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Visceral adiposity and its anatomical distribution as predictors of the metabolic syndrome and cardiometabolic risk factor levels^1,2,3

¹ From the Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, MN (EWD); the Lifespan Health Research Center, Department of Community Health, Boonshoft School of Medicine, Wright State University, Dayton, OH (DR, NR, ML, ACC, WC, SAC, WCC, RMS, and BT); and the Department of Biostatistics, Virginia Commonwealth University, Richmond, VA (SSS)

² Supported by grants no. R01-DK064870, R01-DK064391, R01-HD12252, and R01-HL69995 from the National Institutes of Health.

³ Reprints not available. Address correspondence to EW Demerath, Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, 1300 South Second Street, Suite 300, Minneapolis, MN 55454-1015. E-mail: ewd{at}umn.edu.

	ABSTRACT

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Background: Despite the recognition that central obesity plays a critical role in chronic disease, few large-scale imaging studies have documented human variation in abdominal adipose tissue patterning.

Objective: We aimed to compare the associations between abdominal subcutaneous adipose tissue (ASAT) and visceral abdominal tissue (VAT), which were measured at different locations across the abdomen, and the presence of the metabolic syndrome (MS; National Cholesterol Education Program Adult Treatment Panel III definition) and individual cardiometabolic risk factors.

Design: This study included 713 non-Hispanic whites aged 18–86 y, in whom VAT and ASAT were assessed by using multiple-image magnetic resonance imaging. The anatomical position of the magnetic resonance image containing the maximum VAT area for each subject was used as a measure of VAT patterning. Multivariate linear and logistic regression analyses were used to examine the relation of VAT, ASAT, and VAT patterning to cardiometabolic risk.

Results: VAT mass was a stronger predictor of the MS than was ASAT mass, but ASAT mass (and other measures of subcutaneous adiposity) had signification interactions with VAT mass, whereby elevated ASAT reduced the probability of MS among men with high VAT (P = 0.0008). There was variation across image locations in the association of VAT area with the MS in men, and magnetic resonance images located 4–8 cm above L4–L5 provided the strongest correlations between VAT area and cardiometabolic risk factors. Subjects whose maximum VAT area was higher in the abdomen had higher LDL-cholesterol concentrations (R² = 0.07, P < 0.0001), independent of age and adiposity.

Conclusion: Further studies are needed to confirm the effects of VAT patterning on cardiometabolic risk.

	INTRODUCTION

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Abdominal obesity predisposes persons to diabetes and cardiovascular disease (CVD) (1), and elevated visceral adipose tissue (VAT), in particular, is associated with insulin resistance, dyslipidemia, systemic inflammation, diabetes, hypertension, myocardial infarction, and all-cause mortality (2-14). Whereas VAT may have a uniquely important pathophysiological role in CVD and diabetes, the amount of subcutaneous adipose tissue (SAT) also contributes to obesity-related insulin resistance and the metabolic syndrome (MS), as reviewed by Misra and Vikram (7) and Freedland (15). Indeed, it has been suggested that low SAT mass in the limbs may increase the risk of diabetes (1, 16-20). However, the role of abdominal SAT (ASAT) in buffering the effects of VAT on health is not entirely clear.

Because of cost constraints, most imaging studies have obtained a single magnetic resonance imaging (MRI) or computed tomography image of the abdomen, typically at the L4–L5 intervertebral space, to represent VAT and abdominal SAT (21). However, studies using multiple-image protocols suggest that, compared with images taken in the middle to upper abdomen, ie, near L2–L3, the L4–L5 image is a significantly worse predictor of both total VAT volume (22-25) and CVD risk factor levels and the MS (23, 26, 27). Furthermore, individual variation in the spatial distribution of the VAT depot itself in the upper and lower abdomen has received relatively little attention. Machann et al (28) elegantly illustrated variation in adipose tissue "topography," but epidemiologic studies have yet to examine the spatial patterning of VAT as an independent predictor of health risks.

