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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.

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Presentation on theme: "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."— Presentation transcript:

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: Despres JP. Circulation. 2012;126:

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

4 Obesity is Heterogeneous

5 Diabetes

6 Prediabetes Obesity is Heterogeneous

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

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

9 Year 2 DHS-1 Exam DHS-2 Exam Methods N=732 BMI 30 No DM No 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 43 65% Women 71% Nonwhite

10 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

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

12 Results – Overall Cohort Median (IQR) or %No Diabetes (n=648 ) Incident Diabetes (n=84)P value Family History of Diabetes42%63%<0.001 Waist/Hip ratio0.91 (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

13 Results – Overall Cohort Median (IQR) or %No Diabetes (n=648 ) Incident Diabetes (n=84)P value Family History of Diabetes42%63%<0.001 Waist/Hip ratio0.91 (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 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

14 Results – Overall Cohort Median (IQR) or %No Diabetes (n=648 ) Incident Diabetes (n=84)P value Family History of Diabetes42%63%<0.001 Waist/Hip ratio0.91 (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 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/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/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

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

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

17 Results – Overall Cohort – Incident Diabetes Multivariable analysis: VariableOdds Ratio (95% CI)Χ 2 value Fructosamine (per 1 SD)*2.0 ( )17.7 Visceral fat mass (per 1 SD)*2.4 ( )17.0 Fasting glucose (per 1 SD)*1.9 ( )16.1 Weight gain (per 5 kg)1.3 ( )9.8 Systolic blood pressure (per 10 mm Hg)1.3 ( )7.6 Family history of diabetes2.3 ( )7.1 *Log-transformed

18 Results – Overall Cohort – Incident Diabetes Multivariable analysis: VariableOdds Ratio (95% CI)Χ 2 value Fructosamine (per 1 SD)*2.0 ( )17.7 Visceral fat mass (per 1 SD)*2.4 ( )17.0 Fasting glucose (per 1 SD)*1.9 ( )16.1 Weight gain (per 5 kg)1.3 ( )9.8 Systolic blood pressure (per 10 mm Hg)1.3 ( )7.6 Family history of diabetes2.3 ( )7.1 *Log-transformed

19 Results – Overall Cohort – Incident Diabetes Multivariable analysis: VariableOdds Ratio (95% CI)Χ 2 value Fructosamine (per 1 SD)*2.0 ( )17.7 Visceral fat mass (per 1 SD)*2.4 ( )17.0 Fasting glucose (per 1 SD)*1.9 ( )16.1 Weight gain (per 5 kg)1.3 ( )9.8 Systolic blood pressure (per 10 mm Hg)1.3 ( )7.6 Family history of diabetes2.3 ( )7.1 *Log-transformed

20 Results – Overall Cohort – Incident Diabetes Multivariable analysis: VariableOdds Ratio (95% CI)Χ 2 value Fructosamine (per 1 SD)*2.0 ( )17.7 Visceral fat mass (per 1 SD)*2.4 ( )17.0 Fasting glucose (per 1 SD)*1.9 ( )16.1 Weight gain (per 5 kg)1.3 ( )9.8 Systolic blood pressure (per 10 mm Hg)1.3 ( )7.6 Family history of diabetes2.3 ( )7.1 *Log-transformed

21 Results – Overall Cohort – Incident Diabetes Multivariable analysis: VariableOdds Ratio (95% CI)Χ 2 value Fructosamine (per 1 SD)*2.0 ( )17.7 Visceral fat mass (per 1 SD)*2.4 ( )17.0 Fasting glucose (per 1 SD)*1.9 ( )16.1 Weight gain (per 5 kg)1.3 ( )9.8 Systolic blood pressure (per 10 mm Hg)1.3 ( )7.6 Family history of diabetes2.3 ( )7.1 *Log-transformed

22 Results – Overall Cohort – Incident Diabetes Multivariable analysis: VariableOdds Ratio (95% CI)Χ 2 value Fructosamine (per 1 SD)*2.0 ( )17.7 Visceral fat mass (per 1 SD)*2.4 ( )17.0 Fasting glucose (per 1 SD)*1.9 ( )16.1 Weight gain (per 5 kg)1.3 ( )9.8 Systolic blood pressure (per 10 mm Hg)1.3 ( )7.6 Family history of diabetes2.3 ( )7.1 *Log-transformed

23 Multivariable analysis: *Log-transformed VariableOdds Ratio (95% CI)Χ 2 value Weight gain (per 5 kg)1.5 ( )40.9 Fasting blood glucose (per 1 SD)*1.7 ( )16.0 Age (per 10 years)1.5 ( )10.9 Visceral fat mass (per 1 SD)*1.5 ( )10.8 Fructosamine (per 1 SD)*1.4 ( )10.2 Insulin (per 1 SD)*1.3 ( )6.1 Nonwhite race1.8 ( )5.2 Family history of diabetes1.6 ( )4.8 Results – Subgroup with FBG<100 – Incident Prediabetes or Diabetes

24 Results Prevalence of Subclinical CVD at Baseline Stratified by Diabetes Status

25 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.

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

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

28

29 Visceral Fat stratified by Subgroups

30 Study Population and Follow-Up

31 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/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 Waist/Hip ratio0.91 (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

32 Single slice measurement at L2-L3 level provides excellent accuracy for abdominal fat mass measured at all inter- vertebral levels (R 2 =85-96%) Abdominal MRI Measurements

33 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

34 Model Validation

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

36 Visceral fat and Insulin Resistance are Additive

37 Anthropometric Measures of Abdominal Obesity are Insufficient VariableOdds Ratio (95% CI)X2X2 Waist Circumference (per 1 cm) 0.99 ( )0.01 Log WHR (per 1-SD)1.4 ( )3.0 Added to the Incident Diabetes Model without Visceral Fat

38 Weight Gain over the Study Interval

39 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.

40 Subcutaneous Fat Expandability and Metabolic Health Tran et al. Cell Metab. 2008;7:

41 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.

42 Prior Studies Colditz et al. Ann Intern Med. 1995;122: Stern et al. Ann Intern Med. 2002;136: Schmidt et al. Diabetes Care. 2005;28: Wilson et al. Arch Intern Med. 2007;167: Author, YearStudy PopulationMean Weight or BMISummary of Findings Colditz et al, 1995Nurses Health Study57 kgBMI, Weight gain Stern et al, 2002San Antonio Heart Study24-28 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


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