Localizing Functional Damage in the Neural Retina of...

Retina

Localizing Functional Damage in the Neural Retina ofAdolescents and Young Adults With Type 1 Diabetes

Wylie Tan,1,2 Tom Wright,1 Annie Dupuis,3,4 Ekta Lakhani,1 and Carol Westall1,5

1Department of Ophthalmology and Vision Sciences, The Hospital for Sick Children, Toronto, Canada2School of Optometry and Vision Science, University of Waterloo, Waterloo, Canada3Clinical Research Services, The Hospital for Sick Children, Toronto, Canada4Dalla Lana School of Public Health, University of Toronto, Toronto, Canada5Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, Canada

Correspondence: Carol Westall, De-partment of Ophthalmology andVision Sciences, The Hospital forSick Children, 555 University Ave-nue, Toronto, ON, Canada, M5G1X8;[email protected].

Submitted: September 9, 2013Accepted: February 1, 2014

Citation: Tan W, Wright T, Dupuis A,Lakhani E, Westall C. Localizing func-tional damage in the neural retina ofadolescents and young adults withtype 1 diabetes. Invest Ophthalmol

Vis Sci. 2014;55:2432–2441. DOI:10.1167/iovs.13-13232

PURPOSE. It is unknown which regions of the retina are most susceptible to damage bydiabetes mellitus. We hypothesized that the standard and slow-flash (sf-) multifocalelectroretinogram (mfERG) will localize retinal regions of greatest vulnerability.

METHODS. A total of 55 adolescents and young adults with type 1 diabetes and without diabeticretinopathy (DR) or with mild nonproliferative DR and 54 typically-developing, age-similarcontrol participants underwent mfERG and sf-mfERG testing. The amplitude and implicit timeof the first order response of the standard mfERG and of three multifocal oscillatory potentials(mfOPs) of the sf-mfERG were compared between groups at the level of hexagons, quadrants,and rings using separate mixed model ANOVAs. Spatial mapping of the P values from post hocpairwise comparisons illustrated patterns of retinal dysfunction.

RESULTS. Delays in mfERG implicit times were evident across the tested retinal areas in thediabetes group. Delays in sf-mfERG implicit times were found at different eccentricities foreach mfOP in the diabetes group. The greatest delays were noted in the periphery for mfOP1,in the midperiphery for mfOP2, and in the macular region for mfOP3. There were nosignificant group differences in amplitude for the mfERG and sf-mfERG protocols.

CONCLUSIONS. Delays in mfERG and sf-mfERG responses suggest that the inner retina isparticularly vulnerable to diabetes. Localizing regions of early dysfunction will help guidefuture studies to examine early structural damage associated with DR.

Keywords: type 1 diabetes, multifocal electroretinogram, dysfunction, inner retina, oscillatorypotentials

Diabetic retinopathy (DR) is a common complication ofdiabetes mellitus and the leading cause of new blindness in

young individuals in developed countries.1,2 Approximately 93million people live with some form of DR worldwide.3 Theprevalence of DR has been shown to be higher in individualswith type 1 diabetes (T1D) compared to type 2 diabetes (T2D),with a landmark study reporting 97.5% prevalence of DRamong their participants with 15þ years of T1D.4 The DRprevalence rates have since declined,5,6 with latest globalestimates reporting a prevalence of approximately 77% in thispopulation.3 The DR will, however, continue to be a globalhealth concern because of the expected increase in the numberof people affected by diabetes.7

Hallmark diagnostic features of DR consist of clinicallyvisible microvascular lesions identified through fundus photog-raphy or ophthalmoscopy. These lesions are characterizedbased on the modified Airlie House classification system8 andserve as the standard for diagnosing DR. Retinal dysfunctionoccurs before these vascular lesions are detected clinically,9–11

suggesting that early functional disturbances in the neuralretina may serve as effective biomarkers for DR.

It has long been known that oscillatory potentials of theelectroretinogram (ERG) are one of the earliest electroretino-graphic components to be affected and they have been

suggested to be the most sensitive marker of neuroretinal

dysfunction in diabetes.9–11 The global response properties of

the ERG cannot identify localized regions of early functional

damage. The standard and slow-flash (sf-) multifocal electroret-

inogram (mfERG) may be more ideal tools to localize early

retinal damage. Several studies have assessed localized retinal

dysfunction in participants with diabetes; the majority,

however, focused on adult populations with T2D or a mixture

of participants with T1D and T2D.12–16 The Wisconsin

Epidemiological Study of Diabetic Retinopathy (WESDR)4

emphasized that duration of diabetes is a strong risk factor for

DR and that prevalence of DR is much higher in individuals

with T1D compared to T2D. Therefore, it is of interest to

identify sensitive markers for DR as early as possible in the

disease process, making adolescents and young adults with T1D

an ideal study population.

Few studies have investigated the retina’s vulnerability to

diabetes topographically.17 This study will use the standard

mfERG and sf-mfERG to assess the functional integrity of the

outer and inner neural retina, respectively, in an adolescent and

young adult population with T1D. The mfERG and sf-mfERG

responses will be compared spatially between participants with

and without T1D at the level of hexagons, quadrants, and rings.

Copyright 2014 The Association for Research in Vision and Ophthalmology, Inc.

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Findings from this study will identify regions of the retina thatare most vulnerable to damage by the effects of diabetes.

METHODS

Subjects

A total of 55 adolescents and young adults with T1D andwithout DR, or with mild nonproliferative DR (NPDR) wasrecruited at The Hospital for Sick Children. Inclusion criteriawere duration of T1D of ‡5 years and age 10 to 25 years.Participants with moderate or severe NPDR, or proliferative DRwere excluded either by fundus examination by an ophthal-mologist or based on seven-field, 308 stereoscopic fundusphotographs graded by a retinal specialist according to themodified Airlie House classification system.8 A total of 54typically-developing, age-similar participants without diabetesacted as control participants. All participants with other eyediseases, hemoglobinopathy, high refractive error (worse than65 diopters [D]), poor visual acuity (worse than 0.3 logMAR),neurologic disorders, and those on medications affecting visualor retinal function were excluded. Informed consent wasobtained from all participants after the purpose, protocol, andpotential harms and benefits of the study were explained. Allprocedures were approved by the Research Ethics Board atThe Hospital for Sick Children and were conducted incompliance with the tenets of the Declaration of Helsinki.

Data Acquisition

All participants were tested at The Hospital for Sick Children.Since acute changes in ambient blood glucose are known toaffect mfERG responses,18,19 blood glucose readings weremeasured with a glucose meter (OneTouch Ultra; LifeScan,Inc., Milpitas, CA, USA) at least three times: before psycho-physical testing, before mfERG testing, and after mfERGtesting. Participants with T1D completed light exercises, orwere administered food or insulin, as advised by a registerednurse, to adjust and maintain blood glucose levels within a 4 to10 mmol/L range.

One eye was selected randomly for testing from eachparticipant and the untested eye was occluded. All participantswere assessed for visual acuity (Early Treatment of DiabeticRetinopathy Study [ETDRS], logMAR) and contrast sensitivity(Pelli-Robson). Color vision was assessed with the Hardy-Rand-Rittler (HRR) pseudoisochromatic plates and the Mollon-ReffinMinimalist Test. The tested eye was anesthetized with a topicalcorneal anesthetic (0.5% proparacaine) and dilated pharmaco-logically (2.5% phenylephrine and 1% tropicamide). Refractiveerror was measured post dilation. Date of T1D diagnosis andhemoglobin A1c (HbA1c) values closest to the testing date wereobtained from hospital records.

Multifocal ERG

Participants were tested on the mfERG and sf-mfERG protocolswith the VERIS FMSII Science System (Electro-DiagnosticImaging, Inc., Redwood City, CA, USA) according to Interna-tional Society for Clinical Electrophysiology of Vision (ISCEV)guidelines.20 A red cross was used as the fixation target and itwas enlarged as required to ensure that the target was visibleduring recordings. The FMSII Stimulator was adjusted to ensurethat the mfERG hexagons were in focus.

