1. 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

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.
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:
2. Despres JP. Circulation. 2012;126:

3. 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:

4. 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.

5. 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.

6. Obesity is Heterogeneous
Prediabetes
Diabetes
Prediabetes
Prediabetes
Diabetes

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.
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…

8. 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

9. 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

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

12. 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.

13. 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

14. 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

15. 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.

16. 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.

17. Results – Overall Cohort – Incident Diabetes
Multivariable analysis:
Variable
Odds Ratio (95% CI)
Χ2 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 diabetes
2.3 ( )
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

18. Results – Overall Cohort – Incident Diabetes
Multivariable analysis:
Variable
Odds Ratio (95% CI)
Χ2 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 diabetes
2.3 ( )
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

19. Results – Overall Cohort – Incident Diabetes
Multivariable analysis:
Variable
Odds Ratio (95% CI)
Χ2 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 diabetes
2.3 ( )
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

20. Results – Overall Cohort – Incident Diabetes
Multivariable analysis:
Variable
Odds Ratio (95% CI)
Χ2 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 diabetes
2.3 ( )
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

21. Results – Overall Cohort – Incident Diabetes
Multivariable analysis:
Variable
Odds Ratio (95% CI)
Χ2 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 diabetes
2.3 ( )
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

22. Results – Overall Cohort – Incident Diabetes
Multivariable analysis:
Variable
Odds Ratio (95% CI)
Χ2 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 diabetes
2.3 ( )
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

23. 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 ( )
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)*
10.8
Fructosamine (per 1 SD)*
1.4 ( )
10.2
Insulin (per 1 SD)*
1.3 ( )
6.1
Nonwhite race
1.8 ( )
5.2
Family history of diabetes
1.6 ( )
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

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

26. 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

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

29. Visceral Fat stratified by Subgroups

30. Study Population and Follow-Up

31. 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

32. 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%)

33. 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).

34. Model Validation

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

36. Visceral fat and Insulin Resistance are Additive

37. 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 ( )
0.01
Log WHR (per 1-SD)
1.4 ( )
3.0

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
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:

41. 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.

42. 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:
Wilson et al. Arch Intern Med. 2007;167: