Skip main navigation

Abdominal Visceral and Subcutaneous Adipose Tissue Compartments

Association With Metabolic Risk Factors in the Framingham Heart Study
Originally publishedhttps://doi.org/10.1161/CIRCULATIONAHA.106.675355Circulation. 2007;116:39–48

Abstract

Background— Visceral adipose tissue (VAT) compartments may confer increased metabolic risk. The incremental utility of measuring both visceral and subcutaneous abdominal adipose tissue (SAT) in association with metabolic risk factors and underlying heritability has not been well described in a population-based setting.

Methods and Results— Participants (n=3001) were drawn from the Framingham Heart Study (48% women; mean age, 50 years), were free of clinical cardiovascular disease, and underwent multidetector computed tomography assessment of SAT and VAT volumes between 2002 and 2005. Metabolic risk factors were examined in relation to increments of SAT and VAT after multivariable adjustment. Heritability was calculated using variance-components analysis. Among both women and men, SAT and VAT were significantly associated with blood pressure, fasting plasma glucose, triglycerides, and high-density lipoprotein cholesterol and with increased odds of hypertension, impaired fasting glucose, diabetes mellitus, and metabolic syndrome (P range <0.01). In women, relations between VAT and risk factors were consistently stronger than in men. However, VAT was more strongly correlated with most metabolic risk factors than was SAT. For example, among women and men, both SAT and VAT were associated with increased odds of metabolic syndrome. In women, the odds ratio (OR) of metabolic syndrome per 1–standard deviation increase in VAT (OR, 4.7) was stronger than that for SAT (OR, 3.0; P for difference between SAT and VAT <0.0001); similar differences were noted for men (OR for VAT, 4.2; OR for SAT, 2.5). Furthermore, VAT but not SAT contributed significantly to risk factor variation after adjustment for body mass index and waist circumference (P ≤0.01). Among overweight and obese individuals, the prevalence of hypertension, impaired fasting glucose, and metabolic syndrome increased linearly and significantly across increasing VAT quartiles. Heritability values for SAT and VAT were 57% and 36%, respectively.

Conclusions— Although both SAT and VAT are correlated with metabolic risk factors, VAT remains more strongly associated with an adverse metabolic risk profile even after accounting for standard anthropometric indexes. Our findings are consistent with the hypothesized role of visceral fat as a unique, pathogenic fat depot. Measurement of VAT may provide a more complete understanding of metabolic risk associated with variation in fat distribution.

Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in the United States, affecting roughly 70 million people and accounting for nearly 1 million deaths per year.1 Improvements in CVD risk factor profiles have led to significant reductions in death from CVD over the past 50 years,2 but recent data suggest that the increasing prevalence of obesity may have slowed this rate of decline.3 Rates of overweight and obesity continue to increase,4–6 which suggests that the full impact of the obesity epidemic has yet to be realized.

Clinical Perspective p 48

Obesity, defined by a body mass index (BMI) of at least 30 kg/m2, is a risk factor for multiple CVD risk factors, including hypertension, dyslipidemia, diabetes, and the metabolic syndrome (MetS). BMI is a useful indicator of overall adiposity, and recent National Health and Nutrition Examination Survey data demonstrate that two thirds of the US population is either overweight or obese.6 However, different fat compartments may be associated with differential metabolic risk.7 In particular, the visceral adipose tissue (VAT) compartment may be a unique pathogenic fat depot.8–10 VAT has been termed an endocrine organ, in part because it secretes adipocytokines and other vasoactive substances that can influence the risk of developing metabolic traits.10–16

Waist circumference (WC) is an easily obtainable but imprecise measure of abdominal adiposity17 because it is a function of both the subcutaneous adipose tissue (SAT) and VAT compartments. Therefore, assessment of VAT requires imaging with radiographic techniques such as computed tomography (CT) or magnetic resonance imaging (MRI). Available studies report relations of greater SAT and VAT with a higher prevalence of impaired fasting glucose,9,18 diabetes,9,10,19 insulin resistance,9,20,21 hypertension,22–24 lipids,25–29 MetS,30–32 and risk factor clustering.26 However, current imaging studies evaluating SAT and VAT are limited to small, referral-based samples often enriched for adiposity-related traits.23,25–27,32–34 Furthermore, study samples have often been limited to either women or men, precluding the study of sex differences.22,23,25–28,34–36 Some studies have focused on Japanese Americans or Southeast Asians,19,22,26,29,35,37 ethnic groups with more visceral fat than expected for a given overall BMI.38 We recently demonstrated that multidetector CT permits highly reproducible volumetric measurements of both SAT and VAT.39 In addition, this initial observation suggested that volumetric fat measurements, as opposed to previous studies using single-slice methodology, can accurately characterize the heterogeneity of abdominal fat distribution between individuals and the differences in fat distribution with age and between women and men.

Thus, our aims in a community-based sample of women and men free of CVD across the spectrum of BMI were to assess whether the volume of SAT and VAT are associated with metabolic risk factors cross-sectionally, to assess whether VAT is more strongly associated with metabolic risk factors than is SAT, and to determine whether sophisticated volumetric imaging methods of SAT and VAT provide information about metabolic risk other than that offered by simpler measures such as BMI and WC.

Methods

Study Sample

Participants for this study were drawn from the Framingham Heart Study Multidetector Computed Tomography Study, a population-based substudy of the community-based Framingham Heart Study Offspring and Third-Generation Study cohorts. Beginning in 1948, 5209 men and women 28 to 62 years of age were enrolled in the original cohort of the Framingham Heart Study. The offspring and spouses of the offspring of the original cohort were enrolled in the Offspring Study starting in 1971. Selection criteria and study design have been described elsewhere.40,41 Beginning in 2002, 4095 Third Generation Study participants, who had at least 1 parent in the offspring cohort, were enrolled in the Framingham Heart Study and underwent standard clinic examinations. The standard clinic examination included a physician interview, a physical examination, and laboratory tests. For the present study, the study sample consisted of Offspring and Third Generation Study participants who were part of the multidetector CT substudy.

Between June 2002 and April 2005, 3529 participants (2111 third generation, 1418 offspring participants) underwent multidetector CT assessment of coronary and aortic calcium. Inclusion in this study was weighted toward participants from larger Framingham Heart Study families and those who resided in the greater New England area. Overall, 755 families were included in our analysis. Men had to be ≥35 years of age; women had to be ≥40 years of age and not pregnant; and all participants had to weigh <350 pounds. Of the participants, 433 (222 offspring and 211 third generation) were imaged as participants in an ancillary study using an identical imaging protocol, the National Heart, Lung, and Blood’s Family Heart Study.42 Of the total 3529 subjects imaged, 3394 had interpretable CT measures; of those, 3329 had both SAT and VAT measured; of those, 3124 of them were free of CVD; of those, 3102 attended a contemporaneous examination; and of those, 3001 had a complete covariate profile. Thus, the overall sample size for analysis is 3001.

The study was approved by the institutional review boards of the Boston University Medical Center and Massachusetts General Hospital. All subjects provided written informed consent.

Volumetric Adipose Tissue Imaging

Subjects underwent 8-slice multidetector CT imaging of the abdomen in a supine position as previously described (LightSpeed Ultra, General Electric, Milwaukee, Wis).39 Briefly, 25 contiguous 5-mm-thick slices (120 kVp; 400 mA; gantry rotation time, 500 ms; table feed, 3:1) were acquired covering 125 mm above the level of S1.

