Contributions of total and regional fat mass to risk for cardiovascular disease in older women

R. E. Van Pelt1,3, E. M. Evans1, K. B. Schechtman1,2, A. A. Ehsani1, and W. M. Kohrt1,3

Divisions of 1 Geriatrics/Gerontology and 2 Biostatistics, Department of Internal Medicine, Washington University School of Medicine, St. Louis, Missouri 63110; and 3 Division of Geriatric Medicine, Department of Medicine, University of Colorado Health Sciences Center, Denver, Colorado 80262


    ABSTRACT
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

The aim of this study was to determine whether trunk fat mass, measured by dual-energy X-ray absorptiometry (DEXA), is predictive of insulin resistance and dyslipidemia, independently of arm and leg fat mass, in postmenopausal women. Total and regional body composition was measured by DEXA in 166 healthy, postmenopausal women (66 ± 4 yr). Four primary markers of insulin resistance and dyslipidemia were assessed: 1) area under the curve for the insulin (INSAUC) response to an oral glucose tolerance test (OGTT), 2) product of the OGTT glucose and insulin areas (INSAUC×GLUAUC), 3) serum triglycerides (TG), and 4) high-density lipoprotein (HDL)-cholesterol. Trunk fat mass was the strongest independent predictor of each of the primary dependent variables. In multivariate regression models, trunk fat mass was associated with unfavorable levels of INSAUC, INSAUC×GLUAUC, TG, and HDL-C, whereas leg fat mass was favorably associated with each of these variables. Thus trunk fat is a strong independent predictor of insulin resistance and dyslipidemia in postmenopausal women, whereas leg fat appears to confer protective effects against metabolic dysfunction.

trunk fat; leg fat; disease risk; postmenopausal women


    INTRODUCTION
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

MENOPAUSE IS ASSOCIATED WITH INCREASES in body fatness, particularly in the abdominal region (19, 21). Abdominal adiposity is more strongly associated with the development of type 2 diabetes, coronary artery disease (CAD), and cardiovascular disease-related mortality than is total adiposity (3, 9, 10, 29). Menopause-related central body fat accumulation potentially contributes to the increased incidence of disease observed in postmenopausal, compared with premenopausal, women.

Because upper body obesity is associated with the metabolic and cardiovascular complications of the hyperinsulinemic-dyslipidemic syndrome (7), the assessment of upper body fat accumulation in postmenopausal women is an important screening tool for the prevention of these health complications. Anthropometric (e.g., waist circumference) and soft-tissue imaging [e.g., computed tomography (CT), magnetic resonance imaging (MRI)] measures of abdominal adiposity are associated with poor metabolic health and cardiovascular disease risk factors (6, 17, 25, 26). However, the measurement of regional adiposity by dual-energy X-ray absorptiometry (DEXA) is potentially more accurate than anthropometric measures and more practical and cost effective than CT or MRI scans.

Although the primary application of DEXA is to measure bone mineral density to ascertain risk for osteoporosis, it also provides a measure of total and regional (i.e., trunk, arm, leg) fat mass. It is not known whether trunk fat mass is as strong a predictor of metabolic and cardiovascular disease risk as the commonly used clinical measures of body mass index (BMI) and waist circumference. Thus the primary aim of this study was to determine whether trunk fat mass, measured by DEXA, is a good predictor of insulin resistance and dyslipidemia in postmenopausal women.

Additionally, there is evidence in young and middle-aged women that central, but not peripheral, fat mass is associated with insulin resistance (4) and poor lipid profile (35). Therefore, a second aim of this study was to determine whether the relation of trunk fat mass to insulin resistance and dyslipidemia is independent of arm and leg fat mass in postmenopausal women.


    METHODS
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

Subjects. Body composition and cardiovascular disease risk factors were retrospectively analyzed in 166 healthy, postmenopausal (66 ± 4 yr) women who had participated in research studies conducted at Washington University School of Medicine. All women were >= 2 yr past menopause (18 ± 7 yr), were not using any type of hormone replacement, and were not smokers. They did not have overt heart disease, as assessed by resting and exercise 12-lead electrocardiogram, or diabetes mellitus, as assessed by an oral glucose tolerance test (OGTT). All of the participants provided written informed consent to participate in these studies, which were approved by the Washington University Institutional Review Board.

Body composition. Fat-free mass, whole body fat mass, and regional fat mass (trunk, legs, and arms) were measured using DEXA-enhanced whole body analysis (v5.64, Hologic QDR-1000/W; Waltham, MA). The recommendations of the manufacturer were followed for the designation of regions of interest (i.e., arms, legs, trunk). Lines were initially placed by the computer program and then manually adjusted by a technician. The proximal ends of the lines that separated the arms from the trunk were positioned so as to go through the middle of the axilla; they were then angled outward from the body so that they separated the arms from the trunk. A pelvic triangle was positioned so that one horizontal line was just superior to the iliac crests and the other two lines angled down so that they crossed through the femoral neck regions of both hips and intersected at a point between the legs.

The reproducibility of regional body composition measurements was evaluated in 13 women aged 60-70 yr. Three DEXA procedures were performed at weekly intervals; results therefore reflect both technical and biological variability. Coefficients of variation were calculated for each individual for fat, fat-free, and total masses of the arm, leg, and trunk regions. The average coefficients of variation (%, mean ± SD) for the group were as follows.   

                              
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The technical variance in any of these mass measurements is between 0.1 and 0.5 kg. Thus coefficients of variation (i.e., SD/mean) tend to be larger for the arm region, because the total mass is less than in the leg or trunk regions.

Waist circumference was measured in triplicate at the midpoint between the distal border of the ribs and the top of the iliac crest with the subject in the standing position.

Blood lipids and lipoproteins. Measurements of serum lipid and lipoprotein concentrations were performed in the Core Laboratory for Clinical Studies at Washington University. Total cholesterol (TC) and glycerol-blanked triglycerides (TG) were measured by automated enzymatic commercial kits (Miles/Technicon, Tarrytown, NY). High-density lipoprotein (HDL)-cholesterol was measured in plasma after precipitation of apolipoprotein B-containing lipoproteins by dextran sulfate (50,000 MW) and magnesium (34). Low-density lipoprotein (LDL)-cholesterol was calculated using the Friedewald equation (12). These methods are continuously standardized by the Lipid Standardization Program of the Centers for Disease Control and Prevention.

OGTT. A 75-g OGTT was administered in the morning after an overnight fast. Diet was monitored for 3 days before the OGTT to ensure an intake of >150 g of carbohydrate per day. Blood samples (3.0 ml) were obtained before and 30, 60, 90, 120, and 180 min after glucose ingestion for glucose (glucose oxidase method; Beckman glucose analyzer) and insulin (24) determinations. The total areas under the glucose (GLUAUC) and insulin (INSAUC) curves were calculated using the trapezoidal rule. The INSAUC was used as an index of hyperinsulinemia, and the product of the insulin and glucose areas (INSAUC×GLUAUC) was calculated as an index of peripheral insulin resistance (8, 20, 22).

Blood pressure. After 15 min of supine rest, systolic and diastolic blood pressures (BP) were measured manually using a sphygmomanometer. Three measurements were made at ~5-min intervals and averaged.

Statistics. The primary (INSAUC, INSAUC×GLUAUC, TG, HDL-C) and secondary (TC, LDL-C, GLUAUC, systolic BP, and diastolic BP) outcome variables for analysis in this study were chosen a priori. The primary outcomes were so designated because they have typically been found to relate more closely to abdominal obesity than have the secondary outcomes (7). Pearson correlation coefficients were used to test the hypothesis that trunk fat mass is significantly associated with the primary outcome variables. Stepwise multiple regression and partial correlations were used to test the hypothesis that the association between disease risk and trunk fat mass is independent of arm and leg fat mass. Tertiles of trunk fat mass and leg fat mass were determined for the study cohort. One-way analysis of variance (ANOVA) was then used to compare outcomes in women who had similar levels of trunk fat mass (i.e., middle tertile) but different levels of leg fat mass (low vs. middle vs. high tertile). When data did not satisfy required conditions for a given parametric analysis, appropriate nonparametric analyses were applied. All data are presented as means ± SD, and statistical significance was designated as an alpha -level of 0.05 unless otherwise stated.