With the use of a large database of 1 cm–thick contiguous abdominal MRI images, the present study aimed 1) to examine effects of VAT and ASAT and their interaction with each other on the presence or absence of the MS, 2) to determine whether image location significantly affects the correlations between single image VAT areas and cardiometabolic risk factor levels and the presence or absence of the MS, and 3) to explore variation in the distribution of VAT across the abdomen and its relation to cardiometabolic risk factor levels and the MS.

	SUBJECTS AND METHODS

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

 
Study sample
The sample included 713 healthy non-Hispanic white participants (49% women) aged 18–86 y who were enrolled in 1 of 3 ongoing studies of body composition and CVD risk—the Fels Longitudinal Study, the Miami Valley Family Aging Study, and the Southwest Ohio Family Study—at the Lifespan Health Research Center (Dayton, OH) in 2003–2006. Participants resided in the Dayton-Cincinnati area of southwestern Ohio at the time of recruitment and returned to the study center at regular intervals for serial examinations. Participants were screened in advance to ensure that they were free of any contraindications for MRI. All of the data for a scheduled exam were collected on the same day.

All subjects provided written informed consent. The study protocol was approved by the Institutional Review Board at Wright State University.

Anthropometry and whole-body body composition
Participants wore light clothing (eg, shorts and sleeveless shirts) during measurement. Weight was measured to the nearest 0.01 kg and stature was measured to the nearest 0.01 cm by using a digital scale and a digital stadiometer, respectively. Total-body fat mass (TBF), fat-free mass (FFM), and lower-extremity (LEG) fat mass were measured by using a dual-energy X-ray absorptiometry (DXA) system (Hologic QDR 4500; Hologic Inc, Bedford, MA; software version 9.8D). The triceps subcutaneous skinfold thickness was measured by using Lange calipers.

Magnetic resonance imaging
Abdominal MRI was conducted at the Good Samaritan Hospital Greater Dayton MRI Consortium (Dayton, OH) by using a previously described protocol (22, 29). Images were obtained every 1 cm from the 9th thoracic vertebra (T9) to the first sacral vertebra (S1). Image locations were defined relative to a common anatomical landmark, the L4–L5 intervertebral space. To facilitate comparison of individual image data, we limited our analyses to the set of images with no missing values for all subjects, and this set included images ranging from 20 cm above L4–L5 (+ 20 cm) to 3 cm below L4–L5 (– 3 cm), for a total of 24 images per subject. Segmentation of the axial images into VAT and ASAT areas (cm²) was performed by 2 trained observers using SLICE-O-MATIC image analysis software (version 4.2; Tomovision Inc, Montreal, Canada). Interobserver variation was low (CVs = 5.1% for VAT mass and 1.2% for ASAT mass). VAT and SAT areas were summed across all 24 images to obtain VAT and SAT volumes, and then these volumes were multiplied by 0.916 g/cm³, the density of adipose tissue, to obtain total VAT mass (kg) and total ASAT mass (kg). The spatial distribution, or patterning, of VAT across the abdomen was assessed by identifying the maximum (peak) VAT area for each subject, and recording the measurement location of that image. Individual maximum VAT areas occurred across a broad range of images, from 3 cm below L4–L5 to 19 cm above L4–L5. On average, maximum VAT area occurred 6.7 cm above L4–L5 in men and 3.8 cm above L4–L5 in women (P for sex difference < 0.0001).

