Dissecting the cause of diabetes Translational Medicine Ken C. Chiu, MD, FACE Director,...

Dissecting the cause of diabetesDissecting the cause of diabetesTranslational MedicineTranslational Medicine

Ken C. Chiu, MD, FACEKen C. Chiu, MD, FACEDirector, Endocrinology Fellowship Training ProgramDirector, Endocrinology Fellowship Training Program

Department of Diabetes, Endocrinology, and MetabolismDepartment of Diabetes, Endocrinology, and MetabolismCity of Hope National Medical CenterCity of Hope National Medical Center

Division of EndocrinologyDivision of EndocrinologyHarbor-UCLA Medical CenterHarbor-UCLA Medical Center

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Institutional Clinical and Translational Science Award (1) Institutional Clinical and Translational Science Award (1) RFA-RM-06-002 (Release Date: October 12, 2005)RFA-RM-06-002 (Release Date: October 12, 2005)

This RFA will be administered by This RFA will be administered by – the National Center for Research Resources the National Center for Research Resources

Purpose: Purpose: – Growing barriers between clinical and basic research, along with the ever Growing barriers between clinical and basic research, along with the ever

increasing complexities involved in conducting clinical research, are increasing complexities involved in conducting clinical research, are making it more difficult to translate new knowledge to the clinic -- and making it more difficult to translate new knowledge to the clinic -- and back again to the bench. back again to the bench.

– These challenges are limiting professional interest in the field and These challenges are limiting professional interest in the field and hampering the clinical research enterprise at a time when it should be hampering the clinical research enterprise at a time when it should be expanding. expanding.

Size and Duration of Awards: Size and Duration of Awards: – Awards will vary in size due to the consolidation of multiple programs Awards will vary in size due to the consolidation of multiple programs

within the CTSA program. within the CTSA program. – Applicants may request total costs up to $6 million annually for 5 years Applicants may request total costs up to $6 million annually for 5 years

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Specific objectives Specific objectives – provide opportunities and resources for original research on novel provide opportunities and resources for original research on novel

methods and approaches to translational and clinical science methods and approaches to translational and clinical science – provide the translational technologies and knowledge base for the provide the translational technologies and knowledge base for the

spectrum of clinical and translational science, including all types and spectrum of clinical and translational science, including all types and sizes of studies and specialties sizes of studies and specialties

– integrate translational and clinical science by fostering collaboration integrate translational and clinical science by fostering collaboration between departments and schools of an institution and between between departments and schools of an institution and between institutions and industry institutions and industry

– provide a point of contact for partnerships with industry, foundations provide a point of contact for partnerships with industry, foundations and community physicians as appropriate and community physicians as appropriate

– provide research education, training and career development leading to provide research education, training and career development leading to an advanced degree (Masters or Ph.D.) for the next generation of clinical an advanced degree (Masters or Ph.D.) for the next generation of clinical and translational researchers (including physicians, nurses, dentists, and translational researchers (including physicians, nurses, dentists, pharmacists, and other allied health professionals) pharmacists, and other allied health professionals)

– conduct self-evaluation activities and participate in a national evaluation conduct self-evaluation activities and participate in a national evaluation of the CTSA program of the CTSA program

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Key functions or components of a CTSAKey functions or components of a CTSA– Development of Novel Clinical and Translational Development of Novel Clinical and Translational

Methodologies Methodologies – Pilot and Collaborative Translational and Clinical Studies Pilot and Collaborative Translational and Clinical Studies – Biomedical Informatics Biomedical Informatics – Design, Biostatistics, and Clinical Research Ethics Design, Biostatistics, and Clinical Research Ethics – Regulatory Knowledge and Support Regulatory Knowledge and Support – Participant and Clinical Interactions Resources Participant and Clinical Interactions Resources – Community Engagement Community Engagement – Translational Technologies and Resources Translational Technologies and Resources – Research Education, Training and Career Development Research Education, Training and Career Development

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2006 Awardees2006 Awardees– Columbia University Health Columbia University Health

Sciences Sciences – Duke University Duke University – Mayo Clinic College of Medicine Mayo Clinic College of Medicine – Oregon Health & Science University Oregon Health & Science University – Rockefeller University Rockefeller University – University of California, Davis University of California, Davis – University of California, San University of California, San

Francisco Francisco – University of Pennsylvania University of Pennsylvania – University of Pittsburgh University of Pittsburgh – University of Rochester University of Rochester – University of Texas Health Science University of Texas Health Science

Center at Houston •Center at Houston •– Yale University Yale University

2007 Awardees:2007 Awardees:– Case Western Reserve University Case Western Reserve University – Emory University Emory University – Johns Hopkins Johns Hopkins – University of Chicago University of Chicago – University of Iowa University of Iowa – University of Michigan University of Michigan – University of Texas Southwestern University of Texas Southwestern

Medical Center Medical Center – University of Washington University of Washington – University of Wisconsin University of Wisconsin – Vanderbilt University Vanderbilt University – Washington University Washington University – Weill Cornell Medical College Weill Cornell Medical College

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Oral glucose tolerance tests in normal glucose tolerance, impaired Oral glucose tolerance tests in normal glucose tolerance, impaired glucose tolerance, and diabetesglucose tolerance, and diabetes

Fasting 30 60 90 120

Pla

sma

glu

cose

(m

g/d

L)

Pla

sma

insu

lin

( U

/mL

)

0

100

200

300

400

Fasting 30 60 90 1200

100

200

300

400

Fasting 30 60 90 1200

100

200

300

400

Normal glucose tolerance (n=81)

Impaired glucose tolerance (n=136)

Diabetes (n=85)

Fasting < 110 mg/dL > 110 or < 126 mg/dL > 126 mg/dLInterval < 200 mg/dL > 200 mg/dL2-hour < 140 mg/dL > 140 or < 200 mg/dL > 200 mg/dL

Mean + standard deviation

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Natural history of type 2 diabetesNatural history of type 2 diabetes

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Self-reported prevalence of diabetesSelf-reported prevalence of diabetes

Diabetes Care 2000;23:1278-1283.

1990 1998 Difference %difference

Age-group

18-29 1.5 1.6 0.1 9.1%

30-39 2.1 3.7 1.6 69.9%

40-49 3.6 5.1 1.5 39.8%

50-59 7.5 9.8 2.3 30.9%

60-69 10.9 12.8 1.9 17.1%

70 11.6 12.7 1.1 10.1%

Race

Caucasian-Am 4.6 5.9 1.3 29.3%

African-Am 7.0 8.9 1.9 26.1%

Mexican-Am 5.6 7.7 2.1 37.7%

Other 3.5 6.6 3.1 87.6%

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The effect of aging on insulin sensitivity and beta cell functionThe effect of aging on insulin sensitivity and beta cell functionAdjusted %S and adjusted %B in 149 glucose tolerant Caucasian subjectsAdjusted %S and adjusted %B in 149 glucose tolerant Caucasian subjects

Age

10 20 30 40 50 60 70 80 90

Adj

uste

d %

S

0.10

0.25

0.50

0.75

1.001.251.50

2.00

Age

10 20 30 40 50 60 70 80 90

Adj

uste

d %

B

50

75

100

150

200

250

300

r2 = 0.217

Chiu KC et al. Clinical Endocrinology 2000;53:569-575.

BMI, DBP BMI, DBP

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The effect of aging on fasting plasma glucoseThe effect of aging on fasting plasma glucoseNHANES IIINHANES III


Non-Hispanic White

Age

10 20 30 40 50 60 70 80 90

Fa

sti

ng

pla

sm

a g

luc

os

e (

mg

/dL

)

60

70

80

90

100

110

Non-Hispanic Black

Age

10 20 30 40 50 60 70 80 90

Fa

sti

ng

pla

sm

a g

luc

os

e (

mg

/dL

)

60

70

80

90

100

110

Hispanic

Age

10 20 30 40 50 60 70 80 90

Fa

sti

ng

pla

sm

a g

luc

os

e (

mg

/dL

)

60

70

80

90

100

110

P < 0.0001 P = 0.0015 P < 0.0001

+0.108 (0.090-0.198) mg/dL/year +0.090 (0.036-0.144) mg/dL/year +0.108 (0.072-0.144) mg/dL/year

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The effect of aging on fasting plasma insulinThe effect of aging on fasting plasma insulinNHANES IIINHANES III


Non-Hispanic White

Age

10 20 30 40 50 60 70 80 90

Fas

tin

g p

lasm

a in

suli

n ( U

/mL

)

0

5

10

15

20

25

30

Non-Hispanic Black

Age

10 20 30 40 50 60 70 80 90

Fas

tin

g p

lasm

a in

suli

n ( U

/mL

)

0

5

10

15

20

25

30

Hispanic

Age

10 20 30 40 50 60 70 80 90

Fas

tin

g p

lasm

a in

suli

n ( U

/mL

)

0

5

10

15

20

25

30

N.S. P = 0.04 N.S.