    RESULTS
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

Subject characteristics for body composition and for primary and secondary outcome variables are presented in Table 1. The Pearson correlation analyses indicated that most of the measures of total and regional adiposity were significantly correlated with both the primary and secondary risk factors (Table 2). Trunk fat mass was the strongest independent predictor of each of the primary outcome variables.

                              
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Table 1.   Body composition and metabolic characteristics of the study cohort (n = 166)


                              
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Table 2.   Pearson correlation coefficients between predictors and dependent variables

The set of independent predictors for each of the dependent variables was determined through stepwise regression analyses (Table 3). In these multivariate models, trunk fat mass remained the strongest correlate of each of the primary outcome variables, and leg fat mass was the next most significant independent predictor of these outcomes. Importantly, in these multivariate regression models, trunk fat mass was associated with unfavorable levels of hyperinsulinemia, insulin resistance, TG, and HDL-cholesterol, whereas leg fat mass was favorably associated with each of these variables after adjustments were made for trunk fat mass.

                              
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Table 3.   Independent predictors of dependent variables resulting from multiple stepwise linear regression analysis

To investigate more closely the relative importance of trunk, leg, and arm fat mass as predictors of the four primary dependent variables, partial correlations were determined that adjusted for fat mass in alternate regions. Significant correlations were found between trunk fat mass and each of the dependent measures (Table 4), whether unadjusted or adjusted for leg or arm fat, such that increased trunk fat was predictive of increased disease risk. The analyses for leg and arm fat mass indicated different patterns of association. Arm fat mass was significantly related to three of the primary outcome measures. However, after controlling for trunk fat mass, the correlations of arm fat mass with the dependent variables were no longer significant. Conversely, leg fat mass was a significant independent predictor only of INSAUC×GLUAUC. However, after adjustments for variance in trunk fat mass, all of the partial correlations of leg fat mass with the dependent variables were significant, such that greater leg fat mass was associated with reduced disease risk (i.e., lower levels of hyperinsulinemia, insulin resistance, and TG and higher HDL-cholesterol levels).

                              
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Table 4.   Correlation coefficients for the associations between the primary dependent variables and regional fat mass, either unadjusted or adjusted for fat mass in another region

To describe further the nature of the associations of trunk and leg fat mass with the primary risk factors, subjects were grouped by tertiles of trunk fat mass (3.1-10.7 kg, n = 54; 10.8-15.4 kg, n = 58; 15.7-33.3 kg, n = 54) and then further categorized by tertile of leg fat mass (1.9-9.9 kg, n = 55; 10.0-12.8 kg, n = 57; 12.9-22.8 kg, n = 54). Characteristics of the subjects in the middle tertile of trunk fat mass are presented in Table 5. Despite similar levels of trunk fat mass and waist girth and a higher relative body fat content, women in the high leg fat tertile were less insulin resistant and had lower serum TG than women with low leg fat mass. There was also a strong tendency for women with more leg fat to have higher HDL-cholesterol levels (P = 0.07).

                              
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Table 5.   Characteristics of women in the middle tertile of trunk fat mass (10.8-15.4 kg), categorized by tertile of leg fat mass


    DISCUSSION
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

The results of this study indicate that trunk fat mass measured by DEXA is a strong predictor of disease risk in postmenopausal women. Trunk fat mass was consistently associated with important markers of increased risk, including hyperinsulinemia, insulin resistance, high TG, and low HDL-cholesterol. Furthermore, the relation between trunk fat mass and these risk factors remained strong after controlling for peripheral adiposity. In contrast, leg fat mass became a significant predictor of risk only after adjustment for the extent of trunk adiposity, and the direction of the relation indicated that leg fat mass was favorably associated with disease risk. Thus the results of this study are consistent with the belief that excess adipose tissue in central body regions imparts more risk for cardiovascular disease and type 2 diabetes mellitus than does fat stored in peripheral depots. The results further suggest that, for a given degree of central adiposity, greater peripheral adiposity is associated with a more favorable metabolic profile in postmenopausal women.

To our knowledge, the present study is the first to evaluate the relation between DEXA regional fat and risk for both hyperinsulinemia and dyslipidemia in a relatively large group of postmenopausal women not using hormone replacement therapy. Because exogenous estrogens may independently influence levels of certain risk factors (e.g., HDL-cholesterol) (2, 32), it is important to control for this factor when evaluating the influence of regional body composition on risk for disease in women.