The standard mfERG protocol consisted of a stimulus arraywith 103 hexagons that subtended a field diameter of 408vertical 3458 horizontal. Hexagons were scaled for eccentricitysuch that all responses had approximately equal amplitudesacross a healthy retina. Each hexagon alternated between black

(0 cd/m2) and white (200 cd/m2) following an algorithmicpseudorandom m-sequence (215 � 1) with a base rate of 13.3ms. For every frame, each hexagon had a 50% chance of beingilluminated (mean luminance 100 cd/m2).

The sf-mfERG protocol consisted of a stimulus array of 61hexagons that subtended a similar field to the mfERG.Hexagons were again scaled for eccentricity. Each step of them-sequence (212� 1) consisted of six frames: in the first frame,each hexagon had a 50% chance of being white and in thesubsequent five frames, all hexagons were black. Themultifocal frames were separated by 79.8 ms, allowing forthe development of inner retinal responses.

A bipolar Burian-Allen contact lens electrode (HansenOphthalmic Development Laboratory, Iowa City, IA, USA)was used for recordings with a gold-plated electrode (GrassTechnologies, Warwick, RI) on the forehead to serve as ground.Incoming signals were amplified (350,000) and filtered with ananalog filter (band-pass 10–300 Hz). Additional filtering wasaccomplished with digital filtering for mfERG (band stop filterat 60 Hz power) and sf-mfERG (75 Hz high pass filter). Twoiterations of artifact removal were used and spatial averagingwas not used as this option reduced the spatial accuracy of thedata. Participant fixation was monitored with a built-in infraredfundus camera. Recording time was divided into 16 segmentsfor participant comfort. Segments with fixation loss or noiseartifacts were repeated. Total recording time for the mfERG (8minutes) and sf-mfERG (~7 minutes) protocols was approxi-mately 15 minutes.

Data Analysis

The outcome measures for the mfERG recordings were theamplitude and implicit time of the first order response of eachhexagon. The amplitude was measured from trough-to-peak(N1 to P1) and the implicit time was measured from the time offrame presentation to the time of the major peak (Fig. 1A).

The outcome measures for the sf-mfERG recordings werethe amplitude and implicit time of the three multifocaloscillatory potentials (mfOPs). Peaks and troughs wereidentified with a custom script written in R Statistical AnalysisSoftware Version 2.13.1.21 The script identified peaks and

FIGURE 1. Amplitude (dashed arrow) and implicit time (solid arrow)measurements for the standard mfERG (A) and sf-mfERG with threemfOPs (B) and two mfOPs (C) from a control participant. Gray

columns represent the 5-ms time span searched for an mfOP peak.

Localizing Functional Damage in the Diabetic Retina IOVS j April 2014 j Vol. 55 j No. 4 j 2433


troughs by comparing each data point with the two precedingdata points and the two following data points; a data point wasidentified as a peak if it had the greatest positivity and a troughif it had the greatest negativity within a span of 5 ms (Fig. 1B).The amplitude of each mfOP was measured from trough-to-peak, and amplitudes smaller than 10 nV were considerednoise and omitted manually. Implicit time was measured fromthe time of stimulus presentation to the time of the peak ofeach mfOP. Since the timings of the mfOPs varied withhexagon location, all control data were averaged to find theapproximate timing of mfOP1, mfOP2, and mfOP3 at eachhexagon. These timings acted as guidelines to classify thepeaks identified for all participants. Peaks were classified asmfOP1, mfOP2, or mfOP3 based on the proximity of the peak’simplicit time to the guidelines. In several cases, participantswith and without diabetes had occasional hexagons thatpresented with two mfOPs rather than three. These twomfOPs were classified according to the guidelines and it wasassumed that one of the three mfOPs was missing (Fig. 1C). Itusually was mfOP2 or mfOP3 that was missing, and rarelymfOP1. Almost all participants had at least 1 of 61 hexagonswith a missing mfOP and the maximum number of hexagonswith a missing mfOP in a single participant was approximately30 hexagons.