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The effect of aging on body mass indexThe effect of aging on body mass indexNHANES IIINHANES III


Non-Hispanic White

Age

10 20 30 40 50 60 70 80 90

Bo

dy

ma

ss

in

de

x (

kg

/m2 )

10

20

30

40

50

Non-Hispanic Black

Age

10 20 30 40 50 60 70 80 90

Bo

dy

ma

ss

in

de

x (

kg

/m2 )

15

20

25

30

35

40

45

50

Hispanic

Age

10 20 30 40 50 60 70 80 90

Bo

dy

ma

ss

in

de

x (

kg

/m2 )

10

20

30

40

50

N.S. N.S. N.S.

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The effect of aging on HbA1The effect of aging on HbA1CC NHANES IIINHANES III


Non-Hispanic White

Age

10 20 30 40 50 60 70 80 90

Hb

A1

C (

%)

3

4

5

6

7

Non-Hispanic Black

Age

10 20 30 40 50 60 70 80 90

Hb

A1

C (

%)

3

4

5

6

7

Hispanic

Age

10 20 30 40 50 60 70 80 90

Hb

A1

C (

%)

3

4

5

6

7

P < 0.0001 P = 0.0004 P < 0.0001

+0.008 (0.007-0.009) %/year +0.005 (0.002-0.008) %/year +0.007 (0.005-0.008) %/year

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The effect of aging on insulin sensitivityThe effect of aging on insulin sensitivityNHANES IIINHANES III


Non-Hispanic White

Age

10 20 30 40 50 60 70 80 90

%S

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Non-Hispanic Black

Age

10 20 30 40 50 60 70 80 90

%S

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Hispanic

Age

10 20 30 40 50 60 70 80 90

%S

0.0

0.5

1.0

1.5

2.0

2.5

3.0

N.S. N.S. N.S.

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The effect of aging on beta cell functionThe effect of aging on beta cell functionNHANES IIINHANES III

-0.80 (0.42-1.19) %/year -1.44 (0.52-2.37) %/year -1.25 (0.64-1.85) %/year


Non-Hispanic White

Age

10 20 30 40 50 60 70 80 90

%B

0

50

100

150

200

250

Non-Hispanic Black

Age

10 20 30 40 50 60 70 80 90

%B

0

50

100

150

200

250

Hispanic

Age

10 20 30 40 50 60 70 80 90

%B

0

50

100

150

200

250

P < 0.0001 P = 0.0004 P = 0.0014

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The effect of aging on beta cell functionThe effect of aging on beta cell functionNHANES IIINHANES III

%B -0.80 (0.42-1.19) -1.44 (0.52-2.37) -1.25 (0.64-1.85) %/year

Glucose +0.108 (0.090-0.198) +0.090 (0.036-0.144) +0.108 (0.072-0.144) mg/dL/year

HbA1C +0.008 (0.007-0.009) +0.005 (0.002-0.008) +0.007 (0.005-0.008) %/year

No correlation with insulin sensitivity (%S), plasma insulin, BMI


Non-Hispanic White

Age

10 20 30 40 50 60 70 80 90

%B

0

50

100

150

200

250

Non-Hispanic Black

Age

10 20 30 40 50 60 70 80 90

%B

0

50

100

150

200

250

Hispanic

Age

10 20 30 40 50 60 70 80 90

%B

0

50

100

150

200

250

P < 0.0001 P = 0.0004 P = 0.0014

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Progressive decline of beta cell dysfunctionProgressive decline of beta cell dysfunction

Adapted from UK Prospective Diabetes Study (UKPDS) Group. Diabetes. 1995;44:1249-1258.

Year

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

%B

0

10

20

30

40

50

60

70

80

90

100

Diet Metformin Sulfonylurea

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Oral glucose tolerance testOral glucose tolerance test150 glucose tolerant subjects150 glucose tolerant subjects

1.71 1.99 2.40 2.81 2.28 6.31 16.54 19.21 30.56 16.72Mean25%, 75%10%, 90%

Minutes

Fasting 30 60 90 120

Pla

sma

glu

cose

(m

g/d

L)

0

50

100

150

200

Minutes

Fasting 30 60 90 120P

las

ma

insu

lin ( U

/ml)

0

50

100

150

200

250

Chiu KC et al JCEM 2001:86:1620-1625Chiu KC et al JCEM 2001:86:1620-1625

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Insulin sensitivity and beta cell function in poorly controlled type 2 Insulin sensitivity and beta cell function in poorly controlled type 2 diabetes after intensive insulin therapy for 3-weekdiabetes after intensive insulin therapy for 3-week

Garvey WT et al. Diabetes 1985;34:222-234.

Patients with Type 2 diabetes (n=17, age 50 ± 4 yrs old, duration 7.8 ± 2.1 yrs) had fasting blood glucose 286 ± 17 mg/dL and fasting Insulin 15 ± 2 U/mL. They received intensive insulin therapy for 3 wks by CSII to keep blood glucose < 100 mg/dL. CSII was discontinued at 15 hrs before the repeated study.

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Natural history of type 2 diabetesNatural history of type 2 diabetesBeta cells fail to compensate for insulin resistanceBeta cells fail to compensate for insulin resistance

Genetic factors

Environmental factors

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Assessment of Assessment of beta cell function and insulin sensitivitybeta cell function and insulin sensitivity

Method Beta cell function Insulin sensitivity

FSIVGTT-Minimal model AIR SI

Euglycemic clamp - M

Hyperglycemic clamp 1st & 2nd IR ISI

Insulin suppression test - SSPG

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Comparison of euglycemic and hyperglycemic clampsComparison of euglycemic and hyperglycemic clamps

Euglycemic clampEuglycemic clamp Hyperglycemic clampHyperglycemic clampDeFronzo RA, Tobin JD, Andres R: Glucose clamp technique: a method for quantifying insulin DeFronzo RA, Tobin JD, Andres R: Glucose clamp technique: a method for quantifying insulin secretion and resistance. secretion and resistance. Am J Physiol 1979;237(3):E214-E223.Am J Physiol 1979;237(3):E214-E223.

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Advantages and disadvantages of Advantages and disadvantages of euglycemic and hyperglycemic clampseuglycemic and hyperglycemic clamps

Euglucemic clampEuglucemic clamp– Gold standard for insulin sensitivityGold standard for insulin sensitivity– Constant:Constant: insulin infusion rate (40 insulin infusion rate (40 U/mU/m22/min), plasma glucose (90 mg/dl)/min), plasma glucose (90 mg/dl)– Variables:Variables: plasma insulin (~100 plasma insulin (~100 U/ml, 50-150 U/ml, 50-150 U/ml), glucose infusion U/ml), glucose infusion

raterate– Pitfalls:Pitfalls:

Fails to reflect the variation inherent in endogenous insulin secretionFails to reflect the variation inherent in endogenous insulin secretionReverses the normal gradient between portal and peripheral insulinReverses the normal gradient between portal and peripheral insulinProvides no information about Provides no information about cell function cell function

Hyperglycemic clampHyperglycemic clamp– Constant:Constant: plasma glucose (180 mg/dl)plasma glucose (180 mg/dl)– Variables:Variables: plasma insulin (11-229 plasma insulin (11-229 U/ml), glucose infusion rateU/ml), glucose infusion rate– Pitfalls:Pitfalls:

Raises plasma glucose to an artificially high levelRaises plasma glucose to an artificially high levelIgnore mass effect of glucose (Glucose clearance) Ignore mass effect of glucose (Glucose clearance)

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Comparison of insulin sensitivity indices Comparison of insulin sensitivity indices Euglycemic vs. hyperglycemic clampsEuglycemic vs. hyperglycemic clamps

DeFronzo RA, Tobin JD, Andres RDeFronzo RA, Tobin JD, Andres R: : Glucose clamp Glucose clamp technique: a method for quantifying insulin technique: a method for quantifying insulin secretion and resistance.secretion and resistance. Am J Physiol Am J Physiol 1979;237(3):E214-E223.1979;237(3):E214-E223.

