreas) relyon the transport of sc-injected insulin into the circulatione duration of this transport varies from person to personand is a major mechanism of postprandial GV because ofthe introduced delaysmdasha postprandial excursion has time todevelop due to insulin deciency (relative to health) in itsinitial stages even if a meal bolus is given on time us themodeling and the formal description of sc insulin transportis important for the effectiveness of modern diabetes controlstrategies [62 63] Aer entering the circulation the actionof insulin is determined by the dynamics of insulin-mediatedglucose utilizationmdasha process that has been mathematicallycharacterized by Bergman and Cobellirsquos classic MinimalModel which introduced the mathematical formulation ofinsulin sensitivity [15] a key metabolic parameter that hasbeen the subject of investigation of a number of subsequentstudies [64ndash70] It is nowwell known that insulin sensitivity isenhanced by exercise [71ndash74] methods exist for quantitativeassessment of insulin sensitivity in laboratory [64] and inoutpatient settings [67] including methods for assessmentduring physical activity [75] e processes of gastric emp-tying and glucose appearance in the blood are similarly well-quantied [76 77] It is apparent that a major source of GV

4 Scientica

is rapid onset of hyperglycemia due to consumption of ldquohighglycemic indexrdquo foods especially in large quantity For exam-ple foods with simple carbohydrate and high fat (classicallypizza) present challenges to technology and optimal therapyby resulting in sustained postprandial hyperglycemia

3 Monitoring of BG Fluctuations in Diabetes

31e Frequency of BGObservation Intuitively the aggres-siveness of glucose control in diabetes would depend on thefrequency of glucose measurement For example if only theaverage glycemic state of a patient is available once every fewmonths (as it would be with measurement of HbA1c alone)then control strategies could only target adjustment of long-term average glycemia but would not be able to respondto daily or hourly variation in glucose level Rapid BGchanges would remain largely unnoticed unless they led toacute complications such as severe hypoglycemia or diabeticketoacidosis us the frequency of glucose measurementdetermines to a large extent the aggressiveness of possibletreatments Table 1 presents the frequency and the temporalresolution of commonly used glucose assessment techniquesGenerally HbA1c reects long-term (over 2-3months) bloodglucose average thus the temporal resolution of HbA1c islimited to reect slow changes in average glycemia Self-monitoring of blood glucose (SMBG) is a standard practiceincluding several (eg 2ndash5) BG readings per day us thetemporal resolution of SMBG allows for assessment of dailyBG proles or weekly trends ith the advent of CGM itis now well accepted that BG uctuations are a process intime which has two principal components risk associatedwith the amplitude (variability) of BG changes and timeindicating the rate of event progression Contemporary CGMdevices are capable of producing BG determinations every5ndash10 minutes which provides vast amounts of data withhigh temporal resolution and allows for detailed monitoringof glucose uctuations on a temporal scale of minutesmdashafrequency that enables closed-loop control

32 Self-Monitoring of Blood Glucose Contemporary homeBG meters offer convenient means for frequent and accurateBG determinations through self-monitoring Most devicesare capable of storing BG readings (typically over 150readings) and have interfaces to download these readingsinto a computer e meters are usually accompanied bysoware that has capabilities for basic data analyses (egcalculation of mean BG estimates of the average BG overthe previous two weeks percentages in target hypoglycemicand hyperglycemic zones etc) and log of the data andgraphical representation (eg histograms pie charts) [78ndash81] Analytical methods based on SMBG data are discussedin the next section

33 Continuous Glucose Monitoring Since the advent ofCGM technology 10 years ago [25ndash27] signicant progresshas been made towards versatile and reliable CGM devicesthat not only monitor the entire course of BG day and nightbut also provide feedback to the patient such as alarms when

BG reaches preset low or high levels A number of studieshave documented the benets of CGM [82ndash85] and chartedguidelines for clinical use and its future as a precursor toclosed-loop control [86ndash89] However while CGM has thepotential to revolutionize the control of diabetes it also gen-erates data streams that are both voluminous and complexe utilization of such data requires an understanding ofthe physical biochemical and mathematical principles andproperties involved in this new technology It is important toknow that CGM devices measure glucose concentration in adifferent compartmentmdashthe interstitium Interstitial glucose(IG) uctuations are related to BG presumably via diffusionprocess [90ndash92] To account for the gradient between BGand IG CGM devices are calibrated with capillary glucosewhich brings the typically lower IG concentration to BGlevels Successful calibration would adjust the amplitude ofIG uctuations with respect to BG but would not eliminatethe possible time lag due to BG-to-IG glucose transport andthe sensor processing time (instrument delay) Because sucha time lag could greatly inuence the accuracy of CGM anumber of studies were dedicated to its investigation yieldingvarious results [93ndash96] For example it was hypothesized thatif a glucose fall is due to peripheral glucose consumptionthe physiologic time lag would be negative that is fall in IGwould precede fall in BG [90 97] In most studies IG laggedbehind BG (most of the time) by 4ndash10 minutes regardlessof the direction of BG change [92 93] e formulation ofthe push-pull phenomenon offered reconciliation of theseresults and provided arguments for a more complex BG-IG relationship than a simple constant or directional timelag [96 98] In addition errors from calibration loss ofsensitivity and random noise confound CGM data [99]Nevertheless the accuracy of CGM is increasing and maybe reaching a physiological limit for subcutaneous glucosemonitoring [100ndash103]

In addition to presenting frequent data (eg every 5ndash10minutes) CGM devices typically display directional trendsand BG rate of change and are capable of alerting the patientof upcoming hypo- or hyperglycemia ese features arebased on methods which predict blood glucose and generatealarms and warning messages In the past several yearsthese methods have rapidly evolved from a concept [104] toimplementation in CGM devices such as the Guardian RTand the MiniMed Paradigm REAL-Time System (MedtronicNorhtridge CA USA) [105] and the Freestyle Navigator(Abbott Diabetes Care Alameda CA USA) [106] Alarmsfor particularly rapid rates of BG change (eg greater than2mgdLmin) are available as well (Guardian RT and Dex-com Seven Plus Dexcom San Diego CA USA) Discussionof the methods for testing of the accuracy and the utility ofsuch alarms has been initiated [106ndash108] and the next logicalstepmdashprevention of hypoglycemia via shutoff of the insulinpumpmdashhas been undertaken [109]

4 Assessment of BG Fluctuations in Diabetes

As presented in Table 1 different frequencies of BGmonitor-ing provide data for different types of analytical techniques

Scientica 5

T 1 Frequency of available glucose monitoring technologies

Measure Temporal resolution Reects Methods typically used to present and analyze the data

HbA1c Monthsyears Slow changes in average BG Direct assay and review of values group comparisonswhen treatments are evaluated

Self-monitoring of bloodglucose (SMBG) Daysweeks Daily variation

weekly trends

Mean and standard deviation (SD) coefficient ofvariation (CV) Interquartile range (IQR)119872119872-value(1965) MAGE (1970)lability index (2004)lowhigh BG (risk) Indices (1998)average daily risk range (ADRR 2006)

Continuous glucosemonitoring (CGM) Minuteshours System dynamics

uctuation and periodicity

CONGA (2005)glucose rate of Change amp CGM versions of lowhighBG (risk) indices (2005)time seriesdynamical system analysis

assessing the glycemic state or the BG dynamics of aperson with diabetes A brief account of analytical methodsapplicable to SMBG andor CGM data is given below Someof these methods such as the Risk Analysis of BG data haveentered the design of closed-loop control systems preventinghypoglycemia [110]

41 SMBG-Based Analytical Methods e computation ofmean glucose values from SMBG data is typically used as adescriptor of overall glycemic control Computing pre- andpostmeal averages and their difference can serve as an indica-tion of the effectiveness of premeal bolus timing and amountSimilarly the percentages of SMBG readings within belowor above preset target limits would serve as indication of thegeneral behavior of BG uctuations e suggested limits are70 and 180mgdL (39ndash10mmoll) which create three sug-gested by the DCCT and commonly accepted bands hypo-glycemia (BG le 70mgdL) normoglycemia (70mgdL ltBG le 180mgdL) hyperglycemia (BG gt 180mgdL) [1]Percentage of time within additional bands can be computedas well to emphasize the frequency of extreme glucoseexcursions Computing standard deviation (SD) as ameasureof glucose variability is not recommended because the BGmeasurement scale is highly asymmetric the hypoglycemicrange is numerically narrower than the hyperglycemic rangeand the distribution of the glucose values of an individualis typically quite skewed [111] erefore SD would bepredominantly inuenced by hyperglycemic excursions andwould not be sensitive to hypoglycemia It is also possiblefor condence intervals based on SD to assume unrealisticnegative valuesus as a standard measure of GV we wouldsuggest reporting interquartile range (IQR) which is suitablefor asymmetric distributions Several diabetes-specic met-rics are also available to serve the analysis of SMBG dataincluding the mean amplitude of glucose excursions (MAGE[112]) the 119872119872-value [113] the lability index [114] and thelow and high blood glucose indices (LBGI HBGI) whichreect the risks associated with hypo- and hyperglycemiarespectively [37 115] In a series of studies we have shownthat specic risk analysis of SMBG data could also capturelong-term trends towards increased risk for hypoglycemia

[36ndash38] and could identify 24-hour periods of increased riskfor hypoglycemia [39 116]

42 Risk Analysis of BG Data To provide a avor for theanalytical techniques used for SMBG CGM and closed-loopcontrol data we will present a bit more detail on the conceptfor risk analysis of BG data [117] e risk analysis steps areas follows

421 Symmetrization of the BG Scale A nonlinear transfor-mation is applied to the BG measurements scale to map theentire BG range (20 to 600mgdL or 11 to 333mmoll) to asymmetric intervalis is needed because the distribution ofBG values of a person with diabetes is asymmetric typicallyskewed towards hyperglycemiae BG value of 1125mgdL(625mmoll) is mapped to zero corresponding to zero riskfor hypo- or hyperglycemia (we should note that this is nota normoglycemic or fasting value which in health wouldbe lt100mgdL it is zero-risk value pertinent to diabetes)e analytical form of this transformation is 119891119891119891BG) = 120574120574 120574119891ln 119891BG)120572120572 minus 120573120573) where the parameters are estimated as 120572120572 =1084 120573120573 = 12057312057381 and 120574120574 = 11205730120574 if BG is measured in mgdLand 120572120572 = 10120572120572 120573120573 = 181205721 and 120574120574 = 11205741205744 if BG is measuredin mmoll [111]

422 Assignment of a Risk Value to Each BG Reading Aquadratic risk function is dened as by the formula 119903119903119891BG) =10 120574 119891119891119891BG)120572 e function 119903119903119891BG) ranges from 0 to 100Its minimum value is achieved at BG = 1125mgdL asafe euglycemic BG reading while its maximum is reachedat the extreme ends of the BG scale us 119903119903119891BG) can beinterpreted as a measure of the risk associated with a certainBG level e le branch of this parabola identies the riskof hypoglycemia while the right branch identies the risk ofhyperglycemia

423 Computing Measures of Risk for Hypoglycemia Hyper-glycemia and Glucose Variability Let 1199091199091 119909119909120572hellip 119909119909119899119899 be aseries of 119899119899 BG readings and let 119903119903119903119903119891BG) = 119903119903119891BG) if 119891119891119891BG) lt 0and 0 otherwise 119903119903119903119891BG) = 119903119903119891BG) if 119891119891119891BG) gt 0 and 0

6 Scientica

otherwise en the low and high blood glucose indices arecomputed as follows

LBGI = 1119899119899

11989911989910055761005576119894119894=111990311990311990311990310076491007649119909119909119894119894100766510076652 HBGI = 1

119899119899

11989911989910055761005576119894119894=11199031199031199031007649100764911990911990911989411989410076651007665

2 (1)

us the LBGI is a nonnegative quantity that increaseswhen the number andor extent of low BG readings increasesand the HBGI is nonnegative quantity that increases whenthe number andor extent of high BG readings increasesBased on this same technique we also dene the average dailyrisk range (ADRR) which is a measure of risks associatedwith overall glycemic variability [118] In studies the LBGItypically accounted for 40ndash55 of the variance of futuresignicant hypoglycemia in the subsequent 3ndash6 months [36ndash38] which made it a potent predictor of hypoglycemia basedon SMBGeADRR has been shown superior to traditionalglucose variability measures in terms of risk assessment andprediction of extreme glycemic excursions [118] Specicallyit has been demonstrated that classication of risk for hypo-glycemia based on four ADRR categories low risk ADRR lt20 low-moderate risk 20 le ADRR lt 30 moderate-high risk30 le ADRR lt 40 and high risk ADRR gt 40 resulted inmore than a sixfold increase in risk for hypoglycemia fromthe lowest to the highest risk category [118] In addition thelow and high BG indices have been adapted to continuousmonitoring data [119] and can be used in the same way aswith SMBG to assess the risk for hypo- or hyperglycemia

43 CGM-Based Analytical Methods While traditional risk[119] and variability [120] analyses are still applied to CGMdata the high temporal resolution of CGM brought aboutthe possibility for use of sophisticated analytical methodsassessing system (person) dynamics on the time scale of min-utes is necessitated the development of new technologiesfor data analysis and visualization that are not available forSMBG data Analysis of the BG rate of change (measuredin mgdLmin) is a way to evaluate the dynamics of BGuctuations on the time scale of minutes e BG rate ofchange at 119905119905119894119894mdashis computed as the ratio (BG(119905119905119894119894)minusBG(119905119905119894119894minus1))(119905119905119894119894minus119905119905119894119894minus1) where BG(119905119905119894119894) and BG(119905119905119894119894minus1) are CGM readings taken attimes 119905119905119894119894 and 119905119905119894119894minus1 for example minutes apart Investigationof the frequency of glucose uctuations showed that optimalevaluation of the BG rate of change would be achieved overtime periods of 15minutes [121] for exampleΔ119905119905 = 119905119905119894119894 minus119905119905119894119894minus1 =15 A large variation of the BG rate of change indicates rapidand more pronounced BG uctuations and therefore a lessstable system us the standard deviation of the BG rateof change is a measure of stability of glucose uctuation (weshould note that as opposed to the distribution of BG levelsthe distribution of the BG rate of change is symmetric andtherefore using SD is statistically accurate [122]) e SDof BG rate of change has been introduced as a measure ofstability computed fromCGMdata and is known asCONGAof order 1 In general CONGA122 of order 119899119899 is computedas the standard deviation of CGM readings that are 119899119899 hoursapart reecting glucose stability over these time intervals[123]

Most important to the development of the articial pan-creas algorithms is a class of methods allowing the predictionof BG values ahead in time ese methods typically basedon time-series techniques have been applied successfully ina number of studies [124ndash129] In addition to time seriesneural networks have been used for the prediction of glucoselevels from CGM [130 131] Detailed reviews of CGM dataanalysis methods are presented in [122] including severalgraphs that could be used for the visualization of the rathercomplex CGM data sets and in [132] where a broad reviewof modeling analytical and control techniques for diabetesis provided

5 Control of BG Fluctuations in Diabetes

51 Intraperitoneal Insulin Delivery As detailed in the Intro-duction the articial pancreas idea can be traced back to theearly 70s when external BG regulation in people with dia-betes was achieved by iv glucose measurement and iv infu-sion of glucose and insulin However the intravenous routeof closed-loop control remains cumbersome and unsuitedfor outpatient use An alternative has been presented byimplantable intraperitoneal (ip) systems employing iv sam-pling and ip insulin delivery [133ndash136] e ip infusionroute has several desirable characteristics reproducibility ofinsulin absorption quick time to peak and return to baselineof insulin action near-physiological peripheral insulin levelsand restoration of glucagon response to hypoglycemia andexercise [133 137ndash139] However while ip systems haveachieved excellent BG control their implementation stillrequires considerable surgery and is associated with signi-cant cost Nevertheless the development of less invasive andcheaper implantable ports (eg DiaPort Roche DiagnosticsMannheim Germany) may contribute to the future prolifer-ation of ip insulin delivery [140ndash142]

52e Subcutaneous Route to Closed-Loop Control Follow-ing the progress of minimally invasive subcutaneous CGMthe next logical step was the development of sc closed-loopglucose control which links a CGM device with CSII insulinpump A key element of this combination was a controlalgorithm which monitors BG uctuations and the actionsof the insulin pump and computes insulin delivery rate everyfew minutes [143] Figure 2 presents key milestones in thetimeline of this development

Following the pioneering work of Hovorka et al [144145] and Steil et al [146] in 2006 the Juvenile DiabetesResearch Foundation (JDRF) initiated the Articial PancreasProject and funded several centers in the USA and Europeto carry closed-loop control research [147] In 2008 theUSA National Institutes of Health launched an articialpancreas initiative and in 2010 the European APHomeconsortium was established By the end of the rst decade ofthis century the articial pancreas became a global researchtopic engaging physicians and engineers in unprecedentedcollaboration [148 149]

Scientica 7

2006 2008 2010

The JDRF artificial pancreas consortium

is launched (Kowalski)

Studies of hybrid closed-loop control (Weinzimer

and Tamborlane)

First human trials begin using a system designed entirely in silico UVA

Italy and France(Kovatchev Cobelli Renard)

NIH funds artificial pancreas

EU launches the APHome

artificial pancreas initiative

JDRF multicenter trialof modular control-to-

range

2004 the ADICOLproject

(Hovorka)

First studies of automated sc

closed loop (Steil)

FDA accepts the UVAPadova metabolic simulator as a substitute

to animal trials(Kovatchev Cobelli Dalla

Man and Breton)

Modular control-to-rangeintroduced trials at UVA

Italy and France(Kovatchev

Cobelli and Renard)

The APS introduced(Dassau Doyle

First studies of outpatient closed-loop

control (CobelliRenard Zisser and

Kovatchev )

2012

DiAs first portableAP platform

(Keith-HynesKovatchev)

studies Zisser)

30

2012

F 2 Timeline of the articial pancreas developments in the last decademdashtheoretical work and a number of in-clinic studies leading tothe rst trials of wearable articial pancreas device

53 In Silico Models of the Human Metabolic System Acritical step towards accelerated clinical progress of thearticial pancreas was the development of sophisticatedcomputer simulator of the human metabolic system allowingrapid in silico testing of closed-loop control algorithmsis simulation environment was based on the previouslyintroduced Meal Model of glucose-insulin dynamics [76 77]and was equipped with a ldquopopulationrdquo of in silico imagesof 119873119873 119873 119873119873119873 ldquosubjectsrdquo with type 1 diabetes separated inthree age groups 119873119873 119873 119873119873119873 simulated ldquochildrenrdquo below theage of 11 119873119873 119873 119873119873119873 ldquoadolescentsrdquo 12ndash18 years old and119873119873 119873 119873119873119873 ldquoadultsrdquo e characteristics of these ldquosubjectsrdquo(eg weight daily insulin dose carbohydrate ratio etc)were tailored to span a wide range of intersubject variabilityapproximating the variability observed in people in vivo[150] Simulation experiments allow any CGM device anyinsulin pump and any control algorithm to be linked in aclosed-loop system in silico prior to their use in clinical trialsWith this technology any meal and insulin delivery scenariocan be pilot-tested very efficientlymdasha 24-hour period ofclosed-loop control is simulated in under 2 secondsWe needto emphasize however that good in silico performance of acontrol algorithmdoes not guarantee in vivo performancemdashitonly helps test extreme situations and the stability of thealgorithm and rule out inefficient scenarios us computersimulation is only a prerequisite to but not a substitute forclinical trials

In January 2008 in an unprecedented decision theUSA Food and Drug Administration accepted this computer

simulator as a substitute to animal trials for the testing ofclosed-loop control strategies is opened the eld for effi-cient and cost-effective in silico experiments leading directlyto human studies Only three months later in April 2008 therst human trials began at the University of irginia (USA)Montpellier (France) and Padova (Italy) using a controlsystem designed entirely in silico [151]

54 Control System Designs e rst studies of ovorkaet al [144 145] and Steil et al [146] outlined the twomajor types of closed-loop control algorithms now in use inarticial pancreas systemsmdashmodel-predictive control (MPC[145]) and proportional-integral-derivative (PID [146])respectively By 2007 the blueprints of the contemporarycontrollers were in place including run-to-run control[152ndash154] and linear MPC [155] To date the trials ofsubcutaneous closed-loop control systems have been usingeither PID [146 156] or MPC [157ndash160] but MPC becamethe approach of choice targeted by recent research erewere two important reasons making MPC preferable (i)PID is purely reactive responding to changes in glucoselevel while a properly tuned MPC allows for prediction ofglucose dynamics and as a result for mitigation of the timedelays inherent with subcutaneous glucose monitoring andsubcutaneous insulin infusion [62 63] (ii) MPC allows forrelatively straightforward personalizing of the control usingpatient-specic model parameters In addition MPC couldhave ldquolearningrdquo capabilitiesmdashit has been shown that a class

8 Scientica

of algorithms (known as run-to-run control) can ldquolearnrdquospecics of patientsrsquo daily routine (eg timing of meals) andthen optimize the response to a subsequent meal using thisinformation or account for circadian uctuation in insulinresistance (eg dawn phenomenon observed in some people)[149]

In 2008 a universal research platformmdashthe APSmdashwasintroduced enabling automated communication betweenseveral CGM devices insulin pumps and control algorithms[161] e APS was very instrumental for a number ofinpatient trials of closed-loop control A year later a mod-ular architecture was introduced proposing standardizationsequential testing and clinical deployment of articial pan-creas components [162]

55 Inpatient Clinical Trials Between 2008 and 2011 prom-ising results were reported by several groups [156ndash160 163ndash167] Most of these studies pointed out the superiority ofclosed-loop control over standard CSII therapy in termsof (i) increased time within target glucose range (typically39ndash10mmoll) (ii) reduced incidence of hypoglycemia and(iii) better overnight control Two of these studies [159166] had state-of-the-art randomized cross-over design butlacked automated data transfermdashall CGM readings weretransferred to the controllermanually by the study personneland all insulin pump commands were entered manually aswell To distinguish the various degrees of automation inclosed-loop studies the notion of fully-integrated closed-loop control emerged dened as having all of the followingthree components (i) automated data transfer from theCGM to the controller (ii) real-time control action and (iii)automated command of the insulin pump e rst (andthe largest to date) randomized cross-over study of fully-integrated closed-loop control was published in 2012 [168]However even this contemporary trial of fully automatedCLC which enrolled 38 patients with T1D at three centersand tested two different control algorithms achieving note-worthy glycemic control and prevention of hypoglycemia didnot leave the clinical setting e technology used by thisstudy was still based on a laptop computer wired to a CGMand an insulin pump a system limiting the free movementof the study subjects and too cumbersome to be used beyondhospital connes

5 earale tpatient rticial ancreas e transitionof closed-loop control to ambulatory use began in 2011 withthe development of the Diabetes Assistant (DiAs)mdashthe rstwearable articial pancreas platform based on a smart phonee design characteristics of DiAs included the following

(i) based on readily available inexpensive wearablehardware platform

(ii) computationally capable of running advanced closed-loop control algorithms

(iii) wirelessly connectable to CGM devices and insulinpumps

(iv) capable of broadband communication with a centrallocation for remote monitoring and safety supervi-sion of the participants in outpatient clinical trials

In ctober 2011 the rst two pilot trials of wearableoutpatient articial pancreas were performed simultaneouslyin Padova (Italy) and Montpellier (France) [169] ese 2-day trials allowed the renement of a wearable system andenabled a subsequentmultisite feasibility study of ambulatoryarticial pancreas which was completed recently at theUniversities of Virginia Padova and Montpellier and at theSansumDiabetes Research institute Santa Barbara CAUSAResults from this study are forthcoming

6 Conclusions

Solving the optimization problem of diabetes requiresreplacement of insulin action through insulin injections ororal medications (applicable primarily to type 2 diabetes)which until fully automated closed-loop control becomesavailable would remain a process largely controlled bypatient behavior In engineering terms BG uctuations indiabetes result from the activity of a complex metabolicsystem perturbed by behavioral challenges e frequencyand extent of these challenges and the ability of the personrsquosmetabolic system to absorb them determines the qualityof glycemic control Along with HbA1c the magnitudeand speed of BG uctuations is the primary measurablemarker of glucose control in diabetes ese same quanti-tiesmdashHbA1c and glucose variabilitymdashare also the principalfeedback available to patients to assist with optimization oftheir diabetes control

In the past 30 years the technology for monitoring ofblood glucose levels in diabetes has progressed from assess-ment of average glycemia via HbA1c once in several monthsthrough daily SMBG to minutely continuous glucose mon-itoring e increasing temporal resolution of the moni-toring technology enabled increasingly intensive diabetestreatment from daily insulin injections or oral medicationthrough insulin pump therapy to the articial pancreasis progress is accompanied by increasingly sophisticatedanalytical methods for retrieval of blood glucose data rangingfrom subjective interpretation of glucose values and straight-forward summary statistics through risk and variabilityanalysis to real-time closed-loop control algorithms based oncomplex models of the human metabolism

It is therefore evident that the development of diabetestechnology is accelerating exponentially A primary cata-lyst of this acceleration is unprecedented interdisciplinarycollaboration between physicians chemists engineers andmathematicians As a result a wearable articial pancreassuitable for outpatient use is now within reach

e primary engineering challenges to the widespreadadoption of closed-loop control as a viable therapeutic optionfor diabetes include system connectivity the accuracy ofsubcutaneous glucose sensing and the speed of action ofsubcutaneously injected insulin ese challenges are well

Scientica 9

understood by those working in the eld wireless commu-nication between CGM devices insulin pumps and closed-loop controllers are under development and testing newgenerations of CGM device demonstrate superior accuracyand reliability and new insulin analogs and methods forinsulin delivery are being engineered to approximate asclose as possible the action prole of endogenous insulinIt should be noted however that the signals available toa contemporary closed-loop control system are generallylimited to CGM and insulin delivery data user input aboutcarbohydrate intake and physical activity could be availableas well In contrast the endocrine pancreas receives directand rapid control inputs from other nutrients (eg lipids andamino acids) adjacent cells (somatostatin from the delta cellsand glucagon from alpha cells) incretins and neural signalsus while articial closed-loop control is expected to bevastly superior to the diabetes control methods employed inthe clinical practice today it will continue to be imperfectwhen compared to the natural endocrine regulation of bloodglucose

Acknowledgments

is work was made possible by the JDRF Articial PancreasProject the National Institutes of HealthNIDDK GrantsRO1 DK 51562 and RO1 DK 085623 and by the generoussupport of PBM Science Charlottesville Virginia CA USAand the Frederick Banting Foundation Richmond VirginiaCA USA e author thanks his colleagues at the Universityof Virginia Center forDiabetes Technology for their relentlesswork on articial pancreas development

References

[1] American Diabetes Association ldquoDiagnosis and classicationof diabetes mellitusrdquo Diabetes Care vol 27 pp s5ndashs10 2004

[2] A H Kadish ldquoAutomation control of blood sugarmdashI A ser-vomechanism for glucose monitoring and controlrdquo AmericanJournal of Medical Electronics vol 39 pp 82ndash86 1964

[3] J C Pickup H Keen J A Parsons and K G M M AlbertildquoContinuous subcutaneous insulin infusion an approach toachieving normoglycaemiardquo British Medical Journal vol 1 no6107 pp 204ndash207 1978

[4] W V Tamborlane R S Sherwin M Genel and P FeligldquoReduction to normal of plasma glucose in juvenile diabetes bysubcutaneous administration of insulinwith a portable infusionpumprdquo New England Journal of Medicine vol 300 no 11 pp573ndash578 1979

[5] A M Albisser B S Leibel and T G Ewart ldquoAn articialendocrine pancreasrdquoDiabetes vol 23 no 5 pp 389ndash396 1974

[6] E F Pfeier um Ch and A H Clemens ldquoe articialbeta cell a continuous control of blood sugar by external regu-lation of insulin infusion (glucose controlled insulin infusionsystem)rdquo Hormone and Metabolic Research vol 6 no 5 pp339ndash342 1974

[7] J Mirouze J L Selam T C Pham and D Cavadore ldquoEvalua-tion of exogenous insulin homoeostasis by the artical pancreasin insulin dependent diabetesrdquo Diabetologia vol 13 no 3 pp273ndash278 1977

[8] E W Kraegen L V Campbell and Y O Chia ldquoControlof blood glucose in diabetics using an articial pancreasrdquoAustralian and New Zealand Journal of Medicine vol 7 no 3pp 280ndash286 1977

[9] M Shichiri R Kawamori Y Yamasaki M Inoue Y Shigetaand H Abe ldquoComputer algorithm for the articial pancreaticbeta cellrdquo Articial rgans vol 2 supplement pp 247ndash2501978

[10] A H Clemens P H Chang and R W Myers ldquoe devel-opment of Biostator a Glucose Controlled Insulin InfusionSystem (GCIIS)rdquo Hormone and Metabolic Research vol 7 pp23ndash33 1977

[11] E B Marliss F T Murray and E F Stokes ldquoNormalization ofglycemia in diabetics during meals with insulin and glucagondelivery by the articial pancreasrdquo Diabetes vol 26 no 7 pp663ndash672 1977

[12] J V Santiago A H Clemens W L Clarke and D M KipnisldquoClosed-loop and open-loop devices for blood glucose controlin normal and diabetic subjectsrdquo Diabetes vol 28 no 1 pp71ndash84 1979

[13] U Fischer E Jutzi E J Freyse and E Salzsieder ldquoDerivationand experimental proof of a new algorithm for the articial B-cell based on the individual analysis of the physiological insulin-glucose relationshiprdquo Endokrinologie vol 71 no 1 pp 65ndash751978

[14] R S Parker F J Doyle and N A Peppas ldquoe intravenousroute to blood glucose control a review of control algorithmsfor noninvasive monitoring and regulation in type I diabeticpatientsrdquo IEEE Engineering in Medicine and Biology Magazinevol 20 no 1 pp 65ndash73 2001

[15] R N Bergman Y Z Ider C R Bowden and C CobellildquoQuantitative estimation of insulin sensitivityrdquo e AmericanJournal of Physiology vol 236 no 6 pp E667ndashE677 1979

[16] H M Broekhuyse J D Nelson B Zinman and A M AlbisserldquoComparison of algorithms for the closed-loop control of bloodglucose using the articial beta cellrdquo IEEE Transactions onBiomedical Engineering vol 28 no 10 pp 678ndash687 1981

[17] A H Clemens ldquoFeedback control dynamics for glucosecontrolled insulin infusion systemrdquo Medical Progress throughTechnology vol 6 no 3 pp 91ndash98 1979

[18] C Cobelli and A Ruggeri ldquoEvaluation of portalperipheralroute and of algorithms for insulin delivery in the closed-loop control of glucose in diabetes a modeling studyrdquo IEEETransactions on Biomedical Engineering vol 30 no 2 pp93ndash103 1983

[19] E Salzsieder G Albrecht U Fischer and E J Freyse ldquoKineticmodeling of the glucoregulatory system to improve insulintherapyrdquo IEEE Transactions on Biomedical Engineering vol 32no 10 pp 846ndash855 1985

[20] P Brunetti C Cobelli P Cruciani et al ldquoA simulation study ona self-tuning portable controller of blood glucoserdquo InternationalJournal of Articial rgans vol 16 no 1 pp 51ndash57 1993

[21] U Fischer W Schenk E Salzsieder G Albrecht P Abel andE J Freyse ldquoDoes physiological blood glucose control requirean adaptive control strategyrdquo IEEE Transactions on BiomedicalEngineering vol 34 no 8 pp 575ndash582 1987

[22] J T Sorensen A physiologic model of glucose metabolism inman and its use to design and assess improved insulin therapiesfor diabetes [PhD dissertation] Department of Chemical Engi-neering MIT 1985

[23] R S Parker F J Doyle and N A Peppas ldquoA model-basedalgorithm for blood glucose control in type I diabetic patientsrdquo

10 Scientica

IEEE Transactions on Biomedical Engineering vol 46 no 2 pp148ndash157 1999

[24] J J Mastrototaro ldquoe MiniMed continuous glucose monitor-ing Systemrdquo Diabetes Technology anderapeutics vol 2 no 1pp S13ndashS18 2000

[25] B W Bode ldquoClinical utility of the continuous glucose monitor-ing systemrdquo Diabetes Technology anderapeutics vol 2 no 1supplement pp S35ndashS41 2000

[26] B Feldman R Brazg S Schwartz and R Weinstein ldquoAcontinuous glucose sensor based on wired enzyme technol-ogymdashresults from a 3-day trial in patients with type 1 diabetesrdquoDiabetes Technology anderapeutics vol 5 no 5 pp 769ndash7792003

[27] eDiabetes Control and Complications Trial Research Groupldquoe effect of intensive treatment of diabetes on the develop-ment and progression of long-term complications of insulin-dependent diabetes mellitusrdquoNew England Journal of Medicinevol 329 pp 978ndash986 1993

[28] eDiabetes Control and Complications Trial Research Groupldquoe relationship of glycemic exposure (HbA1c) to the riskof development and progression of retinopathy in the Dia-betes Control and Complications Trialrdquo Diabetes vol 44 pp968ndash983 1995

[29] J M Lachin S Genuth D M Nathan B Zinman and B NRutledge ldquoEffect of glycemic exposure on the risk of microvas-cular complications in the diabetes control and complicationstrial-revisitedrdquo Diabetes vol 57 no 4 pp 995ndash1001 2008

[30] P Reichard and M Pihl ldquoMortality and treatment side-effectsduring long-term intensied conventional insulin treatment inthe Stockholm Diabetes Intervention Studyrdquo Diabetes vol 43no 2 pp 313ndash317 1994

[31] UK Prospective Diabetes Study Group (UKPDS) ldquoIntensiveblood-glucose control with sulphonylureas or insulin comparedwith conventional treatment and risk of complications inpatients with type 2 diabetes (UKPDS 33)rdquo Lancet vol 352 no9131 pp 837ndash853 1998

[32] P A Svendsen T Lauritzen U Soegaard and J NerupldquoGlycosylated haemoglobin and steady-state mean blood glu-cose concentration in type 1 (insulin-dependent) diabetesrdquoDiabetologia vol 23 no 5 pp 403ndash405 1982

[33] J V Santiago ldquoLessons from the diabetes control and compli-cations trialrdquo Diabetes vol 42 no 11 pp 1549ndash1554 1993

[34] eDiabetes Control and Complications Trial Research GroupldquoHypoglycemia in the diabetes control and complications trialrdquoDiabetes vol 46 pp 271ndash286 1997

[35] A E Gold BM Frier KMMacLeod and I J Deary ldquoA struc-tural equation model for predictors of severe hypoglycaemiain patients with insulin-dependent diabetes mellitusrdquo DiabeticMedicine vol 14 pp 309ndash315 1997

[36] D J Cox B P Kovatchev D M Julian et al ldquoFrequency ofsevere hypoglycemia in insulin-dependent diabetesmellitus canbe predicted from self-monitoring blood glucose datardquo Journalof Clinical Endocrinology and Metabolism vol 79 no 6 pp1659ndash1662 1994

[37] B P Kovatchev D J Cox L A Gonder-Frederick D Young-Hyman D Schlundt and W Clarke ldquoAssessment of risk forsevere hypoglycemia among adults with IDDM validation ofthe low blood glucose indexrdquo Diabetes Care vol 21 no 11 pp1870ndash1875 1998

[38] B P Kovatchev D J Cox A Kumar L Gonder-Frederick andW L Clarke ldquoAlgorithmic evaluation of metabolic control and

risk of severe hypoglycemia in type 1 and type 2 diabetes usingself-monitoring blood glucose datardquo Diabetes Technology anderapeutics vol 5 no 5 pp 817ndash828 2003

[39] D J Cox L Gonder-Frederick L Ritterband W Clarke andB P Kovatchev ldquoPrediction of severe hypoglycemiardquo DiabetesCare vol 30 no 6 pp 1370ndash1373 2007

[40] S A Amiel R S Sherwin D C Simonson and W VTamborlane ldquoEffect of intensive insulin therapy on glycemicthresholds for counterregulatory hormone releaserdquo Diabetesvol 37 no 7 pp 901ndash907 1988

[41] S A Amiel W V Tamborlane D C Simonson and RS Sherwin ldquoDefective glucose counterregulation aer strictglycemic control of insulin-dependent diabetes mellitusrdquo NewEngland Journal of Medicine vol 316 no 22 pp 1376ndash13831987

[42] P E Cryer and J E Gerich ldquoGlucose counterregulation hypo-glycemia and intensive insulin therapy in diabetes mellitusrdquoNew England Journal of Medicine vol 313 no 4 pp 232ndash2411985

[43] N H White D A Skor and P E Cryer ldquoIdentication of typeI diabetic patients at increased risk for hypoglycemia duringintensive therapyrdquo New England Journal of Medicine vol 308no 9 pp 485ndash491 1983

[44] P E Cryer ldquoIatrogenic hypoglycemia as a cause ofhypoglycemia-associated autonomic failure in IDDM avicious cyclerdquo Diabetes vol 41 no 3 pp 255ndash260 1992

[45] J N Henderson K V Allen I J Deary and B M FrierldquoHypoglycaemia in insulin-treated Type 2 diabetes frequencysymptoms and impaired awarenessrdquo Diabetic Medicine vol 20no 12 pp 1016ndash1021 2003

[46] P E Cryer Hypoglycemia Pathophysiology Diagnosis andTreatment Oxford University Press New York NY USA 1997

[47] P E Cryer S N Davis and H Shamoon ldquoHypoglycemia indiabetesrdquo Diabetes Care vol 26 no 6 pp 1902ndash1912 2003

[48] B P Childs N G Clark and D J Cox ldquoDening and reportinghypoglycemia in diabetes a report from the American diabetesassociation workgroup on hypoglycemiardquo Diabetes Care vol28 no 5 pp 1245ndash1249 2005

[49] P E Cryer ldquoHypoglycaemia the limiting factor in the glycaemicmanagement of Type I and Type II diabetesrdquo Diabetologia vol45 no 7 pp 937ndash948 2002

[50] P E Cryer ldquoHypoglycemia the limiting factor in the manage-ment of IDDMrdquo Diabetes vol 43 no 11 pp 1378ndash1389 1994

[51] A L McCall and B P Kovatchev ldquoe median is not the onlymessage a clinicianrsquos perspective on mathematical analysis ofglycemic variability and modeling in diabetes mellitusrdquo Journalof Diabetes Science and Technology vol 3 pp 3ndash11 2009

[52] M Brownlee and I B Hirsch ldquoGlycemic variability ahemoglobin A1c-independent risk factor for diabetic compli-cationsrdquo Journal of the American Medical Association vol 295no 14 pp 1707ndash1708 2006

[53] I B Hirsch and M Brownlee ldquoShould minimal blood glucosevariability become the gold standard of glycemic controlrdquoJournal of Diabetes and Its Complications vol 19 no 3 pp178ndash181 2005

[54] K Esposito D Giugliano F Nappo and R Marfella ldquoRegres-sion of carotid atherosclerosis by control of postprandial hyper-glycemia in type 2 diabetes mellitusrdquo Circulation vol 110 no2 pp 214ndash219 2004

[55] S Haffner ldquoe importance of postprandial hyperglycaemia indevelopment of cardiovascular disease in people with diabetes

Scientica 11

pointrdquo International Journal of Clinical Practice no 123 pp24ndash26 2001

[56] LMonnier EMas C Ginet et al ldquoActivation of oxidative stressby acute glucose uctuations compared with sustained chronichyperglycemia in patients with type 2 diabetesrdquo Journal of theAmerican Medical Association vol 295 no 14 pp 1681ndash16872006

[57] T S Temelkova-Kurktschiev C Koehler E Henkel W Leon-hardt K Fuecker and M Hanefeld ldquoPostchallenge plasmaglucose and glycemic spikes are more strongly associated withatherosclerosis than fasting glucose or HbA(1c) levelrdquo DiabetesCare vol 23 no 12 pp 1830ndash1834 2000

[58] W L Clarke D J Cox L Gonder-Frederick D JulianB Kovatchev and D Young-Hyman ldquoBiopsychobehavioralmodel of risk of severe hypoglycemia self-management behav-iorsrdquo Diabetes Care vol 22 no 4 pp 580ndash584 1999

[59] D J Cox L A Gonder-Frederick B P Kovatchev et alldquoBiopsychobehavioral model of severe hypoglycemia II under-standing the risk of severe hypoglycemiardquo Diabetes Care vol22 no 12 pp 2018ndash2025 1999

[60] L Gonder-Frederick D Cox B Kovatchev D Schlundt andW Clarke ldquoA biopsychobehavioral model of risk of severehypoglycemiardquoDiabetes Care vol 20 no 4 pp 661ndash669 1997

[61] B Kovatchev D Cox L Gonder-Frederick D Schlundtand W Clarke ldquoStochastic model of self-regulation decisionmaking exemplied by decisions concerning hypoglycemiardquoHealth Psychology vol 17 no 3 pp 277ndash284 1998

[62] G Nucci and C Cobelli ldquoModels of subcutaneous insulinkinetics A critical reviewrdquo Computer Methods and Programs inBiomedicine vol 62 no 3 pp 249ndash257 2000

[63] M E Wilinska L J Chassin H C Schaller L Schaupp T RPieber and R Hovorka ldquoInsulin kinetics in type-1 diabetescontinuous and bolus delivery of rapid acting insulinrdquo IEEETransactions on Biomedical Engineering vol 52 no 1 pp 3ndash122005

[64] R N Bergman D T Finegood and M Ader ldquoAssessmentof insulin sensitivity in vivordquo Endocrine Reviews vol 236 ppE667ndashE677 1985

[65] R N Bergman ldquoe minimal model of glucose regulation abiographyrdquoAdvances in ExperimentalMedicine and Biology vol537 pp 1ndash19 2003

[66] R N Bergman D J Zaccaro R M Watanabe et al ldquoMinimalmodel-based insulin sensitivity has greater heritability and adifferent genetic basis than homeostasis model assessment orfasting insulinrdquo Diabetes vol 52 no 8 pp 2168ndash2174 2003

[67] A Caumo R N Bergman and C Cobelli ldquoInsulin sensitivityfrom meal tolerance tests in normal subjects a minimal modelindexrdquo Journal of Clinical Endocrinology and Metabolism vol85 no 11 pp 4396ndash4402 2000

[68] J O Clausen K Borch-Johnsen H Ibsen et al ldquoInsulin sen-sitivity index acute insulin response and glucose effectivenessin a population-based sample of 380 young healthy Caucasiansanalysis of the impact of gender body fat physical tness andlife-style factorsrdquo Journal of Clinical Investigation vol 98 no 5pp 1195ndash1209 1996

[69] T C Ni M Ader and R N Bergman ldquoReassessment of glu-cose effectiveness and insulin sensitivity from minimal modelanalysis a theoretical evaluation of the single-compartmentglucose distribution assumptionrdquo Diabetes vol 46 no 11 pp1813ndash1821 1997

[70] S Welch S S P Gebhart R N Bergman and L S PhillipsldquoMinimal model analysis of intravenous glucose tolerance

test-derived insulin sensitivity in diabetic subjectsrdquo Journalof Clinical Endocrinology and Metabolism vol 71 no 6 pp1508ndash1518 1990

[71] D Dawson M A Vincent E J Barrett et al ldquoCapillary recruit-ment in skeletal muscle in response to exercise and hyperin-sulinemia assessed with contrast-enhanced ultrasoundrdquo Amer-ican Journal of Physiology vol 282 no 3 pp E714ndashE720 2002

[72] K J Mikines B Sonne P A Farrell B Tronier and H GalboldquoEffect of physical exercise on sensitivity and responsiveness toinsulin in humansrdquoAmerican Journal of Physiology vol 254 no3 pp E248ndashE259 1988

[73] E A Richter ldquoGlucose utilizationrdquo in Handbook of PhysiologyL B Rowell and J T Shepherd Eds pp 912ndash951 OxfordUniversity Press New York NY USA 1996

[74] D H Wasserman R J Geer D E Rice et al ldquoInteraction ofexercise and insulin action in humansrdquo American Journal ofPhysiology vol 260 no 1 pp E37ndashE45 1991

[75] G Pillonetto A Caumo G Sparacino and C Cobelli ldquoAnew dynamic index of insulin sensitivityrdquo IEEE Transactions onBiomedical Engineering vol 53 no 3 pp 369ndash379 2006

[76] C Dalla Man D M Raimondo R A Rizza and C CobellildquoGIM Simulation soware of meal glucose-insulin modelrdquoJournal of Diabetes Science and Technology vol 1 pp 323ndash3302007

[77] C Dalla Man R A Rizza and C Cobelli ldquoMeal simulationmodel of the glucose-insulin systemrdquo IEEE Transactions onBiomedical Engineering vol 54 no 10 pp 1740ndash1749 2007

[78] W L Clarke D Cox L A Gonder-Frederick W Carter andS L Pohl ldquoEvaluating clinical accuracy of systems for self-monitoring of blood glucoserdquo Diabetes Care vol 10 no 5 pp622ndash628 1987

[79] e Diabetes Research In Children Network (Direcnet) StudyGroup ldquoA multicenter study of the accuracy of the one touchultra home glucose meter in children with type 1 diabetesrdquoDiabetes Technology anderapeutics vol 5 no 6 pp 933ndash9412003

[80] I B Hirsch B W Bode B P Childs et al ldquoSelf-monitoring ofblood glucose (SMBG) in insulin- and non-insulin-using adultswith diabetes consensus recommendations for improvingSMBG accuracy utilization and researchrdquo Diabetes Technologyanderapeutics vol 10 no 6 pp 419ndash439 2008

[81] G Freckmann A Baumstark N Jendrike et al ldquoSystemaccuracy evaluation of 27 blood glucose monitoring systemsaccording to DIN en ISO 15197rdquo Diabetes Technology anderapeutics vol 12 no 3 pp 221ndash231 2010

[82] D Deiss J Bolinder J P Riveline et al ldquoImproved glycemiccontrol in poorly controlled patients with type 1 diabetes usingreal-time continuous glucose monitoringrdquo Diabetes Care vol29 no 12 pp 2730ndash2732 2006

[83] S Garg H Zisser S Schwartz et al ldquoImprovement in glycemicexcursions with a transcutaneous real-time continuous glucosesensor a randomized controlled trialrdquo Diabetes Care vol 29no 1 pp 44ndash50 2006

[84] B P Kovatchev and W L Clarke ldquoContinuous glucose mon-itoring reduces risks for hypo- and hyperglycemia and glucosevariability in diabetesrdquo Diabetes vol 56 1 Article ID 0086OR2007

[85] e Juvenile Diabetes Research Foundation Continuous Glu-cose Monitoring Study Group ldquoContinuous glucose monitor-ing and intensive treatment of type 1 diabetesrdquo New EnglandJournal of Medicine vol 359 pp 1464ndash1476 2008

12 Scientica

[86] D C Klonoff ldquoContinuous glucose monitoring roadmap for21st century diabetes therapyrdquo Diabetes Care vol 28 no 5 pp1231ndash1239 2005

[87] R Hovorka ldquoContinuous glucose monitoring and closed-loopsystemsrdquo Diabetic Medicine vol 23 no 1 pp 1ndash12 2006

[88] D C Klonoff ldquoe articial pancreas how sweet engineeringwill solve bitter problemsrdquo Journal of Diabetes Science andTechnology vol 1 pp 72ndash81 2007

[89] I B Hirsch D Armstrong R M Bergenstal et al ldquoClin-ical application of emerging sensor technologies in diabetesmanagement consensus guidelines for continuous glucosemonitoring (CGM)rdquoDiabetes Technology anderapeutics vol10 no 4 pp 232ndash246 2008

[90] K Rebrin G M Steil W P Van Antwerp and J J Mastro-totaro ldquoSubcutaneous glucose predicts plasma glucose inde-pendent of insulin implications for continuous monitoringrdquoAmerican Journal of Physiology vol 277 no 3 pp E561ndashE5711999

[91] K Rebrin and G M Steil ldquoCan interstitial glucose assessmentreplace blood glucosemeasurementsrdquoDiabetes Technology anderapeutics vol 2 no 3 pp 461ndash472 2000

[92] G M Steil K Rebrin F Hariri et al ldquoInterstitial uid glucosedynamics during insulin-induced hypoglycaemiardquo Diabetolo-gia vol 48 no 9 pp 1833ndash1840 2005

[93] M S Boyne D M Silver J Kaplan and C D Saudek ldquoTimingof changes in interstitial and venous blood glucose measuredwith a continuous subcutaneous glucose sensorrdquo Diabetes vol52 no 11 pp 2790ndash2794 2003

[94] E Kulcu J A Tamada G Reach R O Potts and M JLesho ldquoPhysiological differences between interstitial glucoseand blood glucose measured in human subjectsrdquoDiabetes Carevol 26 no 8 pp 2405ndash2409 2003

[95] P J Stout J R Racchini andM E Hilgers ldquoA novel approach tomitigating the physiological lag between blood and interstitialuid glucose measurementsrdquo Diabetes Technology and era-peutics vol 6 no 5 pp 635ndash644 2004

[96] I M E Wentholt A A M Hart J B L Hoekstra and J HDevries ldquoRelationship between interstitial and blood glucose intype 1 diabetes patients delay and the push-pull phenomenonrevisitedrdquo Diabetes Technology and erapeutics vol 9 no 2pp 169ndash175 2007

[97] K J C Wientjes and A J M Schoonen ldquoDetermination oftime delay between blood and interstitial adipose tissue glucoseconcentration change by microdialysis in healthy volunteersrdquonternational Journal of Articial rgans vol 24 no 12 pp884ndash889 2001

[98] B Aussedat M Dupire-Angel R Gifford J C Klein G SWilson and G Reach ldquoInterstitial glucose concentration andglycemia implications for continuous subcutaneous glucosemonitoringrdquo American Journal of Physiology vol 278 no 4 ppE716ndashE728 2000

[99] B P Kovatchev and W L Clarke ldquoPeculiarities of the con-tinuous glucose monitoring data stream and their impact ondeveloping closed-loop control technologyrdquo Journal of DiabetesScience and Technology vol 2 pp 158ndash163 2008

[100] W L Clarke and B P Kovatchev ldquoContinuous glucose sen-sorsmdashcontinuing questions about clinical accuracyrdquo Journal ofDiabetes Science and Technology vol 1 pp 164ndash170 2007

[101] e Diabetes Research in Children Network (DirecNet) StudyGroup ldquoe accuracy of the guardian RT continuous glucosemonitor in children with type 1 diabetesrdquo Diabetes Technologyanderapeutics vol 10 pp 266ndash272 2008

[102] B Kovatchev S Anderson L Heinemann and W ClarkeldquoComparison of the numerical and clinical accuracy of fourcontinuous glucose monitorsrdquo Diabetes Care vol 31 no 6 pp1160ndash1164 2008

[103] S K Garg J Smith C Beatson B Lopez-Baca M Voelmleand P A Gottlieb ldquoComparison of accuracy and safety ofthe SEVEN and the navigator continuous glucose monitoringsystemsrdquo Diabetes Technology and erapeutics vol 11 no 2pp 65ndash72 2009

[104] T Heise T Koschinsky L Heinemann and V Lodwig ldquoHypo-glycemia warning signal and glucose sensors requirements andconceptsrdquo Diabetes Technology and erapeutics vol 5 no 4pp 563ndash571 2003

[105] B Bode K Gross N Rikalo et al ldquoAlarms based on real-timesensor glucose values alert patients to hypo- and hyperglycemiathe Guardian continuous monitoring systemrdquo Diabetes Tech-nology anderapeutics vol 6 no 2 pp 105ndash113 2004

[106] G McGarraugh and R Bergenstal ldquoDetection of hypoglycemiawith continuous interstitial and traditional blood glucosemonitoring using the FreeStyle navigator continuous glucosemonitoring systemrdquo Diabetes Technology and erapeutics vol11 no 3 pp 145ndash150 2009

[107] S E Noujaim D Horwitz M Sharma and J Marhoul ldquoAccu-racy requirements for a hypoglycemia detector an analyticalmodel to evaluate the effects of bias precision and rate ofglucose changerdquo Journal of Diabetes Science and Technology vol1 pp 653ndash668 2007

[108] W K Ward ldquoe role of new technology in the early detectionof hypoglycemiardquo Diabetes Technology and erapeutics vol 6no 2 pp 115ndash117 2004

[109] B Buckingham E Cobry P Clinton et al ldquoPreventing hypo-glycemia using predictive alarm algorithms and insulin pumpsuspensionrdquo Diabetes Technology and erapeutics vol 11 no2 pp 93ndash97 2009

[110] C S Hughes S D Patek M D Breton and B P KovatchevldquoHypoglycemia prevention via pump attenuation and red-yellow-green ldquotrafficrdquo lights using continuous glucose moni-toring and insulin pump datardquo Journal of Diabetes Science andTechnology vol 4 no 5 pp 1146ndash1155 2010

[111] B P Kovatchev D J Cox L A Gonder-Frederick and WClarke ldquoSymmetrization of the blood glucose measurementscale and its applicationsrdquo Diabetes Care vol 20 no 11 pp1655ndash1658 1997

[112] F J Service G D Molnar J W Rosevear E Ackerman LC Gatewood and W F Taylor ldquoMean amplitude of glycemicexcursions a measure of diabetic instabilityrdquo Diabetes vol 19no 9 pp 644ndash655 1970

[113] J Schlichtkrull O Munck and M Jersild ldquoe M-value anindex of blood glucose control in diabeticsrdquo Acta MedicaScandinavica vol 177 pp 95ndash102 1965

[114] E A Ryan T Shandro K Green et al ldquoAssessment of theseverity of hypoglycemia and glycemic lambility in type 1diabetic subjects undergoing islet in transplantationrdquo Diabetesvol 53 no 4 pp 955ndash962 2004

[115] B P Kovatchev D J Cox L Gonder-Frederick and WL Clarke ldquoMethods for quantifying self-monitoring bloodglucose proles exemplied by an examination of blood glucosepatterns in patients with type 1 and type 2 diabetesrdquo DiabetesTechnology anderapeutics vol 4 no 3 pp 295ndash303 2002

[116] B P Kovatchev D J Cox L S Farhy M Straume L Gonder-Frederick and W L Clarke ldquoEpisodes of severe hypoglycemia

Scientica 13

in type 1 diabetes are preceded and followed within 48 hours bymeasurable disturbances in blood glucoserdquo Journal of ClinicalEndocrinology and Metabolism vol 85 no 11 pp 4287ndash42922000

[117] B P Kovatchev M Straume D J Cox and L S FarhyldquoRisk analysis of blood glucose data a quantitative approach tooptimizing the control of Insulin Dependent Diabetesrdquo Journalof eoretical Medicine vol 3 no 1 pp 1ndash10 2000

[118] B P Kovatchev E Otto D Cox L Gonder-Frederick andW Clarke ldquoEvaluation of a new measure of blood glucosevariability in diabetesrdquo Diabetes Care vol 29 no 11 pp2433ndash2438 2006

[119] B P Kovatchev W L Clarke M Breton K Brayman and AMcCall ldquoQuantifying temporal glucose variability in diabetesvia continuous glucose monitoring mathematical methods andclinical applicationrdquo Diabetes Technology and erapeutics vol7 no 6 pp 849ndash862 2005

[120] D Rodbard ldquoNew and improved methods to characterizeglycemic variability using continuous glucosemonitoringrdquoDia-betes Technology and erapeutics vol 11 no 9 pp 551ndash5652009

[121] M Miller and P Strange ldquoUse of Fourier models for analysisand interpretation of continuous glucose monitoring glucoseprolesrdquo Journal of Diabetes Science and Technology vol 1 pp630ndash638 2007

[122] W Clarke and B Kovatchev ldquoStatistical tools to analyzecontinuous glucose monitor datardquo Diabetes Technology anderapeutics vol 11 pp S45ndashS54 2009

[123] C M Mcdonnell S M Donath S I Vidmar G A Wertherand F J Cameron ldquoA novel approach to continuous glucoseanalysis utilizing glycemic variationrdquo Diabetes Technology anderapeutics vol 7 no 2 pp 253ndash263 2005

[124] G Sparacino F Zanderigo A Maran and C Cobelli ldquoContin-uous glucosemonitoring andhypohyperglycaemia predictionrdquoDiabetes Research and Clinical Practice vol 74 no 2 supple-ment pp S160ndashS163 2006

[125] G Sparacino F Zanderigo G Corazza A Maran AFacchinetti and C Cobelli ldquoGlucose concentration can bepredicted ahead in time from continuous glucose monitoringsensor time-seriesrdquo IEEE Transactions on Biomedical Engineer-ing vol 54 pp 931ndash937 2007

[126] J Reifman S Rajaraman A Gribok and W K Ward ldquoPredic-tive monitoring for improved management of glucose levelsrdquoJournal of Diabetes Science and Technology vol 1 pp 478ndash4862007

[127] F Zanderigo G Sparacino B Kovatchev and C CobellildquoGlucose prediction algorithms from continuous monitoringassessment of accuracy via continuous glucose-error grid anal-ysisrdquo Journal of Diabetes Science and Technology vol 1 pp645ndash651 2007

[128] F Cameron G Niemayer K Gundy-Burlet and B Bucking-ham ldquoStatistical hypoglycemia predictionrdquo Journal of DiabetesScience and Technology vol 2 pp 612ndash621 2008

[129] A Gani A V Gribok S Rajaraman W K Ward and JReifman ldquoPredicting subcutaneous glucose concentration inhumans data-driven glucose modelingrdquo IEEE Transactions onBiomedical Engineering vol 56 no 2 pp 246ndash254 2009

[130] M Eren-Oruklu A Cinar L Quinn andD Smith ldquoEstimationof future glucose concentrations with subject-specic recursivelinear modelsrdquo Diabetes Technology and erapeutics vol 11no 4 pp 243ndash253 2009

[131] S M Pappada B D Cameron and P M Rosman ldquoDevelop-ment of a neural network for prediction of glucose concentra-tion in type 1 diabetes patientsrdquo Journal of Diabetes Science andTechnology vol 2 pp 792ndash801 2008

[132] C Cobelli C Dalla Man G Sparacino L Magni G Nicolaoand B P Kovatchev ldquoDiabetes models signals and controlrdquoIEEE Reviews in Biomedical Engineering vol 2 pp 54ndash96 2009

[133] H LeBlanc D Chauvet P Lombrail and J J Robert ldquoGlycemiccontrol with closed-loop intraperitoneal insulin in type Idiabetesrdquo Diabetes Care vol 9 no 2 pp 124ndash128 1986

[134] C D Saudek J L Selman H A Pitt et al ldquoA preliminary trialof the programmable implantablemedication system for insulindeliveryrdquo New England Journal of Medicine vol 321 no 9 pp574ndash579 1989

[135] C Broussolle N Jeandidier and H Hanaire-Broutin ldquoFrenchmulticentre experience of implantable insulin pumpsrdquo Lancetvol 343 no 8896 pp 514ndash515 1994

[136] H Hanaire-Broutin C Broussolle N Jeandidier et al ldquoFea-sibility of intraperitoneal insulin therapy with programmableimplantable pumps in IDDM a multicenter studyrdquo DiabetesCare vol 18 no 3 pp 388ndash392 1995

[137] P R Oskarsson P E Lins H W Henriksson and U CAdamson ldquoMetabolic and hormonal responses to exercisein type 1 diabetic patients during continuous subcutaneousas compared to continuous intraperitoneal insulin infusionrdquoDiabetes and Metabolism vol 25 no 6 pp 491ndash497 1999

[138] P R Oskarsson P E Lins L Backman and U C AdamsonldquoContinuous intraperitoneal insulin infusion partly restores theglucagon response to hypoglycaemia in type 1 diabetic patientsrdquoDiabetes and Metabolism vol 26 no 2 pp 118ndash124 2000

[139] B Catargi L Meyer V Melki E Renard and N JeandidierldquoComparison of blood glucose stability and HbA1C betweenimplantable insulin pumps using U400 hoe 21pH insulin andexternal pumps using lispro in type 1 diabetic patients a pilotstudyrdquo Diabetes and Metabolism vol 28 no 2 pp 133ndash1372002

[140] E Renard ldquoImplantable closed-loop glucose-sensing andinsulin delivery the future for insulin pump therapyrdquo CurrentOpinion in Pharmacology vol 2 no 6 pp 708ndash716 2002

[141] E Renard G Costalat H Chevassus and J Bringer ldquoArticial120573120573-cell clinical experience toward an implantable closed-loopinsulin delivery systemrdquo Diabetes and Metabolism vol 32 no5 pp 497ndash502 2006

[142] E Renard J Place M Cantwell H Chevassus and C CPalerm ldquoClosed-loop insulin delivery using a subcutaneousglucose sensor and intraperitoneal insulin delivery feasibilitystudy testing a new model for the articial pancreasrdquo DiabetesCare vol 33 no 1 pp 121ndash127 2010

[143] R Bellazzi G Nucci and C Cobelli ldquoe subcutaneousroute to insulin-dependent diabetes therapy closed-loop andpartially closed-loop control strategies for insulin deliveryand measuring glucose concentrationrdquo IEEE Engineering inMedicine and Biology Magazine vol 20 no 1 pp 54ndash64 2001

[144] R Hovorka L J Chassin M E Wilinska et al ldquoClosingthe loop the adicol experiencerdquo Diabetes Technology anderapeutics vol 6 no 3 pp 307ndash318 2004

[145] R Hovorka V Canonico L J Chassin et al ldquoNonlinear modelpredictive control of glucose concentration in subjects withtype 1 diabetesrdquo Physiological Measurement vol 25 no 4 pp905ndash920 2004

14 Scientica

[146] G M Steil K Rebrin C Darwin F Hariri and M F SaadldquoFeasibility of automating insulin delivery for the treatment oftype 1 diabetesrdquo Diabetes vol 55 no 12 pp 3344ndash3350 2006

[147] e JDRF e-Newsletter Emerging Technologies in DiabetesResearch 2006

[148] W L Clarke and B Kovatchev ldquoe articial pancreas howclose are we to closing the looprdquo Pediatric EndocrinologyReviews vol 4 no 4 pp 314ndash316 2007

[149] C Cobelli E Renard and B P Kovatchev ldquoPerspectives indiabetes articial pancreas past present futurerdquo Diabetes vol60 pp 2672ndash2682 2011

[150] B P Kovatchev M D Breton C Dalla Man and C Cobelli ldquoInsilico preclinical trials a proof of concept in closed-loop controlof type 1 diabetesrdquo Journal of Diabetes Science and Technologyvol 3 pp 44ndash55 2009

[151] B Kovatchev C Cobelli E Renard et al ldquoMultinational studyof subcutaneous model-predictive closed-loop control in type1 diabetes mellitus summary of the resultsrdquo Journal of DiabetesScience and Technology vol 4 no 6 pp 1374ndash1381 2010

[152] H Zisser L Jovanovic F Doyle P Ospina and C OwensldquoRun-to-run control of meal-related insulin dosingrdquo DiabetesTechnology anderapeutics vol 7 no 1 pp 48ndash57 2005

[153] C Owens H Zisser L Jovanovic B Srinivasan D Bonvinand F J Doyle III ldquoRun-to-run control of blood glucoseconcentrations for people with type 1 diabetes mellitusrdquo IEEETransactions on Biomedical Engineering vol 53 no 6 pp996ndash1005 2006

[154] C C Palerm H Zisser W C Bevier L Jovanovič and F JDoyle III ldquoPrandial insulin dosing using run-to-run controlapplication of clinical data and medical expertise to dene asuitable performance metricrdquo Diabetes Care vol 30 no 5 pp1131ndash1136 2007

[155] LMagni F Raimondo L Bossi et al ldquoModel predictive controlof type 1 diabetes an in silico trialrdquo Journal of Diabetes Scienceand Technology vol 1 pp 804ndash812 2007

[156] S A Weinzimer G M Steil K L Swan J Dziura N Kurtzand W V Tamborlane ldquoFully automated closed-loop insulindelivery versus semiautomated hybrid control in pediatricpatients with type 1 diabetes using an articial pancreasrdquoDiabetes Care vol 31 no 5 pp 934ndash939 2008

[157] W L Clarke S Anderson M Breton S Patek L Kashmerand B Kovatchev ldquoClosed-loop articial pancreas using sub-cutaneous glucose sensing and insulin delivery and a modelpredictive control algorithm the Virginia experiencerdquo Journalof Diabetes Science and Technology vol 3 no 5 pp 1031ndash10382009

[158] D Bruttomesso A Farret S Costa et al ldquoClosed-loop arti-cial pancreas using subcutaneous glucose sensing and insulindelivery and a model predictive control algorithm preliminarystudies in Padova and Montpellierrdquo Journal of Diabetes Scienceand Technology vol 3 no 5 pp 1014ndash1021 2009

[159] R Hovorka J M Allen D Elleri et al ldquoManual closed-loop insulin delivery in children and adolescents with type 1diabetes a phase 2 randomised crossover trialrdquoe Lancet vol375 no 9716 pp 743ndash751 2010

[160] F H El-khatib S J Russell D M Nathan R G Sutherlin andE R Damiano ldquoA bihormonal closed-loop articial pancreasfor type 1 diabetesrdquo Science Translational Medicine vol 2 no27 Article ID 27ra27 2010

[161] E Dassau H Zisser C C Palerm B A Buckingham LJovanovič and F J Doye III ldquoModular articial 120573120573-cell system a

prototype for clinical researchrdquo Journal of Diabetes Science andTechnology vol 2 pp 863ndash872 2008

[162] B Kovatchev S Patek E Dassau et al ldquoControl to range fordiabetes functionality and modular architecturerdquo Journal ofDiabetes Science and Rechnology vol 3 no 5 pp 1058ndash10652009

[163] E Atlas R Nimri S Miller E A Grunberg and M PhillipldquoMD-logic articial pancreas system a pilot study in adults withtype 1 diabetesrdquo Diabetes Care vol 33 no 5 pp 1072ndash10762010

[164] E Dassau H Zisser M W Percival B Grosman L Jovanovičand F J Doyle III ldquoClinical results of automated articialpancreatic 120573120573-cell system with unannounced meal using multi-parametric MPC and insulin-on-boardrdquo in Proceedings of the70th American Diabetes Association Meeting Diabetes vol 59supplement 1 A94 Orlando Fla USA 2010

[165] E M Renard A Farret J Place C Cobelli B P Kovatchev andMD Breton ldquoClosed-loop insulin delivery using subcutaneousinfusion and glucose sensing and equipped with a dedicatedsafety supervision algorithm improves safety of glucose controlin type 1 diabetesrdquo Diabetologia vol 53 supplement 1 p S252010

[166] R Hovorka K Kumareswaran J Harris et al ldquoOvernightclosed loop insulin delivery articial pancreas in adults withtype 1 diabetes crossover randomized controlled studiesrdquoBritish Medical Journal vol 342 no 7803 Article ID d18552011

[167] H Zisser E Dassau W Bevier et al ldquoInitial evaluation ofa fully automated articial pancreasrdquo in Proceedings of the71st American Diabetes Association Meeting Diabetes vol 60supplement 1 A41 San Diego Calif USA 2011

[168] M D Breton A Farret and D Bruttomesso ldquoFully-integratedarticial pancreas in type 1 diabetes modular closed-loopglucose control maintains near-normoglycemiardquo Diabetes vol61 pp 2230ndash2237 2012

[169] C Cobelli E Renard and B P Kovatchev ldquoPilot studies ofwearable articial pancreas in type 1 diabetesrdquo Diabetes Carevol 35 no 9 pp e65ndashe67 2012

Submit your manuscripts athttpwwwhindawicom

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Oxidative Medicine and Cellular Longevity

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The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Research and TreatmentAIDS

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Gastroenterology Research and Practice

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

4 Scientica

is rapid onset of hyperglycemia due to consumption of ldquohighglycemic indexrdquo foods especially in large quantity For exam-ple foods with simple carbohydrate and high fat (classicallypizza) present challenges to technology and optimal therapyby resulting in sustained postprandial hyperglycemia

3 Monitoring of BG Fluctuations in Diabetes

31e Frequency of BGObservation Intuitively the aggres-siveness of glucose control in diabetes would depend on thefrequency of glucose measurement For example if only theaverage glycemic state of a patient is available once every fewmonths (as it would be with measurement of HbA1c alone)then control strategies could only target adjustment of long-term average glycemia but would not be able to respondto daily or hourly variation in glucose level Rapid BGchanges would remain largely unnoticed unless they led toacute complications such as severe hypoglycemia or diabeticketoacidosis us the frequency of glucose measurementdetermines to a large extent the aggressiveness of possibletreatments Table 1 presents the frequency and the temporalresolution of commonly used glucose assessment techniquesGenerally HbA1c reects long-term (over 2-3months) bloodglucose average thus the temporal resolution of HbA1c islimited to reect slow changes in average glycemia Self-monitoring of blood glucose (SMBG) is a standard practiceincluding several (eg 2ndash5) BG readings per day us thetemporal resolution of SMBG allows for assessment of dailyBG proles or weekly trends ith the advent of CGM itis now well accepted that BG uctuations are a process intime which has two principal components risk associatedwith the amplitude (variability) of BG changes and timeindicating the rate of event progression Contemporary CGMdevices are capable of producing BG determinations every5ndash10 minutes which provides vast amounts of data withhigh temporal resolution and allows for detailed monitoringof glucose uctuations on a temporal scale of minutesmdashafrequency that enables closed-loop control

32 Self-Monitoring of Blood Glucose Contemporary homeBG meters offer convenient means for frequent and accurateBG determinations through self-monitoring Most devicesare capable of storing BG readings (typically over 150readings) and have interfaces to download these readingsinto a computer e meters are usually accompanied bysoware that has capabilities for basic data analyses (egcalculation of mean BG estimates of the average BG overthe previous two weeks percentages in target hypoglycemicand hyperglycemic zones etc) and log of the data andgraphical representation (eg histograms pie charts) [78ndash81] Analytical methods based on SMBG data are discussedin the next section

33 Continuous Glucose Monitoring Since the advent ofCGM technology 10 years ago [25ndash27] signicant progresshas been made towards versatile and reliable CGM devicesthat not only monitor the entire course of BG day and nightbut also provide feedback to the patient such as alarms when

BG reaches preset low or high levels A number of studieshave documented the benets of CGM [82ndash85] and chartedguidelines for clinical use and its future as a precursor toclosed-loop control [86ndash89] However while CGM has thepotential to revolutionize the control of diabetes it also gen-erates data streams that are both voluminous and complexe utilization of such data requires an understanding ofthe physical biochemical and mathematical principles andproperties involved in this new technology It is important toknow that CGM devices measure glucose concentration in adifferent compartmentmdashthe interstitium Interstitial glucose(IG) uctuations are related to BG presumably via diffusionprocess [90ndash92] To account for the gradient between BGand IG CGM devices are calibrated with capillary glucosewhich brings the typically lower IG concentration to BGlevels Successful calibration would adjust the amplitude ofIG uctuations with respect to BG but would not eliminatethe possible time lag due to BG-to-IG glucose transport andthe sensor processing time (instrument delay) Because sucha time lag could greatly inuence the accuracy of CGM anumber of studies were dedicated to its investigation yieldingvarious results [93ndash96] For example it was hypothesized thatif a glucose fall is due to peripheral glucose consumptionthe physiologic time lag would be negative that is fall in IGwould precede fall in BG [90 97] In most studies IG laggedbehind BG (most of the time) by 4ndash10 minutes regardlessof the direction of BG change [92 93] e formulation ofthe push-pull phenomenon offered reconciliation of theseresults and provided arguments for a more complex BG-IG relationship than a simple constant or directional timelag [96 98] In addition errors from calibration loss ofsensitivity and random noise confound CGM data [99]Nevertheless the accuracy of CGM is increasing and maybe reaching a physiological limit for subcutaneous glucosemonitoring [100ndash103]

In addition to presenting frequent data (eg every 5ndash10minutes) CGM devices typically display directional trendsand BG rate of change and are capable of alerting the patientof upcoming hypo- or hyperglycemia ese features arebased on methods which predict blood glucose and generatealarms and warning messages In the past several yearsthese methods have rapidly evolved from a concept [104] toimplementation in CGM devices such as the Guardian RTand the MiniMed Paradigm REAL-Time System (MedtronicNorhtridge CA USA) [105] and the Freestyle Navigator(Abbott Diabetes Care Alameda CA USA) [106] Alarmsfor particularly rapid rates of BG change (eg greater than2mgdLmin) are available as well (Guardian RT and Dex-com Seven Plus Dexcom San Diego CA USA) Discussionof the methods for testing of the accuracy and the utility ofsuch alarms has been initiated [106ndash108] and the next logicalstepmdashprevention of hypoglycemia via shutoff of the insulinpumpmdashhas been undertaken [109]

4 Assessment of BG Fluctuations in Diabetes

As presented in Table 1 different frequencies of BGmonitor-ing provide data for different types of analytical techniques

Scientica 5

T 1 Frequency of available glucose monitoring technologies

Measure Temporal resolution Reects Methods typically used to present and analyze the data

HbA1c Monthsyears Slow changes in average BG Direct assay and review of values group comparisonswhen treatments are evaluated

Self-monitoring of bloodglucose (SMBG) Daysweeks Daily variation

weekly trends

Mean and standard deviation (SD) coefficient ofvariation (CV) Interquartile range (IQR)119872119872-value(1965) MAGE (1970)lability index (2004)lowhigh BG (risk) Indices (1998)average daily risk range (ADRR 2006)

Continuous glucosemonitoring (CGM) Minuteshours System dynamics

uctuation and periodicity

CONGA (2005)glucose rate of Change amp CGM versions of lowhighBG (risk) indices (2005)time seriesdynamical system analysis

assessing the glycemic state or the BG dynamics of aperson with diabetes A brief account of analytical methodsapplicable to SMBG andor CGM data is given below Someof these methods such as the Risk Analysis of BG data haveentered the design of closed-loop control systems preventinghypoglycemia [110]

41 SMBG-Based Analytical Methods e computation ofmean glucose values from SMBG data is typically used as adescriptor of overall glycemic control Computing pre- andpostmeal averages and their difference can serve as an indica-tion of the effectiveness of premeal bolus timing and amountSimilarly the percentages of SMBG readings within belowor above preset target limits would serve as indication of thegeneral behavior of BG uctuations e suggested limits are70 and 180mgdL (39ndash10mmoll) which create three sug-gested by the DCCT and commonly accepted bands hypo-glycemia (BG le 70mgdL) normoglycemia (70mgdL ltBG le 180mgdL) hyperglycemia (BG gt 180mgdL) [1]Percentage of time within additional bands can be computedas well to emphasize the frequency of extreme glucoseexcursions Computing standard deviation (SD) as ameasureof glucose variability is not recommended because the BGmeasurement scale is highly asymmetric the hypoglycemicrange is numerically narrower than the hyperglycemic rangeand the distribution of the glucose values of an individualis typically quite skewed [111] erefore SD would bepredominantly inuenced by hyperglycemic excursions andwould not be sensitive to hypoglycemia It is also possiblefor condence intervals based on SD to assume unrealisticnegative valuesus as a standard measure of GV we wouldsuggest reporting interquartile range (IQR) which is suitablefor asymmetric distributions Several diabetes-specic met-rics are also available to serve the analysis of SMBG dataincluding the mean amplitude of glucose excursions (MAGE[112]) the 119872119872-value [113] the lability index [114] and thelow and high blood glucose indices (LBGI HBGI) whichreect the risks associated with hypo- and hyperglycemiarespectively [37 115] In a series of studies we have shownthat specic risk analysis of SMBG data could also capturelong-term trends towards increased risk for hypoglycemia

[36ndash38] and could identify 24-hour periods of increased riskfor hypoglycemia [39 116]

42 Risk Analysis of BG Data To provide a avor for theanalytical techniques used for SMBG CGM and closed-loopcontrol data we will present a bit more detail on the conceptfor risk analysis of BG data [117] e risk analysis steps areas follows

421 Symmetrization of the BG Scale A nonlinear transfor-mation is applied to the BG measurements scale to map theentire BG range (20 to 600mgdL or 11 to 333mmoll) to asymmetric intervalis is needed because the distribution ofBG values of a person with diabetes is asymmetric typicallyskewed towards hyperglycemiae BG value of 1125mgdL(625mmoll) is mapped to zero corresponding to zero riskfor hypo- or hyperglycemia (we should note that this is nota normoglycemic or fasting value which in health wouldbe lt100mgdL it is zero-risk value pertinent to diabetes)e analytical form of this transformation is 119891119891119891BG) = 120574120574 120574119891ln 119891BG)120572120572 minus 120573120573) where the parameters are estimated as 120572120572 =1084 120573120573 = 12057312057381 and 120574120574 = 11205730120574 if BG is measured in mgdLand 120572120572 = 10120572120572 120573120573 = 181205721 and 120574120574 = 11205741205744 if BG is measuredin mmoll [111]

422 Assignment of a Risk Value to Each BG Reading Aquadratic risk function is dened as by the formula 119903119903119891BG) =10 120574 119891119891119891BG)120572 e function 119903119903119891BG) ranges from 0 to 100Its minimum value is achieved at BG = 1125mgdL asafe euglycemic BG reading while its maximum is reachedat the extreme ends of the BG scale us 119903119903119891BG) can beinterpreted as a measure of the risk associated with a certainBG level e le branch of this parabola identies the riskof hypoglycemia while the right branch identies the risk ofhyperglycemia

423 Computing Measures of Risk for Hypoglycemia Hyper-glycemia and Glucose Variability Let 1199091199091 119909119909120572hellip 119909119909119899119899 be aseries of 119899119899 BG readings and let 119903119903119903119903119891BG) = 119903119903119891BG) if 119891119891119891BG) lt 0and 0 otherwise 119903119903119903119891BG) = 119903119903119891BG) if 119891119891119891BG) gt 0 and 0

6 Scientica

otherwise en the low and high blood glucose indices arecomputed as follows

LBGI = 1119899119899

11989911989910055761005576119894119894=111990311990311990311990310076491007649119909119909119894119894100766510076652 HBGI = 1

119899119899

11989911989910055761005576119894119894=11199031199031199031007649100764911990911990911989411989410076651007665

2 (1)

us the LBGI is a nonnegative quantity that increaseswhen the number andor extent of low BG readings increasesand the HBGI is nonnegative quantity that increases whenthe number andor extent of high BG readings increasesBased on this same technique we also dene the average dailyrisk range (ADRR) which is a measure of risks associatedwith overall glycemic variability [118] In studies the LBGItypically accounted for 40ndash55 of the variance of futuresignicant hypoglycemia in the subsequent 3ndash6 months [36ndash38] which made it a potent predictor of hypoglycemia basedon SMBGeADRR has been shown superior to traditionalglucose variability measures in terms of risk assessment andprediction of extreme glycemic excursions [118] Specicallyit has been demonstrated that classication of risk for hypo-glycemia based on four ADRR categories low risk ADRR lt20 low-moderate risk 20 le ADRR lt 30 moderate-high risk30 le ADRR lt 40 and high risk ADRR gt 40 resulted inmore than a sixfold increase in risk for hypoglycemia fromthe lowest to the highest risk category [118] In addition thelow and high BG indices have been adapted to continuousmonitoring data [119] and can be used in the same way aswith SMBG to assess the risk for hypo- or hyperglycemia

43 CGM-Based Analytical Methods While traditional risk[119] and variability [120] analyses are still applied to CGMdata the high temporal resolution of CGM brought aboutthe possibility for use of sophisticated analytical methodsassessing system (person) dynamics on the time scale of min-utes is necessitated the development of new technologiesfor data analysis and visualization that are not available forSMBG data Analysis of the BG rate of change (measuredin mgdLmin) is a way to evaluate the dynamics of BGuctuations on the time scale of minutes e BG rate ofchange at 119905119905119894119894mdashis computed as the ratio (BG(119905119905119894119894)minusBG(119905119905119894119894minus1))(119905119905119894119894minus119905119905119894119894minus1) where BG(119905119905119894119894) and BG(119905119905119894119894minus1) are CGM readings taken attimes 119905119905119894119894 and 119905119905119894119894minus1 for example minutes apart Investigationof the frequency of glucose uctuations showed that optimalevaluation of the BG rate of change would be achieved overtime periods of 15minutes [121] for exampleΔ119905119905 = 119905119905119894119894 minus119905119905119894119894minus1 =15 A large variation of the BG rate of change indicates rapidand more pronounced BG uctuations and therefore a lessstable system us the standard deviation of the BG rateof change is a measure of stability of glucose uctuation (weshould note that as opposed to the distribution of BG levelsthe distribution of the BG rate of change is symmetric andtherefore using SD is statistically accurate [122]) e SDof BG rate of change has been introduced as a measure ofstability computed fromCGMdata and is known asCONGAof order 1 In general CONGA122 of order 119899119899 is computedas the standard deviation of CGM readings that are 119899119899 hoursapart reecting glucose stability over these time intervals[123]

Most important to the development of the articial pan-creas algorithms is a class of methods allowing the predictionof BG values ahead in time ese methods typically basedon time-series techniques have been applied successfully ina number of studies [124ndash129] In addition to time seriesneural networks have been used for the prediction of glucoselevels from CGM [130 131] Detailed reviews of CGM dataanalysis methods are presented in [122] including severalgraphs that could be used for the visualization of the rathercomplex CGM data sets and in [132] where a broad reviewof modeling analytical and control techniques for diabetesis provided

5 Control of BG Fluctuations in Diabetes

51 Intraperitoneal Insulin Delivery As detailed in the Intro-duction the articial pancreas idea can be traced back to theearly 70s when external BG regulation in people with dia-betes was achieved by iv glucose measurement and iv infu-sion of glucose and insulin However the intravenous routeof closed-loop control remains cumbersome and unsuitedfor outpatient use An alternative has been presented byimplantable intraperitoneal (ip) systems employing iv sam-pling and ip insulin delivery [133ndash136] e ip infusionroute has several desirable characteristics reproducibility ofinsulin absorption quick time to peak and return to baselineof insulin action near-physiological peripheral insulin levelsand restoration of glucagon response to hypoglycemia andexercise [133 137ndash139] However while ip systems haveachieved excellent BG control their implementation stillrequires considerable surgery and is associated with signi-cant cost Nevertheless the development of less invasive andcheaper implantable ports (eg DiaPort Roche DiagnosticsMannheim Germany) may contribute to the future prolifer-ation of ip insulin delivery [140ndash142]

52e Subcutaneous Route to Closed-Loop Control Follow-ing the progress of minimally invasive subcutaneous CGMthe next logical step was the development of sc closed-loopglucose control which links a CGM device with CSII insulinpump A key element of this combination was a controlalgorithm which monitors BG uctuations and the actionsof the insulin pump and computes insulin delivery rate everyfew minutes [143] Figure 2 presents key milestones in thetimeline of this development

Following the pioneering work of Hovorka et al [144145] and Steil et al [146] in 2006 the Juvenile DiabetesResearch Foundation (JDRF) initiated the Articial PancreasProject and funded several centers in the USA and Europeto carry closed-loop control research [147] In 2008 theUSA National Institutes of Health launched an articialpancreas initiative and in 2010 the European APHomeconsortium was established By the end of the rst decade ofthis century the articial pancreas became a global researchtopic engaging physicians and engineers in unprecedentedcollaboration [148 149]

Scientica 7

2006 2008 2010

The JDRF artificial pancreas consortium

is launched (Kowalski)

Studies of hybrid closed-loop control (Weinzimer

and Tamborlane)

First human trials begin using a system designed entirely in silico UVA

Italy and France(Kovatchev Cobelli Renard)

NIH funds artificial pancreas

EU launches the APHome

artificial pancreas initiative

JDRF multicenter trialof modular control-to-

range

2004 the ADICOLproject

(Hovorka)

First studies of automated sc

closed loop (Steil)

FDA accepts the UVAPadova metabolic simulator as a substitute

to animal trials(Kovatchev Cobelli Dalla

Man and Breton)

Modular control-to-rangeintroduced trials at UVA

Italy and France(Kovatchev

Cobelli and Renard)

The APS introduced(Dassau Doyle

First studies of outpatient closed-loop

control (CobelliRenard Zisser and

Kovatchev )

2012

DiAs first portableAP platform

(Keith-HynesKovatchev)

studies Zisser)

30

2012

F 2 Timeline of the articial pancreas developments in the last decademdashtheoretical work and a number of in-clinic studies leading tothe rst trials of wearable articial pancreas device

53 In Silico Models of the Human Metabolic System Acritical step towards accelerated clinical progress of thearticial pancreas was the development of sophisticatedcomputer simulator of the human metabolic system allowingrapid in silico testing of closed-loop control algorithmsis simulation environment was based on the previouslyintroduced Meal Model of glucose-insulin dynamics [76 77]and was equipped with a ldquopopulationrdquo of in silico imagesof 119873119873 119873 119873119873119873 ldquosubjectsrdquo with type 1 diabetes separated inthree age groups 119873119873 119873 119873119873119873 simulated ldquochildrenrdquo below theage of 11 119873119873 119873 119873119873119873 ldquoadolescentsrdquo 12ndash18 years old and119873119873 119873 119873119873119873 ldquoadultsrdquo e characteristics of these ldquosubjectsrdquo(eg weight daily insulin dose carbohydrate ratio etc)were tailored to span a wide range of intersubject variabilityapproximating the variability observed in people in vivo[150] Simulation experiments allow any CGM device anyinsulin pump and any control algorithm to be linked in aclosed-loop system in silico prior to their use in clinical trialsWith this technology any meal and insulin delivery scenariocan be pilot-tested very efficientlymdasha 24-hour period ofclosed-loop control is simulated in under 2 secondsWe needto emphasize however that good in silico performance of acontrol algorithmdoes not guarantee in vivo performancemdashitonly helps test extreme situations and the stability of thealgorithm and rule out inefficient scenarios us computersimulation is only a prerequisite to but not a substitute forclinical trials

In January 2008 in an unprecedented decision theUSA Food and Drug Administration accepted this computer

simulator as a substitute to animal trials for the testing ofclosed-loop control strategies is opened the eld for effi-cient and cost-effective in silico experiments leading directlyto human studies Only three months later in April 2008 therst human trials began at the University of irginia (USA)Montpellier (France) and Padova (Italy) using a controlsystem designed entirely in silico [151]

54 Control System Designs e rst studies of ovorkaet al [144 145] and Steil et al [146] outlined the twomajor types of closed-loop control algorithms now in use inarticial pancreas systemsmdashmodel-predictive control (MPC[145]) and proportional-integral-derivative (PID [146])respectively By 2007 the blueprints of the contemporarycontrollers were in place including run-to-run control[152ndash154] and linear MPC [155] To date the trials ofsubcutaneous closed-loop control systems have been usingeither PID [146 156] or MPC [157ndash160] but MPC becamethe approach of choice targeted by recent research erewere two important reasons making MPC preferable (i)PID is purely reactive responding to changes in glucoselevel while a properly tuned MPC allows for prediction ofglucose dynamics and as a result for mitigation of the timedelays inherent with subcutaneous glucose monitoring andsubcutaneous insulin infusion [62 63] (ii) MPC allows forrelatively straightforward personalizing of the control usingpatient-specic model parameters In addition MPC couldhave ldquolearningrdquo capabilitiesmdashit has been shown that a class

8 Scientica

of algorithms (known as run-to-run control) can ldquolearnrdquospecics of patientsrsquo daily routine (eg timing of meals) andthen optimize the response to a subsequent meal using thisinformation or account for circadian uctuation in insulinresistance (eg dawn phenomenon observed in some people)[149]

In 2008 a universal research platformmdashthe APSmdashwasintroduced enabling automated communication betweenseveral CGM devices insulin pumps and control algorithms[161] e APS was very instrumental for a number ofinpatient trials of closed-loop control A year later a mod-ular architecture was introduced proposing standardizationsequential testing and clinical deployment of articial pan-creas components [162]

55 Inpatient Clinical Trials Between 2008 and 2011 prom-ising results were reported by several groups [156ndash160 163ndash167] Most of these studies pointed out the superiority ofclosed-loop control over standard CSII therapy in termsof (i) increased time within target glucose range (typically39ndash10mmoll) (ii) reduced incidence of hypoglycemia and(iii) better overnight control Two of these studies [159166] had state-of-the-art randomized cross-over design butlacked automated data transfermdashall CGM readings weretransferred to the controllermanually by the study personneland all insulin pump commands were entered manually aswell To distinguish the various degrees of automation inclosed-loop studies the notion of fully-integrated closed-loop control emerged dened as having all of the followingthree components (i) automated data transfer from theCGM to the controller (ii) real-time control action and (iii)automated command of the insulin pump e rst (andthe largest to date) randomized cross-over study of fully-integrated closed-loop control was published in 2012 [168]However even this contemporary trial of fully automatedCLC which enrolled 38 patients with T1D at three centersand tested two different control algorithms achieving note-worthy glycemic control and prevention of hypoglycemia didnot leave the clinical setting e technology used by thisstudy was still based on a laptop computer wired to a CGMand an insulin pump a system limiting the free movementof the study subjects and too cumbersome to be used beyondhospital connes

5 earale tpatient rticial ancreas e transitionof closed-loop control to ambulatory use began in 2011 withthe development of the Diabetes Assistant (DiAs)mdashthe rstwearable articial pancreas platform based on a smart phonee design characteristics of DiAs included the following

(i) based on readily available inexpensive wearablehardware platform

(ii) computationally capable of running advanced closed-loop control algorithms

(iii) wirelessly connectable to CGM devices and insulinpumps

(iv) capable of broadband communication with a centrallocation for remote monitoring and safety supervi-sion of the participants in outpatient clinical trials

In ctober 2011 the rst two pilot trials of wearableoutpatient articial pancreas were performed simultaneouslyin Padova (Italy) and Montpellier (France) [169] ese 2-day trials allowed the renement of a wearable system andenabled a subsequentmultisite feasibility study of ambulatoryarticial pancreas which was completed recently at theUniversities of Virginia Padova and Montpellier and at theSansumDiabetes Research institute Santa Barbara CAUSAResults from this study are forthcoming

6 Conclusions

Solving the optimization problem of diabetes requiresreplacement of insulin action through insulin injections ororal medications (applicable primarily to type 2 diabetes)which until fully automated closed-loop control becomesavailable would remain a process largely controlled bypatient behavior In engineering terms BG uctuations indiabetes result from the activity of a complex metabolicsystem perturbed by behavioral challenges e frequencyand extent of these challenges and the ability of the personrsquosmetabolic system to absorb them determines the qualityof glycemic control Along with HbA1c the magnitudeand speed of BG uctuations is the primary measurablemarker of glucose control in diabetes ese same quanti-tiesmdashHbA1c and glucose variabilitymdashare also the principalfeedback available to patients to assist with optimization oftheir diabetes control

In the past 30 years the technology for monitoring ofblood glucose levels in diabetes has progressed from assess-ment of average glycemia via HbA1c once in several monthsthrough daily SMBG to minutely continuous glucose mon-itoring e increasing temporal resolution of the moni-toring technology enabled increasingly intensive diabetestreatment from daily insulin injections or oral medicationthrough insulin pump therapy to the articial pancreasis progress is accompanied by increasingly sophisticatedanalytical methods for retrieval of blood glucose data rangingfrom subjective interpretation of glucose values and straight-forward summary statistics through risk and variabilityanalysis to real-time closed-loop control algorithms based oncomplex models of the human metabolism

It is therefore evident that the development of diabetestechnology is accelerating exponentially A primary cata-lyst of this acceleration is unprecedented interdisciplinarycollaboration between physicians chemists engineers andmathematicians As a result a wearable articial pancreassuitable for outpatient use is now within reach

e primary engineering challenges to the widespreadadoption of closed-loop control as a viable therapeutic optionfor diabetes include system connectivity the accuracy ofsubcutaneous glucose sensing and the speed of action ofsubcutaneously injected insulin ese challenges are well

Scientica 9

understood by those working in the eld wireless commu-nication between CGM devices insulin pumps and closed-loop controllers are under development and testing newgenerations of CGM device demonstrate superior accuracyand reliability and new insulin analogs and methods forinsulin delivery are being engineered to approximate asclose as possible the action prole of endogenous insulinIt should be noted however that the signals available toa contemporary closed-loop control system are generallylimited to CGM and insulin delivery data user input aboutcarbohydrate intake and physical activity could be availableas well In contrast the endocrine pancreas receives directand rapid control inputs from other nutrients (eg lipids andamino acids) adjacent cells (somatostatin from the delta cellsand glucagon from alpha cells) incretins and neural signalsus while articial closed-loop control is expected to bevastly superior to the diabetes control methods employed inthe clinical practice today it will continue to be imperfectwhen compared to the natural endocrine regulation of bloodglucose

Acknowledgments

is work was made possible by the JDRF Articial PancreasProject the National Institutes of HealthNIDDK GrantsRO1 DK 51562 and RO1 DK 085623 and by the generoussupport of PBM Science Charlottesville Virginia CA USAand the Frederick Banting Foundation Richmond VirginiaCA USA e author thanks his colleagues at the Universityof Virginia Center forDiabetes Technology for their relentlesswork on articial pancreas development

References

[1] American Diabetes Association ldquoDiagnosis and classicationof diabetes mellitusrdquo Diabetes Care vol 27 pp s5ndashs10 2004

[2] A H Kadish ldquoAutomation control of blood sugarmdashI A ser-vomechanism for glucose monitoring and controlrdquo AmericanJournal of Medical Electronics vol 39 pp 82ndash86 1964

[3] J C Pickup H Keen J A Parsons and K G M M AlbertildquoContinuous subcutaneous insulin infusion an approach toachieving normoglycaemiardquo British Medical Journal vol 1 no6107 pp 204ndash207 1978

[4] W V Tamborlane R S Sherwin M Genel and P FeligldquoReduction to normal of plasma glucose in juvenile diabetes bysubcutaneous administration of insulinwith a portable infusionpumprdquo New England Journal of Medicine vol 300 no 11 pp573ndash578 1979

[5] A M Albisser B S Leibel and T G Ewart ldquoAn articialendocrine pancreasrdquoDiabetes vol 23 no 5 pp 389ndash396 1974

[6] E F Pfeier um Ch and A H Clemens ldquoe articialbeta cell a continuous control of blood sugar by external regu-lation of insulin infusion (glucose controlled insulin infusionsystem)rdquo Hormone and Metabolic Research vol 6 no 5 pp339ndash342 1974

[7] J Mirouze J L Selam T C Pham and D Cavadore ldquoEvalua-tion of exogenous insulin homoeostasis by the artical pancreasin insulin dependent diabetesrdquo Diabetologia vol 13 no 3 pp273ndash278 1977

[8] E W Kraegen L V Campbell and Y O Chia ldquoControlof blood glucose in diabetics using an articial pancreasrdquoAustralian and New Zealand Journal of Medicine vol 7 no 3pp 280ndash286 1977

[9] M Shichiri R Kawamori Y Yamasaki M Inoue Y Shigetaand H Abe ldquoComputer algorithm for the articial pancreaticbeta cellrdquo Articial rgans vol 2 supplement pp 247ndash2501978

[10] A H Clemens P H Chang and R W Myers ldquoe devel-opment of Biostator a Glucose Controlled Insulin InfusionSystem (GCIIS)rdquo Hormone and Metabolic Research vol 7 pp23ndash33 1977

[11] E B Marliss F T Murray and E F Stokes ldquoNormalization ofglycemia in diabetics during meals with insulin and glucagondelivery by the articial pancreasrdquo Diabetes vol 26 no 7 pp663ndash672 1977

[12] J V Santiago A H Clemens W L Clarke and D M KipnisldquoClosed-loop and open-loop devices for blood glucose controlin normal and diabetic subjectsrdquo Diabetes vol 28 no 1 pp71ndash84 1979

[13] U Fischer E Jutzi E J Freyse and E Salzsieder ldquoDerivationand experimental proof of a new algorithm for the articial B-cell based on the individual analysis of the physiological insulin-glucose relationshiprdquo Endokrinologie vol 71 no 1 pp 65ndash751978

[14] R S Parker F J Doyle and N A Peppas ldquoe intravenousroute to blood glucose control a review of control algorithmsfor noninvasive monitoring and regulation in type I diabeticpatientsrdquo IEEE Engineering in Medicine and Biology Magazinevol 20 no 1 pp 65ndash73 2001

[15] R N Bergman Y Z Ider C R Bowden and C CobellildquoQuantitative estimation of insulin sensitivityrdquo e AmericanJournal of Physiology vol 236 no 6 pp E667ndashE677 1979

[16] H M Broekhuyse J D Nelson B Zinman and A M AlbisserldquoComparison of algorithms for the closed-loop control of bloodglucose using the articial beta cellrdquo IEEE Transactions onBiomedical Engineering vol 28 no 10 pp 678ndash687 1981

[17] A H Clemens ldquoFeedback control dynamics for glucosecontrolled insulin infusion systemrdquo Medical Progress throughTechnology vol 6 no 3 pp 91ndash98 1979

[18] C Cobelli and A Ruggeri ldquoEvaluation of portalperipheralroute and of algorithms for insulin delivery in the closed-loop control of glucose in diabetes a modeling studyrdquo IEEETransactions on Biomedical Engineering vol 30 no 2 pp93ndash103 1983

[19] E Salzsieder G Albrecht U Fischer and E J Freyse ldquoKineticmodeling of the glucoregulatory system to improve insulintherapyrdquo IEEE Transactions on Biomedical Engineering vol 32no 10 pp 846ndash855 1985

[20] P Brunetti C Cobelli P Cruciani et al ldquoA simulation study ona self-tuning portable controller of blood glucoserdquo InternationalJournal of Articial rgans vol 16 no 1 pp 51ndash57 1993

[21] U Fischer W Schenk E Salzsieder G Albrecht P Abel andE J Freyse ldquoDoes physiological blood glucose control requirean adaptive control strategyrdquo IEEE Transactions on BiomedicalEngineering vol 34 no 8 pp 575ndash582 1987

[22] J T Sorensen A physiologic model of glucose metabolism inman and its use to design and assess improved insulin therapiesfor diabetes [PhD dissertation] Department of Chemical Engi-neering MIT 1985

[23] R S Parker F J Doyle and N A Peppas ldquoA model-basedalgorithm for blood glucose control in type I diabetic patientsrdquo

10 Scientica

IEEE Transactions on Biomedical Engineering vol 46 no 2 pp148ndash157 1999

[24] J J Mastrototaro ldquoe MiniMed continuous glucose monitor-ing Systemrdquo Diabetes Technology anderapeutics vol 2 no 1pp S13ndashS18 2000

[25] B W Bode ldquoClinical utility of the continuous glucose monitor-ing systemrdquo Diabetes Technology anderapeutics vol 2 no 1supplement pp S35ndashS41 2000

[26] B Feldman R Brazg S Schwartz and R Weinstein ldquoAcontinuous glucose sensor based on wired enzyme technol-ogymdashresults from a 3-day trial in patients with type 1 diabetesrdquoDiabetes Technology anderapeutics vol 5 no 5 pp 769ndash7792003

[27] eDiabetes Control and Complications Trial Research Groupldquoe effect of intensive treatment of diabetes on the develop-ment and progression of long-term complications of insulin-dependent diabetes mellitusrdquoNew England Journal of Medicinevol 329 pp 978ndash986 1993

[28] eDiabetes Control and Complications Trial Research Groupldquoe relationship of glycemic exposure (HbA1c) to the riskof development and progression of retinopathy in the Dia-betes Control and Complications Trialrdquo Diabetes vol 44 pp968ndash983 1995

[29] J M Lachin S Genuth D M Nathan B Zinman and B NRutledge ldquoEffect of glycemic exposure on the risk of microvas-cular complications in the diabetes control and complicationstrial-revisitedrdquo Diabetes vol 57 no 4 pp 995ndash1001 2008

[30] P Reichard and M Pihl ldquoMortality and treatment side-effectsduring long-term intensied conventional insulin treatment inthe Stockholm Diabetes Intervention Studyrdquo Diabetes vol 43no 2 pp 313ndash317 1994

[31] UK Prospective Diabetes Study Group (UKPDS) ldquoIntensiveblood-glucose control with sulphonylureas or insulin comparedwith conventional treatment and risk of complications inpatients with type 2 diabetes (UKPDS 33)rdquo Lancet vol 352 no9131 pp 837ndash853 1998

[32] P A Svendsen T Lauritzen U Soegaard and J NerupldquoGlycosylated haemoglobin and steady-state mean blood glu-cose concentration in type 1 (insulin-dependent) diabetesrdquoDiabetologia vol 23 no 5 pp 403ndash405 1982

[33] J V Santiago ldquoLessons from the diabetes control and compli-cations trialrdquo Diabetes vol 42 no 11 pp 1549ndash1554 1993

[34] eDiabetes Control and Complications Trial Research GroupldquoHypoglycemia in the diabetes control and complications trialrdquoDiabetes vol 46 pp 271ndash286 1997

[35] A E Gold BM Frier KMMacLeod and I J Deary ldquoA struc-tural equation model for predictors of severe hypoglycaemiain patients with insulin-dependent diabetes mellitusrdquo DiabeticMedicine vol 14 pp 309ndash315 1997

[36] D J Cox B P Kovatchev D M Julian et al ldquoFrequency ofsevere hypoglycemia in insulin-dependent diabetesmellitus canbe predicted from self-monitoring blood glucose datardquo Journalof Clinical Endocrinology and Metabolism vol 79 no 6 pp1659ndash1662 1994

[37] B P Kovatchev D J Cox L A Gonder-Frederick D Young-Hyman D Schlundt and W Clarke ldquoAssessment of risk forsevere hypoglycemia among adults with IDDM validation ofthe low blood glucose indexrdquo Diabetes Care vol 21 no 11 pp1870ndash1875 1998

[38] B P Kovatchev D J Cox A Kumar L Gonder-Frederick andW L Clarke ldquoAlgorithmic evaluation of metabolic control and

risk of severe hypoglycemia in type 1 and type 2 diabetes usingself-monitoring blood glucose datardquo Diabetes Technology anderapeutics vol 5 no 5 pp 817ndash828 2003

[39] D J Cox L Gonder-Frederick L Ritterband W Clarke andB P Kovatchev ldquoPrediction of severe hypoglycemiardquo DiabetesCare vol 30 no 6 pp 1370ndash1373 2007

[40] S A Amiel R S Sherwin D C Simonson and W VTamborlane ldquoEffect of intensive insulin therapy on glycemicthresholds for counterregulatory hormone releaserdquo Diabetesvol 37 no 7 pp 901ndash907 1988

[41] S A Amiel W V Tamborlane D C Simonson and RS Sherwin ldquoDefective glucose counterregulation aer strictglycemic control of insulin-dependent diabetes mellitusrdquo NewEngland Journal of Medicine vol 316 no 22 pp 1376ndash13831987

[42] P E Cryer and J E Gerich ldquoGlucose counterregulation hypo-glycemia and intensive insulin therapy in diabetes mellitusrdquoNew England Journal of Medicine vol 313 no 4 pp 232ndash2411985

[43] N H White D A Skor and P E Cryer ldquoIdentication of typeI diabetic patients at increased risk for hypoglycemia duringintensive therapyrdquo New England Journal of Medicine vol 308no 9 pp 485ndash491 1983

[44] P E Cryer ldquoIatrogenic hypoglycemia as a cause ofhypoglycemia-associated autonomic failure in IDDM avicious cyclerdquo Diabetes vol 41 no 3 pp 255ndash260 1992

[45] J N Henderson K V Allen I J Deary and B M FrierldquoHypoglycaemia in insulin-treated Type 2 diabetes frequencysymptoms and impaired awarenessrdquo Diabetic Medicine vol 20no 12 pp 1016ndash1021 2003

[46] P E Cryer Hypoglycemia Pathophysiology Diagnosis andTreatment Oxford University Press New York NY USA 1997

[47] P E Cryer S N Davis and H Shamoon ldquoHypoglycemia indiabetesrdquo Diabetes Care vol 26 no 6 pp 1902ndash1912 2003

[48] B P Childs N G Clark and D J Cox ldquoDening and reportinghypoglycemia in diabetes a report from the American diabetesassociation workgroup on hypoglycemiardquo Diabetes Care vol28 no 5 pp 1245ndash1249 2005

[49] P E Cryer ldquoHypoglycaemia the limiting factor in the glycaemicmanagement of Type I and Type II diabetesrdquo Diabetologia vol45 no 7 pp 937ndash948 2002

[50] P E Cryer ldquoHypoglycemia the limiting factor in the manage-ment of IDDMrdquo Diabetes vol 43 no 11 pp 1378ndash1389 1994

[51] A L McCall and B P Kovatchev ldquoe median is not the onlymessage a clinicianrsquos perspective on mathematical analysis ofglycemic variability and modeling in diabetes mellitusrdquo Journalof Diabetes Science and Technology vol 3 pp 3ndash11 2009

[52] M Brownlee and I B Hirsch ldquoGlycemic variability ahemoglobin A1c-independent risk factor for diabetic compli-cationsrdquo Journal of the American Medical Association vol 295no 14 pp 1707ndash1708 2006

[53] I B Hirsch and M Brownlee ldquoShould minimal blood glucosevariability become the gold standard of glycemic controlrdquoJournal of Diabetes and Its Complications vol 19 no 3 pp178ndash181 2005

[54] K Esposito D Giugliano F Nappo and R Marfella ldquoRegres-sion of carotid atherosclerosis by control of postprandial hyper-glycemia in type 2 diabetes mellitusrdquo Circulation vol 110 no2 pp 214ndash219 2004

[55] S Haffner ldquoe importance of postprandial hyperglycaemia indevelopment of cardiovascular disease in people with diabetes

Scientica 11

pointrdquo International Journal of Clinical Practice no 123 pp24ndash26 2001

[56] LMonnier EMas C Ginet et al ldquoActivation of oxidative stressby acute glucose uctuations compared with sustained chronichyperglycemia in patients with type 2 diabetesrdquo Journal of theAmerican Medical Association vol 295 no 14 pp 1681ndash16872006

[57] T S Temelkova-Kurktschiev C Koehler E Henkel W Leon-hardt K Fuecker and M Hanefeld ldquoPostchallenge plasmaglucose and glycemic spikes are more strongly associated withatherosclerosis than fasting glucose or HbA(1c) levelrdquo DiabetesCare vol 23 no 12 pp 1830ndash1834 2000

[58] W L Clarke D J Cox L Gonder-Frederick D JulianB Kovatchev and D Young-Hyman ldquoBiopsychobehavioralmodel of risk of severe hypoglycemia self-management behav-iorsrdquo Diabetes Care vol 22 no 4 pp 580ndash584 1999

[59] D J Cox L A Gonder-Frederick B P Kovatchev et alldquoBiopsychobehavioral model of severe hypoglycemia II under-standing the risk of severe hypoglycemiardquo Diabetes Care vol22 no 12 pp 2018ndash2025 1999

[60] L Gonder-Frederick D Cox B Kovatchev D Schlundt andW Clarke ldquoA biopsychobehavioral model of risk of severehypoglycemiardquoDiabetes Care vol 20 no 4 pp 661ndash669 1997

[61] B Kovatchev D Cox L Gonder-Frederick D Schlundtand W Clarke ldquoStochastic model of self-regulation decisionmaking exemplied by decisions concerning hypoglycemiardquoHealth Psychology vol 17 no 3 pp 277ndash284 1998

[62] G Nucci and C Cobelli ldquoModels of subcutaneous insulinkinetics A critical reviewrdquo Computer Methods and Programs inBiomedicine vol 62 no 3 pp 249ndash257 2000

[63] M E Wilinska L J Chassin H C Schaller L Schaupp T RPieber and R Hovorka ldquoInsulin kinetics in type-1 diabetescontinuous and bolus delivery of rapid acting insulinrdquo IEEETransactions on Biomedical Engineering vol 52 no 1 pp 3ndash122005

[64] R N Bergman D T Finegood and M Ader ldquoAssessmentof insulin sensitivity in vivordquo Endocrine Reviews vol 236 ppE667ndashE677 1985

[65] R N Bergman ldquoe minimal model of glucose regulation abiographyrdquoAdvances in ExperimentalMedicine and Biology vol537 pp 1ndash19 2003

[66] R N Bergman D J Zaccaro R M Watanabe et al ldquoMinimalmodel-based insulin sensitivity has greater heritability and adifferent genetic basis than homeostasis model assessment orfasting insulinrdquo Diabetes vol 52 no 8 pp 2168ndash2174 2003

[67] A Caumo R N Bergman and C Cobelli ldquoInsulin sensitivityfrom meal tolerance tests in normal subjects a minimal modelindexrdquo Journal of Clinical Endocrinology and Metabolism vol85 no 11 pp 4396ndash4402 2000

[68] J O Clausen K Borch-Johnsen H Ibsen et al ldquoInsulin sen-sitivity index acute insulin response and glucose effectivenessin a population-based sample of 380 young healthy Caucasiansanalysis of the impact of gender body fat physical tness andlife-style factorsrdquo Journal of Clinical Investigation vol 98 no 5pp 1195ndash1209 1996

[69] T C Ni M Ader and R N Bergman ldquoReassessment of glu-cose effectiveness and insulin sensitivity from minimal modelanalysis a theoretical evaluation of the single-compartmentglucose distribution assumptionrdquo Diabetes vol 46 no 11 pp1813ndash1821 1997

[70] S Welch S S P Gebhart R N Bergman and L S PhillipsldquoMinimal model analysis of intravenous glucose tolerance

test-derived insulin sensitivity in diabetic subjectsrdquo Journalof Clinical Endocrinology and Metabolism vol 71 no 6 pp1508ndash1518 1990

[71] D Dawson M A Vincent E J Barrett et al ldquoCapillary recruit-ment in skeletal muscle in response to exercise and hyperin-sulinemia assessed with contrast-enhanced ultrasoundrdquo Amer-ican Journal of Physiology vol 282 no 3 pp E714ndashE720 2002

[72] K J Mikines B Sonne P A Farrell B Tronier and H GalboldquoEffect of physical exercise on sensitivity and responsiveness toinsulin in humansrdquoAmerican Journal of Physiology vol 254 no3 pp E248ndashE259 1988

[73] E A Richter ldquoGlucose utilizationrdquo in Handbook of PhysiologyL B Rowell and J T Shepherd Eds pp 912ndash951 OxfordUniversity Press New York NY USA 1996

[74] D H Wasserman R J Geer D E Rice et al ldquoInteraction ofexercise and insulin action in humansrdquo American Journal ofPhysiology vol 260 no 1 pp E37ndashE45 1991

[75] G Pillonetto A Caumo G Sparacino and C Cobelli ldquoAnew dynamic index of insulin sensitivityrdquo IEEE Transactions onBiomedical Engineering vol 53 no 3 pp 369ndash379 2006

[76] C Dalla Man D M Raimondo R A Rizza and C CobellildquoGIM Simulation soware of meal glucose-insulin modelrdquoJournal of Diabetes Science and Technology vol 1 pp 323ndash3302007

[77] C Dalla Man R A Rizza and C Cobelli ldquoMeal simulationmodel of the glucose-insulin systemrdquo IEEE Transactions onBiomedical Engineering vol 54 no 10 pp 1740ndash1749 2007

[78] W L Clarke D Cox L A Gonder-Frederick W Carter andS L Pohl ldquoEvaluating clinical accuracy of systems for self-monitoring of blood glucoserdquo Diabetes Care vol 10 no 5 pp622ndash628 1987

[79] e Diabetes Research In Children Network (Direcnet) StudyGroup ldquoA multicenter study of the accuracy of the one touchultra home glucose meter in children with type 1 diabetesrdquoDiabetes Technology anderapeutics vol 5 no 6 pp 933ndash9412003

[80] I B Hirsch B W Bode B P Childs et al ldquoSelf-monitoring ofblood glucose (SMBG) in insulin- and non-insulin-using adultswith diabetes consensus recommendations for improvingSMBG accuracy utilization and researchrdquo Diabetes Technologyanderapeutics vol 10 no 6 pp 419ndash439 2008

[81] G Freckmann A Baumstark N Jendrike et al ldquoSystemaccuracy evaluation of 27 blood glucose monitoring systemsaccording to DIN en ISO 15197rdquo Diabetes Technology anderapeutics vol 12 no 3 pp 221ndash231 2010

[82] D Deiss J Bolinder J P Riveline et al ldquoImproved glycemiccontrol in poorly controlled patients with type 1 diabetes usingreal-time continuous glucose monitoringrdquo Diabetes Care vol29 no 12 pp 2730ndash2732 2006

[83] S Garg H Zisser S Schwartz et al ldquoImprovement in glycemicexcursions with a transcutaneous real-time continuous glucosesensor a randomized controlled trialrdquo Diabetes Care vol 29no 1 pp 44ndash50 2006

[84] B P Kovatchev and W L Clarke ldquoContinuous glucose mon-itoring reduces risks for hypo- and hyperglycemia and glucosevariability in diabetesrdquo Diabetes vol 56 1 Article ID 0086OR2007

[85] e Juvenile Diabetes Research Foundation Continuous Glu-cose Monitoring Study Group ldquoContinuous glucose monitor-ing and intensive treatment of type 1 diabetesrdquo New EnglandJournal of Medicine vol 359 pp 1464ndash1476 2008

12 Scientica

[86] D C Klonoff ldquoContinuous glucose monitoring roadmap for21st century diabetes therapyrdquo Diabetes Care vol 28 no 5 pp1231ndash1239 2005

[87] R Hovorka ldquoContinuous glucose monitoring and closed-loopsystemsrdquo Diabetic Medicine vol 23 no 1 pp 1ndash12 2006

[88] D C Klonoff ldquoe articial pancreas how sweet engineeringwill solve bitter problemsrdquo Journal of Diabetes Science andTechnology vol 1 pp 72ndash81 2007

[89] I B Hirsch D Armstrong R M Bergenstal et al ldquoClin-ical application of emerging sensor technologies in diabetesmanagement consensus guidelines for continuous glucosemonitoring (CGM)rdquoDiabetes Technology anderapeutics vol10 no 4 pp 232ndash246 2008

[90] K Rebrin G M Steil W P Van Antwerp and J J Mastro-totaro ldquoSubcutaneous glucose predicts plasma glucose inde-pendent of insulin implications for continuous monitoringrdquoAmerican Journal of Physiology vol 277 no 3 pp E561ndashE5711999

[91] K Rebrin and G M Steil ldquoCan interstitial glucose assessmentreplace blood glucosemeasurementsrdquoDiabetes Technology anderapeutics vol 2 no 3 pp 461ndash472 2000

[92] G M Steil K Rebrin F Hariri et al ldquoInterstitial uid glucosedynamics during insulin-induced hypoglycaemiardquo Diabetolo-gia vol 48 no 9 pp 1833ndash1840 2005

[93] M S Boyne D M Silver J Kaplan and C D Saudek ldquoTimingof changes in interstitial and venous blood glucose measuredwith a continuous subcutaneous glucose sensorrdquo Diabetes vol52 no 11 pp 2790ndash2794 2003

[94] E Kulcu J A Tamada G Reach R O Potts and M JLesho ldquoPhysiological differences between interstitial glucoseand blood glucose measured in human subjectsrdquoDiabetes Carevol 26 no 8 pp 2405ndash2409 2003

[95] P J Stout J R Racchini andM E Hilgers ldquoA novel approach tomitigating the physiological lag between blood and interstitialuid glucose measurementsrdquo Diabetes Technology and era-peutics vol 6 no 5 pp 635ndash644 2004

[96] I M E Wentholt A A M Hart J B L Hoekstra and J HDevries ldquoRelationship between interstitial and blood glucose intype 1 diabetes patients delay and the push-pull phenomenonrevisitedrdquo Diabetes Technology and erapeutics vol 9 no 2pp 169ndash175 2007

[97] K J C Wientjes and A J M Schoonen ldquoDetermination oftime delay between blood and interstitial adipose tissue glucoseconcentration change by microdialysis in healthy volunteersrdquonternational Journal of Articial rgans vol 24 no 12 pp884ndash889 2001

[98] B Aussedat M Dupire-Angel R Gifford J C Klein G SWilson and G Reach ldquoInterstitial glucose concentration andglycemia implications for continuous subcutaneous glucosemonitoringrdquo American Journal of Physiology vol 278 no 4 ppE716ndashE728 2000

[99] B P Kovatchev and W L Clarke ldquoPeculiarities of the con-tinuous glucose monitoring data stream and their impact ondeveloping closed-loop control technologyrdquo Journal of DiabetesScience and Technology vol 2 pp 158ndash163 2008

[100] W L Clarke and B P Kovatchev ldquoContinuous glucose sen-sorsmdashcontinuing questions about clinical accuracyrdquo Journal ofDiabetes Science and Technology vol 1 pp 164ndash170 2007

[101] e Diabetes Research in Children Network (DirecNet) StudyGroup ldquoe accuracy of the guardian RT continuous glucosemonitor in children with type 1 diabetesrdquo Diabetes Technologyanderapeutics vol 10 pp 266ndash272 2008

[102] B Kovatchev S Anderson L Heinemann and W ClarkeldquoComparison of the numerical and clinical accuracy of fourcontinuous glucose monitorsrdquo Diabetes Care vol 31 no 6 pp1160ndash1164 2008

[103] S K Garg J Smith C Beatson B Lopez-Baca M Voelmleand P A Gottlieb ldquoComparison of accuracy and safety ofthe SEVEN and the navigator continuous glucose monitoringsystemsrdquo Diabetes Technology and erapeutics vol 11 no 2pp 65ndash72 2009

[104] T Heise T Koschinsky L Heinemann and V Lodwig ldquoHypo-glycemia warning signal and glucose sensors requirements andconceptsrdquo Diabetes Technology and erapeutics vol 5 no 4pp 563ndash571 2003

[105] B Bode K Gross N Rikalo et al ldquoAlarms based on real-timesensor glucose values alert patients to hypo- and hyperglycemiathe Guardian continuous monitoring systemrdquo Diabetes Tech-nology anderapeutics vol 6 no 2 pp 105ndash113 2004

[106] G McGarraugh and R Bergenstal ldquoDetection of hypoglycemiawith continuous interstitial and traditional blood glucosemonitoring using the FreeStyle navigator continuous glucosemonitoring systemrdquo Diabetes Technology and erapeutics vol11 no 3 pp 145ndash150 2009

[107] S E Noujaim D Horwitz M Sharma and J Marhoul ldquoAccu-racy requirements for a hypoglycemia detector an analyticalmodel to evaluate the effects of bias precision and rate ofglucose changerdquo Journal of Diabetes Science and Technology vol1 pp 653ndash668 2007

[108] W K Ward ldquoe role of new technology in the early detectionof hypoglycemiardquo Diabetes Technology and erapeutics vol 6no 2 pp 115ndash117 2004

[109] B Buckingham E Cobry P Clinton et al ldquoPreventing hypo-glycemia using predictive alarm algorithms and insulin pumpsuspensionrdquo Diabetes Technology and erapeutics vol 11 no2 pp 93ndash97 2009

[110] C S Hughes S D Patek M D Breton and B P KovatchevldquoHypoglycemia prevention via pump attenuation and red-yellow-green ldquotrafficrdquo lights using continuous glucose moni-toring and insulin pump datardquo Journal of Diabetes Science andTechnology vol 4 no 5 pp 1146ndash1155 2010

[111] B P Kovatchev D J Cox L A Gonder-Frederick and WClarke ldquoSymmetrization of the blood glucose measurementscale and its applicationsrdquo Diabetes Care vol 20 no 11 pp1655ndash1658 1997

[112] F J Service G D Molnar J W Rosevear E Ackerman LC Gatewood and W F Taylor ldquoMean amplitude of glycemicexcursions a measure of diabetic instabilityrdquo Diabetes vol 19no 9 pp 644ndash655 1970

[113] J Schlichtkrull O Munck and M Jersild ldquoe M-value anindex of blood glucose control in diabeticsrdquo Acta MedicaScandinavica vol 177 pp 95ndash102 1965

[114] E A Ryan T Shandro K Green et al ldquoAssessment of theseverity of hypoglycemia and glycemic lambility in type 1diabetic subjects undergoing islet in transplantationrdquo Diabetesvol 53 no 4 pp 955ndash962 2004

[115] B P Kovatchev D J Cox L Gonder-Frederick and WL Clarke ldquoMethods for quantifying self-monitoring bloodglucose proles exemplied by an examination of blood glucosepatterns in patients with type 1 and type 2 diabetesrdquo DiabetesTechnology anderapeutics vol 4 no 3 pp 295ndash303 2002

[116] B P Kovatchev D J Cox L S Farhy M Straume L Gonder-Frederick and W L Clarke ldquoEpisodes of severe hypoglycemia

Scientica 13

in type 1 diabetes are preceded and followed within 48 hours bymeasurable disturbances in blood glucoserdquo Journal of ClinicalEndocrinology and Metabolism vol 85 no 11 pp 4287ndash42922000

[117] B P Kovatchev M Straume D J Cox and L S FarhyldquoRisk analysis of blood glucose data a quantitative approach tooptimizing the control of Insulin Dependent Diabetesrdquo Journalof eoretical Medicine vol 3 no 1 pp 1ndash10 2000

[118] B P Kovatchev E Otto D Cox L Gonder-Frederick andW Clarke ldquoEvaluation of a new measure of blood glucosevariability in diabetesrdquo Diabetes Care vol 29 no 11 pp2433ndash2438 2006

[119] B P Kovatchev W L Clarke M Breton K Brayman and AMcCall ldquoQuantifying temporal glucose variability in diabetesvia continuous glucose monitoring mathematical methods andclinical applicationrdquo Diabetes Technology and erapeutics vol7 no 6 pp 849ndash862 2005

[120] D Rodbard ldquoNew and improved methods to characterizeglycemic variability using continuous glucosemonitoringrdquoDia-betes Technology and erapeutics vol 11 no 9 pp 551ndash5652009

[121] M Miller and P Strange ldquoUse of Fourier models for analysisand interpretation of continuous glucose monitoring glucoseprolesrdquo Journal of Diabetes Science and Technology vol 1 pp630ndash638 2007

[122] W Clarke and B Kovatchev ldquoStatistical tools to analyzecontinuous glucose monitor datardquo Diabetes Technology anderapeutics vol 11 pp S45ndashS54 2009

[123] C M Mcdonnell S M Donath S I Vidmar G A Wertherand F J Cameron ldquoA novel approach to continuous glucoseanalysis utilizing glycemic variationrdquo Diabetes Technology anderapeutics vol 7 no 2 pp 253ndash263 2005

[124] G Sparacino F Zanderigo A Maran and C Cobelli ldquoContin-uous glucosemonitoring andhypohyperglycaemia predictionrdquoDiabetes Research and Clinical Practice vol 74 no 2 supple-ment pp S160ndashS163 2006

[125] G Sparacino F Zanderigo G Corazza A Maran AFacchinetti and C Cobelli ldquoGlucose concentration can bepredicted ahead in time from continuous glucose monitoringsensor time-seriesrdquo IEEE Transactions on Biomedical Engineer-ing vol 54 pp 931ndash937 2007

[126] J Reifman S Rajaraman A Gribok and W K Ward ldquoPredic-tive monitoring for improved management of glucose levelsrdquoJournal of Diabetes Science and Technology vol 1 pp 478ndash4862007

[127] F Zanderigo G Sparacino B Kovatchev and C CobellildquoGlucose prediction algorithms from continuous monitoringassessment of accuracy via continuous glucose-error grid anal-ysisrdquo Journal of Diabetes Science and Technology vol 1 pp645ndash651 2007

[128] F Cameron G Niemayer K Gundy-Burlet and B Bucking-ham ldquoStatistical hypoglycemia predictionrdquo Journal of DiabetesScience and Technology vol 2 pp 612ndash621 2008

[129] A Gani A V Gribok S Rajaraman W K Ward and JReifman ldquoPredicting subcutaneous glucose concentration inhumans data-driven glucose modelingrdquo IEEE Transactions onBiomedical Engineering vol 56 no 2 pp 246ndash254 2009

[130] M Eren-Oruklu A Cinar L Quinn andD Smith ldquoEstimationof future glucose concentrations with subject-specic recursivelinear modelsrdquo Diabetes Technology and erapeutics vol 11no 4 pp 243ndash253 2009

[131] S M Pappada B D Cameron and P M Rosman ldquoDevelop-ment of a neural network for prediction of glucose concentra-tion in type 1 diabetes patientsrdquo Journal of Diabetes Science andTechnology vol 2 pp 792ndash801 2008

[132] C Cobelli C Dalla Man G Sparacino L Magni G Nicolaoand B P Kovatchev ldquoDiabetes models signals and controlrdquoIEEE Reviews in Biomedical Engineering vol 2 pp 54ndash96 2009

[133] H LeBlanc D Chauvet P Lombrail and J J Robert ldquoGlycemiccontrol with closed-loop intraperitoneal insulin in type Idiabetesrdquo Diabetes Care vol 9 no 2 pp 124ndash128 1986

[134] C D Saudek J L Selman H A Pitt et al ldquoA preliminary trialof the programmable implantablemedication system for insulindeliveryrdquo New England Journal of Medicine vol 321 no 9 pp574ndash579 1989

[135] C Broussolle N Jeandidier and H Hanaire-Broutin ldquoFrenchmulticentre experience of implantable insulin pumpsrdquo Lancetvol 343 no 8896 pp 514ndash515 1994

[136] H Hanaire-Broutin C Broussolle N Jeandidier et al ldquoFea-sibility of intraperitoneal insulin therapy with programmableimplantable pumps in IDDM a multicenter studyrdquo DiabetesCare vol 18 no 3 pp 388ndash392 1995

[137] P R Oskarsson P E Lins H W Henriksson and U CAdamson ldquoMetabolic and hormonal responses to exercisein type 1 diabetic patients during continuous subcutaneousas compared to continuous intraperitoneal insulin infusionrdquoDiabetes and Metabolism vol 25 no 6 pp 491ndash497 1999

[138] P R Oskarsson P E Lins L Backman and U C AdamsonldquoContinuous intraperitoneal insulin infusion partly restores theglucagon response to hypoglycaemia in type 1 diabetic patientsrdquoDiabetes and Metabolism vol 26 no 2 pp 118ndash124 2000

[139] B Catargi L Meyer V Melki E Renard and N JeandidierldquoComparison of blood glucose stability and HbA1C betweenimplantable insulin pumps using U400 hoe 21pH insulin andexternal pumps using lispro in type 1 diabetic patients a pilotstudyrdquo Diabetes and Metabolism vol 28 no 2 pp 133ndash1372002

[140] E Renard ldquoImplantable closed-loop glucose-sensing andinsulin delivery the future for insulin pump therapyrdquo CurrentOpinion in Pharmacology vol 2 no 6 pp 708ndash716 2002

[141] E Renard G Costalat H Chevassus and J Bringer ldquoArticial120573120573-cell clinical experience toward an implantable closed-loopinsulin delivery systemrdquo Diabetes and Metabolism vol 32 no5 pp 497ndash502 2006

[142] E Renard J Place M Cantwell H Chevassus and C CPalerm ldquoClosed-loop insulin delivery using a subcutaneousglucose sensor and intraperitoneal insulin delivery feasibilitystudy testing a new model for the articial pancreasrdquo DiabetesCare vol 33 no 1 pp 121ndash127 2010

[143] R Bellazzi G Nucci and C Cobelli ldquoe subcutaneousroute to insulin-dependent diabetes therapy closed-loop andpartially closed-loop control strategies for insulin deliveryand measuring glucose concentrationrdquo IEEE Engineering inMedicine and Biology Magazine vol 20 no 1 pp 54ndash64 2001

[144] R Hovorka L J Chassin M E Wilinska et al ldquoClosingthe loop the adicol experiencerdquo Diabetes Technology anderapeutics vol 6 no 3 pp 307ndash318 2004

[145] R Hovorka V Canonico L J Chassin et al ldquoNonlinear modelpredictive control of glucose concentration in subjects withtype 1 diabetesrdquo Physiological Measurement vol 25 no 4 pp905ndash920 2004

14 Scientica

[146] G M Steil K Rebrin C Darwin F Hariri and M F SaadldquoFeasibility of automating insulin delivery for the treatment oftype 1 diabetesrdquo Diabetes vol 55 no 12 pp 3344ndash3350 2006

[147] e JDRF e-Newsletter Emerging Technologies in DiabetesResearch 2006

[148] W L Clarke and B Kovatchev ldquoe articial pancreas howclose are we to closing the looprdquo Pediatric EndocrinologyReviews vol 4 no 4 pp 314ndash316 2007

[149] C Cobelli E Renard and B P Kovatchev ldquoPerspectives indiabetes articial pancreas past present futurerdquo Diabetes vol60 pp 2672ndash2682 2011

[150] B P Kovatchev M D Breton C Dalla Man and C Cobelli ldquoInsilico preclinical trials a proof of concept in closed-loop controlof type 1 diabetesrdquo Journal of Diabetes Science and Technologyvol 3 pp 44ndash55 2009

[151] B Kovatchev C Cobelli E Renard et al ldquoMultinational studyof subcutaneous model-predictive closed-loop control in type1 diabetes mellitus summary of the resultsrdquo Journal of DiabetesScience and Technology vol 4 no 6 pp 1374ndash1381 2010

[152] H Zisser L Jovanovic F Doyle P Ospina and C OwensldquoRun-to-run control of meal-related insulin dosingrdquo DiabetesTechnology anderapeutics vol 7 no 1 pp 48ndash57 2005

[153] C Owens H Zisser L Jovanovic B Srinivasan D Bonvinand F J Doyle III ldquoRun-to-run control of blood glucoseconcentrations for people with type 1 diabetes mellitusrdquo IEEETransactions on Biomedical Engineering vol 53 no 6 pp996ndash1005 2006

[154] C C Palerm H Zisser W C Bevier L Jovanovič and F JDoyle III ldquoPrandial insulin dosing using run-to-run controlapplication of clinical data and medical expertise to dene asuitable performance metricrdquo Diabetes Care vol 30 no 5 pp1131ndash1136 2007

[155] LMagni F Raimondo L Bossi et al ldquoModel predictive controlof type 1 diabetes an in silico trialrdquo Journal of Diabetes Scienceand Technology vol 1 pp 804ndash812 2007

[156] S A Weinzimer G M Steil K L Swan J Dziura N Kurtzand W V Tamborlane ldquoFully automated closed-loop insulindelivery versus semiautomated hybrid control in pediatricpatients with type 1 diabetes using an articial pancreasrdquoDiabetes Care vol 31 no 5 pp 934ndash939 2008

[157] W L Clarke S Anderson M Breton S Patek L Kashmerand B Kovatchev ldquoClosed-loop articial pancreas using sub-cutaneous glucose sensing and insulin delivery and a modelpredictive control algorithm the Virginia experiencerdquo Journalof Diabetes Science and Technology vol 3 no 5 pp 1031ndash10382009

[158] D Bruttomesso A Farret S Costa et al ldquoClosed-loop arti-cial pancreas using subcutaneous glucose sensing and insulindelivery and a model predictive control algorithm preliminarystudies in Padova and Montpellierrdquo Journal of Diabetes Scienceand Technology vol 3 no 5 pp 1014ndash1021 2009

[159] R Hovorka J M Allen D Elleri et al ldquoManual closed-loop insulin delivery in children and adolescents with type 1diabetes a phase 2 randomised crossover trialrdquoe Lancet vol375 no 9716 pp 743ndash751 2010

[160] F H El-khatib S J Russell D M Nathan R G Sutherlin andE R Damiano ldquoA bihormonal closed-loop articial pancreasfor type 1 diabetesrdquo Science Translational Medicine vol 2 no27 Article ID 27ra27 2010

[161] E Dassau H Zisser C C Palerm B A Buckingham LJovanovič and F J Doye III ldquoModular articial 120573120573-cell system a

prototype for clinical researchrdquo Journal of Diabetes Science andTechnology vol 2 pp 863ndash872 2008

[162] B Kovatchev S Patek E Dassau et al ldquoControl to range fordiabetes functionality and modular architecturerdquo Journal ofDiabetes Science and Rechnology vol 3 no 5 pp 1058ndash10652009

[163] E Atlas R Nimri S Miller E A Grunberg and M PhillipldquoMD-logic articial pancreas system a pilot study in adults withtype 1 diabetesrdquo Diabetes Care vol 33 no 5 pp 1072ndash10762010

[164] E Dassau H Zisser M W Percival B Grosman L Jovanovičand F J Doyle III ldquoClinical results of automated articialpancreatic 120573120573-cell system with unannounced meal using multi-parametric MPC and insulin-on-boardrdquo in Proceedings of the70th American Diabetes Association Meeting Diabetes vol 59supplement 1 A94 Orlando Fla USA 2010

[165] E M Renard A Farret J Place C Cobelli B P Kovatchev andMD Breton ldquoClosed-loop insulin delivery using subcutaneousinfusion and glucose sensing and equipped with a dedicatedsafety supervision algorithm improves safety of glucose controlin type 1 diabetesrdquo Diabetologia vol 53 supplement 1 p S252010

[166] R Hovorka K Kumareswaran J Harris et al ldquoOvernightclosed loop insulin delivery articial pancreas in adults withtype 1 diabetes crossover randomized controlled studiesrdquoBritish Medical Journal vol 342 no 7803 Article ID d18552011

[167] H Zisser E Dassau W Bevier et al ldquoInitial evaluation ofa fully automated articial pancreasrdquo in Proceedings of the71st American Diabetes Association Meeting Diabetes vol 60supplement 1 A41 San Diego Calif USA 2011

[168] M D Breton A Farret and D Bruttomesso ldquoFully-integratedarticial pancreas in type 1 diabetes modular closed-loopglucose control maintains near-normoglycemiardquo Diabetes vol61 pp 2230ndash2237 2012

[169] C Cobelli E Renard and B P Kovatchev ldquoPilot studies ofwearable articial pancreas in type 1 diabetesrdquo Diabetes Carevol 35 no 9 pp e65ndashe67 2012

Submit your manuscripts athttpwwwhindawicom

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Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Scientica 5

T 1 Frequency of available glucose monitoring technologies

Measure Temporal resolution Reects Methods typically used to present and analyze the data

HbA1c Monthsyears Slow changes in average BG Direct assay and review of values group comparisonswhen treatments are evaluated

Self-monitoring of bloodglucose (SMBG) Daysweeks Daily variation

weekly trends

Mean and standard deviation (SD) coefficient ofvariation (CV) Interquartile range (IQR)119872119872-value(1965) MAGE (1970)lability index (2004)lowhigh BG (risk) Indices (1998)average daily risk range (ADRR 2006)

Continuous glucosemonitoring (CGM) Minuteshours System dynamics

uctuation and periodicity

CONGA (2005)glucose rate of Change amp CGM versions of lowhighBG (risk) indices (2005)time seriesdynamical system analysis

assessing the glycemic state or the BG dynamics of aperson with diabetes A brief account of analytical methodsapplicable to SMBG andor CGM data is given below Someof these methods such as the Risk Analysis of BG data haveentered the design of closed-loop control systems preventinghypoglycemia [110]

41 SMBG-Based Analytical Methods e computation ofmean glucose values from SMBG data is typically used as adescriptor of overall glycemic control Computing pre- andpostmeal averages and their difference can serve as an indica-tion of the effectiveness of premeal bolus timing and amountSimilarly the percentages of SMBG readings within belowor above preset target limits would serve as indication of thegeneral behavior of BG uctuations e suggested limits are70 and 180mgdL (39ndash10mmoll) which create three sug-gested by the DCCT and commonly accepted bands hypo-glycemia (BG le 70mgdL) normoglycemia (70mgdL ltBG le 180mgdL) hyperglycemia (BG gt 180mgdL) [1]Percentage of time within additional bands can be computedas well to emphasize the frequency of extreme glucoseexcursions Computing standard deviation (SD) as ameasureof glucose variability is not recommended because the BGmeasurement scale is highly asymmetric the hypoglycemicrange is numerically narrower than the hyperglycemic rangeand the distribution of the glucose values of an individualis typically quite skewed [111] erefore SD would bepredominantly inuenced by hyperglycemic excursions andwould not be sensitive to hypoglycemia It is also possiblefor condence intervals based on SD to assume unrealisticnegative valuesus as a standard measure of GV we wouldsuggest reporting interquartile range (IQR) which is suitablefor asymmetric distributions Several diabetes-specic met-rics are also available to serve the analysis of SMBG dataincluding the mean amplitude of glucose excursions (MAGE[112]) the 119872119872-value [113] the lability index [114] and thelow and high blood glucose indices (LBGI HBGI) whichreect the risks associated with hypo- and hyperglycemiarespectively [37 115] In a series of studies we have shownthat specic risk analysis of SMBG data could also capturelong-term trends towards increased risk for hypoglycemia

[36ndash38] and could identify 24-hour periods of increased riskfor hypoglycemia [39 116]

42 Risk Analysis of BG Data To provide a avor for theanalytical techniques used for SMBG CGM and closed-loopcontrol data we will present a bit more detail on the conceptfor risk analysis of BG data [117] e risk analysis steps areas follows

421 Symmetrization of the BG Scale A nonlinear transfor-mation is applied to the BG measurements scale to map theentire BG range (20 to 600mgdL or 11 to 333mmoll) to asymmetric intervalis is needed because the distribution ofBG values of a person with diabetes is asymmetric typicallyskewed towards hyperglycemiae BG value of 1125mgdL(625mmoll) is mapped to zero corresponding to zero riskfor hypo- or hyperglycemia (we should note that this is nota normoglycemic or fasting value which in health wouldbe lt100mgdL it is zero-risk value pertinent to diabetes)e analytical form of this transformation is 119891119891119891BG) = 120574120574 120574119891ln 119891BG)120572120572 minus 120573120573) where the parameters are estimated as 120572120572 =1084 120573120573 = 12057312057381 and 120574120574 = 11205730120574 if BG is measured in mgdLand 120572120572 = 10120572120572 120573120573 = 181205721 and 120574120574 = 11205741205744 if BG is measuredin mmoll [111]

422 Assignment of a Risk Value to Each BG Reading Aquadratic risk function is dened as by the formula 119903119903119891BG) =10 120574 119891119891119891BG)120572 e function 119903119903119891BG) ranges from 0 to 100Its minimum value is achieved at BG = 1125mgdL asafe euglycemic BG reading while its maximum is reachedat the extreme ends of the BG scale us 119903119903119891BG) can beinterpreted as a measure of the risk associated with a certainBG level e le branch of this parabola identies the riskof hypoglycemia while the right branch identies the risk ofhyperglycemia

423 Computing Measures of Risk for Hypoglycemia Hyper-glycemia and Glucose Variability Let 1199091199091 119909119909120572hellip 119909119909119899119899 be aseries of 119899119899 BG readings and let 119903119903119903119903119891BG) = 119903119903119891BG) if 119891119891119891BG) lt 0and 0 otherwise 119903119903119903119891BG) = 119903119903119891BG) if 119891119891119891BG) gt 0 and 0

6 Scientica

otherwise en the low and high blood glucose indices arecomputed as follows

LBGI = 1119899119899

11989911989910055761005576119894119894=111990311990311990311990310076491007649119909119909119894119894100766510076652 HBGI = 1

119899119899

11989911989910055761005576119894119894=11199031199031199031007649100764911990911990911989411989410076651007665

2 (1)

us the LBGI is a nonnegative quantity that increaseswhen the number andor extent of low BG readings increasesand the HBGI is nonnegative quantity that increases whenthe number andor extent of high BG readings increasesBased on this same technique we also dene the average dailyrisk range (ADRR) which is a measure of risks associatedwith overall glycemic variability [118] In studies the LBGItypically accounted for 40ndash55 of the variance of futuresignicant hypoglycemia in the subsequent 3ndash6 months [36ndash38] which made it a potent predictor of hypoglycemia basedon SMBGeADRR has been shown superior to traditionalglucose variability measures in terms of risk assessment andprediction of extreme glycemic excursions [118] Specicallyit has been demonstrated that classication of risk for hypo-glycemia based on four ADRR categories low risk ADRR lt20 low-moderate risk 20 le ADRR lt 30 moderate-high risk30 le ADRR lt 40 and high risk ADRR gt 40 resulted inmore than a sixfold increase in risk for hypoglycemia fromthe lowest to the highest risk category [118] In addition thelow and high BG indices have been adapted to continuousmonitoring data [119] and can be used in the same way aswith SMBG to assess the risk for hypo- or hyperglycemia

43 CGM-Based Analytical Methods While traditional risk[119] and variability [120] analyses are still applied to CGMdata the high temporal resolution of CGM brought aboutthe possibility for use of sophisticated analytical methodsassessing system (person) dynamics on the time scale of min-utes is necessitated the development of new technologiesfor data analysis and visualization that are not available forSMBG data Analysis of the BG rate of change (measuredin mgdLmin) is a way to evaluate the dynamics of BGuctuations on the time scale of minutes e BG rate ofchange at 119905119905119894119894mdashis computed as the ratio (BG(119905119905119894119894)minusBG(119905119905119894119894minus1))(119905119905119894119894minus119905119905119894119894minus1) where BG(119905119905119894119894) and BG(119905119905119894119894minus1) are CGM readings taken attimes 119905119905119894119894 and 119905119905119894119894minus1 for example minutes apart Investigationof the frequency of glucose uctuations showed that optimalevaluation of the BG rate of change would be achieved overtime periods of 15minutes [121] for exampleΔ119905119905 = 119905119905119894119894 minus119905119905119894119894minus1 =15 A large variation of the BG rate of change indicates rapidand more pronounced BG uctuations and therefore a lessstable system us the standard deviation of the BG rateof change is a measure of stability of glucose uctuation (weshould note that as opposed to the distribution of BG levelsthe distribution of the BG rate of change is symmetric andtherefore using SD is statistically accurate [122]) e SDof BG rate of change has been introduced as a measure ofstability computed fromCGMdata and is known asCONGAof order 1 In general CONGA122 of order 119899119899 is computedas the standard deviation of CGM readings that are 119899119899 hoursapart reecting glucose stability over these time intervals[123]

Most important to the development of the articial pan-creas algorithms is a class of methods allowing the predictionof BG values ahead in time ese methods typically basedon time-series techniques have been applied successfully ina number of studies [124ndash129] In addition to time seriesneural networks have been used for the prediction of glucoselevels from CGM [130 131] Detailed reviews of CGM dataanalysis methods are presented in [122] including severalgraphs that could be used for the visualization of the rathercomplex CGM data sets and in [132] where a broad reviewof modeling analytical and control techniques for diabetesis provided

5 Control of BG Fluctuations in Diabetes

51 Intraperitoneal Insulin Delivery As detailed in the Intro-duction the articial pancreas idea can be traced back to theearly 70s when external BG regulation in people with dia-betes was achieved by iv glucose measurement and iv infu-sion of glucose and insulin However the intravenous routeof closed-loop control remains cumbersome and unsuitedfor outpatient use An alternative has been presented byimplantable intraperitoneal (ip) systems employing iv sam-pling and ip insulin delivery [133ndash136] e ip infusionroute has several desirable characteristics reproducibility ofinsulin absorption quick time to peak and return to baselineof insulin action near-physiological peripheral insulin levelsand restoration of glucagon response to hypoglycemia andexercise [133 137ndash139] However while ip systems haveachieved excellent BG control their implementation stillrequires considerable surgery and is associated with signi-cant cost Nevertheless the development of less invasive andcheaper implantable ports (eg DiaPort Roche DiagnosticsMannheim Germany) may contribute to the future prolifer-ation of ip insulin delivery [140ndash142]

52e Subcutaneous Route to Closed-Loop Control Follow-ing the progress of minimally invasive subcutaneous CGMthe next logical step was the development of sc closed-loopglucose control which links a CGM device with CSII insulinpump A key element of this combination was a controlalgorithm which monitors BG uctuations and the actionsof the insulin pump and computes insulin delivery rate everyfew minutes [143] Figure 2 presents key milestones in thetimeline of this development

Following the pioneering work of Hovorka et al [144145] and Steil et al [146] in 2006 the Juvenile DiabetesResearch Foundation (JDRF) initiated the Articial PancreasProject and funded several centers in the USA and Europeto carry closed-loop control research [147] In 2008 theUSA National Institutes of Health launched an articialpancreas initiative and in 2010 the European APHomeconsortium was established By the end of the rst decade ofthis century the articial pancreas became a global researchtopic engaging physicians and engineers in unprecedentedcollaboration [148 149]

Scientica 7

2006 2008 2010

The JDRF artificial pancreas consortium

is launched (Kowalski)

Studies of hybrid closed-loop control (Weinzimer

and Tamborlane)

First human trials begin using a system designed entirely in silico UVA

Italy and France(Kovatchev Cobelli Renard)

NIH funds artificial pancreas

EU launches the APHome

artificial pancreas initiative

JDRF multicenter trialof modular control-to-

range

2004 the ADICOLproject

(Hovorka)

First studies of automated sc

closed loop (Steil)

FDA accepts the UVAPadova metabolic simulator as a substitute

to animal trials(Kovatchev Cobelli Dalla

Man and Breton)

Modular control-to-rangeintroduced trials at UVA

Italy and France(Kovatchev

Cobelli and Renard)

The APS introduced(Dassau Doyle

First studies of outpatient closed-loop

control (CobelliRenard Zisser and

Kovatchev )

2012

DiAs first portableAP platform

(Keith-HynesKovatchev)

studies Zisser)

30

2012

F 2 Timeline of the articial pancreas developments in the last decademdashtheoretical work and a number of in-clinic studies leading tothe rst trials of wearable articial pancreas device

53 In Silico Models of the Human Metabolic System Acritical step towards accelerated clinical progress of thearticial pancreas was the development of sophisticatedcomputer simulator of the human metabolic system allowingrapid in silico testing of closed-loop control algorithmsis simulation environment was based on the previouslyintroduced Meal Model of glucose-insulin dynamics [76 77]and was equipped with a ldquopopulationrdquo of in silico imagesof 119873119873 119873 119873119873119873 ldquosubjectsrdquo with type 1 diabetes separated inthree age groups 119873119873 119873 119873119873119873 simulated ldquochildrenrdquo below theage of 11 119873119873 119873 119873119873119873 ldquoadolescentsrdquo 12ndash18 years old and119873119873 119873 119873119873119873 ldquoadultsrdquo e characteristics of these ldquosubjectsrdquo(eg weight daily insulin dose carbohydrate ratio etc)were tailored to span a wide range of intersubject variabilityapproximating the variability observed in people in vivo[150] Simulation experiments allow any CGM device anyinsulin pump and any control algorithm to be linked in aclosed-loop system in silico prior to their use in clinical trialsWith this technology any meal and insulin delivery scenariocan be pilot-tested very efficientlymdasha 24-hour period ofclosed-loop control is simulated in under 2 secondsWe needto emphasize however that good in silico performance of acontrol algorithmdoes not guarantee in vivo performancemdashitonly helps test extreme situations and the stability of thealgorithm and rule out inefficient scenarios us computersimulation is only a prerequisite to but not a substitute forclinical trials

In January 2008 in an unprecedented decision theUSA Food and Drug Administration accepted this computer

simulator as a substitute to animal trials for the testing ofclosed-loop control strategies is opened the eld for effi-cient and cost-effective in silico experiments leading directlyto human studies Only three months later in April 2008 therst human trials began at the University of irginia (USA)Montpellier (France) and Padova (Italy) using a controlsystem designed entirely in silico [151]

54 Control System Designs e rst studies of ovorkaet al [144 145] and Steil et al [146] outlined the twomajor types of closed-loop control algorithms now in use inarticial pancreas systemsmdashmodel-predictive control (MPC[145]) and proportional-integral-derivative (PID [146])respectively By 2007 the blueprints of the contemporarycontrollers were in place including run-to-run control[152ndash154] and linear MPC [155] To date the trials ofsubcutaneous closed-loop control systems have been usingeither PID [146 156] or MPC [157ndash160] but MPC becamethe approach of choice targeted by recent research erewere two important reasons making MPC preferable (i)PID is purely reactive responding to changes in glucoselevel while a properly tuned MPC allows for prediction ofglucose dynamics and as a result for mitigation of the timedelays inherent with subcutaneous glucose monitoring andsubcutaneous insulin infusion [62 63] (ii) MPC allows forrelatively straightforward personalizing of the control usingpatient-specic model parameters In addition MPC couldhave ldquolearningrdquo capabilitiesmdashit has been shown that a class

8 Scientica

of algorithms (known as run-to-run control) can ldquolearnrdquospecics of patientsrsquo daily routine (eg timing of meals) andthen optimize the response to a subsequent meal using thisinformation or account for circadian uctuation in insulinresistance (eg dawn phenomenon observed in some people)[149]

In 2008 a universal research platformmdashthe APSmdashwasintroduced enabling automated communication betweenseveral CGM devices insulin pumps and control algorithms[161] e APS was very instrumental for a number ofinpatient trials of closed-loop control A year later a mod-ular architecture was introduced proposing standardizationsequential testing and clinical deployment of articial pan-creas components [162]

55 Inpatient Clinical Trials Between 2008 and 2011 prom-ising results were reported by several groups [156ndash160 163ndash167] Most of these studies pointed out the superiority ofclosed-loop control over standard CSII therapy in termsof (i) increased time within target glucose range (typically39ndash10mmoll) (ii) reduced incidence of hypoglycemia and(iii) better overnight control Two of these studies [159166] had state-of-the-art randomized cross-over design butlacked automated data transfermdashall CGM readings weretransferred to the controllermanually by the study personneland all insulin pump commands were entered manually aswell To distinguish the various degrees of automation inclosed-loop studies the notion of fully-integrated closed-loop control emerged dened as having all of the followingthree components (i) automated data transfer from theCGM to the controller (ii) real-time control action and (iii)automated command of the insulin pump e rst (andthe largest to date) randomized cross-over study of fully-integrated closed-loop control was published in 2012 [168]However even this contemporary trial of fully automatedCLC which enrolled 38 patients with T1D at three centersand tested two different control algorithms achieving note-worthy glycemic control and prevention of hypoglycemia didnot leave the clinical setting e technology used by thisstudy was still based on a laptop computer wired to a CGMand an insulin pump a system limiting the free movementof the study subjects and too cumbersome to be used beyondhospital connes

5 earale tpatient rticial ancreas e transitionof closed-loop control to ambulatory use began in 2011 withthe development of the Diabetes Assistant (DiAs)mdashthe rstwearable articial pancreas platform based on a smart phonee design characteristics of DiAs included the following

(i) based on readily available inexpensive wearablehardware platform

(ii) computationally capable of running advanced closed-loop control algorithms

(iii) wirelessly connectable to CGM devices and insulinpumps

(iv) capable of broadband communication with a centrallocation for remote monitoring and safety supervi-sion of the participants in outpatient clinical trials

In ctober 2011 the rst two pilot trials of wearableoutpatient articial pancreas were performed simultaneouslyin Padova (Italy) and Montpellier (France) [169] ese 2-day trials allowed the renement of a wearable system andenabled a subsequentmultisite feasibility study of ambulatoryarticial pancreas which was completed recently at theUniversities of Virginia Padova and Montpellier and at theSansumDiabetes Research institute Santa Barbara CAUSAResults from this study are forthcoming

6 Conclusions

Solving the optimization problem of diabetes requiresreplacement of insulin action through insulin injections ororal medications (applicable primarily to type 2 diabetes)which until fully automated closed-loop control becomesavailable would remain a process largely controlled bypatient behavior In engineering terms BG uctuations indiabetes result from the activity of a complex metabolicsystem perturbed by behavioral challenges e frequencyand extent of these challenges and the ability of the personrsquosmetabolic system to absorb them determines the qualityof glycemic control Along with HbA1c the magnitudeand speed of BG uctuations is the primary measurablemarker of glucose control in diabetes ese same quanti-tiesmdashHbA1c and glucose variabilitymdashare also the principalfeedback available to patients to assist with optimization oftheir diabetes control

In the past 30 years the technology for monitoring ofblood glucose levels in diabetes has progressed from assess-ment of average glycemia via HbA1c once in several monthsthrough daily SMBG to minutely continuous glucose mon-itoring e increasing temporal resolution of the moni-toring technology enabled increasingly intensive diabetestreatment from daily insulin injections or oral medicationthrough insulin pump therapy to the articial pancreasis progress is accompanied by increasingly sophisticatedanalytical methods for retrieval of blood glucose data rangingfrom subjective interpretation of glucose values and straight-forward summary statistics through risk and variabilityanalysis to real-time closed-loop control algorithms based oncomplex models of the human metabolism

It is therefore evident that the development of diabetestechnology is accelerating exponentially A primary cata-lyst of this acceleration is unprecedented interdisciplinarycollaboration between physicians chemists engineers andmathematicians As a result a wearable articial pancreassuitable for outpatient use is now within reach

e primary engineering challenges to the widespreadadoption of closed-loop control as a viable therapeutic optionfor diabetes include system connectivity the accuracy ofsubcutaneous glucose sensing and the speed of action ofsubcutaneously injected insulin ese challenges are well

Scientica 9

understood by those working in the eld wireless commu-nication between CGM devices insulin pumps and closed-loop controllers are under development and testing newgenerations of CGM device demonstrate superior accuracyand reliability and new insulin analogs and methods forinsulin delivery are being engineered to approximate asclose as possible the action prole of endogenous insulinIt should be noted however that the signals available toa contemporary closed-loop control system are generallylimited to CGM and insulin delivery data user input aboutcarbohydrate intake and physical activity could be availableas well In contrast the endocrine pancreas receives directand rapid control inputs from other nutrients (eg lipids andamino acids) adjacent cells (somatostatin from the delta cellsand glucagon from alpha cells) incretins and neural signalsus while articial closed-loop control is expected to bevastly superior to the diabetes control methods employed inthe clinical practice today it will continue to be imperfectwhen compared to the natural endocrine regulation of bloodglucose

Acknowledgments

is work was made possible by the JDRF Articial PancreasProject the National Institutes of HealthNIDDK GrantsRO1 DK 51562 and RO1 DK 085623 and by the generoussupport of PBM Science Charlottesville Virginia CA USAand the Frederick Banting Foundation Richmond VirginiaCA USA e author thanks his colleagues at the Universityof Virginia Center forDiabetes Technology for their relentlesswork on articial pancreas development

References

[1] American Diabetes Association ldquoDiagnosis and classicationof diabetes mellitusrdquo Diabetes Care vol 27 pp s5ndashs10 2004

[2] A H Kadish ldquoAutomation control of blood sugarmdashI A ser-vomechanism for glucose monitoring and controlrdquo AmericanJournal of Medical Electronics vol 39 pp 82ndash86 1964

[3] J C Pickup H Keen J A Parsons and K G M M AlbertildquoContinuous subcutaneous insulin infusion an approach toachieving normoglycaemiardquo British Medical Journal vol 1 no6107 pp 204ndash207 1978

[4] W V Tamborlane R S Sherwin M Genel and P FeligldquoReduction to normal of plasma glucose in juvenile diabetes bysubcutaneous administration of insulinwith a portable infusionpumprdquo New England Journal of Medicine vol 300 no 11 pp573ndash578 1979

[5] A M Albisser B S Leibel and T G Ewart ldquoAn articialendocrine pancreasrdquoDiabetes vol 23 no 5 pp 389ndash396 1974

[6] E F Pfeier um Ch and A H Clemens ldquoe articialbeta cell a continuous control of blood sugar by external regu-lation of insulin infusion (glucose controlled insulin infusionsystem)rdquo Hormone and Metabolic Research vol 6 no 5 pp339ndash342 1974

[7] J Mirouze J L Selam T C Pham and D Cavadore ldquoEvalua-tion of exogenous insulin homoeostasis by the artical pancreasin insulin dependent diabetesrdquo Diabetologia vol 13 no 3 pp273ndash278 1977

[8] E W Kraegen L V Campbell and Y O Chia ldquoControlof blood glucose in diabetics using an articial pancreasrdquoAustralian and New Zealand Journal of Medicine vol 7 no 3pp 280ndash286 1977

[9] M Shichiri R Kawamori Y Yamasaki M Inoue Y Shigetaand H Abe ldquoComputer algorithm for the articial pancreaticbeta cellrdquo Articial rgans vol 2 supplement pp 247ndash2501978

[10] A H Clemens P H Chang and R W Myers ldquoe devel-opment of Biostator a Glucose Controlled Insulin InfusionSystem (GCIIS)rdquo Hormone and Metabolic Research vol 7 pp23ndash33 1977

[11] E B Marliss F T Murray and E F Stokes ldquoNormalization ofglycemia in diabetics during meals with insulin and glucagondelivery by the articial pancreasrdquo Diabetes vol 26 no 7 pp663ndash672 1977

[12] J V Santiago A H Clemens W L Clarke and D M KipnisldquoClosed-loop and open-loop devices for blood glucose controlin normal and diabetic subjectsrdquo Diabetes vol 28 no 1 pp71ndash84 1979

[13] U Fischer E Jutzi E J Freyse and E Salzsieder ldquoDerivationand experimental proof of a new algorithm for the articial B-cell based on the individual analysis of the physiological insulin-glucose relationshiprdquo Endokrinologie vol 71 no 1 pp 65ndash751978

[14] R S Parker F J Doyle and N A Peppas ldquoe intravenousroute to blood glucose control a review of control algorithmsfor noninvasive monitoring and regulation in type I diabeticpatientsrdquo IEEE Engineering in Medicine and Biology Magazinevol 20 no 1 pp 65ndash73 2001

[15] R N Bergman Y Z Ider C R Bowden and C CobellildquoQuantitative estimation of insulin sensitivityrdquo e AmericanJournal of Physiology vol 236 no 6 pp E667ndashE677 1979

[16] H M Broekhuyse J D Nelson B Zinman and A M AlbisserldquoComparison of algorithms for the closed-loop control of bloodglucose using the articial beta cellrdquo IEEE Transactions onBiomedical Engineering vol 28 no 10 pp 678ndash687 1981

[17] A H Clemens ldquoFeedback control dynamics for glucosecontrolled insulin infusion systemrdquo Medical Progress throughTechnology vol 6 no 3 pp 91ndash98 1979

[18] C Cobelli and A Ruggeri ldquoEvaluation of portalperipheralroute and of algorithms for insulin delivery in the closed-loop control of glucose in diabetes a modeling studyrdquo IEEETransactions on Biomedical Engineering vol 30 no 2 pp93ndash103 1983

[19] E Salzsieder G Albrecht U Fischer and E J Freyse ldquoKineticmodeling of the glucoregulatory system to improve insulintherapyrdquo IEEE Transactions on Biomedical Engineering vol 32no 10 pp 846ndash855 1985

[20] P Brunetti C Cobelli P Cruciani et al ldquoA simulation study ona self-tuning portable controller of blood glucoserdquo InternationalJournal of Articial rgans vol 16 no 1 pp 51ndash57 1993

[21] U Fischer W Schenk E Salzsieder G Albrecht P Abel andE J Freyse ldquoDoes physiological blood glucose control requirean adaptive control strategyrdquo IEEE Transactions on BiomedicalEngineering vol 34 no 8 pp 575ndash582 1987

[22] J T Sorensen A physiologic model of glucose metabolism inman and its use to design and assess improved insulin therapiesfor diabetes [PhD dissertation] Department of Chemical Engi-neering MIT 1985

[23] R S Parker F J Doyle and N A Peppas ldquoA model-basedalgorithm for blood glucose control in type I diabetic patientsrdquo

10 Scientica

IEEE Transactions on Biomedical Engineering vol 46 no 2 pp148ndash157 1999

[24] J J Mastrototaro ldquoe MiniMed continuous glucose monitor-ing Systemrdquo Diabetes Technology anderapeutics vol 2 no 1pp S13ndashS18 2000

[25] B W Bode ldquoClinical utility of the continuous glucose monitor-ing systemrdquo Diabetes Technology anderapeutics vol 2 no 1supplement pp S35ndashS41 2000

[26] B Feldman R Brazg S Schwartz and R Weinstein ldquoAcontinuous glucose sensor based on wired enzyme technol-ogymdashresults from a 3-day trial in patients with type 1 diabetesrdquoDiabetes Technology anderapeutics vol 5 no 5 pp 769ndash7792003

[27] eDiabetes Control and Complications Trial Research Groupldquoe effect of intensive treatment of diabetes on the develop-ment and progression of long-term complications of insulin-dependent diabetes mellitusrdquoNew England Journal of Medicinevol 329 pp 978ndash986 1993

[28] eDiabetes Control and Complications Trial Research Groupldquoe relationship of glycemic exposure (HbA1c) to the riskof development and progression of retinopathy in the Dia-betes Control and Complications Trialrdquo Diabetes vol 44 pp968ndash983 1995

[29] J M Lachin S Genuth D M Nathan B Zinman and B NRutledge ldquoEffect of glycemic exposure on the risk of microvas-cular complications in the diabetes control and complicationstrial-revisitedrdquo Diabetes vol 57 no 4 pp 995ndash1001 2008

[30] P Reichard and M Pihl ldquoMortality and treatment side-effectsduring long-term intensied conventional insulin treatment inthe Stockholm Diabetes Intervention Studyrdquo Diabetes vol 43no 2 pp 313ndash317 1994

[31] UK Prospective Diabetes Study Group (UKPDS) ldquoIntensiveblood-glucose control with sulphonylureas or insulin comparedwith conventional treatment and risk of complications inpatients with type 2 diabetes (UKPDS 33)rdquo Lancet vol 352 no9131 pp 837ndash853 1998

[32] P A Svendsen T Lauritzen U Soegaard and J NerupldquoGlycosylated haemoglobin and steady-state mean blood glu-cose concentration in type 1 (insulin-dependent) diabetesrdquoDiabetologia vol 23 no 5 pp 403ndash405 1982

[33] J V Santiago ldquoLessons from the diabetes control and compli-cations trialrdquo Diabetes vol 42 no 11 pp 1549ndash1554 1993

[34] eDiabetes Control and Complications Trial Research GroupldquoHypoglycemia in the diabetes control and complications trialrdquoDiabetes vol 46 pp 271ndash286 1997

[35] A E Gold BM Frier KMMacLeod and I J Deary ldquoA struc-tural equation model for predictors of severe hypoglycaemiain patients with insulin-dependent diabetes mellitusrdquo DiabeticMedicine vol 14 pp 309ndash315 1997

[36] D J Cox B P Kovatchev D M Julian et al ldquoFrequency ofsevere hypoglycemia in insulin-dependent diabetesmellitus canbe predicted from self-monitoring blood glucose datardquo Journalof Clinical Endocrinology and Metabolism vol 79 no 6 pp1659ndash1662 1994

[37] B P Kovatchev D J Cox L A Gonder-Frederick D Young-Hyman D Schlundt and W Clarke ldquoAssessment of risk forsevere hypoglycemia among adults with IDDM validation ofthe low blood glucose indexrdquo Diabetes Care vol 21 no 11 pp1870ndash1875 1998

[38] B P Kovatchev D J Cox A Kumar L Gonder-Frederick andW L Clarke ldquoAlgorithmic evaluation of metabolic control and

risk of severe hypoglycemia in type 1 and type 2 diabetes usingself-monitoring blood glucose datardquo Diabetes Technology anderapeutics vol 5 no 5 pp 817ndash828 2003

[39] D J Cox L Gonder-Frederick L Ritterband W Clarke andB P Kovatchev ldquoPrediction of severe hypoglycemiardquo DiabetesCare vol 30 no 6 pp 1370ndash1373 2007

[40] S A Amiel R S Sherwin D C Simonson and W VTamborlane ldquoEffect of intensive insulin therapy on glycemicthresholds for counterregulatory hormone releaserdquo Diabetesvol 37 no 7 pp 901ndash907 1988

[41] S A Amiel W V Tamborlane D C Simonson and RS Sherwin ldquoDefective glucose counterregulation aer strictglycemic control of insulin-dependent diabetes mellitusrdquo NewEngland Journal of Medicine vol 316 no 22 pp 1376ndash13831987

[42] P E Cryer and J E Gerich ldquoGlucose counterregulation hypo-glycemia and intensive insulin therapy in diabetes mellitusrdquoNew England Journal of Medicine vol 313 no 4 pp 232ndash2411985

[43] N H White D A Skor and P E Cryer ldquoIdentication of typeI diabetic patients at increased risk for hypoglycemia duringintensive therapyrdquo New England Journal of Medicine vol 308no 9 pp 485ndash491 1983

[44] P E Cryer ldquoIatrogenic hypoglycemia as a cause ofhypoglycemia-associated autonomic failure in IDDM avicious cyclerdquo Diabetes vol 41 no 3 pp 255ndash260 1992

[45] J N Henderson K V Allen I J Deary and B M FrierldquoHypoglycaemia in insulin-treated Type 2 diabetes frequencysymptoms and impaired awarenessrdquo Diabetic Medicine vol 20no 12 pp 1016ndash1021 2003

[46] P E Cryer Hypoglycemia Pathophysiology Diagnosis andTreatment Oxford University Press New York NY USA 1997

[47] P E Cryer S N Davis and H Shamoon ldquoHypoglycemia indiabetesrdquo Diabetes Care vol 26 no 6 pp 1902ndash1912 2003

[48] B P Childs N G Clark and D J Cox ldquoDening and reportinghypoglycemia in diabetes a report from the American diabetesassociation workgroup on hypoglycemiardquo Diabetes Care vol28 no 5 pp 1245ndash1249 2005

[49] P E Cryer ldquoHypoglycaemia the limiting factor in the glycaemicmanagement of Type I and Type II diabetesrdquo Diabetologia vol45 no 7 pp 937ndash948 2002

[50] P E Cryer ldquoHypoglycemia the limiting factor in the manage-ment of IDDMrdquo Diabetes vol 43 no 11 pp 1378ndash1389 1994

[51] A L McCall and B P Kovatchev ldquoe median is not the onlymessage a clinicianrsquos perspective on mathematical analysis ofglycemic variability and modeling in diabetes mellitusrdquo Journalof Diabetes Science and Technology vol 3 pp 3ndash11 2009

[52] M Brownlee and I B Hirsch ldquoGlycemic variability ahemoglobin A1c-independent risk factor for diabetic compli-cationsrdquo Journal of the American Medical Association vol 295no 14 pp 1707ndash1708 2006

[53] I B Hirsch and M Brownlee ldquoShould minimal blood glucosevariability become the gold standard of glycemic controlrdquoJournal of Diabetes and Its Complications vol 19 no 3 pp178ndash181 2005

[54] K Esposito D Giugliano F Nappo and R Marfella ldquoRegres-sion of carotid atherosclerosis by control of postprandial hyper-glycemia in type 2 diabetes mellitusrdquo Circulation vol 110 no2 pp 214ndash219 2004

[55] S Haffner ldquoe importance of postprandial hyperglycaemia indevelopment of cardiovascular disease in people with diabetes

Scientica 11

pointrdquo International Journal of Clinical Practice no 123 pp24ndash26 2001

[56] LMonnier EMas C Ginet et al ldquoActivation of oxidative stressby acute glucose uctuations compared with sustained chronichyperglycemia in patients with type 2 diabetesrdquo Journal of theAmerican Medical Association vol 295 no 14 pp 1681ndash16872006

[57] T S Temelkova-Kurktschiev C Koehler E Henkel W Leon-hardt K Fuecker and M Hanefeld ldquoPostchallenge plasmaglucose and glycemic spikes are more strongly associated withatherosclerosis than fasting glucose or HbA(1c) levelrdquo DiabetesCare vol 23 no 12 pp 1830ndash1834 2000

[58] W L Clarke D J Cox L Gonder-Frederick D JulianB Kovatchev and D Young-Hyman ldquoBiopsychobehavioralmodel of risk of severe hypoglycemia self-management behav-iorsrdquo Diabetes Care vol 22 no 4 pp 580ndash584 1999

[59] D J Cox L A Gonder-Frederick B P Kovatchev et alldquoBiopsychobehavioral model of severe hypoglycemia II under-standing the risk of severe hypoglycemiardquo Diabetes Care vol22 no 12 pp 2018ndash2025 1999

[60] L Gonder-Frederick D Cox B Kovatchev D Schlundt andW Clarke ldquoA biopsychobehavioral model of risk of severehypoglycemiardquoDiabetes Care vol 20 no 4 pp 661ndash669 1997

[61] B Kovatchev D Cox L Gonder-Frederick D Schlundtand W Clarke ldquoStochastic model of self-regulation decisionmaking exemplied by decisions concerning hypoglycemiardquoHealth Psychology vol 17 no 3 pp 277ndash284 1998

[62] G Nucci and C Cobelli ldquoModels of subcutaneous insulinkinetics A critical reviewrdquo Computer Methods and Programs inBiomedicine vol 62 no 3 pp 249ndash257 2000

[63] M E Wilinska L J Chassin H C Schaller L Schaupp T RPieber and R Hovorka ldquoInsulin kinetics in type-1 diabetescontinuous and bolus delivery of rapid acting insulinrdquo IEEETransactions on Biomedical Engineering vol 52 no 1 pp 3ndash122005

[64] R N Bergman D T Finegood and M Ader ldquoAssessmentof insulin sensitivity in vivordquo Endocrine Reviews vol 236 ppE667ndashE677 1985

[65] R N Bergman ldquoe minimal model of glucose regulation abiographyrdquoAdvances in ExperimentalMedicine and Biology vol537 pp 1ndash19 2003

[66] R N Bergman D J Zaccaro R M Watanabe et al ldquoMinimalmodel-based insulin sensitivity has greater heritability and adifferent genetic basis than homeostasis model assessment orfasting insulinrdquo Diabetes vol 52 no 8 pp 2168ndash2174 2003

[67] A Caumo R N Bergman and C Cobelli ldquoInsulin sensitivityfrom meal tolerance tests in normal subjects a minimal modelindexrdquo Journal of Clinical Endocrinology and Metabolism vol85 no 11 pp 4396ndash4402 2000

[68] J O Clausen K Borch-Johnsen H Ibsen et al ldquoInsulin sen-sitivity index acute insulin response and glucose effectivenessin a population-based sample of 380 young healthy Caucasiansanalysis of the impact of gender body fat physical tness andlife-style factorsrdquo Journal of Clinical Investigation vol 98 no 5pp 1195ndash1209 1996

[69] T C Ni M Ader and R N Bergman ldquoReassessment of glu-cose effectiveness and insulin sensitivity from minimal modelanalysis a theoretical evaluation of the single-compartmentglucose distribution assumptionrdquo Diabetes vol 46 no 11 pp1813ndash1821 1997

[70] S Welch S S P Gebhart R N Bergman and L S PhillipsldquoMinimal model analysis of intravenous glucose tolerance

test-derived insulin sensitivity in diabetic subjectsrdquo Journalof Clinical Endocrinology and Metabolism vol 71 no 6 pp1508ndash1518 1990

[71] D Dawson M A Vincent E J Barrett et al ldquoCapillary recruit-ment in skeletal muscle in response to exercise and hyperin-sulinemia assessed with contrast-enhanced ultrasoundrdquo Amer-ican Journal of Physiology vol 282 no 3 pp E714ndashE720 2002

[72] K J Mikines B Sonne P A Farrell B Tronier and H GalboldquoEffect of physical exercise on sensitivity and responsiveness toinsulin in humansrdquoAmerican Journal of Physiology vol 254 no3 pp E248ndashE259 1988

[73] E A Richter ldquoGlucose utilizationrdquo in Handbook of PhysiologyL B Rowell and J T Shepherd Eds pp 912ndash951 OxfordUniversity Press New York NY USA 1996

[74] D H Wasserman R J Geer D E Rice et al ldquoInteraction ofexercise and insulin action in humansrdquo American Journal ofPhysiology vol 260 no 1 pp E37ndashE45 1991

[75] G Pillonetto A Caumo G Sparacino and C Cobelli ldquoAnew dynamic index of insulin sensitivityrdquo IEEE Transactions onBiomedical Engineering vol 53 no 3 pp 369ndash379 2006

[76] C Dalla Man D M Raimondo R A Rizza and C CobellildquoGIM Simulation soware of meal glucose-insulin modelrdquoJournal of Diabetes Science and Technology vol 1 pp 323ndash3302007

[77] C Dalla Man R A Rizza and C Cobelli ldquoMeal simulationmodel of the glucose-insulin systemrdquo IEEE Transactions onBiomedical Engineering vol 54 no 10 pp 1740ndash1749 2007

[78] W L Clarke D Cox L A Gonder-Frederick W Carter andS L Pohl ldquoEvaluating clinical accuracy of systems for self-monitoring of blood glucoserdquo Diabetes Care vol 10 no 5 pp622ndash628 1987

[79] e Diabetes Research In Children Network (Direcnet) StudyGroup ldquoA multicenter study of the accuracy of the one touchultra home glucose meter in children with type 1 diabetesrdquoDiabetes Technology anderapeutics vol 5 no 6 pp 933ndash9412003

[80] I B Hirsch B W Bode B P Childs et al ldquoSelf-monitoring ofblood glucose (SMBG) in insulin- and non-insulin-using adultswith diabetes consensus recommendations for improvingSMBG accuracy utilization and researchrdquo Diabetes Technologyanderapeutics vol 10 no 6 pp 419ndash439 2008

[81] G Freckmann A Baumstark N Jendrike et al ldquoSystemaccuracy evaluation of 27 blood glucose monitoring systemsaccording to DIN en ISO 15197rdquo Diabetes Technology anderapeutics vol 12 no 3 pp 221ndash231 2010

[82] D Deiss J Bolinder J P Riveline et al ldquoImproved glycemiccontrol in poorly controlled patients with type 1 diabetes usingreal-time continuous glucose monitoringrdquo Diabetes Care vol29 no 12 pp 2730ndash2732 2006

[83] S Garg H Zisser S Schwartz et al ldquoImprovement in glycemicexcursions with a transcutaneous real-time continuous glucosesensor a randomized controlled trialrdquo Diabetes Care vol 29no 1 pp 44ndash50 2006

[84] B P Kovatchev and W L Clarke ldquoContinuous glucose mon-itoring reduces risks for hypo- and hyperglycemia and glucosevariability in diabetesrdquo Diabetes vol 56 1 Article ID 0086OR2007

[85] e Juvenile Diabetes Research Foundation Continuous Glu-cose Monitoring Study Group ldquoContinuous glucose monitor-ing and intensive treatment of type 1 diabetesrdquo New EnglandJournal of Medicine vol 359 pp 1464ndash1476 2008

12 Scientica

[86] D C Klonoff ldquoContinuous glucose monitoring roadmap for21st century diabetes therapyrdquo Diabetes Care vol 28 no 5 pp1231ndash1239 2005

[87] R Hovorka ldquoContinuous glucose monitoring and closed-loopsystemsrdquo Diabetic Medicine vol 23 no 1 pp 1ndash12 2006

[88] D C Klonoff ldquoe articial pancreas how sweet engineeringwill solve bitter problemsrdquo Journal of Diabetes Science andTechnology vol 1 pp 72ndash81 2007

[89] I B Hirsch D Armstrong R M Bergenstal et al ldquoClin-ical application of emerging sensor technologies in diabetesmanagement consensus guidelines for continuous glucosemonitoring (CGM)rdquoDiabetes Technology anderapeutics vol10 no 4 pp 232ndash246 2008

[90] K Rebrin G M Steil W P Van Antwerp and J J Mastro-totaro ldquoSubcutaneous glucose predicts plasma glucose inde-pendent of insulin implications for continuous monitoringrdquoAmerican Journal of Physiology vol 277 no 3 pp E561ndashE5711999

[91] K Rebrin and G M Steil ldquoCan interstitial glucose assessmentreplace blood glucosemeasurementsrdquoDiabetes Technology anderapeutics vol 2 no 3 pp 461ndash472 2000

[92] G M Steil K Rebrin F Hariri et al ldquoInterstitial uid glucosedynamics during insulin-induced hypoglycaemiardquo Diabetolo-gia vol 48 no 9 pp 1833ndash1840 2005

[93] M S Boyne D M Silver J Kaplan and C D Saudek ldquoTimingof changes in interstitial and venous blood glucose measuredwith a continuous subcutaneous glucose sensorrdquo Diabetes vol52 no 11 pp 2790ndash2794 2003

[94] E Kulcu J A Tamada G Reach R O Potts and M JLesho ldquoPhysiological differences between interstitial glucoseand blood glucose measured in human subjectsrdquoDiabetes Carevol 26 no 8 pp 2405ndash2409 2003

[95] P J Stout J R Racchini andM E Hilgers ldquoA novel approach tomitigating the physiological lag between blood and interstitialuid glucose measurementsrdquo Diabetes Technology and era-peutics vol 6 no 5 pp 635ndash644 2004

[96] I M E Wentholt A A M Hart J B L Hoekstra and J HDevries ldquoRelationship between interstitial and blood glucose intype 1 diabetes patients delay and the push-pull phenomenonrevisitedrdquo Diabetes Technology and erapeutics vol 9 no 2pp 169ndash175 2007

[97] K J C Wientjes and A J M Schoonen ldquoDetermination oftime delay between blood and interstitial adipose tissue glucoseconcentration change by microdialysis in healthy volunteersrdquonternational Journal of Articial rgans vol 24 no 12 pp884ndash889 2001

[98] B Aussedat M Dupire-Angel R Gifford J C Klein G SWilson and G Reach ldquoInterstitial glucose concentration andglycemia implications for continuous subcutaneous glucosemonitoringrdquo American Journal of Physiology vol 278 no 4 ppE716ndashE728 2000

[99] B P Kovatchev and W L Clarke ldquoPeculiarities of the con-tinuous glucose monitoring data stream and their impact ondeveloping closed-loop control technologyrdquo Journal of DiabetesScience and Technology vol 2 pp 158ndash163 2008

[100] W L Clarke and B P Kovatchev ldquoContinuous glucose sen-sorsmdashcontinuing questions about clinical accuracyrdquo Journal ofDiabetes Science and Technology vol 1 pp 164ndash170 2007

[101] e Diabetes Research in Children Network (DirecNet) StudyGroup ldquoe accuracy of the guardian RT continuous glucosemonitor in children with type 1 diabetesrdquo Diabetes Technologyanderapeutics vol 10 pp 266ndash272 2008

[102] B Kovatchev S Anderson L Heinemann and W ClarkeldquoComparison of the numerical and clinical accuracy of fourcontinuous glucose monitorsrdquo Diabetes Care vol 31 no 6 pp1160ndash1164 2008

[103] S K Garg J Smith C Beatson B Lopez-Baca M Voelmleand P A Gottlieb ldquoComparison of accuracy and safety ofthe SEVEN and the navigator continuous glucose monitoringsystemsrdquo Diabetes Technology and erapeutics vol 11 no 2pp 65ndash72 2009

[104] T Heise T Koschinsky L Heinemann and V Lodwig ldquoHypo-glycemia warning signal and glucose sensors requirements andconceptsrdquo Diabetes Technology and erapeutics vol 5 no 4pp 563ndash571 2003

[105] B Bode K Gross N Rikalo et al ldquoAlarms based on real-timesensor glucose values alert patients to hypo- and hyperglycemiathe Guardian continuous monitoring systemrdquo Diabetes Tech-nology anderapeutics vol 6 no 2 pp 105ndash113 2004

[106] G McGarraugh and R Bergenstal ldquoDetection of hypoglycemiawith continuous interstitial and traditional blood glucosemonitoring using the FreeStyle navigator continuous glucosemonitoring systemrdquo Diabetes Technology and erapeutics vol11 no 3 pp 145ndash150 2009

[107] S E Noujaim D Horwitz M Sharma and J Marhoul ldquoAccu-racy requirements for a hypoglycemia detector an analyticalmodel to evaluate the effects of bias precision and rate ofglucose changerdquo Journal of Diabetes Science and Technology vol1 pp 653ndash668 2007

[108] W K Ward ldquoe role of new technology in the early detectionof hypoglycemiardquo Diabetes Technology and erapeutics vol 6no 2 pp 115ndash117 2004

[109] B Buckingham E Cobry P Clinton et al ldquoPreventing hypo-glycemia using predictive alarm algorithms and insulin pumpsuspensionrdquo Diabetes Technology and erapeutics vol 11 no2 pp 93ndash97 2009

[110] C S Hughes S D Patek M D Breton and B P KovatchevldquoHypoglycemia prevention via pump attenuation and red-yellow-green ldquotrafficrdquo lights using continuous glucose moni-toring and insulin pump datardquo Journal of Diabetes Science andTechnology vol 4 no 5 pp 1146ndash1155 2010

[111] B P Kovatchev D J Cox L A Gonder-Frederick and WClarke ldquoSymmetrization of the blood glucose measurementscale and its applicationsrdquo Diabetes Care vol 20 no 11 pp1655ndash1658 1997

[112] F J Service G D Molnar J W Rosevear E Ackerman LC Gatewood and W F Taylor ldquoMean amplitude of glycemicexcursions a measure of diabetic instabilityrdquo Diabetes vol 19no 9 pp 644ndash655 1970

[113] J Schlichtkrull O Munck and M Jersild ldquoe M-value anindex of blood glucose control in diabeticsrdquo Acta MedicaScandinavica vol 177 pp 95ndash102 1965

[114] E A Ryan T Shandro K Green et al ldquoAssessment of theseverity of hypoglycemia and glycemic lambility in type 1diabetic subjects undergoing islet in transplantationrdquo Diabetesvol 53 no 4 pp 955ndash962 2004

[115] B P Kovatchev D J Cox L Gonder-Frederick and WL Clarke ldquoMethods for quantifying self-monitoring bloodglucose proles exemplied by an examination of blood glucosepatterns in patients with type 1 and type 2 diabetesrdquo DiabetesTechnology anderapeutics vol 4 no 3 pp 295ndash303 2002

[116] B P Kovatchev D J Cox L S Farhy M Straume L Gonder-Frederick and W L Clarke ldquoEpisodes of severe hypoglycemia

Scientica 13

in type 1 diabetes are preceded and followed within 48 hours bymeasurable disturbances in blood glucoserdquo Journal of ClinicalEndocrinology and Metabolism vol 85 no 11 pp 4287ndash42922000

[117] B P Kovatchev M Straume D J Cox and L S FarhyldquoRisk analysis of blood glucose data a quantitative approach tooptimizing the control of Insulin Dependent Diabetesrdquo Journalof eoretical Medicine vol 3 no 1 pp 1ndash10 2000

[118] B P Kovatchev E Otto D Cox L Gonder-Frederick andW Clarke ldquoEvaluation of a new measure of blood glucosevariability in diabetesrdquo Diabetes Care vol 29 no 11 pp2433ndash2438 2006

[119] B P Kovatchev W L Clarke M Breton K Brayman and AMcCall ldquoQuantifying temporal glucose variability in diabetesvia continuous glucose monitoring mathematical methods andclinical applicationrdquo Diabetes Technology and erapeutics vol7 no 6 pp 849ndash862 2005

[120] D Rodbard ldquoNew and improved methods to characterizeglycemic variability using continuous glucosemonitoringrdquoDia-betes Technology and erapeutics vol 11 no 9 pp 551ndash5652009

[121] M Miller and P Strange ldquoUse of Fourier models for analysisand interpretation of continuous glucose monitoring glucoseprolesrdquo Journal of Diabetes Science and Technology vol 1 pp630ndash638 2007

[122] W Clarke and B Kovatchev ldquoStatistical tools to analyzecontinuous glucose monitor datardquo Diabetes Technology anderapeutics vol 11 pp S45ndashS54 2009

[123] C M Mcdonnell S M Donath S I Vidmar G A Wertherand F J Cameron ldquoA novel approach to continuous glucoseanalysis utilizing glycemic variationrdquo Diabetes Technology anderapeutics vol 7 no 2 pp 253ndash263 2005

[124] G Sparacino F Zanderigo A Maran and C Cobelli ldquoContin-uous glucosemonitoring andhypohyperglycaemia predictionrdquoDiabetes Research and Clinical Practice vol 74 no 2 supple-ment pp S160ndashS163 2006

[125] G Sparacino F Zanderigo G Corazza A Maran AFacchinetti and C Cobelli ldquoGlucose concentration can bepredicted ahead in time from continuous glucose monitoringsensor time-seriesrdquo IEEE Transactions on Biomedical Engineer-ing vol 54 pp 931ndash937 2007

[126] J Reifman S Rajaraman A Gribok and W K Ward ldquoPredic-tive monitoring for improved management of glucose levelsrdquoJournal of Diabetes Science and Technology vol 1 pp 478ndash4862007

[127] F Zanderigo G Sparacino B Kovatchev and C CobellildquoGlucose prediction algorithms from continuous monitoringassessment of accuracy via continuous glucose-error grid anal-ysisrdquo Journal of Diabetes Science and Technology vol 1 pp645ndash651 2007

[128] F Cameron G Niemayer K Gundy-Burlet and B Bucking-ham ldquoStatistical hypoglycemia predictionrdquo Journal of DiabetesScience and Technology vol 2 pp 612ndash621 2008

[129] A Gani A V Gribok S Rajaraman W K Ward and JReifman ldquoPredicting subcutaneous glucose concentration inhumans data-driven glucose modelingrdquo IEEE Transactions onBiomedical Engineering vol 56 no 2 pp 246ndash254 2009

[130] M Eren-Oruklu A Cinar L Quinn andD Smith ldquoEstimationof future glucose concentrations with subject-specic recursivelinear modelsrdquo Diabetes Technology and erapeutics vol 11no 4 pp 243ndash253 2009

[131] S M Pappada B D Cameron and P M Rosman ldquoDevelop-ment of a neural network for prediction of glucose concentra-tion in type 1 diabetes patientsrdquo Journal of Diabetes Science andTechnology vol 2 pp 792ndash801 2008

[132] C Cobelli C Dalla Man G Sparacino L Magni G Nicolaoand B P Kovatchev ldquoDiabetes models signals and controlrdquoIEEE Reviews in Biomedical Engineering vol 2 pp 54ndash96 2009

[133] H LeBlanc D Chauvet P Lombrail and J J Robert ldquoGlycemiccontrol with closed-loop intraperitoneal insulin in type Idiabetesrdquo Diabetes Care vol 9 no 2 pp 124ndash128 1986

[134] C D Saudek J L Selman H A Pitt et al ldquoA preliminary trialof the programmable implantablemedication system for insulindeliveryrdquo New England Journal of Medicine vol 321 no 9 pp574ndash579 1989

[135] C Broussolle N Jeandidier and H Hanaire-Broutin ldquoFrenchmulticentre experience of implantable insulin pumpsrdquo Lancetvol 343 no 8896 pp 514ndash515 1994

[136] H Hanaire-Broutin C Broussolle N Jeandidier et al ldquoFea-sibility of intraperitoneal insulin therapy with programmableimplantable pumps in IDDM a multicenter studyrdquo DiabetesCare vol 18 no 3 pp 388ndash392 1995

[137] P R Oskarsson P E Lins H W Henriksson and U CAdamson ldquoMetabolic and hormonal responses to exercisein type 1 diabetic patients during continuous subcutaneousas compared to continuous intraperitoneal insulin infusionrdquoDiabetes and Metabolism vol 25 no 6 pp 491ndash497 1999

[138] P R Oskarsson P E Lins L Backman and U C AdamsonldquoContinuous intraperitoneal insulin infusion partly restores theglucagon response to hypoglycaemia in type 1 diabetic patientsrdquoDiabetes and Metabolism vol 26 no 2 pp 118ndash124 2000

[139] B Catargi L Meyer V Melki E Renard and N JeandidierldquoComparison of blood glucose stability and HbA1C betweenimplantable insulin pumps using U400 hoe 21pH insulin andexternal pumps using lispro in type 1 diabetic patients a pilotstudyrdquo Diabetes and Metabolism vol 28 no 2 pp 133ndash1372002

[140] E Renard ldquoImplantable closed-loop glucose-sensing andinsulin delivery the future for insulin pump therapyrdquo CurrentOpinion in Pharmacology vol 2 no 6 pp 708ndash716 2002

[141] E Renard G Costalat H Chevassus and J Bringer ldquoArticial120573120573-cell clinical experience toward an implantable closed-loopinsulin delivery systemrdquo Diabetes and Metabolism vol 32 no5 pp 497ndash502 2006

[142] E Renard J Place M Cantwell H Chevassus and C CPalerm ldquoClosed-loop insulin delivery using a subcutaneousglucose sensor and intraperitoneal insulin delivery feasibilitystudy testing a new model for the articial pancreasrdquo DiabetesCare vol 33 no 1 pp 121ndash127 2010

[143] R Bellazzi G Nucci and C Cobelli ldquoe subcutaneousroute to insulin-dependent diabetes therapy closed-loop andpartially closed-loop control strategies for insulin deliveryand measuring glucose concentrationrdquo IEEE Engineering inMedicine and Biology Magazine vol 20 no 1 pp 54ndash64 2001

[144] R Hovorka L J Chassin M E Wilinska et al ldquoClosingthe loop the adicol experiencerdquo Diabetes Technology anderapeutics vol 6 no 3 pp 307ndash318 2004

[145] R Hovorka V Canonico L J Chassin et al ldquoNonlinear modelpredictive control of glucose concentration in subjects withtype 1 diabetesrdquo Physiological Measurement vol 25 no 4 pp905ndash920 2004

14 Scientica

[146] G M Steil K Rebrin C Darwin F Hariri and M F SaadldquoFeasibility of automating insulin delivery for the treatment oftype 1 diabetesrdquo Diabetes vol 55 no 12 pp 3344ndash3350 2006

[147] e JDRF e-Newsletter Emerging Technologies in DiabetesResearch 2006

[148] W L Clarke and B Kovatchev ldquoe articial pancreas howclose are we to closing the looprdquo Pediatric EndocrinologyReviews vol 4 no 4 pp 314ndash316 2007

[149] C Cobelli E Renard and B P Kovatchev ldquoPerspectives indiabetes articial pancreas past present futurerdquo Diabetes vol60 pp 2672ndash2682 2011

[150] B P Kovatchev M D Breton C Dalla Man and C Cobelli ldquoInsilico preclinical trials a proof of concept in closed-loop controlof type 1 diabetesrdquo Journal of Diabetes Science and Technologyvol 3 pp 44ndash55 2009

[151] B Kovatchev C Cobelli E Renard et al ldquoMultinational studyof subcutaneous model-predictive closed-loop control in type1 diabetes mellitus summary of the resultsrdquo Journal of DiabetesScience and Technology vol 4 no 6 pp 1374ndash1381 2010

[152] H Zisser L Jovanovic F Doyle P Ospina and C OwensldquoRun-to-run control of meal-related insulin dosingrdquo DiabetesTechnology anderapeutics vol 7 no 1 pp 48ndash57 2005

[153] C Owens H Zisser L Jovanovic B Srinivasan D Bonvinand F J Doyle III ldquoRun-to-run control of blood glucoseconcentrations for people with type 1 diabetes mellitusrdquo IEEETransactions on Biomedical Engineering vol 53 no 6 pp996ndash1005 2006

[154] C C Palerm H Zisser W C Bevier L Jovanovič and F JDoyle III ldquoPrandial insulin dosing using run-to-run controlapplication of clinical data and medical expertise to dene asuitable performance metricrdquo Diabetes Care vol 30 no 5 pp1131ndash1136 2007

[155] LMagni F Raimondo L Bossi et al ldquoModel predictive controlof type 1 diabetes an in silico trialrdquo Journal of Diabetes Scienceand Technology vol 1 pp 804ndash812 2007

[156] S A Weinzimer G M Steil K L Swan J Dziura N Kurtzand W V Tamborlane ldquoFully automated closed-loop insulindelivery versus semiautomated hybrid control in pediatricpatients with type 1 diabetes using an articial pancreasrdquoDiabetes Care vol 31 no 5 pp 934ndash939 2008

[157] W L Clarke S Anderson M Breton S Patek L Kashmerand B Kovatchev ldquoClosed-loop articial pancreas using sub-cutaneous glucose sensing and insulin delivery and a modelpredictive control algorithm the Virginia experiencerdquo Journalof Diabetes Science and Technology vol 3 no 5 pp 1031ndash10382009

[158] D Bruttomesso A Farret S Costa et al ldquoClosed-loop arti-cial pancreas using subcutaneous glucose sensing and insulindelivery and a model predictive control algorithm preliminarystudies in Padova and Montpellierrdquo Journal of Diabetes Scienceand Technology vol 3 no 5 pp 1014ndash1021 2009

[159] R Hovorka J M Allen D Elleri et al ldquoManual closed-loop insulin delivery in children and adolescents with type 1diabetes a phase 2 randomised crossover trialrdquoe Lancet vol375 no 9716 pp 743ndash751 2010

[160] F H El-khatib S J Russell D M Nathan R G Sutherlin andE R Damiano ldquoA bihormonal closed-loop articial pancreasfor type 1 diabetesrdquo Science Translational Medicine vol 2 no27 Article ID 27ra27 2010

[161] E Dassau H Zisser C C Palerm B A Buckingham LJovanovič and F J Doye III ldquoModular articial 120573120573-cell system a

prototype for clinical researchrdquo Journal of Diabetes Science andTechnology vol 2 pp 863ndash872 2008

[162] B Kovatchev S Patek E Dassau et al ldquoControl to range fordiabetes functionality and modular architecturerdquo Journal ofDiabetes Science and Rechnology vol 3 no 5 pp 1058ndash10652009

[163] E Atlas R Nimri S Miller E A Grunberg and M PhillipldquoMD-logic articial pancreas system a pilot study in adults withtype 1 diabetesrdquo Diabetes Care vol 33 no 5 pp 1072ndash10762010

[164] E Dassau H Zisser M W Percival B Grosman L Jovanovičand F J Doyle III ldquoClinical results of automated articialpancreatic 120573120573-cell system with unannounced meal using multi-parametric MPC and insulin-on-boardrdquo in Proceedings of the70th American Diabetes Association Meeting Diabetes vol 59supplement 1 A94 Orlando Fla USA 2010

[165] E M Renard A Farret J Place C Cobelli B P Kovatchev andMD Breton ldquoClosed-loop insulin delivery using subcutaneousinfusion and glucose sensing and equipped with a dedicatedsafety supervision algorithm improves safety of glucose controlin type 1 diabetesrdquo Diabetologia vol 53 supplement 1 p S252010

[166] R Hovorka K Kumareswaran J Harris et al ldquoOvernightclosed loop insulin delivery articial pancreas in adults withtype 1 diabetes crossover randomized controlled studiesrdquoBritish Medical Journal vol 342 no 7803 Article ID d18552011

[167] H Zisser E Dassau W Bevier et al ldquoInitial evaluation ofa fully automated articial pancreasrdquo in Proceedings of the71st American Diabetes Association Meeting Diabetes vol 60supplement 1 A41 San Diego Calif USA 2011

[168] M D Breton A Farret and D Bruttomesso ldquoFully-integratedarticial pancreas in type 1 diabetes modular closed-loopglucose control maintains near-normoglycemiardquo Diabetes vol61 pp 2230ndash2237 2012

[169] C Cobelli E Renard and B P Kovatchev ldquoPilot studies ofwearable articial pancreas in type 1 diabetesrdquo Diabetes Carevol 35 no 9 pp e65ndashe67 2012

Submit your manuscripts athttpwwwhindawicom

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Behavioural Neurology

EndocrinologyInternational Journal of

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Disease Markers

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Oxidative Medicine and Cellular Longevity

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The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

6 Scientica

otherwise en the low and high blood glucose indices arecomputed as follows

LBGI = 1119899119899

11989911989910055761005576119894119894=111990311990311990311990310076491007649119909119909119894119894100766510076652 HBGI = 1

119899119899

11989911989910055761005576119894119894=11199031199031199031007649100764911990911990911989411989410076651007665

2 (1)

us the LBGI is a nonnegative quantity that increaseswhen the number andor extent of low BG readings increasesand the HBGI is nonnegative quantity that increases whenthe number andor extent of high BG readings increasesBased on this same technique we also dene the average dailyrisk range (ADRR) which is a measure of risks associatedwith overall glycemic variability [118] In studies the LBGItypically accounted for 40ndash55 of the variance of futuresignicant hypoglycemia in the subsequent 3ndash6 months [36ndash38] which made it a potent predictor of hypoglycemia basedon SMBGeADRR has been shown superior to traditionalglucose variability measures in terms of risk assessment andprediction of extreme glycemic excursions [118] Specicallyit has been demonstrated that classication of risk for hypo-glycemia based on four ADRR categories low risk ADRR lt20 low-moderate risk 20 le ADRR lt 30 moderate-high risk30 le ADRR lt 40 and high risk ADRR gt 40 resulted inmore than a sixfold increase in risk for hypoglycemia fromthe lowest to the highest risk category [118] In addition thelow and high BG indices have been adapted to continuousmonitoring data [119] and can be used in the same way aswith SMBG to assess the risk for hypo- or hyperglycemia

43 CGM-Based Analytical Methods While traditional risk[119] and variability [120] analyses are still applied to CGMdata the high temporal resolution of CGM brought aboutthe possibility for use of sophisticated analytical methodsassessing system (person) dynamics on the time scale of min-utes is necessitated the development of new technologiesfor data analysis and visualization that are not available forSMBG data Analysis of the BG rate of change (measuredin mgdLmin) is a way to evaluate the dynamics of BGuctuations on the time scale of minutes e BG rate ofchange at 119905119905119894119894mdashis computed as the ratio (BG(119905119905119894119894)minusBG(119905119905119894119894minus1))(119905119905119894119894minus119905119905119894119894minus1) where BG(119905119905119894119894) and BG(119905119905119894119894minus1) are CGM readings taken attimes 119905119905119894119894 and 119905119905119894119894minus1 for example minutes apart Investigationof the frequency of glucose uctuations showed that optimalevaluation of the BG rate of change would be achieved overtime periods of 15minutes [121] for exampleΔ119905119905 = 119905119905119894119894 minus119905119905119894119894minus1 =15 A large variation of the BG rate of change indicates rapidand more pronounced BG uctuations and therefore a lessstable system us the standard deviation of the BG rateof change is a measure of stability of glucose uctuation (weshould note that as opposed to the distribution of BG levelsthe distribution of the BG rate of change is symmetric andtherefore using SD is statistically accurate [122]) e SDof BG rate of change has been introduced as a measure ofstability computed fromCGMdata and is known asCONGAof order 1 In general CONGA122 of order 119899119899 is computedas the standard deviation of CGM readings that are 119899119899 hoursapart reecting glucose stability over these time intervals[123]

Most important to the development of the articial pan-creas algorithms is a class of methods allowing the predictionof BG values ahead in time ese methods typically basedon time-series techniques have been applied successfully ina number of studies [124ndash129] In addition to time seriesneural networks have been used for the prediction of glucoselevels from CGM [130 131] Detailed reviews of CGM dataanalysis methods are presented in [122] including severalgraphs that could be used for the visualization of the rathercomplex CGM data sets and in [132] where a broad reviewof modeling analytical and control techniques for diabetesis provided

5 Control of BG Fluctuations in Diabetes

51 Intraperitoneal Insulin Delivery As detailed in the Intro-duction the articial pancreas idea can be traced back to theearly 70s when external BG regulation in people with dia-betes was achieved by iv glucose measurement and iv infu-sion of glucose and insulin However the intravenous routeof closed-loop control remains cumbersome and unsuitedfor outpatient use An alternative has been presented byimplantable intraperitoneal (ip) systems employing iv sam-pling and ip insulin delivery [133ndash136] e ip infusionroute has several desirable characteristics reproducibility ofinsulin absorption quick time to peak and return to baselineof insulin action near-physiological peripheral insulin levelsand restoration of glucagon response to hypoglycemia andexercise [133 137ndash139] However while ip systems haveachieved excellent BG control their implementation stillrequires considerable surgery and is associated with signi-cant cost Nevertheless the development of less invasive andcheaper implantable ports (eg DiaPort Roche DiagnosticsMannheim Germany) may contribute to the future prolifer-ation of ip insulin delivery [140ndash142]

52e Subcutaneous Route to Closed-Loop Control Follow-ing the progress of minimally invasive subcutaneous CGMthe next logical step was the development of sc closed-loopglucose control which links a CGM device with CSII insulinpump A key element of this combination was a controlalgorithm which monitors BG uctuations and the actionsof the insulin pump and computes insulin delivery rate everyfew minutes [143] Figure 2 presents key milestones in thetimeline of this development

Following the pioneering work of Hovorka et al [144145] and Steil et al [146] in 2006 the Juvenile DiabetesResearch Foundation (JDRF) initiated the Articial PancreasProject and funded several centers in the USA and Europeto carry closed-loop control research [147] In 2008 theUSA National Institutes of Health launched an articialpancreas initiative and in 2010 the European APHomeconsortium was established By the end of the rst decade ofthis century the articial pancreas became a global researchtopic engaging physicians and engineers in unprecedentedcollaboration [148 149]

Scientica 7

2006 2008 2010

The JDRF artificial pancreas consortium

is launched (Kowalski)

Studies of hybrid closed-loop control (Weinzimer

and Tamborlane)

First human trials begin using a system designed entirely in silico UVA

Italy and France(Kovatchev Cobelli Renard)

NIH funds artificial pancreas

EU launches the APHome

artificial pancreas initiative

JDRF multicenter trialof modular control-to-

range

2004 the ADICOLproject

(Hovorka)

First studies of automated sc

closed loop (Steil)

FDA accepts the UVAPadova metabolic simulator as a substitute

to animal trials(Kovatchev Cobelli Dalla

Man and Breton)

Modular control-to-rangeintroduced trials at UVA

Italy and France(Kovatchev

Cobelli and Renard)

The APS introduced(Dassau Doyle

First studies of outpatient closed-loop

control (CobelliRenard Zisser and

Kovatchev )

2012

DiAs first portableAP platform

(Keith-HynesKovatchev)

studies Zisser)

30

2012

F 2 Timeline of the articial pancreas developments in the last decademdashtheoretical work and a number of in-clinic studies leading tothe rst trials of wearable articial pancreas device

53 In Silico Models of the Human Metabolic System Acritical step towards accelerated clinical progress of thearticial pancreas was the development of sophisticatedcomputer simulator of the human metabolic system allowingrapid in silico testing of closed-loop control algorithmsis simulation environment was based on the previouslyintroduced Meal Model of glucose-insulin dynamics [76 77]and was equipped with a ldquopopulationrdquo of in silico imagesof 119873119873 119873 119873119873119873 ldquosubjectsrdquo with type 1 diabetes separated inthree age groups 119873119873 119873 119873119873119873 simulated ldquochildrenrdquo below theage of 11 119873119873 119873 119873119873119873 ldquoadolescentsrdquo 12ndash18 years old and119873119873 119873 119873119873119873 ldquoadultsrdquo e characteristics of these ldquosubjectsrdquo(eg weight daily insulin dose carbohydrate ratio etc)were tailored to span a wide range of intersubject variabilityapproximating the variability observed in people in vivo[150] Simulation experiments allow any CGM device anyinsulin pump and any control algorithm to be linked in aclosed-loop system in silico prior to their use in clinical trialsWith this technology any meal and insulin delivery scenariocan be pilot-tested very efficientlymdasha 24-hour period ofclosed-loop control is simulated in under 2 secondsWe needto emphasize however that good in silico performance of acontrol algorithmdoes not guarantee in vivo performancemdashitonly helps test extreme situations and the stability of thealgorithm and rule out inefficient scenarios us computersimulation is only a prerequisite to but not a substitute forclinical trials

In January 2008 in an unprecedented decision theUSA Food and Drug Administration accepted this computer

simulator as a substitute to animal trials for the testing ofclosed-loop control strategies is opened the eld for effi-cient and cost-effective in silico experiments leading directlyto human studies Only three months later in April 2008 therst human trials began at the University of irginia (USA)Montpellier (France) and Padova (Italy) using a controlsystem designed entirely in silico [151]

54 Control System Designs e rst studies of ovorkaet al [144 145] and Steil et al [146] outlined the twomajor types of closed-loop control algorithms now in use inarticial pancreas systemsmdashmodel-predictive control (MPC[145]) and proportional-integral-derivative (PID [146])respectively By 2007 the blueprints of the contemporarycontrollers were in place including run-to-run control[152ndash154] and linear MPC [155] To date the trials ofsubcutaneous closed-loop control systems have been usingeither PID [146 156] or MPC [157ndash160] but MPC becamethe approach of choice targeted by recent research erewere two important reasons making MPC preferable (i)PID is purely reactive responding to changes in glucoselevel while a properly tuned MPC allows for prediction ofglucose dynamics and as a result for mitigation of the timedelays inherent with subcutaneous glucose monitoring andsubcutaneous insulin infusion [62 63] (ii) MPC allows forrelatively straightforward personalizing of the control usingpatient-specic model parameters In addition MPC couldhave ldquolearningrdquo capabilitiesmdashit has been shown that a class

8 Scientica

of algorithms (known as run-to-run control) can ldquolearnrdquospecics of patientsrsquo daily routine (eg timing of meals) andthen optimize the response to a subsequent meal using thisinformation or account for circadian uctuation in insulinresistance (eg dawn phenomenon observed in some people)[149]

In 2008 a universal research platformmdashthe APSmdashwasintroduced enabling automated communication betweenseveral CGM devices insulin pumps and control algorithms[161] e APS was very instrumental for a number ofinpatient trials of closed-loop control A year later a mod-ular architecture was introduced proposing standardizationsequential testing and clinical deployment of articial pan-creas components [162]

55 Inpatient Clinical Trials Between 2008 and 2011 prom-ising results were reported by several groups [156ndash160 163ndash167] Most of these studies pointed out the superiority ofclosed-loop control over standard CSII therapy in termsof (i) increased time within target glucose range (typically39ndash10mmoll) (ii) reduced incidence of hypoglycemia and(iii) better overnight control Two of these studies [159166] had state-of-the-art randomized cross-over design butlacked automated data transfermdashall CGM readings weretransferred to the controllermanually by the study personneland all insulin pump commands were entered manually aswell To distinguish the various degrees of automation inclosed-loop studies the notion of fully-integrated closed-loop control emerged dened as having all of the followingthree components (i) automated data transfer from theCGM to the controller (ii) real-time control action and (iii)automated command of the insulin pump e rst (andthe largest to date) randomized cross-over study of fully-integrated closed-loop control was published in 2012 [168]However even this contemporary trial of fully automatedCLC which enrolled 38 patients with T1D at three centersand tested two different control algorithms achieving note-worthy glycemic control and prevention of hypoglycemia didnot leave the clinical setting e technology used by thisstudy was still based on a laptop computer wired to a CGMand an insulin pump a system limiting the free movementof the study subjects and too cumbersome to be used beyondhospital connes

5 earale tpatient rticial ancreas e transitionof closed-loop control to ambulatory use began in 2011 withthe development of the Diabetes Assistant (DiAs)mdashthe rstwearable articial pancreas platform based on a smart phonee design characteristics of DiAs included the following

(i) based on readily available inexpensive wearablehardware platform

(ii) computationally capable of running advanced closed-loop control algorithms

(iii) wirelessly connectable to CGM devices and insulinpumps

(iv) capable of broadband communication with a centrallocation for remote monitoring and safety supervi-sion of the participants in outpatient clinical trials

In ctober 2011 the rst two pilot trials of wearableoutpatient articial pancreas were performed simultaneouslyin Padova (Italy) and Montpellier (France) [169] ese 2-day trials allowed the renement of a wearable system andenabled a subsequentmultisite feasibility study of ambulatoryarticial pancreas which was completed recently at theUniversities of Virginia Padova and Montpellier and at theSansumDiabetes Research institute Santa Barbara CAUSAResults from this study are forthcoming

6 Conclusions

Solving the optimization problem of diabetes requiresreplacement of insulin action through insulin injections ororal medications (applicable primarily to type 2 diabetes)which until fully automated closed-loop control becomesavailable would remain a process largely controlled bypatient behavior In engineering terms BG uctuations indiabetes result from the activity of a complex metabolicsystem perturbed by behavioral challenges e frequencyand extent of these challenges and the ability of the personrsquosmetabolic system to absorb them determines the qualityof glycemic control Along with HbA1c the magnitudeand speed of BG uctuations is the primary measurablemarker of glucose control in diabetes ese same quanti-tiesmdashHbA1c and glucose variabilitymdashare also the principalfeedback available to patients to assist with optimization oftheir diabetes control

In the past 30 years the technology for monitoring ofblood glucose levels in diabetes has progressed from assess-ment of average glycemia via HbA1c once in several monthsthrough daily SMBG to minutely continuous glucose mon-itoring e increasing temporal resolution of the moni-toring technology enabled increasingly intensive diabetestreatment from daily insulin injections or oral medicationthrough insulin pump therapy to the articial pancreasis progress is accompanied by increasingly sophisticatedanalytical methods for retrieval of blood glucose data rangingfrom subjective interpretation of glucose values and straight-forward summary statistics through risk and variabilityanalysis to real-time closed-loop control algorithms based oncomplex models of the human metabolism

It is therefore evident that the development of diabetestechnology is accelerating exponentially A primary cata-lyst of this acceleration is unprecedented interdisciplinarycollaboration between physicians chemists engineers andmathematicians As a result a wearable articial pancreassuitable for outpatient use is now within reach

e primary engineering challenges to the widespreadadoption of closed-loop control as a viable therapeutic optionfor diabetes include system connectivity the accuracy ofsubcutaneous glucose sensing and the speed of action ofsubcutaneously injected insulin ese challenges are well

Scientica 9

understood by those working in the eld wireless commu-nication between CGM devices insulin pumps and closed-loop controllers are under development and testing newgenerations of CGM device demonstrate superior accuracyand reliability and new insulin analogs and methods forinsulin delivery are being engineered to approximate asclose as possible the action prole of endogenous insulinIt should be noted however that the signals available toa contemporary closed-loop control system are generallylimited to CGM and insulin delivery data user input aboutcarbohydrate intake and physical activity could be availableas well In contrast the endocrine pancreas receives directand rapid control inputs from other nutrients (eg lipids andamino acids) adjacent cells (somatostatin from the delta cellsand glucagon from alpha cells) incretins and neural signalsus while articial closed-loop control is expected to bevastly superior to the diabetes control methods employed inthe clinical practice today it will continue to be imperfectwhen compared to the natural endocrine regulation of bloodglucose

Acknowledgments

is work was made possible by the JDRF Articial PancreasProject the National Institutes of HealthNIDDK GrantsRO1 DK 51562 and RO1 DK 085623 and by the generoussupport of PBM Science Charlottesville Virginia CA USAand the Frederick Banting Foundation Richmond VirginiaCA USA e author thanks his colleagues at the Universityof Virginia Center forDiabetes Technology for their relentlesswork on articial pancreas development

References

[1] American Diabetes Association ldquoDiagnosis and classicationof diabetes mellitusrdquo Diabetes Care vol 27 pp s5ndashs10 2004

[2] A H Kadish ldquoAutomation control of blood sugarmdashI A ser-vomechanism for glucose monitoring and controlrdquo AmericanJournal of Medical Electronics vol 39 pp 82ndash86 1964

[3] J C Pickup H Keen J A Parsons and K G M M AlbertildquoContinuous subcutaneous insulin infusion an approach toachieving normoglycaemiardquo British Medical Journal vol 1 no6107 pp 204ndash207 1978

[4] W V Tamborlane R S Sherwin M Genel and P FeligldquoReduction to normal of plasma glucose in juvenile diabetes bysubcutaneous administration of insulinwith a portable infusionpumprdquo New England Journal of Medicine vol 300 no 11 pp573ndash578 1979

[5] A M Albisser B S Leibel and T G Ewart ldquoAn articialendocrine pancreasrdquoDiabetes vol 23 no 5 pp 389ndash396 1974

[6] E F Pfeier um Ch and A H Clemens ldquoe articialbeta cell a continuous control of blood sugar by external regu-lation of insulin infusion (glucose controlled insulin infusionsystem)rdquo Hormone and Metabolic Research vol 6 no 5 pp339ndash342 1974

[7] J Mirouze J L Selam T C Pham and D Cavadore ldquoEvalua-tion of exogenous insulin homoeostasis by the artical pancreasin insulin dependent diabetesrdquo Diabetologia vol 13 no 3 pp273ndash278 1977

[8] E W Kraegen L V Campbell and Y O Chia ldquoControlof blood glucose in diabetics using an articial pancreasrdquoAustralian and New Zealand Journal of Medicine vol 7 no 3pp 280ndash286 1977

[9] M Shichiri R Kawamori Y Yamasaki M Inoue Y Shigetaand H Abe ldquoComputer algorithm for the articial pancreaticbeta cellrdquo Articial rgans vol 2 supplement pp 247ndash2501978

[10] A H Clemens P H Chang and R W Myers ldquoe devel-opment of Biostator a Glucose Controlled Insulin InfusionSystem (GCIIS)rdquo Hormone and Metabolic Research vol 7 pp23ndash33 1977

[11] E B Marliss F T Murray and E F Stokes ldquoNormalization ofglycemia in diabetics during meals with insulin and glucagondelivery by the articial pancreasrdquo Diabetes vol 26 no 7 pp663ndash672 1977

[12] J V Santiago A H Clemens W L Clarke and D M KipnisldquoClosed-loop and open-loop devices for blood glucose controlin normal and diabetic subjectsrdquo Diabetes vol 28 no 1 pp71ndash84 1979

[13] U Fischer E Jutzi E J Freyse and E Salzsieder ldquoDerivationand experimental proof of a new algorithm for the articial B-cell based on the individual analysis of the physiological insulin-glucose relationshiprdquo Endokrinologie vol 71 no 1 pp 65ndash751978

[14] R S Parker F J Doyle and N A Peppas ldquoe intravenousroute to blood glucose control a review of control algorithmsfor noninvasive monitoring and regulation in type I diabeticpatientsrdquo IEEE Engineering in Medicine and Biology Magazinevol 20 no 1 pp 65ndash73 2001

[15] R N Bergman Y Z Ider C R Bowden and C CobellildquoQuantitative estimation of insulin sensitivityrdquo e AmericanJournal of Physiology vol 236 no 6 pp E667ndashE677 1979

[16] H M Broekhuyse J D Nelson B Zinman and A M AlbisserldquoComparison of algorithms for the closed-loop control of bloodglucose using the articial beta cellrdquo IEEE Transactions onBiomedical Engineering vol 28 no 10 pp 678ndash687 1981

[17] A H Clemens ldquoFeedback control dynamics for glucosecontrolled insulin infusion systemrdquo Medical Progress throughTechnology vol 6 no 3 pp 91ndash98 1979

[18] C Cobelli and A Ruggeri ldquoEvaluation of portalperipheralroute and of algorithms for insulin delivery in the closed-loop control of glucose in diabetes a modeling studyrdquo IEEETransactions on Biomedical Engineering vol 30 no 2 pp93ndash103 1983

[19] E Salzsieder G Albrecht U Fischer and E J Freyse ldquoKineticmodeling of the glucoregulatory system to improve insulintherapyrdquo IEEE Transactions on Biomedical Engineering vol 32no 10 pp 846ndash855 1985

[20] P Brunetti C Cobelli P Cruciani et al ldquoA simulation study ona self-tuning portable controller of blood glucoserdquo InternationalJournal of Articial rgans vol 16 no 1 pp 51ndash57 1993

[21] U Fischer W Schenk E Salzsieder G Albrecht P Abel andE J Freyse ldquoDoes physiological blood glucose control requirean adaptive control strategyrdquo IEEE Transactions on BiomedicalEngineering vol 34 no 8 pp 575ndash582 1987

[22] J T Sorensen A physiologic model of glucose metabolism inman and its use to design and assess improved insulin therapiesfor diabetes [PhD dissertation] Department of Chemical Engi-neering MIT 1985

[23] R S Parker F J Doyle and N A Peppas ldquoA model-basedalgorithm for blood glucose control in type I diabetic patientsrdquo

10 Scientica

IEEE Transactions on Biomedical Engineering vol 46 no 2 pp148ndash157 1999

[24] J J Mastrototaro ldquoe MiniMed continuous glucose monitor-ing Systemrdquo Diabetes Technology anderapeutics vol 2 no 1pp S13ndashS18 2000

[25] B W Bode ldquoClinical utility of the continuous glucose monitor-ing systemrdquo Diabetes Technology anderapeutics vol 2 no 1supplement pp S35ndashS41 2000

[26] B Feldman R Brazg S Schwartz and R Weinstein ldquoAcontinuous glucose sensor based on wired enzyme technol-ogymdashresults from a 3-day trial in patients with type 1 diabetesrdquoDiabetes Technology anderapeutics vol 5 no 5 pp 769ndash7792003

[27] eDiabetes Control and Complications Trial Research Groupldquoe effect of intensive treatment of diabetes on the develop-ment and progression of long-term complications of insulin-dependent diabetes mellitusrdquoNew England Journal of Medicinevol 329 pp 978ndash986 1993

[28] eDiabetes Control and Complications Trial Research Groupldquoe relationship of glycemic exposure (HbA1c) to the riskof development and progression of retinopathy in the Dia-betes Control and Complications Trialrdquo Diabetes vol 44 pp968ndash983 1995

[29] J M Lachin S Genuth D M Nathan B Zinman and B NRutledge ldquoEffect of glycemic exposure on the risk of microvas-cular complications in the diabetes control and complicationstrial-revisitedrdquo Diabetes vol 57 no 4 pp 995ndash1001 2008

[30] P Reichard and M Pihl ldquoMortality and treatment side-effectsduring long-term intensied conventional insulin treatment inthe Stockholm Diabetes Intervention Studyrdquo Diabetes vol 43no 2 pp 313ndash317 1994

[31] UK Prospective Diabetes Study Group (UKPDS) ldquoIntensiveblood-glucose control with sulphonylureas or insulin comparedwith conventional treatment and risk of complications inpatients with type 2 diabetes (UKPDS 33)rdquo Lancet vol 352 no9131 pp 837ndash853 1998

[32] P A Svendsen T Lauritzen U Soegaard and J NerupldquoGlycosylated haemoglobin and steady-state mean blood glu-cose concentration in type 1 (insulin-dependent) diabetesrdquoDiabetologia vol 23 no 5 pp 403ndash405 1982

[33] J V Santiago ldquoLessons from the diabetes control and compli-cations trialrdquo Diabetes vol 42 no 11 pp 1549ndash1554 1993

[34] eDiabetes Control and Complications Trial Research GroupldquoHypoglycemia in the diabetes control and complications trialrdquoDiabetes vol 46 pp 271ndash286 1997

[35] A E Gold BM Frier KMMacLeod and I J Deary ldquoA struc-tural equation model for predictors of severe hypoglycaemiain patients with insulin-dependent diabetes mellitusrdquo DiabeticMedicine vol 14 pp 309ndash315 1997

[36] D J Cox B P Kovatchev D M Julian et al ldquoFrequency ofsevere hypoglycemia in insulin-dependent diabetesmellitus canbe predicted from self-monitoring blood glucose datardquo Journalof Clinical Endocrinology and Metabolism vol 79 no 6 pp1659ndash1662 1994

[37] B P Kovatchev D J Cox L A Gonder-Frederick D Young-Hyman D Schlundt and W Clarke ldquoAssessment of risk forsevere hypoglycemia among adults with IDDM validation ofthe low blood glucose indexrdquo Diabetes Care vol 21 no 11 pp1870ndash1875 1998

[38] B P Kovatchev D J Cox A Kumar L Gonder-Frederick andW L Clarke ldquoAlgorithmic evaluation of metabolic control and

risk of severe hypoglycemia in type 1 and type 2 diabetes usingself-monitoring blood glucose datardquo Diabetes Technology anderapeutics vol 5 no 5 pp 817ndash828 2003

[39] D J Cox L Gonder-Frederick L Ritterband W Clarke andB P Kovatchev ldquoPrediction of severe hypoglycemiardquo DiabetesCare vol 30 no 6 pp 1370ndash1373 2007

[40] S A Amiel R S Sherwin D C Simonson and W VTamborlane ldquoEffect of intensive insulin therapy on glycemicthresholds for counterregulatory hormone releaserdquo Diabetesvol 37 no 7 pp 901ndash907 1988

[41] S A Amiel W V Tamborlane D C Simonson and RS Sherwin ldquoDefective glucose counterregulation aer strictglycemic control of insulin-dependent diabetes mellitusrdquo NewEngland Journal of Medicine vol 316 no 22 pp 1376ndash13831987

[42] P E Cryer and J E Gerich ldquoGlucose counterregulation hypo-glycemia and intensive insulin therapy in diabetes mellitusrdquoNew England Journal of Medicine vol 313 no 4 pp 232ndash2411985

[43] N H White D A Skor and P E Cryer ldquoIdentication of typeI diabetic patients at increased risk for hypoglycemia duringintensive therapyrdquo New England Journal of Medicine vol 308no 9 pp 485ndash491 1983

[44] P E Cryer ldquoIatrogenic hypoglycemia as a cause ofhypoglycemia-associated autonomic failure in IDDM avicious cyclerdquo Diabetes vol 41 no 3 pp 255ndash260 1992

[45] J N Henderson K V Allen I J Deary and B M FrierldquoHypoglycaemia in insulin-treated Type 2 diabetes frequencysymptoms and impaired awarenessrdquo Diabetic Medicine vol 20no 12 pp 1016ndash1021 2003

[46] P E Cryer Hypoglycemia Pathophysiology Diagnosis andTreatment Oxford University Press New York NY USA 1997

[47] P E Cryer S N Davis and H Shamoon ldquoHypoglycemia indiabetesrdquo Diabetes Care vol 26 no 6 pp 1902ndash1912 2003

[48] B P Childs N G Clark and D J Cox ldquoDening and reportinghypoglycemia in diabetes a report from the American diabetesassociation workgroup on hypoglycemiardquo Diabetes Care vol28 no 5 pp 1245ndash1249 2005

[49] P E Cryer ldquoHypoglycaemia the limiting factor in the glycaemicmanagement of Type I and Type II diabetesrdquo Diabetologia vol45 no 7 pp 937ndash948 2002

[50] P E Cryer ldquoHypoglycemia the limiting factor in the manage-ment of IDDMrdquo Diabetes vol 43 no 11 pp 1378ndash1389 1994

[51] A L McCall and B P Kovatchev ldquoe median is not the onlymessage a clinicianrsquos perspective on mathematical analysis ofglycemic variability and modeling in diabetes mellitusrdquo Journalof Diabetes Science and Technology vol 3 pp 3ndash11 2009

[52] M Brownlee and I B Hirsch ldquoGlycemic variability ahemoglobin A1c-independent risk factor for diabetic compli-cationsrdquo Journal of the American Medical Association vol 295no 14 pp 1707ndash1708 2006

[53] I B Hirsch and M Brownlee ldquoShould minimal blood glucosevariability become the gold standard of glycemic controlrdquoJournal of Diabetes and Its Complications vol 19 no 3 pp178ndash181 2005

[54] K Esposito D Giugliano F Nappo and R Marfella ldquoRegres-sion of carotid atherosclerosis by control of postprandial hyper-glycemia in type 2 diabetes mellitusrdquo Circulation vol 110 no2 pp 214ndash219 2004

[55] S Haffner ldquoe importance of postprandial hyperglycaemia indevelopment of cardiovascular disease in people with diabetes

Scientica 11

pointrdquo International Journal of Clinical Practice no 123 pp24ndash26 2001

[56] LMonnier EMas C Ginet et al ldquoActivation of oxidative stressby acute glucose uctuations compared with sustained chronichyperglycemia in patients with type 2 diabetesrdquo Journal of theAmerican Medical Association vol 295 no 14 pp 1681ndash16872006

[57] T S Temelkova-Kurktschiev C Koehler E Henkel W Leon-hardt K Fuecker and M Hanefeld ldquoPostchallenge plasmaglucose and glycemic spikes are more strongly associated withatherosclerosis than fasting glucose or HbA(1c) levelrdquo DiabetesCare vol 23 no 12 pp 1830ndash1834 2000

[58] W L Clarke D J Cox L Gonder-Frederick D JulianB Kovatchev and D Young-Hyman ldquoBiopsychobehavioralmodel of risk of severe hypoglycemia self-management behav-iorsrdquo Diabetes Care vol 22 no 4 pp 580ndash584 1999

[59] D J Cox L A Gonder-Frederick B P Kovatchev et alldquoBiopsychobehavioral model of severe hypoglycemia II under-standing the risk of severe hypoglycemiardquo Diabetes Care vol22 no 12 pp 2018ndash2025 1999

[60] L Gonder-Frederick D Cox B Kovatchev D Schlundt andW Clarke ldquoA biopsychobehavioral model of risk of severehypoglycemiardquoDiabetes Care vol 20 no 4 pp 661ndash669 1997

[61] B Kovatchev D Cox L Gonder-Frederick D Schlundtand W Clarke ldquoStochastic model of self-regulation decisionmaking exemplied by decisions concerning hypoglycemiardquoHealth Psychology vol 17 no 3 pp 277ndash284 1998

[62] G Nucci and C Cobelli ldquoModels of subcutaneous insulinkinetics A critical reviewrdquo Computer Methods and Programs inBiomedicine vol 62 no 3 pp 249ndash257 2000

[63] M E Wilinska L J Chassin H C Schaller L Schaupp T RPieber and R Hovorka ldquoInsulin kinetics in type-1 diabetescontinuous and bolus delivery of rapid acting insulinrdquo IEEETransactions on Biomedical Engineering vol 52 no 1 pp 3ndash122005

[64] R N Bergman D T Finegood and M Ader ldquoAssessmentof insulin sensitivity in vivordquo Endocrine Reviews vol 236 ppE667ndashE677 1985

[65] R N Bergman ldquoe minimal model of glucose regulation abiographyrdquoAdvances in ExperimentalMedicine and Biology vol537 pp 1ndash19 2003

[66] R N Bergman D J Zaccaro R M Watanabe et al ldquoMinimalmodel-based insulin sensitivity has greater heritability and adifferent genetic basis than homeostasis model assessment orfasting insulinrdquo Diabetes vol 52 no 8 pp 2168ndash2174 2003

[67] A Caumo R N Bergman and C Cobelli ldquoInsulin sensitivityfrom meal tolerance tests in normal subjects a minimal modelindexrdquo Journal of Clinical Endocrinology and Metabolism vol85 no 11 pp 4396ndash4402 2000

[68] J O Clausen K Borch-Johnsen H Ibsen et al ldquoInsulin sen-sitivity index acute insulin response and glucose effectivenessin a population-based sample of 380 young healthy Caucasiansanalysis of the impact of gender body fat physical tness andlife-style factorsrdquo Journal of Clinical Investigation vol 98 no 5pp 1195ndash1209 1996

[69] T C Ni M Ader and R N Bergman ldquoReassessment of glu-cose effectiveness and insulin sensitivity from minimal modelanalysis a theoretical evaluation of the single-compartmentglucose distribution assumptionrdquo Diabetes vol 46 no 11 pp1813ndash1821 1997

[70] S Welch S S P Gebhart R N Bergman and L S PhillipsldquoMinimal model analysis of intravenous glucose tolerance

test-derived insulin sensitivity in diabetic subjectsrdquo Journalof Clinical Endocrinology and Metabolism vol 71 no 6 pp1508ndash1518 1990

[71] D Dawson M A Vincent E J Barrett et al ldquoCapillary recruit-ment in skeletal muscle in response to exercise and hyperin-sulinemia assessed with contrast-enhanced ultrasoundrdquo Amer-ican Journal of Physiology vol 282 no 3 pp E714ndashE720 2002

[72] K J Mikines B Sonne P A Farrell B Tronier and H GalboldquoEffect of physical exercise on sensitivity and responsiveness toinsulin in humansrdquoAmerican Journal of Physiology vol 254 no3 pp E248ndashE259 1988

[73] E A Richter ldquoGlucose utilizationrdquo in Handbook of PhysiologyL B Rowell and J T Shepherd Eds pp 912ndash951 OxfordUniversity Press New York NY USA 1996

[74] D H Wasserman R J Geer D E Rice et al ldquoInteraction ofexercise and insulin action in humansrdquo American Journal ofPhysiology vol 260 no 1 pp E37ndashE45 1991

[75] G Pillonetto A Caumo G Sparacino and C Cobelli ldquoAnew dynamic index of insulin sensitivityrdquo IEEE Transactions onBiomedical Engineering vol 53 no 3 pp 369ndash379 2006

[76] C Dalla Man D M Raimondo R A Rizza and C CobellildquoGIM Simulation soware of meal glucose-insulin modelrdquoJournal of Diabetes Science and Technology vol 1 pp 323ndash3302007

[77] C Dalla Man R A Rizza and C Cobelli ldquoMeal simulationmodel of the glucose-insulin systemrdquo IEEE Transactions onBiomedical Engineering vol 54 no 10 pp 1740ndash1749 2007

[78] W L Clarke D Cox L A Gonder-Frederick W Carter andS L Pohl ldquoEvaluating clinical accuracy of systems for self-monitoring of blood glucoserdquo Diabetes Care vol 10 no 5 pp622ndash628 1987

[79] e Diabetes Research In Children Network (Direcnet) StudyGroup ldquoA multicenter study of the accuracy of the one touchultra home glucose meter in children with type 1 diabetesrdquoDiabetes Technology anderapeutics vol 5 no 6 pp 933ndash9412003

[80] I B Hirsch B W Bode B P Childs et al ldquoSelf-monitoring ofblood glucose (SMBG) in insulin- and non-insulin-using adultswith diabetes consensus recommendations for improvingSMBG accuracy utilization and researchrdquo Diabetes Technologyanderapeutics vol 10 no 6 pp 419ndash439 2008

[81] G Freckmann A Baumstark N Jendrike et al ldquoSystemaccuracy evaluation of 27 blood glucose monitoring systemsaccording to DIN en ISO 15197rdquo Diabetes Technology anderapeutics vol 12 no 3 pp 221ndash231 2010

[82] D Deiss J Bolinder J P Riveline et al ldquoImproved glycemiccontrol in poorly controlled patients with type 1 diabetes usingreal-time continuous glucose monitoringrdquo Diabetes Care vol29 no 12 pp 2730ndash2732 2006

[83] S Garg H Zisser S Schwartz et al ldquoImprovement in glycemicexcursions with a transcutaneous real-time continuous glucosesensor a randomized controlled trialrdquo Diabetes Care vol 29no 1 pp 44ndash50 2006

[84] B P Kovatchev and W L Clarke ldquoContinuous glucose mon-itoring reduces risks for hypo- and hyperglycemia and glucosevariability in diabetesrdquo Diabetes vol 56 1 Article ID 0086OR2007

[85] e Juvenile Diabetes Research Foundation Continuous Glu-cose Monitoring Study Group ldquoContinuous glucose monitor-ing and intensive treatment of type 1 diabetesrdquo New EnglandJournal of Medicine vol 359 pp 1464ndash1476 2008

12 Scientica

[86] D C Klonoff ldquoContinuous glucose monitoring roadmap for21st century diabetes therapyrdquo Diabetes Care vol 28 no 5 pp1231ndash1239 2005

[87] R Hovorka ldquoContinuous glucose monitoring and closed-loopsystemsrdquo Diabetic Medicine vol 23 no 1 pp 1ndash12 2006

[88] D C Klonoff ldquoe articial pancreas how sweet engineeringwill solve bitter problemsrdquo Journal of Diabetes Science andTechnology vol 1 pp 72ndash81 2007

[89] I B Hirsch D Armstrong R M Bergenstal et al ldquoClin-ical application of emerging sensor technologies in diabetesmanagement consensus guidelines for continuous glucosemonitoring (CGM)rdquoDiabetes Technology anderapeutics vol10 no 4 pp 232ndash246 2008

[90] K Rebrin G M Steil W P Van Antwerp and J J Mastro-totaro ldquoSubcutaneous glucose predicts plasma glucose inde-pendent of insulin implications for continuous monitoringrdquoAmerican Journal of Physiology vol 277 no 3 pp E561ndashE5711999

[91] K Rebrin and G M Steil ldquoCan interstitial glucose assessmentreplace blood glucosemeasurementsrdquoDiabetes Technology anderapeutics vol 2 no 3 pp 461ndash472 2000

[92] G M Steil K Rebrin F Hariri et al ldquoInterstitial uid glucosedynamics during insulin-induced hypoglycaemiardquo Diabetolo-gia vol 48 no 9 pp 1833ndash1840 2005

[93] M S Boyne D M Silver J Kaplan and C D Saudek ldquoTimingof changes in interstitial and venous blood glucose measuredwith a continuous subcutaneous glucose sensorrdquo Diabetes vol52 no 11 pp 2790ndash2794 2003

[94] E Kulcu J A Tamada G Reach R O Potts and M JLesho ldquoPhysiological differences between interstitial glucoseand blood glucose measured in human subjectsrdquoDiabetes Carevol 26 no 8 pp 2405ndash2409 2003

[95] P J Stout J R Racchini andM E Hilgers ldquoA novel approach tomitigating the physiological lag between blood and interstitialuid glucose measurementsrdquo Diabetes Technology and era-peutics vol 6 no 5 pp 635ndash644 2004

[96] I M E Wentholt A A M Hart J B L Hoekstra and J HDevries ldquoRelationship between interstitial and blood glucose intype 1 diabetes patients delay and the push-pull phenomenonrevisitedrdquo Diabetes Technology and erapeutics vol 9 no 2pp 169ndash175 2007

[97] K J C Wientjes and A J M Schoonen ldquoDetermination oftime delay between blood and interstitial adipose tissue glucoseconcentration change by microdialysis in healthy volunteersrdquonternational Journal of Articial rgans vol 24 no 12 pp884ndash889 2001

[98] B Aussedat M Dupire-Angel R Gifford J C Klein G SWilson and G Reach ldquoInterstitial glucose concentration andglycemia implications for continuous subcutaneous glucosemonitoringrdquo American Journal of Physiology vol 278 no 4 ppE716ndashE728 2000

[99] B P Kovatchev and W L Clarke ldquoPeculiarities of the con-tinuous glucose monitoring data stream and their impact ondeveloping closed-loop control technologyrdquo Journal of DiabetesScience and Technology vol 2 pp 158ndash163 2008

[100] W L Clarke and B P Kovatchev ldquoContinuous glucose sen-sorsmdashcontinuing questions about clinical accuracyrdquo Journal ofDiabetes Science and Technology vol 1 pp 164ndash170 2007

[101] e Diabetes Research in Children Network (DirecNet) StudyGroup ldquoe accuracy of the guardian RT continuous glucosemonitor in children with type 1 diabetesrdquo Diabetes Technologyanderapeutics vol 10 pp 266ndash272 2008

[102] B Kovatchev S Anderson L Heinemann and W ClarkeldquoComparison of the numerical and clinical accuracy of fourcontinuous glucose monitorsrdquo Diabetes Care vol 31 no 6 pp1160ndash1164 2008

[103] S K Garg J Smith C Beatson B Lopez-Baca M Voelmleand P A Gottlieb ldquoComparison of accuracy and safety ofthe SEVEN and the navigator continuous glucose monitoringsystemsrdquo Diabetes Technology and erapeutics vol 11 no 2pp 65ndash72 2009

[104] T Heise T Koschinsky L Heinemann and V Lodwig ldquoHypo-glycemia warning signal and glucose sensors requirements andconceptsrdquo Diabetes Technology and erapeutics vol 5 no 4pp 563ndash571 2003

[105] B Bode K Gross N Rikalo et al ldquoAlarms based on real-timesensor glucose values alert patients to hypo- and hyperglycemiathe Guardian continuous monitoring systemrdquo Diabetes Tech-nology anderapeutics vol 6 no 2 pp 105ndash113 2004

[106] G McGarraugh and R Bergenstal ldquoDetection of hypoglycemiawith continuous interstitial and traditional blood glucosemonitoring using the FreeStyle navigator continuous glucosemonitoring systemrdquo Diabetes Technology and erapeutics vol11 no 3 pp 145ndash150 2009

[107] S E Noujaim D Horwitz M Sharma and J Marhoul ldquoAccu-racy requirements for a hypoglycemia detector an analyticalmodel to evaluate the effects of bias precision and rate ofglucose changerdquo Journal of Diabetes Science and Technology vol1 pp 653ndash668 2007

[108] W K Ward ldquoe role of new technology in the early detectionof hypoglycemiardquo Diabetes Technology and erapeutics vol 6no 2 pp 115ndash117 2004

[109] B Buckingham E Cobry P Clinton et al ldquoPreventing hypo-glycemia using predictive alarm algorithms and insulin pumpsuspensionrdquo Diabetes Technology and erapeutics vol 11 no2 pp 93ndash97 2009

[110] C S Hughes S D Patek M D Breton and B P KovatchevldquoHypoglycemia prevention via pump attenuation and red-yellow-green ldquotrafficrdquo lights using continuous glucose moni-toring and insulin pump datardquo Journal of Diabetes Science andTechnology vol 4 no 5 pp 1146ndash1155 2010

[111] B P Kovatchev D J Cox L A Gonder-Frederick and WClarke ldquoSymmetrization of the blood glucose measurementscale and its applicationsrdquo Diabetes Care vol 20 no 11 pp1655ndash1658 1997

[112] F J Service G D Molnar J W Rosevear E Ackerman LC Gatewood and W F Taylor ldquoMean amplitude of glycemicexcursions a measure of diabetic instabilityrdquo Diabetes vol 19no 9 pp 644ndash655 1970

[113] J Schlichtkrull O Munck and M Jersild ldquoe M-value anindex of blood glucose control in diabeticsrdquo Acta MedicaScandinavica vol 177 pp 95ndash102 1965

[114] E A Ryan T Shandro K Green et al ldquoAssessment of theseverity of hypoglycemia and glycemic lambility in type 1diabetic subjects undergoing islet in transplantationrdquo Diabetesvol 53 no 4 pp 955ndash962 2004

[115] B P Kovatchev D J Cox L Gonder-Frederick and WL Clarke ldquoMethods for quantifying self-monitoring bloodglucose proles exemplied by an examination of blood glucosepatterns in patients with type 1 and type 2 diabetesrdquo DiabetesTechnology anderapeutics vol 4 no 3 pp 295ndash303 2002

[116] B P Kovatchev D J Cox L S Farhy M Straume L Gonder-Frederick and W L Clarke ldquoEpisodes of severe hypoglycemia

Scientica 13

in type 1 diabetes are preceded and followed within 48 hours bymeasurable disturbances in blood glucoserdquo Journal of ClinicalEndocrinology and Metabolism vol 85 no 11 pp 4287ndash42922000

[117] B P Kovatchev M Straume D J Cox and L S FarhyldquoRisk analysis of blood glucose data a quantitative approach tooptimizing the control of Insulin Dependent Diabetesrdquo Journalof eoretical Medicine vol 3 no 1 pp 1ndash10 2000

[118] B P Kovatchev E Otto D Cox L Gonder-Frederick andW Clarke ldquoEvaluation of a new measure of blood glucosevariability in diabetesrdquo Diabetes Care vol 29 no 11 pp2433ndash2438 2006

[119] B P Kovatchev W L Clarke M Breton K Brayman and AMcCall ldquoQuantifying temporal glucose variability in diabetesvia continuous glucose monitoring mathematical methods andclinical applicationrdquo Diabetes Technology and erapeutics vol7 no 6 pp 849ndash862 2005

[120] D Rodbard ldquoNew and improved methods to characterizeglycemic variability using continuous glucosemonitoringrdquoDia-betes Technology and erapeutics vol 11 no 9 pp 551ndash5652009

[121] M Miller and P Strange ldquoUse of Fourier models for analysisand interpretation of continuous glucose monitoring glucoseprolesrdquo Journal of Diabetes Science and Technology vol 1 pp630ndash638 2007

[122] W Clarke and B Kovatchev ldquoStatistical tools to analyzecontinuous glucose monitor datardquo Diabetes Technology anderapeutics vol 11 pp S45ndashS54 2009

[123] C M Mcdonnell S M Donath S I Vidmar G A Wertherand F J Cameron ldquoA novel approach to continuous glucoseanalysis utilizing glycemic variationrdquo Diabetes Technology anderapeutics vol 7 no 2 pp 253ndash263 2005

[124] G Sparacino F Zanderigo A Maran and C Cobelli ldquoContin-uous glucosemonitoring andhypohyperglycaemia predictionrdquoDiabetes Research and Clinical Practice vol 74 no 2 supple-ment pp S160ndashS163 2006

[125] G Sparacino F Zanderigo G Corazza A Maran AFacchinetti and C Cobelli ldquoGlucose concentration can bepredicted ahead in time from continuous glucose monitoringsensor time-seriesrdquo IEEE Transactions on Biomedical Engineer-ing vol 54 pp 931ndash937 2007

[126] J Reifman S Rajaraman A Gribok and W K Ward ldquoPredic-tive monitoring for improved management of glucose levelsrdquoJournal of Diabetes Science and Technology vol 1 pp 478ndash4862007

[127] F Zanderigo G Sparacino B Kovatchev and C CobellildquoGlucose prediction algorithms from continuous monitoringassessment of accuracy via continuous glucose-error grid anal-ysisrdquo Journal of Diabetes Science and Technology vol 1 pp645ndash651 2007

[128] F Cameron G Niemayer K Gundy-Burlet and B Bucking-ham ldquoStatistical hypoglycemia predictionrdquo Journal of DiabetesScience and Technology vol 2 pp 612ndash621 2008

[129] A Gani A V Gribok S Rajaraman W K Ward and JReifman ldquoPredicting subcutaneous glucose concentration inhumans data-driven glucose modelingrdquo IEEE Transactions onBiomedical Engineering vol 56 no 2 pp 246ndash254 2009

[130] M Eren-Oruklu A Cinar L Quinn andD Smith ldquoEstimationof future glucose concentrations with subject-specic recursivelinear modelsrdquo Diabetes Technology and erapeutics vol 11no 4 pp 243ndash253 2009

[131] S M Pappada B D Cameron and P M Rosman ldquoDevelop-ment of a neural network for prediction of glucose concentra-tion in type 1 diabetes patientsrdquo Journal of Diabetes Science andTechnology vol 2 pp 792ndash801 2008

[132] C Cobelli C Dalla Man G Sparacino L Magni G Nicolaoand B P Kovatchev ldquoDiabetes models signals and controlrdquoIEEE Reviews in Biomedical Engineering vol 2 pp 54ndash96 2009

[133] H LeBlanc D Chauvet P Lombrail and J J Robert ldquoGlycemiccontrol with closed-loop intraperitoneal insulin in type Idiabetesrdquo Diabetes Care vol 9 no 2 pp 124ndash128 1986

[134] C D Saudek J L Selman H A Pitt et al ldquoA preliminary trialof the programmable implantablemedication system for insulindeliveryrdquo New England Journal of Medicine vol 321 no 9 pp574ndash579 1989

[135] C Broussolle N Jeandidier and H Hanaire-Broutin ldquoFrenchmulticentre experience of implantable insulin pumpsrdquo Lancetvol 343 no 8896 pp 514ndash515 1994

[136] H Hanaire-Broutin C Broussolle N Jeandidier et al ldquoFea-sibility of intraperitoneal insulin therapy with programmableimplantable pumps in IDDM a multicenter studyrdquo DiabetesCare vol 18 no 3 pp 388ndash392 1995

[137] P R Oskarsson P E Lins H W Henriksson and U CAdamson ldquoMetabolic and hormonal responses to exercisein type 1 diabetic patients during continuous subcutaneousas compared to continuous intraperitoneal insulin infusionrdquoDiabetes and Metabolism vol 25 no 6 pp 491ndash497 1999

[138] P R Oskarsson P E Lins L Backman and U C AdamsonldquoContinuous intraperitoneal insulin infusion partly restores theglucagon response to hypoglycaemia in type 1 diabetic patientsrdquoDiabetes and Metabolism vol 26 no 2 pp 118ndash124 2000

[139] B Catargi L Meyer V Melki E Renard and N JeandidierldquoComparison of blood glucose stability and HbA1C betweenimplantable insulin pumps using U400 hoe 21pH insulin andexternal pumps using lispro in type 1 diabetic patients a pilotstudyrdquo Diabetes and Metabolism vol 28 no 2 pp 133ndash1372002

[140] E Renard ldquoImplantable closed-loop glucose-sensing andinsulin delivery the future for insulin pump therapyrdquo CurrentOpinion in Pharmacology vol 2 no 6 pp 708ndash716 2002

[141] E Renard G Costalat H Chevassus and J Bringer ldquoArticial120573120573-cell clinical experience toward an implantable closed-loopinsulin delivery systemrdquo Diabetes and Metabolism vol 32 no5 pp 497ndash502 2006

[142] E Renard J Place M Cantwell H Chevassus and C CPalerm ldquoClosed-loop insulin delivery using a subcutaneousglucose sensor and intraperitoneal insulin delivery feasibilitystudy testing a new model for the articial pancreasrdquo DiabetesCare vol 33 no 1 pp 121ndash127 2010

[143] R Bellazzi G Nucci and C Cobelli ldquoe subcutaneousroute to insulin-dependent diabetes therapy closed-loop andpartially closed-loop control strategies for insulin deliveryand measuring glucose concentrationrdquo IEEE Engineering inMedicine and Biology Magazine vol 20 no 1 pp 54ndash64 2001

[144] R Hovorka L J Chassin M E Wilinska et al ldquoClosingthe loop the adicol experiencerdquo Diabetes Technology anderapeutics vol 6 no 3 pp 307ndash318 2004

[145] R Hovorka V Canonico L J Chassin et al ldquoNonlinear modelpredictive control of glucose concentration in subjects withtype 1 diabetesrdquo Physiological Measurement vol 25 no 4 pp905ndash920 2004

14 Scientica

[146] G M Steil K Rebrin C Darwin F Hariri and M F SaadldquoFeasibility of automating insulin delivery for the treatment oftype 1 diabetesrdquo Diabetes vol 55 no 12 pp 3344ndash3350 2006

[147] e JDRF e-Newsletter Emerging Technologies in DiabetesResearch 2006

[148] W L Clarke and B Kovatchev ldquoe articial pancreas howclose are we to closing the looprdquo Pediatric EndocrinologyReviews vol 4 no 4 pp 314ndash316 2007

[149] C Cobelli E Renard and B P Kovatchev ldquoPerspectives indiabetes articial pancreas past present futurerdquo Diabetes vol60 pp 2672ndash2682 2011

[150] B P Kovatchev M D Breton C Dalla Man and C Cobelli ldquoInsilico preclinical trials a proof of concept in closed-loop controlof type 1 diabetesrdquo Journal of Diabetes Science and Technologyvol 3 pp 44ndash55 2009

[151] B Kovatchev C Cobelli E Renard et al ldquoMultinational studyof subcutaneous model-predictive closed-loop control in type1 diabetes mellitus summary of the resultsrdquo Journal of DiabetesScience and Technology vol 4 no 6 pp 1374ndash1381 2010

[152] H Zisser L Jovanovic F Doyle P Ospina and C OwensldquoRun-to-run control of meal-related insulin dosingrdquo DiabetesTechnology anderapeutics vol 7 no 1 pp 48ndash57 2005

[153] C Owens H Zisser L Jovanovic B Srinivasan D Bonvinand F J Doyle III ldquoRun-to-run control of blood glucoseconcentrations for people with type 1 diabetes mellitusrdquo IEEETransactions on Biomedical Engineering vol 53 no 6 pp996ndash1005 2006

[154] C C Palerm H Zisser W C Bevier L Jovanovič and F JDoyle III ldquoPrandial insulin dosing using run-to-run controlapplication of clinical data and medical expertise to dene asuitable performance metricrdquo Diabetes Care vol 30 no 5 pp1131ndash1136 2007

[155] LMagni F Raimondo L Bossi et al ldquoModel predictive controlof type 1 diabetes an in silico trialrdquo Journal of Diabetes Scienceand Technology vol 1 pp 804ndash812 2007

[156] S A Weinzimer G M Steil K L Swan J Dziura N Kurtzand W V Tamborlane ldquoFully automated closed-loop insulindelivery versus semiautomated hybrid control in pediatricpatients with type 1 diabetes using an articial pancreasrdquoDiabetes Care vol 31 no 5 pp 934ndash939 2008

[157] W L Clarke S Anderson M Breton S Patek L Kashmerand B Kovatchev ldquoClosed-loop articial pancreas using sub-cutaneous glucose sensing and insulin delivery and a modelpredictive control algorithm the Virginia experiencerdquo Journalof Diabetes Science and Technology vol 3 no 5 pp 1031ndash10382009

[158] D Bruttomesso A Farret S Costa et al ldquoClosed-loop arti-cial pancreas using subcutaneous glucose sensing and insulindelivery and a model predictive control algorithm preliminarystudies in Padova and Montpellierrdquo Journal of Diabetes Scienceand Technology vol 3 no 5 pp 1014ndash1021 2009

[159] R Hovorka J M Allen D Elleri et al ldquoManual closed-loop insulin delivery in children and adolescents with type 1diabetes a phase 2 randomised crossover trialrdquoe Lancet vol375 no 9716 pp 743ndash751 2010