All left eye recordings were converted to the right eye, suchthat temporal regions of the left eye were compared totemporal regions of the right eye and similarly for the nasalregions.

Mixed Model ANOVA

To examine spatial differences in retinal function, between-group comparisons were made at the level of hexagons,quadrants, and rings with separate mixed model ANOVAs. ThemfERG and sf-mfERG stimulus patterns were divided into fourquadrants (along horizontal and vertical meridians from thefovea) and rings (six for mfERG and five for sf-mfERG, Fig. 2).

Quadrant and ring amplitude, and implicit time responses werecomprised of the average of the amplitude and implicit time ofall of the hexagons within each region. For mfERG and sf-mfERG mixed model ANOVAs, the independent variable wasgroup (T1D or control), the dependent variable was amplitudeor implicit time, and the repeated measure variable washexagon, quadrant, or ring.

Mixed model analyses were performed using SAS version9.2 (SAS Institute, Inc., Rockville, MD, USA). Group meanswere compared using post hoc pairwise comparisons.Bonferroni corrections were applied to account for themultiple comparisons: a ¼ 0.05 was divided by the numberof comparisons or levels within the repeated measure variable.The Bonferroni adjustment is most appropriate when hypoth-esis tests are independent. As observations across hexagons,quadrants, and rings were correlated, the Bonferroni correc-tion in this case is overly conservative. Thus, all results arepresented as actual P values for the reader’s discretion.

RESULTS

Demographic data and psychophysical testing results for allparticipants are shown in the Table. There were no significantbetween-group differences in age, visual acuity, or contrastsensitivity. Two participants with T1D had mild red-green colorvision defects. Most participants with T1D (45/53) werediagnosed before the age of 10 and approximately 70% hadbeen diagnosed with T1D for ‡8 years. Date of diagnosis wasnot available for two participants. The HbA1c values were notavailable for six participants because they were over the age of18 and had left The Hospital for Sick Children. Over 89% of theparticipants with T1D had HbA1c levels above the recom-mended HbA1c target of 7% set by the Canadian DiabetesAssociation.22 Eight participants with diabetes had early NPDR.After qualitative comparisons between the fundus photographsand mfERG traces of these eight participants, no correlations

FIGURE 2. Stimulus array for mfERG (A–C) and sf-mfERG (D–F), divided into hexagons (A, D), quadrants (B, E), and rings (C, F).



between morphologic NPDR findings and local mfERG delayswere found.

Data from most of the participants, with and withoutdiabetes, have been included in prior reports.23–25 This studyuses a different analysis technique to assess the spatialrelationships of the mfERG and sf-mfERG data, and uses thepeak-picking technique to analyze waveforms rather thanwaveform fitting techniques.

The mixed model analyses were conducted for the mfERGand sf-mfERG data. Rerunning the models for the sf-mfERG dataafter removing the observations with two mfOPs only yieldedsimilar results, possibly because the number of hexagons withonly two mfOPs was relatively small (accounting for 15% and13% of all data for participants with and without diabetes,respectively) compared to the hexagons with three mfOPs.Rerunning the mixed models after removing the participantswith mild NPDR also returned the same results. The analysespresented herein included all of the mfERG and sf-mfERG datafrom participants with and without mild NPDR.

Mixed Models for mfERG

Mixed model analyses for mfERG implicit time yielded maineffects for group at the level of hexagons (P < 0.0001),quadrants (P < 0.0001), and rings (P < 0.0001), but not foramplitude. The P values for the post hoc pairwise comparisonsfrom the hexagon analysis are presented in a spatial color mapto illustrate the distribution of dysfunction (Fig. 3).

Positive (þ) and negative (�) signs denote whetherparticipants with T1D had earlier or delayed implicit times,respectively, compared to control participants. Implicit times

were delayed in participants with T1D compared to controlparticipants at most locations (>60%, a¼ 0.05). Implicit timesof participants with T1D were significantly delayed at threehexagons only with the Bonferroni correction (a¼ 0.0005). Itis important to note, however, that the Bonferroni correction isthe most conservative correction for multiple comparisons.Spatial mapping helps to visualize the number of hexagons thatare approaching significance. Over 50% of the hexagons have aP < 0.02, indicating that implicit times of participants withT1D are delayed compared to controls across the majority ofthe retina. Figure 4 shows representative mfERG trace arraysfrom 2 participants with diabetes.

Quadrant (Fig. 5) and ring (Fig. 6) analyses showed thatimplicit time of participants with T1D were significantlydelayed compared to control participants in all quadrants andeccentricities except at the fovea. These comparisons weresignificant after the Bonferroni correction (a ¼ 0.01 forquadrants and a¼ 0.0008 for rings).

Mixed Models for sf-mfERG

Mixed model analyses for mfOP1, mfOP2, and mfOP3 of the sf-mfERG yielded significant between-group differences forimplicit time, but not for amplitude. Significant main effectswere found for mfOP1 at the level of hexagons (P ¼ 0.0003)and quadrants (P < 0.0001). Significant ring differences werefound for mfOP2 (P¼ 0.007) and mfOP3 (P¼ 0.0008), mfOP1approached significance (P ¼ 0.06).

The P values for the post hoc pairwise comparisons at thelevel of hexagons were mapped spatially in color plots (Fig. 7).The areas of interest are those in which the implicit time of

TABLE. Demographic Data and Psychophysical Testing Results for Participants with T1D and Control Participants

T1D Participants, n ¼ 55 Control Participants, n ¼ 54

Sex, male/female 26/29 19/35

NPDR, n 8 –

Age at testing, y 16.6 6 2.3 (12.1–22.1) 17.1 6 3.6 (10.7–25.1)

Age at diagnosis, y 7.2 6 3.9 (1.4–16.3) –

Duration of T1D, y 9.5 6 3.1 (3.9–15.6) –

HbA1c, % 8.6 6 1.3 (6.4–12.3) –

Visual acuity, logMAR �0.01 6 0.12 (�0.28–0.28) �0.07 6 0.13 (�0.60–0.24)

Contrast sensitivity 1.68 6 0.13 (1.05–2.05) 1.69 6 0.16 (1.05–2.20)

Data presented as mean 6 SD (range).

FIGURE 3. Spatial color map representing the mfERG implicit time differences between groups at the level of hexagons (illustrated on an unscaledhexagon array).



FIGURE 4. Examples of representative mfERG trace arrays from two participants (A, B) with diabetes. Blue traces represent individual patient dataand dashed black traces represent average control data. Shaded hexagons indicate responses that are significantly delayed (>2 SD from controls)(illustrated on unscaled hexagon arrays).



participants with T1D was delayed significantly compared to

control participants at a ¼ 0.05 (color toward the red end of

the spectrum with negative sign). The distribution of these

delayed regions was different for each mfOP: the red hexagons

were concentrated in the periphery for mfOP1, in the

midperiphery for mfOP2, and in the macular region for

mfOP3. Although there are minimal areas with significant

differences with the Bonferroni correction (a ¼ 0.0008), it is

important to note the pattern in the distribution of P values.

Ring analysis showed a similar distribution of delays (Fig. 7):

the greatest implicit time delays were observed in Ring 4 for

mfOP1 (P < 0.0001), Ring 3 for mfOP2 (P < 0.0001), and Ring

2 for mfOP3 (P < 0.0001). These differences are significant

with the Bonferroni correction (a ¼ 0.01). Figure 8 shows

FIGURE 5. Comparison of mfERG implicit time between groups at the level of quadrants. Data presented as mean 6 95% confidence limits.Bonferroni correction, P < 0.01.

FIGURE 6. Comparison of mfERG implicit time between groups at the level of rings. Data presented as mean 6 95% confidence limits. Bonferronicorrection, P < 0.0008.



representative sf-mfERG trace arrays from two participantswith diabetes.