Abdominal Adipose Tissue Measurements

SAT and VAT volumes were assessed (Aquarius 3D Workstation, TeraRecon Inc, San Mateo, Calif). To identify pixels containing fat, an image display window width of −195 to −45 Hounsfield units (HU) and a window center of −120 HU were used. The abdominal muscular wall separating the visceral from the subcutaneous compartment was traced manually. Interreader reproducibility was assessed by 2 independent readers measuring VAT and SAT on a subset of 100 randomly selected participants.39 Interclass correlations for interreader comparisons were 0.992 for VAT and 0.997 for SAT. Similarly high correlations were noted for intrareader comparisons.

Risk Factor and Covariate Assessment

Risk factors and covariates were measured at the contemporaneous examination. BMI, defined as weight (in kilograms) divided by the square of height (in meters), was measured at each index examination. WC was measured at the level of the umbilicus. Fasting plasma glucose, total and high-density lipoprotein (HDL) cholesterol, and triglycerides were measured on fasting morning samples. Diabetes was defined as a fasting plasma glucose level ≥126 mg/dL at a Framingham examination or treatment with either insulin or a hypoglycemic agent. Participants were considered current smokers if they had smoked at least 1 cigarette per day for the previous year. Assessed through a series of physician-administered questions, alcohol use was dichotomized on the basis of consumption of >14 drinks per week (in men) or 7 drinks per week (in women). Physical activity, assessed with a questionnaire, is a score based on the average daily number of hours of sleep and sedentary, slight, moderate, and heavy activity of the participant. Women were considered menopausal if their periods had stopped for ≥1 year. Impaired fasting glucose was defined as a fasting plasma glucose level of 100 to 125 mg/dL among those not treated for diabetes. Hypertension was defined as systolic blood pressure ≥140 mm Hg, diastolic blood pressure ≥90 mm Hg, or on treatment. MetS was defined from modified Adult Treatment Panel criteria.43

Statistical Analysis

SAT and VAT were normally distributed. Sex-specific age-adjusted Pearson correlation coefficients were used to assess simple correlations between SAT and VAT and metabolic risk factors. Multivariable linear and logistic regression was used to assess the significance of covariate-adjusted cross-sectional relations between continuous and dichotomous metabolic risk factors and SAT and VAT. For continuous risk factors, the covariate-adjusted average change in risk factor per 1–standard deviation (SD) increase in adipose tissue was estimated; for dichotomous risk factors, the change in odds of the risk factor prevalence per 1-SD increase in adipose tissue was estimated. All models were sex specific to account for the strong sex interactions observed. Covariates in all models included age, smoking (3-level variable: current/former/never smoker), physical activity, alcohol use (dichotomized at >14 drinks per week in men or >7 drinks per week in women), menopausal status, and hormone replacement therapy. In addition, lipid treatment, hypertension treatment, and diabetes treatment were included as covariates in models for HDL cholesterol, log triglycerides, systolic and diastolic blood pressures, and fasting plasma glucose, respectively. R2 values were computed for continuous models and c statistics were computed for dichotomous models to assess the relative contribution of SAT and VAT to explain the outcomes (risk factors). For each risk factor, tests for the significance of the difference between the SAT and VAT regression coefficients were carried out within a multivariate standardized regression (in which variables were first standardized to a mean of 0 and an SD of 1) to assess the relative importance of each adipose tissue measurement in predicting the risk factor. To assess the incremental utility of adding VAT to models that contain BMI or WC, the above multivariate analyses were repeated for VAT with BMI and WC added as covariates in the multivariate regression models. Similar models were not examined for SAT because models with SAT alone did not yield higher R2 or c statistics than models that included BMI and WC alone. As a secondary analysis, the above multivariate regressions were rerun using the general estimating equation linear and logistic regression44 to account for correlations among related individuals (siblings) in the study sample. SAS version 8.0 was used to perform all computations; a 2-tailed value of P<0.05 was considered significant.44

Heritability Analysis

Heritability quantifies the proportion of trait variability resulting from the additive effect of genes; the contributions of both genes and early common environment cannot be differentiated. Heritability calculations rely on the assumption that trait variation can be partitioned into genetic, known covariates and environmental (unknown) components. It is further assumed that the genetic component is polygenic; there is no variation attributable to dominance components. To determine the heritability of SAT and VAT, we created sex-specific and cohort-specific residuals from multivariable regression after adjusting for age, age squared, smoking, and menopausal status using the overall sample with interpretable CT scans. Residuals were then pooled, and SOLAR45 was used to calculate heritabilities using variance-components analysis.

The authors had full access to and take full responsibility for the integrity of the data. All authors have read and agree to the manuscript as written.

Results

Overall, 1452 women and 1549 men were available for analysis. The mean age was 50 years (Table 1); approximately one quarter of the sample was hypertensive, 5% had diabetes, and approximately one third had MetS. Approximately half of the women were postmenopausal.

TABLE 1. Clinical Characteristics of Study Participants Free of Clinical CVD Who Underwent Radiographic Assessment of SAT and VAT Volumes

Women (n=1452)Men (n=1549)
Data are presented as mean±SD when appropriate.
*Median (25th, 75th percentiles).
†Fasting plasma glucose of 100 to 125 mg/dL; percentage is based on those without diabetes.
‡Defined as >14 drinks per week (men) or >7 drinks per week (women).
Age, y51±949±10
BMI, kg/m226.7±5.428.3±4.4
WC, cm92±14103±11
Triglycerides,* mg/dL92 (65, 134)113 (76, 171)
HDL cholesterol, mg/dL62±1746±12
Total cholesterol, mg/dL197±36196±34
Systolic blood pressure, mm Hg119±17123±14
Diastolic blood pressure, mm Hg73±978±9
Hypertension, %2428
Fasting plasma glucose, mg/dL95±16101±20
Impaired fasting glucose, %1838
Diabetes mellitus, %4.26.1
MetS, %2535
Smoking, %
    Current1213
    Former4234
    Never4653
Postmenopausal, %48
Hormone replacement therapy, %23
Alcohol use, %1516
SAT, cm33071±14442603±1187
VAT, cm31306±8072159±967

The mean SAT volume was 3071±1444 cm3 in women and 2603±1187 cm3 in men. The mean VAT volume in women was 1306±807 cm3 and in men was 2159±967 cm3.

Correlations With SAT and VAT

Correlations of SAT and VAT with metabolic risk factors are shown in Table 2. SAT was positively correlated with age in women (r=0.13, P<0.001) but not men, and VAT was positively correlated with age in both sexes (r=0.36 in women and men, P<0.001). SAT and VAT were highly correlated, with an age-adjusted correlation coefficient between SAT and VAT of 0.71 (P<0.0001) in women and 0.58 (P<0.0001) in men. Both BMI and WC were strongly correlated with SAT and VAT after adjustment for age (Table 2). All risk factors were highly correlated with both SAT and VAT, except for serum total cholesterol with SAT in men and physical activity index with VAT in men.