[160] F H El-khatib S J Russell D M Nathan R G Sutherlin andE R Damiano ldquoA bihormonal closed-loop articial pancreasfor type 1 diabetesrdquo Science Translational Medicine vol 2 no27 Article ID 27ra27 2010

[161] E Dassau H Zisser C C Palerm B A Buckingham LJovanovič and F J Doye III ldquoModular articial 120573120573-cell system a

prototype for clinical researchrdquo Journal of Diabetes Science andTechnology vol 2 pp 863ndash872 2008

[162] B Kovatchev S Patek E Dassau et al ldquoControl to range fordiabetes functionality and modular architecturerdquo Journal ofDiabetes Science and Rechnology vol 3 no 5 pp 1058ndash10652009

[163] E Atlas R Nimri S Miller E A Grunberg and M PhillipldquoMD-logic articial pancreas system a pilot study in adults withtype 1 diabetesrdquo Diabetes Care vol 33 no 5 pp 1072ndash10762010

[164] E Dassau H Zisser M W Percival B Grosman L Jovanovičand F J Doyle III ldquoClinical results of automated articialpancreatic 120573120573-cell system with unannounced meal using multi-parametric MPC and insulin-on-boardrdquo in Proceedings of the70th American Diabetes Association Meeting Diabetes vol 59supplement 1 A94 Orlando Fla USA 2010

[165] E M Renard A Farret J Place C Cobelli B P Kovatchev andMD Breton ldquoClosed-loop insulin delivery using subcutaneousinfusion and glucose sensing and equipped with a dedicatedsafety supervision algorithm improves safety of glucose controlin type 1 diabetesrdquo Diabetologia vol 53 supplement 1 p S252010

[166] R Hovorka K Kumareswaran J Harris et al ldquoOvernightclosed loop insulin delivery articial pancreas in adults withtype 1 diabetes crossover randomized controlled studiesrdquoBritish Medical Journal vol 342 no 7803 Article ID d18552011

[167] H Zisser E Dassau W Bevier et al ldquoInitial evaluation ofa fully automated articial pancreasrdquo in Proceedings of the71st American Diabetes Association Meeting Diabetes vol 60supplement 1 A41 San Diego Calif USA 2011

[168] M D Breton A Farret and D Bruttomesso ldquoFully-integratedarticial pancreas in type 1 diabetes modular closed-loopglucose control maintains near-normoglycemiardquo Diabetes vol61 pp 2230ndash2237 2012

[169] C Cobelli E Renard and B P Kovatchev ldquoPilot studies ofwearable articial pancreas in type 1 diabetesrdquo Diabetes Carevol 35 no 9 pp e65ndashe67 2012

Submit your manuscripts athttpwwwhindawicom

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

EndocrinologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

OncologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Oxidative Medicine and Cellular Longevity

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Computational and Mathematical Methods in Medicine

OphthalmologyJournal of

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Research and TreatmentAIDS

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Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Scientica 7

2006 2008 2010

The JDRF artificial pancreas consortium

is launched (Kowalski)

Studies of hybrid closed-loop control (Weinzimer

and Tamborlane)

First human trials begin using a system designed entirely in silico UVA

Italy and France(Kovatchev Cobelli Renard)

NIH funds artificial pancreas

EU launches the APHome

artificial pancreas initiative

JDRF multicenter trialof modular control-to-

range

2004 the ADICOLproject

(Hovorka)

First studies of automated sc

closed loop (Steil)

FDA accepts the UVAPadova metabolic simulator as a substitute

to animal trials(Kovatchev Cobelli Dalla

Man and Breton)

Modular control-to-rangeintroduced trials at UVA

Italy and France(Kovatchev

Cobelli and Renard)

The APS introduced(Dassau Doyle

First studies of outpatient closed-loop

control (CobelliRenard Zisser and

Kovatchev )

2012

DiAs first portableAP platform

(Keith-HynesKovatchev)

studies Zisser)

30

2012

F 2 Timeline of the articial pancreas developments in the last decademdashtheoretical work and a number of in-clinic studies leading tothe rst trials of wearable articial pancreas device

53 In Silico Models of the Human Metabolic System Acritical step towards accelerated clinical progress of thearticial pancreas was the development of sophisticatedcomputer simulator of the human metabolic system allowingrapid in silico testing of closed-loop control algorithmsis simulation environment was based on the previouslyintroduced Meal Model of glucose-insulin dynamics [76 77]and was equipped with a ldquopopulationrdquo of in silico imagesof 119873119873 119873 119873119873119873 ldquosubjectsrdquo with type 1 diabetes separated inthree age groups 119873119873 119873 119873119873119873 simulated ldquochildrenrdquo below theage of 11 119873119873 119873 119873119873119873 ldquoadolescentsrdquo 12ndash18 years old and119873119873 119873 119873119873119873 ldquoadultsrdquo e characteristics of these ldquosubjectsrdquo(eg weight daily insulin dose carbohydrate ratio etc)were tailored to span a wide range of intersubject variabilityapproximating the variability observed in people in vivo[150] Simulation experiments allow any CGM device anyinsulin pump and any control algorithm to be linked in aclosed-loop system in silico prior to their use in clinical trialsWith this technology any meal and insulin delivery scenariocan be pilot-tested very efficientlymdasha 24-hour period ofclosed-loop control is simulated in under 2 secondsWe needto emphasize however that good in silico performance of acontrol algorithmdoes not guarantee in vivo performancemdashitonly helps test extreme situations and the stability of thealgorithm and rule out inefficient scenarios us computersimulation is only a prerequisite to but not a substitute forclinical trials

In January 2008 in an unprecedented decision theUSA Food and Drug Administration accepted this computer

simulator as a substitute to animal trials for the testing ofclosed-loop control strategies is opened the eld for effi-cient and cost-effective in silico experiments leading directlyto human studies Only three months later in April 2008 therst human trials began at the University of irginia (USA)Montpellier (France) and Padova (Italy) using a controlsystem designed entirely in silico [151]

54 Control System Designs e rst studies of ovorkaet al [144 145] and Steil et al [146] outlined the twomajor types of closed-loop control algorithms now in use inarticial pancreas systemsmdashmodel-predictive control (MPC[145]) and proportional-integral-derivative (PID [146])respectively By 2007 the blueprints of the contemporarycontrollers were in place including run-to-run control[152ndash154] and linear MPC [155] To date the trials ofsubcutaneous closed-loop control systems have been usingeither PID [146 156] or MPC [157ndash160] but MPC becamethe approach of choice targeted by recent research erewere two important reasons making MPC preferable (i)PID is purely reactive responding to changes in glucoselevel while a properly tuned MPC allows for prediction ofglucose dynamics and as a result for mitigation of the timedelays inherent with subcutaneous glucose monitoring andsubcutaneous insulin infusion [62 63] (ii) MPC allows forrelatively straightforward personalizing of the control usingpatient-specic model parameters In addition MPC couldhave ldquolearningrdquo capabilitiesmdashit has been shown that a class

8 Scientica

of algorithms (known as run-to-run control) can ldquolearnrdquospecics of patientsrsquo daily routine (eg timing of meals) andthen optimize the response to a subsequent meal using thisinformation or account for circadian uctuation in insulinresistance (eg dawn phenomenon observed in some people)[149]

In 2008 a universal research platformmdashthe APSmdashwasintroduced enabling automated communication betweenseveral CGM devices insulin pumps and control algorithms[161] e APS was very instrumental for a number ofinpatient trials of closed-loop control A year later a mod-ular architecture was introduced proposing standardizationsequential testing and clinical deployment of articial pan-creas components [162]

55 Inpatient Clinical Trials Between 2008 and 2011 prom-ising results were reported by several groups [156ndash160 163ndash167] Most of these studies pointed out the superiority ofclosed-loop control over standard CSII therapy in termsof (i) increased time within target glucose range (typically39ndash10mmoll) (ii) reduced incidence of hypoglycemia and(iii) better overnight control Two of these studies [159166] had state-of-the-art randomized cross-over design butlacked automated data transfermdashall CGM readings weretransferred to the controllermanually by the study personneland all insulin pump commands were entered manually aswell To distinguish the various degrees of automation inclosed-loop studies the notion of fully-integrated closed-loop control emerged dened as having all of the followingthree components (i) automated data transfer from theCGM to the controller (ii) real-time control action and (iii)automated command of the insulin pump e rst (andthe largest to date) randomized cross-over study of fully-integrated closed-loop control was published in 2012 [168]However even this contemporary trial of fully automatedCLC which enrolled 38 patients with T1D at three centersand tested two different control algorithms achieving note-worthy glycemic control and prevention of hypoglycemia didnot leave the clinical setting e technology used by thisstudy was still based on a laptop computer wired to a CGMand an insulin pump a system limiting the free movementof the study subjects and too cumbersome to be used beyondhospital connes

5 earale tpatient rticial ancreas e transitionof closed-loop control to ambulatory use began in 2011 withthe development of the Diabetes Assistant (DiAs)mdashthe rstwearable articial pancreas platform based on a smart phonee design characteristics of DiAs included the following

(i) based on readily available inexpensive wearablehardware platform

(ii) computationally capable of running advanced closed-loop control algorithms

(iii) wirelessly connectable to CGM devices and insulinpumps

(iv) capable of broadband communication with a centrallocation for remote monitoring and safety supervi-sion of the participants in outpatient clinical trials

In ctober 2011 the rst two pilot trials of wearableoutpatient articial pancreas were performed simultaneouslyin Padova (Italy) and Montpellier (France) [169] ese 2-day trials allowed the renement of a wearable system andenabled a subsequentmultisite feasibility study of ambulatoryarticial pancreas which was completed recently at theUniversities of Virginia Padova and Montpellier and at theSansumDiabetes Research institute Santa Barbara CAUSAResults from this study are forthcoming

6 Conclusions

Solving the optimization problem of diabetes requiresreplacement of insulin action through insulin injections ororal medications (applicable primarily to type 2 diabetes)which until fully automated closed-loop control becomesavailable would remain a process largely controlled bypatient behavior In engineering terms BG uctuations indiabetes result from the activity of a complex metabolicsystem perturbed by behavioral challenges e frequencyand extent of these challenges and the ability of the personrsquosmetabolic system to absorb them determines the qualityof glycemic control Along with HbA1c the magnitudeand speed of BG uctuations is the primary measurablemarker of glucose control in diabetes ese same quanti-tiesmdashHbA1c and glucose variabilitymdashare also the principalfeedback available to patients to assist with optimization oftheir diabetes control

In the past 30 years the technology for monitoring ofblood glucose levels in diabetes has progressed from assess-ment of average glycemia via HbA1c once in several monthsthrough daily SMBG to minutely continuous glucose mon-itoring e increasing temporal resolution of the moni-toring technology enabled increasingly intensive diabetestreatment from daily insulin injections or oral medicationthrough insulin pump therapy to the articial pancreasis progress is accompanied by increasingly sophisticatedanalytical methods for retrieval of blood glucose data rangingfrom subjective interpretation of glucose values and straight-forward summary statistics through risk and variabilityanalysis to real-time closed-loop control algorithms based oncomplex models of the human metabolism

It is therefore evident that the development of diabetestechnology is accelerating exponentially A primary cata-lyst of this acceleration is unprecedented interdisciplinarycollaboration between physicians chemists engineers andmathematicians As a result a wearable articial pancreassuitable for outpatient use is now within reach

e primary engineering challenges to the widespreadadoption of closed-loop control as a viable therapeutic optionfor diabetes include system connectivity the accuracy ofsubcutaneous glucose sensing and the speed of action ofsubcutaneously injected insulin ese challenges are well

Scientica 9

understood by those working in the eld wireless commu-nication between CGM devices insulin pumps and closed-loop controllers are under development and testing newgenerations of CGM device demonstrate superior accuracyand reliability and new insulin analogs and methods forinsulin delivery are being engineered to approximate asclose as possible the action prole of endogenous insulinIt should be noted however that the signals available toa contemporary closed-loop control system are generallylimited to CGM and insulin delivery data user input aboutcarbohydrate intake and physical activity could be availableas well In contrast the endocrine pancreas receives directand rapid control inputs from other nutrients (eg lipids andamino acids) adjacent cells (somatostatin from the delta cellsand glucagon from alpha cells) incretins and neural signalsus while articial closed-loop control is expected to bevastly superior to the diabetes control methods employed inthe clinical practice today it will continue to be imperfectwhen compared to the natural endocrine regulation of bloodglucose

Acknowledgments

is work was made possible by the JDRF Articial PancreasProject the National Institutes of HealthNIDDK GrantsRO1 DK 51562 and RO1 DK 085623 and by the generoussupport of PBM Science Charlottesville Virginia CA USAand the Frederick Banting Foundation Richmond VirginiaCA USA e author thanks his colleagues at the Universityof Virginia Center forDiabetes Technology for their relentlesswork on articial pancreas development

References

[1] American Diabetes Association ldquoDiagnosis and classicationof diabetes mellitusrdquo Diabetes Care vol 27 pp s5ndashs10 2004

[2] A H Kadish ldquoAutomation control of blood sugarmdashI A ser-vomechanism for glucose monitoring and controlrdquo AmericanJournal of Medical Electronics vol 39 pp 82ndash86 1964

[3] J C Pickup H Keen J A Parsons and K G M M AlbertildquoContinuous subcutaneous insulin infusion an approach toachieving normoglycaemiardquo British Medical Journal vol 1 no6107 pp 204ndash207 1978

[4] W V Tamborlane R S Sherwin M Genel and P FeligldquoReduction to normal of plasma glucose in juvenile diabetes bysubcutaneous administration of insulinwith a portable infusionpumprdquo New England Journal of Medicine vol 300 no 11 pp573ndash578 1979

[5] A M Albisser B S Leibel and T G Ewart ldquoAn articialendocrine pancreasrdquoDiabetes vol 23 no 5 pp 389ndash396 1974

[6] E F Pfeier um Ch and A H Clemens ldquoe articialbeta cell a continuous control of blood sugar by external regu-lation of insulin infusion (glucose controlled insulin infusionsystem)rdquo Hormone and Metabolic Research vol 6 no 5 pp339ndash342 1974

[7] J Mirouze J L Selam T C Pham and D Cavadore ldquoEvalua-tion of exogenous insulin homoeostasis by the artical pancreasin insulin dependent diabetesrdquo Diabetologia vol 13 no 3 pp273ndash278 1977

[8] E W Kraegen L V Campbell and Y O Chia ldquoControlof blood glucose in diabetics using an articial pancreasrdquoAustralian and New Zealand Journal of Medicine vol 7 no 3pp 280ndash286 1977

[9] M Shichiri R Kawamori Y Yamasaki M Inoue Y Shigetaand H Abe ldquoComputer algorithm for the articial pancreaticbeta cellrdquo Articial rgans vol 2 supplement pp 247ndash2501978

[10] A H Clemens P H Chang and R W Myers ldquoe devel-opment of Biostator a Glucose Controlled Insulin InfusionSystem (GCIIS)rdquo Hormone and Metabolic Research vol 7 pp23ndash33 1977

[11] E B Marliss F T Murray and E F Stokes ldquoNormalization ofglycemia in diabetics during meals with insulin and glucagondelivery by the articial pancreasrdquo Diabetes vol 26 no 7 pp663ndash672 1977

[12] J V Santiago A H Clemens W L Clarke and D M KipnisldquoClosed-loop and open-loop devices for blood glucose controlin normal and diabetic subjectsrdquo Diabetes vol 28 no 1 pp71ndash84 1979

[13] U Fischer E Jutzi E J Freyse and E Salzsieder ldquoDerivationand experimental proof of a new algorithm for the articial B-cell based on the individual analysis of the physiological insulin-glucose relationshiprdquo Endokrinologie vol 71 no 1 pp 65ndash751978

[14] R S Parker F J Doyle and N A Peppas ldquoe intravenousroute to blood glucose control a review of control algorithmsfor noninvasive monitoring and regulation in type I diabeticpatientsrdquo IEEE Engineering in Medicine and Biology Magazinevol 20 no 1 pp 65ndash73 2001

[15] R N Bergman Y Z Ider C R Bowden and C CobellildquoQuantitative estimation of insulin sensitivityrdquo e AmericanJournal of Physiology vol 236 no 6 pp E667ndashE677 1979

[16] H M Broekhuyse J D Nelson B Zinman and A M AlbisserldquoComparison of algorithms for the closed-loop control of bloodglucose using the articial beta cellrdquo IEEE Transactions onBiomedical Engineering vol 28 no 10 pp 678ndash687 1981

[17] A H Clemens ldquoFeedback control dynamics for glucosecontrolled insulin infusion systemrdquo Medical Progress throughTechnology vol 6 no 3 pp 91ndash98 1979

[18] C Cobelli and A Ruggeri ldquoEvaluation of portalperipheralroute and of algorithms for insulin delivery in the closed-loop control of glucose in diabetes a modeling studyrdquo IEEETransactions on Biomedical Engineering vol 30 no 2 pp93ndash103 1983

[19] E Salzsieder G Albrecht U Fischer and E J Freyse ldquoKineticmodeling of the glucoregulatory system to improve insulintherapyrdquo IEEE Transactions on Biomedical Engineering vol 32no 10 pp 846ndash855 1985

[20] P Brunetti C Cobelli P Cruciani et al ldquoA simulation study ona self-tuning portable controller of blood glucoserdquo InternationalJournal of Articial rgans vol 16 no 1 pp 51ndash57 1993

[21] U Fischer W Schenk E Salzsieder G Albrecht P Abel andE J Freyse ldquoDoes physiological blood glucose control requirean adaptive control strategyrdquo IEEE Transactions on BiomedicalEngineering vol 34 no 8 pp 575ndash582 1987

[22] J T Sorensen A physiologic model of glucose metabolism inman and its use to design and assess improved insulin therapiesfor diabetes [PhD dissertation] Department of Chemical Engi-neering MIT 1985

[23] R S Parker F J Doyle and N A Peppas ldquoA model-basedalgorithm for blood glucose control in type I diabetic patientsrdquo

10 Scientica

IEEE Transactions on Biomedical Engineering vol 46 no 2 pp148ndash157 1999

[24] J J Mastrototaro ldquoe MiniMed continuous glucose monitor-ing Systemrdquo Diabetes Technology anderapeutics vol 2 no 1pp S13ndashS18 2000

[25] B W Bode ldquoClinical utility of the continuous glucose monitor-ing systemrdquo Diabetes Technology anderapeutics vol 2 no 1supplement pp S35ndashS41 2000

[26] B Feldman R Brazg S Schwartz and R Weinstein ldquoAcontinuous glucose sensor based on wired enzyme technol-ogymdashresults from a 3-day trial in patients with type 1 diabetesrdquoDiabetes Technology anderapeutics vol 5 no 5 pp 769ndash7792003

[27] eDiabetes Control and Complications Trial Research Groupldquoe effect of intensive treatment of diabetes on the develop-ment and progression of long-term complications of insulin-dependent diabetes mellitusrdquoNew England Journal of Medicinevol 329 pp 978ndash986 1993

[28] eDiabetes Control and Complications Trial Research Groupldquoe relationship of glycemic exposure (HbA1c) to the riskof development and progression of retinopathy in the Dia-betes Control and Complications Trialrdquo Diabetes vol 44 pp968ndash983 1995

[29] J M Lachin S Genuth D M Nathan B Zinman and B NRutledge ldquoEffect of glycemic exposure on the risk of microvas-cular complications in the diabetes control and complicationstrial-revisitedrdquo Diabetes vol 57 no 4 pp 995ndash1001 2008

[30] P Reichard and M Pihl ldquoMortality and treatment side-effectsduring long-term intensied conventional insulin treatment inthe Stockholm Diabetes Intervention Studyrdquo Diabetes vol 43no 2 pp 313ndash317 1994

[31] UK Prospective Diabetes Study Group (UKPDS) ldquoIntensiveblood-glucose control with sulphonylureas or insulin comparedwith conventional treatment and risk of complications inpatients with type 2 diabetes (UKPDS 33)rdquo Lancet vol 352 no9131 pp 837ndash853 1998

[32] P A Svendsen T Lauritzen U Soegaard and J NerupldquoGlycosylated haemoglobin and steady-state mean blood glu-cose concentration in type 1 (insulin-dependent) diabetesrdquoDiabetologia vol 23 no 5 pp 403ndash405 1982

[33] J V Santiago ldquoLessons from the diabetes control and compli-cations trialrdquo Diabetes vol 42 no 11 pp 1549ndash1554 1993

[34] eDiabetes Control and Complications Trial Research GroupldquoHypoglycemia in the diabetes control and complications trialrdquoDiabetes vol 46 pp 271ndash286 1997

[35] A E Gold BM Frier KMMacLeod and I J Deary ldquoA struc-tural equation model for predictors of severe hypoglycaemiain patients with insulin-dependent diabetes mellitusrdquo DiabeticMedicine vol 14 pp 309ndash315 1997

[36] D J Cox B P Kovatchev D M Julian et al ldquoFrequency ofsevere hypoglycemia in insulin-dependent diabetesmellitus canbe predicted from self-monitoring blood glucose datardquo Journalof Clinical Endocrinology and Metabolism vol 79 no 6 pp1659ndash1662 1994

[37] B P Kovatchev D J Cox L A Gonder-Frederick D Young-Hyman D Schlundt and W Clarke ldquoAssessment of risk forsevere hypoglycemia among adults with IDDM validation ofthe low blood glucose indexrdquo Diabetes Care vol 21 no 11 pp1870ndash1875 1998

[38] B P Kovatchev D J Cox A Kumar L Gonder-Frederick andW L Clarke ldquoAlgorithmic evaluation of metabolic control and

risk of severe hypoglycemia in type 1 and type 2 diabetes usingself-monitoring blood glucose datardquo Diabetes Technology anderapeutics vol 5 no 5 pp 817ndash828 2003

[39] D J Cox L Gonder-Frederick L Ritterband W Clarke andB P Kovatchev ldquoPrediction of severe hypoglycemiardquo DiabetesCare vol 30 no 6 pp 1370ndash1373 2007

[40] S A Amiel R S Sherwin D C Simonson and W VTamborlane ldquoEffect of intensive insulin therapy on glycemicthresholds for counterregulatory hormone releaserdquo Diabetesvol 37 no 7 pp 901ndash907 1988

[41] S A Amiel W V Tamborlane D C Simonson and RS Sherwin ldquoDefective glucose counterregulation aer strictglycemic control of insulin-dependent diabetes mellitusrdquo NewEngland Journal of Medicine vol 316 no 22 pp 1376ndash13831987

[42] P E Cryer and J E Gerich ldquoGlucose counterregulation hypo-glycemia and intensive insulin therapy in diabetes mellitusrdquoNew England Journal of Medicine vol 313 no 4 pp 232ndash2411985

[43] N H White D A Skor and P E Cryer ldquoIdentication of typeI diabetic patients at increased risk for hypoglycemia duringintensive therapyrdquo New England Journal of Medicine vol 308no 9 pp 485ndash491 1983

[44] P E Cryer ldquoIatrogenic hypoglycemia as a cause ofhypoglycemia-associated autonomic failure in IDDM avicious cyclerdquo Diabetes vol 41 no 3 pp 255ndash260 1992

[45] J N Henderson K V Allen I J Deary and B M FrierldquoHypoglycaemia in insulin-treated Type 2 diabetes frequencysymptoms and impaired awarenessrdquo Diabetic Medicine vol 20no 12 pp 1016ndash1021 2003

[46] P E Cryer Hypoglycemia Pathophysiology Diagnosis andTreatment Oxford University Press New York NY USA 1997

[47] P E Cryer S N Davis and H Shamoon ldquoHypoglycemia indiabetesrdquo Diabetes Care vol 26 no 6 pp 1902ndash1912 2003

[48] B P Childs N G Clark and D J Cox ldquoDening and reportinghypoglycemia in diabetes a report from the American diabetesassociation workgroup on hypoglycemiardquo Diabetes Care vol28 no 5 pp 1245ndash1249 2005

[49] P E Cryer ldquoHypoglycaemia the limiting factor in the glycaemicmanagement of Type I and Type II diabetesrdquo Diabetologia vol45 no 7 pp 937ndash948 2002

[50] P E Cryer ldquoHypoglycemia the limiting factor in the manage-ment of IDDMrdquo Diabetes vol 43 no 11 pp 1378ndash1389 1994

[51] A L McCall and B P Kovatchev ldquoe median is not the onlymessage a clinicianrsquos perspective on mathematical analysis ofglycemic variability and modeling in diabetes mellitusrdquo Journalof Diabetes Science and Technology vol 3 pp 3ndash11 2009

[52] M Brownlee and I B Hirsch ldquoGlycemic variability ahemoglobin A1c-independent risk factor for diabetic compli-cationsrdquo Journal of the American Medical Association vol 295no 14 pp 1707ndash1708 2006

[53] I B Hirsch and M Brownlee ldquoShould minimal blood glucosevariability become the gold standard of glycemic controlrdquoJournal of Diabetes and Its Complications vol 19 no 3 pp178ndash181 2005

[54] K Esposito D Giugliano F Nappo and R Marfella ldquoRegres-sion of carotid atherosclerosis by control of postprandial hyper-glycemia in type 2 diabetes mellitusrdquo Circulation vol 110 no2 pp 214ndash219 2004

[55] S Haffner ldquoe importance of postprandial hyperglycaemia indevelopment of cardiovascular disease in people with diabetes

Scientica 11

pointrdquo International Journal of Clinical Practice no 123 pp24ndash26 2001

[56] LMonnier EMas C Ginet et al ldquoActivation of oxidative stressby acute glucose uctuations compared with sustained chronichyperglycemia in patients with type 2 diabetesrdquo Journal of theAmerican Medical Association vol 295 no 14 pp 1681ndash16872006

[57] T S Temelkova-Kurktschiev C Koehler E Henkel W Leon-hardt K Fuecker and M Hanefeld ldquoPostchallenge plasmaglucose and glycemic spikes are more strongly associated withatherosclerosis than fasting glucose or HbA(1c) levelrdquo DiabetesCare vol 23 no 12 pp 1830ndash1834 2000

[58] W L Clarke D J Cox L Gonder-Frederick D JulianB Kovatchev and D Young-Hyman ldquoBiopsychobehavioralmodel of risk of severe hypoglycemia self-management behav-iorsrdquo Diabetes Care vol 22 no 4 pp 580ndash584 1999

[59] D J Cox L A Gonder-Frederick B P Kovatchev et alldquoBiopsychobehavioral model of severe hypoglycemia II under-standing the risk of severe hypoglycemiardquo Diabetes Care vol22 no 12 pp 2018ndash2025 1999

[60] L Gonder-Frederick D Cox B Kovatchev D Schlundt andW Clarke ldquoA biopsychobehavioral model of risk of severehypoglycemiardquoDiabetes Care vol 20 no 4 pp 661ndash669 1997

[61] B Kovatchev D Cox L Gonder-Frederick D Schlundtand W Clarke ldquoStochastic model of self-regulation decisionmaking exemplied by decisions concerning hypoglycemiardquoHealth Psychology vol 17 no 3 pp 277ndash284 1998

[62] G Nucci and C Cobelli ldquoModels of subcutaneous insulinkinetics A critical reviewrdquo Computer Methods and Programs inBiomedicine vol 62 no 3 pp 249ndash257 2000

[63] M E Wilinska L J Chassin H C Schaller L Schaupp T RPieber and R Hovorka ldquoInsulin kinetics in type-1 diabetescontinuous and bolus delivery of rapid acting insulinrdquo IEEETransactions on Biomedical Engineering vol 52 no 1 pp 3ndash122005

[64] R N Bergman D T Finegood and M Ader ldquoAssessmentof insulin sensitivity in vivordquo Endocrine Reviews vol 236 ppE667ndashE677 1985

[65] R N Bergman ldquoe minimal model of glucose regulation abiographyrdquoAdvances in ExperimentalMedicine and Biology vol537 pp 1ndash19 2003

[66] R N Bergman D J Zaccaro R M Watanabe et al ldquoMinimalmodel-based insulin sensitivity has greater heritability and adifferent genetic basis than homeostasis model assessment orfasting insulinrdquo Diabetes vol 52 no 8 pp 2168ndash2174 2003

[67] A Caumo R N Bergman and C Cobelli ldquoInsulin sensitivityfrom meal tolerance tests in normal subjects a minimal modelindexrdquo Journal of Clinical Endocrinology and Metabolism vol85 no 11 pp 4396ndash4402 2000

[68] J O Clausen K Borch-Johnsen H Ibsen et al ldquoInsulin sen-sitivity index acute insulin response and glucose effectivenessin a population-based sample of 380 young healthy Caucasiansanalysis of the impact of gender body fat physical tness andlife-style factorsrdquo Journal of Clinical Investigation vol 98 no 5pp 1195ndash1209 1996

[69] T C Ni M Ader and R N Bergman ldquoReassessment of glu-cose effectiveness and insulin sensitivity from minimal modelanalysis a theoretical evaluation of the single-compartmentglucose distribution assumptionrdquo Diabetes vol 46 no 11 pp1813ndash1821 1997

[70] S Welch S S P Gebhart R N Bergman and L S PhillipsldquoMinimal model analysis of intravenous glucose tolerance

test-derived insulin sensitivity in diabetic subjectsrdquo Journalof Clinical Endocrinology and Metabolism vol 71 no 6 pp1508ndash1518 1990

[71] D Dawson M A Vincent E J Barrett et al ldquoCapillary recruit-ment in skeletal muscle in response to exercise and hyperin-sulinemia assessed with contrast-enhanced ultrasoundrdquo Amer-ican Journal of Physiology vol 282 no 3 pp E714ndashE720 2002

[72] K J Mikines B Sonne P A Farrell B Tronier and H GalboldquoEffect of physical exercise on sensitivity and responsiveness toinsulin in humansrdquoAmerican Journal of Physiology vol 254 no3 pp E248ndashE259 1988

[73] E A Richter ldquoGlucose utilizationrdquo in Handbook of PhysiologyL B Rowell and J T Shepherd Eds pp 912ndash951 OxfordUniversity Press New York NY USA 1996

[74] D H Wasserman R J Geer D E Rice et al ldquoInteraction ofexercise and insulin action in humansrdquo American Journal ofPhysiology vol 260 no 1 pp E37ndashE45 1991

[75] G Pillonetto A Caumo G Sparacino and C Cobelli ldquoAnew dynamic index of insulin sensitivityrdquo IEEE Transactions onBiomedical Engineering vol 53 no 3 pp 369ndash379 2006

[76] C Dalla Man D M Raimondo R A Rizza and C CobellildquoGIM Simulation soware of meal glucose-insulin modelrdquoJournal of Diabetes Science and Technology vol 1 pp 323ndash3302007

[77] C Dalla Man R A Rizza and C Cobelli ldquoMeal simulationmodel of the glucose-insulin systemrdquo IEEE Transactions onBiomedical Engineering vol 54 no 10 pp 1740ndash1749 2007

[78] W L Clarke D Cox L A Gonder-Frederick W Carter andS L Pohl ldquoEvaluating clinical accuracy of systems for self-monitoring of blood glucoserdquo Diabetes Care vol 10 no 5 pp622ndash628 1987

[79] e Diabetes Research In Children Network (Direcnet) StudyGroup ldquoA multicenter study of the accuracy of the one touchultra home glucose meter in children with type 1 diabetesrdquoDiabetes Technology anderapeutics vol 5 no 6 pp 933ndash9412003

[80] I B Hirsch B W Bode B P Childs et al ldquoSelf-monitoring ofblood glucose (SMBG) in insulin- and non-insulin-using adultswith diabetes consensus recommendations for improvingSMBG accuracy utilization and researchrdquo Diabetes Technologyanderapeutics vol 10 no 6 pp 419ndash439 2008

[81] G Freckmann A Baumstark N Jendrike et al ldquoSystemaccuracy evaluation of 27 blood glucose monitoring systemsaccording to DIN en ISO 15197rdquo Diabetes Technology anderapeutics vol 12 no 3 pp 221ndash231 2010

[82] D Deiss J Bolinder J P Riveline et al ldquoImproved glycemiccontrol in poorly controlled patients with type 1 diabetes usingreal-time continuous glucose monitoringrdquo Diabetes Care vol29 no 12 pp 2730ndash2732 2006

[83] S Garg H Zisser S Schwartz et al ldquoImprovement in glycemicexcursions with a transcutaneous real-time continuous glucosesensor a randomized controlled trialrdquo Diabetes Care vol 29no 1 pp 44ndash50 2006

[84] B P Kovatchev and W L Clarke ldquoContinuous glucose mon-itoring reduces risks for hypo- and hyperglycemia and glucosevariability in diabetesrdquo Diabetes vol 56 1 Article ID 0086OR2007

[85] e Juvenile Diabetes Research Foundation Continuous Glu-cose Monitoring Study Group ldquoContinuous glucose monitor-ing and intensive treatment of type 1 diabetesrdquo New EnglandJournal of Medicine vol 359 pp 1464ndash1476 2008

12 Scientica

[86] D C Klonoff ldquoContinuous glucose monitoring roadmap for21st century diabetes therapyrdquo Diabetes Care vol 28 no 5 pp1231ndash1239 2005

[87] R Hovorka ldquoContinuous glucose monitoring and closed-loopsystemsrdquo Diabetic Medicine vol 23 no 1 pp 1ndash12 2006

[88] D C Klonoff ldquoe articial pancreas how sweet engineeringwill solve bitter problemsrdquo Journal of Diabetes Science andTechnology vol 1 pp 72ndash81 2007

[89] I B Hirsch D Armstrong R M Bergenstal et al ldquoClin-ical application of emerging sensor technologies in diabetesmanagement consensus guidelines for continuous glucosemonitoring (CGM)rdquoDiabetes Technology anderapeutics vol10 no 4 pp 232ndash246 2008

[90] K Rebrin G M Steil W P Van Antwerp and J J Mastro-totaro ldquoSubcutaneous glucose predicts plasma glucose inde-pendent of insulin implications for continuous monitoringrdquoAmerican Journal of Physiology vol 277 no 3 pp E561ndashE5711999

[91] K Rebrin and G M Steil ldquoCan interstitial glucose assessmentreplace blood glucosemeasurementsrdquoDiabetes Technology anderapeutics vol 2 no 3 pp 461ndash472 2000

[92] G M Steil K Rebrin F Hariri et al ldquoInterstitial uid glucosedynamics during insulin-induced hypoglycaemiardquo Diabetolo-gia vol 48 no 9 pp 1833ndash1840 2005

[93] M S Boyne D M Silver J Kaplan and C D Saudek ldquoTimingof changes in interstitial and venous blood glucose measuredwith a continuous subcutaneous glucose sensorrdquo Diabetes vol52 no 11 pp 2790ndash2794 2003

[94] E Kulcu J A Tamada G Reach R O Potts and M JLesho ldquoPhysiological differences between interstitial glucoseand blood glucose measured in human subjectsrdquoDiabetes Carevol 26 no 8 pp 2405ndash2409 2003

[95] P J Stout J R Racchini andM E Hilgers ldquoA novel approach tomitigating the physiological lag between blood and interstitialuid glucose measurementsrdquo Diabetes Technology and era-peutics vol 6 no 5 pp 635ndash644 2004

[96] I M E Wentholt A A M Hart J B L Hoekstra and J HDevries ldquoRelationship between interstitial and blood glucose intype 1 diabetes patients delay and the push-pull phenomenonrevisitedrdquo Diabetes Technology and erapeutics vol 9 no 2pp 169ndash175 2007

[97] K J C Wientjes and A J M Schoonen ldquoDetermination oftime delay between blood and interstitial adipose tissue glucoseconcentration change by microdialysis in healthy volunteersrdquonternational Journal of Articial rgans vol 24 no 12 pp884ndash889 2001

[98] B Aussedat M Dupire-Angel R Gifford J C Klein G SWilson and G Reach ldquoInterstitial glucose concentration andglycemia implications for continuous subcutaneous glucosemonitoringrdquo American Journal of Physiology vol 278 no 4 ppE716ndashE728 2000

[99] B P Kovatchev and W L Clarke ldquoPeculiarities of the con-tinuous glucose monitoring data stream and their impact ondeveloping closed-loop control technologyrdquo Journal of DiabetesScience and Technology vol 2 pp 158ndash163 2008

[100] W L Clarke and B P Kovatchev ldquoContinuous glucose sen-sorsmdashcontinuing questions about clinical accuracyrdquo Journal ofDiabetes Science and Technology vol 1 pp 164ndash170 2007

[101] e Diabetes Research in Children Network (DirecNet) StudyGroup ldquoe accuracy of the guardian RT continuous glucosemonitor in children with type 1 diabetesrdquo Diabetes Technologyanderapeutics vol 10 pp 266ndash272 2008

[102] B Kovatchev S Anderson L Heinemann and W ClarkeldquoComparison of the numerical and clinical accuracy of fourcontinuous glucose monitorsrdquo Diabetes Care vol 31 no 6 pp1160ndash1164 2008

[103] S K Garg J Smith C Beatson B Lopez-Baca M Voelmleand P A Gottlieb ldquoComparison of accuracy and safety ofthe SEVEN and the navigator continuous glucose monitoringsystemsrdquo Diabetes Technology and erapeutics vol 11 no 2pp 65ndash72 2009

[104] T Heise T Koschinsky L Heinemann and V Lodwig ldquoHypo-glycemia warning signal and glucose sensors requirements andconceptsrdquo Diabetes Technology and erapeutics vol 5 no 4pp 563ndash571 2003

[105] B Bode K Gross N Rikalo et al ldquoAlarms based on real-timesensor glucose values alert patients to hypo- and hyperglycemiathe Guardian continuous monitoring systemrdquo Diabetes Tech-nology anderapeutics vol 6 no 2 pp 105ndash113 2004

[106] G McGarraugh and R Bergenstal ldquoDetection of hypoglycemiawith continuous interstitial and traditional blood glucosemonitoring using the FreeStyle navigator continuous glucosemonitoring systemrdquo Diabetes Technology and erapeutics vol11 no 3 pp 145ndash150 2009

[107] S E Noujaim D Horwitz M Sharma and J Marhoul ldquoAccu-racy requirements for a hypoglycemia detector an analyticalmodel to evaluate the effects of bias precision and rate ofglucose changerdquo Journal of Diabetes Science and Technology vol1 pp 653ndash668 2007

[108] W K Ward ldquoe role of new technology in the early detectionof hypoglycemiardquo Diabetes Technology and erapeutics vol 6no 2 pp 115ndash117 2004

[109] B Buckingham E Cobry P Clinton et al ldquoPreventing hypo-glycemia using predictive alarm algorithms and insulin pumpsuspensionrdquo Diabetes Technology and erapeutics vol 11 no2 pp 93ndash97 2009

[110] C S Hughes S D Patek M D Breton and B P KovatchevldquoHypoglycemia prevention via pump attenuation and red-yellow-green ldquotrafficrdquo lights using continuous glucose moni-toring and insulin pump datardquo Journal of Diabetes Science andTechnology vol 4 no 5 pp 1146ndash1155 2010

[111] B P Kovatchev D J Cox L A Gonder-Frederick and WClarke ldquoSymmetrization of the blood glucose measurementscale and its applicationsrdquo Diabetes Care vol 20 no 11 pp1655ndash1658 1997

[112] F J Service G D Molnar J W Rosevear E Ackerman LC Gatewood and W F Taylor ldquoMean amplitude of glycemicexcursions a measure of diabetic instabilityrdquo Diabetes vol 19no 9 pp 644ndash655 1970

[113] J Schlichtkrull O Munck and M Jersild ldquoe M-value anindex of blood glucose control in diabeticsrdquo Acta MedicaScandinavica vol 177 pp 95ndash102 1965

[114] E A Ryan T Shandro K Green et al ldquoAssessment of theseverity of hypoglycemia and glycemic lambility in type 1diabetic subjects undergoing islet in transplantationrdquo Diabetesvol 53 no 4 pp 955ndash962 2004

[115] B P Kovatchev D J Cox L Gonder-Frederick and WL Clarke ldquoMethods for quantifying self-monitoring bloodglucose proles exemplied by an examination of blood glucosepatterns in patients with type 1 and type 2 diabetesrdquo DiabetesTechnology anderapeutics vol 4 no 3 pp 295ndash303 2002

[116] B P Kovatchev D J Cox L S Farhy M Straume L Gonder-Frederick and W L Clarke ldquoEpisodes of severe hypoglycemia

Scientica 13

in type 1 diabetes are preceded and followed within 48 hours bymeasurable disturbances in blood glucoserdquo Journal of ClinicalEndocrinology and Metabolism vol 85 no 11 pp 4287ndash42922000

[117] B P Kovatchev M Straume D J Cox and L S FarhyldquoRisk analysis of blood glucose data a quantitative approach tooptimizing the control of Insulin Dependent Diabetesrdquo Journalof eoretical Medicine vol 3 no 1 pp 1ndash10 2000

[118] B P Kovatchev E Otto D Cox L Gonder-Frederick andW Clarke ldquoEvaluation of a new measure of blood glucosevariability in diabetesrdquo Diabetes Care vol 29 no 11 pp2433ndash2438 2006

[119] B P Kovatchev W L Clarke M Breton K Brayman and AMcCall ldquoQuantifying temporal glucose variability in diabetesvia continuous glucose monitoring mathematical methods andclinical applicationrdquo Diabetes Technology and erapeutics vol7 no 6 pp 849ndash862 2005

[120] D Rodbard ldquoNew and improved methods to characterizeglycemic variability using continuous glucosemonitoringrdquoDia-betes Technology and erapeutics vol 11 no 9 pp 551ndash5652009

[121] M Miller and P Strange ldquoUse of Fourier models for analysisand interpretation of continuous glucose monitoring glucoseprolesrdquo Journal of Diabetes Science and Technology vol 1 pp630ndash638 2007

[122] W Clarke and B Kovatchev ldquoStatistical tools to analyzecontinuous glucose monitor datardquo Diabetes Technology anderapeutics vol 11 pp S45ndashS54 2009

[123] C M Mcdonnell S M Donath S I Vidmar G A Wertherand F J Cameron ldquoA novel approach to continuous glucoseanalysis utilizing glycemic variationrdquo Diabetes Technology anderapeutics vol 7 no 2 pp 253ndash263 2005

[124] G Sparacino F Zanderigo A Maran and C Cobelli ldquoContin-uous glucosemonitoring andhypohyperglycaemia predictionrdquoDiabetes Research and Clinical Practice vol 74 no 2 supple-ment pp S160ndashS163 2006

[125] G Sparacino F Zanderigo G Corazza A Maran AFacchinetti and C Cobelli ldquoGlucose concentration can bepredicted ahead in time from continuous glucose monitoringsensor time-seriesrdquo IEEE Transactions on Biomedical Engineer-ing vol 54 pp 931ndash937 2007

[126] J Reifman S Rajaraman A Gribok and W K Ward ldquoPredic-tive monitoring for improved management of glucose levelsrdquoJournal of Diabetes Science and Technology vol 1 pp 478ndash4862007

[127] F Zanderigo G Sparacino B Kovatchev and C CobellildquoGlucose prediction algorithms from continuous monitoringassessment of accuracy via continuous glucose-error grid anal-ysisrdquo Journal of Diabetes Science and Technology vol 1 pp645ndash651 2007

[128] F Cameron G Niemayer K Gundy-Burlet and B Bucking-ham ldquoStatistical hypoglycemia predictionrdquo Journal of DiabetesScience and Technology vol 2 pp 612ndash621 2008

[129] A Gani A V Gribok S Rajaraman W K Ward and JReifman ldquoPredicting subcutaneous glucose concentration inhumans data-driven glucose modelingrdquo IEEE Transactions onBiomedical Engineering vol 56 no 2 pp 246ndash254 2009

[130] M Eren-Oruklu A Cinar L Quinn andD Smith ldquoEstimationof future glucose concentrations with subject-specic recursivelinear modelsrdquo Diabetes Technology and erapeutics vol 11no 4 pp 243ndash253 2009

[131] S M Pappada B D Cameron and P M Rosman ldquoDevelop-ment of a neural network for prediction of glucose concentra-tion in type 1 diabetes patientsrdquo Journal of Diabetes Science andTechnology vol 2 pp 792ndash801 2008

[132] C Cobelli C Dalla Man G Sparacino L Magni G Nicolaoand B P Kovatchev ldquoDiabetes models signals and controlrdquoIEEE Reviews in Biomedical Engineering vol 2 pp 54ndash96 2009

[133] H LeBlanc D Chauvet P Lombrail and J J Robert ldquoGlycemiccontrol with closed-loop intraperitoneal insulin in type Idiabetesrdquo Diabetes Care vol 9 no 2 pp 124ndash128 1986

[134] C D Saudek J L Selman H A Pitt et al ldquoA preliminary trialof the programmable implantablemedication system for insulindeliveryrdquo New England Journal of Medicine vol 321 no 9 pp574ndash579 1989

[135] C Broussolle N Jeandidier and H Hanaire-Broutin ldquoFrenchmulticentre experience of implantable insulin pumpsrdquo Lancetvol 343 no 8896 pp 514ndash515 1994

[136] H Hanaire-Broutin C Broussolle N Jeandidier et al ldquoFea-sibility of intraperitoneal insulin therapy with programmableimplantable pumps in IDDM a multicenter studyrdquo DiabetesCare vol 18 no 3 pp 388ndash392 1995

[137] P R Oskarsson P E Lins H W Henriksson and U CAdamson ldquoMetabolic and hormonal responses to exercisein type 1 diabetic patients during continuous subcutaneousas compared to continuous intraperitoneal insulin infusionrdquoDiabetes and Metabolism vol 25 no 6 pp 491ndash497 1999

[138] P R Oskarsson P E Lins L Backman and U C AdamsonldquoContinuous intraperitoneal insulin infusion partly restores theglucagon response to hypoglycaemia in type 1 diabetic patientsrdquoDiabetes and Metabolism vol 26 no 2 pp 118ndash124 2000

[139] B Catargi L Meyer V Melki E Renard and N JeandidierldquoComparison of blood glucose stability and HbA1C betweenimplantable insulin pumps using U400 hoe 21pH insulin andexternal pumps using lispro in type 1 diabetic patients a pilotstudyrdquo Diabetes and Metabolism vol 28 no 2 pp 133ndash1372002

[140] E Renard ldquoImplantable closed-loop glucose-sensing andinsulin delivery the future for insulin pump therapyrdquo CurrentOpinion in Pharmacology vol 2 no 6 pp 708ndash716 2002

[141] E Renard G Costalat H Chevassus and J Bringer ldquoArticial120573120573-cell clinical experience toward an implantable closed-loopinsulin delivery systemrdquo Diabetes and Metabolism vol 32 no5 pp 497ndash502 2006

[142] E Renard J Place M Cantwell H Chevassus and C CPalerm ldquoClosed-loop insulin delivery using a subcutaneousglucose sensor and intraperitoneal insulin delivery feasibilitystudy testing a new model for the articial pancreasrdquo DiabetesCare vol 33 no 1 pp 121ndash127 2010

[143] R Bellazzi G Nucci and C Cobelli ldquoe subcutaneousroute to insulin-dependent diabetes therapy closed-loop andpartially closed-loop control strategies for insulin deliveryand measuring glucose concentrationrdquo IEEE Engineering inMedicine and Biology Magazine vol 20 no 1 pp 54ndash64 2001

[144] R Hovorka L J Chassin M E Wilinska et al ldquoClosingthe loop the adicol experiencerdquo Diabetes Technology anderapeutics vol 6 no 3 pp 307ndash318 2004

[145] R Hovorka V Canonico L J Chassin et al ldquoNonlinear modelpredictive control of glucose concentration in subjects withtype 1 diabetesrdquo Physiological Measurement vol 25 no 4 pp905ndash920 2004

14 Scientica

[146] G M Steil K Rebrin C Darwin F Hariri and M F SaadldquoFeasibility of automating insulin delivery for the treatment oftype 1 diabetesrdquo Diabetes vol 55 no 12 pp 3344ndash3350 2006

[147] e JDRF e-Newsletter Emerging Technologies in DiabetesResearch 2006

[148] W L Clarke and B Kovatchev ldquoe articial pancreas howclose are we to closing the looprdquo Pediatric EndocrinologyReviews vol 4 no 4 pp 314ndash316 2007

[149] C Cobelli E Renard and B P Kovatchev ldquoPerspectives indiabetes articial pancreas past present futurerdquo Diabetes vol60 pp 2672ndash2682 2011

[150] B P Kovatchev M D Breton C Dalla Man and C Cobelli ldquoInsilico preclinical trials a proof of concept in closed-loop controlof type 1 diabetesrdquo Journal of Diabetes Science and Technologyvol 3 pp 44ndash55 2009

[151] B Kovatchev C Cobelli E Renard et al ldquoMultinational studyof subcutaneous model-predictive closed-loop control in type1 diabetes mellitus summary of the resultsrdquo Journal of DiabetesScience and Technology vol 4 no 6 pp 1374ndash1381 2010

[152] H Zisser L Jovanovic F Doyle P Ospina and C OwensldquoRun-to-run control of meal-related insulin dosingrdquo DiabetesTechnology anderapeutics vol 7 no 1 pp 48ndash57 2005

[153] C Owens H Zisser L Jovanovic B Srinivasan D Bonvinand F J Doyle III ldquoRun-to-run control of blood glucoseconcentrations for people with type 1 diabetes mellitusrdquo IEEETransactions on Biomedical Engineering vol 53 no 6 pp996ndash1005 2006

[154] C C Palerm H Zisser W C Bevier L Jovanovič and F JDoyle III ldquoPrandial insulin dosing using run-to-run controlapplication of clinical data and medical expertise to dene asuitable performance metricrdquo Diabetes Care vol 30 no 5 pp1131ndash1136 2007

[155] LMagni F Raimondo L Bossi et al ldquoModel predictive controlof type 1 diabetes an in silico trialrdquo Journal of Diabetes Scienceand Technology vol 1 pp 804ndash812 2007

[156] S A Weinzimer G M Steil K L Swan J Dziura N Kurtzand W V Tamborlane ldquoFully automated closed-loop insulindelivery versus semiautomated hybrid control in pediatricpatients with type 1 diabetes using an articial pancreasrdquoDiabetes Care vol 31 no 5 pp 934ndash939 2008

[157] W L Clarke S Anderson M Breton S Patek L Kashmerand B Kovatchev ldquoClosed-loop articial pancreas using sub-cutaneous glucose sensing and insulin delivery and a modelpredictive control algorithm the Virginia experiencerdquo Journalof Diabetes Science and Technology vol 3 no 5 pp 1031ndash10382009

[158] D Bruttomesso A Farret S Costa et al ldquoClosed-loop arti-cial pancreas using subcutaneous glucose sensing and insulindelivery and a model predictive control algorithm preliminarystudies in Padova and Montpellierrdquo Journal of Diabetes Scienceand Technology vol 3 no 5 pp 1014ndash1021 2009

[159] R Hovorka J M Allen D Elleri et al ldquoManual closed-loop insulin delivery in children and adolescents with type 1diabetes a phase 2 randomised crossover trialrdquoe Lancet vol375 no 9716 pp 743ndash751 2010

[160] F H El-khatib S J Russell D M Nathan R G Sutherlin andE R Damiano ldquoA bihormonal closed-loop articial pancreasfor type 1 diabetesrdquo Science Translational Medicine vol 2 no27 Article ID 27ra27 2010

[161] E Dassau H Zisser C C Palerm B A Buckingham LJovanovič and F J Doye III ldquoModular articial 120573120573-cell system a

prototype for clinical researchrdquo Journal of Diabetes Science andTechnology vol 2 pp 863ndash872 2008

[162] B Kovatchev S Patek E Dassau et al ldquoControl to range fordiabetes functionality and modular architecturerdquo Journal ofDiabetes Science and Rechnology vol 3 no 5 pp 1058ndash10652009

[163] E Atlas R Nimri S Miller E A Grunberg and M PhillipldquoMD-logic articial pancreas system a pilot study in adults withtype 1 diabetesrdquo Diabetes Care vol 33 no 5 pp 1072ndash10762010

[164] E Dassau H Zisser M W Percival B Grosman L Jovanovičand F J Doyle III ldquoClinical results of automated articialpancreatic 120573120573-cell system with unannounced meal using multi-parametric MPC and insulin-on-boardrdquo in Proceedings of the70th American Diabetes Association Meeting Diabetes vol 59supplement 1 A94 Orlando Fla USA 2010

[165] E M Renard A Farret J Place C Cobelli B P Kovatchev andMD Breton ldquoClosed-loop insulin delivery using subcutaneousinfusion and glucose sensing and equipped with a dedicatedsafety supervision algorithm improves safety of glucose controlin type 1 diabetesrdquo Diabetologia vol 53 supplement 1 p S252010

[166] R Hovorka K Kumareswaran J Harris et al ldquoOvernightclosed loop insulin delivery articial pancreas in adults withtype 1 diabetes crossover randomized controlled studiesrdquoBritish Medical Journal vol 342 no 7803 Article ID d18552011

[167] H Zisser E Dassau W Bevier et al ldquoInitial evaluation ofa fully automated articial pancreasrdquo in Proceedings of the71st American Diabetes Association Meeting Diabetes vol 60supplement 1 A41 San Diego Calif USA 2011

[168] M D Breton A Farret and D Bruttomesso ldquoFully-integratedarticial pancreas in type 1 diabetes modular closed-loopglucose control maintains near-normoglycemiardquo Diabetes vol61 pp 2230ndash2237 2012

[169] C Cobelli E Renard and B P Kovatchev ldquoPilot studies ofwearable articial pancreas in type 1 diabetesrdquo Diabetes Carevol 35 no 9 pp e65ndashe67 2012

Submit your manuscripts athttpwwwhindawicom

Stem CellsInternational

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Behavioural Neurology

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Disease Markers

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

OncologyJournal of

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Oxidative Medicine and Cellular Longevity

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PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

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Computational and Mathematical Methods in Medicine

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Research and TreatmentAIDS

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Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

8 Scientica

of algorithms (known as run-to-run control) can ldquolearnrdquospecics of patientsrsquo daily routine (eg timing of meals) andthen optimize the response to a subsequent meal using thisinformation or account for circadian uctuation in insulinresistance (eg dawn phenomenon observed in some people)[149]

In 2008 a universal research platformmdashthe APSmdashwasintroduced enabling automated communication betweenseveral CGM devices insulin pumps and control algorithms[161] e APS was very instrumental for a number ofinpatient trials of closed-loop control A year later a mod-ular architecture was introduced proposing standardizationsequential testing and clinical deployment of articial pan-creas components [162]

55 Inpatient Clinical Trials Between 2008 and 2011 prom-ising results were reported by several groups [156ndash160 163ndash167] Most of these studies pointed out the superiority ofclosed-loop control over standard CSII therapy in termsof (i) increased time within target glucose range (typically39ndash10mmoll) (ii) reduced incidence of hypoglycemia and(iii) better overnight control Two of these studies [159166] had state-of-the-art randomized cross-over design butlacked automated data transfermdashall CGM readings weretransferred to the controllermanually by the study personneland all insulin pump commands were entered manually aswell To distinguish the various degrees of automation inclosed-loop studies the notion of fully-integrated closed-loop control emerged dened as having all of the followingthree components (i) automated data transfer from theCGM to the controller (ii) real-time control action and (iii)automated command of the insulin pump e rst (andthe largest to date) randomized cross-over study of fully-integrated closed-loop control was published in 2012 [168]However even this contemporary trial of fully automatedCLC which enrolled 38 patients with T1D at three centersand tested two different control algorithms achieving note-worthy glycemic control and prevention of hypoglycemia didnot leave the clinical setting e technology used by thisstudy was still based on a laptop computer wired to a CGMand an insulin pump a system limiting the free movementof the study subjects and too cumbersome to be used beyondhospital connes

5 earale tpatient rticial ancreas e transitionof closed-loop control to ambulatory use began in 2011 withthe development of the Diabetes Assistant (DiAs)mdashthe rstwearable articial pancreas platform based on a smart phonee design characteristics of DiAs included the following

(i) based on readily available inexpensive wearablehardware platform

(ii) computationally capable of running advanced closed-loop control algorithms

(iii) wirelessly connectable to CGM devices and insulinpumps

(iv) capable of broadband communication with a centrallocation for remote monitoring and safety supervi-sion of the participants in outpatient clinical trials

In ctober 2011 the rst two pilot trials of wearableoutpatient articial pancreas were performed simultaneouslyin Padova (Italy) and Montpellier (France) [169] ese 2-day trials allowed the renement of a wearable system andenabled a subsequentmultisite feasibility study of ambulatoryarticial pancreas which was completed recently at theUniversities of Virginia Padova and Montpellier and at theSansumDiabetes Research institute Santa Barbara CAUSAResults from this study are forthcoming

6 Conclusions

Solving the optimization problem of diabetes requiresreplacement of insulin action through insulin injections ororal medications (applicable primarily to type 2 diabetes)which until fully automated closed-loop control becomesavailable would remain a process largely controlled bypatient behavior In engineering terms BG uctuations indiabetes result from the activity of a complex metabolicsystem perturbed by behavioral challenges e frequencyand extent of these challenges and the ability of the personrsquosmetabolic system to absorb them determines the qualityof glycemic control Along with HbA1c the magnitudeand speed of BG uctuations is the primary measurablemarker of glucose control in diabetes ese same quanti-tiesmdashHbA1c and glucose variabilitymdashare also the principalfeedback available to patients to assist with optimization oftheir diabetes control

In the past 30 years the technology for monitoring ofblood glucose levels in diabetes has progressed from assess-ment of average glycemia via HbA1c once in several monthsthrough daily SMBG to minutely continuous glucose mon-itoring e increasing temporal resolution of the moni-toring technology enabled increasingly intensive diabetestreatment from daily insulin injections or oral medicationthrough insulin pump therapy to the articial pancreasis progress is accompanied by increasingly sophisticatedanalytical methods for retrieval of blood glucose data rangingfrom subjective interpretation of glucose values and straight-forward summary statistics through risk and variabilityanalysis to real-time closed-loop control algorithms based oncomplex models of the human metabolism

It is therefore evident that the development of diabetestechnology is accelerating exponentially A primary cata-lyst of this acceleration is unprecedented interdisciplinarycollaboration between physicians chemists engineers andmathematicians As a result a wearable articial pancreassuitable for outpatient use is now within reach

e primary engineering challenges to the widespreadadoption of closed-loop control as a viable therapeutic optionfor diabetes include system connectivity the accuracy ofsubcutaneous glucose sensing and the speed of action ofsubcutaneously injected insulin ese challenges are well

Scientica 9

understood by those working in the eld wireless commu-nication between CGM devices insulin pumps and closed-loop controllers are under development and testing newgenerations of CGM device demonstrate superior accuracyand reliability and new insulin analogs and methods forinsulin delivery are being engineered to approximate asclose as possible the action prole of endogenous insulinIt should be noted however that the signals available toa contemporary closed-loop control system are generallylimited to CGM and insulin delivery data user input aboutcarbohydrate intake and physical activity could be availableas well In contrast the endocrine pancreas receives directand rapid control inputs from other nutrients (eg lipids andamino acids) adjacent cells (somatostatin from the delta cellsand glucagon from alpha cells) incretins and neural signalsus while articial closed-loop control is expected to bevastly superior to the diabetes control methods employed inthe clinical practice today it will continue to be imperfectwhen compared to the natural endocrine regulation of bloodglucose

Acknowledgments

is work was made possible by the JDRF Articial PancreasProject the National Institutes of HealthNIDDK GrantsRO1 DK 51562 and RO1 DK 085623 and by the generoussupport of PBM Science Charlottesville Virginia CA USAand the Frederick Banting Foundation Richmond VirginiaCA USA e author thanks his colleagues at the Universityof Virginia Center forDiabetes Technology for their relentlesswork on articial pancreas development

References

[1] American Diabetes Association ldquoDiagnosis and classicationof diabetes mellitusrdquo Diabetes Care vol 27 pp s5ndashs10 2004

[2] A H Kadish ldquoAutomation control of blood sugarmdashI A ser-vomechanism for glucose monitoring and controlrdquo AmericanJournal of Medical Electronics vol 39 pp 82ndash86 1964

[3] J C Pickup H Keen J A Parsons and K G M M AlbertildquoContinuous subcutaneous insulin infusion an approach toachieving normoglycaemiardquo British Medical Journal vol 1 no6107 pp 204ndash207 1978

[4] W V Tamborlane R S Sherwin M Genel and P FeligldquoReduction to normal of plasma glucose in juvenile diabetes bysubcutaneous administration of insulinwith a portable infusionpumprdquo New England Journal of Medicine vol 300 no 11 pp573ndash578 1979

[5] A M Albisser B S Leibel and T G Ewart ldquoAn articialendocrine pancreasrdquoDiabetes vol 23 no 5 pp 389ndash396 1974

[6] E F Pfeier um Ch and A H Clemens ldquoe articialbeta cell a continuous control of blood sugar by external regu-lation of insulin infusion (glucose controlled insulin infusionsystem)rdquo Hormone and Metabolic Research vol 6 no 5 pp339ndash342 1974

[7] J Mirouze J L Selam T C Pham and D Cavadore ldquoEvalua-tion of exogenous insulin homoeostasis by the artical pancreasin insulin dependent diabetesrdquo Diabetologia vol 13 no 3 pp273ndash278 1977

[8] E W Kraegen L V Campbell and Y O Chia ldquoControlof blood glucose in diabetics using an articial pancreasrdquoAustralian and New Zealand Journal of Medicine vol 7 no 3pp 280ndash286 1977

[9] M Shichiri R Kawamori Y Yamasaki M Inoue Y Shigetaand H Abe ldquoComputer algorithm for the articial pancreaticbeta cellrdquo Articial rgans vol 2 supplement pp 247ndash2501978

[10] A H Clemens P H Chang and R W Myers ldquoe devel-opment of Biostator a Glucose Controlled Insulin InfusionSystem (GCIIS)rdquo Hormone and Metabolic Research vol 7 pp23ndash33 1977

[11] E B Marliss F T Murray and E F Stokes ldquoNormalization ofglycemia in diabetics during meals with insulin and glucagondelivery by the articial pancreasrdquo Diabetes vol 26 no 7 pp663ndash672 1977

[12] J V Santiago A H Clemens W L Clarke and D M KipnisldquoClosed-loop and open-loop devices for blood glucose controlin normal and diabetic subjectsrdquo Diabetes vol 28 no 1 pp71ndash84 1979

