Dysfunctional Adiposity and the Risk of Prediabetes and

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 -47 2. Despres JP. Circulation. 2012; 126: 1301 -13

Obesity is Heterogeneous

Obesity is Heterogeneous Diabetes

Obesity is Heterogeneous 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.

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

Methods • Body Composition and Abdominal Fat Distribution Incident Diabetes MRI and DEXA N=732 BMI ≥ 30 No DM No CVD • Blood Biomarkers • Cardiac Structure and Function • FBG ≥ 126 • non-FBG ≥ 200 • Hgb A 1 C ≥ 6. 5 CT and MRI Mean Age 43 65% Women 71% Nonwhite 2002 2000 2007 2009 Weight Gain Year 1 2 DHS-1 Exam 3 4 5 6 7 8 9 DHS-2 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 BMI = 31

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) <0. 001 Systolic Blood Pressure (mm. Hg) 123 (115, 134) 131 (122, 144) <0. 001 Glucose (mg/d. L) 93 (87, 100) 101 (92, 114) <0. 001 Fructosamine (µmol/L) 199 (188, 210) 211 (196, 224) <0. 001 Triglycerides (mg/d. L) 99 (70, 146) 124 (90, 187) 0. 001

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) <0. 001 Systolic Blood Pressure (mm. Hg) 123 (115, 134) 131 (122, 144) <0. 001 Glucose (mg/d. L) 93 (87, 100) 101 (92, 114) <0. 001 Fructosamine (µmol/L) 199 (188, 210) 211 (196, 224) <0. 001 Triglycerides (mg/d. L) 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/m. L) 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) <0. 001 Systolic Blood Pressure (mm. Hg) 123 (115, 134) 131 (122, 144) <0. 001 Glucose (mg/d. L) 93 (87, 100) 101 (92, 114) <0. 001 Fructosamine (µmol/L) 199 (188, 210) 211 (196, 224) <0. 001 Triglycerides (mg/d. L) 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/m. L) 5. 9 (4. 3, 8. 4) 5. 0 (3. 4, 7. 8) 0. 04 Body Mass Index (kg/m 2) 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/d. L) 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

Results – Overall Cohort – Incident Diabetes Multivariable analysis: Variable Odds Ratio (95% CI) Χ 2 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

Results – Subgroup with FBG<100 – Incident Prediabetes or Diabetes Multivariable analysis: Variable Odds Ratio (95% CI) Χ 2 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)* 1. 5 (1. 2 -1. 9) 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 *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.

Clinical Implications ? Risk Stratification Intensive Lifestyle Modification Pharmacologic Therapy Bariatric Surgery

IJ Neeland coauthors Dysfunctional Adiposity and the Risk of Prediabetes and Type 2 Diabetes in Obese Adults Available at www. jama. com Copyright restrictions apply. jamanetwork. com

Visceral Fat stratified by Subgroups

Study Population and Follow-Up

Non-Participants 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/m 2) 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 0. 91 (0. 85, 0. 98) 0. 92 (0. 87, 0. 98) 0. 08 211 (28. 8) 96 (27. 8) 0. 50 290 (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 35. 5 (29. 2, 43. 4) 34. 1 (28. 0, 42. 7) 0. 08 2. 5 (1. 9, 3. 1) 2. 5 (2. 0, 3. 1) 0. 84 Variable Waist/Hip ratio Impaired Fasting Glucose, No. (%) Family History of Diabetes, No. (%) Total Fat Mass (kg) Abdominal Visceral Fat (kg)

Abdominal MRI Measurements Single slice measurement at L 2 -L 3 level provides excellent accuracy for abdominal fat mass measured at all intervertebral levels (R 2=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 A 1 C • 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) X 2 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 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 A 1 C 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 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/m 2 BMI, Blood pressure, TGs, HDL-C Schmidt et al, 2005 Atherosclerosis Risk in Communities Study 26 kg/m 2 Waist circumference, TGs, HDL-C Wilson et al, 2007 Framingham Offspring Cohort Study 27 kg/m 2 BMI, Blood pressure, TGs, HDL-C 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