Quadrant analysis showed that mfOP1 is consistently moredelayed in participants with T1D compared to controlparticipants in all four retinal quadrants: superior temporal(P < 0.0001), superior nasal (P¼ 0.007), inferior temporal (P¼0.004), inferior nasal (P ¼ 0.001).

DISCUSSION

This study found no change in mfERG amplitude, but mfERGimplicit times were delayed across the retina in the diabetesgroup. This is consistent with mfERG literature, where therehas been no consensus as to whether mfERG amplitude isaffected by diabetes,14,26,27 but multifocal ERG implicit timeshas been reported consistently as delayed in participants withdiabetes,14,23,27 and delayed to greater extents with increasingDR severity.26,28

Quadrant and ring analyses of the mfERG data revealed thatthe majority of the retina is affected by diabetes, as participantswith T1D showed response delays across all quadrants andeccentricities. Bearse et al.12 similarly studied and mappedretinal dysfunction in participants with diabetes, and found aspatial preference of vulnerability to the effects of diabetes.They used an sf-mfERG protocol, rather than the mfERGprotocol, to evaluate first-order N1, P1, and N2 implicit times,and found that P1 implicit time delays are most frequent in the

inferior and peripheral retina, and, thus, concluded theseregions are most vulnerable to damage. A few key factorsdifferentiate our studies, including differences in the protocolused, the population studied, and the analysis technique.Bearse et al.12 studied an adult population with a mix ofparticipants with T1D and T2D, whereas our study examinedan adolescent and young adult population with T1D only. Theadvantages of studying biomarkers with a younger rather thanan older adult population have been discussed previously.4

Bearse et al.12 plotted the frequency of delays at each hexagonand defined the region most frequently associated withabnormalities as the region of greatest vulnerability. In ourstudy, the region of greatest vulnerability was defined as theregion in which the mean implicit time of participants withT1D differed most significantly with the mean implicit time ofcontrol participants.

A comprehensive review conducted by Hood29 examinedstudies that used the mfERG to assess different retinal diseases.He derived a guide to locate the site of damage within theretina based on characteristic changes to the mfERG waveform.No amplitude changes and a small implicit time delay (<3 ms)are indicative of damage within the inner plexiform layer.29 Inour study, there was no change in amplitude between groupsand subtle (<1 ms), but significant implicit time delays in thediabetes group, suggesting dysfunction at the level of the innerplexiform layer in our participants with T1D.

Inner retinal function was assessed with the sf-mfERGprotocol and participants with T1D showed implicit time

FIGURE 7. Spatial color maps illustrating the distribution of mfOP implicit time differences between groups: both hexagon and ring analyses showthat each mfOP is most delayed at different eccentricities (illustrated on unscaled hexagon arrays).



delays again. Each mfOP was delayed at different eccentricities,with the greatest delays in the periphery for mfOP1, in themidperiphery for mfOP2, and in the macular region for mfOP3.Delays in mfOPs have been reported consistently in studiesinvolving participants with diabetes.17,30,31 Kurtenbach et al.17

studied mfOP topography in adolescents with T1D with the sf-mfERG. A few features in their study differentiate it from ourstudy, including differences in quadrant divisions, the identi-fication of two mfOPS in the first order response instead ofthree mfOPs, a smaller stimulus size, and a faster flashsequence. Also, they did not take multiple comparisons intoconsideration or control for blood sugar levels in theirparticipants with diabetes, which have been shown to affectthe mfERG.19,32 Despite these differences, their ring analysisyielded results that were similar to our findings: significantimplicit time delays were found peripherally for mfOP1 (~178–308) and midperipherally for mfOP2 (~118–228).

The exact origin of mfOPs is unknown, but past microelec-trode studies have isolated the responses to the innerplexiform layer and suggested that each mfOP may havedifferent cellular origins.33 Our finding that each of the mfOPdelays varied with eccentricity supports the idea of differentcellular origins. In the primate retina, the mfOPs have beendescribed as high frequency mfOPs and low frequencymfOPs.34 High frequency mfOPs correspond to early mfOPs(OP1-3) and are generated mainly by ganglion cells. Lowfrequency mfOPs correspond to early and late mfOPs, and aremainly generated by amacrine cells and hyperpolarizingsecond-order cells (OFF-bipolar and horizontal cells). Thus,the three mfOPs in the current study may originate fromganglion, amacrine, and interplexiform cells, and the mfOPsmay reflect negative feedback activity between these celltypes, but further investigation is needed to identify thespecific origins of these mfOPs and the mechanism involved inthe generation of the observed patterns of dysfunction.