[13] U Fischer E Jutzi E J Freyse and E Salzsieder ldquoDerivationand experimental proof of a new algorithm for the articial B-cell based on the individual analysis of the physiological insulin-glucose relationshiprdquo Endokrinologie vol 71 no 1 pp 65ndash751978

[14] R S Parker F J Doyle and N A Peppas ldquoe intravenousroute to blood glucose control a review of control algorithmsfor noninvasive monitoring and regulation in type I diabeticpatientsrdquo IEEE Engineering in Medicine and Biology Magazinevol 20 no 1 pp 65ndash73 2001

[15] R N Bergman Y Z Ider C R Bowden and C CobellildquoQuantitative estimation of insulin sensitivityrdquo e AmericanJournal of Physiology vol 236 no 6 pp E667ndashE677 1979

[16] H M Broekhuyse J D Nelson B Zinman and A M AlbisserldquoComparison of algorithms for the closed-loop control of bloodglucose using the articial beta cellrdquo IEEE Transactions onBiomedical Engineering vol 28 no 10 pp 678ndash687 1981

[17] A H Clemens ldquoFeedback control dynamics for glucosecontrolled insulin infusion systemrdquo Medical Progress throughTechnology vol 6 no 3 pp 91ndash98 1979

[18] C Cobelli and A Ruggeri ldquoEvaluation of portalperipheralroute and of algorithms for insulin delivery in the closed-loop control of glucose in diabetes a modeling studyrdquo IEEETransactions on Biomedical Engineering vol 30 no 2 pp93ndash103 1983

[19] E Salzsieder G Albrecht U Fischer and E J Freyse ldquoKineticmodeling of the glucoregulatory system to improve insulintherapyrdquo IEEE Transactions on Biomedical Engineering vol 32no 10 pp 846ndash855 1985

[20] P Brunetti C Cobelli P Cruciani et al ldquoA simulation study ona self-tuning portable controller of blood glucoserdquo InternationalJournal of Articial rgans vol 16 no 1 pp 51ndash57 1993

[21] U Fischer W Schenk E Salzsieder G Albrecht P Abel andE J Freyse ldquoDoes physiological blood glucose control requirean adaptive control strategyrdquo IEEE Transactions on BiomedicalEngineering vol 34 no 8 pp 575ndash582 1987

[22] J T Sorensen A physiologic model of glucose metabolism inman and its use to design and assess improved insulin therapiesfor diabetes [PhD dissertation] Department of Chemical Engi-neering MIT 1985

[23] R S Parker F J Doyle and N A Peppas ldquoA model-basedalgorithm for blood glucose control in type I diabetic patientsrdquo

10 Scientica

IEEE Transactions on Biomedical Engineering vol 46 no 2 pp148ndash157 1999

[24] J J Mastrototaro ldquoe MiniMed continuous glucose monitor-ing Systemrdquo Diabetes Technology anderapeutics vol 2 no 1pp S13ndashS18 2000

[25] B W Bode ldquoClinical utility of the continuous glucose monitor-ing systemrdquo Diabetes Technology anderapeutics vol 2 no 1supplement pp S35ndashS41 2000

[26] B Feldman R Brazg S Schwartz and R Weinstein ldquoAcontinuous glucose sensor based on wired enzyme technol-ogymdashresults from a 3-day trial in patients with type 1 diabetesrdquoDiabetes Technology anderapeutics vol 5 no 5 pp 769ndash7792003

[27] eDiabetes Control and Complications Trial Research Groupldquoe effect of intensive treatment of diabetes on the develop-ment and progression of long-term complications of insulin-dependent diabetes mellitusrdquoNew England Journal of Medicinevol 329 pp 978ndash986 1993

[28] eDiabetes Control and Complications Trial Research Groupldquoe relationship of glycemic exposure (HbA1c) to the riskof development and progression of retinopathy in the Dia-betes Control and Complications Trialrdquo Diabetes vol 44 pp968ndash983 1995

[29] J M Lachin S Genuth D M Nathan B Zinman and B NRutledge ldquoEffect of glycemic exposure on the risk of microvas-cular complications in the diabetes control and complicationstrial-revisitedrdquo Diabetes vol 57 no 4 pp 995ndash1001 2008

[30] P Reichard and M Pihl ldquoMortality and treatment side-effectsduring long-term intensied conventional insulin treatment inthe Stockholm Diabetes Intervention Studyrdquo Diabetes vol 43no 2 pp 313ndash317 1994

[31] UK Prospective Diabetes Study Group (UKPDS) ldquoIntensiveblood-glucose control with sulphonylureas or insulin comparedwith conventional treatment and risk of complications inpatients with type 2 diabetes (UKPDS 33)rdquo Lancet vol 352 no9131 pp 837ndash853 1998

[32] P A Svendsen T Lauritzen U Soegaard and J NerupldquoGlycosylated haemoglobin and steady-state mean blood glu-cose concentration in type 1 (insulin-dependent) diabetesrdquoDiabetologia vol 23 no 5 pp 403ndash405 1982

[33] J V Santiago ldquoLessons from the diabetes control and compli-cations trialrdquo Diabetes vol 42 no 11 pp 1549ndash1554 1993

[34] eDiabetes Control and Complications Trial Research GroupldquoHypoglycemia in the diabetes control and complications trialrdquoDiabetes vol 46 pp 271ndash286 1997

[35] A E Gold BM Frier KMMacLeod and I J Deary ldquoA struc-tural equation model for predictors of severe hypoglycaemiain patients with insulin-dependent diabetes mellitusrdquo DiabeticMedicine vol 14 pp 309ndash315 1997

[36] D J Cox B P Kovatchev D M Julian et al ldquoFrequency ofsevere hypoglycemia in insulin-dependent diabetesmellitus canbe predicted from self-monitoring blood glucose datardquo Journalof Clinical Endocrinology and Metabolism vol 79 no 6 pp1659ndash1662 1994

[37] B P Kovatchev D J Cox L A Gonder-Frederick D Young-Hyman D Schlundt and W Clarke ldquoAssessment of risk forsevere hypoglycemia among adults with IDDM validation ofthe low blood glucose indexrdquo Diabetes Care vol 21 no 11 pp1870ndash1875 1998

[38] B P Kovatchev D J Cox A Kumar L Gonder-Frederick andW L Clarke ldquoAlgorithmic evaluation of metabolic control and

risk of severe hypoglycemia in type 1 and type 2 diabetes usingself-monitoring blood glucose datardquo Diabetes Technology anderapeutics vol 5 no 5 pp 817ndash828 2003

[39] D J Cox L Gonder-Frederick L Ritterband W Clarke andB P Kovatchev ldquoPrediction of severe hypoglycemiardquo DiabetesCare vol 30 no 6 pp 1370ndash1373 2007

[40] S A Amiel R S Sherwin D C Simonson and W VTamborlane ldquoEffect of intensive insulin therapy on glycemicthresholds for counterregulatory hormone releaserdquo Diabetesvol 37 no 7 pp 901ndash907 1988

[41] S A Amiel W V Tamborlane D C Simonson and RS Sherwin ldquoDefective glucose counterregulation aer strictglycemic control of insulin-dependent diabetes mellitusrdquo NewEngland Journal of Medicine vol 316 no 22 pp 1376ndash13831987

[42] P E Cryer and J E Gerich ldquoGlucose counterregulation hypo-glycemia and intensive insulin therapy in diabetes mellitusrdquoNew England Journal of Medicine vol 313 no 4 pp 232ndash2411985

[43] N H White D A Skor and P E Cryer ldquoIdentication of typeI diabetic patients at increased risk for hypoglycemia duringintensive therapyrdquo New England Journal of Medicine vol 308no 9 pp 485ndash491 1983

[44] P E Cryer ldquoIatrogenic hypoglycemia as a cause ofhypoglycemia-associated autonomic failure in IDDM avicious cyclerdquo Diabetes vol 41 no 3 pp 255ndash260 1992

[45] J N Henderson K V Allen I J Deary and B M FrierldquoHypoglycaemia in insulin-treated Type 2 diabetes frequencysymptoms and impaired awarenessrdquo Diabetic Medicine vol 20no 12 pp 1016ndash1021 2003

[46] P E Cryer Hypoglycemia Pathophysiology Diagnosis andTreatment Oxford University Press New York NY USA 1997

[47] P E Cryer S N Davis and H Shamoon ldquoHypoglycemia indiabetesrdquo Diabetes Care vol 26 no 6 pp 1902ndash1912 2003

[48] B P Childs N G Clark and D J Cox ldquoDening and reportinghypoglycemia in diabetes a report from the American diabetesassociation workgroup on hypoglycemiardquo Diabetes Care vol28 no 5 pp 1245ndash1249 2005

[49] P E Cryer ldquoHypoglycaemia the limiting factor in the glycaemicmanagement of Type I and Type II diabetesrdquo Diabetologia vol45 no 7 pp 937ndash948 2002

[50] P E Cryer ldquoHypoglycemia the limiting factor in the manage-ment of IDDMrdquo Diabetes vol 43 no 11 pp 1378ndash1389 1994

[51] A L McCall and B P Kovatchev ldquoe median is not the onlymessage a clinicianrsquos perspective on mathematical analysis ofglycemic variability and modeling in diabetes mellitusrdquo Journalof Diabetes Science and Technology vol 3 pp 3ndash11 2009

[52] M Brownlee and I B Hirsch ldquoGlycemic variability ahemoglobin A1c-independent risk factor for diabetic compli-cationsrdquo Journal of the American Medical Association vol 295no 14 pp 1707ndash1708 2006

[53] I B Hirsch and M Brownlee ldquoShould minimal blood glucosevariability become the gold standard of glycemic controlrdquoJournal of Diabetes and Its Complications vol 19 no 3 pp178ndash181 2005

[54] K Esposito D Giugliano F Nappo and R Marfella ldquoRegres-sion of carotid atherosclerosis by control of postprandial hyper-glycemia in type 2 diabetes mellitusrdquo Circulation vol 110 no2 pp 214ndash219 2004

[55] S Haffner ldquoe importance of postprandial hyperglycaemia indevelopment of cardiovascular disease in people with diabetes

Scientica 11

pointrdquo International Journal of Clinical Practice no 123 pp24ndash26 2001

[56] LMonnier EMas C Ginet et al ldquoActivation of oxidative stressby acute glucose uctuations compared with sustained chronichyperglycemia in patients with type 2 diabetesrdquo Journal of theAmerican Medical Association vol 295 no 14 pp 1681ndash16872006

[57] T S Temelkova-Kurktschiev C Koehler E Henkel W Leon-hardt K Fuecker and M Hanefeld ldquoPostchallenge plasmaglucose and glycemic spikes are more strongly associated withatherosclerosis than fasting glucose or HbA(1c) levelrdquo DiabetesCare vol 23 no 12 pp 1830ndash1834 2000

[58] W L Clarke D J Cox L Gonder-Frederick D JulianB Kovatchev and D Young-Hyman ldquoBiopsychobehavioralmodel of risk of severe hypoglycemia self-management behav-iorsrdquo Diabetes Care vol 22 no 4 pp 580ndash584 1999

[59] D J Cox L A Gonder-Frederick B P Kovatchev et alldquoBiopsychobehavioral model of severe hypoglycemia II under-standing the risk of severe hypoglycemiardquo Diabetes Care vol22 no 12 pp 2018ndash2025 1999

[60] L Gonder-Frederick D Cox B Kovatchev D Schlundt andW Clarke ldquoA biopsychobehavioral model of risk of severehypoglycemiardquoDiabetes Care vol 20 no 4 pp 661ndash669 1997

[61] B Kovatchev D Cox L Gonder-Frederick D Schlundtand W Clarke ldquoStochastic model of self-regulation decisionmaking exemplied by decisions concerning hypoglycemiardquoHealth Psychology vol 17 no 3 pp 277ndash284 1998

[62] G Nucci and C Cobelli ldquoModels of subcutaneous insulinkinetics A critical reviewrdquo Computer Methods and Programs inBiomedicine vol 62 no 3 pp 249ndash257 2000

[63] M E Wilinska L J Chassin H C Schaller L Schaupp T RPieber and R Hovorka ldquoInsulin kinetics in type-1 diabetescontinuous and bolus delivery of rapid acting insulinrdquo IEEETransactions on Biomedical Engineering vol 52 no 1 pp 3ndash122005

[64] R N Bergman D T Finegood and M Ader ldquoAssessmentof insulin sensitivity in vivordquo Endocrine Reviews vol 236 ppE667ndashE677 1985

[65] R N Bergman ldquoe minimal model of glucose regulation abiographyrdquoAdvances in ExperimentalMedicine and Biology vol537 pp 1ndash19 2003

[66] R N Bergman D J Zaccaro R M Watanabe et al ldquoMinimalmodel-based insulin sensitivity has greater heritability and adifferent genetic basis than homeostasis model assessment orfasting insulinrdquo Diabetes vol 52 no 8 pp 2168ndash2174 2003

[67] A Caumo R N Bergman and C Cobelli ldquoInsulin sensitivityfrom meal tolerance tests in normal subjects a minimal modelindexrdquo Journal of Clinical Endocrinology and Metabolism vol85 no 11 pp 4396ndash4402 2000

[68] J O Clausen K Borch-Johnsen H Ibsen et al ldquoInsulin sen-sitivity index acute insulin response and glucose effectivenessin a population-based sample of 380 young healthy Caucasiansanalysis of the impact of gender body fat physical tness andlife-style factorsrdquo Journal of Clinical Investigation vol 98 no 5pp 1195ndash1209 1996

[69] T C Ni M Ader and R N Bergman ldquoReassessment of glu-cose effectiveness and insulin sensitivity from minimal modelanalysis a theoretical evaluation of the single-compartmentglucose distribution assumptionrdquo Diabetes vol 46 no 11 pp1813ndash1821 1997

[70] S Welch S S P Gebhart R N Bergman and L S PhillipsldquoMinimal model analysis of intravenous glucose tolerance

test-derived insulin sensitivity in diabetic subjectsrdquo Journalof Clinical Endocrinology and Metabolism vol 71 no 6 pp1508ndash1518 1990

[71] D Dawson M A Vincent E J Barrett et al ldquoCapillary recruit-ment in skeletal muscle in response to exercise and hyperin-sulinemia assessed with contrast-enhanced ultrasoundrdquo Amer-ican Journal of Physiology vol 282 no 3 pp E714ndashE720 2002

[72] K J Mikines B Sonne P A Farrell B Tronier and H GalboldquoEffect of physical exercise on sensitivity and responsiveness toinsulin in humansrdquoAmerican Journal of Physiology vol 254 no3 pp E248ndashE259 1988

[73] E A Richter ldquoGlucose utilizationrdquo in Handbook of PhysiologyL B Rowell and J T Shepherd Eds pp 912ndash951 OxfordUniversity Press New York NY USA 1996

[74] D H Wasserman R J Geer D E Rice et al ldquoInteraction ofexercise and insulin action in humansrdquo American Journal ofPhysiology vol 260 no 1 pp E37ndashE45 1991

[75] G Pillonetto A Caumo G Sparacino and C Cobelli ldquoAnew dynamic index of insulin sensitivityrdquo IEEE Transactions onBiomedical Engineering vol 53 no 3 pp 369ndash379 2006

[76] C Dalla Man D M Raimondo R A Rizza and C CobellildquoGIM Simulation soware of meal glucose-insulin modelrdquoJournal of Diabetes Science and Technology vol 1 pp 323ndash3302007

[77] C Dalla Man R A Rizza and C Cobelli ldquoMeal simulationmodel of the glucose-insulin systemrdquo IEEE Transactions onBiomedical Engineering vol 54 no 10 pp 1740ndash1749 2007

[78] W L Clarke D Cox L A Gonder-Frederick W Carter andS L Pohl ldquoEvaluating clinical accuracy of systems for self-monitoring of blood glucoserdquo Diabetes Care vol 10 no 5 pp622ndash628 1987

[79] e Diabetes Research In Children Network (Direcnet) StudyGroup ldquoA multicenter study of the accuracy of the one touchultra home glucose meter in children with type 1 diabetesrdquoDiabetes Technology anderapeutics vol 5 no 6 pp 933ndash9412003

[80] I B Hirsch B W Bode B P Childs et al ldquoSelf-monitoring ofblood glucose (SMBG) in insulin- and non-insulin-using adultswith diabetes consensus recommendations for improvingSMBG accuracy utilization and researchrdquo Diabetes Technologyanderapeutics vol 10 no 6 pp 419ndash439 2008

[81] G Freckmann A Baumstark N Jendrike et al ldquoSystemaccuracy evaluation of 27 blood glucose monitoring systemsaccording to DIN en ISO 15197rdquo Diabetes Technology anderapeutics vol 12 no 3 pp 221ndash231 2010

[82] D Deiss J Bolinder J P Riveline et al ldquoImproved glycemiccontrol in poorly controlled patients with type 1 diabetes usingreal-time continuous glucose monitoringrdquo Diabetes Care vol29 no 12 pp 2730ndash2732 2006

[83] S Garg H Zisser S Schwartz et al ldquoImprovement in glycemicexcursions with a transcutaneous real-time continuous glucosesensor a randomized controlled trialrdquo Diabetes Care vol 29no 1 pp 44ndash50 2006

[84] B P Kovatchev and W L Clarke ldquoContinuous glucose mon-itoring reduces risks for hypo- and hyperglycemia and glucosevariability in diabetesrdquo Diabetes vol 56 1 Article ID 0086OR2007

[85] e Juvenile Diabetes Research Foundation Continuous Glu-cose Monitoring Study Group ldquoContinuous glucose monitor-ing and intensive treatment of type 1 diabetesrdquo New EnglandJournal of Medicine vol 359 pp 1464ndash1476 2008

12 Scientica

[86] D C Klonoff ldquoContinuous glucose monitoring roadmap for21st century diabetes therapyrdquo Diabetes Care vol 28 no 5 pp1231ndash1239 2005

[87] R Hovorka ldquoContinuous glucose monitoring and closed-loopsystemsrdquo Diabetic Medicine vol 23 no 1 pp 1ndash12 2006

[88] D C Klonoff ldquoe articial pancreas how sweet engineeringwill solve bitter problemsrdquo Journal of Diabetes Science andTechnology vol 1 pp 72ndash81 2007

[89] I B Hirsch D Armstrong R M Bergenstal et al ldquoClin-ical application of emerging sensor technologies in diabetesmanagement consensus guidelines for continuous glucosemonitoring (CGM)rdquoDiabetes Technology anderapeutics vol10 no 4 pp 232ndash246 2008

[90] K Rebrin G M Steil W P Van Antwerp and J J Mastro-totaro ldquoSubcutaneous glucose predicts plasma glucose inde-pendent of insulin implications for continuous monitoringrdquoAmerican Journal of Physiology vol 277 no 3 pp E561ndashE5711999

[91] K Rebrin and G M Steil ldquoCan interstitial glucose assessmentreplace blood glucosemeasurementsrdquoDiabetes Technology anderapeutics vol 2 no 3 pp 461ndash472 2000

[92] G M Steil K Rebrin F Hariri et al ldquoInterstitial uid glucosedynamics during insulin-induced hypoglycaemiardquo Diabetolo-gia vol 48 no 9 pp 1833ndash1840 2005

[93] M S Boyne D M Silver J Kaplan and C D Saudek ldquoTimingof changes in interstitial and venous blood glucose measuredwith a continuous subcutaneous glucose sensorrdquo Diabetes vol52 no 11 pp 2790ndash2794 2003

[94] E Kulcu J A Tamada G Reach R O Potts and M JLesho ldquoPhysiological differences between interstitial glucoseand blood glucose measured in human subjectsrdquoDiabetes Carevol 26 no 8 pp 2405ndash2409 2003

[95] P J Stout J R Racchini andM E Hilgers ldquoA novel approach tomitigating the physiological lag between blood and interstitialuid glucose measurementsrdquo Diabetes Technology and era-peutics vol 6 no 5 pp 635ndash644 2004

[96] I M E Wentholt A A M Hart J B L Hoekstra and J HDevries ldquoRelationship between interstitial and blood glucose intype 1 diabetes patients delay and the push-pull phenomenonrevisitedrdquo Diabetes Technology and erapeutics vol 9 no 2pp 169ndash175 2007

[97] K J C Wientjes and A J M Schoonen ldquoDetermination oftime delay between blood and interstitial adipose tissue glucoseconcentration change by microdialysis in healthy volunteersrdquonternational Journal of Articial rgans vol 24 no 12 pp884ndash889 2001

[98] B Aussedat M Dupire-Angel R Gifford J C Klein G SWilson and G Reach ldquoInterstitial glucose concentration andglycemia implications for continuous subcutaneous glucosemonitoringrdquo American Journal of Physiology vol 278 no 4 ppE716ndashE728 2000

[99] B P Kovatchev and W L Clarke ldquoPeculiarities of the con-tinuous glucose monitoring data stream and their impact ondeveloping closed-loop control technologyrdquo Journal of DiabetesScience and Technology vol 2 pp 158ndash163 2008

[100] W L Clarke and B P Kovatchev ldquoContinuous glucose sen-sorsmdashcontinuing questions about clinical accuracyrdquo Journal ofDiabetes Science and Technology vol 1 pp 164ndash170 2007

[101] e Diabetes Research in Children Network (DirecNet) StudyGroup ldquoe accuracy of the guardian RT continuous glucosemonitor in children with type 1 diabetesrdquo Diabetes Technologyanderapeutics vol 10 pp 266ndash272 2008

[102] B Kovatchev S Anderson L Heinemann and W ClarkeldquoComparison of the numerical and clinical accuracy of fourcontinuous glucose monitorsrdquo Diabetes Care vol 31 no 6 pp1160ndash1164 2008

[103] S K Garg J Smith C Beatson B Lopez-Baca M Voelmleand P A Gottlieb ldquoComparison of accuracy and safety ofthe SEVEN and the navigator continuous glucose monitoringsystemsrdquo Diabetes Technology and erapeutics vol 11 no 2pp 65ndash72 2009

[104] T Heise T Koschinsky L Heinemann and V Lodwig ldquoHypo-glycemia warning signal and glucose sensors requirements andconceptsrdquo Diabetes Technology and erapeutics vol 5 no 4pp 563ndash571 2003

[105] B Bode K Gross N Rikalo et al ldquoAlarms based on real-timesensor glucose values alert patients to hypo- and hyperglycemiathe Guardian continuous monitoring systemrdquo Diabetes Tech-nology anderapeutics vol 6 no 2 pp 105ndash113 2004

[106] G McGarraugh and R Bergenstal ldquoDetection of hypoglycemiawith continuous interstitial and traditional blood glucosemonitoring using the FreeStyle navigator continuous glucosemonitoring systemrdquo Diabetes Technology and erapeutics vol11 no 3 pp 145ndash150 2009

[107] S E Noujaim D Horwitz M Sharma and J Marhoul ldquoAccu-racy requirements for a hypoglycemia detector an analyticalmodel to evaluate the effects of bias precision and rate ofglucose changerdquo Journal of Diabetes Science and Technology vol1 pp 653ndash668 2007

[108] W K Ward ldquoe role of new technology in the early detectionof hypoglycemiardquo Diabetes Technology and erapeutics vol 6no 2 pp 115ndash117 2004

[109] B Buckingham E Cobry P Clinton et al ldquoPreventing hypo-glycemia using predictive alarm algorithms and insulin pumpsuspensionrdquo Diabetes Technology and erapeutics vol 11 no2 pp 93ndash97 2009

[110] C S Hughes S D Patek M D Breton and B P KovatchevldquoHypoglycemia prevention via pump attenuation and red-yellow-green ldquotrafficrdquo lights using continuous glucose moni-toring and insulin pump datardquo Journal of Diabetes Science andTechnology vol 4 no 5 pp 1146ndash1155 2010

[111] B P Kovatchev D J Cox L A Gonder-Frederick and WClarke ldquoSymmetrization of the blood glucose measurementscale and its applicationsrdquo Diabetes Care vol 20 no 11 pp1655ndash1658 1997

[112] F J Service G D Molnar J W Rosevear E Ackerman LC Gatewood and W F Taylor ldquoMean amplitude of glycemicexcursions a measure of diabetic instabilityrdquo Diabetes vol 19no 9 pp 644ndash655 1970

[113] J Schlichtkrull O Munck and M Jersild ldquoe M-value anindex of blood glucose control in diabeticsrdquo Acta MedicaScandinavica vol 177 pp 95ndash102 1965

[114] E A Ryan T Shandro K Green et al ldquoAssessment of theseverity of hypoglycemia and glycemic lambility in type 1diabetic subjects undergoing islet in transplantationrdquo Diabetesvol 53 no 4 pp 955ndash962 2004

[115] B P Kovatchev D J Cox L Gonder-Frederick and WL Clarke ldquoMethods for quantifying self-monitoring bloodglucose proles exemplied by an examination of blood glucosepatterns in patients with type 1 and type 2 diabetesrdquo DiabetesTechnology anderapeutics vol 4 no 3 pp 295ndash303 2002

[116] B P Kovatchev D J Cox L S Farhy M Straume L Gonder-Frederick and W L Clarke ldquoEpisodes of severe hypoglycemia

Scientica 13

in type 1 diabetes are preceded and followed within 48 hours bymeasurable disturbances in blood glucoserdquo Journal of ClinicalEndocrinology and Metabolism vol 85 no 11 pp 4287ndash42922000

[117] B P Kovatchev M Straume D J Cox and L S FarhyldquoRisk analysis of blood glucose data a quantitative approach tooptimizing the control of Insulin Dependent Diabetesrdquo Journalof eoretical Medicine vol 3 no 1 pp 1ndash10 2000

[118] B P Kovatchev E Otto D Cox L Gonder-Frederick andW Clarke ldquoEvaluation of a new measure of blood glucosevariability in diabetesrdquo Diabetes Care vol 29 no 11 pp2433ndash2438 2006

[119] B P Kovatchev W L Clarke M Breton K Brayman and AMcCall ldquoQuantifying temporal glucose variability in diabetesvia continuous glucose monitoring mathematical methods andclinical applicationrdquo Diabetes Technology and erapeutics vol7 no 6 pp 849ndash862 2005

[120] D Rodbard ldquoNew and improved methods to characterizeglycemic variability using continuous glucosemonitoringrdquoDia-betes Technology and erapeutics vol 11 no 9 pp 551ndash5652009

[121] M Miller and P Strange ldquoUse of Fourier models for analysisand interpretation of continuous glucose monitoring glucoseprolesrdquo Journal of Diabetes Science and Technology vol 1 pp630ndash638 2007

[122] W Clarke and B Kovatchev ldquoStatistical tools to analyzecontinuous glucose monitor datardquo Diabetes Technology anderapeutics vol 11 pp S45ndashS54 2009

[123] C M Mcdonnell S M Donath S I Vidmar G A Wertherand F J Cameron ldquoA novel approach to continuous glucoseanalysis utilizing glycemic variationrdquo Diabetes Technology anderapeutics vol 7 no 2 pp 253ndash263 2005

[124] G Sparacino F Zanderigo A Maran and C Cobelli ldquoContin-uous glucosemonitoring andhypohyperglycaemia predictionrdquoDiabetes Research and Clinical Practice vol 74 no 2 supple-ment pp S160ndashS163 2006

[125] G Sparacino F Zanderigo G Corazza A Maran AFacchinetti and C Cobelli ldquoGlucose concentration can bepredicted ahead in time from continuous glucose monitoringsensor time-seriesrdquo IEEE Transactions on Biomedical Engineer-ing vol 54 pp 931ndash937 2007

[126] J Reifman S Rajaraman A Gribok and W K Ward ldquoPredic-tive monitoring for improved management of glucose levelsrdquoJournal of Diabetes Science and Technology vol 1 pp 478ndash4862007

[127] F Zanderigo G Sparacino B Kovatchev and C CobellildquoGlucose prediction algorithms from continuous monitoringassessment of accuracy via continuous glucose-error grid anal-ysisrdquo Journal of Diabetes Science and Technology vol 1 pp645ndash651 2007

[128] F Cameron G Niemayer K Gundy-Burlet and B Bucking-ham ldquoStatistical hypoglycemia predictionrdquo Journal of DiabetesScience and Technology vol 2 pp 612ndash621 2008

[129] A Gani A V Gribok S Rajaraman W K Ward and JReifman ldquoPredicting subcutaneous glucose concentration inhumans data-driven glucose modelingrdquo IEEE Transactions onBiomedical Engineering vol 56 no 2 pp 246ndash254 2009

[130] M Eren-Oruklu A Cinar L Quinn andD Smith ldquoEstimationof future glucose concentrations with subject-specic recursivelinear modelsrdquo Diabetes Technology and erapeutics vol 11no 4 pp 243ndash253 2009

[131] S M Pappada B D Cameron and P M Rosman ldquoDevelop-ment of a neural network for prediction of glucose concentra-tion in type 1 diabetes patientsrdquo Journal of Diabetes Science andTechnology vol 2 pp 792ndash801 2008

[132] C Cobelli C Dalla Man G Sparacino L Magni G Nicolaoand B P Kovatchev ldquoDiabetes models signals and controlrdquoIEEE Reviews in Biomedical Engineering vol 2 pp 54ndash96 2009

[133] H LeBlanc D Chauvet P Lombrail and J J Robert ldquoGlycemiccontrol with closed-loop intraperitoneal insulin in type Idiabetesrdquo Diabetes Care vol 9 no 2 pp 124ndash128 1986

[134] C D Saudek J L Selman H A Pitt et al ldquoA preliminary trialof the programmable implantablemedication system for insulindeliveryrdquo New England Journal of Medicine vol 321 no 9 pp574ndash579 1989

[135] C Broussolle N Jeandidier and H Hanaire-Broutin ldquoFrenchmulticentre experience of implantable insulin pumpsrdquo Lancetvol 343 no 8896 pp 514ndash515 1994

[136] H Hanaire-Broutin C Broussolle N Jeandidier et al ldquoFea-sibility of intraperitoneal insulin therapy with programmableimplantable pumps in IDDM a multicenter studyrdquo DiabetesCare vol 18 no 3 pp 388ndash392 1995

[137] P R Oskarsson P E Lins H W Henriksson and U CAdamson ldquoMetabolic and hormonal responses to exercisein type 1 diabetic patients during continuous subcutaneousas compared to continuous intraperitoneal insulin infusionrdquoDiabetes and Metabolism vol 25 no 6 pp 491ndash497 1999

[138] P R Oskarsson P E Lins L Backman and U C AdamsonldquoContinuous intraperitoneal insulin infusion partly restores theglucagon response to hypoglycaemia in type 1 diabetic patientsrdquoDiabetes and Metabolism vol 26 no 2 pp 118ndash124 2000

[139] B Catargi L Meyer V Melki E Renard and N JeandidierldquoComparison of blood glucose stability and HbA1C betweenimplantable insulin pumps using U400 hoe 21pH insulin andexternal pumps using lispro in type 1 diabetic patients a pilotstudyrdquo Diabetes and Metabolism vol 28 no 2 pp 133ndash1372002

[140] E Renard ldquoImplantable closed-loop glucose-sensing andinsulin delivery the future for insulin pump therapyrdquo CurrentOpinion in Pharmacology vol 2 no 6 pp 708ndash716 2002

[141] E Renard G Costalat H Chevassus and J Bringer ldquoArticial120573120573-cell clinical experience toward an implantable closed-loopinsulin delivery systemrdquo Diabetes and Metabolism vol 32 no5 pp 497ndash502 2006

[142] E Renard J Place M Cantwell H Chevassus and C CPalerm ldquoClosed-loop insulin delivery using a subcutaneousglucose sensor and intraperitoneal insulin delivery feasibilitystudy testing a new model for the articial pancreasrdquo DiabetesCare vol 33 no 1 pp 121ndash127 2010

[143] R Bellazzi G Nucci and C Cobelli ldquoe subcutaneousroute to insulin-dependent diabetes therapy closed-loop andpartially closed-loop control strategies for insulin deliveryand measuring glucose concentrationrdquo IEEE Engineering inMedicine and Biology Magazine vol 20 no 1 pp 54ndash64 2001

[144] R Hovorka L J Chassin M E Wilinska et al ldquoClosingthe loop the adicol experiencerdquo Diabetes Technology anderapeutics vol 6 no 3 pp 307ndash318 2004

[145] R Hovorka V Canonico L J Chassin et al ldquoNonlinear modelpredictive control of glucose concentration in subjects withtype 1 diabetesrdquo Physiological Measurement vol 25 no 4 pp905ndash920 2004

14 Scientica

[146] G M Steil K Rebrin C Darwin F Hariri and M F SaadldquoFeasibility of automating insulin delivery for the treatment oftype 1 diabetesrdquo Diabetes vol 55 no 12 pp 3344ndash3350 2006

[147] e JDRF e-Newsletter Emerging Technologies in DiabetesResearch 2006

[148] W L Clarke and B Kovatchev ldquoe articial pancreas howclose are we to closing the looprdquo Pediatric EndocrinologyReviews vol 4 no 4 pp 314ndash316 2007

[149] C Cobelli E Renard and B P Kovatchev ldquoPerspectives indiabetes articial pancreas past present futurerdquo Diabetes vol60 pp 2672ndash2682 2011

[150] B P Kovatchev M D Breton C Dalla Man and C Cobelli ldquoInsilico preclinical trials a proof of concept in closed-loop controlof type 1 diabetesrdquo Journal of Diabetes Science and Technologyvol 3 pp 44ndash55 2009

[151] B Kovatchev C Cobelli E Renard et al ldquoMultinational studyof subcutaneous model-predictive closed-loop control in type1 diabetes mellitus summary of the resultsrdquo Journal of DiabetesScience and Technology vol 4 no 6 pp 1374ndash1381 2010

[152] H Zisser L Jovanovic F Doyle P Ospina and C OwensldquoRun-to-run control of meal-related insulin dosingrdquo DiabetesTechnology anderapeutics vol 7 no 1 pp 48ndash57 2005

[153] C Owens H Zisser L Jovanovic B Srinivasan D Bonvinand F J Doyle III ldquoRun-to-run control of blood glucoseconcentrations for people with type 1 diabetes mellitusrdquo IEEETransactions on Biomedical Engineering vol 53 no 6 pp996ndash1005 2006

[154] C C Palerm H Zisser W C Bevier L Jovanovič and F JDoyle III ldquoPrandial insulin dosing using run-to-run controlapplication of clinical data and medical expertise to dene asuitable performance metricrdquo Diabetes Care vol 30 no 5 pp1131ndash1136 2007

[155] LMagni F Raimondo L Bossi et al ldquoModel predictive controlof type 1 diabetes an in silico trialrdquo Journal of Diabetes Scienceand Technology vol 1 pp 804ndash812 2007

[156] S A Weinzimer G M Steil K L Swan J Dziura N Kurtzand W V Tamborlane ldquoFully automated closed-loop insulindelivery versus semiautomated hybrid control in pediatricpatients with type 1 diabetes using an articial pancreasrdquoDiabetes Care vol 31 no 5 pp 934ndash939 2008

[157] W L Clarke S Anderson M Breton S Patek L Kashmerand B Kovatchev ldquoClosed-loop articial pancreas using sub-cutaneous glucose sensing and insulin delivery and a modelpredictive control algorithm the Virginia experiencerdquo Journalof Diabetes Science and Technology vol 3 no 5 pp 1031ndash10382009

[158] D Bruttomesso A Farret S Costa et al ldquoClosed-loop arti-cial pancreas using subcutaneous glucose sensing and insulindelivery and a model predictive control algorithm preliminarystudies in Padova and Montpellierrdquo Journal of Diabetes Scienceand Technology vol 3 no 5 pp 1014ndash1021 2009

[159] R Hovorka J M Allen D Elleri et al ldquoManual closed-loop insulin delivery in children and adolescents with type 1diabetes a phase 2 randomised crossover trialrdquoe Lancet vol375 no 9716 pp 743ndash751 2010

[160] F H El-khatib S J Russell D M Nathan R G Sutherlin andE R Damiano ldquoA bihormonal closed-loop articial pancreasfor type 1 diabetesrdquo Science Translational Medicine vol 2 no27 Article ID 27ra27 2010

[161] E Dassau H Zisser C C Palerm B A Buckingham LJovanovič and F J Doye III ldquoModular articial 120573120573-cell system a

prototype for clinical researchrdquo Journal of Diabetes Science andTechnology vol 2 pp 863ndash872 2008

[162] B Kovatchev S Patek E Dassau et al ldquoControl to range fordiabetes functionality and modular architecturerdquo Journal ofDiabetes Science and Rechnology vol 3 no 5 pp 1058ndash10652009

[163] E Atlas R Nimri S Miller E A Grunberg and M PhillipldquoMD-logic articial pancreas system a pilot study in adults withtype 1 diabetesrdquo Diabetes Care vol 33 no 5 pp 1072ndash10762010

[164] E Dassau H Zisser M W Percival B Grosman L Jovanovičand F J Doyle III ldquoClinical results of automated articialpancreatic 120573120573-cell system with unannounced meal using multi-parametric MPC and insulin-on-boardrdquo in Proceedings of the70th American Diabetes Association Meeting Diabetes vol 59supplement 1 A94 Orlando Fla USA 2010

[165] E M Renard A Farret J Place C Cobelli B P Kovatchev andMD Breton ldquoClosed-loop insulin delivery using subcutaneousinfusion and glucose sensing and equipped with a dedicatedsafety supervision algorithm improves safety of glucose controlin type 1 diabetesrdquo Diabetologia vol 53 supplement 1 p S252010

[166] R Hovorka K Kumareswaran J Harris et al ldquoOvernightclosed loop insulin delivery articial pancreas in adults withtype 1 diabetes crossover randomized controlled studiesrdquoBritish Medical Journal vol 342 no 7803 Article ID d18552011

[167] H Zisser E Dassau W Bevier et al ldquoInitial evaluation ofa fully automated articial pancreasrdquo in Proceedings of the71st American Diabetes Association Meeting Diabetes vol 60supplement 1 A41 San Diego Calif USA 2011

[168] M D Breton A Farret and D Bruttomesso ldquoFully-integratedarticial pancreas in type 1 diabetes modular closed-loopglucose control maintains near-normoglycemiardquo Diabetes vol61 pp 2230ndash2237 2012

[169] C Cobelli E Renard and B P Kovatchev ldquoPilot studies ofwearable articial pancreas in type 1 diabetesrdquo Diabetes Carevol 35 no 9 pp e65ndashe67 2012

Submit your manuscripts athttpwwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Behavioural Neurology

EndocrinologyInternational Journal of

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Disease Markers

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PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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ObesityJournal of

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Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Scientica 9

understood by those working in the eld wireless commu-nication between CGM devices insulin pumps and closed-loop controllers are under development and testing newgenerations of CGM device demonstrate superior accuracyand reliability and new insulin analogs and methods forinsulin delivery are being engineered to approximate asclose as possible the action prole of endogenous insulinIt should be noted however that the signals available toa contemporary closed-loop control system are generallylimited to CGM and insulin delivery data user input aboutcarbohydrate intake and physical activity could be availableas well In contrast the endocrine pancreas receives directand rapid control inputs from other nutrients (eg lipids andamino acids) adjacent cells (somatostatin from the delta cellsand glucagon from alpha cells) incretins and neural signalsus while articial closed-loop control is expected to bevastly superior to the diabetes control methods employed inthe clinical practice today it will continue to be imperfectwhen compared to the natural endocrine regulation of bloodglucose

Acknowledgments

is work was made possible by the JDRF Articial PancreasProject the National Institutes of HealthNIDDK GrantsRO1 DK 51562 and RO1 DK 085623 and by the generoussupport of PBM Science Charlottesville Virginia CA USAand the Frederick Banting Foundation Richmond VirginiaCA USA e author thanks his colleagues at the Universityof Virginia Center forDiabetes Technology for their relentlesswork on articial pancreas development

References

[1] American Diabetes Association ldquoDiagnosis and classicationof diabetes mellitusrdquo Diabetes Care vol 27 pp s5ndashs10 2004

[2] A H Kadish ldquoAutomation control of blood sugarmdashI A ser-vomechanism for glucose monitoring and controlrdquo AmericanJournal of Medical Electronics vol 39 pp 82ndash86 1964

[3] J C Pickup H Keen J A Parsons and K G M M AlbertildquoContinuous subcutaneous insulin infusion an approach toachieving normoglycaemiardquo British Medical Journal vol 1 no6107 pp 204ndash207 1978

[4] W V Tamborlane R S Sherwin M Genel and P FeligldquoReduction to normal of plasma glucose in juvenile diabetes bysubcutaneous administration of insulinwith a portable infusionpumprdquo New England Journal of Medicine vol 300 no 11 pp573ndash578 1979

[5] A M Albisser B S Leibel and T G Ewart ldquoAn articialendocrine pancreasrdquoDiabetes vol 23 no 5 pp 389ndash396 1974

[6] E F Pfeier um Ch and A H Clemens ldquoe articialbeta cell a continuous control of blood sugar by external regu-lation of insulin infusion (glucose controlled insulin infusionsystem)rdquo Hormone and Metabolic Research vol 6 no 5 pp339ndash342 1974

[7] J Mirouze J L Selam T C Pham and D Cavadore ldquoEvalua-tion of exogenous insulin homoeostasis by the artical pancreasin insulin dependent diabetesrdquo Diabetologia vol 13 no 3 pp273ndash278 1977

[8] E W Kraegen L V Campbell and Y O Chia ldquoControlof blood glucose in diabetics using an articial pancreasrdquoAustralian and New Zealand Journal of Medicine vol 7 no 3pp 280ndash286 1977

[9] M Shichiri R Kawamori Y Yamasaki M Inoue Y Shigetaand H Abe ldquoComputer algorithm for the articial pancreaticbeta cellrdquo Articial rgans vol 2 supplement pp 247ndash2501978

[10] A H Clemens P H Chang and R W Myers ldquoe devel-opment of Biostator a Glucose Controlled Insulin InfusionSystem (GCIIS)rdquo Hormone and Metabolic Research vol 7 pp23ndash33 1977

[11] E B Marliss F T Murray and E F Stokes ldquoNormalization ofglycemia in diabetics during meals with insulin and glucagondelivery by the articial pancreasrdquo Diabetes vol 26 no 7 pp663ndash672 1977

[12] J V Santiago A H Clemens W L Clarke and D M KipnisldquoClosed-loop and open-loop devices for blood glucose controlin normal and diabetic subjectsrdquo Diabetes vol 28 no 1 pp71ndash84 1979

[13] U Fischer E Jutzi E J Freyse and E Salzsieder ldquoDerivationand experimental proof of a new algorithm for the articial B-cell based on the individual analysis of the physiological insulin-glucose relationshiprdquo Endokrinologie vol 71 no 1 pp 65ndash751978

[14] R S Parker F J Doyle and N A Peppas ldquoe intravenousroute to blood glucose control a review of control algorithmsfor noninvasive monitoring and regulation in type I diabeticpatientsrdquo IEEE Engineering in Medicine and Biology Magazinevol 20 no 1 pp 65ndash73 2001

[15] R N Bergman Y Z Ider C R Bowden and C CobellildquoQuantitative estimation of insulin sensitivityrdquo e AmericanJournal of Physiology vol 236 no 6 pp E667ndashE677 1979

[16] H M Broekhuyse J D Nelson B Zinman and A M AlbisserldquoComparison of algorithms for the closed-loop control of bloodglucose using the articial beta cellrdquo IEEE Transactions onBiomedical Engineering vol 28 no 10 pp 678ndash687 1981

[17] A H Clemens ldquoFeedback control dynamics for glucosecontrolled insulin infusion systemrdquo Medical Progress throughTechnology vol 6 no 3 pp 91ndash98 1979

[18] C Cobelli and A Ruggeri ldquoEvaluation of portalperipheralroute and of algorithms for insulin delivery in the closed-loop control of glucose in diabetes a modeling studyrdquo IEEETransactions on Biomedical Engineering vol 30 no 2 pp93ndash103 1983

[19] E Salzsieder G Albrecht U Fischer and E J Freyse ldquoKineticmodeling of the glucoregulatory system to improve insulintherapyrdquo IEEE Transactions on Biomedical Engineering vol 32no 10 pp 846ndash855 1985

[20] P Brunetti C Cobelli P Cruciani et al ldquoA simulation study ona self-tuning portable controller of blood glucoserdquo InternationalJournal of Articial rgans vol 16 no 1 pp 51ndash57 1993

[21] U Fischer W Schenk E Salzsieder G Albrecht P Abel andE J Freyse ldquoDoes physiological blood glucose control requirean adaptive control strategyrdquo IEEE Transactions on BiomedicalEngineering vol 34 no 8 pp 575ndash582 1987

[22] J T Sorensen A physiologic model of glucose metabolism inman and its use to design and assess improved insulin therapiesfor diabetes [PhD dissertation] Department of Chemical Engi-neering MIT 1985

[23] R S Parker F J Doyle and N A Peppas ldquoA model-basedalgorithm for blood glucose control in type I diabetic patientsrdquo

10 Scientica

IEEE Transactions on Biomedical Engineering vol 46 no 2 pp148ndash157 1999

[24] J J Mastrototaro ldquoe MiniMed continuous glucose monitor-ing Systemrdquo Diabetes Technology anderapeutics vol 2 no 1pp S13ndashS18 2000

[25] B W Bode ldquoClinical utility of the continuous glucose monitor-ing systemrdquo Diabetes Technology anderapeutics vol 2 no 1supplement pp S35ndashS41 2000

[26] B Feldman R Brazg S Schwartz and R Weinstein ldquoAcontinuous glucose sensor based on wired enzyme technol-ogymdashresults from a 3-day trial in patients with type 1 diabetesrdquoDiabetes Technology anderapeutics vol 5 no 5 pp 769ndash7792003

[27] eDiabetes Control and Complications Trial Research Groupldquoe effect of intensive treatment of diabetes on the develop-ment and progression of long-term complications of insulin-dependent diabetes mellitusrdquoNew England Journal of Medicinevol 329 pp 978ndash986 1993

[28] eDiabetes Control and Complications Trial Research Groupldquoe relationship of glycemic exposure (HbA1c) to the riskof development and progression of retinopathy in the Dia-betes Control and Complications Trialrdquo Diabetes vol 44 pp968ndash983 1995

[29] J M Lachin S Genuth D M Nathan B Zinman and B NRutledge ldquoEffect of glycemic exposure on the risk of microvas-cular complications in the diabetes control and complicationstrial-revisitedrdquo Diabetes vol 57 no 4 pp 995ndash1001 2008

[30] P Reichard and M Pihl ldquoMortality and treatment side-effectsduring long-term intensied conventional insulin treatment inthe Stockholm Diabetes Intervention Studyrdquo Diabetes vol 43no 2 pp 313ndash317 1994

[31] UK Prospective Diabetes Study Group (UKPDS) ldquoIntensiveblood-glucose control with sulphonylureas or insulin comparedwith conventional treatment and risk of complications inpatients with type 2 diabetes (UKPDS 33)rdquo Lancet vol 352 no9131 pp 837ndash853 1998

[32] P A Svendsen T Lauritzen U Soegaard and J NerupldquoGlycosylated haemoglobin and steady-state mean blood glu-cose concentration in type 1 (insulin-dependent) diabetesrdquoDiabetologia vol 23 no 5 pp 403ndash405 1982

[33] J V Santiago ldquoLessons from the diabetes control and compli-cations trialrdquo Diabetes vol 42 no 11 pp 1549ndash1554 1993

[34] eDiabetes Control and Complications Trial Research GroupldquoHypoglycemia in the diabetes control and complications trialrdquoDiabetes vol 46 pp 271ndash286 1997

[35] A E Gold BM Frier KMMacLeod and I J Deary ldquoA struc-tural equation model for predictors of severe hypoglycaemiain patients with insulin-dependent diabetes mellitusrdquo DiabeticMedicine vol 14 pp 309ndash315 1997

[36] D J Cox B P Kovatchev D M Julian et al ldquoFrequency ofsevere hypoglycemia in insulin-dependent diabetesmellitus canbe predicted from self-monitoring blood glucose datardquo Journalof Clinical Endocrinology and Metabolism vol 79 no 6 pp1659ndash1662 1994

[37] B P Kovatchev D J Cox L A Gonder-Frederick D Young-Hyman D Schlundt and W Clarke ldquoAssessment of risk forsevere hypoglycemia among adults with IDDM validation ofthe low blood glucose indexrdquo Diabetes Care vol 21 no 11 pp1870ndash1875 1998

[38] B P Kovatchev D J Cox A Kumar L Gonder-Frederick andW L Clarke ldquoAlgorithmic evaluation of metabolic control and

risk of severe hypoglycemia in type 1 and type 2 diabetes usingself-monitoring blood glucose datardquo Diabetes Technology anderapeutics vol 5 no 5 pp 817ndash828 2003

[39] D J Cox L Gonder-Frederick L Ritterband W Clarke andB P Kovatchev ldquoPrediction of severe hypoglycemiardquo DiabetesCare vol 30 no 6 pp 1370ndash1373 2007

[40] S A Amiel R S Sherwin D C Simonson and W VTamborlane ldquoEffect of intensive insulin therapy on glycemicthresholds for counterregulatory hormone releaserdquo Diabetesvol 37 no 7 pp 901ndash907 1988

[41] S A Amiel W V Tamborlane D C Simonson and RS Sherwin ldquoDefective glucose counterregulation aer strictglycemic control of insulin-dependent diabetes mellitusrdquo NewEngland Journal of Medicine vol 316 no 22 pp 1376ndash13831987

[42] P E Cryer and J E Gerich ldquoGlucose counterregulation hypo-glycemia and intensive insulin therapy in diabetes mellitusrdquoNew England Journal of Medicine vol 313 no 4 pp 232ndash2411985

[43] N H White D A Skor and P E Cryer ldquoIdentication of typeI diabetic patients at increased risk for hypoglycemia duringintensive therapyrdquo New England Journal of Medicine vol 308no 9 pp 485ndash491 1983

[44] P E Cryer ldquoIatrogenic hypoglycemia as a cause ofhypoglycemia-associated autonomic failure in IDDM avicious cyclerdquo Diabetes vol 41 no 3 pp 255ndash260 1992

[45] J N Henderson K V Allen I J Deary and B M FrierldquoHypoglycaemia in insulin-treated Type 2 diabetes frequencysymptoms and impaired awarenessrdquo Diabetic Medicine vol 20no 12 pp 1016ndash1021 2003

[46] P E Cryer Hypoglycemia Pathophysiology Diagnosis andTreatment Oxford University Press New York NY USA 1997

[47] P E Cryer S N Davis and H Shamoon ldquoHypoglycemia indiabetesrdquo Diabetes Care vol 26 no 6 pp 1902ndash1912 2003

[48] B P Childs N G Clark and D J Cox ldquoDening and reportinghypoglycemia in diabetes a report from the American diabetesassociation workgroup on hypoglycemiardquo Diabetes Care vol28 no 5 pp 1245ndash1249 2005

[49] P E Cryer ldquoHypoglycaemia the limiting factor in the glycaemicmanagement of Type I and Type II diabetesrdquo Diabetologia vol45 no 7 pp 937ndash948 2002

[50] P E Cryer ldquoHypoglycemia the limiting factor in the manage-ment of IDDMrdquo Diabetes vol 43 no 11 pp 1378ndash1389 1994

[51] A L McCall and B P Kovatchev ldquoe median is not the onlymessage a clinicianrsquos perspective on mathematical analysis ofglycemic variability and modeling in diabetes mellitusrdquo Journalof Diabetes Science and Technology vol 3 pp 3ndash11 2009

[52] M Brownlee and I B Hirsch ldquoGlycemic variability ahemoglobin A1c-independent risk factor for diabetic compli-cationsrdquo Journal of the American Medical Association vol 295no 14 pp 1707ndash1708 2006

[53] I B Hirsch and M Brownlee ldquoShould minimal blood glucosevariability become the gold standard of glycemic controlrdquoJournal of Diabetes and Its Complications vol 19 no 3 pp178ndash181 2005

[54] K Esposito D Giugliano F Nappo and R Marfella ldquoRegres-sion of carotid atherosclerosis by control of postprandial hyper-glycemia in type 2 diabetes mellitusrdquo Circulation vol 110 no2 pp 214ndash219 2004

[55] S Haffner ldquoe importance of postprandial hyperglycaemia indevelopment of cardiovascular disease in people with diabetes

Scientica 11

pointrdquo International Journal of Clinical Practice no 123 pp24ndash26 2001

[56] LMonnier EMas C Ginet et al ldquoActivation of oxidative stressby acute glucose uctuations compared with sustained chronichyperglycemia in patients with type 2 diabetesrdquo Journal of theAmerican Medical Association vol 295 no 14 pp 1681ndash16872006

[57] T S Temelkova-Kurktschiev C Koehler E Henkel W Leon-hardt K Fuecker and M Hanefeld ldquoPostchallenge plasmaglucose and glycemic spikes are more strongly associated withatherosclerosis than fasting glucose or HbA(1c) levelrdquo DiabetesCare vol 23 no 12 pp 1830ndash1834 2000

[58] W L Clarke D J Cox L Gonder-Frederick D JulianB Kovatchev and D Young-Hyman ldquoBiopsychobehavioralmodel of risk of severe hypoglycemia self-management behav-iorsrdquo Diabetes Care vol 22 no 4 pp 580ndash584 1999

[59] D J Cox L A Gonder-Frederick B P Kovatchev et alldquoBiopsychobehavioral model of severe hypoglycemia II under-standing the risk of severe hypoglycemiardquo Diabetes Care vol22 no 12 pp 2018ndash2025 1999

[60] L Gonder-Frederick D Cox B Kovatchev D Schlundt andW Clarke ldquoA biopsychobehavioral model of risk of severehypoglycemiardquoDiabetes Care vol 20 no 4 pp 661ndash669 1997

[61] B Kovatchev D Cox L Gonder-Frederick D Schlundtand W Clarke ldquoStochastic model of self-regulation decisionmaking exemplied by decisions concerning hypoglycemiardquoHealth Psychology vol 17 no 3 pp 277ndash284 1998

[62] G Nucci and C Cobelli ldquoModels of subcutaneous insulinkinetics A critical reviewrdquo Computer Methods and Programs inBiomedicine vol 62 no 3 pp 249ndash257 2000

[63] M E Wilinska L J Chassin H C Schaller L Schaupp T RPieber and R Hovorka ldquoInsulin kinetics in type-1 diabetescontinuous and bolus delivery of rapid acting insulinrdquo IEEETransactions on Biomedical Engineering vol 52 no 1 pp 3ndash122005

[64] R N Bergman D T Finegood and M Ader ldquoAssessmentof insulin sensitivity in vivordquo Endocrine Reviews vol 236 ppE667ndashE677 1985

[65] R N Bergman ldquoe minimal model of glucose regulation abiographyrdquoAdvances in ExperimentalMedicine and Biology vol537 pp 1ndash19 2003

[66] R N Bergman D J Zaccaro R M Watanabe et al ldquoMinimalmodel-based insulin sensitivity has greater heritability and adifferent genetic basis than homeostasis model assessment orfasting insulinrdquo Diabetes vol 52 no 8 pp 2168ndash2174 2003

[67] A Caumo R N Bergman and C Cobelli ldquoInsulin sensitivityfrom meal tolerance tests in normal subjects a minimal modelindexrdquo Journal of Clinical Endocrinology and Metabolism vol85 no 11 pp 4396ndash4402 2000

[68] J O Clausen K Borch-Johnsen H Ibsen et al ldquoInsulin sen-sitivity index acute insulin response and glucose effectivenessin a population-based sample of 380 young healthy Caucasiansanalysis of the impact of gender body fat physical tness andlife-style factorsrdquo Journal of Clinical Investigation vol 98 no 5pp 1195ndash1209 1996

[69] T C Ni M Ader and R N Bergman ldquoReassessment of glu-cose effectiveness and insulin sensitivity from minimal modelanalysis a theoretical evaluation of the single-compartmentglucose distribution assumptionrdquo Diabetes vol 46 no 11 pp1813ndash1821 1997

[70] S Welch S S P Gebhart R N Bergman and L S PhillipsldquoMinimal model analysis of intravenous glucose tolerance

test-derived insulin sensitivity in diabetic subjectsrdquo Journalof Clinical Endocrinology and Metabolism vol 71 no 6 pp1508ndash1518 1990

[71] D Dawson M A Vincent E J Barrett et al ldquoCapillary recruit-ment in skeletal muscle in response to exercise and hyperin-sulinemia assessed with contrast-enhanced ultrasoundrdquo Amer-ican Journal of Physiology vol 282 no 3 pp E714ndashE720 2002

[72] K J Mikines B Sonne P A Farrell B Tronier and H GalboldquoEffect of physical exercise on sensitivity and responsiveness toinsulin in humansrdquoAmerican Journal of Physiology vol 254 no3 pp E248ndashE259 1988

[73] E A Richter ldquoGlucose utilizationrdquo in Handbook of PhysiologyL B Rowell and J T Shepherd Eds pp 912ndash951 OxfordUniversity Press New York NY USA 1996

[74] D H Wasserman R J Geer D E Rice et al ldquoInteraction ofexercise and insulin action in humansrdquo American Journal ofPhysiology vol 260 no 1 pp E37ndashE45 1991

[75] G Pillonetto A Caumo G Sparacino and C Cobelli ldquoAnew dynamic index of insulin sensitivityrdquo IEEE Transactions onBiomedical Engineering vol 53 no 3 pp 369ndash379 2006

[76] C Dalla Man D M Raimondo R A Rizza and C CobellildquoGIM Simulation soware of meal glucose-insulin modelrdquoJournal of Diabetes Science and Technology vol 1 pp 323ndash3302007

[77] C Dalla Man R A Rizza and C Cobelli ldquoMeal simulationmodel of the glucose-insulin systemrdquo IEEE Transactions onBiomedical Engineering vol 54 no 10 pp 1740ndash1749 2007

[78] W L Clarke D Cox L A Gonder-Frederick W Carter andS L Pohl ldquoEvaluating clinical accuracy of systems for self-monitoring of blood glucoserdquo Diabetes Care vol 10 no 5 pp622ndash628 1987

[79] e Diabetes Research In Children Network (Direcnet) StudyGroup ldquoA multicenter study of the accuracy of the one touchultra home glucose meter in children with type 1 diabetesrdquoDiabetes Technology anderapeutics vol 5 no 6 pp 933ndash9412003

[80] I B Hirsch B W Bode B P Childs et al ldquoSelf-monitoring ofblood glucose (SMBG) in insulin- and non-insulin-using adultswith diabetes consensus recommendations for improvingSMBG accuracy utilization and researchrdquo Diabetes Technologyanderapeutics vol 10 no 6 pp 419ndash439 2008

[81] G Freckmann A Baumstark N Jendrike et al ldquoSystemaccuracy evaluation of 27 blood glucose monitoring systemsaccording to DIN en ISO 15197rdquo Diabetes Technology anderapeutics vol 12 no 3 pp 221ndash231 2010

[82] D Deiss J Bolinder J P Riveline et al ldquoImproved glycemiccontrol in poorly controlled patients with type 1 diabetes usingreal-time continuous glucose monitoringrdquo Diabetes Care vol29 no 12 pp 2730ndash2732 2006

[83] S Garg H Zisser S Schwartz et al ldquoImprovement in glycemicexcursions with a transcutaneous real-time continuous glucosesensor a randomized controlled trialrdquo Diabetes Care vol 29no 1 pp 44ndash50 2006

[84] B P Kovatchev and W L Clarke ldquoContinuous glucose mon-itoring reduces risks for hypo- and hyperglycemia and glucosevariability in diabetesrdquo Diabetes vol 56 1 Article ID 0086OR2007

[85] e Juvenile Diabetes Research Foundation Continuous Glu-cose Monitoring Study Group ldquoContinuous glucose monitor-ing and intensive treatment of type 1 diabetesrdquo New EnglandJournal of Medicine vol 359 pp 1464ndash1476 2008

12 Scientica

[86] D C Klonoff ldquoContinuous glucose monitoring roadmap for21st century diabetes therapyrdquo Diabetes Care vol 28 no 5 pp1231ndash1239 2005

[87] R Hovorka ldquoContinuous glucose monitoring and closed-loopsystemsrdquo Diabetic Medicine vol 23 no 1 pp 1ndash12 2006

[88] D C Klonoff ldquoe articial pancreas how sweet engineeringwill solve bitter problemsrdquo Journal of Diabetes Science andTechnology vol 1 pp 72ndash81 2007

[89] I B Hirsch D Armstrong R M Bergenstal et al ldquoClin-ical application of emerging sensor technologies in diabetesmanagement consensus guidelines for continuous glucosemonitoring (CGM)rdquoDiabetes Technology anderapeutics vol10 no 4 pp 232ndash246 2008

[90] K Rebrin G M Steil W P Van Antwerp and J J Mastro-totaro ldquoSubcutaneous glucose predicts plasma glucose inde-pendent of insulin implications for continuous monitoringrdquoAmerican Journal of Physiology vol 277 no 3 pp E561ndashE5711999

[91] K Rebrin and G M Steil ldquoCan interstitial glucose assessmentreplace blood glucosemeasurementsrdquoDiabetes Technology anderapeutics vol 2 no 3 pp 461ndash472 2000

[92] G M Steil K Rebrin F Hariri et al ldquoInterstitial uid glucosedynamics during insulin-induced hypoglycaemiardquo Diabetolo-gia vol 48 no 9 pp 1833ndash1840 2005

[93] M S Boyne D M Silver J Kaplan and C D Saudek ldquoTimingof changes in interstitial and venous blood glucose measuredwith a continuous subcutaneous glucose sensorrdquo Diabetes vol52 no 11 pp 2790ndash2794 2003

[94] E Kulcu J A Tamada G Reach R O Potts and M JLesho ldquoPhysiological differences between interstitial glucoseand blood glucose measured in human subjectsrdquoDiabetes Carevol 26 no 8 pp 2405ndash2409 2003

[95] P J Stout J R Racchini andM E Hilgers ldquoA novel approach tomitigating the physiological lag between blood and interstitialuid glucose measurementsrdquo Diabetes Technology and era-peutics vol 6 no 5 pp 635ndash644 2004

[96] I M E Wentholt A A M Hart J B L Hoekstra and J HDevries ldquoRelationship between interstitial and blood glucose intype 1 diabetes patients delay and the push-pull phenomenonrevisitedrdquo Diabetes Technology and erapeutics vol 9 no 2pp 169ndash175 2007