TABLE 2. Age-Adjusted Pearson Correlation Coefficients Between Metabolic Risk Factors and SAT and VAT Volumes

WomenMen
SATVATSATVAT
*P<0.01;
P<0.001.
Age0.130.360.030.36
BMI0.880.750.830.71
WC0.870.780.880.73
Log triglycerides0.310.460.180.37
HDL cholesterol−0.25−0.35−0.17−0.33
Total cholesterol0.110.150.020.08*
Systolic blood pressure0.260.300.180.24
Diastolic blood pressure0.260.280.210.27
Blood glucose0.230.340.120.19
Physical activity index−0.14−0.09*−0.08*−0.03

Multivariable-Adjusted Regressions With SAT, VAT, and Metabolic Risk Factor Variables

Results of multivariable-adjusted general linear regression analyses for SAT and VAT for both continuous and dichotomous metabolic risk factors are shown in Table 3. In women, per 1-SD increase in SAT, systolic blood pressure increased on average 3.9±0.4 mm Hg (±1 SE), whereas VAT was 4.8±0.4 mm Hg higher. For systolic blood pressure in women, the difference between the magnitude of effect of the SAT versus VAT was not significant (P=0.10; Table 3). In men, the magnitude of the association of the average systolic blood pressure increase per 1-SD increase in VAT was larger than for SAT (3.3 versus 2.3 mm Hg, respectively; P=0.01 for difference in the regression coefficients between SAT and VAT). Similar results were obtained for diastolic blood pressure.

TABLE 3. Sex-Specific Multivariable-Adjusted*Regressions for SAT and VAT With Continuous Metabolic Risk Factors (Top) and Dichotomous Risk Factors (Bottom)

WomenMen
MV-Adjusted Residual Effect SizeP for Either SAT or VATP for SAT vs VATResidual Effect Size After MV/BMI/WC AdjustmentMV-Adjusted Residual Effect SizeP for Either SAT or VATP for SAT vs VATResidual Effect Size After MV/BMI/WC AdjustmentP for Sex Interaction
MV indicates multivariable; SBP, systolic blood pressure; DBP, diastolic blood pressure; FPG, fasting plasma glucose; TG, triglycerides; HTN, hypertension; IFG, impaired fasting glucose; and DM, diastolic mellitus. Data presented include effect size (the average change in risk factor±SE) per 1 SD in adipose tissue for continuous data, and the change in odds of the condition per 1 SD of adipose tissue with 95% CIs for dichotomous data.
*Adjusted for age, smoking, alcohol use, physical activity, and menopausal status (women only), hormone replacement therapy (women only); for blood pressure, FPG, HDL cholesterol, and log triglycerides, an additional covariate of treatment for HTN, diabetes, or lipid disorders, respectively, was included.
†For SAT or VAT in the model.
‡For SAT vs VAT difference.
SBP
    SAT3.9±0.4<0.00012.3±0.3<0.00010.01
    VAT4.8±0.4<0.00010.102.5±0.73.3±0.4<0.00010.011.8±0.05<0.0001
DBP
    SAT2.2±0.2<0.00011.9±0.2<0.00010.77
    VAT2.6±0.3<0.00010.331.4±0.42.6±0.2<0.00010.0081.5±0.30.01
FPG
    SAT3.4±0.3<0.00011.6±0.40.00020.03
    VAT4.8±0.4<0.0001<0.00013.4±0.63.1±0.5<0.00010.0011.8±0.7<0.0001
Log TG
    SAT0.14±0.01<0.00010.10±0.010.0030.16
    VAT0.23±0.01<0.0001<0.00010.19±0.020.22±0.01<0.0001<0.00010.22±0.020.0002
HDL
    SAT−3.9±0.4<0.0001−2.0±0.3<0.00010.006
    VAT−5.9±0.4<0.0001<0.0001−4.5±0.7−4.5±0.3<0.0001<0.0001−3.8±0.5<0.0001
HTN
    SAT1.7 (1.5–2.0)<0.00011.5 (1.4–1.7)<0.00010.89
    VAT2.1 (1.8–2.4)<0.0001<0.00011.6 (1.3–2.0)1.9 (1.6–2.1)<0.00010.0061.6 (1.3–1.9)0.01
IFG
    SAT2.0 (1.7–2.3)<0.00011.5 (1.3–1.7)<0.00010.04
    VAT2.5 (2.1–2.9)<0.00010.0012.1 (1.7–2.6)1.8 (1.6–2.0)<0.00010.0051.5 (1.2–1.8)<0.0001
DM
    SAT1.6 (1.2–2.0)0.0071.6 (1.3–1.9)<0.00010.27
    VAT2.1 (1.6–2.6)<0.00010.00031.9 (1.3–2.7)1.6 (1.3–2.0)<0.00010.910.9 (0.7–1.3)0.03
MetS
    SAT3.0 (2.6–3.5)<0.00012.5 (2.2–2.8)<0.00010.77
    VAT4.7 (3.9–5.7)<0.0001<0.00011.9 (1.3–2.7)4.2 (3.5–5.0)<0.0001<0.00012.6 (2.1–3.2)0.002

In women and men, the association of both SAT and VAT with continuous measures of metabolic risk factors was highly significant. For fasting plasma glucose, the effect of VAT was stronger than that of SAT (P<0.0001 for difference in women, P=0.001 in men). Strong and significant results for log triglycerides and HDL cholesterol followed similar patterns (Table 3).

Highly significant associations with SAT and VAT also were noted for dichotomous risk factor variables. Among women and men, both SAT and VAT were associated with an increased odds of hypertension (Table 3). In women, the odds ratio of hypertension per 1-SD increase in VAT (odds ratio, 2.1) was stronger than that for SAT (odds ratio, 1.7; P=0.001 for difference between SAT and VAT); similar differences were noted for men. Similar highly significant differences also were noted for impaired fasting glucose, diabetes, and MetS and are presented in Table 3.

The magnitude of association between VAT and all risk factors examined was consistently greater for women than for men (Table 3). Weaker sex differences were observed for SAT.

Residual Effect of VAT in Multivariable Models That Contain BMI and WC

To address whether radiographic imaging of abdominal adipose tissue explains variation in metabolic risk factors over and above the contribution of BMI and WC, we examined the residual effect size of each metabolic risk factor from multivariable models that additionally contained BMI and WC. Because models with BMI and WC routinely yielded higher R2 or c statistic than models with SAT (Table I in the online-only Data Supplement), the addition of all 3 variables into one model was not pursued. For example, in women, SAT plus covariates were associated with 21% of the variation in log triglycerides (R2=0.21), VAT plus covariates were associated with 30% of the variation in log triglycerides, and both BMI and WC plus the covariates were associated with 26% of the variation in triglycerides. Models with VAT, BMI, and WC demonstrated significant additional contribution of VAT for all variables except diabetes in men. Statistically significant residual effect sizes for VAT were observed for all metabolic risk factors except diabetes in men (Table 3).

Risk Factor Distribution Based on Quartiles of VAT

Because VAT adds to risk factor variation above and beyond BMI and WC, we assessed the impact of stratifying individuals by VAT quartile within clinically defined categories of BMI (normal weight, BMI <25 kg/m2; overweight, BMI of 25 to 29.9 kg/m2; and obese, BMI ≥30 kg/m2). Thirty-three percent of the sample was normal weight, 41% was overweight, and 26% was obese. Among normal-weight, overweight, and obese individuals, there was a highly statistically significant stepwise linear increase in the prevalence of the MetS across quartiles of VAT in both women and men (see the Figure) after adjustment for age and BMI; similar relations were noted for additional risk factors, including hypertension and impaired fasting glucose.

Prevalence of hypertension (HTN), impaired fasting glucose (IFG), diabetes (DM), and MetS among normal-weight (A), overweight (B), and obese (C) individuals. Probability values represent those for linear trend across VAT quartiles and are adjusted for age and BMI.