Cardiometabolic risk factor measures and the metabolic syndrome
Blood samples were obtained after a 12-h fast and collected by using a evacuated tube containing EDTA. Fasting plasma glucose concentrations were measured in-house by using the Vitros Chemistry Products VITROS GLU DT method (Ortho-Clinical Diagnostics, Raritan, NJ) on the VITROS DT60 analyzer. Fasting triglycerides (TG) and the HDL- and LDL-cholesterol fractions were measured at the Medical Research Laboratory (Cincinnati, OH), which is participating in the Centers for Disease Control and Prevention's Lipid Standardization Program. Fasting insulin, measured by radioimmunoassay, was available for a subset of subjects. The homeostasis model assessment score, an estimate of insulin resistance, was calculated from fasting insulin and glucose values (30). High-sensitivity C-reactive protein was measured in a subset of participants with the use of an immunochemiluminometric method. Trained technicians measured waist circumference to the nearest centimeter immediately superior to the left iliac crest according to protocols of the National Health and Nutrition Examination Survey. Seated blood pressures were measured with a mercury sphygmomanometer after 5 min of rest, and appropriate cuff size was chosen on the basis of the arm circumference of the participant. The means of the last 2 of 3 seated systolic and diastolic blood pressure measurements were used for analysis. The MS was defined according to the updated criteria of the National Cholesterol Education Program Adult Treatment Panel III (31). The prevalence of the MS was significantly higher in men than women (31.2% and 24.0%, respectively; P = 0.02). Sex differences also were found in the prevalence of all MS components (P < 0.05, data not shown) and in all body-composition measures, and therefore analyses were stratified by sex.

Statistical analysis
Skewness and kurtosis of the study variables were examined, and variables were transformed by using the natural log function only when necessary to meet statistical assumptions for general linear models (32). The sample initially included 776 subjects who underwent abdominal MRI; 22 were removed because of diabetes (11 men and 11 women), and 41 subjects with values >3 SD above the mean for >1 cardiometabolic risk factor were removed (25 men and 16 women), to yield the final sample of 713. Differences in means between the sexes were tested by using unpaired t tests.

Sex-specific logistic regression models adjusted for age, age², body mass index (BMI; in kg/m²), current smoking status (yes or no), physical activity level [using the sport index of the Baecke questionnaire (33)], and FFM were tested to examine the effect of VAT, ASAT, and BMI on the odds of the MS. In analyses of the interaction between VAT and various SAT measures (ie, ASAT, triceps skinfold thickness, and LEG fat mass) on the odds of the MS, we evaluated these measures as continuous variables in logistic regression models. Then, for illustrative purposes, we created tertiles to plot the probability of the MS in subjects with high (top tertile) compared with lower (bottom 2 tertiles) subcutaneous adiposity across all levels of VAT mass. PROC GENMOD was used to compare odds ratios (ORs) of the MS in subjects with high and lower subcutaneous adiposity at particular levels of VAT mass.

Differences in the association between VAT area measured at different single image locations across the abdomen and at different cardiometabolic risk factor levels were tested by comparing each age-adjusted Pearson correlation coefficient with the highest correlation coefficient for each risk factor with the use of the Hotelling-Williams test (34). Then, using sex-specific logistic regression models adjusted for age, age², BMI, current smoking status (yes or no), physical activity level, and FFM, we compared the ORs (and 95% CIs) for VAT measured at different single image locations as a predictor of the MS. ORs were based on a 1-SD increase in each of the predictor variables. Goodness-of-fit for the logistic regression models was assessed by using the c-statistic and the Hosmer-Lemeshow test, and the Akaike Information Criterion (AIC) was used to compare the final models. Multicollinearity was tested by using the variance inflation factor, and models were excluded if the variance inflation factor was >10.

VAT patterning was examined by using the anatomical position (image number) of the maximum VAT area for each person. VAT area generally follows a curvilinear pattern across the abdominal region, and it shows a peak, or maximum VAT area, for each subject; see Demerath et al (35) for further description. The anatomical location of the maximum VAT area increased in 1-cm increments from image location no. 1 (in which case peak VAT area occurred 3 cm below L4–L5) to image location no. 22 (in which case peak VAT area occurred 18 cm above L4–L5); there were no subjects with maximum VAT area at L4–L5 + 19 cm or L4–L5 + 20 cm. Maximum VAT area location was entered into sex-stratified general linear and logistic regression models as a continuous variable alongside age, age², VAT mass, smoking, and physical activity to test the independent effects of VAT patterning on cardiometabolic risk factors and the MS.

Statistical analyses were carried out with the use of SAS software (version 9.2; SAS Institute, Cary, NC). Two-tailed ( = 0.05) tests of significance were used.