Mitrakou A et al (J Gerich):Mitrakou A et al (J Gerich): Simultaneous assessment Simultaneous assessment of insulin secrection and insulin sensitivity using a of insulin secrection and insulin sensitivity using a hyperglycemic clamp.hyperglycemic clamp. JCEM 1992;75:379-382. JCEM 1992;75:379-382.

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Comparison of hyperglycemic clamp and FSIVGTTComparison of hyperglycemic clamp and FSIVGTT

20 gram glucose IV bolusKahn SE, Porte D Jr

Am J Med 1988;85(S5A):4-8.Mean + SE

Time (minutes)

-40

-20 0 20 40 60 80 100

120

140

160

180

200

Pla

sma

insu

lin ( U

/ml)

0

20

40

60

80

100

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Requirement for hyperglycemic clampRequirement for hyperglycemic clamp

2 angiocatheters (G20 or larger)2 angiocatheters (G20 or larger)EDTA tubes: 5 ml X 24, 3 ml X 17EDTA tubes: 5 ml X 24, 3 ml X 17– 5 ml for glucose and insulin: -30, -20, -10, 2.5, 5, 7.5, 10, 20, 30, 40, 5 ml for glucose and insulin: -30, -20, -10, 2.5, 5, 7.5, 10, 20, 30, 40,

50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 18050, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180– 3 ml for glucose: 15, 25, 35, 45, 55, 65, 75, 85, 95, 105, 115, 125, 135, 3 ml for glucose: 15, 25, 35, 45, 55, 65, 75, 85, 95, 105, 115, 125, 135,

145, 155, 165, 175145, 155, 165, 175One IV infusion set with extensionOne IV infusion set with extensionOne IV infusion pumpOne IV infusion pumpYSI glucose analyzer (or Beckman glucose analyzer)YSI glucose analyzer (or Beckman glucose analyzer)MicrocentrifugeMicrocentrifuge0.5 ml Microfuge tubes: 420.5 ml Microfuge tubes: 4250 ml 50% glucose solution: 2 bottles50 ml 50% glucose solution: 2 bottles1000 ml 30% glucose solution: 3 bags1000 ml 30% glucose solution: 3 bagsSyringes: 5 ml X 24, 3 ml X 58Syringes: 5 ml X 24, 3 ml X 58Ice basketIce basket

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Protocol for hyperglycemic clampProtocol for hyperglycemic clamp

8:00 AM after a 13-hr fast8:00 AM after a 13-hr fast

Voiding and measurement of weight and heightVoiding and measurement of weight and height

Two angiocatheters, one on each armTwo angiocatheters, one on each arm

Plasma samples at -30, -20, -10 minutesPlasma samples at -30, -20, -10 minutes

T=0, a bolus of 50% glucose solution within the first minute T=0, a bolus of 50% glucose solution within the first minute – based on body surface area (11.4 gm/mbased on body surface area (11.4 gm/m22) )

T=15, continuous infusion of 30% Dextrose solution T=15, continuous infusion of 30% Dextrose solution – Plasma glucose > 225 mg/dl, infuse at 30 ml/hrPlasma glucose > 225 mg/dl, infuse at 30 ml/hr– Plasma glucose Plasma glucose << 225 mg/dl, infuse at 60 ml/hr 225 mg/dl, infuse at 60 ml/hr

Plasma glucose every 5 minutes Plasma glucose every 5 minutes

Plasma insulin at 2.5, 5, 7.5, 10, 20, 40, 60, 80, 100, 120, 130, 140, 150, Plasma insulin at 2.5, 5, 7.5, 10, 20, 40, 60, 80, 100, 120, 130, 140, 150, 160, 170, and 180 minutes. 160, 170, and 180 minutes.

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CalculationCalculation

First phase insulin response (1stIR)First phase insulin response (1stIR)– the sum of the plasma insulin concentrations at 2.5, 5.0, 7.5, and 10 the sum of the plasma insulin concentrations at 2.5, 5.0, 7.5, and 10

minutes.minutes.

Second phase insulin response (2ndIR)Second phase insulin response (2ndIR)– the average plasma insulin concentration during the last hour of the the average plasma insulin concentration during the last hour of the

clamp (T=130, 140, 150, 160, 170, 180 min) clamp (T=130, 140, 150, 160, 170, 180 min)

Insulin sensitivity index (ISI)Insulin sensitivity index (ISI)– dividing the average glucose infusion rate during the last hour of dividing the average glucose infusion rate during the last hour of

each clamp (each clamp (M/mM/m22/min) by the average plasma insulin /min) by the average plasma insulin concentration (pM) during the last hour of the clamp (T=130, 140, concentration (pM) during the last hour of the clamp (T=130, 140, 150, 160, 170, 180 min) 150, 160, 170, 180 min)

Glucose clearance (GGlucose clearance (GClCl))

– dividing ISI with steady-state plasma glucose concentration. dividing ISI with steady-state plasma glucose concentration.

KCC 10/23/2008KCC 10/23/2008

Advantage of the current hyperglycemic protocolAdvantage of the current hyperglycemic protocol

1stIR1stIR– 1stIR increases progressively with increasing plasma glucose 1stIR increases progressively with increasing plasma glucose

concentration up to 180 mg/dl, above which it tends to plateauconcentration up to 180 mg/dl, above which it tends to plateau– > 99% (except one out of 153 subjects) have a peak plasma glucose > 99% (except one out of 153 subjects) have a peak plasma glucose

concentration greater 200 mg/dlconcentration greater 200 mg/dl– Therefore, the current protocol is sufficient to induce a maximal Therefore, the current protocol is sufficient to induce a maximal

stimulation to beta cellsstimulation to beta cells– No correlation between 1stIR and amount of glucose infusion at T=0 No correlation between 1stIR and amount of glucose infusion at T=0 – Therefore, 1stIR is independent of the amount of glucose infused Therefore, 1stIR is independent of the amount of glucose infused

2ndIR 2ndIR – 2ndIR increases as the steady-state plasma glucose increases, up to 2ndIR increases as the steady-state plasma glucose increases, up to

306 mg/dl 306 mg/dl – Glucosuria, when plasma glucose greater than 180-200 mg/dl Glucosuria, when plasma glucose greater than 180-200 mg/dl – Glucosuria will introduce another variable (urinary glucose loss)Glucosuria will introduce another variable (urinary glucose loss)– No glucosuria under the current protocolNo glucosuria under the current protocol

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Hyperglycemic clamp Hyperglycemic clamp (STD, n=153)(STD, n=153)

Mean + STD

Time (minutes)

-40 -20 0 20 40 60 80 100 120 140 160 180 200

Pla

sm

a g

luc

os

e (m

g/d

l)P

las

ma

insu

lin ( U

/ml)

Glu

co

se

infu

sio

n r

ate

(m

l/h)

-50

0

50

100

150

200

250

300

350

400

Plasma glucosePlasma insulinGlucose infusion rate

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Infusion Rate During Hyperglycemic ClampInfusion Rate During Hyperglycemic Clamp30% glucose solution (n=153)30% glucose solution (n=153)

Mean + Max/Min

Time (minutes)

-40 -20 0 20 40 60 80 100 120 140 160 180 200

Glu

cose

infu

sio

n r

ate

(ml/h

)

-100

0

100

200

300

400

500

600

700

KCC 10/23/2008KCC 10/23/2008

Hyperglycemic clamp Hyperglycemic clamp (SE, n=153)(SE, n=153)

Mean + SE

Time (minutes)

-40 -20 0 20 40 60 80 100 120 140 160 180 200

Pla

sm

a g

luc

os

e (

mg

/dl)

Pla

sm

a in

su

lin ( U

/ml)

Glu

co

se

infu

sio

n r

ate

(m

l/h)

0

50

100

150

200

250

300

Plasma glucosePlasma insulinGlucose infusion rate

KCC 10/23/2008KCC 10/23/2008

The study of the impact of ethnicity on insulin sensitivity and beta cell function

78 glucose tolerant and normotensive subjects78 glucose tolerant and normotensive subjects

Insulin sensitivity index(M/m2/min/pM)

1 2 3 4 5 7.510 20 30 4050 75100

1st

ph

ase

insu

lin r

esp

on

se(p

M)

200

300

400500

750

1000

2000

3000

40005000

7500

10000

p < 0.0001r2= 0.3848

Insulin sensitivity index(M/m2/min/pM)

1 2 3 4 5 7.510 20 30 4050 75100

2nd

ph

ase

insu

lin r

esp

on

se(p

M)

50

75

100

200

300

400500

750

1000

2000

p < 0.0001r2= 0.5671

Chiu KC et al. Diabetes Care 2000;23:1353-1358.