Secondary Analysis

When education status was included as an additional covariate, relations between SAT and VAT and the continuous and dichotomous metabolic risk factors were not materially different (data not shown). When analyses were rerun using the general estimating equation procedure, the resulting probability values were not substantively changed from those discussed above (data not shown).

Heritability Analysis

Heritability for SAT was 57%, whereas heritability for VAT was 36%.

Discussion

In our community-based sample, volumetric CT measures of both SAT and VAT were correlated with multiple metabolic risk factors, although risk factor correlations with VAT were consistently significantly stronger than those for SAT. VAT, not SAT, provided information above and beyond simple clinical anthropometrics, including BMI and WC, and consistently provided differences in risk factor stratification among individuals who were overweight and obese. VAT was more strongly associated with metabolic risk factors in women than in men. Finally, both SAT and VAT are heritable traits.

VAT has traditionally been considered the more pathogenic adipose tissue compartment compared with SAT, but data confirming these relations using high-resolution volumetric imaging assessments in large, community-based samples of women and men have been lacking. The mechanism of increased metabolic risk is hypothesized to be related to the metabolically active adipose tissue found in the visceral region,7–16 in addition to the drainage of these substances directly into the portal circulation.46 Multiple small studies have demonstrated that the visceral fat compartment is metabolically active, secreting such vasoactive substances as inflammatory markers,15,47 adipocytokines,10,13,48,49 markers of hemostasis and fibrinolysis (including plasminogen activator inhibitor-1),50,51 and growth factors (including vascular endothelial growth factor),52 which may contribute to its role in cardiometabolic risk factor manifestation.53–55

Our results are consistent with these prior findings in small studies and extend these findings to a well-characterized, community-based sample of men and women in that we show that all cardiometabolic risk factors examined were more strongly associated with VAT than SAT. We also extend the current literature to note statistically significant, albeit weaker, correlations with SAT. Although SAT and VAT are highly correlated with each other, we used the R2 (for continuous variables) and c statistic (for dichotomous variables) to determine the total variance explained by SAT and VAT. We also performed a formal test of the difference in the β-coefficients for SAT compared with VAT in relation to the outcome variables, and in nearly every situation, the β-coefficient from the regression model was stronger for VAT than for SAT. Of note, SAT volume is greater than VAT volume in both women and men. Therefore, although the effect sizes between VAT and risk factors may be higher, it is possible that SAT volume actually contributes to more absolute risk.

Of interest are recent findings from the Dallas Heart Study, which examined metabolic risk factors relations in 1934 black and white women and men with SAT and VAT as assessed by MRI.56 An important difference between our study and the Dallas Heart Study is the inclusion of percent body fat in regression models. Given that we do not have a measure of percent body fat, our findings are not directly comparable. Nonetheless, the results of Vega et al56 also show considerably higher R2 for models that include VAT than for SAT, particularly among white participants, and increased R2 for models that include VAT above and beyond percent body fat and WC.

Our results show that both SAT and VAT are associated positively with prevalence of hypertension, but only VAT provides significant information above and beyond BMI and WC. Other studies have demonstrated relations between VAT and hypertension.22–24,57 Among Japanese Americans and whites, VAT but not SAT was associated with hypertension, even after adjustment for BMI and WC,22,57 whereas among blacks, both SAT and VAT were associated with hypertension in men and women,24 underscoring the relative importance of fat depots among different ethnic groups.38

We also found that both SAT and VAT were associated with triglycerides and HDL cholesterol in women and men. Our results build on those of others, which confirm the known association between VAT and lipids.25,27–29 However, we extend these findings to include strong and significant relations of SAT with HDL cholesterol and triglycerides. The primary difference with prior studies may be our large sample size in a community-based cohort compared with the few other studies that were adequately powered to compare the difference between SAT and VAT.25,29

Similarly, for impaired fasting glucose and diabetes, multiple prior studies have demonstrated relations between VAT and prediabetic hyperglycemia and diabetes,9,10,18,19 but few have yielded significant relations for SAT. However, SAT has been shown in multiple studies to be more strongly associated with insulin resistance than is VAT; this has been reviewed previously.58 Some studies of insulin resistance have demonstrated stronger correlations with SAT than with VAT,20 especially in women. In the Insulin Resistance Atherosclerosis Study (IRAS), both SAT and VAT were important correlates of insulin resistance.21 One small study of 47 women and men demonstrated equivalent correlations of deep SAT and VAT with respect to cardiometabolic risk factors.59

Although our results show that VAT is more highly correlated with MetS than is SAT, SAT was an important correlate of the MetS. These findings are in contrast to prior studies, which have reported that SAT is only weakly associated with MetS. For example, in the Health, Aging, and Body Composition (Health ABC) Study, SAT was associated only with MetS in normal-weight and overweight men31; however, unlike our study, the Health ABC Study focused primarily on older individuals.32 Therefore, SAT may be an important adipose tissue compartment that should not be overlooked. Only 1 very small intervention study has been conducted to date to examine the relation of SAT reduction with metabolic variables: In a small study of 15 women who underwent large-volume liposuction, despite the loss of nearly 10 kg subcutaneous fat, improvements in cardiometabolic risk factors were not observed.60 However, the small sample size, associated low power, and inclusion of morbidly obese study participants make it difficult to rule out a beneficial effect.

The strong correlation between SAT and cardiometabolic risk factors may be driven by the results from some20,21,58 but not all37,61 studies that have shown that insulin sensitivity is related to SAT and VAT. In addition to insulin resistance, relations between specific fat depots and adipocytokines may be responsible for mediating the relations with risk factors. In particular, leptin has been shown to be equally, if not more, correlated to SAT compared with VAT.62 Leptin also has been implicated in vascular dysfunction,63–66 which suggests another potential mechanism whereby SAT may be associated with cardiometabolic risk factors.

Despite the statistically significant results observed with both SAT and VAT, only VAT provided information above and beyond BMI and WC, suggesting that VAT may be a unique pathogenic fat depot. Similar findings have been noted among Japanese Americans, for whom VAT but not SAT was associated with hypertension, even after adjustment for BMI and WC.22,57 Unfortunately, we were unable to analyze SAT in the same models as BMI and WC because of the high collinearity between the variables. In fact, the R2 of SAT versus BMI/WC is much higher for SAT than for VAT (0.76 versus 0.54 for men, 0.81 versus 0.64 for women).

Sex Differences

In our study, we found evidence for sex interactions in that increasing volumes of SAT and of VAT were consistently and more strongly associated with more adverse risk factors levels in women than in men. To the best of our knowledge, our findings are the most comprehensive examination of sex differences reported to date. In the Health ABC Study, a significant sex interaction was observed between VAT and diabetes.10 Among women and men from the Quebec Family Study and the Health, Risk factors, Exercise Training, and Genetics (HERITAGE) Family Study, only in women were higher amounts of VAT associated with adverse CVD risk factor profiles.67 The cause of these sex differences is uncertain but may be related to the higher amount of hepatic free fatty acid delivery derived from lipolysis from VAT that has been observed in women than in men.16

Heritability

We demonstrate moderate to high heritability for VAT and SAT, respectively, indicating that a significant portion of the variability in these traits is familial. Two prior studies that investigated the heritability of intra-abdominal fat depots68,69 have found estimates for VAT ranging from 42% to 56% and estimates for SAT of 42%. Differences between our findings and prior published work may be due to the inclusion of younger participants with lower mean BMI, exclusion of certain metabolic conditions, and inclusion in a fitness study, which may have biased estimates. Overall, these findings suggest that a significant proportion of variability in VAT and SAT may be due to genetic causes. Further research is warranted to explore this further, including uncovering genomic regions of linkage and novel candidate genes.