Our finding that leg fat mass appeared to have favorable effects on disease risk factors after adjustment for central adiposity is seemingly consistent with previous investigations, with the caveat that sex hormone status was not controlled for in those studies. Williams et al. (35) found that trunk fatness was associated with unfavorable serum lipid and lipoprotein levels in 224 women aged 17-77 yr. Moreover, leg fatness was related to a favorable lipid profile after statistical adjustment for other measures of fat distribution, although the strengths of the relationships did not appear to be as strong as in the present study. They found that leg fat independently accounted for 1 and 4% of the variance in serum HDL-cholesterol and TG, respectively, compared with 6 and 12% in our study. Given the wide age range of women studied by Williams et al., the weaker correlations may reflect greater heterogeneity in factors other than adiposity that influenced lipid profiles. For example, although the partial correlations of measures of adiposity with lipid parameters were adjusted for menopausal status (pre- vs. postmenopausal), there was no apparent control over estrogen use by postmenopausal women or oral contraceptive use by premenopausal women, both of which have independent effects on the serum lipid profile (2, 32). Another difference between the studies that may explain some of the discordance is the manner in which regional fat measures were expressed. Williams et al. presented trunk, leg, and arm fat as a percentage rather than as a mass, as in the present study. However, it was not clear whether the percentages of fat were relative to total body fat mass or to the total mass of each region of interest. It should also be noted that, in addition to the regional measures of adiposity by DEXA, the study of Williams et al. included measures of abdominal subcutaneous and visceral adiposity by CT. The favorable influence of leg fat on all of the lipid measures remained significant after adjustment for both DEXA and CT measures of fat distribution. Furthermore, trunk fat measured by DEXA was the strongest independent determinant of total cholesterol, LDL-cholesterol, and TG, emphasizing the utility of this measure in evaluating disease risk.

Terry et al. (31) also reported favorable effects of leg fatness on the serum lipid profile of 130 overweight premenopausal women aged 25-49 yr. After adjusting for variance in waist circumference, these authors found favorable independent correlations of thigh girth with serum TG and HDL-cholesterol, but not TC or LDL-cholesterol. Although estrogen (oral contraceptives) use was not controlled for in that study, those findings, using anthropometric measures of regional adiposity, are similar to our DEXA-based findings.

Our results and those of others (7, 31, 35) clearly indicate that trunk fat is a deleterious risk factor for cardiovascular disease (insulin resistance and dyslipidemia) in women but also suggest that some degree of protection may be afforded by the propensity to deposit fat in gluteal-femoral depots. This is likely an overly simplistic view, as further discrimination of fat depots within these anatomic regions may also influence risk for disease. In the abdomen, for example, adipose tissue stored in visceral regions appears to confer greater disease risk than adipose tissue in subcutaneous depots (7, 23, 35), although this remains controversial (11). It has also been suggested that the location of fat within the thigh influences disease risk (13, 30). Fat in nonsubcutaneous depots (i.e., stored within muscle, around muscle fibers) was related to insulin resistance in obese individuals, whereas there was no such correlation with subcutaneous thigh fat (13). A positive association between insulin resistance and intramuscular lipid concentrations has also been observed (18, 28). Taken together, these findings suggest that certain depots of fat within the thigh (i.e., intramuscular) are predictive of insulin resistance and may, therefore, confer increased risk for type 2 diabetes mellitus and cardiovascular disease.

Why, then, do we and others (31, 35) find that increased leg fat mass appears to be favorably associated with CAD risk factors after adjusting for central adiposity? It is important to note that, even in those individuals who have obvious fat accumulation within thigh muscle regions, the vast majority of fat is in subcutaneous regions. For example, Goodpaster et al. (13) found that only 2-6% of leg fat was located intramuscularly, even in obese individuals. Because the majority of fat in the legs is in subcutaneous depots, it seems plausible that the apparent protective effect of increased leg fat mass is simply indicative of a propensity to store fat subcutaneously. It is possible that those women who have a relatively large leg fat mass, presumably subcutaneous, also store a relatively larger proportion of abdominal fat in subcutaneous (rather than visceral) depots and thus appear to be at less risk for CAD. Because DEXA cannot distinguish between subcutaneous and visceral abdominal fat depots, or between subcutaneous and intramuscular peripheral fat depots, this contention will require further evaluation with more sophisticated imaging procedures.