Our findings from the mfERG and sf-mfERG protocolsimplicated the inner plexiform layer as a region particularly

vulnerable to the effects of diabetes. Many studies havesuggested that hypoxia may have a key role in inner retinaldysfunction.10,35 The inner retina is supplied by the retinalcirculation, and experimental disruption of this circulation inanimal studies results in hypoxia and reduces or eliminatesOPs.9,36 Altered rod photoreceptor function has been reportedin participants with diabetes.37 It has been postulated thatbecause rods are compromised in diabetes, the oxygenrequirements for dark adaptation increase. The outer retinarequires more oxygen and imposes additional hypoxic stresseson the inner retina, resulting in inner retinal dysfunction.37 Inaddition, early changes in retinal astrocytes have beenidentified in the inner and peripheral retina of diabetes-induced rats, and these changes are coincident with earlyretinal dysfunction and vascular changes.38 Since astrocytes areglial cells that modulate neuronal and vascular function, theiralterations may have a key role in inner retinal dysfunction.38

Altered glial cells, hypoxia, and retinal stress may havecontributed to the retinal dysfunction observed in ourparticipants with T1D.

Certain factors may have influenced our results. Themajority of our study population was at or around the age ofpuberty. Puberty is a risk factor for the development ofDR.22,24,39 Hormonal fluctuations associated with puberty posechallenges for those with diabetes in controlling their bloodglucose levels.22 These fluctuations may affect retinal function.One of the strengths of our study is that ambient blood glucosewas controlled for and monitored throughout testing. Otherfactors that affect the mfERG are age and high refractive errors.Multifocal ERG amplitude decreases with increasing age andrefractive errors.10,40 Our participants with T1D were com-pared to age-similar control participants and the majority ofparticipants were refracted. For the participants who were notrefracted (15/55 patients and 26/54 controls), a refractive errorworse than 65 D was ruled out if the participant had visualacuity better than 0.3 logMAR.

FIGURE 8. Representative sf-mfERG trace arrays for two participants with diabetes (A, B). Blue traces represent individual patient data and dashed

black traces represent average control data. The same trace array is repeated to demonstrate the hexagons with delayed implicit times for mfOP1(green), mfOP2 (yellow), mfOP3 (red); the leftmost arrays show a composite of all hexagons with implicit time delays (illustrated on unscaledhexagon arrays).



The main findings of this study offer insight about thespecific regions of the retina that are most susceptible todamage from the effects of diabetes. The inner retina isparticularly vulnerable and may suffer the effects of hypoxia,leading to early vascular changes. Spatial mapping of localdysfunction is important because identifying the regions ofgreatest vulnerability greatly increases the sensitivity of findingearly structural changes in the retina.

Acknowledgments

The authors thank Wai Ching Lam, MD, FRCSC, for grading fundusphotographs, Marcia Wilson, RN, for titrating and monitoring theblood glucose levels of participants with diabetes during testing,Melissa Cotesta, OC(C), for conducting refractions, CynthiaVandenHoven, BAA, CRA, for fundus photography, Cheng Lin,BASc, for technical assistance with creating spatial color maps, andCarole Panton, DBO(D), CO, OC(C), for editing the manuscript.

Supported by the Canadian Institute for Health Research (CW),Canadian Foundation for Innovation (CW), Vision Science Re-search Program Graduate Student Scholarship (WT), Banting andBest Diabetes Novo Nordisk Graduate Scholarship (WT), andSickKids Restracomp Graduate Scholarship (WT).

Disclosure: W. Tan, None; T. Wright, None; A. Dupuis, None; E.Lakhani, None; C. Westall, None

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