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. As we are all well aware, the prevalence of diabetes and obesity has reached epidemic proportions. In fact, increasing rates… However, the development of diabetes among obese individuals is heterogeneous. Indeed, many obese persons remain free from metabolic disease their entire lifetime. Thus, BMI alone does not adequately discriminate the risk of diabetes in this population. Thus, there is an unmet clinical need to identify factors that help to differentiate… 1. Wijeysundera et al. JAMA. 2010;303:1841-47 2. Despres JP. Circulation. 2012;126:1301-13

Rates of Diabetes and Obesity on the Rise Changes in CVD Risk Factors from 1994 to 2005 in Ontario, Canada Relative % Change Body Mass Index Smoking Physical Inactivity Diabetes Systolic BP Total Cholesterol Wijeysundera et al. JAMA. 2010;303:1841-47

Obesity is Heterogeneous Increasing rates of diabetes and obesity have contributed to a slowed decline in CVD event rates in the population.1 Diabetes development is heterogeneous among obese individuals and BMI does not adequately discriminate risk in this population.2 Factors that differentiate obese persons who will develop prediabetes and diabetes from those who will remain metabolically healthy have not been well characterized.

Obesity is Heterogeneous Diabetes Diabetes Increasing rates of diabetes and obesity have contributed to a slowed decline in CVD event rates in the population.1 Diabetes development is heterogeneous among obese individuals and BMI does not adequately discriminate risk in this population.2 Factors that differentiate obese persons who will develop prediabetes and diabetes from those who will remain metabolically healthy have not been well characterized.

Obesity is Heterogeneous Prediabetes Diabetes Prediabetes Prediabetes Diabetes

Study Aim Investigate associations between markers of general and dysfunctional adiposity and risk of incident prediabetes and diabetes in multiethnic cohort of obese adults. Adipose tissue dysfunction is characterized by ectopic fat deposition in the abdominal viscera and liver, inflammatory and adipokine dysregulation, and insulin resistance Specifically markers of general and dysfunctional adiposity… Therefore, we aimed to investigate associations between adiposity phenotypes, specifically markers of general and dysfunctional adiposity, and the risk…

The Dallas Heart Study Genetic Markers Biomarkers Imaging Cohort F/U EBCT Cardiac MRI Aortic MRI MRI Abdomen DEXA n3500 n3000 n=6101 Representative Population Sample Cohort F/U

Methods 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 N=732 BMI ≥ 30 No DM No CVD Mean Age 43 65% Women 71% Nonwhite 2000 2002 2007 6 8 3 4 5 7 9 2009 DHS-2 Exam Weight Gain Multivariable logistic regression using a backward selection strategy to identify independent associations of variables with incident prediabetes and diabetes. 1 2 Year DHS-1 Exam Subgroup with FBG<100 (n=512)  Incident Prediabetes

Baseline Measurements: Body Composition Dual energy x-ray absorptiometry Total fat mass Total lean mass Percent body fat Truncal fat mass Lower body fat mass

Abdominal MRI Patient #1: 21 AA Female BMI = 36 Patient #2: 59 W Male

Results – Overall Cohort Median (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) Systolic Blood Pressure (mmHg) 123 (115, 134) 131 (122, 144) Glucose (mg/dL) 93 (87, 100) 101 (92, 114) Fructosamine (µmol/L) 199 (188, 210) 211 (196, 224) Triglycerides (mg/dL) 99 (70, 146) 124 (90, 187) 0.001 In the overall cohort, 84, or 11.5% of participants developed incident diabetes.

Results – Overall Cohort Median (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) Systolic Blood Pressure (mmHg) 123 (115, 134) 131 (122, 144) Glucose (mg/dL) 93 (87, 100) 101 (92, 114) Fructosamine (µmol/L) 199 (188, 210) 211 (196, 224) Triglycerides (mg/dL) 99 (70, 146) 124 (90, 187) 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

Results – Overall Cohort Median (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) Systolic Blood Pressure (mmHg) 123 (115, 134) 131 (122, 144) Glucose (mg/dL) 93 (87, 100) 101 (92, 114) Fructosamine (µmol/L) 199 (188, 210) 211 (196, 224) Triglycerides (mg/dL) 99 (70, 146) 124 (90, 187) 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 Cohort Diabetes Incidence by Sex-Specific Tertiles of Abdominal Fat Distribution Diabetes incidence increased significantly across sex-specific tertiles of visceral fat mass, but no association was seen for abdominal subcutaneous fat. Similarly, no trend with diabetes incidence was observed across tertiles of total body fat.

Results – Overall Cohort Diabetes Incidence by Sex-Specific Tertiles of Abdominal Fat Distribution Diabetes incidence increased significantly across sex-specific tertiles of visceral fat mass, but no association was seen for abdominal subcutaneous fat. Similarly, no trend with diabetes incidence was observed across tertiles of total body fat.