Alternatively, we cannot discount the possibility that there are genetic differences between upper- and lower-body-overweight women. Recent evidence indicates that there may be several loci determining propensity to store fat in the abdominal region (27). It is possible that those women who tend to store fat in central body regions are genetically predisposed to insulin resistance and dyslipidemia and, conversely, those who store fat in the lower body simply have a more favorable genetic predisposition for these risk factors.

There are potential physiological reasons why truncal adiposity may increase, and lower-extremity adiposity decrease, risk for metabolic dysfunction, related to the heterogeneity of regional adipose tissue metabolism. In general, in vitro data demonstrate that adipocytes from visceral abdominal regions are more sensitive to lipolytic stimuli and more resistant to suppression of lipolysis by insulin than are adipocytes from gluteal-femoral subcutaneous regions (16, 33); the metabolic characteristics of adipocytes from subcutaneous abdominal regions tend to be intermediate to these (1). There are some, albeit few, in vivo data that support these findings (15). On the basis of these regional differences in the regulation of lipolysis, it would be reasonable to expect that the daily systemic flux of free fatty acids, per unit of fat mass, would be higher in individuals with a preponderance of abdominal fat than in those with lower body fat localization, due both to a heightened sensitivity to the activation of lipolysis and to an impaired suppression of lipolysis in abdominal adipocytes. Furthermore, abdominal fat may directly impact hepatic free fatty acid flux due to its proximity to the portal circulation and, consequently, increase TG synthesis and decrease hepatic insulin clearance (14, 23). There is also evidence to suggest that adipocytes have distinct intrinsic characteristics (e.g., fatty acid-binding proteins and enzymes of fat metabolism) that further contribute to the heterogeneity in FFA handling by the various fat depots (5).

There are at least three limitations to the present study that should be noted. First, INSAUC and INSAUC×GLUCAUC are only surrogate indexes of insulin resistance. Whether more direct measure of insulin resistance (i.e., glucose disposal during a hyperinsulinemic-euglycemic clamp) would yield equivalent results is unknown. Second, regional adiposity explains a relatively low percentage (~38%) of the variability in these indexes of insulin resistance (total r2 = 0.376). Thus, as mentioned above, other factors such as genetic predisposition must be important. Third, the use of DEXA to assess regional body composition (other than bone mineral density) is not routinely done at present in the clinical setting, so the use of DEXA for this purpose has yet to be standardized outside the research setting. Consequently, whether DEXA can be applied generally as a tool to identify women at greatest risk for the hyperinsulinemic-dyslipidemic syndrome is unknown.

In summary, we observed consistently strong associations between trunk fat mass measured by DEXA and markers of insulin resistance and dyslipidemia that were independent of arm or leg fat mass in postmenopausal women. Additionally, leg fat mass was associated with a more favorable metabolic profile after adjustment for risk attributable to central adiposity, whereas arm fat mass had no such association. Thus the results indicate that DEXA measures of central and peripheral adiposity (i.e., trunk and leg fat mass) are independent predictors of disease risk in postmenopausal women. Although the mechanisms for the discordant effects of central vs. peripheral adiposity on disease risk remain to be determined, the findings provide further support for the concept that total adiposity does not adequately indicate the extent of disease risk in postmenopausal women. The usefulness of BMI in assessing disease risk in women is therefore questionable. However, because DEXA is widely used as a screening tool to identify postmenopausal women at risk for osteoporosis, its utility as a means of assessing regional adiposity as a predictor of risk for diabetes and cardiovascular disease is appealing.


    ACKNOWLEDGEMENTS

This research was supported by the following awards from the National Institutes of Health: Claude Pepper Older Americans Independence Center, AG-13629; R01 AG-18198; General Clinical Research Center, RR-00036; and Diabetes Research and Training Center, DK-20579.


    FOOTNOTES

Address for reprint requests and other correspondence: R. E. Van Pelt, Division of Geriatric Medicine, Univ. of Colorado Health Sciences Center, 4200 E. Ninth Ave., Campus Box B-179, Denver, CO 80262 (E-mail: rachael.vanpelt{at}uchsc.edu).

