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Keywords:

  • abdominal obesity;
  • adults;
  • body composition;
  • body fat distribution;
  • magnetic resonance

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

Objective: We tested sex, race, and age differences in the patterning of visceral adipose tissue (VAT) and subcutaneous adipose tissue.

Research Methods and Procedures: Contiguous 1-cm-thick magnetic resonance (MR) images of the abdomen were collected from 820 African-American and white adults. Repeated-measures ANOVA was used to examine the effects of image location, sex, race, and age (≥50 vs. <50 years) on adipose tissue areas. Maximum VAT area was identified for each subject from the raw data.

Results: Compared to women, men had greater total VAT volume (p < 0.0001), and their maximum VAT area occurred higher in the abdomen (p < 0.0001). Among white men, maximim VAT area most frequently occurred 5 to 10 cm above L4-L5, whereas in the other groups, maximim VAT area most frequently occurred 1 to 4 cm above L4-L5 (p < 0.0001). African-American men had greater total VAT volume than African-American women (p < 0.01), but this sex difference was only significant using single images cranial to L4-L5 + 2 cm. Age-related increases in VAT tended to be greatest 5 to 10 cm above L4-L5 in men and near L4-L5 in women.

Discussion: A single MR image 5 to 10 cm above L4-L5 may allow more accurate conclusions than the L4-L5 image regarding group differences in visceral adiposity.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

Elevated visceral adipose tissue (VAT)1 is associated with insulin resistance and dyslipidemia, as well as systemic inflammation (1, 2, 3, 4, 5, 6, 7), and is an independent risk factor for incident type 2 diabetes (8), myocardial infarction (9), hypertension (10), and all-cause mortality (11).

To date, studies have focused primarily on the relationship between health outcomes and measures of total VAT or abdominal subcutaneous adipose tissue (SAT), whereas individual and group differences in the patterning of VAT deposition have yet to be fully examined. Given that the deleterious effects of excess VAT are thought to lie, at least in part, in the direct flow of high levels of free fatty acids (FFAs) from VAT to the liver through the portal venous system (3, 12, 13), it is possible that not only the amount but also the spatial distribution of VAT within the abdomen has consequences for insulin resistance and lipoprotein synthesis. A recent study documented differences in the patterning, or topography, of VAT between insulin-resistant (IR) and insulin-sensitive (IS) men (14). IR men had a higher ratio of VAT to total adipose tissue at specific locations in the trunk, but lower adipose tissue on the upper extremities, compared with BMI-matched IS men. The risk of the metabolic syndrome may also be significantly greater using VAT area measured higher in the abdomen (odds ratio ∼8.0) than VAT measured lower in the abdomen (odds ratio ∼3.9) (15), suggesting that not only the amount of VAT, but also its anatomical patterning, may affect disease risk.

VAT is typically measured using a single magnetic resonance image (MRI) or computed tomography (CT) image at the L4-L5 intervertebral space (16). If there are systematic race, sex, or age differences in the distribution of VAT and SAT across the abdomen, using the same single MRI or CT image to represent total VAT may not be appropriate for all population subgroups. Despite landmark studies showing anatomic variation in VAT area (17, 18, 19, 20, 21, 22) and studies showing significant effects of measurement site on estimation of total VAT volume by a single image (23, 24, 25, 26), the hypothesis that race, sex, and age influence the spatial distribution of VAT has yet to be directly addressed in a large-scale study.

Using a large database of contiguous MR images of the abdomen, we previously found that the VAT area from a single image located 6 cm above the L4-L5 intervertebral space is the best proxy for total VAT volume (27). The objective of this analysis is to examine race, sex, and age differences in the patterning of VAT and SAT, focusing particularly on the magnitude and significance of group differences in VAT and SAT areas measured at the conventional L4-L5 site compared with group differences measured at other locations.

Research Methods and Procedures

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

Subjects

The sample included 820 healthy subjects (300 white men, 392 white women, 48 African-American men, and 80 African-American women), 18 to 88 years of age. All subjects were enrolled in three ongoing, community-based studies conducted by the Lifespan Health Research Center in Dayton, OH: the Fels Longitudinal Study, the Miami Valley Family Aging Study, and the Southwest Ohio Family Study. All subjects were seen between 2003 and 2006. Subjects resided in the Dayton-Cincinnati area of southwestern Ohio at the time they were recruited into the studies and returned to the study center at regular intervals for follow-up. The Miami Valley Family Aging Study is based on women from a local physician's practice (Springfield, OH) who had large numbers of siblings living in the area; the study included both whites and African Americans. The Southwest Ohio Family Study families were assembled using probands screened during the Multiple Risk Factor Intervention Trial who also had large numbers of family members living in the catchment area; both whites and African Americans are represented in that study. The Fels Longitudinal Study has been previously described and focuses on assessing individual variation in growth and development and their relationship to chronic disease risk in adulthood; subjects are predominately white. Subjects were not selected on the basis of having (or not having) any particular medical condition. Race was self-reported by the subjects. Subjects were pre-screened to insure that they were free of any contraindications for MRI, and the study protocols and informed consent documents were approved by the Wright State University Institutional Review Board before subject participation.

Anthropometry and Body Composition

Weight was measured to the nearest 0.01 kg and stature to the nearest 0.01 cm using a digital scale and a digital stadiometer. DXA, using a Hologic QDR 4500 Elite X-ray densitometer (Bedford, MA), was used in the fast whole body scanning mode to estimate total body fat mass (TBF).

MRI

Abdominal MRI was conducted at the Good Samaritan Hospital Greater Dayton MRI Consortium in Dayton, OH. Images were obtained with a Siemens Magnetom Vision 1.5-T whole body scanner (Siemens Canada, Mississauga, Canada) using a T1-weighted fast-spin echo pulse sequence (TR 322 ms, TE 12 ms) with body coil. A breath-hold sequence (∼22 s/acquisition) was used to minimize the effects of respiratory motion on the images. All images were acquired on a 256 × 256-mm matrix and a 480-mm field of view. Images were obtained every 10 mm from the ninth thoracic vertebra (T9) to the first sacral vertebra (S1). Image location was defined relative to a common anatomical landmark, the L4-L5 intervertebral space. Segmentation of the axial images into VAT and SAT areas was performed by two trained observers using image analysis software (slice-O-matic, version 4.2; Tomovision, Montreal, Quebec, Canada). VAT and SAT areas were summed across all images to obtain total VAT and SAT volumes. Further details on the methods are found elsewhere (23).

Statistical Analysis

For all study variables, differences in means between each race-sex group were tested using a general linear model with six pairwise contrasts. Adjustments were made for the comparison-wise error rate first by using the Bonferroni method (where the critical p value was reduced to 0.05/6, or p = 0.008) and then by using the Tukey-Kramer test. In neither case did the interpretation of pairwise differences change; therefore, we presented the unadjusted p values.

Our primary interest was to describe variation in the pattern of VAT and SAT deposition, which required us to account for within-individual correlations in adipose tissue area between image locations. Initially, we plotted all of the data by individual subject to gain a sense of the general pattern of VAT and SAT by image location and its variation between individuals, as shown in Figure 1. We used repeated-measures ANOVA within the SAS PROC MIXED procedure to test for significant effects of sex, race (African-American or white), age (≥50 or <50 years), and image location (from −3 to +20 cm from L4-L5) on SAT and VAT area. Several covariance structures were tested to account for the within-individual (i.e., inter-image) correlations of VAT and SAT measures, and a compound symmetry covariance structure was found to be the most parsimonious. Peak VAT location was determined by examining each individual's raw data and identifying the image, of the 24 collected, that had the highest VAT area and noting that image number (from −3 to +20) for each subject. Means were calculated on this variable, called “peak VAT location,” by race and sex, and Student's t tests were used to test sex differences in mean peak VAT location, within race. Sex and race differences in the proportion of subjects who had their peak VAT area at different points up and down the spine distribution were determined across a number of 4-cm wide image ranges (i.e., −3 to 0, +1 to +4, +5 to +8, +9 to +12, +13 to +16, and +17 cm or higher), and group differences were tested using the χ2 test. We used the Hotelling test to determine which body composition measures had the highest correlation with age for each race and sex group (28).

image

Figure 1. : Individual variation in VAT patterning.

