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

1. 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

2. 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

3. Obesity is Heterogeneous

4. Obesity is Heterogeneous Diabetes Diabetes

5. Obesity is Heterogeneous Prediabetes Diabetes Prediabetes Prediabetes Diabetes

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

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

8. 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 2007 2002 2000 2009 Weight Gain 3 4 5 7 2 1 6 8 9 Year DHS-1 Exam DHS-2 Exam Subgroup with FBG<100 (n=512)  Incident Prediabetes

9. 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

10. Abdominal MRI Patient #1: 21 AA Female BMI = 36 Patient #2: 59 W Male BMI = 31

11. Results – Overall Cohort

12. Results – Overall Cohort

13. Results – Overall Cohort

14. Results – Overall Cohort Diabetes Incidence by Sex-Specific Tertiles of Abdominal Fat Distribution

15. Results – Overall Cohort Diabetes Incidence by Sex-Specific Tertiles of Abdominal Fat Distribution

16. Results – Overall Cohort – Incident Diabetes Multivariable analysis: *Log-transformed

17. Results – Overall Cohort – Incident Diabetes Multivariable analysis: *Log-transformed

18. Results – Overall Cohort – Incident Diabetes Multivariable analysis: *Log-transformed

19. Results – Overall Cohort – Incident Diabetes Multivariable analysis: *Log-transformed

20. Results – Overall Cohort – Incident Diabetes Multivariable analysis: *Log-transformed

21. Results – Overall Cohort – Incident Diabetes Multivariable analysis: *Log-transformed

22. Results – Subgroup with FBG<100 – Incident Prediabetes or Diabetes Multivariable analysis: *Log-transformed

23. Results Prevalence of Subclinical CVD at Baseline Stratified by Diabetes Status

24. 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.

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

26. IJ Neeland and coauthors Dysfunctional Adiposity and the Risk of Prediabetes and Type 2 Diabetes in Obese Adults

28. Visceral Fat stratified by Subgroups

29. Study Population and Follow-Up

30. Non-Participants

31. 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%)

32. 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).

33. Model Validation

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

35. Visceral fat and Insulin Resistance are Additive

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

37. Weight Gain over the Study Interval

38. 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.

39. Subcutaneous Fat Expandability and Metabolic Health Tran et al. Cell Metab. 2008;7:410-420

40. 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.

41. Prior Studies 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