Implications

The relation of MetS and its components with increasing VAT quartiles, particularly in overweight and obese individuals, suggests that VAT in particular may confer increasing risk within clinically defined categories of body weight. Two thirds of our study sample were either overweight or obese, statistics that mirror national data.6 Our data suggest that further observational and possibly interventional studies are warranted to test the impact of weight reduction and, in particular, the reduction of VAT on metabolic risk factors and overall CVD risk. Additionally, our work demonstrates strong and significant results for SAT and VAT in relation to cardiometabolic risk factors, suggesting that SAT should not be overlooked in regional adipose tissue research. Nonetheless, we note that the proportion of overall variation of VAT and of SAT with metabolic risk factors is moderate at best. This finding, which has been observed previously,56 suggests that other factors not accounted for in this study may be responsible for the variation in metabolic risk factors. In fact, many of these traits have a substantial heritable component, with reported heritabilities for glucose being 34% to 51%70; for systolic blood pressure, 53%71; and for total cholesterol, 40%.72 Therefore, the potential role of gene–adiposity interactions should be considered in future research.

Strengths and Limitations

Strengths of our study include the use of a community-based sample with participants not enriched for adiposity-related traits. Routine screening of metabolic risk factors was performed, and adjustment was made for several potential confounders. We used a highly reproducible volumetric method of SAT and VAT assessment, which accounts for heterogeneity of fat distribution throughout the abdomen. We were able to assess the role of SAT and VAT above and beyond clinical anthropometry. Our study participants were primarily middle-aged, allowing assessment of relations between fat compartments and risk factors in the absence of significant comorbidity. Lastly, we have a large sample with adequate power to detect potentially smaller but significant relations with SAT. Limitations include the cross-sectional design; because the associations are not prospective, causality cannot be inferred. Because the Framingham Offspring Study is primarily a white sample, generalizability to other ethnic groups is uncertain. For example, Japanese Americans and Southeast Asians have groups with more visceral fat than expected for a given overall BMI,38 whereas blacks have less visceral fat than do whites for a given BMI.73 Although we accounted for sibling–sibling correlations, current analytical methods did not allow us to account for all familial relations. Because we did not subdivide subcutaneous fat into superficial and deep compartments, we cannot comment on the relative importance of these compartments. Furthermore, we measured only abdominal, not truncal, SAT. Truncal SAT has been shown to be more correlated to insulin resistance than is abdominal SAT in men.58,74 Finally, we had only radiographic CT measures of intra-abdominal fat, not other techniques such as MRI or ultrasound. MRI may provide a better resolution of fat depots, and ultrasound may be a reasonable alternative to CT75 and is less invasive. Neither MRI nor ultrasound places patients at risk of radiation exposure, but MRI is limited by its expense and amount of time needed to perform the actual scan.

Conclusions

Both SAT and VAT are associated with an adverse metabolic risk profile. However, only VAT provides information above and beyond easily obtainable clinical anthropometric measurements. Measurement of VAT may provide a more complete understanding of metabolic risk, and further studies are warranted to prospectively assess the impact of the lowering of VAT and SAT on the incidence of MetS and CVD.

Sources of Funding

This work was supported by the National Heart, Lung and Blood Institute’s Framingham Heart Study (N01-HC-25195). Dr Meigs is supported by an American Diabetes Association Career Development Award. Dr Vasan is supported in part by 2K24HL04334 (National Heart, Lung, and Blood Institute, National Institutes of Health).

Disclosures

Dr Meigs has been the recipient of research grants from GlaxoSmithKline, Pfizer, and Wyeth and has served on Advisory Boards for GlaxoSmithKline, Merck, Pfizer, and Lilly. The other authors report no conflicts.

Footnotes

Correspondence to Caroline S. Fox, MD, MPH, 73 Mt Wayte Ave, Ste 2, Framingham, MA 01702. E-mail

References

  • 1 Thom T, Haase N, Rosamond W, Howard VJ, Rumsfeld J, Manolio T, Zheng ZJ, Flegal K, O’Donnell C, Kittner S, Lloyd-Jones D, Goff DC Jr, Hong Y, Adams R, Friday G, Furie K, Gorelick P, Kissela B, Marler J, Meigs J, Roger V, Sidney S, Sorlie P, Steinberger J, Wasserthiel-Smoller S, Wilson M, Wolf P; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics–2006 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation. 2006; 113: e85–e151.LinkGoogle Scholar
  • 2 Fox CS, Evans JC, Larson MG, Kannel WB, Levy D. Temporal trends in coronary heart disease mortality and sudden cardiac death from 1950 to 1999: the Framingham Heart Study. Circulation. 2004; 110: 522–527.LinkGoogle Scholar
  • 3 Hu FB, Stampfer MJ, Manson JE, Grodstein F, Colditz GA, Speizer FE, Willett WC. Trends in the incidence of coronary heart disease and changes in diet and lifestyle in women. N Engl J Med. 2000; 343: 530–537.CrossrefMedlineGoogle Scholar
  • 4 Flegal KM, Carroll MD, Kuczmarski RJ, Johnson CL. Overweight and obesity in the United States: prevalence and trends, 1960–1994. Int J Obes Relat Metab Disord. 1998; 22: 39–47.CrossrefMedlineGoogle Scholar
  • 5 Flegal KM, Carroll MD, Ogden CL, Johnson CL. Prevalence and trends in obesity among US adults, 1999–2000. JAMA. 2002; 288: 1723–1727.CrossrefMedlineGoogle Scholar
  • 6 Ogden CL, Carroll MD, Curtin LR, McDowell MA, Tabak CJ, Flegal KM. Prevalence of overweight and obesity in the United States, 1999–2004. JAMA. 2006; 295: 1549–1555.CrossrefMedlineGoogle Scholar
  • 7 Poirier P, Despres JP. Waist circumference, visceral obesity, and cardiovascular risk. J Cardiopulm Rehabil. 2003; 23: 161–169.CrossrefMedlineGoogle Scholar
  • 8 Klein S. The case of visceral fat: argument for the defense. J Clin Invest. 2004; 113: 1530–1532.CrossrefMedlineGoogle Scholar
  • 9 Goodpaster BH, Krishnaswami S, Resnick H, Kelley DE, Haggerty C, Harris TB, Schwartz AV, Kritchevsky S, Newman AB. Association between regional adipose tissue distribution and both type 2 diabetes and impaired glucose tolerance in elderly men and women. Diabetes Care. 2003; 26: 372–379.CrossrefMedlineGoogle Scholar
  • 10 Kanaya AM, Harris T, Goodpaster BH, Tylavsky F, Cummings SR. Adipocytokines attenuate the association between visceral adiposity and diabetes in older adults. Diabetes Care. 2004; 27: 1375–1380.CrossrefMedlineGoogle Scholar
  • 11 Matsuzawa Y. Therapy insight: adipocytokines in metabolic syndrome and related cardiovascular disease. Nat Clin Pract Cardiovasc Med. 2006; 3: 35–42.CrossrefMedlineGoogle Scholar
  • 12 Wajchenberg BL. Subcutaneous and visceral adipose tissue: their relation to the metabolic syndrome. Endocr Rev. 2000; 21: 697–738.CrossrefMedlineGoogle Scholar
  • 13 Yatagai T, Nagasaka S, Taniguchi A, Fukushima M, Nakamura T, Kuroe A, Nakai Y, Ishibashi S. Hypoadiponectinemia is associated with visceral fat accumulation and insulin resistance in Japanese men with type 2 diabetes mellitus. Metabolism. 2003; 52: 1274–1278.CrossrefMedlineGoogle Scholar
  • 14 Bacha F, Saad R, Gungor N, Arslanian SA. Adiponectin in youth: relationship to visceral adiposity, insulin sensitivity, and beta-cell function. Diabetes Care. 2004; 27: 547–552.CrossrefMedlineGoogle Scholar
  • 15 Saijo Y, Kiyota N, Kawasaki Y, Miyazaki Y, Kashimura J, Fukuda M, Kishi R. Relationship between C-reactive protein and visceral adipose tissue in healthy Japanese subjects. Diabetes Obes Metab. 2004; 6: 249–258.CrossrefMedlineGoogle Scholar
  • 16 Nielsen S, Guo Z, Johnson CM, Hensrud DD, Jensen MD. Splanchnic lipolysis in human obesity. J Clin Invest. 2004; 113: 1582–1588.CrossrefMedlineGoogle Scholar
  • 17 Kuk JL, Lee S, Heymsfield SB, Ross R. Waist circumference and abdominal adipose tissue distribution: influence of age and sex. Am J Clin Nutr. 2005; 81: 1330–1334.CrossrefMedlineGoogle Scholar
  • 18 Hayashi T, Boyko EJ, Leonetti DL, McNeely MJ, Newell-Morris L, Kahn SE, Fujimoto WY. Visceral adiposity and the risk of impaired glucose tolerance: a prospective study among Japanese Americans. Diabetes Care. 2003; 26: 650–655.CrossrefMedlineGoogle Scholar
  • 19 Boyko EJ, Fujimoto WY, Leonetti DL, Newell-Morris L. Visceral adiposity and risk of type 2 diabetes: a prospective study among Japanese Americans. Diabetes Care. 2000; 23: 465–471.CrossrefMedlineGoogle Scholar
  • 20 Tulloch-Reid MK, Hanson RL, Sebring NG, Reynolds JC, Premkumar A, Genovese DJ, Sumner AE. Both subcutaneous and visceral adipose tissue correlate highly with insulin resistance in African Americans. Obes Res. 2004; 12: 1352–1359.CrossrefMedlineGoogle Scholar
  • 21 Wagenknecht LE, Langefeld CD, Scherzinger AL, Norris JM, Haffner SM, Saad MF, Bergman RN. Insulin sensitivity, insulin secretion, and abdominal fat: the Insulin Resistance Atherosclerosis Study (IRAS) Family Study. Diabetes. 2003; 52: 2490–2496.CrossrefMedlineGoogle Scholar
  • 22 Hayashi T, Boyko EJ, Leonetti DL, McNeely MJ, Newell-Morris L, Kahn SE, Fujimoto WY. Visceral adiposity is an independent predictor of incident hypertension in Japanese Americans. Ann Intern Med. 2004; 140: 992–1000.CrossrefMedlineGoogle Scholar
  • 23 Sironi AM, Gastaldelli A, Mari A, Ciociaro D, Positano V, Buzzigoli E, Ghione S, Turchi S, Lombardi M, Ferrannini E. Visceral fat in hypertension: influence on insulin resistance and beta-cell function. Hypertension. 2004; 44: 127–133.LinkGoogle Scholar
  • 24 Ding J, Visser M, Kritchevsky SB, Nevitt M, Newman A, Sutton-Tyrrell K, Harris TB. The association of regional fat depots with hypertension in older persons of white and African American ethnicity. Am J Hypertens. 2004; 17: 971–976.CrossrefMedlineGoogle Scholar
  • 25 Pascot A, Lemieux S, Lemieux I, Prud’homme D, Tremblay A, Bouchard C, Nadeau A, Couillard C, Tchernof A, Bergeron J, Despres JP. Age-related increase in visceral adipose tissue and body fat and the metabolic risk profile of premenopausal women. Diabetes Care. 1999; 22: 1471–1478.CrossrefMedlineGoogle Scholar
  • 26 Nagaretani H, Nakamura T, Funahashi T, Kotani K, Miyanaga M, Tokunaga K, Takahashi M, Nishizawa H, Kishida K, Kuriyama H, Hotta K, Yamashita S, Matsuzawa Y. Visceral fat is a major contributor for multiple risk factor clustering in Japanese men with impaired glucose tolerance. Diabetes Care. 2001; 24: 2127–2133.CrossrefMedlineGoogle Scholar
  • 27 Nicklas BJ, Penninx BW, Ryan AS, Berman DM, Lynch NA, Dennis KE. Visceral adipose tissue cutoffs associated with metabolic risk factors for coronary heart disease in women. Diabetes Care. 2003; 26: 1413–1420.CrossrefMedlineGoogle Scholar
  • 28 Lemieux S, Prud’homme D, Moorjani S, Tremblay A, Bouchard C, Lupien PJ, Despres JP. Do elevated levels of abdominal visceral adipose tissue contribute to age-related differences in plasma lipoprotein concentrations in men? Atherosclerosis. 1995; 118: 155–164.CrossrefMedlineGoogle Scholar
  • 29 Kobayashi H, Nakamura T, Miyaoka K, Nishida M, Funahashi T, Yamashita S, Matsuzawa Y. Visceral fat accumulation contributes to insulin resistance, small-sized low-density lipoprotein, and progression of coronary artery disease in middle-aged non-obese Japanese men. Jpn Circ J. 2001; 65: 193–199.CrossrefMedlineGoogle Scholar
  • 30 Mori Y, Hoshino K, Yokota K, Yokose T, Tajima N. Increased visceral fat and impaired glucose tolerance predict the increased risk of metabolic syndrome in Japanese middle-aged men. Exp Clin Endocrinol Diabetes. 2005; 113: 334–339.CrossrefMedlineGoogle Scholar
  • 31 Goodpaster BH, Krishnaswami S, Harris TB, Katsiaras A, Kritchevsky SB, Simonsick EM, Nevitt M, Holvoet P, Newman AB. Obesity, regional body fat distribution, and the metabolic syndrome in older men and women. Arch Intern Med. 2005; 165: 777–783.CrossrefMedlineGoogle Scholar
  • 32 Carr DB, Utzschneider KM, Hull RL, Kodama K, Retzlaff BM, Brunzell JD, Shofer JB, Fish BE, Knopp RH, Kahn SE. Intra-abdominal fat is a major determinant of the National Cholesterol Education Program Adult Treatment Panel III criteria for the metabolic syndrome. Diabetes. 2004; 53: 2087–2094.CrossrefMedlineGoogle Scholar
  • 33 von Eyben FE, Mouritsen E, Holm J, Montvilas P, Dimcevski G, Suciu G, Helleberg I, Kristensen L, von Eyben R. Intra-abdominal obesity and metabolic risk factors: a study of young adults. Int J Obes Relat Metab Disord. 2003; 27: 941–949.CrossrefMedlineGoogle Scholar
  • 34 Ross R, Freeman J, Hudson R, Janssen I. Abdominal obesity, muscle composition, and insulin resistance in premenopausal women. J Clin Endocrinol Metab. 2002; 87: 5044–5051.CrossrefMedlineGoogle Scholar
  • 35 Fujimoto WY, Bergstrom RW, Boyko EJ, Chen KW, Leonetti DL, Newell-Morris L, Shofer JB, Wahl PW. Visceral adiposity and incident coronary heart disease in Japanese-American men: the 10-year follow-up results of the Seattle Japanese-American Community Diabetes Study. Diabetes Care. 1999; 22: 1808–1812.CrossrefMedlineGoogle Scholar
  • 36 Hernandez-Ono A, Monter-Carreola G, Zamora-Gonzalez J, Cardoso-Saldana G, Posadas-Sanchez R, Torres-Tamayo M, Posadas-Romero C. Association of visceral fat with coronary risk factors in a population-based sample of postmenopausal women. Int J Obes Relat Metab Disord. 2002; 26: 33–39.CrossrefMedlineGoogle Scholar
  • 37 Tong J, Fujimoto WY, Kahn SE, Weigle DS, McNeely MJ, Leonetti DL, Shofer JB, Boyko EJ. Insulin, C-peptide, and leptin concentrations predict increased visceral adiposity at 5- and 10-year follow-ups in nondiabetic Japanese Americans. Diabetes. 2005; 54: 985–990.CrossrefMedlineGoogle Scholar
  • 38 Park YW, Allison DB, Heymsfield SB, Gallagher D. Larger amounts of visceral adipose tissue in Asian Americans. Obes Res. 2001; 9: 381–387.CrossrefMedlineGoogle Scholar
  • 39 Maurovich-Horvat P, Massaro J, Fox CS, Moselewski F, O’Donnell CJ, Hoffmann U. Comparison of anthropometric, area- and volume-based assessment of abdominal subcutaneous and visceral adipose tissue volumes using multi-detector computed tomography. Int J Obes (Lond). 2007; 31: 500–506.CrossrefMedlineGoogle Scholar
  • 40 Dawber TR, Kannel WB, Lyell LP. An approach to longitudinal studies in a community: the Framingham Heart Study. Ann N Y Acad Sci. 1963; 107: 539–556.CrossrefMedlineGoogle Scholar
  • 41 Shurtleff D. Some characteristics related to the incidence of cardiovascular disease and death: Framingham study, 18-year follow-up. In: Kannel WB, Fordon T, eds. The Framingham Study: An Epidemiological Investigation of Cardiovascular Disease. Washington, DC: Department of Health, Education, and Welfare; 1973. DHEW publication No. NIH 74–599, section 30.Google Scholar
  • 42 Hopkins PN, Ellison RC, Province MA, Pankow JS, Carr JJ, Arnett DK, Lewis CE, Heiss G, Hunt SC. Association of coronary artery calcified plaque with clinical coronary heart disease in the National Heart, Lung, and Blood Institute’s Family Heart Study. Am J Cardiol. 2006; 97: 1564–1569.CrossrefMedlineGoogle Scholar
  • 43 Executive Summary of the third report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA. 2001; 285: 2486–2497.CrossrefMedlineGoogle Scholar
  • 44 SAS/STAT User’s Guide, Version 8. Cary, NC: SAS Institute Inc; 2000.Google Scholar
  • 45 Almasy L, Blangero J. Multipoint quantitative-trait linkage analysis in general pedigrees. Am J Hum Genet. 1998; 62: 1198–1211.CrossrefMedlineGoogle Scholar
  • 46 Bjorntorp P. “Portal” adipose tissue as a generator of risk factors for cardiovascular disease and diabetes. Arteriosclerosis. 1990; 10: 493–496.LinkGoogle Scholar
  • 47 Lemieux I, Pascot A, Prud’homme D, Almeras N, Bogaty P, Nadeau A, Bergeron J, Despres JP. Elevated C-reactive protein: another component of the atherothrombotic profile of abdominal obesity. Arterioscler Thromb Vasc Biol. 2001; 21: 961–967.CrossrefMedlineGoogle Scholar
  • 48 Azuma K, Katsukawa F, Oguchi S, Murata M, Yamazaki H, Shimada A, Saruta T. Correlation between serum resistin level and adiposity in obese individuals. Obes Res. 2003; 11: 997–1001.CrossrefMedlineGoogle Scholar
  • 49 Fukuhara A, Matsuda M, Nishizawa M, Segawa K, Tanaka M, Kishimoto K, Matsuki Y, Murakami M, Ichisaka T, Murakami H, Watanabe E, Takagi T, Akiyoshi M, Ohtsubo T, Kihara S, Yamashita S, Makishima M, Funahashi T, Yamanaka S, Hiramatsu R, Matsuzawa Y, Shimomura I. Visfatin: a protein secreted by visceral fat that mimics the effects of insulin. Science. 2005; 307: 426–430.CrossrefMedlineGoogle Scholar
  • 50 Cigolini M, Targher G, Bergamo Andreis I, Tonoli M, Agostino G, De Sandre G. Visceral fat accumulation and its relation to plasma hemostatic factors in healthy men. Arterioscler Thromb Vasc Biol. 1996; 16: 368–374.CrossrefMedlineGoogle Scholar
  • 51 Mertens I, Van Gaal LF. Visceral fat as a determinant of fibrinolysis and hemostasis. Semin Vasc Med. 2005; 5: 48–55.CrossrefMedlineGoogle Scholar
  • 52 Miyazawa-Hoshimoto S, Takahashi K, Bujo H, Hashimoto N, Saito Y. Elevated serum vascular endothelial growth factor is associated with visceral fat accumulation in human obese subjects. Diabetologia. 2003; 46: 1483–1488.CrossrefMedlineGoogle Scholar
  • 53 Giusti V, Suter M, Verdumo C, Gaillard RC, Burckhardt P, Pralong FP. Molecular determinants of human adipose tissue: differences between visceral and subcutaneous compartments in obese women. J Clin Endocrinol Metab. 2004; 89: 1379–1384.CrossrefMedlineGoogle Scholar
  • 54 Arner P. Regional differences in protein production by human adipose tissue. Biochem Soc Trans. 2001; 29: 72–75.CrossrefMedlineGoogle Scholar
  • 55 Dusserre E, Moulin P, Vidal H. Differences in mRNA expression of the proteins secreted by the adipocytes in human subcutaneous and visceral adipose tissues. Biochim Biophys Acta. 2000; 1500: 88–96.CrossrefMedlineGoogle Scholar
  • 56 Vega GL, Adams-Huet B, Peshock R, Willett D, Shah B, Grundy SM. Influence of body fat content and distribution on variation in metabolic risk. J Clin Endocrinol Metab. 2006; 91: 4459–4466.CrossrefMedlineGoogle Scholar
  • 57 Hayashi T, Boyko EJ, Leonetti DL, McNeely MJ, Newell-Morris L, Kahn SE, Fujimoto WY. Visceral adiposity and the prevalence of hypertension in Japanese Americans. Circulation. 2003; 108: 1718–1723.LinkGoogle Scholar
  • 58 Garg A. Regional adiposity and insulin resistance. J Clin Endocrinol Metab. 2004; 89: 4206–4210.CrossrefMedlineGoogle Scholar
  • 59 Kelley DE, Thaete FL, Troost F, Huwe T, Goodpaster BH. Subdivisions of subcutaneous abdominal adipose tissue and insulin resistance. Am J Physiol Endocrinol Metab. 2000; 278: E941–E948.CrossrefMedlineGoogle Scholar
  • 60 Klein S, Fontana L, Young VL, Coggan AR, Kilo C, Patterson BW, Mohammed BS. Absence of an effect of liposuction on insulin action and risk factors for coronary heart disease. N Engl J Med. 2004; 350: 2549–2557.CrossrefMedlineGoogle Scholar
  • 61 Goodpaster BH, Kelley DE, Wing RR, Meier A, Thaete FL. Effects of weight loss on regional fat distribution and insulin sensitivity in obesity. Diabetes. 1999; 48: 839–847.CrossrefMedlineGoogle Scholar
  • 62 Ruhl CE, Everhart JE, Ding J, Goodpaster BH, Kanaya AM, Simonsick EM, Tylavsky FA, Harris TB. Serum leptin concentrations and body adipose measures in older black and white adults. Am J Clin Nutr. 2004; 80: 576–583.CrossrefMedlineGoogle Scholar
  • 63 Sundell J, Huupponen R, Raitakari OT, Nuutila P, Knuuti J. High serum leptin is associated with attenuated coronary vasoreactivity. Obes Res. 2003; 11: 776–782.CrossrefMedlineGoogle Scholar
  • 64 Yamagishi S, Inagaki Y, Amano S, Okamoto T, Takeuchi M. Up-regulation of vascular endothelial growth factor and down-regulation of pigment epithelium-derived factor messenger ribonucleic acid levels in leptin-exposed cultured retinal pericytes. Int J Tissue React. 2002; 24: 137–142.MedlineGoogle Scholar
  • 65 Cooke JP, Oka RK. Does leptin cause vascular disease? Circulation. 2002; 106: 1904–1905.LinkGoogle Scholar
  • 66 Singhal A, Farooqi IS, Cole TJ, O’Rahilly S, Fewtrell M, Kattenhorn M, Lucas A, Deanfield J. Influence of leptin on arterial distensibility: a novel link between obesity and cardiovascular disease? Circulation. 2002; 106: 1919–1924.LinkGoogle Scholar
  • 67 Tanaka S, Togashi K, Rankinen T, Perusse L, Leon AS, Rao DC, Skinner JS, Wilmore JH, Despres JP, Bouchard C. Sex differences in the relationships of abdominal fat to cardiovascular disease risk among normal-weight white subjects. Int J Obes Relat Metab Disord. 2004; 28: 320–323.CrossrefMedlineGoogle Scholar
  • 68 Hong Y, Rice T, Gagnon J, Despres JP, Nadeau A, Perusse L, Bouchard C, Leon AS, Skinner JS, Wilmore JH, Rao DC. Familial clustering of insulin and abdominal visceral fat: the HERITAGE Family Study. J Clin Endocrinol Metab. 1998; 83: 4239–4245.MedlineGoogle Scholar
  • 69 Perusse L, Despres JP, Lemieux S, Rice T, Rao DC, Bouchard C. Familial aggregation of abdominal visceral fat level: results from the Quebec Family Study. Metabolism. 1996; 45: 378–382.CrossrefMedlineGoogle Scholar
  • 70 Meigs JB, Panhuysen CI, Myers RH, Wilson PW, Cupples LA. A genome-wide scan for loci linked to plasma levels of glucose and HbA(1c) in a community-based sample of Caucasian pedigrees: the Framingham Offspring Study. Diabetes. 2002; 51: 833–840.CrossrefMedlineGoogle Scholar
  • 71 Levy D, DeStefano AL, Larson MG, O’Donnell CJ, Lifton RP, Gavras H, Cupples LA, Myers RH. Evidence for a gene influencing blood pressure on chromosome 17: genome scan linkage results for longitudinal blood pressure phenotypes in subjects from the Framingham Heart Study. Hypertension. 2000; 36: 477–483.CrossrefMedlineGoogle Scholar
  • 72 Mathias RA, Roy-Gagnon MH, Justice CM, Papanicolaou GJ, Fan YT, Pugh EW, Wilson AF. Comparison of year-of-exam- and age-matched estimates of heritability in the Framingham Heart Study data. BMC Genet. 2003; 4 (suppl 1): S36.CrossrefMedlineGoogle Scholar
  • 73 Tittelbach TJ, Berman DM, Nicklas BJ, Ryan AS, Goldberg AP. Racial differences in adipocyte size and relationship to the metabolic syndrome in obese women. Obes Res. 2004; 12: 990–998.CrossrefMedlineGoogle Scholar
  • 74 Abate N, Garg A, Peshock RM, Stray-Gundersen J, Grundy SM. Relationships of generalized and regional adiposity to insulin sensitivity in men. J Clin Invest. 1995; 96: 88–98.CrossrefMedlineGoogle Scholar
  • 75 Stolk RP, Wink O, Zelissen PM, Meijer R, van Gils AP, Grobbee DE. Validity and reproducibility of ultrasonography for the measurement of intra-abdominal adipose tissue. Int J Obes Relat Metab Disord. 2001; 25: 1346–1351.CrossrefMedlineGoogle Scholar
circulationahaCirculationCirculationCirculation0009-73221524-4539Lippincott Williams & Wilkins
CLINICAL PERSPECTIVE03072007