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Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

Adipose Tissue Differences by Sex and Race

Characteristics of the study subjects are summarized by race and sex in Table 1. Within race, the expected sex differences were observed for TBF (women greater than men), SAT volume and area measures (women greater than men), and VAT volume and area measures (men greater than women). One exception was that African-American men did not have greater L4-L5 VAT area than African-American women (112 vs. 102 cm2, p = 0.32). Within sex, African-American and white men did not differ in mean abdominal circumference, TBF, total SAT volume, or L4- L5 SAT area, but white men had greater total VAT volume than African-American men, as well as greater L4-L5 VAT area and peak VAT area. Race differences were the opposite in women; whereas African-American and white women did not differ in total VAT volume, L4-L5 VAT area, or peak VAT area, African-American women had significantly higher abdominal circumference, TBF, total SAT volume, and L4-L5 SAT area compared with white women.

Table 1. . Characteristics of the subjects*
 MenWomen
 WhiteAfrican-AmericanWhiteAfrican-American
  • TBF, total body fat; SAT, subcutaneous adipose tissue; VAT, visceral adipose tissue. Values are mean ± standard deviation.

  • *

    Groups having the same superscript letter are not significantly different from one another, p> 0.05, and groups having different superscript letters are statistically different from one another (p < 0.05). For example, total VAT volume was significantly different between white men, African-American men, and white women (all different superscripts) but was not different between white women and African-American women (same superscript).

N3004839280
Age (yrs)44.7 ± 16.4a46.2 ± 18.4a46.5 ± 16.0a48.0 ± 14.9a
BMI (kg/m2)27.6 ± 4.9a27.6 ± 4.8a27.0 ± 5.9a30.8 ± 6.8b
Stature (cm)179.0 ± 6.7a176.3 ± 7.9b164.1 ± 6.6c163.4 ± 5.7c
Abdominal circumference (cm)100.6 ± 13.8a98.2 ± 15.1a92.5 ± 13.9b101.6 ± 16.2a
TBF (kg)20.4 ± 8.0a19.4 ± 8.4a25.8 ± 9.7b31.0 ± 11.7c
Total SAT volume (liters)3.92 ± 2.11a3.92 ± 2.40a4.86 ± 2.80b6.58 ± 3.42c
Total VAT volume (liters)3.40 ± 2.12a2.48 ± 1.66b1.69 ± 1.24c1.72 ± 1.03c
L4-L5 SAT area (cm2)245 ± 135a262 ± 153a322 ± 162b420 ± 196c
L4-L5 VAT area (cm2)141 ± 90a112 ± 77b94 ± 65b102 ± 62b
Peak VAT area (cm2)201 ± 118a154 ± 99b110 ± 75c117 ± 68c

Variation in the Distribution of SAT by Sex and Race

Race and sex differences in the pattern of SAT distribution are shown in Figure 2. Means and standard error estimates from the models are shown in the figure, and the p values for the effects of measurement location, as well as sex, race, and the interaction terms (sex × race, race × location, and sex × location) are included in a footnote to the figure. Within the anatomical region examined for this analysis, measurement location (p < 0.0001), as well as race (0.0003) and sex (<0.0001), had significant effects on SAT. SAT areas were greatest in the lower abdominal region and decreased linearly toward the upper abdominal/thoracic region in all race-sex groups. The most remarkable feature of the SAT distribution pattern was the excess SAT area among African-American women compared with white women; on average, abdominal SAT area was 25% to 35% greater in African-American women than in white women at each measurement site. In contrast, SAT area did not differ between African-American and white men (p for race within sex = 0.78).

image

Figure 2. : Means and standard errors for SAT areas by race, sex, and measurement location: model estimates. p Values for parameters are as follows: sex, p < 0.0001; race, p = 0.0003; measurement location, p < 0.0001; sex × race, p = 0.001; sex × measurement location, p < 0.0001; race × measurement location, p < 0.0001.