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 Findings were similar when HOMA-IR was substituted for fasting glucose and were insensitive to forcing age, sex, and race into the model or to excluding participants diagnosed exclusively by Hgb A1C. *Log-transformed

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 Findings were similar when HOMA-IR was substituted for fasting glucose and were insensitive to forcing age, sex, and race into the model or to excluding participants diagnosed exclusively by Hgb A1C. *Log-transformed

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 Findings were similar when HOMA-IR was substituted for fasting glucose and were insensitive to forcing age, sex, and race into the model or to excluding participants diagnosed exclusively by Hgb A1C. *Log-transformed

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 Findings were similar when HOMA-IR was substituted for fasting glucose and were insensitive to forcing age, sex, and race into the model or to excluding participants diagnosed exclusively by Hgb A1C. *Log-transformed

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 Findings were similar when HOMA-IR was substituted for fasting glucose and were insensitive to forcing age, sex, and race into the model or to excluding participants diagnosed exclusively by Hgb A1C. *Log-transformed

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 Findings were similar when HOMA-IR was substituted for fasting glucose and were insensitive to forcing age, sex, and race into the model or to excluding participants diagnosed exclusively by Hgb A1C. *Log-transformed

Results – Subgroup with FBG<100 – Incident Prediabetes or Diabetes Multivariable analysis: Variable Odds Ratio (95% CI) Χ2 value Weight gain (per 5 kg) 1.5 (1.3-1.6) 40.9 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 Visceral fat mass (per 1 SD)* 10.8 Fructosamine (per 1 SD)* 1.4 (1.1-1.8) 10.2 Insulin (per 1 SD)* 1.3 (1.1-1.7) 6.1 Nonwhite race 1.8 (1.1-2.9) 5.2 Family history of diabetes 1.6 (1.1-2.4) 4.8 Similar findings were seen when HOMA-IR was substituted for fasting glucose and insulin, when participants diagnosed exclusively by Hgb A1C criteria were excluded, or when participants prescribed weight-modifying diabetic medications (insulin, thiazolidinediones, or metformin) during follow-up were excluded. Unadjusted associations of visceral fat consistent across subgroups of sex and race. *Log-transformed

Results Prevalence 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. …and suggest that body fat distribution may be one of the key variables explaining the metabolic heterogeneity of obesity and its related CVD risk.

Intensive Lifestyle Modification Pharmacologic Therapy Clinical Implications ? Risk Stratification May help to identify appropriate candidates for intensive lifestyle modification, medical therapy, and bariatric surgery. Development of novel therapies that modify adipose tissue distribution may improve metabolic and cardiovascular outcomes in obese individuals. Preventing weight gain among those already obese may favorably impact metabolic health, even if weight loss cannot be achieved. Intensive Lifestyle Modification Pharmacologic Therapy Bariatric Surgery

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

Non-Participants Variable Participated 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) Abdominal Visceral Fat (kg) 2.5 (1.9, 3.1) 2.5 (2.0, 3.1) 0.84

Abdominal MRI Measurements Single slice measurement at L2-L3 level provides excellent accuracy for abdominal fat mass measured at all inter- vertebral levels (R2=85-96%)

Multivariable Models 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).

Model Validation

Diagnoses Exclusively by Hgb A1C Diabetes: 12/84 = 14% Prediabetes: 67/161 = 42% Findings insensitive to excluding these participants from the multivariable models.

Visceral fat and Insulin Resistance are Additive

Anthropometric Measures of Abdominal Obesity are Insufficient Added to the Incident Diabetes Model without Visceral Fat 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

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 The group with SC fat transplanted into the VIS cavity exhibited decreased body weight, total fat mass, and glucose and insulin levels. These mice also exhibited improved insulin sensitivity during hyperinsulinemic-euglycemic clamps with increased whole-body glucose uptake, glucose uptake into endogenous fat, and insulin suppression of hepatic glucose production. These effects were observed to a lesser extent with SC fat transplanted to the SC area, whereas VIS fat transplanted to the VIS area was without effect. Tran et al. Cell Metab. 2008;7:410-420

Strengths and Limitations 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;122:481-86 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, 2005 Atherosclerosis Risk in Communities Study 26 kg/m2 Waist circumference, TGs, HDL-C Wilson et al, 2007 Framingham Offspring Cohort Study 27 kg/m2 Prior population-based studies in primarily non-obese populations demonstrated that markers of general adiposity were associated with incident diabetes. Colditz et al. Ann Intern Med. 1995;122:481-86 Stern et al. Ann Intern Med. 2002;136:575-81 Schmidt et al. Diabetes Care. 2005;28:2013-18 Wilson et al. Arch Intern Med. 2007;167:1068-74