[97] K J C Wientjes and A J M Schoonen ldquoDetermination oftime delay between blood and interstitial adipose tissue glucoseconcentration change by microdialysis in healthy volunteersrdquonternational Journal of Articial rgans vol 24 no 12 pp884ndash889 2001

[98] B Aussedat M Dupire-Angel R Gifford J C Klein G SWilson and G Reach ldquoInterstitial glucose concentration andglycemia implications for continuous subcutaneous glucosemonitoringrdquo American Journal of Physiology vol 278 no 4 ppE716ndashE728 2000

[99] B P Kovatchev and W L Clarke ldquoPeculiarities of the con-tinuous glucose monitoring data stream and their impact ondeveloping closed-loop control technologyrdquo Journal of DiabetesScience and Technology vol 2 pp 158ndash163 2008

[100] W L Clarke and B P Kovatchev ldquoContinuous glucose sen-sorsmdashcontinuing questions about clinical accuracyrdquo Journal ofDiabetes Science and Technology vol 1 pp 164ndash170 2007

[101] e Diabetes Research in Children Network (DirecNet) StudyGroup ldquoe accuracy of the guardian RT continuous glucosemonitor in children with type 1 diabetesrdquo Diabetes Technologyanderapeutics vol 10 pp 266ndash272 2008

[102] B Kovatchev S Anderson L Heinemann and W ClarkeldquoComparison of the numerical and clinical accuracy of fourcontinuous glucose monitorsrdquo Diabetes Care vol 31 no 6 pp1160ndash1164 2008

[103] S K Garg J Smith C Beatson B Lopez-Baca M Voelmleand P A Gottlieb ldquoComparison of accuracy and safety ofthe SEVEN and the navigator continuous glucose monitoringsystemsrdquo Diabetes Technology and erapeutics vol 11 no 2pp 65ndash72 2009

[104] T Heise T Koschinsky L Heinemann and V Lodwig ldquoHypo-glycemia warning signal and glucose sensors requirements andconceptsrdquo Diabetes Technology and erapeutics vol 5 no 4pp 563ndash571 2003

[105] B Bode K Gross N Rikalo et al ldquoAlarms based on real-timesensor glucose values alert patients to hypo- and hyperglycemiathe Guardian continuous monitoring systemrdquo Diabetes Tech-nology anderapeutics vol 6 no 2 pp 105ndash113 2004

[106] G McGarraugh and R Bergenstal ldquoDetection of hypoglycemiawith continuous interstitial and traditional blood glucosemonitoring using the FreeStyle navigator continuous glucosemonitoring systemrdquo Diabetes Technology and erapeutics vol11 no 3 pp 145ndash150 2009

[107] S E Noujaim D Horwitz M Sharma and J Marhoul ldquoAccu-racy requirements for a hypoglycemia detector an analyticalmodel to evaluate the effects of bias precision and rate ofglucose changerdquo Journal of Diabetes Science and Technology vol1 pp 653ndash668 2007

[108] W K Ward ldquoe role of new technology in the early detectionof hypoglycemiardquo Diabetes Technology and erapeutics vol 6no 2 pp 115ndash117 2004

[109] B Buckingham E Cobry P Clinton et al ldquoPreventing hypo-glycemia using predictive alarm algorithms and insulin pumpsuspensionrdquo Diabetes Technology and erapeutics vol 11 no2 pp 93ndash97 2009

[110] C S Hughes S D Patek M D Breton and B P KovatchevldquoHypoglycemia prevention via pump attenuation and red-yellow-green ldquotrafficrdquo lights using continuous glucose moni-toring and insulin pump datardquo Journal of Diabetes Science andTechnology vol 4 no 5 pp 1146ndash1155 2010

[111] B P Kovatchev D J Cox L A Gonder-Frederick and WClarke ldquoSymmetrization of the blood glucose measurementscale and its applicationsrdquo Diabetes Care vol 20 no 11 pp1655ndash1658 1997

[112] F J Service G D Molnar J W Rosevear E Ackerman LC Gatewood and W F Taylor ldquoMean amplitude of glycemicexcursions a measure of diabetic instabilityrdquo Diabetes vol 19no 9 pp 644ndash655 1970

[113] J Schlichtkrull O Munck and M Jersild ldquoe M-value anindex of blood glucose control in diabeticsrdquo Acta MedicaScandinavica vol 177 pp 95ndash102 1965

[114] E A Ryan T Shandro K Green et al ldquoAssessment of theseverity of hypoglycemia and glycemic lambility in type 1diabetic subjects undergoing islet in transplantationrdquo Diabetesvol 53 no 4 pp 955ndash962 2004

[115] B P Kovatchev D J Cox L Gonder-Frederick and WL Clarke ldquoMethods for quantifying self-monitoring bloodglucose proles exemplied by an examination of blood glucosepatterns in patients with type 1 and type 2 diabetesrdquo DiabetesTechnology anderapeutics vol 4 no 3 pp 295ndash303 2002

[116] B P Kovatchev D J Cox L S Farhy M Straume L Gonder-Frederick and W L Clarke ldquoEpisodes of severe hypoglycemia

Scientica 13

in type 1 diabetes are preceded and followed within 48 hours bymeasurable disturbances in blood glucoserdquo Journal of ClinicalEndocrinology and Metabolism vol 85 no 11 pp 4287ndash42922000

[117] B P Kovatchev M Straume D J Cox and L S FarhyldquoRisk analysis of blood glucose data a quantitative approach tooptimizing the control of Insulin Dependent Diabetesrdquo Journalof eoretical Medicine vol 3 no 1 pp 1ndash10 2000

[118] B P Kovatchev E Otto D Cox L Gonder-Frederick andW Clarke ldquoEvaluation of a new measure of blood glucosevariability in diabetesrdquo Diabetes Care vol 29 no 11 pp2433ndash2438 2006

[119] B P Kovatchev W L Clarke M Breton K Brayman and AMcCall ldquoQuantifying temporal glucose variability in diabetesvia continuous glucose monitoring mathematical methods andclinical applicationrdquo Diabetes Technology and erapeutics vol7 no 6 pp 849ndash862 2005

[120] D Rodbard ldquoNew and improved methods to characterizeglycemic variability using continuous glucosemonitoringrdquoDia-betes Technology and erapeutics vol 11 no 9 pp 551ndash5652009

[121] M Miller and P Strange ldquoUse of Fourier models for analysisand interpretation of continuous glucose monitoring glucoseprolesrdquo Journal of Diabetes Science and Technology vol 1 pp630ndash638 2007

[122] W Clarke and B Kovatchev ldquoStatistical tools to analyzecontinuous glucose monitor datardquo Diabetes Technology anderapeutics vol 11 pp S45ndashS54 2009

[123] C M Mcdonnell S M Donath S I Vidmar G A Wertherand F J Cameron ldquoA novel approach to continuous glucoseanalysis utilizing glycemic variationrdquo Diabetes Technology anderapeutics vol 7 no 2 pp 253ndash263 2005

[124] G Sparacino F Zanderigo A Maran and C Cobelli ldquoContin-uous glucosemonitoring andhypohyperglycaemia predictionrdquoDiabetes Research and Clinical Practice vol 74 no 2 supple-ment pp S160ndashS163 2006

[125] G Sparacino F Zanderigo G Corazza A Maran AFacchinetti and C Cobelli ldquoGlucose concentration can bepredicted ahead in time from continuous glucose monitoringsensor time-seriesrdquo IEEE Transactions on Biomedical Engineer-ing vol 54 pp 931ndash937 2007

[126] J Reifman S Rajaraman A Gribok and W K Ward ldquoPredic-tive monitoring for improved management of glucose levelsrdquoJournal of Diabetes Science and Technology vol 1 pp 478ndash4862007

[127] F Zanderigo G Sparacino B Kovatchev and C CobellildquoGlucose prediction algorithms from continuous monitoringassessment of accuracy via continuous glucose-error grid anal-ysisrdquo Journal of Diabetes Science and Technology vol 1 pp645ndash651 2007

[128] F Cameron G Niemayer K Gundy-Burlet and B Bucking-ham ldquoStatistical hypoglycemia predictionrdquo Journal of DiabetesScience and Technology vol 2 pp 612ndash621 2008

[129] A Gani A V Gribok S Rajaraman W K Ward and JReifman ldquoPredicting subcutaneous glucose concentration inhumans data-driven glucose modelingrdquo IEEE Transactions onBiomedical Engineering vol 56 no 2 pp 246ndash254 2009

[130] M Eren-Oruklu A Cinar L Quinn andD Smith ldquoEstimationof future glucose concentrations with subject-specic recursivelinear modelsrdquo Diabetes Technology and erapeutics vol 11no 4 pp 243ndash253 2009

[131] S M Pappada B D Cameron and P M Rosman ldquoDevelop-ment of a neural network for prediction of glucose concentra-tion in type 1 diabetes patientsrdquo Journal of Diabetes Science andTechnology vol 2 pp 792ndash801 2008

[132] C Cobelli C Dalla Man G Sparacino L Magni G Nicolaoand B P Kovatchev ldquoDiabetes models signals and controlrdquoIEEE Reviews in Biomedical Engineering vol 2 pp 54ndash96 2009

[133] H LeBlanc D Chauvet P Lombrail and J J Robert ldquoGlycemiccontrol with closed-loop intraperitoneal insulin in type Idiabetesrdquo Diabetes Care vol 9 no 2 pp 124ndash128 1986

[134] C D Saudek J L Selman H A Pitt et al ldquoA preliminary trialof the programmable implantablemedication system for insulindeliveryrdquo New England Journal of Medicine vol 321 no 9 pp574ndash579 1989

[135] C Broussolle N Jeandidier and H Hanaire-Broutin ldquoFrenchmulticentre experience of implantable insulin pumpsrdquo Lancetvol 343 no 8896 pp 514ndash515 1994

[136] H Hanaire-Broutin C Broussolle N Jeandidier et al ldquoFea-sibility of intraperitoneal insulin therapy with programmableimplantable pumps in IDDM a multicenter studyrdquo DiabetesCare vol 18 no 3 pp 388ndash392 1995

[137] P R Oskarsson P E Lins H W Henriksson and U CAdamson ldquoMetabolic and hormonal responses to exercisein type 1 diabetic patients during continuous subcutaneousas compared to continuous intraperitoneal insulin infusionrdquoDiabetes and Metabolism vol 25 no 6 pp 491ndash497 1999

[138] P R Oskarsson P E Lins L Backman and U C AdamsonldquoContinuous intraperitoneal insulin infusion partly restores theglucagon response to hypoglycaemia in type 1 diabetic patientsrdquoDiabetes and Metabolism vol 26 no 2 pp 118ndash124 2000

[139] B Catargi L Meyer V Melki E Renard and N JeandidierldquoComparison of blood glucose stability and HbA1C betweenimplantable insulin pumps using U400 hoe 21pH insulin andexternal pumps using lispro in type 1 diabetic patients a pilotstudyrdquo Diabetes and Metabolism vol 28 no 2 pp 133ndash1372002

[140] E Renard ldquoImplantable closed-loop glucose-sensing andinsulin delivery the future for insulin pump therapyrdquo CurrentOpinion in Pharmacology vol 2 no 6 pp 708ndash716 2002

[141] E Renard G Costalat H Chevassus and J Bringer ldquoArticial120573120573-cell clinical experience toward an implantable closed-loopinsulin delivery systemrdquo Diabetes and Metabolism vol 32 no5 pp 497ndash502 2006

[142] E Renard J Place M Cantwell H Chevassus and C CPalerm ldquoClosed-loop insulin delivery using a subcutaneousglucose sensor and intraperitoneal insulin delivery feasibilitystudy testing a new model for the articial pancreasrdquo DiabetesCare vol 33 no 1 pp 121ndash127 2010

[143] R Bellazzi G Nucci and C Cobelli ldquoe subcutaneousroute to insulin-dependent diabetes therapy closed-loop andpartially closed-loop control strategies for insulin deliveryand measuring glucose concentrationrdquo IEEE Engineering inMedicine and Biology Magazine vol 20 no 1 pp 54ndash64 2001

[144] R Hovorka L J Chassin M E Wilinska et al ldquoClosingthe loop the adicol experiencerdquo Diabetes Technology anderapeutics vol 6 no 3 pp 307ndash318 2004

[145] R Hovorka V Canonico L J Chassin et al ldquoNonlinear modelpredictive control of glucose concentration in subjects withtype 1 diabetesrdquo Physiological Measurement vol 25 no 4 pp905ndash920 2004

14 Scientica

[146] G M Steil K Rebrin C Darwin F Hariri and M F SaadldquoFeasibility of automating insulin delivery for the treatment oftype 1 diabetesrdquo Diabetes vol 55 no 12 pp 3344ndash3350 2006

[147] e JDRF e-Newsletter Emerging Technologies in DiabetesResearch 2006

[148] W L Clarke and B Kovatchev ldquoe articial pancreas howclose are we to closing the looprdquo Pediatric EndocrinologyReviews vol 4 no 4 pp 314ndash316 2007

[149] C Cobelli E Renard and B P Kovatchev ldquoPerspectives indiabetes articial pancreas past present futurerdquo Diabetes vol60 pp 2672ndash2682 2011

[150] B P Kovatchev M D Breton C Dalla Man and C Cobelli ldquoInsilico preclinical trials a proof of concept in closed-loop controlof type 1 diabetesrdquo Journal of Diabetes Science and Technologyvol 3 pp 44ndash55 2009

[151] B Kovatchev C Cobelli E Renard et al ldquoMultinational studyof subcutaneous model-predictive closed-loop control in type1 diabetes mellitus summary of the resultsrdquo Journal of DiabetesScience and Technology vol 4 no 6 pp 1374ndash1381 2010

[152] H Zisser L Jovanovic F Doyle P Ospina and C OwensldquoRun-to-run control of meal-related insulin dosingrdquo DiabetesTechnology anderapeutics vol 7 no 1 pp 48ndash57 2005

[153] C Owens H Zisser L Jovanovic B Srinivasan D Bonvinand F J Doyle III ldquoRun-to-run control of blood glucoseconcentrations for people with type 1 diabetes mellitusrdquo IEEETransactions on Biomedical Engineering vol 53 no 6 pp996ndash1005 2006

[154] C C Palerm H Zisser W C Bevier L Jovanovič and F JDoyle III ldquoPrandial insulin dosing using run-to-run controlapplication of clinical data and medical expertise to dene asuitable performance metricrdquo Diabetes Care vol 30 no 5 pp1131ndash1136 2007

[155] LMagni F Raimondo L Bossi et al ldquoModel predictive controlof type 1 diabetes an in silico trialrdquo Journal of Diabetes Scienceand Technology vol 1 pp 804ndash812 2007

[156] S A Weinzimer G M Steil K L Swan J Dziura N Kurtzand W V Tamborlane ldquoFully automated closed-loop insulindelivery versus semiautomated hybrid control in pediatricpatients with type 1 diabetes using an articial pancreasrdquoDiabetes Care vol 31 no 5 pp 934ndash939 2008

[157] W L Clarke S Anderson M Breton S Patek L Kashmerand B Kovatchev ldquoClosed-loop articial pancreas using sub-cutaneous glucose sensing and insulin delivery and a modelpredictive control algorithm the Virginia experiencerdquo Journalof Diabetes Science and Technology vol 3 no 5 pp 1031ndash10382009

[158] D Bruttomesso A Farret S Costa et al ldquoClosed-loop arti-cial pancreas using subcutaneous glucose sensing and insulindelivery and a model predictive control algorithm preliminarystudies in Padova and Montpellierrdquo Journal of Diabetes Scienceand Technology vol 3 no 5 pp 1014ndash1021 2009

[159] R Hovorka J M Allen D Elleri et al ldquoManual closed-loop insulin delivery in children and adolescents with type 1diabetes a phase 2 randomised crossover trialrdquoe Lancet vol375 no 9716 pp 743ndash751 2010

[160] F H El-khatib S J Russell D M Nathan R G Sutherlin andE R Damiano ldquoA bihormonal closed-loop articial pancreasfor type 1 diabetesrdquo Science Translational Medicine vol 2 no27 Article ID 27ra27 2010

[161] E Dassau H Zisser C C Palerm B A Buckingham LJovanovič and F J Doye III ldquoModular articial 120573120573-cell system a

prototype for clinical researchrdquo Journal of Diabetes Science andTechnology vol 2 pp 863ndash872 2008

[162] B Kovatchev S Patek E Dassau et al ldquoControl to range fordiabetes functionality and modular architecturerdquo Journal ofDiabetes Science and Rechnology vol 3 no 5 pp 1058ndash10652009

[163] E Atlas R Nimri S Miller E A Grunberg and M PhillipldquoMD-logic articial pancreas system a pilot study in adults withtype 1 diabetesrdquo Diabetes Care vol 33 no 5 pp 1072ndash10762010

[164] E Dassau H Zisser M W Percival B Grosman L Jovanovičand F J Doyle III ldquoClinical results of automated articialpancreatic 120573120573-cell system with unannounced meal using multi-parametric MPC and insulin-on-boardrdquo in Proceedings of the70th American Diabetes Association Meeting Diabetes vol 59supplement 1 A94 Orlando Fla USA 2010

[165] E M Renard A Farret J Place C Cobelli B P Kovatchev andMD Breton ldquoClosed-loop insulin delivery using subcutaneousinfusion and glucose sensing and equipped with a dedicatedsafety supervision algorithm improves safety of glucose controlin type 1 diabetesrdquo Diabetologia vol 53 supplement 1 p S252010

[166] R Hovorka K Kumareswaran J Harris et al ldquoOvernightclosed loop insulin delivery articial pancreas in adults withtype 1 diabetes crossover randomized controlled studiesrdquoBritish Medical Journal vol 342 no 7803 Article ID d18552011

[167] H Zisser E Dassau W Bevier et al ldquoInitial evaluation ofa fully automated articial pancreasrdquo in Proceedings of the71st American Diabetes Association Meeting Diabetes vol 60supplement 1 A41 San Diego Calif USA 2011

[168] M D Breton A Farret and D Bruttomesso ldquoFully-integratedarticial pancreas in type 1 diabetes modular closed-loopglucose control maintains near-normoglycemiardquo Diabetes vol61 pp 2230ndash2237 2012

[169] C Cobelli E Renard and B P Kovatchev ldquoPilot studies ofwearable articial pancreas in type 1 diabetesrdquo Diabetes Carevol 35 no 9 pp e65ndashe67 2012

Submit your manuscripts athttpwwwhindawicom

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Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

10 Scientica

IEEE Transactions on Biomedical Engineering vol 46 no 2 pp148ndash157 1999

[24] J J Mastrototaro ldquoe MiniMed continuous glucose monitor-ing Systemrdquo Diabetes Technology anderapeutics vol 2 no 1pp S13ndashS18 2000

[25] B W Bode ldquoClinical utility of the continuous glucose monitor-ing systemrdquo Diabetes Technology anderapeutics vol 2 no 1supplement pp S35ndashS41 2000

[26] B Feldman R Brazg S Schwartz and R Weinstein ldquoAcontinuous glucose sensor based on wired enzyme technol-ogymdashresults from a 3-day trial in patients with type 1 diabetesrdquoDiabetes Technology anderapeutics vol 5 no 5 pp 769ndash7792003

[27] eDiabetes Control and Complications Trial Research Groupldquoe effect of intensive treatment of diabetes on the develop-ment and progression of long-term complications of insulin-dependent diabetes mellitusrdquoNew England Journal of Medicinevol 329 pp 978ndash986 1993

[28] eDiabetes Control and Complications Trial Research Groupldquoe relationship of glycemic exposure (HbA1c) to the riskof development and progression of retinopathy in the Dia-betes Control and Complications Trialrdquo Diabetes vol 44 pp968ndash983 1995

[29] J M Lachin S Genuth D M Nathan B Zinman and B NRutledge ldquoEffect of glycemic exposure on the risk of microvas-cular complications in the diabetes control and complicationstrial-revisitedrdquo Diabetes vol 57 no 4 pp 995ndash1001 2008

[30] P Reichard and M Pihl ldquoMortality and treatment side-effectsduring long-term intensied conventional insulin treatment inthe Stockholm Diabetes Intervention Studyrdquo Diabetes vol 43no 2 pp 313ndash317 1994

[31] UK Prospective Diabetes Study Group (UKPDS) ldquoIntensiveblood-glucose control with sulphonylureas or insulin comparedwith conventional treatment and risk of complications inpatients with type 2 diabetes (UKPDS 33)rdquo Lancet vol 352 no9131 pp 837ndash853 1998

[32] P A Svendsen T Lauritzen U Soegaard and J NerupldquoGlycosylated haemoglobin and steady-state mean blood glu-cose concentration in type 1 (insulin-dependent) diabetesrdquoDiabetologia vol 23 no 5 pp 403ndash405 1982

[33] J V Santiago ldquoLessons from the diabetes control and compli-cations trialrdquo Diabetes vol 42 no 11 pp 1549ndash1554 1993

[34] eDiabetes Control and Complications Trial Research GroupldquoHypoglycemia in the diabetes control and complications trialrdquoDiabetes vol 46 pp 271ndash286 1997

[35] A E Gold BM Frier KMMacLeod and I J Deary ldquoA struc-tural equation model for predictors of severe hypoglycaemiain patients with insulin-dependent diabetes mellitusrdquo DiabeticMedicine vol 14 pp 309ndash315 1997

[36] D J Cox B P Kovatchev D M Julian et al ldquoFrequency ofsevere hypoglycemia in insulin-dependent diabetesmellitus canbe predicted from self-monitoring blood glucose datardquo Journalof Clinical Endocrinology and Metabolism vol 79 no 6 pp1659ndash1662 1994

[37] B P Kovatchev D J Cox L A Gonder-Frederick D Young-Hyman D Schlundt and W Clarke ldquoAssessment of risk forsevere hypoglycemia among adults with IDDM validation ofthe low blood glucose indexrdquo Diabetes Care vol 21 no 11 pp1870ndash1875 1998

[38] B P Kovatchev D J Cox A Kumar L Gonder-Frederick andW L Clarke ldquoAlgorithmic evaluation of metabolic control and

risk of severe hypoglycemia in type 1 and type 2 diabetes usingself-monitoring blood glucose datardquo Diabetes Technology anderapeutics vol 5 no 5 pp 817ndash828 2003

[39] D J Cox L Gonder-Frederick L Ritterband W Clarke andB P Kovatchev ldquoPrediction of severe hypoglycemiardquo DiabetesCare vol 30 no 6 pp 1370ndash1373 2007

[40] S A Amiel R S Sherwin D C Simonson and W VTamborlane ldquoEffect of intensive insulin therapy on glycemicthresholds for counterregulatory hormone releaserdquo Diabetesvol 37 no 7 pp 901ndash907 1988

[41] S A Amiel W V Tamborlane D C Simonson and RS Sherwin ldquoDefective glucose counterregulation aer strictglycemic control of insulin-dependent diabetes mellitusrdquo NewEngland Journal of Medicine vol 316 no 22 pp 1376ndash13831987

[42] P E Cryer and J E Gerich ldquoGlucose counterregulation hypo-glycemia and intensive insulin therapy in diabetes mellitusrdquoNew England Journal of Medicine vol 313 no 4 pp 232ndash2411985

[43] N H White D A Skor and P E Cryer ldquoIdentication of typeI diabetic patients at increased risk for hypoglycemia duringintensive therapyrdquo New England Journal of Medicine vol 308no 9 pp 485ndash491 1983

[44] P E Cryer ldquoIatrogenic hypoglycemia as a cause ofhypoglycemia-associated autonomic failure in IDDM avicious cyclerdquo Diabetes vol 41 no 3 pp 255ndash260 1992

[45] J N Henderson K V Allen I J Deary and B M FrierldquoHypoglycaemia in insulin-treated Type 2 diabetes frequencysymptoms and impaired awarenessrdquo Diabetic Medicine vol 20no 12 pp 1016ndash1021 2003

[46] P E Cryer Hypoglycemia Pathophysiology Diagnosis andTreatment Oxford University Press New York NY USA 1997

[47] P E Cryer S N Davis and H Shamoon ldquoHypoglycemia indiabetesrdquo Diabetes Care vol 26 no 6 pp 1902ndash1912 2003

[48] B P Childs N G Clark and D J Cox ldquoDening and reportinghypoglycemia in diabetes a report from the American diabetesassociation workgroup on hypoglycemiardquo Diabetes Care vol28 no 5 pp 1245ndash1249 2005

[49] P E Cryer ldquoHypoglycaemia the limiting factor in the glycaemicmanagement of Type I and Type II diabetesrdquo Diabetologia vol45 no 7 pp 937ndash948 2002

[50] P E Cryer ldquoHypoglycemia the limiting factor in the manage-ment of IDDMrdquo Diabetes vol 43 no 11 pp 1378ndash1389 1994

[51] A L McCall and B P Kovatchev ldquoe median is not the onlymessage a clinicianrsquos perspective on mathematical analysis ofglycemic variability and modeling in diabetes mellitusrdquo Journalof Diabetes Science and Technology vol 3 pp 3ndash11 2009

[52] M Brownlee and I B Hirsch ldquoGlycemic variability ahemoglobin A1c-independent risk factor for diabetic compli-cationsrdquo Journal of the American Medical Association vol 295no 14 pp 1707ndash1708 2006

[53] I B Hirsch and M Brownlee ldquoShould minimal blood glucosevariability become the gold standard of glycemic controlrdquoJournal of Diabetes and Its Complications vol 19 no 3 pp178ndash181 2005

[54] K Esposito D Giugliano F Nappo and R Marfella ldquoRegres-sion of carotid atherosclerosis by control of postprandial hyper-glycemia in type 2 diabetes mellitusrdquo Circulation vol 110 no2 pp 214ndash219 2004

[55] S Haffner ldquoe importance of postprandial hyperglycaemia indevelopment of cardiovascular disease in people with diabetes

Scientica 11

pointrdquo International Journal of Clinical Practice no 123 pp24ndash26 2001

[56] LMonnier EMas C Ginet et al ldquoActivation of oxidative stressby acute glucose uctuations compared with sustained chronichyperglycemia in patients with type 2 diabetesrdquo Journal of theAmerican Medical Association vol 295 no 14 pp 1681ndash16872006

[57] T S Temelkova-Kurktschiev C Koehler E Henkel W Leon-hardt K Fuecker and M Hanefeld ldquoPostchallenge plasmaglucose and glycemic spikes are more strongly associated withatherosclerosis than fasting glucose or HbA(1c) levelrdquo DiabetesCare vol 23 no 12 pp 1830ndash1834 2000

[58] W L Clarke D J Cox L Gonder-Frederick D JulianB Kovatchev and D Young-Hyman ldquoBiopsychobehavioralmodel of risk of severe hypoglycemia self-management behav-iorsrdquo Diabetes Care vol 22 no 4 pp 580ndash584 1999

[59] D J Cox L A Gonder-Frederick B P Kovatchev et alldquoBiopsychobehavioral model of severe hypoglycemia II under-standing the risk of severe hypoglycemiardquo Diabetes Care vol22 no 12 pp 2018ndash2025 1999

[60] L Gonder-Frederick D Cox B Kovatchev D Schlundt andW Clarke ldquoA biopsychobehavioral model of risk of severehypoglycemiardquoDiabetes Care vol 20 no 4 pp 661ndash669 1997

[61] B Kovatchev D Cox L Gonder-Frederick D Schlundtand W Clarke ldquoStochastic model of self-regulation decisionmaking exemplied by decisions concerning hypoglycemiardquoHealth Psychology vol 17 no 3 pp 277ndash284 1998

[62] G Nucci and C Cobelli ldquoModels of subcutaneous insulinkinetics A critical reviewrdquo Computer Methods and Programs inBiomedicine vol 62 no 3 pp 249ndash257 2000

[63] M E Wilinska L J Chassin H C Schaller L Schaupp T RPieber and R Hovorka ldquoInsulin kinetics in type-1 diabetescontinuous and bolus delivery of rapid acting insulinrdquo IEEETransactions on Biomedical Engineering vol 52 no 1 pp 3ndash122005

[64] R N Bergman D T Finegood and M Ader ldquoAssessmentof insulin sensitivity in vivordquo Endocrine Reviews vol 236 ppE667ndashE677 1985

[65] R N Bergman ldquoe minimal model of glucose regulation abiographyrdquoAdvances in ExperimentalMedicine and Biology vol537 pp 1ndash19 2003

[66] R N Bergman D J Zaccaro R M Watanabe et al ldquoMinimalmodel-based insulin sensitivity has greater heritability and adifferent genetic basis than homeostasis model assessment orfasting insulinrdquo Diabetes vol 52 no 8 pp 2168ndash2174 2003

[67] A Caumo R N Bergman and C Cobelli ldquoInsulin sensitivityfrom meal tolerance tests in normal subjects a minimal modelindexrdquo Journal of Clinical Endocrinology and Metabolism vol85 no 11 pp 4396ndash4402 2000

[68] J O Clausen K Borch-Johnsen H Ibsen et al ldquoInsulin sen-sitivity index acute insulin response and glucose effectivenessin a population-based sample of 380 young healthy Caucasiansanalysis of the impact of gender body fat physical tness andlife-style factorsrdquo Journal of Clinical Investigation vol 98 no 5pp 1195ndash1209 1996

[69] T C Ni M Ader and R N Bergman ldquoReassessment of glu-cose effectiveness and insulin sensitivity from minimal modelanalysis a theoretical evaluation of the single-compartmentglucose distribution assumptionrdquo Diabetes vol 46 no 11 pp1813ndash1821 1997

[70] S Welch S S P Gebhart R N Bergman and L S PhillipsldquoMinimal model analysis of intravenous glucose tolerance

test-derived insulin sensitivity in diabetic subjectsrdquo Journalof Clinical Endocrinology and Metabolism vol 71 no 6 pp1508ndash1518 1990

[71] D Dawson M A Vincent E J Barrett et al ldquoCapillary recruit-ment in skeletal muscle in response to exercise and hyperin-sulinemia assessed with contrast-enhanced ultrasoundrdquo Amer-ican Journal of Physiology vol 282 no 3 pp E714ndashE720 2002

[72] K J Mikines B Sonne P A Farrell B Tronier and H GalboldquoEffect of physical exercise on sensitivity and responsiveness toinsulin in humansrdquoAmerican Journal of Physiology vol 254 no3 pp E248ndashE259 1988

[73] E A Richter ldquoGlucose utilizationrdquo in Handbook of PhysiologyL B Rowell and J T Shepherd Eds pp 912ndash951 OxfordUniversity Press New York NY USA 1996

[74] D H Wasserman R J Geer D E Rice et al ldquoInteraction ofexercise and insulin action in humansrdquo American Journal ofPhysiology vol 260 no 1 pp E37ndashE45 1991

[75] G Pillonetto A Caumo G Sparacino and C Cobelli ldquoAnew dynamic index of insulin sensitivityrdquo IEEE Transactions onBiomedical Engineering vol 53 no 3 pp 369ndash379 2006

[76] C Dalla Man D M Raimondo R A Rizza and C CobellildquoGIM Simulation soware of meal glucose-insulin modelrdquoJournal of Diabetes Science and Technology vol 1 pp 323ndash3302007

[77] C Dalla Man R A Rizza and C Cobelli ldquoMeal simulationmodel of the glucose-insulin systemrdquo IEEE Transactions onBiomedical Engineering vol 54 no 10 pp 1740ndash1749 2007

[78] W L Clarke D Cox L A Gonder-Frederick W Carter andS L Pohl ldquoEvaluating clinical accuracy of systems for self-monitoring of blood glucoserdquo Diabetes Care vol 10 no 5 pp622ndash628 1987

[79] e Diabetes Research In Children Network (Direcnet) StudyGroup ldquoA multicenter study of the accuracy of the one touchultra home glucose meter in children with type 1 diabetesrdquoDiabetes Technology anderapeutics vol 5 no 6 pp 933ndash9412003

[80] I B Hirsch B W Bode B P Childs et al ldquoSelf-monitoring ofblood glucose (SMBG) in insulin- and non-insulin-using adultswith diabetes consensus recommendations for improvingSMBG accuracy utilization and researchrdquo Diabetes Technologyanderapeutics vol 10 no 6 pp 419ndash439 2008

[81] G Freckmann A Baumstark N Jendrike et al ldquoSystemaccuracy evaluation of 27 blood glucose monitoring systemsaccording to DIN en ISO 15197rdquo Diabetes Technology anderapeutics vol 12 no 3 pp 221ndash231 2010

[82] D Deiss J Bolinder J P Riveline et al ldquoImproved glycemiccontrol in poorly controlled patients with type 1 diabetes usingreal-time continuous glucose monitoringrdquo Diabetes Care vol29 no 12 pp 2730ndash2732 2006

[83] S Garg H Zisser S Schwartz et al ldquoImprovement in glycemicexcursions with a transcutaneous real-time continuous glucosesensor a randomized controlled trialrdquo Diabetes Care vol 29no 1 pp 44ndash50 2006

[84] B P Kovatchev and W L Clarke ldquoContinuous glucose mon-itoring reduces risks for hypo- and hyperglycemia and glucosevariability in diabetesrdquo Diabetes vol 56 1 Article ID 0086OR2007

[85] e Juvenile Diabetes Research Foundation Continuous Glu-cose Monitoring Study Group ldquoContinuous glucose monitor-ing and intensive treatment of type 1 diabetesrdquo New EnglandJournal of Medicine vol 359 pp 1464ndash1476 2008

12 Scientica

[86] D C Klonoff ldquoContinuous glucose monitoring roadmap for21st century diabetes therapyrdquo Diabetes Care vol 28 no 5 pp1231ndash1239 2005

[87] R Hovorka ldquoContinuous glucose monitoring and closed-loopsystemsrdquo Diabetic Medicine vol 23 no 1 pp 1ndash12 2006

[88] D C Klonoff ldquoe articial pancreas how sweet engineeringwill solve bitter problemsrdquo Journal of Diabetes Science andTechnology vol 1 pp 72ndash81 2007

[89] I B Hirsch D Armstrong R M Bergenstal et al ldquoClin-ical application of emerging sensor technologies in diabetesmanagement consensus guidelines for continuous glucosemonitoring (CGM)rdquoDiabetes Technology anderapeutics vol10 no 4 pp 232ndash246 2008

[90] K Rebrin G M Steil W P Van Antwerp and J J Mastro-totaro ldquoSubcutaneous glucose predicts plasma glucose inde-pendent of insulin implications for continuous monitoringrdquoAmerican Journal of Physiology vol 277 no 3 pp E561ndashE5711999

[91] K Rebrin and G M Steil ldquoCan interstitial glucose assessmentreplace blood glucosemeasurementsrdquoDiabetes Technology anderapeutics vol 2 no 3 pp 461ndash472 2000

[92] G M Steil K Rebrin F Hariri et al ldquoInterstitial uid glucosedynamics during insulin-induced hypoglycaemiardquo Diabetolo-gia vol 48 no 9 pp 1833ndash1840 2005

[93] M S Boyne D M Silver J Kaplan and C D Saudek ldquoTimingof changes in interstitial and venous blood glucose measuredwith a continuous subcutaneous glucose sensorrdquo Diabetes vol52 no 11 pp 2790ndash2794 2003

[94] E Kulcu J A Tamada G Reach R O Potts and M JLesho ldquoPhysiological differences between interstitial glucoseand blood glucose measured in human subjectsrdquoDiabetes Carevol 26 no 8 pp 2405ndash2409 2003

[95] P J Stout J R Racchini andM E Hilgers ldquoA novel approach tomitigating the physiological lag between blood and interstitialuid glucose measurementsrdquo Diabetes Technology and era-peutics vol 6 no 5 pp 635ndash644 2004

[96] I M E Wentholt A A M Hart J B L Hoekstra and J HDevries ldquoRelationship between interstitial and blood glucose intype 1 diabetes patients delay and the push-pull phenomenonrevisitedrdquo Diabetes Technology and erapeutics vol 9 no 2pp 169ndash175 2007

[97] K J C Wientjes and A J M Schoonen ldquoDetermination oftime delay between blood and interstitial adipose tissue glucoseconcentration change by microdialysis in healthy volunteersrdquonternational Journal of Articial rgans vol 24 no 12 pp884ndash889 2001

[98] B Aussedat M Dupire-Angel R Gifford J C Klein G SWilson and G Reach ldquoInterstitial glucose concentration andglycemia implications for continuous subcutaneous glucosemonitoringrdquo American Journal of Physiology vol 278 no 4 ppE716ndashE728 2000

[99] B P Kovatchev and W L Clarke ldquoPeculiarities of the con-tinuous glucose monitoring data stream and their impact ondeveloping closed-loop control technologyrdquo Journal of DiabetesScience and Technology vol 2 pp 158ndash163 2008

[100] W L Clarke and B P Kovatchev ldquoContinuous glucose sen-sorsmdashcontinuing questions about clinical accuracyrdquo Journal ofDiabetes Science and Technology vol 1 pp 164ndash170 2007

[101] e Diabetes Research in Children Network (DirecNet) StudyGroup ldquoe accuracy of the guardian RT continuous glucosemonitor in children with type 1 diabetesrdquo Diabetes Technologyanderapeutics vol 10 pp 266ndash272 2008

[102] B Kovatchev S Anderson L Heinemann and W ClarkeldquoComparison of the numerical and clinical accuracy of fourcontinuous glucose monitorsrdquo Diabetes Care vol 31 no 6 pp1160ndash1164 2008

[103] S K Garg J Smith C Beatson B Lopez-Baca M Voelmleand P A Gottlieb ldquoComparison of accuracy and safety ofthe SEVEN and the navigator continuous glucose monitoringsystemsrdquo Diabetes Technology and erapeutics vol 11 no 2pp 65ndash72 2009

[104] T Heise T Koschinsky L Heinemann and V Lodwig ldquoHypo-glycemia warning signal and glucose sensors requirements andconceptsrdquo Diabetes Technology and erapeutics vol 5 no 4pp 563ndash571 2003

[105] B Bode K Gross N Rikalo et al ldquoAlarms based on real-timesensor glucose values alert patients to hypo- and hyperglycemiathe Guardian continuous monitoring systemrdquo Diabetes Tech-nology anderapeutics vol 6 no 2 pp 105ndash113 2004

[106] G McGarraugh and R Bergenstal ldquoDetection of hypoglycemiawith continuous interstitial and traditional blood glucosemonitoring using the FreeStyle navigator continuous glucosemonitoring systemrdquo Diabetes Technology and erapeutics vol11 no 3 pp 145ndash150 2009

[107] S E Noujaim D Horwitz M Sharma and J Marhoul ldquoAccu-racy requirements for a hypoglycemia detector an analyticalmodel to evaluate the effects of bias precision and rate ofglucose changerdquo Journal of Diabetes Science and Technology vol1 pp 653ndash668 2007

[108] W K Ward ldquoe role of new technology in the early detectionof hypoglycemiardquo Diabetes Technology and erapeutics vol 6no 2 pp 115ndash117 2004

[109] B Buckingham E Cobry P Clinton et al ldquoPreventing hypo-glycemia using predictive alarm algorithms and insulin pumpsuspensionrdquo Diabetes Technology and erapeutics vol 11 no2 pp 93ndash97 2009

[110] C S Hughes S D Patek M D Breton and B P KovatchevldquoHypoglycemia prevention via pump attenuation and red-yellow-green ldquotrafficrdquo lights using continuous glucose moni-toring and insulin pump datardquo Journal of Diabetes Science andTechnology vol 4 no 5 pp 1146ndash1155 2010

[111] B P Kovatchev D J Cox L A Gonder-Frederick and WClarke ldquoSymmetrization of the blood glucose measurementscale and its applicationsrdquo Diabetes Care vol 20 no 11 pp1655ndash1658 1997

[112] F J Service G D Molnar J W Rosevear E Ackerman LC Gatewood and W F Taylor ldquoMean amplitude of glycemicexcursions a measure of diabetic instabilityrdquo Diabetes vol 19no 9 pp 644ndash655 1970

[113] J Schlichtkrull O Munck and M Jersild ldquoe M-value anindex of blood glucose control in diabeticsrdquo Acta MedicaScandinavica vol 177 pp 95ndash102 1965

[114] E A Ryan T Shandro K Green et al ldquoAssessment of theseverity of hypoglycemia and glycemic lambility in type 1diabetic subjects undergoing islet in transplantationrdquo Diabetesvol 53 no 4 pp 955ndash962 2004

[115] B P Kovatchev D J Cox L Gonder-Frederick and WL Clarke ldquoMethods for quantifying self-monitoring bloodglucose proles exemplied by an examination of blood glucosepatterns in patients with type 1 and type 2 diabetesrdquo DiabetesTechnology anderapeutics vol 4 no 3 pp 295ndash303 2002

[116] B P Kovatchev D J Cox L S Farhy M Straume L Gonder-Frederick and W L Clarke ldquoEpisodes of severe hypoglycemia

Scientica 13

in type 1 diabetes are preceded and followed within 48 hours bymeasurable disturbances in blood glucoserdquo Journal of ClinicalEndocrinology and Metabolism vol 85 no 11 pp 4287ndash42922000

[117] B P Kovatchev M Straume D J Cox and L S FarhyldquoRisk analysis of blood glucose data a quantitative approach tooptimizing the control of Insulin Dependent Diabetesrdquo Journalof eoretical Medicine vol 3 no 1 pp 1ndash10 2000

[118] B P Kovatchev E Otto D Cox L Gonder-Frederick andW Clarke ldquoEvaluation of a new measure of blood glucosevariability in diabetesrdquo Diabetes Care vol 29 no 11 pp2433ndash2438 2006

[119] B P Kovatchev W L Clarke M Breton K Brayman and AMcCall ldquoQuantifying temporal glucose variability in diabetesvia continuous glucose monitoring mathematical methods andclinical applicationrdquo Diabetes Technology and erapeutics vol7 no 6 pp 849ndash862 2005

[120] D Rodbard ldquoNew and improved methods to characterizeglycemic variability using continuous glucosemonitoringrdquoDia-betes Technology and erapeutics vol 11 no 9 pp 551ndash5652009

[121] M Miller and P Strange ldquoUse of Fourier models for analysisand interpretation of continuous glucose monitoring glucoseprolesrdquo Journal of Diabetes Science and Technology vol 1 pp630ndash638 2007

[122] W Clarke and B Kovatchev ldquoStatistical tools to analyzecontinuous glucose monitor datardquo Diabetes Technology anderapeutics vol 11 pp S45ndashS54 2009

[123] C M Mcdonnell S M Donath S I Vidmar G A Wertherand F J Cameron ldquoA novel approach to continuous glucoseanalysis utilizing glycemic variationrdquo Diabetes Technology anderapeutics vol 7 no 2 pp 253ndash263 2005

[124] G Sparacino F Zanderigo A Maran and C Cobelli ldquoContin-uous glucosemonitoring andhypohyperglycaemia predictionrdquoDiabetes Research and Clinical Practice vol 74 no 2 supple-ment pp S160ndashS163 2006

[125] G Sparacino F Zanderigo G Corazza A Maran AFacchinetti and C Cobelli ldquoGlucose concentration can bepredicted ahead in time from continuous glucose monitoringsensor time-seriesrdquo IEEE Transactions on Biomedical Engineer-ing vol 54 pp 931ndash937 2007

[126] J Reifman S Rajaraman A Gribok and W K Ward ldquoPredic-tive monitoring for improved management of glucose levelsrdquoJournal of Diabetes Science and Technology vol 1 pp 478ndash4862007

[127] F Zanderigo G Sparacino B Kovatchev and C CobellildquoGlucose prediction algorithms from continuous monitoringassessment of accuracy via continuous glucose-error grid anal-ysisrdquo Journal of Diabetes Science and Technology vol 1 pp645ndash651 2007

[128] F Cameron G Niemayer K Gundy-Burlet and B Bucking-ham ldquoStatistical hypoglycemia predictionrdquo Journal of DiabetesScience and Technology vol 2 pp 612ndash621 2008

[129] A Gani A V Gribok S Rajaraman W K Ward and JReifman ldquoPredicting subcutaneous glucose concentration inhumans data-driven glucose modelingrdquo IEEE Transactions onBiomedical Engineering vol 56 no 2 pp 246ndash254 2009

[130] M Eren-Oruklu A Cinar L Quinn andD Smith ldquoEstimationof future glucose concentrations with subject-specic recursivelinear modelsrdquo Diabetes Technology and erapeutics vol 11no 4 pp 243ndash253 2009

[131] S M Pappada B D Cameron and P M Rosman ldquoDevelop-ment of a neural network for prediction of glucose concentra-tion in type 1 diabetes patientsrdquo Journal of Diabetes Science andTechnology vol 2 pp 792ndash801 2008

[132] C Cobelli C Dalla Man G Sparacino L Magni G Nicolaoand B P Kovatchev ldquoDiabetes models signals and controlrdquoIEEE Reviews in Biomedical Engineering vol 2 pp 54ndash96 2009

[133] H LeBlanc D Chauvet P Lombrail and J J Robert ldquoGlycemiccontrol with closed-loop intraperitoneal insulin in type Idiabetesrdquo Diabetes Care vol 9 no 2 pp 124ndash128 1986

[134] C D Saudek J L Selman H A Pitt et al ldquoA preliminary trialof the programmable implantablemedication system for insulindeliveryrdquo New England Journal of Medicine vol 321 no 9 pp574ndash579 1989

[135] C Broussolle N Jeandidier and H Hanaire-Broutin ldquoFrenchmulticentre experience of implantable insulin pumpsrdquo Lancetvol 343 no 8896 pp 514ndash515 1994

[136] H Hanaire-Broutin C Broussolle N Jeandidier et al ldquoFea-sibility of intraperitoneal insulin therapy with programmableimplantable pumps in IDDM a multicenter studyrdquo DiabetesCare vol 18 no 3 pp 388ndash392 1995

[137] P R Oskarsson P E Lins H W Henriksson and U CAdamson ldquoMetabolic and hormonal responses to exercisein type 1 diabetic patients during continuous subcutaneousas compared to continuous intraperitoneal insulin infusionrdquoDiabetes and Metabolism vol 25 no 6 pp 491ndash497 1999

[138] P R Oskarsson P E Lins L Backman and U C AdamsonldquoContinuous intraperitoneal insulin infusion partly restores theglucagon response to hypoglycaemia in type 1 diabetic patientsrdquoDiabetes and Metabolism vol 26 no 2 pp 118ndash124 2000

[139] B Catargi L Meyer V Melki E Renard and N JeandidierldquoComparison of blood glucose stability and HbA1C betweenimplantable insulin pumps using U400 hoe 21pH insulin andexternal pumps using lispro in type 1 diabetic patients a pilotstudyrdquo Diabetes and Metabolism vol 28 no 2 pp 133ndash1372002

[140] E Renard ldquoImplantable closed-loop glucose-sensing andinsulin delivery the future for insulin pump therapyrdquo CurrentOpinion in Pharmacology vol 2 no 6 pp 708ndash716 2002

[141] E Renard G Costalat H Chevassus and J Bringer ldquoArticial120573120573-cell clinical experience toward an implantable closed-loopinsulin delivery systemrdquo Diabetes and Metabolism vol 32 no5 pp 497ndash502 2006

[142] E Renard J Place M Cantwell H Chevassus and C CPalerm ldquoClosed-loop insulin delivery using a subcutaneousglucose sensor and intraperitoneal insulin delivery feasibilitystudy testing a new model for the articial pancreasrdquo DiabetesCare vol 33 no 1 pp 121ndash127 2010

[143] R Bellazzi G Nucci and C Cobelli ldquoe subcutaneousroute to insulin-dependent diabetes therapy closed-loop andpartially closed-loop control strategies for insulin deliveryand measuring glucose concentrationrdquo IEEE Engineering inMedicine and Biology Magazine vol 20 no 1 pp 54ndash64 2001

[144] R Hovorka L J Chassin M E Wilinska et al ldquoClosingthe loop the adicol experiencerdquo Diabetes Technology anderapeutics vol 6 no 3 pp 307ndash318 2004

[145] R Hovorka V Canonico L J Chassin et al ldquoNonlinear modelpredictive control of glucose concentration in subjects withtype 1 diabetesrdquo Physiological Measurement vol 25 no 4 pp905ndash920 2004

14 Scientica

[146] G M Steil K Rebrin C Darwin F Hariri and M F SaadldquoFeasibility of automating insulin delivery for the treatment oftype 1 diabetesrdquo Diabetes vol 55 no 12 pp 3344ndash3350 2006

[147] e JDRF e-Newsletter Emerging Technologies in DiabetesResearch 2006

[148] W L Clarke and B Kovatchev ldquoe articial pancreas howclose are we to closing the looprdquo Pediatric EndocrinologyReviews vol 4 no 4 pp 314ndash316 2007

[149] C Cobelli E Renard and B P Kovatchev ldquoPerspectives indiabetes articial pancreas past present futurerdquo Diabetes vol60 pp 2672ndash2682 2011

[150] B P Kovatchev M D Breton C Dalla Man and C Cobelli ldquoInsilico preclinical trials a proof of concept in closed-loop controlof type 1 diabetesrdquo Journal of Diabetes Science and Technologyvol 3 pp 44ndash55 2009

[151] B Kovatchev C Cobelli E Renard et al ldquoMultinational studyof subcutaneous model-predictive closed-loop control in type1 diabetes mellitus summary of the resultsrdquo Journal of DiabetesScience and Technology vol 4 no 6 pp 1374ndash1381 2010

[152] H Zisser L Jovanovic F Doyle P Ospina and C OwensldquoRun-to-run control of meal-related insulin dosingrdquo DiabetesTechnology anderapeutics vol 7 no 1 pp 48ndash57 2005

[153] C Owens H Zisser L Jovanovic B Srinivasan D Bonvinand F J Doyle III ldquoRun-to-run control of blood glucoseconcentrations for people with type 1 diabetes mellitusrdquo IEEETransactions on Biomedical Engineering vol 53 no 6 pp996ndash1005 2006

[154] C C Palerm H Zisser W C Bevier L Jovanovič and F JDoyle III ldquoPrandial insulin dosing using run-to-run controlapplication of clinical data and medical expertise to dene asuitable performance metricrdquo Diabetes Care vol 30 no 5 pp1131ndash1136 2007

[155] LMagni F Raimondo L Bossi et al ldquoModel predictive controlof type 1 diabetes an in silico trialrdquo Journal of Diabetes Scienceand Technology vol 1 pp 804ndash812 2007

[156] S A Weinzimer G M Steil K L Swan J Dziura N Kurtzand W V Tamborlane ldquoFully automated closed-loop insulindelivery versus semiautomated hybrid control in pediatricpatients with type 1 diabetes using an articial pancreasrdquoDiabetes Care vol 31 no 5 pp 934ndash939 2008

[157] W L Clarke S Anderson M Breton S Patek L Kashmerand B Kovatchev ldquoClosed-loop articial pancreas using sub-cutaneous glucose sensing and insulin delivery and a modelpredictive control algorithm the Virginia experiencerdquo Journalof Diabetes Science and Technology vol 3 no 5 pp 1031ndash10382009

[158] D Bruttomesso A Farret S Costa et al ldquoClosed-loop arti-cial pancreas using subcutaneous glucose sensing and insulindelivery and a model predictive control algorithm preliminarystudies in Padova and Montpellierrdquo Journal of Diabetes Scienceand Technology vol 3 no 5 pp 1014ndash1021 2009

[159] R Hovorka J M Allen D Elleri et al ldquoManual closed-loop insulin delivery in children and adolescents with type 1diabetes a phase 2 randomised crossover trialrdquoe Lancet vol375 no 9716 pp 743ndash751 2010

[160] F H El-khatib S J Russell D M Nathan R G Sutherlin andE R Damiano ldquoA bihormonal closed-loop articial pancreasfor type 1 diabetesrdquo Science Translational Medicine vol 2 no27 Article ID 27ra27 2010

[161] E Dassau H Zisser C C Palerm B A Buckingham LJovanovič and F J Doye III ldquoModular articial 120573120573-cell system a

prototype for clinical researchrdquo Journal of Diabetes Science andTechnology vol 2 pp 863ndash872 2008

[162] B Kovatchev S Patek E Dassau et al ldquoControl to range fordiabetes functionality and modular architecturerdquo Journal ofDiabetes Science and Rechnology vol 3 no 5 pp 1058ndash10652009

[163] E Atlas R Nimri S Miller E A Grunberg and M PhillipldquoMD-logic articial pancreas system a pilot study in adults withtype 1 diabetesrdquo Diabetes Care vol 33 no 5 pp 1072ndash10762010

[164] E Dassau H Zisser M W Percival B Grosman L Jovanovičand F J Doyle III ldquoClinical results of automated articialpancreatic 120573120573-cell system with unannounced meal using multi-parametric MPC and insulin-on-boardrdquo in Proceedings of the70th American Diabetes Association Meeting Diabetes vol 59supplement 1 A94 Orlando Fla USA 2010

[165] E M Renard A Farret J Place C Cobelli B P Kovatchev andMD Breton ldquoClosed-loop insulin delivery using subcutaneousinfusion and glucose sensing and equipped with a dedicatedsafety supervision algorithm improves safety of glucose controlin type 1 diabetesrdquo Diabetologia vol 53 supplement 1 p S252010

[166] R Hovorka K Kumareswaran J Harris et al ldquoOvernightclosed loop insulin delivery articial pancreas in adults withtype 1 diabetes crossover randomized controlled studiesrdquoBritish Medical Journal vol 342 no 7803 Article ID d18552011

[167] H Zisser E Dassau W Bevier et al ldquoInitial evaluation ofa fully automated articial pancreasrdquo in Proceedings of the71st American Diabetes Association Meeting Diabetes vol 60supplement 1 A41 San Diego Calif USA 2011

[168] M D Breton A Farret and D Bruttomesso ldquoFully-integratedarticial pancreas in type 1 diabetes modular closed-loopglucose control maintains near-normoglycemiardquo Diabetes vol61 pp 2230ndash2237 2012

[169] C Cobelli E Renard and B P Kovatchev ldquoPilot studies ofwearable articial pancreas in type 1 diabetesrdquo Diabetes Carevol 35 no 9 pp e65ndashe67 2012

Submit your manuscripts athttpwwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

EndocrinologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

OncologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Oxidative Medicine and Cellular Longevity

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational and Mathematical Methods in Medicine

OphthalmologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Diabetes ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Research and TreatmentAIDS

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Gastroenterology Research and Practice

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Scientica 11

pointrdquo International Journal of Clinical Practice no 123 pp24ndash26 2001

[56] LMonnier EMas C Ginet et al ldquoActivation of oxidative stressby acute glucose uctuations compared with sustained chronichyperglycemia in patients with type 2 diabetesrdquo Journal of theAmerican Medical Association vol 295 no 14 pp 1681ndash16872006

[57] T S Temelkova-Kurktschiev C Koehler E Henkel W Leon-hardt K Fuecker and M Hanefeld ldquoPostchallenge plasmaglucose and glycemic spikes are more strongly associated withatherosclerosis than fasting glucose or HbA(1c) levelrdquo DiabetesCare vol 23 no 12 pp 1830ndash1834 2000

[58] W L Clarke D J Cox L Gonder-Frederick D JulianB Kovatchev and D Young-Hyman ldquoBiopsychobehavioralmodel of risk of severe hypoglycemia self-management behav-iorsrdquo Diabetes Care vol 22 no 4 pp 580ndash584 1999

[59] D J Cox L A Gonder-Frederick B P Kovatchev et alldquoBiopsychobehavioral model of severe hypoglycemia II under-standing the risk of severe hypoglycemiardquo Diabetes Care vol22 no 12 pp 2018ndash2025 1999

[60] L Gonder-Frederick D Cox B Kovatchev D Schlundt andW Clarke ldquoA biopsychobehavioral model of risk of severehypoglycemiardquoDiabetes Care vol 20 no 4 pp 661ndash669 1997

[61] B Kovatchev D Cox L Gonder-Frederick D Schlundtand W Clarke ldquoStochastic model of self-regulation decisionmaking exemplied by decisions concerning hypoglycemiardquoHealth Psychology vol 17 no 3 pp 277ndash284 1998

[62] G Nucci and C Cobelli ldquoModels of subcutaneous insulinkinetics A critical reviewrdquo Computer Methods and Programs inBiomedicine vol 62 no 3 pp 249ndash257 2000

[63] M E Wilinska L J Chassin H C Schaller L Schaupp T RPieber and R Hovorka ldquoInsulin kinetics in type-1 diabetescontinuous and bolus delivery of rapid acting insulinrdquo IEEETransactions on Biomedical Engineering vol 52 no 1 pp 3ndash122005

[64] R N Bergman D T Finegood and M Ader ldquoAssessmentof insulin sensitivity in vivordquo Endocrine Reviews vol 236 ppE667ndashE677 1985

[65] R N Bergman ldquoe minimal model of glucose regulation abiographyrdquoAdvances in ExperimentalMedicine and Biology vol537 pp 1ndash19 2003

[66] R N Bergman D J Zaccaro R M Watanabe et al ldquoMinimalmodel-based insulin sensitivity has greater heritability and adifferent genetic basis than homeostasis model assessment orfasting insulinrdquo Diabetes vol 52 no 8 pp 2168ndash2174 2003

[67] A Caumo R N Bergman and C Cobelli ldquoInsulin sensitivityfrom meal tolerance tests in normal subjects a minimal modelindexrdquo Journal of Clinical Endocrinology and Metabolism vol85 no 11 pp 4396ndash4402 2000

[68] J O Clausen K Borch-Johnsen H Ibsen et al ldquoInsulin sen-sitivity index acute insulin response and glucose effectivenessin a population-based sample of 380 young healthy Caucasiansanalysis of the impact of gender body fat physical tness andlife-style factorsrdquo Journal of Clinical Investigation vol 98 no 5pp 1195ndash1209 1996

[69] T C Ni M Ader and R N Bergman ldquoReassessment of glu-cose effectiveness and insulin sensitivity from minimal modelanalysis a theoretical evaluation of the single-compartmentglucose distribution assumptionrdquo Diabetes vol 46 no 11 pp1813ndash1821 1997

[70] S Welch S S P Gebhart R N Bergman and L S PhillipsldquoMinimal model analysis of intravenous glucose tolerance

test-derived insulin sensitivity in diabetic subjectsrdquo Journalof Clinical Endocrinology and Metabolism vol 71 no 6 pp1508ndash1518 1990

[71] D Dawson M A Vincent E J Barrett et al ldquoCapillary recruit-ment in skeletal muscle in response to exercise and hyperin-sulinemia assessed with contrast-enhanced ultrasoundrdquo Amer-ican Journal of Physiology vol 282 no 3 pp E714ndashE720 2002

[72] K J Mikines B Sonne P A Farrell B Tronier and H GalboldquoEffect of physical exercise on sensitivity and responsiveness toinsulin in humansrdquoAmerican Journal of Physiology vol 254 no3 pp E248ndashE259 1988

[73] E A Richter ldquoGlucose utilizationrdquo in Handbook of PhysiologyL B Rowell and J T Shepherd Eds pp 912ndash951 OxfordUniversity Press New York NY USA 1996

[74] D H Wasserman R J Geer D E Rice et al ldquoInteraction ofexercise and insulin action in humansrdquo American Journal ofPhysiology vol 260 no 1 pp E37ndashE45 1991

[75] G Pillonetto A Caumo G Sparacino and C Cobelli ldquoAnew dynamic index of insulin sensitivityrdquo IEEE Transactions onBiomedical Engineering vol 53 no 3 pp 369ndash379 2006

[76] C Dalla Man D M Raimondo R A Rizza and C CobellildquoGIM Simulation soware of meal glucose-insulin modelrdquoJournal of Diabetes Science and Technology vol 1 pp 323ndash3302007

[77] C Dalla Man R A Rizza and C Cobelli ldquoMeal simulationmodel of the glucose-insulin systemrdquo IEEE Transactions onBiomedical Engineering vol 54 no 10 pp 1740ndash1749 2007

[78] W L Clarke D Cox L A Gonder-Frederick W Carter andS L Pohl ldquoEvaluating clinical accuracy of systems for self-monitoring of blood glucoserdquo Diabetes Care vol 10 no 5 pp622ndash628 1987

[79] e Diabetes Research In Children Network (Direcnet) StudyGroup ldquoA multicenter study of the accuracy of the one touchultra home glucose meter in children with type 1 diabetesrdquoDiabetes Technology anderapeutics vol 5 no 6 pp 933ndash9412003

[80] I B Hirsch B W Bode B P Childs et al ldquoSelf-monitoring ofblood glucose (SMBG) in insulin- and non-insulin-using adultswith diabetes consensus recommendations for improvingSMBG accuracy utilization and researchrdquo Diabetes Technologyanderapeutics vol 10 no 6 pp 419ndash439 2008

[81] G Freckmann A Baumstark N Jendrike et al ldquoSystemaccuracy evaluation of 27 blood glucose monitoring systemsaccording to DIN en ISO 15197rdquo Diabetes Technology anderapeutics vol 12 no 3 pp 221ndash231 2010

[82] D Deiss J Bolinder J P Riveline et al ldquoImproved glycemiccontrol in poorly controlled patients with type 1 diabetes usingreal-time continuous glucose monitoringrdquo Diabetes Care vol29 no 12 pp 2730ndash2732 2006

[83] S Garg H Zisser S Schwartz et al ldquoImprovement in glycemicexcursions with a transcutaneous real-time continuous glucosesensor a randomized controlled trialrdquo Diabetes Care vol 29no 1 pp 44ndash50 2006

[84] B P Kovatchev and W L Clarke ldquoContinuous glucose mon-itoring reduces risks for hypo- and hyperglycemia and glucosevariability in diabetesrdquo Diabetes vol 56 1 Article ID 0086OR2007

[85] e Juvenile Diabetes Research Foundation Continuous Glu-cose Monitoring Study Group ldquoContinuous glucose monitor-ing and intensive treatment of type 1 diabetesrdquo New EnglandJournal of Medicine vol 359 pp 1464ndash1476 2008

12 Scientica

[86] D C Klonoff ldquoContinuous glucose monitoring roadmap for21st century diabetes therapyrdquo Diabetes Care vol 28 no 5 pp1231ndash1239 2005

