Dysfunctional Adiposity and the Risk of Prediabetes and Type 2 Diabetes in Obese Adults James A de Lemos, MD University of Texas Southwestern Medical Center.

Dysfunctional Adiposity and the Risk of Prediabetes and Type 2

Diabetes in Obese Adults

James A de Lemos, MD

University of Texas Southwestern Medical Center

Study Rationale• Increasing rates of diabetes and obesity have

contributed to a slowed decline in CVD.1

• Diabetes development is heterogeneous and BMI does not adequately discriminate risk.2

• Previous studies – Cross sectional with little longitudinal data– Not focused on obese– Ethnically homogeneous– Limited application of advanced imaging

• Factors that differentiate obese persons who will develop prediabetes and diabetes from those who will remain metabolically healthy have not been well characterized.

1. Wijeysundera et al. JAMA. 2010;303:1841-472. Despres JP. Circulation. 2012;126:1301-13

Obesity is Heterogeneous


Diabetes

Diabetes

Diabetes

DiabetesPrediabetes

PrediabetesPrediabetes


Study Aim

Investigate associations between markers of general and dysfunctional adiposity and risk of incident prediabetes and diabetes in multiethnic cohort of obese adults.

Imaging

Cohort F/U

GeneticMarkers

The Dallas Heart Study

Biomarkers

Representative Population Sample

EBCTCardiac MRIAortic MRIMRI AbdomenDEXA

n=6101

n3500

n3000

2002

1

2000

Year 2DHS-1 Exam

2007

6 83 4 5 7 9

2009

DHS-2 Exam

Methods

N=732BMI ≥ 30

No DMNo CVD

• Body Composition and Abdominal Fat Distribution

MRI and DEXA

• Blood Biomarkers

• Cardiac Structure and Function CT and MRI

Incident Diabetes

• FBG ≥ 126

• non-FBG ≥ 200

• Hgb A1C ≥ 6.5

Weight Gain

Subgroup with FBG<100 (n=512) Incident Prediabetes

Mean Age 4365% Women71% Nonwhite

Baseline Measurements: Body Composition

Abdominal MRIPatient #1: 21 AA Female

BMI = 36Patient #2: 59 W Male

BMI = 31

Results – Overall CohortMedian (IQR) or % No Diabetes (n=648) Incident Diabetes (n=84) P value

Family History of Diabetes 42% 63% <0.001

Waist/Hip ratio 0.91 (0.85, 0.97) 0.95 (0.90, 1.00) <0.001

Systolic Blood Pressure (mmHg)

123 (115, 134) 131 (122, 144) <0.001

Glucose (mg/dL) 93 (87, 100) 101 (92, 114) <0.001

Fructosamine (µmol/L) 199 (188, 210) 211 (196, 224) <0.001

Triglycerides (mg/dL) 99 (70, 146) 124 (90, 187) 0.001



Waist/Hip ratio 0.91 (0.85, 0.97) 0.95 (0.90, 1.00) <0.001


123 (115, 134) 131 (122, 144) <0.001

Glucose (mg/dL) 93 (87, 100) 101 (92, 114) <0.001



Lower Body Fat (kg) 12.6 (9.6, 16.3) 11.2 (9.0, 15.1) 0.02

Adiponectin (ng/mL) 5.9 (4.3, 8.4) 5.0 (3.4, 7.8) 0.04



Waist/Hip ratio 0.91 (0.85, 0.97) 0.95 (0.90, 1.00) <0.001


123 (115, 134) 131 (122, 144) <0.001

Glucose (mg/dL) 93 (87, 100) 101 (92, 114) <0.001



Lower Body Fat (kg) 12.6 (9.6, 16.3) 11.2 (9.0, 15.1) 0.02

Adiponectin (ng/mL) 5.9 (4.3, 8.4) 5.0 (3.4, 7.8) 0.04

Body Mass Index (kg/m2) 34.9 (31.9, 38.9) 35.4 (33.0, 39.3) 0.35

Total Body Fat (kg) 35.5 (29.3, 43.4) 35.3 (28.8, 42.7) 0.51

HDL Cholesterol (mg/dL) 46 (39, 54) 45 (38, 54) 0.48

C-reactive protein (mg/L) 4.4 (2.2, 9.4) 3.6 (1.9, 9.3) 0.40

Results – Overall CohortDiabetes Incidence by Sex-Specific Tertiles

of Abdominal Fat Distribution

Results – Overall CohortDiabetes Incidence by Sex-Specific Tertiles

of Abdominal Fat Distribution

Results – Overall Cohort – Incident Diabetes

Multivariable analysis:

Variable Odds Ratio (95% CI) Χ2 value

Fructosamine (per 1 SD)* 2.0 (1.4-2.7) 17.7

Visceral fat mass (per 1 SD)* 2.4 (1.6-3.7) 17.0

Fasting glucose (per 1 SD)* 1.9 (1.4-2.6) 16.1

Weight gain (per 5 kg) 1.3 (1.1-1.2) 9.8

Systolic blood pressure (per 10 mm Hg)

1.3 (1.1-1.5) 7.6

Family history of diabetes 2.3 (1.3-4.3) 7.1

*Log-transformed









1.3 (1.1-1.5) 7.6


*Log-transformed









1.3 (1.1-1.5) 7.6


*Log-transformed









1.3 (1.1-1.5) 7.6


*Log-transformed









1.3 (1.1-1.5) 7.6


*Log-transformed









1.3 (1.1-1.5) 7.6


*Log-transformed


*Log-transformed



Fasting blood glucose (per 1 SD)* 1.7 (1.3-2.1) 16.0

Age (per 10 years) 1.5 (1.2-1.9) 10.9



Insulin (per 1 SD)* 1.3 (1.1-1.7) 6.1

Nonwhite race 1.8 (1.1-2.9) 5.2


Results – Subgroup with FBG<100 – Incident Prediabetes or Diabetes

ResultsPrevalence of Subclinical CVD at Baseline

Stratified by Diabetes Status

Conclusions

• Dysfunctional adiposity phenotype associated with incident prediabetes and diabetes in obese population.– Excess visceral fat mass

– Insulin resistance

• No association between general adiposity and incident prediabetes or diabetes.

• Obesity is a heterogeneous disorder with distinct adiposity sub-phenotypes.

Clinical Implications

Risk Stratification

?

Intensive Lifestyle

Modification

Pharmacologic Therapy

Bariatric Surgery

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IJ Neeland and coauthors

Dysfunctional Adiposity and the Risk of Prediabetes and Type 2 Diabetes in Obese Adults

Visceral Fat stratified by Subgroups

Study Population and Follow-Up

VariableParticipated in DHS-2

(n=732)

Did not participate in DHS-2(n=345)

P-value

Weight (kg) 98.4 (87.5, 109.8) 98.0 (87.1, 109.3) 0.69

Body Mass Index (kg/m2) 35.0 (32.0, 38.9) 34.4 (31.8, 38.6) 0.21

Waist Circumference (cm) 109.0 (101.0, 117.5) 108.7 (101.5, 116.5) 0.68

Waist/Hip ratio 0.91 (0.85, 0.98) 0.92 (0.87, 0.98) 0.08

Impaired Fasting Glucose, No. (%)

211 (28.8) 96 (27.8) 0.50

Family History of Diabetes, No. (%)

290 (44.1) 129 (42.6) 0.66

Hypertension, No. (%) 258 (35.8) 132 (38.7) 0.36

Metabolic Syndrome, No. (%)

348 (47.5) 164 (47.5) 1.00

Total Fat Mass (kg) 35.5 (29.2, 43.4) 34.1 (28.0, 42.7) 0.08

Abdominal Visceral Fat (kg)

2.5 (1.9, 3.1) 2.5 (2.0, 3.1) 0.84

Non-Participants

Single slicemeasurementat L2-L3 levelprovidesexcellentaccuracyfor abdominalfat mass measured at all inter-vertebral levels(R2=85-96%)

Abdominal MRI Measurements Correlation coefficient (r)

Study and reference

Method Location of single

slice Tot-SAT : SS-SAT Tot-VAT : SS-VAT

Borkan et al 1 (n=8 M)

CT 6, 4, 2 cm above, at, and 2, 4, 6 cm below umbilicus

0.99 0.95-0.99

Tokunaga et al 2 (n= 8 M)

CT Umbilicus 0.99 ---

Kvist et al 3

(n=17 M, 10 F) CT L3-L4 --- 0.94-0.98

L4-L5 0.98-0.99 Ross et al 4 (n=27 M)

MRI 15 cm above L4-

L5 --- 0.96

10 cm above L4-L5 (corresponding approximately to

L2-L3 level)

--- 0.96

5 cm above L4-L5 --- 0.97 L4-L5 0.97 0.95 5 cm below L4-L5 --- 0.89

Armellini et al 5 (n=18 M, 72 F)

CT T12-L1 --- 0.95

L2-L3 --- 0.98 L3-L4 --- 0.95 L4-L5 --- 0.92

Abate et al 6 (n=49 M)

MRI T12-L1 0.93 0.83

L1-L2 0.94 0.89 L2-L3 0.92 0.92 L3-L4 0.89 0.93 L4-L5 0.87 0.87 L5-S1 0.96 0.86

• Criteria for entry = 0.1

• Criteria for backward selection = 0.05

• Assessment for Overfitting: Shrinkage coefficient calculated as: [Likelihood model chi-square-p]/Likelihood model chi-square, where p=# of covariates in the model– Incidence diabetes = 0.94– Incident prediabetes or diabetes = 0.95

• Evaluation for Collinearity: Variance inflation factors (VIFs) calculated using the dependent variable from logistic regression analysis as a dependent variable in a linear regression. No evidence of collinearity found (VIFs all <1.7).

Multivariable Models

For model validation, we used 500 bootstrap samples to see how the beta coefficients, and hence, odds ratios, changed across variations within our dataset. We used bootstrapping as opposed to cross-validation, as the entire dataset is used for model development in the samples and we wanted to preserve the inclusion of as many events as possible. Bootstrapping also provides fairly unbiased estimates. The odds ratios changed minimally.

T2DM Model Full sample 500 Bootstrap Replications

Fasting Glucose 1.88 (1.38-2.56) 1.90 (1.31-2.77) Family History 2.32 (1.25-4.29) 2.42 (1.24-4.73) Systolic BP 1.26 (1.07-1.48) 1.27 (1.06-1.52) Visceral fat 2.42 (1.59-3.68) 2.49 (1.62-3.84) Fructosamine 1.95 (1.43-2.67) 1.97 (1.38-2.82) Weight gain 1.06 (1.02-1.10) 1.06 (1.02-1.11) The c-statistic based on the original dataset was 0.845, while the bootstrap c-statistics derived from fitting the bootstrapped equation to the original dataset on average was 0.835. Thus, the diminution in c-statistics is small. Furthermore, we were only interested in determining associations with incident diabetes and not constructing a risk prediction model.

Model Validation

• Diabetes: 12/84 = 14%

• Prediabetes: 67/161 = 42%

• Findings insensitive to excluding these participants from the multivariable models.

Diagnoses Exclusively by Hgb A1C

Visceral fat and Insulin Resistance are Additive

Anthropometric Measures of Abdominal Obesity are Insufficient

Variable Odds Ratio (95% CI) X2

Waist Circumference (per 1 cm)

0.99 (0.97-1.0) 0.01

Log WHR (per 1-SD) 1.4 (0.96-2.0) 3.0

Added to the Incident Diabetes Model without Visceral Fat

Weight Gain over the Study Interval

Potential Mechanisms

• ↓ Subcutaneous fat storage = ↑ Visceral and ectopic fat

• Resistance to diabetes may be due to shunting excess fat away from ectopic sites and preferentially depositing it in the lower body subcutaneous compartment.

• Visceral fat and insulin resistance may contribute to subclinical CVD prior to the clinical manifestations of metabolic disease.

Subcutaneous Fat Expandability and Metabolic Health

Tran et al. Cell Metab. 2008;7:410-420

Strengths and Limitations• Strengths :

– diverse sample of adults applicable to the general obese population

– extensive and detailed phenotyping using advanced imaging and laboratory techniques

– longitudinal follow-up in a prospective cohort

• Limitations: – absence of glucose tolerance testing in the DHS and of Hgb A1C

measurements in DHS-1 – modest number of diabetes events– time of pre-diabetes or diabetes onset not available. – findings not necessarily generalizable to individuals older than

age 65 or of Asian descent/ethnicity.

Prior Studies

Colditz et al. Ann Intern Med. 1995;122:481-86Stern et al. Ann Intern Med. 2002;136:575-81Schmidt et al. Diabetes Care. 2005;28:2013-18Wilson et al. Arch Intern Med. 2007;167:1068-74

Author, Year Study Population Mean Weight or BMI Summary of Findings

Colditz et al, 1995 Nurses Health Study 57 kg BMI, Weight gain

Stern et al, 2002 San Antonio Heart Study 24-28 kg/m2 BMI, Blood pressure, TGs, HDL-C

Schmidt et al, 2005Atherosclerosis Risk in

Communities Study26 kg/m2 Waist circumference, TGs, HDL-C

Wilson et al, 2007Framingham Offspring Cohort

Study27 kg/m2 BMI, Blood pressure, TGs, HDL-C

Dysfunctional Adiposity and the Risk of Prediabetes and Type 2 Diabetes in Obese Adults James A de Lemos, MD University of Texas Southwestern Medical Center.

Documents

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mri incident diabetes

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prediabetes obesity

risk of prediabetes