	RESULTS

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Descriptive statistics
Characteristics of the study subjects are summarized by sex in Table 1. Mean age was 43 y; 70% of the subjects were overweight (37.7% of women and 44.6% of men) or obese (31.2% of women and 27.6% of men), and expected sex differences in body composition and cardiometabolic risk factor levels were observed.

Table 2

Table 3

Figure 1

View larger version (22K): [in this window] [in a new window]   FIGURE 1.. Interaction between visceral adipose tissue (VAT) and abdominal subcutaneous adipose tissue (ASAT) mass (by tertile) for the probability of the metabolic syndrome in men (probit estimates), n = 363.

Table 4

Figure 2

View larger version (21K): [in this window] [in a new window]   FIGURE 2.. Odds of the metabolic syndrome (National Cholesterol Education Program definition) associated with a 1-SD increase in visceral adipose tissue (VAT) area at twenty-four 1-cm–thick contiguous image locations spanning from 3 cm below L4–L5 (L4–L5 – 3) to 20 cm above L4–L5 (L4–L5 + 20), adjusted for age, smoking status, physical activity, and fat-free mass, by sex. n = 363 men and 350 women. MRI, magnetic resonance imaging.

Table 5

Figure 3

View larger version (21K): [in this window] [in a new window]   FIGURE 3.. Relation of visceral adipose tissue (VAT) patterning (location of maximum VAT area) to LDL-cholesterol concentrations in men (n = 363). Individual predicted values from a multivariate-adjusted regression model also included age, VAT mass, smoking status, fat-free mass, and physical activity.

	DISCUSSION

TOP ABSTRACT INTRODUCTION SUBJECTS AND METHODS RESULTS DISCUSSION REFERENCES

We hypothesized that greater ASAT mass, LEG fat mass, and triceps skinfold thickness may have protective effects (ie, may be associated with lower odds of theMS) in this sample of healthy adults. Goodpaster et al (16) showed in the Health, Aging, and Body Composition Study that older adults with the MS tend to have lower percentage body fat than those without the MS. In the present study, a significant interaction between VAT mass and subcutaneous adiposity traits was found in men: elevated ASAT mass and peripheral subcutaneous fat measures were deleterious when VAT mass was low, but they became more protective against the MS (ie, they reduced the OR) as VAT mass increased. There were relatively few subjects with discordant values on these measures [eg, only 4% of men were in the top tertile for VAT mass (>4.2 kg) and the bottom tertile for ASAT mass (<2.2 kg)]. The relative rarity of such subjects most likely led to the relatively wide CIs of our probability estimates at the tails of the distribution of VAT, and it may explain why smaller studies have not identified this protective effect at all. Nonetheless, our conclusions are based primarily on the significance of the interaction term in the continuous-variable models, and neither the figures nor the tables show estimates that extended past actually observed data points. Our finding is consistent with the paradigm of ectopic fat accumulation, in which increased metabolic risk stems from exceeding the capacity of the subcutaneous adipocytes to differentiate and accommodate excess circulating lipids, which results in ectopic deposition of lipids—that is, deposition in the liver, muscles, pericardium, and visceral compartments (1, 17, 18); this outcome was reviewed by Bays et al (39). The present study is, to our knowledge, the first to suggest that elevated abdominal SAT mass, as well as peripheral or extremity SAT, may have protective effects in the presence of high VAT.

Kuk et al (23) found that, whereas VAT area was a significant predictor of the MS at all 8 measurement locations they examined, the odds were twice as high for VAT area measured in the upper abdomen [near L1-L2 (unadjusted OR = 8.8)] than for VAT area measured at L4–L5 (unadjusted OR: 3.9; confidence limits not provided), which indicates that the single MRI or computed tomography image chosen for analysis could affect the perceived severity of risk associated with elevated VAT. Shen et al (27) reported that the L4–L5 image never had the highest correlation with MS risk factors and often had a significantly lower correlation than did total VAT volume or images located superior to L4–L5. They suggested choosing a single image 10–15 cm above L4–L5 in men and either 5 cm above or 5 cm below L4–L5 in women. In the present study, the wide CIs around the estimates precluded our choosing any one particular image as statistically superior to the others, but the highest risk was associated with midabdominal images (near L2–L3 in men and near L3–L4 in women), rather than with lower abdominal images (near L4–L5). By looking at individual risk factors, we identified a set of contiguous images containing the statistically equivalent, highest correlations with risk factor levels for each sex, and these ranges included the L4–L5 image 50% of the time. In contrast, images located 4–8 cm above L4–L5 provided correlations that were consistently high in magnitude for every risk factor examined (other than LDL), in both men and women. Therefore, we would recommend that, if a small number of images are to be analyzed, an image located in that region [which we have found to lie close to the L2–L3 intervertebral space (35)], rather than the L4–L5 image, would best capture the relation between VAT and cardiometabolic risk factors in both sexes.