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Variation in the Distribution of VAT by Sex and Race

VAT area varied significantly by image location (p for location < 0.0001), with a demonstrable peak in mean VAT area in the lower to middle abdomen (Figure 3). Men had greater VAT areas than women (p for sex < 0.0001), and whites had greater VAT areas than African Americans (p for race, p = 0.007). However, there was a significant sex × race interaction (p = 0.003), such that VAT was significantly higher in white men than in black men, but similar in white women and African-American women at every measurement location. The magnitude of race differences in VAT also varied by measurement location (race × measurement location effect: p < 0.0001); the location showing the greatest race difference in men was in the mid abdomen (at ∼5 to 10 cm above L4-L5).

image

Figure 3. : Means and standard errors for VAT areas by race, sex, and measurement location: model estimates. p Values for parameters are as follows: sex, p < 0.0001; race, p = 0.007; measurement location, p < 0.0001; sex × race, p = 0.003; sex × measurement location, p < 0.0001; race × measurement location, p < 0.0001.

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Another way to assess variation in VAT patterning is to identify for each individual the single image that has the maximum VAT area and determine whether this is different, on average, by race and sex. As would be expected from Figure 2, the observed measurement location having the greatest VAT area (peak VAT location) was anatomically higher in men than in women for both races. Mean peak VAT location was 6.2 cm above L4-L5 in white men vs. 1.6 cm above L4-L5 in white women (p < 0.0001) and 5.6 cm above L4-L5 in African-American men vs. 1.6 cm above L4-L5 in African-American women (p < 0.0001). The frequency distribution of the peak VAT location is provided in Figure 4 and shows that peak VAT was most often located between 1 and 4 cm above L4-L5 in African-American women, white women, and African-American men. In contrast, white men displayed peak VAT most frequently 5 to 8 cm above L4-L5. In a significant number of women (30% to 35%), peak VAT occurred below L4-L5, whereas this occurred in <10% of men. Similarly, peak VAT occurred 9 to 12 cm above L4-L5 in 25% to 30% of men but did so in only 10% of women. The frequency distributions for peak VAT location were significantly different by sex in both races (whites: p = 0.007; African Americans: p = 0.0011) and were significantly different by race in men only (men: p = 0.04; women: p = 0.42).

image

Figure 4. : Frequency distribution of the maximum (peak) VAT area location.

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Variation in the Distribution of VAT by Age

All adiposity measures increased with age (Table 2), but total VAT volume increased more rapidly with age than did other body composition measures in all groups except African-American men (Hotelling test: p = 0.01 to p = 0.0001). To examine whether there were age-related differences in the pattern of VAT deposition across the abdomen, we examined mean VAT areas from our repeated-measures ANOVA for individuals ≥50 and <50 years of age. Given the smaller number of African-American subjects, we included only white subjects in this analysis. An increase in VAT with age was seen across all measurement locations (p age effect < 0.0001), and there were, again, significant sex × location (p < 0.0001) and age × location (p < 0.0001) interactions, whereby both sex differences and age-related increases in VAT areas differed by measurement location. These findings are shown in Figure 5, which shows a cross-over in mean VAT area between younger men and older women; older women had greater VAT area than younger men at sites near L4-L5 but lesser VAT area than younger men at most other locations. Thus, L4-L5 VAT area was higher in older women than in younger men, despite the fact that total VAT volume was also lower in older white women (2.0 liters) compared with both older (4.3 liters) and younger (2.6 liters) white men (p < 0.0001 for each contrast).

Table 2. . Correlations between age and body composition measures by race and sex
VariablesWhite menAfrican-American menWhite womenAfrican-American women
  • TBF, total body fat; VAT, visceral adipose tissue; SAT, subcutaneous adipose tissue. Correlation between age and body composition measure is significantly different from zero:

  • *

    p < 0.0001

  • p < 0.05

  • p < 0.01.

  • Correlation between age and VAT volume significantly greater than correlation between age and other body composition measures as measured by the Hotelling test (28) is marked in bold.

BMI (kg/m2)0.24*0.260.160.19
Abdominal circumference (cm)0.46*0.55*0.29*0.23
TBF (kg)0.35*0.480.180.21
Total VAT volume (L)0.50*0.55*0.42*0.40
Total SAT volume (L)0.180.380.120.08
image

Figure 5. : Means and standard errors for VAT areas by sex, age (≥50 or <50 years), and measurement location: model estimates (whites only). p Values for parameters are as follows: sex, p < 0.0001; age, p < 0.0001; measurement location, p < 0.0001; sex × age, p = 0.001; sex × measurement location, p < 0.0001; race × measurement location, p < 0.0001; age × measurement location, p < 0.0001.