[87] R Hovorka ldquoContinuous glucose monitoring and closed-loopsystemsrdquo Diabetic Medicine vol 23 no 1 pp 1ndash12 2006

[88] D C Klonoff ldquoe articial pancreas how sweet engineeringwill solve bitter problemsrdquo Journal of Diabetes Science andTechnology vol 1 pp 72ndash81 2007

[89] I B Hirsch D Armstrong R M Bergenstal et al ldquoClin-ical application of emerging sensor technologies in diabetesmanagement consensus guidelines for continuous glucosemonitoring (CGM)rdquoDiabetes Technology anderapeutics vol10 no 4 pp 232ndash246 2008

[90] K Rebrin G M Steil W P Van Antwerp and J J Mastro-totaro ldquoSubcutaneous glucose predicts plasma glucose inde-pendent of insulin implications for continuous monitoringrdquoAmerican Journal of Physiology vol 277 no 3 pp E561ndashE5711999

[91] K Rebrin and G M Steil ldquoCan interstitial glucose assessmentreplace blood glucosemeasurementsrdquoDiabetes Technology anderapeutics vol 2 no 3 pp 461ndash472 2000

[92] G M Steil K Rebrin F Hariri et al ldquoInterstitial uid glucosedynamics during insulin-induced hypoglycaemiardquo Diabetolo-gia vol 48 no 9 pp 1833ndash1840 2005

[93] M S Boyne D M Silver J Kaplan and C D Saudek ldquoTimingof changes in interstitial and venous blood glucose measuredwith a continuous subcutaneous glucose sensorrdquo Diabetes vol52 no 11 pp 2790ndash2794 2003

[94] E Kulcu J A Tamada G Reach R O Potts and M JLesho ldquoPhysiological differences between interstitial glucoseand blood glucose measured in human subjectsrdquoDiabetes Carevol 26 no 8 pp 2405ndash2409 2003

[95] P J Stout J R Racchini andM E Hilgers ldquoA novel approach tomitigating the physiological lag between blood and interstitialuid glucose measurementsrdquo Diabetes Technology and era-peutics vol 6 no 5 pp 635ndash644 2004

[96] I M E Wentholt A A M Hart J B L Hoekstra and J HDevries ldquoRelationship between interstitial and blood glucose intype 1 diabetes patients delay and the push-pull phenomenonrevisitedrdquo Diabetes Technology and erapeutics vol 9 no 2pp 169ndash175 2007

[97] K J C Wientjes and A J M Schoonen ldquoDetermination oftime delay between blood and interstitial adipose tissue glucoseconcentration change by microdialysis in healthy volunteersrdquonternational Journal of Articial rgans vol 24 no 12 pp884ndash889 2001

[98] B Aussedat M Dupire-Angel R Gifford J C Klein G SWilson and G Reach ldquoInterstitial glucose concentration andglycemia implications for continuous subcutaneous glucosemonitoringrdquo American Journal of Physiology vol 278 no 4 ppE716ndashE728 2000

[99] B P Kovatchev and W L Clarke ldquoPeculiarities of the con-tinuous glucose monitoring data stream and their impact ondeveloping closed-loop control technologyrdquo Journal of DiabetesScience and Technology vol 2 pp 158ndash163 2008

[100] W L Clarke and B P Kovatchev ldquoContinuous glucose sen-sorsmdashcontinuing questions about clinical accuracyrdquo Journal ofDiabetes Science and Technology vol 1 pp 164ndash170 2007

[101] e Diabetes Research in Children Network (DirecNet) StudyGroup ldquoe accuracy of the guardian RT continuous glucosemonitor in children with type 1 diabetesrdquo Diabetes Technologyanderapeutics vol 10 pp 266ndash272 2008

[102] B Kovatchev S Anderson L Heinemann and W ClarkeldquoComparison of the numerical and clinical accuracy of fourcontinuous glucose monitorsrdquo Diabetes Care vol 31 no 6 pp1160ndash1164 2008

[103] S K Garg J Smith C Beatson B Lopez-Baca M Voelmleand P A Gottlieb ldquoComparison of accuracy and safety ofthe SEVEN and the navigator continuous glucose monitoringsystemsrdquo Diabetes Technology and erapeutics vol 11 no 2pp 65ndash72 2009

[104] T Heise T Koschinsky L Heinemann and V Lodwig ldquoHypo-glycemia warning signal and glucose sensors requirements andconceptsrdquo Diabetes Technology and erapeutics vol 5 no 4pp 563ndash571 2003

[105] B Bode K Gross N Rikalo et al ldquoAlarms based on real-timesensor glucose values alert patients to hypo- and hyperglycemiathe Guardian continuous monitoring systemrdquo Diabetes Tech-nology anderapeutics vol 6 no 2 pp 105ndash113 2004

[106] G McGarraugh and R Bergenstal ldquoDetection of hypoglycemiawith continuous interstitial and traditional blood glucosemonitoring using the FreeStyle navigator continuous glucosemonitoring systemrdquo Diabetes Technology and erapeutics vol11 no 3 pp 145ndash150 2009

[107] S E Noujaim D Horwitz M Sharma and J Marhoul ldquoAccu-racy requirements for a hypoglycemia detector an analyticalmodel to evaluate the effects of bias precision and rate ofglucose changerdquo Journal of Diabetes Science and Technology vol1 pp 653ndash668 2007

[108] W K Ward ldquoe role of new technology in the early detectionof hypoglycemiardquo Diabetes Technology and erapeutics vol 6no 2 pp 115ndash117 2004

[109] B Buckingham E Cobry P Clinton et al ldquoPreventing hypo-glycemia using predictive alarm algorithms and insulin pumpsuspensionrdquo Diabetes Technology and erapeutics vol 11 no2 pp 93ndash97 2009

[110] C S Hughes S D Patek M D Breton and B P KovatchevldquoHypoglycemia prevention via pump attenuation and red-yellow-green ldquotrafficrdquo lights using continuous glucose moni-toring and insulin pump datardquo Journal of Diabetes Science andTechnology vol 4 no 5 pp 1146ndash1155 2010

[111] B P Kovatchev D J Cox L A Gonder-Frederick and WClarke ldquoSymmetrization of the blood glucose measurementscale and its applicationsrdquo Diabetes Care vol 20 no 11 pp1655ndash1658 1997

[112] F J Service G D Molnar J W Rosevear E Ackerman LC Gatewood and W F Taylor ldquoMean amplitude of glycemicexcursions a measure of diabetic instabilityrdquo Diabetes vol 19no 9 pp 644ndash655 1970

[113] J Schlichtkrull O Munck and M Jersild ldquoe M-value anindex of blood glucose control in diabeticsrdquo Acta MedicaScandinavica vol 177 pp 95ndash102 1965

[114] E A Ryan T Shandro K Green et al ldquoAssessment of theseverity of hypoglycemia and glycemic lambility in type 1diabetic subjects undergoing islet in transplantationrdquo Diabetesvol 53 no 4 pp 955ndash962 2004

[115] B P Kovatchev D J Cox L Gonder-Frederick and WL Clarke ldquoMethods for quantifying self-monitoring bloodglucose proles exemplied by an examination of blood glucosepatterns in patients with type 1 and type 2 diabetesrdquo DiabetesTechnology anderapeutics vol 4 no 3 pp 295ndash303 2002

[116] B P Kovatchev D J Cox L S Farhy M Straume L Gonder-Frederick and W L Clarke ldquoEpisodes of severe hypoglycemia

Scientica 13

in type 1 diabetes are preceded and followed within 48 hours bymeasurable disturbances in blood glucoserdquo Journal of ClinicalEndocrinology and Metabolism vol 85 no 11 pp 4287ndash42922000

[117] B P Kovatchev M Straume D J Cox and L S FarhyldquoRisk analysis of blood glucose data a quantitative approach tooptimizing the control of Insulin Dependent Diabetesrdquo Journalof eoretical Medicine vol 3 no 1 pp 1ndash10 2000

[118] B P Kovatchev E Otto D Cox L Gonder-Frederick andW Clarke ldquoEvaluation of a new measure of blood glucosevariability in diabetesrdquo Diabetes Care vol 29 no 11 pp2433ndash2438 2006

[119] B P Kovatchev W L Clarke M Breton K Brayman and AMcCall ldquoQuantifying temporal glucose variability in diabetesvia continuous glucose monitoring mathematical methods andclinical applicationrdquo Diabetes Technology and erapeutics vol7 no 6 pp 849ndash862 2005

[120] D Rodbard ldquoNew and improved methods to characterizeglycemic variability using continuous glucosemonitoringrdquoDia-betes Technology and erapeutics vol 11 no 9 pp 551ndash5652009

[121] M Miller and P Strange ldquoUse of Fourier models for analysisand interpretation of continuous glucose monitoring glucoseprolesrdquo Journal of Diabetes Science and Technology vol 1 pp630ndash638 2007

[122] W Clarke and B Kovatchev ldquoStatistical tools to analyzecontinuous glucose monitor datardquo Diabetes Technology anderapeutics vol 11 pp S45ndashS54 2009

[123] C M Mcdonnell S M Donath S I Vidmar G A Wertherand F J Cameron ldquoA novel approach to continuous glucoseanalysis utilizing glycemic variationrdquo Diabetes Technology anderapeutics vol 7 no 2 pp 253ndash263 2005

[124] G Sparacino F Zanderigo A Maran and C Cobelli ldquoContin-uous glucosemonitoring andhypohyperglycaemia predictionrdquoDiabetes Research and Clinical Practice vol 74 no 2 supple-ment pp S160ndashS163 2006

[125] G Sparacino F Zanderigo G Corazza A Maran AFacchinetti and C Cobelli ldquoGlucose concentration can bepredicted ahead in time from continuous glucose monitoringsensor time-seriesrdquo IEEE Transactions on Biomedical Engineer-ing vol 54 pp 931ndash937 2007

[126] J Reifman S Rajaraman A Gribok and W K Ward ldquoPredic-tive monitoring for improved management of glucose levelsrdquoJournal of Diabetes Science and Technology vol 1 pp 478ndash4862007

[127] F Zanderigo G Sparacino B Kovatchev and C CobellildquoGlucose prediction algorithms from continuous monitoringassessment of accuracy via continuous glucose-error grid anal-ysisrdquo Journal of Diabetes Science and Technology vol 1 pp645ndash651 2007

[128] F Cameron G Niemayer K Gundy-Burlet and B Bucking-ham ldquoStatistical hypoglycemia predictionrdquo Journal of DiabetesScience and Technology vol 2 pp 612ndash621 2008

[129] A Gani A V Gribok S Rajaraman W K Ward and JReifman ldquoPredicting subcutaneous glucose concentration inhumans data-driven glucose modelingrdquo IEEE Transactions onBiomedical Engineering vol 56 no 2 pp 246ndash254 2009

[130] M Eren-Oruklu A Cinar L Quinn andD Smith ldquoEstimationof future glucose concentrations with subject-specic recursivelinear modelsrdquo Diabetes Technology and erapeutics vol 11no 4 pp 243ndash253 2009

[131] S M Pappada B D Cameron and P M Rosman ldquoDevelop-ment of a neural network for prediction of glucose concentra-tion in type 1 diabetes patientsrdquo Journal of Diabetes Science andTechnology vol 2 pp 792ndash801 2008

[132] C Cobelli C Dalla Man G Sparacino L Magni G Nicolaoand B P Kovatchev ldquoDiabetes models signals and controlrdquoIEEE Reviews in Biomedical Engineering vol 2 pp 54ndash96 2009

[133] H LeBlanc D Chauvet P Lombrail and J J Robert ldquoGlycemiccontrol with closed-loop intraperitoneal insulin in type Idiabetesrdquo Diabetes Care vol 9 no 2 pp 124ndash128 1986

[134] C D Saudek J L Selman H A Pitt et al ldquoA preliminary trialof the programmable implantablemedication system for insulindeliveryrdquo New England Journal of Medicine vol 321 no 9 pp574ndash579 1989

[135] C Broussolle N Jeandidier and H Hanaire-Broutin ldquoFrenchmulticentre experience of implantable insulin pumpsrdquo Lancetvol 343 no 8896 pp 514ndash515 1994

[136] H Hanaire-Broutin C Broussolle N Jeandidier et al ldquoFea-sibility of intraperitoneal insulin therapy with programmableimplantable pumps in IDDM a multicenter studyrdquo DiabetesCare vol 18 no 3 pp 388ndash392 1995

[137] P R Oskarsson P E Lins H W Henriksson and U CAdamson ldquoMetabolic and hormonal responses to exercisein type 1 diabetic patients during continuous subcutaneousas compared to continuous intraperitoneal insulin infusionrdquoDiabetes and Metabolism vol 25 no 6 pp 491ndash497 1999

[138] P R Oskarsson P E Lins L Backman and U C AdamsonldquoContinuous intraperitoneal insulin infusion partly restores theglucagon response to hypoglycaemia in type 1 diabetic patientsrdquoDiabetes and Metabolism vol 26 no 2 pp 118ndash124 2000

[139] B Catargi L Meyer V Melki E Renard and N JeandidierldquoComparison of blood glucose stability and HbA1C betweenimplantable insulin pumps using U400 hoe 21pH insulin andexternal pumps using lispro in type 1 diabetic patients a pilotstudyrdquo Diabetes and Metabolism vol 28 no 2 pp 133ndash1372002

[140] E Renard ldquoImplantable closed-loop glucose-sensing andinsulin delivery the future for insulin pump therapyrdquo CurrentOpinion in Pharmacology vol 2 no 6 pp 708ndash716 2002

[141] E Renard G Costalat H Chevassus and J Bringer ldquoArticial120573120573-cell clinical experience toward an implantable closed-loopinsulin delivery systemrdquo Diabetes and Metabolism vol 32 no5 pp 497ndash502 2006

[142] E Renard J Place M Cantwell H Chevassus and C CPalerm ldquoClosed-loop insulin delivery using a subcutaneousglucose sensor and intraperitoneal insulin delivery feasibilitystudy testing a new model for the articial pancreasrdquo DiabetesCare vol 33 no 1 pp 121ndash127 2010

[143] R Bellazzi G Nucci and C Cobelli ldquoe subcutaneousroute to insulin-dependent diabetes therapy closed-loop andpartially closed-loop control strategies for insulin deliveryand measuring glucose concentrationrdquo IEEE Engineering inMedicine and Biology Magazine vol 20 no 1 pp 54ndash64 2001

[144] R Hovorka L J Chassin M E Wilinska et al ldquoClosingthe loop the adicol experiencerdquo Diabetes Technology anderapeutics vol 6 no 3 pp 307ndash318 2004

[145] R Hovorka V Canonico L J Chassin et al ldquoNonlinear modelpredictive control of glucose concentration in subjects withtype 1 diabetesrdquo Physiological Measurement vol 25 no 4 pp905ndash920 2004

14 Scientica

[146] G M Steil K Rebrin C Darwin F Hariri and M F SaadldquoFeasibility of automating insulin delivery for the treatment oftype 1 diabetesrdquo Diabetes vol 55 no 12 pp 3344ndash3350 2006

[147] e JDRF e-Newsletter Emerging Technologies in DiabetesResearch 2006

[148] W L Clarke and B Kovatchev ldquoe articial pancreas howclose are we to closing the looprdquo Pediatric EndocrinologyReviews vol 4 no 4 pp 314ndash316 2007

[149] C Cobelli E Renard and B P Kovatchev ldquoPerspectives indiabetes articial pancreas past present futurerdquo Diabetes vol60 pp 2672ndash2682 2011

[150] B P Kovatchev M D Breton C Dalla Man and C Cobelli ldquoInsilico preclinical trials a proof of concept in closed-loop controlof type 1 diabetesrdquo Journal of Diabetes Science and Technologyvol 3 pp 44ndash55 2009

[151] B Kovatchev C Cobelli E Renard et al ldquoMultinational studyof subcutaneous model-predictive closed-loop control in type1 diabetes mellitus summary of the resultsrdquo Journal of DiabetesScience and Technology vol 4 no 6 pp 1374ndash1381 2010

[152] H Zisser L Jovanovic F Doyle P Ospina and C OwensldquoRun-to-run control of meal-related insulin dosingrdquo DiabetesTechnology anderapeutics vol 7 no 1 pp 48ndash57 2005

[153] C Owens H Zisser L Jovanovic B Srinivasan D Bonvinand F J Doyle III ldquoRun-to-run control of blood glucoseconcentrations for people with type 1 diabetes mellitusrdquo IEEETransactions on Biomedical Engineering vol 53 no 6 pp996ndash1005 2006

[154] C C Palerm H Zisser W C Bevier L Jovanovič and F JDoyle III ldquoPrandial insulin dosing using run-to-run controlapplication of clinical data and medical expertise to dene asuitable performance metricrdquo Diabetes Care vol 30 no 5 pp1131ndash1136 2007

[155] LMagni F Raimondo L Bossi et al ldquoModel predictive controlof type 1 diabetes an in silico trialrdquo Journal of Diabetes Scienceand Technology vol 1 pp 804ndash812 2007

[156] S A Weinzimer G M Steil K L Swan J Dziura N Kurtzand W V Tamborlane ldquoFully automated closed-loop insulindelivery versus semiautomated hybrid control in pediatricpatients with type 1 diabetes using an articial pancreasrdquoDiabetes Care vol 31 no 5 pp 934ndash939 2008

[157] W L Clarke S Anderson M Breton S Patek L Kashmerand B Kovatchev ldquoClosed-loop articial pancreas using sub-cutaneous glucose sensing and insulin delivery and a modelpredictive control algorithm the Virginia experiencerdquo Journalof Diabetes Science and Technology vol 3 no 5 pp 1031ndash10382009

[158] D Bruttomesso A Farret S Costa et al ldquoClosed-loop arti-cial pancreas using subcutaneous glucose sensing and insulindelivery and a model predictive control algorithm preliminarystudies in Padova and Montpellierrdquo Journal of Diabetes Scienceand Technology vol 3 no 5 pp 1014ndash1021 2009

[159] R Hovorka J M Allen D Elleri et al ldquoManual closed-loop insulin delivery in children and adolescents with type 1diabetes a phase 2 randomised crossover trialrdquoe Lancet vol375 no 9716 pp 743ndash751 2010

[160] F H El-khatib S J Russell D M Nathan R G Sutherlin andE R Damiano ldquoA bihormonal closed-loop articial pancreasfor type 1 diabetesrdquo Science Translational Medicine vol 2 no27 Article ID 27ra27 2010

[161] E Dassau H Zisser C C Palerm B A Buckingham LJovanovič and F J Doye III ldquoModular articial 120573120573-cell system a

prototype for clinical researchrdquo Journal of Diabetes Science andTechnology vol 2 pp 863ndash872 2008

[162] B Kovatchev S Patek E Dassau et al ldquoControl to range fordiabetes functionality and modular architecturerdquo Journal ofDiabetes Science and Rechnology vol 3 no 5 pp 1058ndash10652009

[163] E Atlas R Nimri S Miller E A Grunberg and M PhillipldquoMD-logic articial pancreas system a pilot study in adults withtype 1 diabetesrdquo Diabetes Care vol 33 no 5 pp 1072ndash10762010

[164] E Dassau H Zisser M W Percival B Grosman L Jovanovičand F J Doyle III ldquoClinical results of automated articialpancreatic 120573120573-cell system with unannounced meal using multi-parametric MPC and insulin-on-boardrdquo in Proceedings of the70th American Diabetes Association Meeting Diabetes vol 59supplement 1 A94 Orlando Fla USA 2010

[165] E M Renard A Farret J Place C Cobelli B P Kovatchev andMD Breton ldquoClosed-loop insulin delivery using subcutaneousinfusion and glucose sensing and equipped with a dedicatedsafety supervision algorithm improves safety of glucose controlin type 1 diabetesrdquo Diabetologia vol 53 supplement 1 p S252010

[166] R Hovorka K Kumareswaran J Harris et al ldquoOvernightclosed loop insulin delivery articial pancreas in adults withtype 1 diabetes crossover randomized controlled studiesrdquoBritish Medical Journal vol 342 no 7803 Article ID d18552011

[167] H Zisser E Dassau W Bevier et al ldquoInitial evaluation ofa fully automated articial pancreasrdquo in Proceedings of the71st American Diabetes Association Meeting Diabetes vol 60supplement 1 A41 San Diego Calif USA 2011

[168] M D Breton A Farret and D Bruttomesso ldquoFully-integratedarticial pancreas in type 1 diabetes modular closed-loopglucose control maintains near-normoglycemiardquo Diabetes vol61 pp 2230ndash2237 2012

[169] C Cobelli E Renard and B P Kovatchev ldquoPilot studies ofwearable articial pancreas in type 1 diabetesrdquo Diabetes Carevol 35 no 9 pp e65ndashe67 2012

Submit your manuscripts athttpwwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

EndocrinologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

OncologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Oxidative Medicine and Cellular Longevity

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational and Mathematical Methods in Medicine

OphthalmologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Diabetes ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Research and TreatmentAIDS

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Gastroenterology Research and Practice

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

12 Scientica

[86] D C Klonoff ldquoContinuous glucose monitoring roadmap for21st century diabetes therapyrdquo Diabetes Care vol 28 no 5 pp1231ndash1239 2005

[87] R Hovorka ldquoContinuous glucose monitoring and closed-loopsystemsrdquo Diabetic Medicine vol 23 no 1 pp 1ndash12 2006

[88] D C Klonoff ldquoe articial pancreas how sweet engineeringwill solve bitter problemsrdquo Journal of Diabetes Science andTechnology vol 1 pp 72ndash81 2007

[89] I B Hirsch D Armstrong R M Bergenstal et al ldquoClin-ical application of emerging sensor technologies in diabetesmanagement consensus guidelines for continuous glucosemonitoring (CGM)rdquoDiabetes Technology anderapeutics vol10 no 4 pp 232ndash246 2008

[90] K Rebrin G M Steil W P Van Antwerp and J J Mastro-totaro ldquoSubcutaneous glucose predicts plasma glucose inde-pendent of insulin implications for continuous monitoringrdquoAmerican Journal of Physiology vol 277 no 3 pp E561ndashE5711999

[91] K Rebrin and G M Steil ldquoCan interstitial glucose assessmentreplace blood glucosemeasurementsrdquoDiabetes Technology anderapeutics vol 2 no 3 pp 461ndash472 2000

[92] G M Steil K Rebrin F Hariri et al ldquoInterstitial uid glucosedynamics during insulin-induced hypoglycaemiardquo Diabetolo-gia vol 48 no 9 pp 1833ndash1840 2005

[93] M S Boyne D M Silver J Kaplan and C D Saudek ldquoTimingof changes in interstitial and venous blood glucose measuredwith a continuous subcutaneous glucose sensorrdquo Diabetes vol52 no 11 pp 2790ndash2794 2003

[94] E Kulcu J A Tamada G Reach R O Potts and M JLesho ldquoPhysiological differences between interstitial glucoseand blood glucose measured in human subjectsrdquoDiabetes Carevol 26 no 8 pp 2405ndash2409 2003

[95] P J Stout J R Racchini andM E Hilgers ldquoA novel approach tomitigating the physiological lag between blood and interstitialuid glucose measurementsrdquo Diabetes Technology and era-peutics vol 6 no 5 pp 635ndash644 2004

[96] I M E Wentholt A A M Hart J B L Hoekstra and J HDevries ldquoRelationship between interstitial and blood glucose intype 1 diabetes patients delay and the push-pull phenomenonrevisitedrdquo Diabetes Technology and erapeutics vol 9 no 2pp 169ndash175 2007

[97] K J C Wientjes and A J M Schoonen ldquoDetermination oftime delay between blood and interstitial adipose tissue glucoseconcentration change by microdialysis in healthy volunteersrdquonternational Journal of Articial rgans vol 24 no 12 pp884ndash889 2001

[98] B Aussedat M Dupire-Angel R Gifford J C Klein G SWilson and G Reach ldquoInterstitial glucose concentration andglycemia implications for continuous subcutaneous glucosemonitoringrdquo American Journal of Physiology vol 278 no 4 ppE716ndashE728 2000

[99] B P Kovatchev and W L Clarke ldquoPeculiarities of the con-tinuous glucose monitoring data stream and their impact ondeveloping closed-loop control technologyrdquo Journal of DiabetesScience and Technology vol 2 pp 158ndash163 2008

[100] W L Clarke and B P Kovatchev ldquoContinuous glucose sen-sorsmdashcontinuing questions about clinical accuracyrdquo Journal ofDiabetes Science and Technology vol 1 pp 164ndash170 2007

[101] e Diabetes Research in Children Network (DirecNet) StudyGroup ldquoe accuracy of the guardian RT continuous glucosemonitor in children with type 1 diabetesrdquo Diabetes Technologyanderapeutics vol 10 pp 266ndash272 2008

[102] B Kovatchev S Anderson L Heinemann and W ClarkeldquoComparison of the numerical and clinical accuracy of fourcontinuous glucose monitorsrdquo Diabetes Care vol 31 no 6 pp1160ndash1164 2008

[103] S K Garg J Smith C Beatson B Lopez-Baca M Voelmleand P A Gottlieb ldquoComparison of accuracy and safety ofthe SEVEN and the navigator continuous glucose monitoringsystemsrdquo Diabetes Technology and erapeutics vol 11 no 2pp 65ndash72 2009

[104] T Heise T Koschinsky L Heinemann and V Lodwig ldquoHypo-glycemia warning signal and glucose sensors requirements andconceptsrdquo Diabetes Technology and erapeutics vol 5 no 4pp 563ndash571 2003

[105] B Bode K Gross N Rikalo et al ldquoAlarms based on real-timesensor glucose values alert patients to hypo- and hyperglycemiathe Guardian continuous monitoring systemrdquo Diabetes Tech-nology anderapeutics vol 6 no 2 pp 105ndash113 2004

[106] G McGarraugh and R Bergenstal ldquoDetection of hypoglycemiawith continuous interstitial and traditional blood glucosemonitoring using the FreeStyle navigator continuous glucosemonitoring systemrdquo Diabetes Technology and erapeutics vol11 no 3 pp 145ndash150 2009

[107] S E Noujaim D Horwitz M Sharma and J Marhoul ldquoAccu-racy requirements for a hypoglycemia detector an analyticalmodel to evaluate the effects of bias precision and rate ofglucose changerdquo Journal of Diabetes Science and Technology vol1 pp 653ndash668 2007

[108] W K Ward ldquoe role of new technology in the early detectionof hypoglycemiardquo Diabetes Technology and erapeutics vol 6no 2 pp 115ndash117 2004

[109] B Buckingham E Cobry P Clinton et al ldquoPreventing hypo-glycemia using predictive alarm algorithms and insulin pumpsuspensionrdquo Diabetes Technology and erapeutics vol 11 no2 pp 93ndash97 2009

[110] C S Hughes S D Patek M D Breton and B P KovatchevldquoHypoglycemia prevention via pump attenuation and red-yellow-green ldquotrafficrdquo lights using continuous glucose moni-toring and insulin pump datardquo Journal of Diabetes Science andTechnology vol 4 no 5 pp 1146ndash1155 2010

[111] B P Kovatchev D J Cox L A Gonder-Frederick and WClarke ldquoSymmetrization of the blood glucose measurementscale and its applicationsrdquo Diabetes Care vol 20 no 11 pp1655ndash1658 1997

[112] F J Service G D Molnar J W Rosevear E Ackerman LC Gatewood and W F Taylor ldquoMean amplitude of glycemicexcursions a measure of diabetic instabilityrdquo Diabetes vol 19no 9 pp 644ndash655 1970

[113] J Schlichtkrull O Munck and M Jersild ldquoe M-value anindex of blood glucose control in diabeticsrdquo Acta MedicaScandinavica vol 177 pp 95ndash102 1965

[114] E A Ryan T Shandro K Green et al ldquoAssessment of theseverity of hypoglycemia and glycemic lambility in type 1diabetic subjects undergoing islet in transplantationrdquo Diabetesvol 53 no 4 pp 955ndash962 2004

[115] B P Kovatchev D J Cox L Gonder-Frederick and WL Clarke ldquoMethods for quantifying self-monitoring bloodglucose proles exemplied by an examination of blood glucosepatterns in patients with type 1 and type 2 diabetesrdquo DiabetesTechnology anderapeutics vol 4 no 3 pp 295ndash303 2002

[116] B P Kovatchev D J Cox L S Farhy M Straume L Gonder-Frederick and W L Clarke ldquoEpisodes of severe hypoglycemia

Scientica 13

in type 1 diabetes are preceded and followed within 48 hours bymeasurable disturbances in blood glucoserdquo Journal of ClinicalEndocrinology and Metabolism vol 85 no 11 pp 4287ndash42922000

[117] B P Kovatchev M Straume D J Cox and L S FarhyldquoRisk analysis of blood glucose data a quantitative approach tooptimizing the control of Insulin Dependent Diabetesrdquo Journalof eoretical Medicine vol 3 no 1 pp 1ndash10 2000

[118] B P Kovatchev E Otto D Cox L Gonder-Frederick andW Clarke ldquoEvaluation of a new measure of blood glucosevariability in diabetesrdquo Diabetes Care vol 29 no 11 pp2433ndash2438 2006

[119] B P Kovatchev W L Clarke M Breton K Brayman and AMcCall ldquoQuantifying temporal glucose variability in diabetesvia continuous glucose monitoring mathematical methods andclinical applicationrdquo Diabetes Technology and erapeutics vol7 no 6 pp 849ndash862 2005

[120] D Rodbard ldquoNew and improved methods to characterizeglycemic variability using continuous glucosemonitoringrdquoDia-betes Technology and erapeutics vol 11 no 9 pp 551ndash5652009

[121] M Miller and P Strange ldquoUse of Fourier models for analysisand interpretation of continuous glucose monitoring glucoseprolesrdquo Journal of Diabetes Science and Technology vol 1 pp630ndash638 2007

[122] W Clarke and B Kovatchev ldquoStatistical tools to analyzecontinuous glucose monitor datardquo Diabetes Technology anderapeutics vol 11 pp S45ndashS54 2009

[123] C M Mcdonnell S M Donath S I Vidmar G A Wertherand F J Cameron ldquoA novel approach to continuous glucoseanalysis utilizing glycemic variationrdquo Diabetes Technology anderapeutics vol 7 no 2 pp 253ndash263 2005

[124] G Sparacino F Zanderigo A Maran and C Cobelli ldquoContin-uous glucosemonitoring andhypohyperglycaemia predictionrdquoDiabetes Research and Clinical Practice vol 74 no 2 supple-ment pp S160ndashS163 2006

[125] G Sparacino F Zanderigo G Corazza A Maran AFacchinetti and C Cobelli ldquoGlucose concentration can bepredicted ahead in time from continuous glucose monitoringsensor time-seriesrdquo IEEE Transactions on Biomedical Engineer-ing vol 54 pp 931ndash937 2007

[126] J Reifman S Rajaraman A Gribok and W K Ward ldquoPredic-tive monitoring for improved management of glucose levelsrdquoJournal of Diabetes Science and Technology vol 1 pp 478ndash4862007

[127] F Zanderigo G Sparacino B Kovatchev and C CobellildquoGlucose prediction algorithms from continuous monitoringassessment of accuracy via continuous glucose-error grid anal-ysisrdquo Journal of Diabetes Science and Technology vol 1 pp645ndash651 2007

[128] F Cameron G Niemayer K Gundy-Burlet and B Bucking-ham ldquoStatistical hypoglycemia predictionrdquo Journal of DiabetesScience and Technology vol 2 pp 612ndash621 2008

[129] A Gani A V Gribok S Rajaraman W K Ward and JReifman ldquoPredicting subcutaneous glucose concentration inhumans data-driven glucose modelingrdquo IEEE Transactions onBiomedical Engineering vol 56 no 2 pp 246ndash254 2009

[130] M Eren-Oruklu A Cinar L Quinn andD Smith ldquoEstimationof future glucose concentrations with subject-specic recursivelinear modelsrdquo Diabetes Technology and erapeutics vol 11no 4 pp 243ndash253 2009

[131] S M Pappada B D Cameron and P M Rosman ldquoDevelop-ment of a neural network for prediction of glucose concentra-tion in type 1 diabetes patientsrdquo Journal of Diabetes Science andTechnology vol 2 pp 792ndash801 2008

[132] C Cobelli C Dalla Man G Sparacino L Magni G Nicolaoand B P Kovatchev ldquoDiabetes models signals and controlrdquoIEEE Reviews in Biomedical Engineering vol 2 pp 54ndash96 2009

[133] H LeBlanc D Chauvet P Lombrail and J J Robert ldquoGlycemiccontrol with closed-loop intraperitoneal insulin in type Idiabetesrdquo Diabetes Care vol 9 no 2 pp 124ndash128 1986

[134] C D Saudek J L Selman H A Pitt et al ldquoA preliminary trialof the programmable implantablemedication system for insulindeliveryrdquo New England Journal of Medicine vol 321 no 9 pp574ndash579 1989

[135] C Broussolle N Jeandidier and H Hanaire-Broutin ldquoFrenchmulticentre experience of implantable insulin pumpsrdquo Lancetvol 343 no 8896 pp 514ndash515 1994

[136] H Hanaire-Broutin C Broussolle N Jeandidier et al ldquoFea-sibility of intraperitoneal insulin therapy with programmableimplantable pumps in IDDM a multicenter studyrdquo DiabetesCare vol 18 no 3 pp 388ndash392 1995

[137] P R Oskarsson P E Lins H W Henriksson and U CAdamson ldquoMetabolic and hormonal responses to exercisein type 1 diabetic patients during continuous subcutaneousas compared to continuous intraperitoneal insulin infusionrdquoDiabetes and Metabolism vol 25 no 6 pp 491ndash497 1999

[138] P R Oskarsson P E Lins L Backman and U C AdamsonldquoContinuous intraperitoneal insulin infusion partly restores theglucagon response to hypoglycaemia in type 1 diabetic patientsrdquoDiabetes and Metabolism vol 26 no 2 pp 118ndash124 2000

[139] B Catargi L Meyer V Melki E Renard and N JeandidierldquoComparison of blood glucose stability and HbA1C betweenimplantable insulin pumps using U400 hoe 21pH insulin andexternal pumps using lispro in type 1 diabetic patients a pilotstudyrdquo Diabetes and Metabolism vol 28 no 2 pp 133ndash1372002

[140] E Renard ldquoImplantable closed-loop glucose-sensing andinsulin delivery the future for insulin pump therapyrdquo CurrentOpinion in Pharmacology vol 2 no 6 pp 708ndash716 2002

[141] E Renard G Costalat H Chevassus and J Bringer ldquoArticial120573120573-cell clinical experience toward an implantable closed-loopinsulin delivery systemrdquo Diabetes and Metabolism vol 32 no5 pp 497ndash502 2006

[142] E Renard J Place M Cantwell H Chevassus and C CPalerm ldquoClosed-loop insulin delivery using a subcutaneousglucose sensor and intraperitoneal insulin delivery feasibilitystudy testing a new model for the articial pancreasrdquo DiabetesCare vol 33 no 1 pp 121ndash127 2010

[143] R Bellazzi G Nucci and C Cobelli ldquoe subcutaneousroute to insulin-dependent diabetes therapy closed-loop andpartially closed-loop control strategies for insulin deliveryand measuring glucose concentrationrdquo IEEE Engineering inMedicine and Biology Magazine vol 20 no 1 pp 54ndash64 2001

[144] R Hovorka L J Chassin M E Wilinska et al ldquoClosingthe loop the adicol experiencerdquo Diabetes Technology anderapeutics vol 6 no 3 pp 307ndash318 2004

[145] R Hovorka V Canonico L J Chassin et al ldquoNonlinear modelpredictive control of glucose concentration in subjects withtype 1 diabetesrdquo Physiological Measurement vol 25 no 4 pp905ndash920 2004

14 Scientica

[146] G M Steil K Rebrin C Darwin F Hariri and M F SaadldquoFeasibility of automating insulin delivery for the treatment oftype 1 diabetesrdquo Diabetes vol 55 no 12 pp 3344ndash3350 2006

[147] e JDRF e-Newsletter Emerging Technologies in DiabetesResearch 2006

[148] W L Clarke and B Kovatchev ldquoe articial pancreas howclose are we to closing the looprdquo Pediatric EndocrinologyReviews vol 4 no 4 pp 314ndash316 2007

[149] C Cobelli E Renard and B P Kovatchev ldquoPerspectives indiabetes articial pancreas past present futurerdquo Diabetes vol60 pp 2672ndash2682 2011

[150] B P Kovatchev M D Breton C Dalla Man and C Cobelli ldquoInsilico preclinical trials a proof of concept in closed-loop controlof type 1 diabetesrdquo Journal of Diabetes Science and Technologyvol 3 pp 44ndash55 2009

[151] B Kovatchev C Cobelli E Renard et al ldquoMultinational studyof subcutaneous model-predictive closed-loop control in type1 diabetes mellitus summary of the resultsrdquo Journal of DiabetesScience and Technology vol 4 no 6 pp 1374ndash1381 2010

[152] H Zisser L Jovanovic F Doyle P Ospina and C OwensldquoRun-to-run control of meal-related insulin dosingrdquo DiabetesTechnology anderapeutics vol 7 no 1 pp 48ndash57 2005

[153] C Owens H Zisser L Jovanovic B Srinivasan D Bonvinand F J Doyle III ldquoRun-to-run control of blood glucoseconcentrations for people with type 1 diabetes mellitusrdquo IEEETransactions on Biomedical Engineering vol 53 no 6 pp996ndash1005 2006

[154] C C Palerm H Zisser W C Bevier L Jovanovič and F JDoyle III ldquoPrandial insulin dosing using run-to-run controlapplication of clinical data and medical expertise to dene asuitable performance metricrdquo Diabetes Care vol 30 no 5 pp1131ndash1136 2007

[155] LMagni F Raimondo L Bossi et al ldquoModel predictive controlof type 1 diabetes an in silico trialrdquo Journal of Diabetes Scienceand Technology vol 1 pp 804ndash812 2007

[156] S A Weinzimer G M Steil K L Swan J Dziura N Kurtzand W V Tamborlane ldquoFully automated closed-loop insulindelivery versus semiautomated hybrid control in pediatricpatients with type 1 diabetes using an articial pancreasrdquoDiabetes Care vol 31 no 5 pp 934ndash939 2008

[157] W L Clarke S Anderson M Breton S Patek L Kashmerand B Kovatchev ldquoClosed-loop articial pancreas using sub-cutaneous glucose sensing and insulin delivery and a modelpredictive control algorithm the Virginia experiencerdquo Journalof Diabetes Science and Technology vol 3 no 5 pp 1031ndash10382009

[158] D Bruttomesso A Farret S Costa et al ldquoClosed-loop arti-cial pancreas using subcutaneous glucose sensing and insulindelivery and a model predictive control algorithm preliminarystudies in Padova and Montpellierrdquo Journal of Diabetes Scienceand Technology vol 3 no 5 pp 1014ndash1021 2009

[159] R Hovorka J M Allen D Elleri et al ldquoManual closed-loop insulin delivery in children and adolescents with type 1diabetes a phase 2 randomised crossover trialrdquoe Lancet vol375 no 9716 pp 743ndash751 2010

[160] F H El-khatib S J Russell D M Nathan R G Sutherlin andE R Damiano ldquoA bihormonal closed-loop articial pancreasfor type 1 diabetesrdquo Science Translational Medicine vol 2 no27 Article ID 27ra27 2010

[161] E Dassau H Zisser C C Palerm B A Buckingham LJovanovič and F J Doye III ldquoModular articial 120573120573-cell system a

prototype for clinical researchrdquo Journal of Diabetes Science andTechnology vol 2 pp 863ndash872 2008

[162] B Kovatchev S Patek E Dassau et al ldquoControl to range fordiabetes functionality and modular architecturerdquo Journal ofDiabetes Science and Rechnology vol 3 no 5 pp 1058ndash10652009

[163] E Atlas R Nimri S Miller E A Grunberg and M PhillipldquoMD-logic articial pancreas system a pilot study in adults withtype 1 diabetesrdquo Diabetes Care vol 33 no 5 pp 1072ndash10762010

[164] E Dassau H Zisser M W Percival B Grosman L Jovanovičand F J Doyle III ldquoClinical results of automated articialpancreatic 120573120573-cell system with unannounced meal using multi-parametric MPC and insulin-on-boardrdquo in Proceedings of the70th American Diabetes Association Meeting Diabetes vol 59supplement 1 A94 Orlando Fla USA 2010

[165] E M Renard A Farret J Place C Cobelli B P Kovatchev andMD Breton ldquoClosed-loop insulin delivery using subcutaneousinfusion and glucose sensing and equipped with a dedicatedsafety supervision algorithm improves safety of glucose controlin type 1 diabetesrdquo Diabetologia vol 53 supplement 1 p S252010

[166] R Hovorka K Kumareswaran J Harris et al ldquoOvernightclosed loop insulin delivery articial pancreas in adults withtype 1 diabetes crossover randomized controlled studiesrdquoBritish Medical Journal vol 342 no 7803 Article ID d18552011

[167] H Zisser E Dassau W Bevier et al ldquoInitial evaluation ofa fully automated articial pancreasrdquo in Proceedings of the71st American Diabetes Association Meeting Diabetes vol 60supplement 1 A41 San Diego Calif USA 2011

[168] M D Breton A Farret and D Bruttomesso ldquoFully-integratedarticial pancreas in type 1 diabetes modular closed-loopglucose control maintains near-normoglycemiardquo Diabetes vol61 pp 2230ndash2237 2012

[169] C Cobelli E Renard and B P Kovatchev ldquoPilot studies ofwearable articial pancreas in type 1 diabetesrdquo Diabetes Carevol 35 no 9 pp e65ndashe67 2012

Submit your manuscripts athttpwwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

EndocrinologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

OncologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Oxidative Medicine and Cellular Longevity

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational and Mathematical Methods in Medicine

OphthalmologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Diabetes ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Research and TreatmentAIDS

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Gastroenterology Research and Practice

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Scientica 13

in type 1 diabetes are preceded and followed within 48 hours bymeasurable disturbances in blood glucoserdquo Journal of ClinicalEndocrinology and Metabolism vol 85 no 11 pp 4287ndash42922000

[117] B P Kovatchev M Straume D J Cox and L S FarhyldquoRisk analysis of blood glucose data a quantitative approach tooptimizing the control of Insulin Dependent Diabetesrdquo Journalof eoretical Medicine vol 3 no 1 pp 1ndash10 2000

[118] B P Kovatchev E Otto D Cox L Gonder-Frederick andW Clarke ldquoEvaluation of a new measure of blood glucosevariability in diabetesrdquo Diabetes Care vol 29 no 11 pp2433ndash2438 2006

[119] B P Kovatchev W L Clarke M Breton K Brayman and AMcCall ldquoQuantifying temporal glucose variability in diabetesvia continuous glucose monitoring mathematical methods andclinical applicationrdquo Diabetes Technology and erapeutics vol7 no 6 pp 849ndash862 2005

[120] D Rodbard ldquoNew and improved methods to characterizeglycemic variability using continuous glucosemonitoringrdquoDia-betes Technology and erapeutics vol 11 no 9 pp 551ndash5652009

[121] M Miller and P Strange ldquoUse of Fourier models for analysisand interpretation of continuous glucose monitoring glucoseprolesrdquo Journal of Diabetes Science and Technology vol 1 pp630ndash638 2007

[122] W Clarke and B Kovatchev ldquoStatistical tools to analyzecontinuous glucose monitor datardquo Diabetes Technology anderapeutics vol 11 pp S45ndashS54 2009

[123] C M Mcdonnell S M Donath S I Vidmar G A Wertherand F J Cameron ldquoA novel approach to continuous glucoseanalysis utilizing glycemic variationrdquo Diabetes Technology anderapeutics vol 7 no 2 pp 253ndash263 2005

[124] G Sparacino F Zanderigo A Maran and C Cobelli ldquoContin-uous glucosemonitoring andhypohyperglycaemia predictionrdquoDiabetes Research and Clinical Practice vol 74 no 2 supple-ment pp S160ndashS163 2006

[125] G Sparacino F Zanderigo G Corazza A Maran AFacchinetti and C Cobelli ldquoGlucose concentration can bepredicted ahead in time from continuous glucose monitoringsensor time-seriesrdquo IEEE Transactions on Biomedical Engineer-ing vol 54 pp 931ndash937 2007

[126] J Reifman S Rajaraman A Gribok and W K Ward ldquoPredic-tive monitoring for improved management of glucose levelsrdquoJournal of Diabetes Science and Technology vol 1 pp 478ndash4862007

[127] F Zanderigo G Sparacino B Kovatchev and C CobellildquoGlucose prediction algorithms from continuous monitoringassessment of accuracy via continuous glucose-error grid anal-ysisrdquo Journal of Diabetes Science and Technology vol 1 pp645ndash651 2007

[128] F Cameron G Niemayer K Gundy-Burlet and B Bucking-ham ldquoStatistical hypoglycemia predictionrdquo Journal of DiabetesScience and Technology vol 2 pp 612ndash621 2008

[129] A Gani A V Gribok S Rajaraman W K Ward and JReifman ldquoPredicting subcutaneous glucose concentration inhumans data-driven glucose modelingrdquo IEEE Transactions onBiomedical Engineering vol 56 no 2 pp 246ndash254 2009

[130] M Eren-Oruklu A Cinar L Quinn andD Smith ldquoEstimationof future glucose concentrations with subject-specic recursivelinear modelsrdquo Diabetes Technology and erapeutics vol 11no 4 pp 243ndash253 2009

[131] S M Pappada B D Cameron and P M Rosman ldquoDevelop-ment of a neural network for prediction of glucose concentra-tion in type 1 diabetes patientsrdquo Journal of Diabetes Science andTechnology vol 2 pp 792ndash801 2008

[132] C Cobelli C Dalla Man G Sparacino L Magni G Nicolaoand B P Kovatchev ldquoDiabetes models signals and controlrdquoIEEE Reviews in Biomedical Engineering vol 2 pp 54ndash96 2009

[133] H LeBlanc D Chauvet P Lombrail and J J Robert ldquoGlycemiccontrol with closed-loop intraperitoneal insulin in type Idiabetesrdquo Diabetes Care vol 9 no 2 pp 124ndash128 1986

[134] C D Saudek J L Selman H A Pitt et al ldquoA preliminary trialof the programmable implantablemedication system for insulindeliveryrdquo New England Journal of Medicine vol 321 no 9 pp574ndash579 1989

[135] C Broussolle N Jeandidier and H Hanaire-Broutin ldquoFrenchmulticentre experience of implantable insulin pumpsrdquo Lancetvol 343 no 8896 pp 514ndash515 1994

[136] H Hanaire-Broutin C Broussolle N Jeandidier et al ldquoFea-sibility of intraperitoneal insulin therapy with programmableimplantable pumps in IDDM a multicenter studyrdquo DiabetesCare vol 18 no 3 pp 388ndash392 1995

[137] P R Oskarsson P E Lins H W Henriksson and U CAdamson ldquoMetabolic and hormonal responses to exercisein type 1 diabetic patients during continuous subcutaneousas compared to continuous intraperitoneal insulin infusionrdquoDiabetes and Metabolism vol 25 no 6 pp 491ndash497 1999

[138] P R Oskarsson P E Lins L Backman and U C AdamsonldquoContinuous intraperitoneal insulin infusion partly restores theglucagon response to hypoglycaemia in type 1 diabetic patientsrdquoDiabetes and Metabolism vol 26 no 2 pp 118ndash124 2000

[139] B Catargi L Meyer V Melki E Renard and N JeandidierldquoComparison of blood glucose stability and HbA1C betweenimplantable insulin pumps using U400 hoe 21pH insulin andexternal pumps using lispro in type 1 diabetic patients a pilotstudyrdquo Diabetes and Metabolism vol 28 no 2 pp 133ndash1372002

[140] E Renard ldquoImplantable closed-loop glucose-sensing andinsulin delivery the future for insulin pump therapyrdquo CurrentOpinion in Pharmacology vol 2 no 6 pp 708ndash716 2002

[141] E Renard G Costalat H Chevassus and J Bringer ldquoArticial120573120573-cell clinical experience toward an implantable closed-loopinsulin delivery systemrdquo Diabetes and Metabolism vol 32 no5 pp 497ndash502 2006

[142] E Renard J Place M Cantwell H Chevassus and C CPalerm ldquoClosed-loop insulin delivery using a subcutaneousglucose sensor and intraperitoneal insulin delivery feasibilitystudy testing a new model for the articial pancreasrdquo DiabetesCare vol 33 no 1 pp 121ndash127 2010

[143] R Bellazzi G Nucci and C Cobelli ldquoe subcutaneousroute to insulin-dependent diabetes therapy closed-loop andpartially closed-loop control strategies for insulin deliveryand measuring glucose concentrationrdquo IEEE Engineering inMedicine and Biology Magazine vol 20 no 1 pp 54ndash64 2001

[144] R Hovorka L J Chassin M E Wilinska et al ldquoClosingthe loop the adicol experiencerdquo Diabetes Technology anderapeutics vol 6 no 3 pp 307ndash318 2004

[145] R Hovorka V Canonico L J Chassin et al ldquoNonlinear modelpredictive control of glucose concentration in subjects withtype 1 diabetesrdquo Physiological Measurement vol 25 no 4 pp905ndash920 2004

14 Scientica

[146] G M Steil K Rebrin C Darwin F Hariri and M F SaadldquoFeasibility of automating insulin delivery for the treatment oftype 1 diabetesrdquo Diabetes vol 55 no 12 pp 3344ndash3350 2006

[147] e JDRF e-Newsletter Emerging Technologies in DiabetesResearch 2006

[148] W L Clarke and B Kovatchev ldquoe articial pancreas howclose are we to closing the looprdquo Pediatric EndocrinologyReviews vol 4 no 4 pp 314ndash316 2007

[149] C Cobelli E Renard and B P Kovatchev ldquoPerspectives indiabetes articial pancreas past present futurerdquo Diabetes vol60 pp 2672ndash2682 2011

[150] B P Kovatchev M D Breton C Dalla Man and C Cobelli ldquoInsilico preclinical trials a proof of concept in closed-loop controlof type 1 diabetesrdquo Journal of Diabetes Science and Technologyvol 3 pp 44ndash55 2009

[151] B Kovatchev C Cobelli E Renard et al ldquoMultinational studyof subcutaneous model-predictive closed-loop control in type1 diabetes mellitus summary of the resultsrdquo Journal of DiabetesScience and Technology vol 4 no 6 pp 1374ndash1381 2010

[152] H Zisser L Jovanovic F Doyle P Ospina and C OwensldquoRun-to-run control of meal-related insulin dosingrdquo DiabetesTechnology anderapeutics vol 7 no 1 pp 48ndash57 2005

[153] C Owens H Zisser L Jovanovic B Srinivasan D Bonvinand F J Doyle III ldquoRun-to-run control of blood glucoseconcentrations for people with type 1 diabetes mellitusrdquo IEEETransactions on Biomedical Engineering vol 53 no 6 pp996ndash1005 2006

[154] C C Palerm H Zisser W C Bevier L Jovanovič and F JDoyle III ldquoPrandial insulin dosing using run-to-run controlapplication of clinical data and medical expertise to dene asuitable performance metricrdquo Diabetes Care vol 30 no 5 pp1131ndash1136 2007

[155] LMagni F Raimondo L Bossi et al ldquoModel predictive controlof type 1 diabetes an in silico trialrdquo Journal of Diabetes Scienceand Technology vol 1 pp 804ndash812 2007

[156] S A Weinzimer G M Steil K L Swan J Dziura N Kurtzand W V Tamborlane ldquoFully automated closed-loop insulindelivery versus semiautomated hybrid control in pediatricpatients with type 1 diabetes using an articial pancreasrdquoDiabetes Care vol 31 no 5 pp 934ndash939 2008

[157] W L Clarke S Anderson M Breton S Patek L Kashmerand B Kovatchev ldquoClosed-loop articial pancreas using sub-cutaneous glucose sensing and insulin delivery and a modelpredictive control algorithm the Virginia experiencerdquo Journalof Diabetes Science and Technology vol 3 no 5 pp 1031ndash10382009

[158] D Bruttomesso A Farret S Costa et al ldquoClosed-loop arti-cial pancreas using subcutaneous glucose sensing and insulindelivery and a model predictive control algorithm preliminarystudies in Padova and Montpellierrdquo Journal of Diabetes Scienceand Technology vol 3 no 5 pp 1014ndash1021 2009

[159] R Hovorka J M Allen D Elleri et al ldquoManual closed-loop insulin delivery in children and adolescents with type 1diabetes a phase 2 randomised crossover trialrdquoe Lancet vol375 no 9716 pp 743ndash751 2010

[160] F H El-khatib S J Russell D M Nathan R G Sutherlin andE R Damiano ldquoA bihormonal closed-loop articial pancreasfor type 1 diabetesrdquo Science Translational Medicine vol 2 no27 Article ID 27ra27 2010

[161] E Dassau H Zisser C C Palerm B A Buckingham LJovanovič and F J Doye III ldquoModular articial 120573120573-cell system a

prototype for clinical researchrdquo Journal of Diabetes Science andTechnology vol 2 pp 863ndash872 2008

[162] B Kovatchev S Patek E Dassau et al ldquoControl to range fordiabetes functionality and modular architecturerdquo Journal ofDiabetes Science and Rechnology vol 3 no 5 pp 1058ndash10652009

[163] E Atlas R Nimri S Miller E A Grunberg and M PhillipldquoMD-logic articial pancreas system a pilot study in adults withtype 1 diabetesrdquo Diabetes Care vol 33 no 5 pp 1072ndash10762010

[164] E Dassau H Zisser M W Percival B Grosman L Jovanovičand F J Doyle III ldquoClinical results of automated articialpancreatic 120573120573-cell system with unannounced meal using multi-parametric MPC and insulin-on-boardrdquo in Proceedings of the70th American Diabetes Association Meeting Diabetes vol 59supplement 1 A94 Orlando Fla USA 2010

[165] E M Renard A Farret J Place C Cobelli B P Kovatchev andMD Breton ldquoClosed-loop insulin delivery using subcutaneousinfusion and glucose sensing and equipped with a dedicatedsafety supervision algorithm improves safety of glucose controlin type 1 diabetesrdquo Diabetologia vol 53 supplement 1 p S252010

[166] R Hovorka K Kumareswaran J Harris et al ldquoOvernightclosed loop insulin delivery articial pancreas in adults withtype 1 diabetes crossover randomized controlled studiesrdquoBritish Medical Journal vol 342 no 7803 Article ID d18552011

[167] H Zisser E Dassau W Bevier et al ldquoInitial evaluation ofa fully automated articial pancreasrdquo in Proceedings of the71st American Diabetes Association Meeting Diabetes vol 60supplement 1 A41 San Diego Calif USA 2011

[168] M D Breton A Farret and D Bruttomesso ldquoFully-integratedarticial pancreas in type 1 diabetes modular closed-loopglucose control maintains near-normoglycemiardquo Diabetes vol61 pp 2230ndash2237 2012

[169] C Cobelli E Renard and B P Kovatchev ldquoPilot studies ofwearable articial pancreas in type 1 diabetesrdquo Diabetes Carevol 35 no 9 pp e65ndashe67 2012

Submit your manuscripts athttpwwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

EndocrinologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

OncologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Oxidative Medicine and Cellular Longevity

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational and Mathematical Methods in Medicine

OphthalmologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Diabetes ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Research and TreatmentAIDS

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Gastroenterology Research and Practice

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

14 Scientica

[146] G M Steil K Rebrin C Darwin F Hariri and M F SaadldquoFeasibility of automating insulin delivery for the treatment oftype 1 diabetesrdquo Diabetes vol 55 no 12 pp 3344ndash3350 2006

[147] e JDRF e-Newsletter Emerging Technologies in DiabetesResearch 2006

[148] W L Clarke and B Kovatchev ldquoe articial pancreas howclose are we to closing the looprdquo Pediatric EndocrinologyReviews vol 4 no 4 pp 314ndash316 2007

[149] C Cobelli E Renard and B P Kovatchev ldquoPerspectives indiabetes articial pancreas past present futurerdquo Diabetes vol60 pp 2672ndash2682 2011

[150] B P Kovatchev M D Breton C Dalla Man and C Cobelli ldquoInsilico preclinical trials a proof of concept in closed-loop controlof type 1 diabetesrdquo Journal of Diabetes Science and Technologyvol 3 pp 44ndash55 2009

[151] B Kovatchev C Cobelli E Renard et al ldquoMultinational studyof subcutaneous model-predictive closed-loop control in type1 diabetes mellitus summary of the resultsrdquo Journal of DiabetesScience and Technology vol 4 no 6 pp 1374ndash1381 2010

[152] H Zisser L Jovanovic F Doyle P Ospina and C OwensldquoRun-to-run control of meal-related insulin dosingrdquo DiabetesTechnology anderapeutics vol 7 no 1 pp 48ndash57 2005

[153] C Owens H Zisser L Jovanovic B Srinivasan D Bonvinand F J Doyle III ldquoRun-to-run control of blood glucoseconcentrations for people with type 1 diabetes mellitusrdquo IEEETransactions on Biomedical Engineering vol 53 no 6 pp996ndash1005 2006

[154] C C Palerm H Zisser W C Bevier L Jovanovič and F JDoyle III ldquoPrandial insulin dosing using run-to-run controlapplication of clinical data and medical expertise to dene asuitable performance metricrdquo Diabetes Care vol 30 no 5 pp1131ndash1136 2007

[155] LMagni F Raimondo L Bossi et al ldquoModel predictive controlof type 1 diabetes an in silico trialrdquo Journal of Diabetes Scienceand Technology vol 1 pp 804ndash812 2007

[156] S A Weinzimer G M Steil K L Swan J Dziura N Kurtzand W V Tamborlane ldquoFully automated closed-loop insulindelivery versus semiautomated hybrid control in pediatricpatients with type 1 diabetes using an articial pancreasrdquoDiabetes Care vol 31 no 5 pp 934ndash939 2008

[157] W L Clarke S Anderson M Breton S Patek L Kashmerand B Kovatchev ldquoClosed-loop articial pancreas using sub-cutaneous glucose sensing and insulin delivery and a modelpredictive control algorithm the Virginia experiencerdquo Journalof Diabetes Science and Technology vol 3 no 5 pp 1031ndash10382009

[158] D Bruttomesso A Farret S Costa et al ldquoClosed-loop arti-cial pancreas using subcutaneous glucose sensing and insulindelivery and a model predictive control algorithm preliminarystudies in Padova and Montpellierrdquo Journal of Diabetes Scienceand Technology vol 3 no 5 pp 1014ndash1021 2009

[159] R Hovorka J M Allen D Elleri et al ldquoManual closed-loop insulin delivery in children and adolescents with type 1diabetes a phase 2 randomised crossover trialrdquoe Lancet vol375 no 9716 pp 743ndash751 2010

[160] F H El-khatib S J Russell D M Nathan R G Sutherlin andE R Damiano ldquoA bihormonal closed-loop articial pancreasfor type 1 diabetesrdquo Science Translational Medicine vol 2 no27 Article ID 27ra27 2010

[161] E Dassau H Zisser C C Palerm B A Buckingham LJovanovič and F J Doye III ldquoModular articial 120573120573-cell system a

prototype for clinical researchrdquo Journal of Diabetes Science andTechnology vol 2 pp 863ndash872 2008

[162] B Kovatchev S Patek E Dassau et al ldquoControl to range fordiabetes functionality and modular architecturerdquo Journal ofDiabetes Science and Rechnology vol 3 no 5 pp 1058ndash10652009

[163] E Atlas R Nimri S Miller E A Grunberg and M PhillipldquoMD-logic articial pancreas system a pilot study in adults withtype 1 diabetesrdquo Diabetes Care vol 33 no 5 pp 1072ndash10762010

[164] E Dassau H Zisser M W Percival B Grosman L Jovanovičand F J Doyle III ldquoClinical results of automated articialpancreatic 120573120573-cell system with unannounced meal using multi-parametric MPC and insulin-on-boardrdquo in Proceedings of the70th American Diabetes Association Meeting Diabetes vol 59supplement 1 A94 Orlando Fla USA 2010

[165] E M Renard A Farret J Place C Cobelli B P Kovatchev andMD Breton ldquoClosed-loop insulin delivery using subcutaneousinfusion and glucose sensing and equipped with a dedicatedsafety supervision algorithm improves safety of glucose controlin type 1 diabetesrdquo Diabetologia vol 53 supplement 1 p S252010

[166] R Hovorka K Kumareswaran J Harris et al ldquoOvernightclosed loop insulin delivery articial pancreas in adults withtype 1 diabetes crossover randomized controlled studiesrdquoBritish Medical Journal vol 342 no 7803 Article ID d18552011

[167] H Zisser E Dassau W Bevier et al ldquoInitial evaluation ofa fully automated articial pancreasrdquo in Proceedings of the71st American Diabetes Association Meeting Diabetes vol 60supplement 1 A41 San Diego Calif USA 2011

[168] M D Breton A Farret and D Bruttomesso ldquoFully-integratedarticial pancreas in type 1 diabetes modular closed-loopglucose control maintains near-normoglycemiardquo Diabetes vol61 pp 2230ndash2237 2012

[169] C Cobelli E Renard and B P Kovatchev ldquoPilot studies ofwearable articial pancreas in type 1 diabetesrdquo Diabetes Carevol 35 no 9 pp e65ndashe67 2012

Submit your manuscripts athttpwwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

EndocrinologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

OncologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Oxidative Medicine and Cellular Longevity

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational and Mathematical Methods in Medicine

OphthalmologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Diabetes ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Research and TreatmentAIDS

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Gastroenterology Research and Practice

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom

Submit your manuscripts athttpwwwhindawicom

Stem CellsInternational

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MEDIATORSINFLAMMATION

of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Behavioural Neurology

EndocrinologyInternational Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Disease Markers

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

BioMed Research International

OncologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Oxidative Medicine and Cellular Longevity

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

PPAR Research

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Immunology ResearchHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

ObesityJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Computational and Mathematical Methods in Medicine

OphthalmologyJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Diabetes ResearchJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Research and TreatmentAIDS

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Gastroenterology Research and Practice

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Parkinsonrsquos Disease

Evidence-Based Complementary and Alternative Medicine

Volume 2014Hindawi Publishing Corporationhttpwwwhindawicom