KCC 10/23/2008KCC 10/23/2008

Ethnic difference in insulin sensitivity Adjusted beta cell function with insulin sensitivity

Ad

jus

ted

1st

ph

ase

insu

lin

res

po

ns

e(p

M)

0

500

1000

1500

2000

2500

Ad

jus

ted

2n

d p

ha

se i

ns

ulin

re

spo

nse

(pM

)

0

100

200

300

400

500

600

700

Ad

jus

ted

ins

ulin

sen

sit

ivit

y in

dex

(M

/m2/m

in/p

M)

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

10.00p = 0.00008

A B C H A B C H A B C H

Geometric mean + standard error1st phase insulin response was adjusted for insulin sensitivity index.2nd phase insulin response was adjusted for insulin sensitivity index, SBP and DBP.Insulin sensitivity index was adjusted for gender, age, WHR, and DBP.

Chiu KC et al. Diabetes Care 2000;23:1353-1358.

KCC 10/23/2008KCC 10/23/2008

September 2000September 2000

Ken C. Chiu, M.D.Ken C. Chiu, M.D.

Pejman Cohan, M.D.Pejman Cohan, M.D.

Nancy P. Lee, Pharm.D. Nancy P. Lee, Pharm.D.

Lee-Ming Chuang, M.D.Lee-Ming Chuang, M.D.

Diabetes CareDiabetes Care 2000;23:1353-1358.2000;23:1353-1358.

Insulin sensitivity differs among ethnic groups with a Insulin sensitivity differs among ethnic groups with a compensatory response in beta cell functioncompensatory response in beta cell function

KCC 10/23/2008KCC 10/23/2008

Goal of genetic studyGoal of genetic studyGoal of genetic studyGoal of genetic study

Define the genetic factors that contribute etiology Define the genetic factors that contribute etiology

Improve risk assessment with genetic influenceImprove risk assessment with genetic influence

Understand the interaction between genes and Understand the interaction between genes and environmentsenvironments

Explore the genetic influence on the response to Explore the genetic influence on the response to interventions: pharmacological and non-interventions: pharmacological and non-pharmacologicalpharmacological

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Genetic ContributionGenetic Contribution

PHHI Type 2 diabetes TB

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The impact of American Indian heritage on the prevalence The impact of American Indian heritage on the prevalence of type 2 diabetesof type 2 diabetes

The Prevalence of type 2 diabetes in Mexican AmericansThe Prevalence of type 2 diabetes in Mexican Americans

The impact of American Indian heritage on the prevalence The impact of American Indian heritage on the prevalence of type 2 diabetesof type 2 diabetes

The Prevalence of type 2 diabetes in Mexican AmericansThe Prevalence of type 2 diabetes in Mexican Americans

0

2

4

6

8

10

12

14

16

0% 18% 27% 46%

Native American Admixture

Pre

vale

nce

Gardner et al Diabetes 1984

KCC 10/23/2008KCC 10/23/2008

The incidence of type 2 diabetes in monozygotic twinsThe incidence of type 2 diabetes in monozygotic twinsThe incidence of type 2 diabetes in monozygotic twinsThe incidence of type 2 diabetes in monozygotic twins

Concordant92%

Discordant8%

Tattersall et al Lancet 1972

KCC 10/23/2008KCC 10/23/2008

Impact of family history on incidence of type 2 diabetesImpact of family history on incidence of type 2 diabetes

Harris Diabetes Care 1991Knowler et al Diab/Met Rev 1990

Relative risk of diabetes by numbers of family members with diabetes

Race 1 vs. 0 2 vs. 0

Caucasians 1.8 3.6

African Americans 1.5 3.0

Pima Indians 2.0 2.8

KCC 10/23/2008KCC 10/23/2008

The influence of physical activity The influence of physical activity on the risk of type 2 diabeteson the risk of type 2 diabetes

The influence of physical activity The influence of physical activity on the risk of type 2 diabeteson the risk of type 2 diabetes

>26 24-25.99

< 24

> 2000

500 - 1999

< 500

0

1

2

3

4

5R

elat

ive

risk

Body mass index

Physical activity

Helmrich et al NEJM 1991

p < 0.001

p = 0.007

KCC 10/23/2008KCC 10/23/2008

There is a good correlation between the number of There is a good correlation between the number of bath tubs per country and rates of diabetes.bath tubs per country and rates of diabetes.

There is a good correlation between the number of There is a good correlation between the number of bath tubs per country and rates of diabetes.bath tubs per country and rates of diabetes.

Kelly M. WestKelly M. West Father of Diabetes EpidemiologistFather of Diabetes Epidemiologist

Pathogenesis of type 2 diabetesPathogenesis of type 2 diabetesPathogenesis of type 2 diabetesPathogenesis of type 2 diabetes

Geneticfactors

Environmentalfactors

Insulinresistance

-celldysfunction

Overtdiabetes

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Genetic vs. environmental factorGenetic vs. environmental factor

My son looks like meMy son looks like me

Genetic factorGenetic factor

My son looks like my neighborMy son looks like my neighbor

Environmental factorEnvironmental factor

KCC 10/23/2008KCC 10/23/2008

SS

SS: The degree of familial clustering of a disease: The degree of familial clustering of a disease

SS = K = KSS/K/K

KKSS:: the risk for siblings of patientsthe risk for siblings of patients

K:K: the risk for population (prevalence)the risk for population (prevalence)SS

PHHI:PHHI: 12,50012,500 (25%/0.002%)(25%/0.002%) 1/50,0001/50,000MODY:MODY: 200200 (50%/0.25%)(50%/0.25%) 1/4001/400

Type 1 diabetes:Type 1 diabetes: 1515 (6%/0.4%)(6%/0.4%) 1/2501/250Type 2 diabetes:Type 2 diabetes:2.5-3.52.5-3.5 (12.5-17.5%/5%)(12.5-17.5%/5%) 1/201/20

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Heritability of insulin sensitivityHeritability of insulin sensitivity

PopulationPopulation PedigreePedigree TraitTrait hh22

AmericansAmericans 3333 SSII 20%20%

American CaucasiansAmerican Caucasians 2626 SSII 33%33%

Finnish Finnish 184184 SSII 28%28%

Pima IndiansPima Indians 153153 MMhighhigh 49%49%

Pima IndiansPima Indians 153153 MMlowlow 38%38%

Finnish TwinsFinnish Twins 4141 MM 37%37%

Elbein SC et al J CEM 1999;84:1398-1403; Martin BC et al Diabetes 1992;41:850-854; Sakul H et al Am J Hum Elbein SC et al J CEM 1999;84:1398-1403; Martin BC et al Diabetes 1992;41:850-854; Sakul H et al Am J Hum Genet 1997;60:651-656; Watanabe RM et al Hum Hered 1999;49:159-168; Lehtovirta M et al Diabetologia Genet 1997;60:651-656; Watanabe RM et al Hum Hered 1999;49:159-168; Lehtovirta M et al Diabetologia 2000;43:285-293.2000;43:285-293.

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Heritability of beta cell functionHeritability of beta cell function

PopulationPopulation PedigreePedigree TraitTrait hh22

American CaucasiansAmerican Caucasians 2626 AIRAIRglucoseglucose 29%29%

Finnish CaucasiansFinnish Caucasians 184184 AIRAIRglucoseglucose 35%35%

Danish TwinsDanish Twins 303303 InsulinInsulin30 min30 min 47%47%

Finnish TwinsFinnish Twins 4242 1stPH1stPH 55%55%

Finnish TwinsFinnish Twins 4242 2ndPH2ndPH 57%57%

Elbein SC et al J Clin Endocrinol Metab 1999;84:1398-1403; Watanabe RM et al Hum Hered 1999;49:159-168; Elbein SC et al J Clin Endocrinol Metab 1999;84:1398-1403; Watanabe RM et al Hum Hered 1999;49:159-168; Poulsen P et al Diabetologia 1999; 42:139-145; Lehtovirta M et al Diabetologia 2000; 43:285-293.Poulsen P et al Diabetologia 1999; 42:139-145; Lehtovirta M et al Diabetologia 2000; 43:285-293.