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Location of MRIs Relative to Anatomical Landmarks

We examined scout images from a random subset of 50 subjects from each race and sex group to determine the relationship between our MRI image locations, which are on a 1-cm interval scale above and below L4-L5, and anatomical (vertebral) landmarks to provide a clearer interpretation of our results. On average, L3-L4 occurred ∼4 cm above L4-L5, L2-L3 occurred ∼8 cm above L4-L5, and L1-L2 occurred ∼12 cm above L4-L5. Mean differences among race and sex groups ranged from 0.3 to 1.0 cm. Thus, the anatomical site providing the best discrimination of sex and race differences (i.e., 5 to 10 cm above L4-L5) was near L2-L3.

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

Despite the fundamental involvement of central (abdominal) adiposity in the etiology of diabetes and coronary heart disease (29, 30), there is no consensus on a standardized method for its measurement, perhaps because the complexity of adipose tissue endocrinology and metabolism has only recently been appreciated (3, 6, 7, 31).

Typically, a single MRI or CT image taken near the umbilicus (at the L4-L5 or L3-L4 intervertebral space) is used to represent abdominal subcutaneous and visceral adiposity (16, 32). One of the main findings of this study was that use of a single image low in the abdomen may provide an inaccurate representation of sex differences in visceral adiposity. Mean VAT area tended to be relatively small in men at L4-L5 but relatively large in women at that location, and the peak VAT area was located more often in the middle-to-upper abdomen in men (5 to 10 cm above L4-L5 or near L2-L3), whereas peak VAT was usually located in the lower abdomen (near L4-L5) in women. This was particularly true of white men who had a uniquely broad distribution of peak VAT location, with many white men carrying their peak VAT 11 to 17 cm above L4-L5 (near L1). These results suggest a sex-specific patterning of VAT that is not adequately captured using current standard single-image protocols, which will underestimate total VAT volume in men compared with women. We liken this to using two different times of day for collecting fasting blood samples in men and women. In both cases, there would be systematic differences in measurement that could affect the perceived relationship between VAT (or blood assay values) and disease risk in men compared with women. Although adjusting for sex, race, and age could statistically remove this measurement error in a pooled analysis, the important issue of how adiposity affects disease risk in men vs. women and in different ethnic and racial groups would be obscured by doing so.

That VAT increased in women from the upper abdomen down to around L4-L5 is at odds with some previous studies that found that VAT area decreases from L1-L4 in women, as in men (22, 26). Although hormonal factors are likely involved in changes in the absolute amount and patterning of VAT with age, these factors are not likely to explain the disparate findings, because we saw the same pattern in older as well as in younger women (Figure 5). Furthermore, one of the other studies was in older women (26), and the other was in younger women (22). It is possible that our study's denser concentration of MRIs within the abdomen and the much greater number of women may have allowed for small slice-by-slice differences in VAT areas to be resolved. It is important that these findings be replicated by others who have contiguous image data of the abdomen in women. It has been previously suggested that men tend to carry VAT higher in the abdomen than do women (16), but the precise location of peak VAT in men vs. women and the significance of race differences in this phenomenon have not, to our knowledge, been reported elsewhere.

We also found that, in men, race differences in VAT varied significantly by measurement location. It is well-known that African-American men tend to have lower VAT than white men, whereas VAT is more similar between white and African-American women (33, 34, 35, 36, 37), but possible race differences in the pattern of VAT deposition across different anatomical locations have not been examined. Differences in VAT between African-American and white men were greater near L2-L3 than near L4-L5. At L4-L5 for example, VAT area was 30 cm2 greater in white than in African-American men, whereas 10 cm above L4-L5, VAT was 60 cm2 greater in white than in African-American men. With small numbers of subjects, measurement location could, therefore, affect the significance of L4-L5 VAT differences between African-American and white men.