Visceral adipose tissue (VAT) compartments may confer increased metabolic risk. The incremental utility of measuring both VAT and subcutaneous abdominal adipose tissue (SAT) in association with metabolic risk factors and underlying heritability has not been well described in a population-based setting. Participants from the Framingham Heart Study underwent multidetector computed tomography assessment of SAT and VAT volumes. SAT and VAT were significantly associated with increased odds of hypertension, impaired fasting glucose, diabetes, and metabolic syndrome. In women, relations between VAT and risk factors were consistently stronger than in men. VAT was more strongly correlated with most metabolic risk factors than was SAT. Furthermore, VAT but not SAT contributed significantly to risk factor variation after adjustment for body mass index and waist circumference. Among overweight and obese individuals, the prevalence of hypertension, impaired fasting glucose, and metabolic syndrome increased linearly and significantly across increasing VAT quartiles. Heritability values for SAT and VAT were 57% and 36%, respectively. Although both SAT and VAT are correlated with metabolic risk factors, VAT remains more strongly associated with an adverse metabolic risk profile even after accounting for standard anthropometric indices. Our findings are consistent with the hypothesized role of visceral fat as a unique, pathogenic fat depot. Measurement of VAT may provide a more complete understanding of metabolic risk associated with variation in fat distribution.

Guest Editor for this article was Robert H. Eckel, MD.

The online-only Data Supplement, which consists of a table, is available with this article at http://circ.ahajournals.org/cgi/content/full/CIRCULATIONAHA.106.675355/DC1.

eLetters(0)

eLetters should relate to an article recently published in the journal and are not a forum for providing unpublished data. Comments are reviewed for appropriate use of tone and language. Comments are not peer-reviewed. Acceptable comments are posted to the journal website only. Comments are not published in an issue and are not indexed in PubMed. Comments should be no longer than 500 words and will only be posted online. References are limited to 10. Authors of the article cited in the comment will be invited to reply, as appropriate.

Comments and feedback on AHA/ASA Scientific Statements and Guidelines should be directed to the AHA/ASA Manuscript Oversight Committee via its Correspondence page.