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

10.1152/ajpendo.00467.2001

Received 17 October 2001; accepted in final form 24 December 2001.


    REFERENCES
TOP
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
REFERENCES

1.   Arner, P. Differences in lipolysis between human subcutaneous and omental adipose tissues. Ann Med 27: 435-438, 1995[ISI][Medline].

2.   Binder, EF, Williams DB, Schechtman KB, Jeffe DB, and Kohrt WM. Effects of hormone replacement therapy on serum lipids in elderly women. Ann Intern Med 134: 754-760, 2001.

3.   Björntorp, P. Abdominal obesity and the development of noninsulin-dependent diabetes mellitus. Diabetes Metab Rev 4: 615-622, 1988[ISI][Medline].

4.   Carey, DG, Jenkins AB, Campbell LV, Freund J, and Chisholm DJ. Abdominal fat and insulin resistance in normal and overweight women. Direct measurements reveal a strong relationship in subjects at both low and high risk of NIDDM. Diabetes 45: 633-638, 1996[Abstract].

5.   Caserta, F, Tchkonia T, Civelek VN, Prentki M, Brown NF, McGarry JD, Forse RA, Corkey BE, Hamilton JA, and Kirkland JL. Fat depot origin affects fatty acid handling in cultured rat and human preadipocytes. Am J Physiol Endocrinol Metab 280: E238-E247, 2001[Abstract/Free Full Text].

6.   Cefalu, WT, Wang ZQ, Werbel S, Bell-Farrow A, Crouse JR, III, Hinson WH, Terry JG, and Anderson R. Contribution of visceral fat mass to the insulin resistance of aging. Metabolism 44: 954-959, 1995[ISI][Medline].

7.   Després, J-P. The insulin resistance-dyslipidemic syndrome of visceral obesity: effect on patient's risk. Obes Res 6: S8-S17, 1998[Abstract].

8.   Evans, EM, Van Pelt RE, Binder EF, Williams DB, Ehsani AA, and Kohrt WM. Effects of HRT and exercise training on insulin action, glucose tolerance, and body composition in older women. J Appl Physiol 90: 2033-2040, 2001[Abstract/Free Full Text].

9.   Folsom, A, Kaye S, Sellers T, Hong CP, Cerhan J, Potter J, and Proctor DN. Body fat distribution and 5-year risk of death in older women. JAMA 269: 483-487, 1993[Abstract].

10.   Folsom, AR, Prineas RJ, Kaye SA, and Munger RG. Incidence of hypertension and stroke in relation to body fat distribution and other risk factors in older women. Stroke 21: 701-706, 1992[Abstract].

11.   Frayn, KN. Visceral fat and insulin resistance---causative or correlative? Br J Nutr 83: 71-77, 2000.

12.   Friedewald, W, Levy R, and Fredrickson D. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without the use of the preparative ultracentrifuge. Clin Chem 18: 499-502, 1972[Abstract/Free Full Text].

13.   Goodpaster, BH, Thaete FL, and Kelley DE. Thigh adipose tissue distribution is associated with insulin resistance in obesity and in type 2 diabetes mellitus. Am J Clin Nutr 71: 885-892, 2000[Abstract/Free Full Text].

14.   Jensen, MD. Health consequences of fat distribution. Horm Res 48: 88-92, 1997[ISI][Medline].

15.   Jensen, MD. Lipolysis: contribution from regional fat. Annu Rev Nutr 17: 127-139, 1997[ISI][Medline].

16.   Kissebah, AH, Vydelingum N, Murray R, Evans DJ, Hartz AJ, Kalkhoff RK, and Adams PW. Relation of body fat distribution to metabolic complications of obesity. J Clin Endocrinol Metab 54: 254-260, 1982[Abstract].

17.   Kohrt, WM, Kirwan JP, Staten MA, Bourey RE, King DS, and Holloszy JO. Insulin resistance in aging is related to abdominal obesity. Diabetes 42: 273-281, 1993[Abstract].

18.   Krssak, M, Falk Peterson K, Dresner A, DiPietro L, Vogel SM, Rothman DL, Roden M, and Shulman GI. Intramyocellular lipid concentrations are correlated with insulin sensitivity in humans: a 1H NMR spectroscopy study. Diabetologia 42: 113-116, 1999[ISI][Medline].