Because of the variation in the location of peak VAT area by sex and race, African-American men and women in our study were indistinguishable from each other in VAT area in the vicinity of L4-L5. The CARDIA and HERITAGE studies, which measured VAT at a single image taken at the L4-L5 location, similarly found no sex difference in VAT among African Americans (34, 35). Sumner et al. (38) suggested that using a single image at L4-L5 is sub-optimal for quantifying sex differences in VAT area among African Americans because of differences in the distribution of VAT across the abdomen in the two genders; our study confirms this using contiguous MR image data and further suggests that an image collected 5 to 10 cm above L4-L5 (i.e., near L2-L3) will improve the likelihood of identifying the more subtle sex differences in VAT that exist in African Americans.

Abdominal adipose tissue is known to increase with age in both sexes (39, 40, 41, 42, 43), with women developing a more “android” or centralized body fat distribution as they age, particularly after menopause (44, 45, 46, 47, 48). Our results showed that age-related increases in VAT varied by both sex and measurement site. In the vicinity of L4-L5, VAT area was greater in older women than in younger men. At most measurement sites, however, this was not the case; women ≥50 years of age typically had significantly less VAT than both older and younger men. Using a measurement location such as L4-L5 alone could, therefore, lead to the conclusion that postmenopausal women have similar or greater VAT than younger men, when, in fact, their total VAT volume is significantly lower.

Waist circumference is now established as an important predictor of cardiovascular disease risk, independently of the risk posed by elevated BMI (49, 50). However, waist circumference is measured at a variety of landmarks, including the last rib, the umbilicus, and at the iliac crest. Circumferences at these locations tend to be highly correlated with one another (48), but we found a significant variation in VAT for each 1 cm distance above or below L4- L5 (near the umbilicus in most individuals), particularly in men, suggesting that the location of the waist circumference measure could also impact the prediction of disease risk. Furthermore, this study suggests that the observed ethnic differences in the relationship between waist circumference and chronic disease (51) may lie in the fact that the same waist circumference measure may not be optimum for approximating VAT in all population subgroups. Our subsequent work will identify an external measurement appropriate to population-based studies that captures peak VAT area in men better than waist circumference and will also examine whether measurement site significantly impacts the relationship between VAT and cardiometabolic risk factors. At this point, researchers might consider adding a subcostal abdominal circumference, in addition to a circumference taken at the umbilicus or the suprailiac crest, to better capture peak VAT area in men.

The major strength of this study is the availability of contiguous 10-mm-thick MRI data in a large sample of adults that allowed us to make precise distinctions in VAT deposition by race, age, and sex. These observations are, to our knowledge, unique. There were a number of limitations, including the fact that we did not decompose VAT into intraperitoneal and extraperitoneal subcompartments and the fact that there were too few African-American subjects to assess the impact of age on VAT patterning. We also did not include individuals of Hispanic or Asian origin, children, or special clinical populations; therefore, our results should be applied cautiously in groups other than healthy white and African-American adults.

In conclusion, our group and others have previously used multiple image protocols to suggest that images located 5 to 10 cm above L4-L5 (i.e., near L2-L3) are better approximations of total VAT mass than the L4-L5 image (15, 23, 26, 52, 53). An additional justification for selecting a single image in this region is presented here. Because of group differences in the patterning or topography of visceral adiposity, the excess VAT volume characteristic of men compared with women and of African-American men compared with African-American women will be underestimated, or even unapparent, when using a single image taken in the near vicinity of L4-L5. Use of the L4-L5 measurement site may also overestimate the relative gain in visceral adiposity in older women vs. men. Significant public health emphasis is now placed on waist circumference as a predictor of cardiometabolic risk; this study suggests that observed differences in the relationship between waist circumference and disease may stem, in part, from human variation in the pattern of VAT accrual.

Acknowledgments

  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References

This study was supported by National Institutes of Health Grants R01-DK064870, R01-DK 064391, R01-HD12252, and R01-HL69995.

Footnotes
  • The costs of publication of this article were defrayed, in part, by the payment of page charges. This article must, therefore, be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

  • 1

    Nonstandard abbreviations: VAT, visceral adipose tissue; SAT, subcutaneous adipose tissue; FFA, free fatty acid; IR, insulin resistant; IS, insulin sensitive; MRI, magnetic resonance imaging; CT, computed tomography; TBF, total body fat.

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  1. Top of page
  2. Abstract
  3. Introduction
  4. Research Methods and Procedures
  5. Results
  6. Discussion
  7. Acknowledgments
  8. References
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