Related to this finding is the documented metabolic heterogeneity within the VAT depot itself; upper abdominal VAT primarily comprises the highly lipolytic mesenteric and omental adipose tissues, which are drained by the portal vein, whereas lower abdominal VAT has a greater proportion of retroperitoneal adipose tissue, which is drained nonportally (40-43). Given these differences, it is possible that not only the amount but also the spatial distribution of VAT within the abdomen has consequences for cardiometabolic risk. Novel findings from the present study are that persons vary greatly in the patterning of VAT (represented in the present analysis by the anatomical location of the maximum VAT area for each subject), that the location of the maximum VAT area tends to move upward (ie, cranially) in the abdomen with increasing age and adiposity, and that LDL-cholesterol concentrations are related to VAT patterning in men and women, independent of age and adiposity. However, the lack of an effect of VAT patterning on the odds of the MS and other risk factors indicates the need for further analysis and replication from studies with multiple-image VAT data.

A limitation of the present study is that risk factor data were available only for non-Hispanic whites; the same relations between VAT and disease risk factors may not apply in other racial-ethnic groups. Furthermore, the subjects were essentially a convenience sample including all nondiabetic subjects in 3 ongoing studies of body composition and CVD risk factors in the greater Dayton area. It is possible that the nonrandom nature of the sampling design (ie, it was restricted to relatively healthy volunteers) may have biased the relations between different adiposity measures and cardiometabolic risk. However, our results are similar to those of other studies documenting the greater risk associated with VAT than with SAT, as well as the effects of image location on cardiometabolic risk factor levels (23, 27). In addition, we adjusted for important possible confounders of the disease risk–body composition relation, including physical activity level and cigarette smoking.

In conclusion, although VAT mass was a stronger predictor of the MS than was ASAT, elevated ASAT mass (and other subcutaneous fat measures) were found to protect against the MS in men with high VAT mass, as predicted by the ectopic fat paradigm. Our findings also indicate that the L4–L5 intervertebral space is not optimal for detecting the relation of VAT to disease risk. Rather, an image located 4–8 cm cranially from L4–L5 would more strongly and consistently correlate with cardiometabolic risk factor levels. Finally, we identified a novel aspect of abdominal adiposity—the spatial patterning or topography of VAT—that appears to stem primarily from differences in age and the amount of visceral fat, but that had independent effects on LDL-cholesterol concentrations. Further work is needed to identify the genetic and environmental determinants of ectopic fat deposition and VAT patterning.

	ACKNOWLEDGMENTS

 
We are grateful for the critical contributions of the Data Collection 374 staff of the Lifespan Health Research Center; the participants in the Southwest Ohio Family Heart Study, the Fels Longitudinal Study, and the Miami Valley Family Aging Study; and Carol Cottom and Kimberly Ritter for their analysis of the magnetic resonance imaging images.

The contributions of the authors are as follows—EWD: analyzed the data and wrote the Introduction, Results, and Discussion sections of the manuscript; DR: analyzed the data and wrote the Subjects and Methods section of the manuscript; NR: collected the data and edited the manuscript; SSS: oversaw analysis of the data; ML: analyzed data and edited the manuscript; ACC: edited the manuscript; WC: analyzed the magnetic resonance imaging data; and SAC, WCC, RMS, and BT: edited the manuscript. None of the authors had a personal or financial conflict of interest.

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