19.   Lemieux, S, Prud'homme D, Bouchard C, Tremblay A, and Després J-P. Seven-year changes in body fat and visceral adipose tissue in women: associations with indexes of plasma glucose-insulin homeostasis. Diabetes Care 19: 983-991, 1996[Abstract].

20.   Levine, R, and Hagan RD. Carbohydrate homeostasis. I. N Engl J Med 283: 175-183, 1970[ISI][Medline].

21.   Ley, CJ, Lees B, and Stevenson JC. Sex- and menopause-associated changes in body-fat distribution. Am J Clin Nutr 55: 950-954, 1992[Abstract].

22.   Matsuda, M, and DeFronzo RA. Insulin sensitivity indices obtained from oral glucose tolerance testing. Diabetes Care 22: 1462-1470, 1999[Abstract].

23.   Montague, CT, and O'Rahilly S. The perils of portliness: causes and consequences of visceral adiposity. Diabetes 49: 883-888, 2000[Abstract].

24.   Morgan, DR, and Lazarow A. Immunoassay of insulin: two antibody system. Diabetes 12: 115-126, 1963[ISI].

25.   Peiris, AN, Sothmann MS, Hennes MI, Lee MB, Wilson CR, Gustafson AB, and Kissebah AH. Relative contribution of obesity and body fat distribution to alterations in glucose insulin homeostasis: predictive values of selected indices in premenopausal women. Am J Clin Nutr 49: 758-764, 1989[Abstract].

26.   Peiris, AN, Sothmann MS, Hoffmann RG, Hennes MI, Wilson CR, Gustafson AB, and Kissebah AH. Adiposity, fat distribution, and cardiovascular risk. Ann Intern Med 110: 867-872, 1989[ISI][Medline].

27.   Perusse, L, Rice T, Chagnon YC, Despres J-P, Lemieux S, Roy S, Lacaille M, Ho-Kim MA, Province MA, Rao DC, and Bouchard C. A genome-wide scan for abdominal fat assessed by computed tomography in the Quebec Family Study. Diabetes 50: 614-621, 2001[Abstract/Free Full Text].

28.   Phillips, DI, Caddy S, Ilic V, Fielding BA, Frayn KN, Borthwick AC, and Taylor R. Intramuscular triglyceride and muscle insulin sensitivity: evidence for a relationship in nondiabetic subjects. Metabolism 45: 947-950, 1996[ISI][Medline].

29.   Rexrode, K, Carey V, Hennekens C, Walters E, Colditz G, Stampfer M, Willet W, and Manson J. Abdominal adiposity and coronary heart disease in women. JAMA 280: 1843-1848, 1998[Abstract/Free Full Text].

30.   Ryan, AS, and Nicklas BJ. Age-related changes in fat deposition in mid-thigh muscle in women: relationships with metabolic cardiovascular disease risk factors. Int J Obes Relat Metab Disord 23: 126-132, 1999[Medline].

31.   Terry, RB, Stefanick ML, Haskell WL, and Wood PD. Contribution of regional adipose tissue depots to plasma lipoprotein concentrations in overweight men and women: possible protective effects of thigh fat. Metabolism 40: 733-740, 1991[ISI][Medline].

32.   Vaziri, SM, Evans JC, Larson MG, and Wilson PW. The impact of female hormone usage on the lipid profile. The Framingham Offspring Study. Arch Intern Med 153: 2200-2206, 1993[Abstract].

33.   Wahrenberg, H, Lönnqvist F, and Arner P. Mechanisms underlying regional differences in lipolysis in human adipose tissue. J Clin Invest 84: 458-467, 1989[ISI][Medline].

34.   Warnick, GR, Benderson J, and Albers JJ. Dextran sulfate-Mg2+ precipitation procedure for quantification of high-density lipoprotein cholesterol. Clin Chem 28: 1379-1388, 1982[Free Full Text].

35.   Williams, MJ, Hunter GR, Kekes-Szabo T, Snyder S, and Treuth MS. Regional fat distribution in women and risk of cardiovascular disease. Am J Clin Nutr 65: 855-860, 1997[Abstract].


Am J Physiol Endocrinol Metab 282(5):E1023-E1028
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