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Approaches in the Genetic StudiesApproaches in the Genetic Studies

Candidate gene approachCandidate gene approach

– Based on known physiology Based on known physiology

– Forward genetic approachForward genetic approach

– Hypothesis testingHypothesis testing

Genomewide approachGenomewide approach

– Without a prioriWithout a priori

– Reverse genetic approachReverse genetic approach

– Hypothesis generatingHypothesis generating

KCC 10/23/2008KCC 10/23/2008

Tools for the Genetic StudiesTools for the Genetic Studies

Parametric methodParametric method– Linkage studyLinkage study

Effective for the large effectsEffective for the large effectsHigh penetrance High penetrance Mendelian inheritanceMendelian inheritance

Non-parametric methodsNon-parametric methods– Sib-pair studySib-pair study– Transmission disequilibrium TestTransmission disequilibrium Test– Association studyAssociation study

Effective for the modest effectEffective for the modest effectMedian-to-low penetranceMedian-to-low penetranceNon-Mendelian inheritanceNon-Mendelian inheritance

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GlucokinaseGlucokinase

Lancet 1992;339:1307-1310.

Molecular Endocrinology 1992;6:1070-1071.

Diabetes 1992;41:843-849.

Diabetologia 1992;35:632-638.

Diabetes 1993; 42:579-582.

FAMILIAL FORM OF NESIDIOBLASTOSISFAMILIAL FORM OF NESIDIOBLASTOSISPersistent Hyperinsulinemic Hypoglycemia of InfancyPersistent Hyperinsulinemic Hypoglycemia of Infancy

A Positional Candidate Gene ApproachA Positional Candidate Gene Approach

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PHHI and type 2 diabetesPHHI and type 2 diabetes

High incidence of diabetes during or after pubertyHigh incidence of diabetes during or after puberty – impaired glucose-induced insulin secretionimpaired glucose-induced insulin secretion– decreased glucose disposal ratedecreased glucose disposal rate

Leibowitz G et al JCEM Leibowitz G et al JCEM 1995;80:386-392.1995;80:386-392.

Regeneration of pancreas after near total pancreatectomyRegeneration of pancreas after near total pancreatectomy

Diseases of pancreatic islets (insulin)Diseases of pancreatic islets (insulin)

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Persistent Hyperinsulinemic Hypoglycemia of InfancyPersistent Hyperinsulinemic Hypoglycemia of Infancy

PHHIPHHI– The most common cause of persistent hypoglycemia in infancy The most common cause of persistent hypoglycemia in infancy

AKAAKA– Nesidioblastosis, familial hyperinsulinism, congenital hyperinsulinism Nesidioblastosis, familial hyperinsulinism, congenital hyperinsulinism

Incidence per live birthsIncidence per live births– 1 in 100,000-50,0001 in 100,000-50,000– 1 in 2,675 in Saudi1 in 2,675 in Saudi– 1 in 3,000 in Ashkenazi Jewish1 in 3,000 in Ashkenazi Jewish

Autosomal recessive disorder Autosomal recessive disorder – Consanguinity; sex ratio close to 1; segregation ratio, 0.254 Consanguinity; sex ratio close to 1; segregation ratio, 0.254

Diagnostic criteriaDiagnostic criteria1.1. blood glucose levels less than 48 mg/dlblood glucose levels less than 48 mg/dl2.2. nonsuppressed insulin levels during hypoglycemianonsuppressed insulin levels during hypoglycemia3.3. inappropriately low free fatty acids and ketone bodies during hypoglycemiainappropriately low free fatty acids and ketone bodies during hypoglycemia4.4. glycemic response of greater than 30 mg/dl to 0.03 mg/kg glucagon injectionglycemic response of greater than 30 mg/dl to 0.03 mg/kg glucagon injection5.5. glucose requirement to maintain euglycemia greater than 15 mg/kg-min; glucose requirement to maintain euglycemia greater than 15 mg/kg-min; 6.6. absence of ketone bodies in urine. absence of ketone bodies in urine.

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Mapping of PHHIMapping of PHHI

Glaser B, Chiu KC et al. Nat Genet 1994;7:185-188.

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Two point LOD score for PHHI and genetic markers on Two point LOD score for PHHI and genetic markers on chromosome 11pchromosome 11p

Glaser B, Chiu KC, et al. Nat Genet 1994;7:185-188.

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Association of PHHI with D11S921 and D11S419Association of PHHI with D11S921 and D11S419in 12 Ashkenazi Jewish pedigreesin 12 Ashkenazi Jewish pedigrees

MarkerHaplotype

Transmitted to PHHI offspring

Transmitted to non-PHHI offspring

D11S921-D11S419

3-2 12 3

5-2 5 3

4-1 0 2

3-1 1 6

4-2 0 6

1-1 22 11

5-1 11 11

2-2 0 1

2-2 0 1

2=10.0 (d.f..=1, P<0.002)


MarkerAllele

Transmitted to PHHI offspring

Transmitted to non-PHHI offspring

D11S921

1 2 1

2 0 1

3 13 10

4 0 8

5 6 4

2=1.84 (d.f..=1, P=0.17)

D11S419

1 4 10

2 17 13

3 0 1

2=3.60 (d.f..=1, P<0.05)

KCC 10/23/2008KCC 10/23/2008

Recombination mapping of the PHHI gene to the region Recombination mapping of the PHHI gene to the region between D11S926 and D11S921between D11S926 and D11S921


KCC 10/23/2008KCC 10/23/2008

MappingMapping of of

PHHIPHHI


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Linkage analysis of autosomal recessive form of PHHILinkage analysis of autosomal recessive form of PHHI

15 families15 families – (12 Ashkenazi Jewish, 2 Arab, 1 non-Jewish Caucasian)(12 Ashkenazi Jewish, 2 Arab, 1 non-Jewish Caucasian)

Linkage analysisLinkage analysis– Chromosome 11p14-15.1 Chromosome 11p14-15.1 – LOD Score = 9.5, LOD Score = 9.5, = 0 at D11S921= 0 at D11S921

Recombinant mappingRecombinant mapping– 6.6 cM interval between D11S921 and D11S9286.6 cM interval between D11S921 and D11S928

Founder effectFounder effect– 12 Jewish families: D11S921 and D11S41912 Jewish families: D11S921 and D11S419

Glaser B, Chiu KC et al Nature Genetics Glaser B, Chiu KC et al Nature Genetics 1994:185-188.1994:185-188.

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Recombination mapping of PHHI in 21 families (1)Recombination mapping of PHHI in 21 families (1)

Glaser B, Chiu KC et al Hum Molecul Genet 1995;4:879-886.

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Recombination mapping of PHHI in 21 families (2)Recombination mapping of PHHI in 21 families (2)


KCC 10/23/2008KCC 10/23/2008

Refined mapping of PHHI in 21 families Refined mapping of PHHI in 21 families Two point LOD scoreTwo point LOD score


KCC 10/23/2008KCC 10/23/2008 Glaser B, Chiu KC et al Hum Molecul Genet 1995;4:879-886.

KCC 10/23/2008KCC 10/23/2008

Refine mapping of the autosomal dominant form of PHHIRefine mapping of the autosomal dominant form of PHHI

21 families21 families – (18 Ashkenazi Jewish, 2 Arab, 1 non-Jewish Caucasian)(18 Ashkenazi Jewish, 2 Arab, 1 non-Jewish Caucasian)

Linkage analysisLinkage analysis– Chromosome 11p15.1 Chromosome 11p15.1 – LOD Score = 13.28, LOD Score = 13.28, = 0 at D11S902= 0 at D11S902

Recombinant mappingRecombinant mapping– 0.8 cM interval between D11S419 and D11S13100.8 cM interval between D11S419 and D11S1310

Founder effectFounder effect– 18 Jewish families: D11S926 and D11S1308 (10.3 cM)18 Jewish families: D11S926 and D11S1308 (10.3 cM)

Glaser B, Chiu KC et al Hum Mol Genet Glaser B, Chiu KC et al Hum Mol Genet 1995;4:879-886.1995;4:879-886.

KCC 10/23/2008KCC 10/23/2008

Linkage analysis of autosomal dominant form of PHHILinkage analysis of autosomal dominant form of PHHI

5 families5 families – consanguineous families of Saudi Arabian originconsanguineous families of Saudi Arabian origin– 2 first-cousin and 3 second-cousin matings2 first-cousin and 3 second-cousin matings

Linkage analysisLinkage analysis– Chromosome 11pChromosome 11p– LOD Score = 5.02, LOD Score = 5.02, = 0 at D11S926= 0 at D11S926

Recombinant mappingRecombinant mapping– 4 cM interval between D11S926 and D11S8994 cM interval between D11S926 and D11S899

Thomas PM et al Am J Hum Gen Thomas PM et al Am J Hum Gen 1995;56:416-421.1995;56:416-421.

