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Donald W. Reynolds Cardiovascular Clinical Research Center and the Department of Internal Medicine (G.L.V., R.P., D.W., S.M.G.), Center for Human Nutrition (G.L.V., B.S., S.M.G.), and Departments of Clinical Sciences and Internal Medicine (B.A.-H.), University of Texas Southwestern Medical Center, Dallas, Texas 75390
Address all correspondence and requests for reprints to: Scott M. Grundy, M.D., Ph.D., Center for Human Nutrition (Y3-206), University of Texas Southwestern Medical Center, 5323 Harry Hines Boulevard, Dallas, Texas 75390-9052. E-mail: scott.grundy{at}utsouthwestern.edu.
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Abstract |
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Participants: A representative sample of 1934 Black and White women and men of the Dallas Heart Study participated in the study.
Design: We measured the fat in total body, trunk, and lower body with dual-energy x-ray absorptiometry and in abdominal compartments (sc, ip, and retroperitoneal) with magnetic resonance imaging. Other measurements included body mass index (BMI), waist circumference, blood pressure, plasma lipids, glucose, insulin (including homeostasis model), and C-reactive protein.
Results: In all groups, total body fat correlated positively with key metabolic risk factors, i.e. homeostasis model, triglyceride/high-density lipoprotein-cholesterol ratios, C-reactive protein, and blood pressure; however, it explained less than one third of the variability of all the risk factors. After adjustment for total body fat, truncal fat conferred additional positive correlation with risk factors. Furthermore, with multivariable regression analysis, ip fat conferred independent correlation with plasma lipids beyond a combination of other compartments including truncal fat. Still, except for insulin levels, all combinations including ip fat still explained less than one third of the variability in risk-factor levels. Conversely, lower body fat correlated negatively with risk factors; i.e. lower body fat appeared to offer some protection against risk factors.
Conclusions: Body fat distribution has some influence on risk factors beyond total body fat content. Both waist circumference and BMI significantly predicted risk factors after adjustment for total body fat, and for clinical purposes, most of the predictive power for men was contained in waist circumference, whereas for women, BMI and waist circumference were similarly predictive. Finally, even though the correlations between combined body fat parameters and risk factors explained only a portion of the variation in the latter, the average number of categorical metabolic risk factors increased progressively with increasing obesity. Hence, obesity seemingly has more clinical impact than revealed in these correlative studies.
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Introduction |
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Body fat content and distribution can be measured by hydrodensitometry, bioelectrical impedance, and dual-energy x-ray absorptiometry (DXA). DXA measures total body fat and fat contents of lower body, trunk, and arms. Computed tomography and magnetic resonance imaging (MRI) allow for more detailed analysis of the relative amounts of body fat in the various abdominal compartments (visceral vs. retroperitoneal). In this study, we assessed amounts and distributions of body fat in a large, representative sample of Black and White men and women from Dallas County (Dallas Heart Study) to address three questions: 1) whether patterns of fat distribution differ according to ethnicity and gender, 2) whether any subcompartments of adipose tissue contribute to metabolic risk factors beyond total body fat, and 3) whether simple clinical measures of body fat content and distribution (BMI, waist circumference, and waist-to-hip ratio) relate to risk factors as well as do more precise measurements of body fat compartments.
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Subjects and Methods |
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The Dallas Heart Study is a multiethnic, probability-based sample of Dallas County in which Blacks were systematically oversampled to obtain 50% Black subjects (26). All participants consented to a protocol approved by our institutional review board. Structured interviews and weight and blood pressure measurements were performed at home in 6101 individuals aged 18–65 yr (54% Black). Within 1 month, a second home visit was done on 3398 participants, ages 30–65 yr (52% Black), to obtain blood and urine. Within 3 months thereafter, 2971 underwent a third visit including height, weight, anthropometric measurements, MRI of the abdomen, and DXA scan. Hispanics and other ethnicities (n = 568) were excluded because of relatively small sample sizes. Subjects with diabetes also were excluded (n = 264). The remainder (n = 205) failed to complete either the DXA scan or the abdominal MRI study. Among these, 49 subjects did not complete the DXA scan for the following reasons: exceeded weight limit for table (n = 45), declined test (n = 3), and test canceled for acute illness (n = 1). Another 156 subjects did not complete the MRI scan for the following reasons: declined test (n = 10), required oxygen tank (n = 1), inability to hold breath (n = 1), failure to fit camera (n = 4), claustrophobic (n = 110), and metal in body (n = 30). Thus, the present study included 1934 Dallas Heart Study subjects (478 Black men, 395 White men, 643 Black women, and 418 White women) who underwent both DXA and MRI scans.
Risk factor measurements
Height, weight, waist circumference, blood pressure, fasting plasma lipids, glucose, insulin, and C-reactive protein (CRP) were measured as described previously (26). Insulin sensitivity was estimated with the HOMA2 computer model (HOMA Calculator version 2.2) (27). Abnormal homeostasis assessment (HOMA) values were defined as above 1.63, which was the 75th percentile for HOMA derived from the 1169 subjects of this study who had a BMI less than 30 kg/m2. The large Bruneck study reported similar results (28). CRP measurements were performed using the Roche/Hitachi 912 System, Tina-quant assay, (Roche Diagnostics, Indianapolis, IN) (29).
DXA measurement of body fat parameters
DXA scanning was performed on a Delphi W scanner (Hologic Inc., Bedford, MA) equipped with a fan beam (30) and Discovery software (version 12.2). Fat mass was measured in total body, trunk, lower body, and upper extremity. DXA data consisted of total fat mass (kg), fat-free mass (kg), and bone mineral mass (kg) of the trunk, upper and lower extremities, and head. The trunk region was defined as the region below the chin, the region delineated by vertical lines within the left and right glenoid fossae and bordering laterally to the ribs, and the region delineated by oblique lines that cross the femoral necks and converge below the pubic symphysis. Lower body fat includes all fat below these oblique lines. The upper extremities are defined by the region extending outside the right and left glenoid fossa and comprising the upper and lower arms and hands.
Quantification of abdominal fat by MRI
Measurements of abdominal compartments of body fat were performed using 1.5 Tesla MRI scanners (Intera; Philips Medical Systems, Best, The Netherlands). The entire abdomen from the diaphragm to the pelvis was scanned using contiguous axial 10-mm slices as previously described (31). We have previously shown that a single MRI slice at the L2-L3 level accurately predicts total sc, ip, and retroperitoneal adipose tissue mass in men (32). To determine whether the same relationship holds in women, we performed an identical analysis in 10 men and 10 women whose BMIs were representative of the total range of BMIs and ethnic distribution of the Dallas Heart Study cohort. For each compartment, Pearson correlation coefficients were determined by comparing the tissue mass measured across all slices with the compartment mass measured at each lumbar intervertebral level (T12-L1, L1-L2, L2-L3, L3-L4, L4-L5, L5-S1). In both men and women, the L2-L3 had the highest and most consistent correlation coefficients for all three compartments, similar to our previous study done only in men. Regression equations using the measurements at the L2-L3 level were then used to estimate total compartment adipose tissue mass in the remainder of the cohort (32).
Statistical analysis
Two-way ANOVA was performed to compare the ethnic and gender groups. Pairwise group comparisons were made with the least-squares means contrasts from the ANOVA models. Some variables were positively (right) skewed and were log transformed before parametric analyses to meet analysis assumptions. Nonparametric Spearman correlations coefficients (rs) were used to evaluate the association among the body composition variables and the cardiovascular risk factor variables. Within each gender and ethnic group, correlation coefficients were compared using methods described by Meng et al. (33). Fisher’s Z transformation was used to compare correlation coefficients between groups. Spearman partial correlation coefficients were computed to adjust for percent total body fat. Multiple regression analysis was performed to describe the proportion of the variability of metabolic risk factors explained by percent total fat (model 1), with and without other predictor variables. BMI, waist circumference, percent truncal fat, and subcompartments (percent ip fat and percent sc fat) were selected based on clinical judgment for inclusion in the models with two independent variables (models 2–6). Then, for models 7, 8, and 9, percent ip fat was added to models 2, 3, and 4 to assess whether this compartment could further contribute to the variability of metabolic risk factors. Because the independent variables are known to be correlated, the possibility of multicollinearity in the regression models was evaluated with variance inflation factor statistics and found that these models were unaffected by collinearity problems. Analyses were performed with SAS version 9.1.3 (SAS Institute, Cary, NC). Two-tailed P values are reported, without adjustments for multiple testing. Results are expressed as mean and SD or median and 10th to 90th percentile unless otherwise noted.
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Results |
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). Although mean BMI was similar in Black and White men, mean waist circumferences of White men were significantly higher than of Black men. White men also had significantly larger waist circumferences than White women. Black women had both significantly higher mean BMIs and mean waist circumferences than White women, but the mean waist circumference in Black women was not significantly different from Black men.
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Relative and absolute amounts of fat in the sc, ip, and retroperitoneal compartments by MRI showed that for all groups the major fraction of abdominal fat was located in the sc compartment. In absolute mass, women had more abdominal fat than men, because of a larger proportion and amount of body fat being located in women’s sc compartment. Average sc fat masses for Black and White men were 3.4 ± 2.0 kg and 3.7 ± 1.6 kg, mean ± SD, respectively, whereas for Black and White women, they were 6.1 ± 3.1 kg and 4.8 ± 2.6 kg, respectively (for all men vs. women, P < 0.0001 by ANOVA). Amounts of ip fat were somewhat higher in men than women: Black men and White men, 1.3 ± 0.6 and 1.7 ± 0.7 kg, respectively; Black and White women, 1.1 ± 0.5 and 1.1 ± 0.5 kg, respectively (for all men vs. women, P < 0.0001 by ANOVA). The ratios of abdominal sc-to-ip fat were much higher in women compared with men (for all men vs. women, P < 0.0001 by ANOVA).
Relationships among BMI, waist circumference, and body fat compartments
Both BMI and waist circumference were strongly correlated with percent body fat and truncal fat (Table 2). In men, waist circumference correlated better with percent total body fat than did BMI; the opposite was true for women. In all four groups, waist circumference was more strongly correlated with abdominal fat, particularly the ip and retroperitoneal compartments, as measured by MRI, than was BMI.
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Table 3 first shows univariate correlations between body fat parameters and four measures of metabolic risk: HOMA (a measure of insulin resistance), triglyceride (TG)/high-density lipoprotein-cholesterol (HDL-C) ratio (a correlate of insulin resistance), CRP, and systolic blood pressure. In both Black and White men, percent total body fat correlated strongest with HOMA among the different risk factors. The same was true for Black and White women except that CRP was as highly correlated as HOMA. In general, body fat compartments were not more highly correlated univariately with risk factors than was percent total body fat except for abdominal sc fat in Black women. Table 3 also gives partial correlation coefficients for each after adjustment for percent total body fat. Partial correlation coefficients for subcompartments of truncal fat often showed modest additional and significant positive correlations with risk factors after adjustment for percent total body fat. Percent truncal fat itself generally added as much or more than its subcompartments, although ip fat was similar to truncal fat for TG/HDL-C ratios. In contrast, lower body fat was negatively correlated with risk factors when adjusted for percent total body fat.
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Regression analysis of metabolic risk factors and body fat distribution
Because of the widely held view that ip (visceral) adiposity has a powerful influence on metabolic risk factors, multiple regression analysis was performed to examine the effect of ip fat (Tables 4 and 5 for men and women, respectively). To provide more detail, individual metabolic risk factors, TG, HDL-C, systolic and diastolic blood pressure, glucose, insulin, and CRP as well as TG/HDL-C, were examined. In this analysis, percent total body fat explained more of the variance in insulin levels than for other risk factors, except for CRP in women (model 1). If all compartments of adipose tissue were to be equal in their metabolic activity, none of them would show an additional effect beyond that of percent total body fat. In models 2–6, addition of one other body fat parameter often explained a significantly higher proportion of the variance in several of the risk factors. But to focus on ip fat, we added it as a second independent variable in model 6 and as a third predictor variable (models 7–9). For TG, TG/HDL-C, and blood pressure, the regression coefficient for percent ip fat was statistically significant in models 6–9 for all groups. For insulin and CRP, the contribution of percent ip fat was noted in some groups but was not as consistent or substantial.
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Although metabolic risk factors are continuous variables, for clinical purposes they often are presented as categorical variables (1). Therefore, in Fig. 1, we plotted the number of categorical metabolic risk factors vs. increments in waist circumference (<88 cm, 88–102 cm, and >102 cm). The risk factors included TG at least 150 mg/dl, HDL-C less than 40 mg/dl (men) or HDL-C less than 50 mg/dl (women), HOMA at least 1.63, systolic blood pressure at least 130 mm Hg or diastolic blood pressure at least 85 mm Hg or on antihypertensive medication, and CRP at least 3 mg/liter. In men, the median number of risk factors was not increased until the waist circumference exceeded 102 cm. In women, additional risk factors appeared at 88 cm. No significant difference in the relationship between number of risk factors and waist circumference was seen between the two races.
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Discussion |
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Ethnic and gender differences in body fat content and distribution
There is a prevailing view that Black women have more body fat than White women (34, 35, 36, 37). We found that Black women had significantly higher BMIs than did White women, but percentages of body fat were similar. Higher BMIs in Black women may result from differences in fat-free mass (e.g. muscle and bone). Mean BMIs were similar for White and Black men, yet percent total body fat was significantly higher in White men. Thus, any higher prevalence of metabolic disorders, such as diabetes (38), in the black population cannot be ascribed to a higher percent body fat.
Well-known differences in percent body fat and fat distribution between genders were observed. But importantly, both Black and White women on average had greater masses of total fat and truncal fat than did their male counterparts. Women, moreover, had approximately twice the lower body fat of men. These findings raise the possibility that women in general have a fat-storage capacity in sc adipose tissue exceeding that of men.
Influence of total body fat on risk factors
Total body fat percentage correlated positively with all studied risk factors, but most strongly for insulin resistance, shown by fasting insulin and HOMA, and for CRP in women. Still, less than one third of the variation in risk factor levels could be explained by percent total body fat. Other influences therefore must modify the severity of metabolic risk factors even in the presence of excess body fat. Among these may be the distribution of body fat.
Truncal fat and abdominal fat compartments
Upper body fat, especially truncal fat, is reported to worsen risk factors (3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25). We found that after adjustment for percent total body fat, percent truncal fat indeed showed positive partial correlation with the risk factors. Thus, percent truncal fat acts on risk factors beyond percent total body fat. Whether this incremental effect can be ascribed mainly to sc fat in the trunk (or abdomen) or to ip fat is disputed (3, 4, 5, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25). We observed that both abdominal sc fat and ip fat carried incremental prediction over total body fat, but the relative importance of the two could not be ascertained simply by partial correlations. One exception was for TG/HDL-C ratios in which ip fat generally was more highly correlated than was abdominal sc fat. A mechanism favored by some is that ip fat secretes its substances directly into the portal circulation and thus may directly modify the liver’s production and removal of plasma lipoproteins (39).
Because of the widely held view that ip fat is uniquely important for metabolic risk (5, 6, 18, 23, 24, 25), we examined regression models in which percent ip fat was added as second and third predictive variables. For TG/HDL-C and systolic blood pressure, the regression coefficient for percent ip fat was statistically significant for all groups. This suggests that ip fat in fact has predictive power beyond total fat and truncal sc fat. For other risk factors, i.e. insulin levels and CRP, percent ip fat contributed independently in some groups but was not as consistent. These analyses reveal the complexity of attempting to ascribe independent predictive power to multiple collinear variables, but with this said, it appears that ip fat does impart independent prediction for some risk factors, especially dyslipidemia. Even so, across the full range of risk-factor levels, all fat parameters together still account for a minority of variation in levels.
Protective effect of lower body adipose tissue
A potentially important observation is that percent lower body fat generally was, independent of percent total fat, negatively correlated with risk factors in both men and women. The mechanism of this seemingly protective effect is worthy of consideration. One intriguing possibility is that the presence of plentiful amounts of lower-body adipose tissue serves as a fat reservoir to guard against ectopic fat deposition in visceral depots, liver, and muscle. Support for this concept comes from the Hoorn Study (40), which observed that fat mass in the legs was negatively correlated with glucose and HOMA levels. If lower body fat is protective, excess upper body fat may be a sign of a deficient lower-body fat reservoir and hence an indicator of predisposition of ectopic fat accumulation.
Predictive power of waist circumference and BMI
Measurement of waist circumference may be a simple means to determine whether adipose tissue stores are metabolically overloaded. An important finding in both Black and White men was that waist circumference was equally, if not more strongly, correlated with the other body fat parameters, including percent total body fat and body fat subcompartments than was BMI. In men, waist circumference was highly correlated with truncal fat (by DXA) and with total abdominal fat (by MRI) but less well with ip fat. Nonetheless, for both Black and White men, waist circumference was generally superior to BMI as a measure of body fat content and body fat distribution. In contrast, in women who in general have more lower-body fat, BMI was better than waist circumference as an indicator of percent total body fat, although not of abdominal fat compartments. Overall, BMI was not a better predictor of risk factors than was waist circumference, particularly for dyslipidemia in which waist circumferences was more robust. Waist-to-hip ratio did not predict risk factors better than waist circumference. For clinical purposes, waist circumference appears to be as good as if not better than body fat compartmentalization for evaluating metabolic risk in both men and women.
Obesity threshold and metabolic risk
Both body fat contents and metabolic risk factors are continuous variables. Yet it must be noted that for all risk factors, body fat parameters accounted for less than one third of their variability. Such might denigrate the importance of body fat for metabolic risk. On the other hand, these findings may be an incomplete picture. For example, for clinical purposes, both body fat measures and risk factors are typically expressed as categorical variables. This allows for easier identification of persons at higher risk. When categorical increments in waist circumference were plotted against the average number of categorical metabolic risk factors, men with lower waist circumferences (<102 cm) had fewer risk factors than those with larger girths (Fig. 1
). The same was true for women, starting at a waist circumference of at least 88 cm. They also suggest that thresholds of obesity are required for metabolic risk factors to become clinically significant. These findings support the currently recommended thresholds for defining abdominal obesity (1, 2) in the United States.
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Acknowledgments |
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Footnotes |
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B.S. was a Doris Duke Clinical Research Fellow.
First Published Online August 22, 2006
Abbreviations: BMI, Body mass index; CRP, C-reactive protein; CVD, cardiovascular disease; DXA, dual-energy x-ray absorptiometry; HDL-C, high-density lipoprotein-cholesterol; HOMA, homeostasis assessment; MRI, magnetic resonance imaging; TG, triglyceride.
Received April 14, 2006.
Accepted August 14, 2006.
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