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
Diabetes
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.
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
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 A1C ≥ 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
(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
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
(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
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
(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/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
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)
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
*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
*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
*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
*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
*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
*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)*
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 and 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/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
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 L2-L3 level
provides
excellent
accuracy
for abdominal
fat mass
measured at
all intervertebral levels
(R2=85-96%)
Correlation coefficient (r)
Study and
reference
Method
Borkan et al
(n=8 M)
1
CT
Tokunaga et al
(n= 8 M)
2
3
Kvist et al
(n=17 M, 10 F)
Ross et al
(n=27 M)
Tot-VAT : SS-VAT
0.99
0.95-0.99
Umbilicus
0.99
---
CT
L3-L4
---
0.94-0.98
MRI
5
Abate et al
(n=49 M)
Tot-SAT : SS-SAT
CT
4
Armellini et al
(n=18 M, 72 F)
Location of single
slice
6, 4, 2 cm above,
at, and 2, 4, 6 cm
below umbilicus
CT
6
MRI
L4-L5
15 cm above L4L5
10 cm above L4L5 (corresponding
approximately to
L2-L3 level)
5 cm above L4-L5
L4-L5
5 cm below L4-L5
0.98-0.99
---
0.96
---
0.96
--0.97
---
0.97
0.95
0.89
T12-L1
---
0.95
L2-L3
L3-L4
L4-L5
-------
0.98
0.95
0.92
T12-L1
0.93
0.83
L1-L2
L2-L3
L3-L4
L4-L5
L5-S1
0.94
0.92
0.89
0.87
0.96
0.89
0.92
0.93
0.87
0.86
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
For model validation, we used 500 bootstrap samples to see how the beta coefficients, and
hence, odds ratios, changed across variations within our dataset. We used bootstrapping as
opposed to cross-validation, as the entire dataset is used for model development in the samples
and we wanted to preserve the inclusion of as many events as possible. Bootstrapping also
provides fairly unbiased estimates. The odds ratios changed minimally.
T2DM Model
Fasting Glucose
Family History
Systolic BP
Visceral fat
Fructosamine
Weight gain
Full sample
1.88 (1.38-2.56)
2.32 (1.25-4.29)
1.26 (1.07-1.48)
2.42 (1.59-3.68)
1.95 (1.43-2.67)
1.06 (1.02-1.10)
500 Bootstrap Replications
1.90 (1.31-2.77)
2.42 (1.24-4.73)
1.27 (1.06-1.52)
2.49 (1.62-3.84)
1.97 (1.38-2.82)
1.06 (1.02-1.11)
The c-statistic based on the original dataset was 0.845, while the bootstrap c-statistics derived
from fitting the bootstrapped equation to the original dataset on average was 0.835. Thus, the
diminution in c-statistics is small. Furthermore, we were only interested in determining
associations with incident diabetes and not constructing a risk prediction model.
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
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 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
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
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