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A trivial pursuitA trivial pursuit

'common disease-common variant' hypothesis'common disease-common variant' hypothesis – Lander ES.Lander ES. The new genomics: global views of biology. The new genomics: global views of biology.

ScienceScience 1996;274:536-539. 1996;274:536-539.

– Collins FS, Guyer MS & Charkravarti A.Collins FS, Guyer MS & Charkravarti A. Variations on a theme: cataloging human Variations on a theme: cataloging human DNA sequence variation.DNA sequence variation.

ScienceScience 1997;278:1580-1581. 1997;278:1580-1581.

FactsFacts– Variants with large effects are always rare.Variants with large effects are always rare.– Common variants rarely have large effects.Common variants rarely have large effects.

Common diseaseCommon disease– Interactions of numerous common variants that have small to modest effects.Interactions of numerous common variants that have small to modest effects.

KCC 10/23/2008KCC 10/23/2008

Genetic markers for Insulin ResistanceGenetic markers for Insulin Resistance

D/I SNP at angiotensin converting enzymeD/I SNP at angiotensin converting enzyme– African Am: %S (African Am: %S (9.3%9.3%), GDR (), GDR (7. 7%7. 7%))

Chiu KC et al. Metabolism Chiu KC et al. Metabolism 1997;46:395-399.1997;46:395-399.

-258 at Hepatic glucokinase promoter-258 at Hepatic glucokinase promoter– African Am: %S (African Am: %S (13.6%13.6%), GDR (), GDR (9.2%9.2%) )

Chiu KC et al. BBRC Chiu KC et al. BBRC 1996;221:614-618.1996;221:614-618.

– Asian Indians: %S (Asian Indians: %S (11.8%11.8%))Chiu KC et al. BMC Genetics Chiu KC et al. BMC Genetics 2000;1:2 2000;1:2

FoK I at vitamin D receptorFoK I at vitamin D receptor– Caucasian Am: %S (Caucasian Am: %S (15.2%15.2%))

Chiu KC et al. BMC Medical Genetics 2001;2:2 Chiu KC et al. BMC Medical Genetics 2001;2:2

A54T at intestinal fatty acid binding protein 2A54T at intestinal fatty acid binding protein 2– Caucasian Am: %S (Caucasian Am: %S (6.04%6.04%), ISI), ISIMM ( (4.28%4.28%), ISI), ISISS ( (4.94%4.94%))

Chiu KC et al. Eur J Clin Investig 2001;31:521-527Chiu KC et al. Eur J Clin Investig 2001;31:521-527

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Genetic markers for beta cell dysfunctionGenetic markers for beta cell dysfunction

– I27L SNP: I27L SNP: 12%12% in 1stIR and in 1stIR and 6%6% in 2ndIR. in 2ndIR. Chiu KC et al. Diabetes Medicine 2003;20:225-230. Chiu KC et al. Diabetes Medicine 2003;20:225-230.

– I27L SNP: I27L SNP: 14%14% in in 1stIR and 1stIR and 11%11% in in 2ndIR.2ndIR. Chiu KC et al. Pancreas 2003;27(2):E23-E28. Chiu KC et al. Pancreas 2003;27(2):E23-E28.

– I27L SNP: I27L SNP: 9% 9% in 1stPHS and in 1stPHS and 7%7% in 2ndPHS. in 2ndPHS. Chiu KC et al. Euro J Endo 2003;148:641-647. Chiu KC et al. Euro J Endo 2003;148:641-647.

Hepatocyte nuclear factor 1Hepatocyte nuclear factor 1 (HNF1 (HNF1) ) – MODY3 (maturity-onset diabetes of the young) MODY3 (maturity-onset diabetes of the young) – 631 amino acids in human; 628 amino acids in rat and mouse631 amino acids in human; 628 amino acids in rat and mouse

I27L: I27L: Isoleucine (Isoleucine (AATC) TC) Leucine ( Leucine (CCTC): TC): withwithin the dimerization domainin the dimerization domain – Between 2 MODY3 mutations, G20R and G31DBetween 2 MODY3 mutations, G20R and G31D

Homo sapiensHomo sapiens LESLESGGLSKEALLSKEALIIGALGALGGEPGPYEPGPYRattus norvegicusRattus norvegicus 81%81% LESGLSKEALLESGLSKEALIIGALGEPGPYGALGEPGPYMus musculusMus musculus 81%81% LESGLSKEALLESGLSKEALIIGALGEPGPYGALGEPGPYCricetinae gen. sp.Cricetinae gen. sp. 81%81% LESGLSKEALLESGLSKEALIIGALGEPGPYGALGEPGPY Gallus gallusGallus gallus 66%66% LESGLLESGLTTKEKETTLLIIKKGGLLSSEEAEAEPYPYXenopus laevisXenopus laevis 61%61% LESGLESGYTYTKEALKEALKKKALKALADGDNADGDNY Y Danio rerioDanio rerio 52%52% LLGGSGLSKESGLSKELLLLIIQQAAMMGGDLERDLEREE

KCC 10/23/2008KCC 10/23/2008

Lessons from the Genetics StudiesLessons from the Genetics StudiesType 2 diabetesType 2 diabetes

Monogenetic diabetes is uncommon (<5%).Monogenetic diabetes is uncommon (<5%).

Individual genetic factor only contributes a modest effect Individual genetic factor only contributes a modest effect on either insulin sensitivity or b cell function.on either insulin sensitivity or b cell function.

It is very unlikely that a single genetic defect plays a key It is very unlikely that a single genetic defect plays a key role in the pathogenesis of type 2 diabetes.role in the pathogenesis of type 2 diabetes.

Any given genetic factor exits in both diabetic and non-Any given genetic factor exits in both diabetic and non-diabetic subjects.diabetic subjects.

It is impossible and unpractical to apply gene therapy in It is impossible and unpractical to apply gene therapy in the treatment of diabetes.the treatment of diabetes.

The major application of genetics is pharmacogenetic.The major application of genetics is pharmacogenetic.

Hypovitaminosis DHypovitaminosis Da cause or biomarker for diabetes?a cause or biomarker for diabetes?

Ken C. Chiu, MD, FACEKen C. Chiu, MD, FACEDirector, Endocrinology Fellowship Training ProgramDirector, Endocrinology Fellowship Training Program

Department of Diabetes, Endocrinology, and MetabolismDepartment of Diabetes, Endocrinology, and MetabolismCity of Hope National Medical CenterCity of Hope National Medical Center

Division of EndocrinologyDivision of EndocrinologyHarbor-UCLA Medical CenterHarbor-UCLA Medical Center

KCC 10/23/2008KCC 10/23/2008

Vitamin D publications

Norman AW Am J Clin Nutr 2008;88(suppl):491S–499S.

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Vitamin D receptor polymorphism and bone densityVitamin D receptor polymorphism and bone density

Prediction of bone density from vitamin D receptor alleles

Lumbar bone density in 22 dizygotic twins (left panel) and in all twins (right panel)

Morrison et al. Nature 1994;367:284-287.

Vitamin D-receptor gene polymorphisms and bone density in prepubertal American girls of Mexican descent (age: 6.7-11.7 yrs)

Bone density in 100 normal girls

Sainz J et al. NEJM 1997;337:77-82.

KCC 10/23/2008KCC 10/23/2008

Bone density and diabetesBone density and diabetes Type 1 diabetesType 1 diabetes

10-20% reduction in bone density at the onset of type 1 diabetes10-20% reduction in bone density at the onset of type 1 diabetes Levin M et al. NEJM Levin M et al. NEJM

1976;294:241-245.1976;294:241-245.

Observations: Observations: alkaline phosphatase, alkaline phosphatase, 1,25-(OH)1,25-(OH)22DD33,,25-OHD25-OHD33, , intestinal intestinal CaCa2+2+ absorption, Mg absorption, Mg2+2+ deficiency, deficiency, PTH, PTH, osteocalcinosteocalcin

Mechanism: ? insulin deficiency induces osteoporosis by either impairing bone Mechanism: ? insulin deficiency induces osteoporosis by either impairing bone formation or by increasing bone lossformation or by increasing bone loss

Type 2 diabetesType 2 diabetes Bone density in patients with type 2 diabetes is similar or higher than that of age-Bone density in patients with type 2 diabetes is similar or higher than that of age-

matched, non-diabetic population.matched, non-diabetic population. Mechanism: ? hyperinsulinemiaMechanism: ? hyperinsulinemia

KCC 10/23/2008KCC 10/23/2008

Fok I polymorphism at the vitamin D receptor locusFok I polymorphism at the vitamin D receptor locus

Chiu KC, Chuang LM et al. BMC Medical Genetics 2001;2:2

minutes

Fasting 30 60 90 120

Pla

sma

gluc

ose

(mg/

dL)

60

80

100

120

140

160

Ff + ffFF

minutes

Fasting 30 60 90 120

Pla

sma

insu

lin (

U/m

L)

0

20

40

60

80

100

Ff + ff FF

a

b c

d

a: p=0.005; b: p=0.009;c: p=0.05;d: p=0.04 for geometric mean (p=0.009, IAUC)

Fok I f ATG GAG GCA ATG GCG Activity Met Glu Ala Met Ala

F ACG GAG GCA ATG GCG Activity Met Ala

Insulin sensitivity (%S)Insulin sensitivity (%S)Univariate analysisUnivariate analysis

Ff/ff: 0.53 (0.47, 0.59)Ff/ff: 0.53 (0.47, 0.59) FF: 0.78 (0.52, 1.06)FF: 0.78 (0.52, 1.06)

PP=0.006=0.006

Multivariate analysisMultivariate analysisFok I: 15.6% in %SFok I: 15.6% in %S

PP=0.039=0.039

Beta cell function (%B)Beta cell function (%B)Univariate analysisUnivariate analysis Ff/ff: 162 (144, 180)Ff/ff: 162 (144, 180) FF: 152 (112, 192)FF: 152 (112, 192)

n.s.n.s.

Multivariate analysisMultivariate analysis Fok I: none in %BFok I: none in %B

KCC 10/23/2008KCC 10/23/2008

Insulin sensitivity and intact parathyroid hormoneInsulin sensitivity and intact parathyroid hormone52 glucose tolerant and normotensive subjects52 glucose tolerant and normotensive subjects

Intact parathyorid hormone (pg/ml)

10 20 30 40 50 60 70 80

Insu

lin s

ensi

tivity

inde

x (

M/m

2 /min

/pM

)

2.00

4.00

6.00

8.0010.00

15.00

20.0025.0030.00

• N: 52 (F 31, M 21)• Age: 26 ± 1 years (19 - 40)• BMI: 24.5 ± 0.6 (17.6 - 36.5)• WHR: 0.79 ± 0.01 (0.65 - 1.03)• Univariate analysis:

r2 = 0.104, p = 0.02• Regression analysis

r2 = 0.405Covariate p iPTH 0.019WHR 0.001Gender 0.010Age < 0.001

Chiu KC, Chuang LM et al. Metabolism 2000;49:1501-1505.

KCC 10/23/2008KCC 10/23/2008

Insulin sensitivity and beta cell functionInsulin sensitivity and beta cell function126 glucose tolerant and normotensive subjects126 glucose tolerant and normotensive subjects

ISI (M/m2/min/pM)

1 2 3 4 5 7 10 15 2025

1stI

R (

pM

)

250

500

750

1000

2000

3000

40005000

7500

10000

ISI (M/m2/min/pM)

1 2 3 4 5 7 10 15 2025

2nd

IR (

pM

)50

75

100

250

500

750

1000

1500

2000

r =-0.5517P<0.0001

r =-0.7470P<0.0001

Chiu KC et al. Am J Clin Nutr 2004;79:820-825.

KCC 10/23/2008KCC 10/23/2008

25-hydroxyvitamin D and insulin sensitivity25-hydroxyvitamin D and insulin sensitivity

lnlnISI=0.3473+(0.4304xISI=0.3473+(0.4304xlnln25OHD) 25OHD) PP < 0.0001, r < 0.0001, r2 2 = 0.2116= 0.2116Covariate entered rCovariate entered r22 PP

– Gender Gender 0.0687 0.0687 0.00030.0003– 25-OHD 25-OHD 0.0610 0.0610 0.00070.0007– BMIBMI 0.0542 0.0542 0.00130.0013– DBPDBP 0.0466 0.0466 0.00280.0028– AgeAge 0.0338 0.0338 0.01050.0105– Ethnicity Ethnicity 0.0393 0.0393 0.05390.0539

Covariates considered: gender, age, ethnicity, Covariates considered: gender, age, ethnicity, season, SBP, DBP, BMI, WHR, and 25OHD.season, SBP, DBP, BMI, WHR, and 25OHD.

25OHD (ng/ml)

2 5 10 20 30 4050 75 100

ISI (

M/m

2/m

in/p

M)

1

2

3

4

5

7.5

10

15

20

30


KCC 10/23/2008KCC 10/23/2008

25-hydroxyvitamin D and beta cell function25-hydroxyvitamin D and beta cell function

ISI r2=0.3435 P < 0.0001 ISI r2= 0.5459 P < 0.0001 Age r2= 0.0500 P = 0.0107

*Covariates: gender, age, ethnicity, season, SBP, DBP, BMI, WHR, 25OHD, and ISI.

25OHD (ng/ml)

2 5 10 20 30 4050 75 100

1stI

R (

pM

)

250

500

750

1000

2000

3000

40005000

7500

10000

25OHD (ng/ml)

2 5 10 20 30 4050 75 1002n

dIR

(p

M)

50

100100

200

300

500

1000

2000

3000

5000

p<0.0001r=-0.3507

p=0.0045r=-0.2560


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Optimal serum 25-hydroxyvitamin D concentrationOptimal serum 25-hydroxyvitamin D concentration

Bischoff-Ferrari HA et al. Am J Clin Nutr 2006;84:18-28.

Optimal 25(OH)D

Starting at30 ng/mL(70 nmol/L)

Best between36-40 ng/mL(70-100 nmol/L)

BMD: bone mineral densityFractureColorectal cancerLower extremity functionPeriodontal disease

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Definition of hypovitaminosis DDefinition of hypovitaminosis D

1,569 subjects in France (43-511,569 subjects in France (43-51ooN)N)PTH plateaus at 36 pg/mlPTH plateaus at 36 pg/ml25-OHD deflects at 78 nmol/l (31 ng/ml)25-OHD deflects at 78 nmol/l (31 ng/ml)

Chapuy MC Osteoporosis Int 1997;7:439-443.Chapuy MC Osteoporosis Int 1997;7:439-443.

Malabanan A, Veronikis IE, Holick MF. Redefining vitamin D deficiency.

Lancet 1998;351:805-806.

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Inadequacy of 400 or 800 IU of vitamin D supplementInadequacy of 400 or 800 IU of vitamin D supplement

Lips P et al. JCEM 1988;67:644-650.

The effect of vitamin D supplementation for 1 yr was studied in 72 people living in a nursing home. The subjects were randomized into 3 groups: control, and 400 or 800 IU vitamin D3 every day.

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Vitamin D in foodVitamin D in food Daily supplement: 1,000 IU/day

Cod Liver Oil, 1 table spoonCod Liver Oil, 1 table spoon 1,360 IU1,360 IU

Salmon, cooked, 3Salmon, cooked, 3 1/2 1/2 ozoz 360 IU360 IU

Mackerel, cooked, 3 Mackerel, cooked, 3 1/21/2 oz oz 345 IU345 IU

Sardines, 3 Sardines, 3 1/21/2 oz oz 270 IU270 IU

Eel, cooked, 3 Eel, cooked, 3 1/21/2 oz oz 200 IU200 IU

Milk, D fortified, 8 ouncesMilk, D fortified, 8 ounces 98 IU98 IU

Margarine, fortified, 1 tbs.Margarine, fortified, 1 tbs. 60 IU60 IU

Cereal grain bar, D fortifiedCereal grain bar, D fortified 50 IU50 IU

Dry cereal, D fortified, Dry cereal, D fortified, 3/4 3/4 cupcup 40-50 IU40-50 IU

Liver, beef, cooked, 3 1/2 ozLiver, beef, cooked, 3 1/2 oz 30 IU30 IU

Egg, 1 whole (vitamin D is Egg, 1 whole (vitamin D is present in the yolk)present in the yolk)

25 IU25 IU

KCC 10/23/2008KCC 10/23/2008

Vitamin D replacement strategyVitamin D replacement strategy

Weekly X 8

25-h

ydro

xyvi

tam

in D

(n

g/m

L)

0

10

20

30

40

50

Before After

QOD X 15

25-h

ydro

xyvi

tam

in D

(n

g/m

L)

0

10

20

30

40

50

Before After

Median75%, 25%90%, 10%

Ergocalciferol 50,000 IU every week for 8 weeks. Check 25OHD in 1-2 weeks Malabanan A, Veronikis IE, Holick MF.

Lancet 1998;351:805-806.

Ergocalciferol 50,000 IU every other day for 15 doses, then vitamin D 1,000 IU daily. Check 25OHD in 3 months.

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Baseline 25-OHD concentration predicts the risk of T2DM in 17 years Baseline 25-OHD concentration predicts the risk of T2DM in 17 years Relative risk of type 2 diabetes among quartiles of serum 25-OHD concentration (Relative risk of type 2 diabetes among quartiles of serum 25-OHD concentration (nn = 4,097) = 4,097)

Mattila C et al. Diabetes Care 2007;30: 2569-2570.

Serum 25OHD quartile

1 2 3 4 P for trend

N 1,051 1,079 986 981

n 52 62 39 30

Mean serum 25OHD (ng/mL)

9 14 19 28

Model 1 1.00 1.18 (0.81–1.72) 0.79 (0.51–1.22) 0.60 (0.36–0.98) 0.01

Model 2 1.00 1.15 (0.79–1.69) 0.83 (0.53–1.29) 0.70 (0.42–1.16) 0.07

Model 3 1.00 1.10 (0.75–1.61) 0.80 (0.51–1.25) 0.67 (0.41–1.11) 0.05

Model 4 1.00 1.23 (0.80–1.89) 0.97 (0.59–1.58) 0.58 (0.32–1.06) 0.06

Data are RR or RR (95% CI) unless otherwise indicated. * Quartile 1, < 12 ng/mL; 2, 12-16 ng/mL; 3, 17-21 ng/mL; and 4, > 22 ng/mL. Model 1: RRs adjusted for age (continuous variable), sex, and month of collecting blood samples; N (all data) = 4,097 and n (type 2 diabetes cases) = 183. Model 2: RRs were adjusted for all of the factors in model 1 plus BMI (< 23, 23-24.9, 25.0-27.4, 27.5-29.9, or > 30 kg/m2), leisure-time exercise (little, occasionally, or regularly), smoking (never smokers, ex-smokers, a cigar or pipe or < 20, 20-29, or > 29 cigarettes/day), and education (< 7, 7-9, 10-12, or > 12 years); N = 4,083 and n = 183. Model 3: RRs were adjusted for all of the factors in model 2 plus blood pressure (normotension, borderline, mild, or definite hypertension); N = 4,030 and n = 181. Model 4: RRs were adjusted for all of the factors in model 2, and the first 5 years of follow-up were excluded; N = 4,044 and n = 144.

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Supplemental vitamin D intake in relation to type 2 diabetes in women in the Supplemental vitamin D intake in relation to type 2 diabetes in women in the Nurses’ Health Study (20 years)Nurses’ Health Study (20 years)

Pittas AG et al Diabetes Care 2006;29:650-656.

Supplemental vitamin D (IU/day)Supplemental vitamin D (IU/day) < 100 101-200 201-300 301-400 > 400 P

No. of new cases of diabetes 2,956 678 497 347 365

Follow-up person-years 947,379 206,980 155,352 125,792 145,455

RR (95% CI)

Age adjusted 1.00 0.87(0.80–0.95)

0.88 (0.80–0.97)

0.81 (0.72–0.90)

0.79 (0.71-0.88)

<0.001

Multivariate without diet* 1.00 0.92 (0.84–1.00)

0.97 (0.89–1.07)

0.90 (0.81–1.01)

0.88 (0.79-0.98)

0.007

Multivariate with diet** Multivariate with diet** 1.00 0.91 (0.83–1.00)

0.96 (0.85–1.07)

0.88 (0.76–1.00)

0.84 (0.73–0.97)

0.01

Multivariate with diet + calcium 1.00 0.92 (0.84–1.01)

0.97 (0.87–1.09)

0.90 (0.78–1.03)

0.87 (0.75–1.00)

0.04

* RR adjusted for age, BMI, hypertension, family history of diabetes, smoking, physical activity, caffeine, alcohol, and state of residence (southern states [California, Florida, and Texas] or northern states [Connecticut, Maryland, Massachusetts, Michigan, New Jersey, New York, Ohio, and Pennsylvania]).

** RR adjusted for everything in footnote * plus type of fat (saturated, polyunsaturated, or trans), cereal fiber, glycemic load, magnesium, and retinol.

KCC 10/23/2008KCC 10/23/2008

Multiple linear regression analysis of the association between baseline 25-OHD (nmol/l) Multiple linear regression analysis of the association between baseline 25-OHD (nmol/l) and 10-year follow-up blood glucose concentration and 10-year follow-up blood glucose concentration

The Ely Study 1990–2000The Ely Study 1990–2000

Model 1 Model 2 Model 3

beta (95% CI) P beta (95% CI) P beta (95% CI) P

Fasting glucose (mmol/l)

-0.0023 0.019 -0.0018 0.082 -0.0019 0.074(-0.004 to -0.0004) (-0.004 to 0.0002) (-0.004 to 0.0002)

2-h glucose (mmol/l)

-0.0097 0.006 -0.0094 0.011 -0.0100 0.013(-0.017 to -0.0030) (-0.017 to -0.0020) (-0.018 to -0.0020)

Model 1: adjusted for baseline outcome variable, age, sex, smoking status, season, and BMI. Model 2: adjusted as in model 1 plus PTH, calcium, and IGF-1. Model 3: adjusted as in model 2 plus physical activity score and occupational social class.

Forouhi NG et al. Diabetes 2008;57:2619–2625.

Model 1 Model 2 Model 3

Increase by 10 ng/mL

Fasting glucose (mg/dL) -1.035 -0.81 -0.855

2-h glucose (mg/dL) -4.365 -4.23 -4.5

Increase by 20 ng/mL

Fasting glucose (mg/dL) -2.07 -1.62 -1.71

2-h glucose (mg/dL) -8.73 -8.46 -9.00

KCC 10/23/2008KCC 10/23/2008

Effects of combined calcium and vitamin D supplementation on FPG and HOMA-IR in subgroups by baseline glucose tolerance status

Pittas AG et al. Diabetes Care 2007;30:980–986.

Treatment with 500 mg calcium citrate and 700 IU vitamin D3 (open bars) or placebos (solid bars) daily for 3 years. Changes in FPG and HOMA-IR are expected means + SEM by general linear model analysis after adjustment for baseline values and risk factors for type 2 diabetes (age, sex, BMI, physical activity, and smoking). There were no differences in C-reactive protein or interleukin-6 between the two treatment arms in either subgroup.

KCC 10/23/2008KCC 10/23/2008

Glucose homeostasis in VDR knockout miceGlucose homeostasis in VDR knockout mice

Oral glucose tolerance in 10-wk-old male wild-type and homozygous VDR knockout mice fed a Oral glucose tolerance in 10-wk-old male wild-type and homozygous VDR knockout mice fed a standard (standard (AA) or a rescue diet () or a rescue diet (BB). ). Data represent mean ± SE. Data represent mean ± SE. **PP < 0.05 vs. wild-type by < 0.05 vs. wild-type by tt test or test or UU test. test.

Zeitz U et al. FASEB J 2003;17:509-511.

KCC 10/23/2008KCC 10/23/2008

Fighting tuberculosis with vitamin DFighting tuberculosis with vitamin D

• LL-37 (AKA cathelicidin) is an inducible antimicrobicidal peptides (AMP).

• Two related pathways: 1. In skin (ketinocyte), sunlight induces

formation of vitamin D and VDR expression.

2. In macrophage, TB-associated molecule binds to TLR2/1 (Toll-like receptor), resulting in expression of CYP27B1 and VDR.

• 1,25-D3, joins with the VDR and activates the gene encoding LL-37, leading to an increase in cellular LL-37 and enhanced microbicidal activity of the phagocyte.

Liu PT et al. Science 2006;311:1770-1773.

• Expression of VDR and CYP27B1• 25-hydroxyvitamin D• Non-endocrine function of vitamin D

Zasloff M. Nature Medicine 2006;12:388-390.

?

CYP27B1

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