AJP-Endo Articles in PresS. Published on January 8, 2002 as DOI 10.1152/ajpendo.00467.2001
CONTRIBUTIONS OF TOTAL AND REGIONAL FAT MASS TO RISK FOR
CARDIOVASCULAR DISEASE IN OLDER WOMEN
R.E. Van Pelt, Ph.D.1,3, E.M. Evans, Ph.D.1, K.B. Schechtman, Ph.D.1,2,
A.A. Ehsani, M.D.1 and W.M. Kohrt, Ph.D.1,3
Department of Internal Medicine
Divisions of Geriatrics/Gerontology1
and Biostatistics2
Washington University School of Medicine
St. Louis, MO 63110
and
3
Department of Medicine
Division of Geriatric Medicine
University of Colorado Health Sciences Center
Denver, Colorado 80262
Running Head: Central fat after menopause
Address for Correspondence:
Rachael E. Van Pelt, Ph.D.
Division of Geriatric Medicine
University of Colorado Health Sciences Center
4200 E Ninth Ave, Campus Box B-179
Denver, CO 80262
voice: 303-315-4693
fax: 303-315-8669
e-mail: rachael.vanpelt@uchsc.edu
Copyright 2002 by the American Physiological Society.
1
Abstract
The aim of this study was to determine whether trunk fat mass, measured by
dual x-ray absorptiometry (DXA), is predictive of insulin resistance and dyslipidemia,
independent of arm and leg fat mass, in postmenopausal women. Total and regional
body composition was measured by DXA in 166 healthy postmenopausal women
(mean±SD; 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 x
GLUAUC), 3) serum triglycerides (TG), and 4) 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 x 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, while leg fat
appears to confer protective effects against metabolic dysfunction.
Keywords: Trunk fat, leg fat, disease risk, postmenopausal women
2
The 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 (17)
(6;25;26). However, the measurement of regional adiposity by dual-energy x-ray absorptiometry
(DXA) is potentially more accurate than anthropometric measures and more practical and costeffective than CT or MRI scans.
Although the primary application of DXA is to measure bone mineral density to ascertain
risk for osteoporosis, it also provides a measure of total and regional (i.e., trunk, arms, legs) fat
mass. It is not known whether trunk fat mass is as strong of a predictor of metabolic and
cardiovascular disease risk as the commonly used clinical measures, BMI and waist
circumference. Thus, the primary aim of this study was to determine whether trunk fat mass,
measured by DXA, 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,
3
a second aim of this study was to determine whether the relations of trunk fat mass with insulin
resistance and dyslipidemia are independent of arm and leg fat mass in postmenopausal women.
Methods
Subjects. Body composition and cardiovascular disease risk factors were retrospectively
analyzed in 166 healthy postmenopausal (mean±SD, 66±4 yr) women that had participated in
research studies conducted at Washington University School of Medicine. All women were at least
2 years past menopause (18±7 yr), 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
ECG, 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, leg and arm)
were measured using DXA 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 away 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 to 70 yr. Three DXA procedures were performed at weekly intervals; results
therefore reflect both technical and biological variability. Coefficients of variation were calculated
4
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:
Fat mass
Fat-free mass
Total mass
Arms
5.3 ± 2.3
4.4 ± 2.5
3.6 ± 1.4
Legs
2.1 ± 1.1
2.7 ± 1.3
2.0 ± 0.7
Trunk
4.1 ± 2.4
2.8 ± 1.4
1.9 ± 0.9
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 mid-point 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 (50,000MW) 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.
Glucose tolerance test. A 75-g OGTT was administered in the morning after an overnight
fast. Diet was monitored for 3 days prior to 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 x GLUAUC) was
calculated as an index of peripheral insulin resistance (8;20;22).
5
Blood pressure. After 15 minutes of supine rest, systolic and diastolic blood pressures (BP)
were measured manually using a sphygmomanometer. Three measurements were made at
approximately 5-minute intervals and averaged.
Statistics. The primary (INSAUC, INSAUC x 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 with 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 mean±SD
and statistical significance was designated as an alpha level of 0.05 unless otherwise stated.
Results
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 each of the primary outcome 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
6
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, triglycerides, and HDL-cholesterol, whereas leg fat mass was
favorably associated with each of these variables after adjusting for trunk fat mass.
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 x GLUAUC.
However, after adjusting 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 triglycerides,
and higher HDL-cholesterol levels).
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 to 10.7 kg, n=54; 10.8 to 15.4
kg, n=58; 15.7 to 33.3 kg, n=54) and then further categorized by tertile of leg fat mass (1.9 to 9.9
kg, n=55; 10.0 to 12.8 kg, n=57; 12.9 to 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 triglycerides than women with low leg fat mass. There was
also a strong trend for women with more leg fat to have higher HDL-cholesterol levels (p=0.07).
7
Discussion
The results of this study indicate that trunk fat mass measured by DXA 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
triglycerides, and low HDL-cholesterol. Further, the relations 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 adjusting for the extent of trunk adiposity, and the
direction of the relationships 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 current study was the first to evaluate the relation between DXA
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 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 to 77 yr. Moreover, leg fatness was related with a favorable lipid profile after statistical
adjustment for other measures of fat distribution, although the strengths of the relationships did
8
not appear to be as strong as in the current study. They found that leg fat independently
accounted for 1% and 4% of the variance in serum HDL-cholesterol and triglycerides,
respectively, compared with 6% and 12% in our study. Given the wide age range of women
studied by Williams et al. (35), 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. (35)
presented trunk, leg, and arm fat as a percentage rather than as a mass, as in the current 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 DXA, the study of Williams et al. (35) 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 adjusting for both DXA and CT measures of fat distribution.
Furthermore, trunk fat measured by DXA was the strongest independent determinant of total
cholesterol, LDL-cholesterol, and triglycerides, 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 to 49 yr. After adjusting for variance in waist
circumference, they found favorable independent correlations of thigh girth with serum
triglycerides and HDL-cholesterol, but not total cholesterol or LDL-cholesterol. Although estrogen
(oral contraceptives) use was not controlled for in this study, these findings, using anthropometic
measures of regional adiposity, are similar to our DXA-based findings.
9
Our results and those of others (7;31;35) clearly indicate that trunk fat has a deleterious
effect on risk 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 glutealfemoral 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 found to be 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 found that only
2-6% of leg fat was located intramuscularly, even in obese individuals (13). 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 DXA cannot distinguish between
subcutaneous and visceral abdominal fat depots, or between subcutaneous and intramuscular
10
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 physiologic 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
abdominal visceral 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 abdominal subcutaneous regions tend to be
intermediate to these (1). There are some, albeit few, in vivo data that support these findings (15).
Based on these regional differences in the regulation of lipolysis, it would be reasonable to expect
that the daily systemic flux of free fatty acids (FFA), 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 FFA flux
due to its proximity to the portal circulation and, consequently, increase triglyceride 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).
11
There are at least three limitations to the present study that should be noted. First, INSAUC
and INSAUCxGLUCAUC are only surrogate indices 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 indices of insulin resistance (Total R2=0.376). Thus,
as mentioned above, other factors such as genetic predisposition must be important. Third, the
use of DXA to assess regional body composition (other than bone mineral density) is not routinely
done at present in the clinical setting so the use of DXA for this purpose has yet to be
standardized outside of the research setting. Consequently, whether DXA can be generally
applied 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 DXA, 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 adjusting for risk attributable to central adiposity, whereas
arm fat mass had no association. Thus, the results indicate that DXA 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 versus
peripheral adiposity on disease risk remain to be determined, the findings provide further support
for the concept that total adiposity does not adequately indicate extent of disease risk in
postmenopausal women. The usefulness of BMI in assessing disease risk in women is therefore
questionable. However, because DXA 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.
12
References
1. Arner, P. Differences in lipolysis between human subcutaneous and omental adipose tissues.
Ann.Med. 27: 435-438, 1995.
2. Binder, E. F., D. B. Williams, K. B. Schechtman, D. B. Jeffe, and W. M. Kohrt. Effects of
hormone replacement therapy on serum lipids in elderly women. Ann.Int.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.
4. Carey, D. G., A. B. Jenkins, L. V. Campbell, J. Freund, and D. J. Chisholm. 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.
5. Caserta, F., T. Tchkonia, V. N. Civelek, M. Prentki, N. F. Brown, J. D. McGarry, R. A. Forse, B.
E. Corkey, J. A. Hamilton, and J. L. Kirkland. Fat depot origin affects fatty acid handling in
cultured rat and human preadipocytes. Am.J.Physiol. (Endocrin Metab) 280: E238-E247, 2001.
6. Cefalu, W. T., Z. Q. Wang, S. Werbel, A. Bell-Farrow, J. R. 3. Crouse, W. H. Hinson, J. G.
Terry, and R. Anderson. Contribution of visceral fat mass to the insulin resistance of aging.
Metabolism 44: 954-959, 1995.
7. Despres, J.-P. The insulin resistance-dyslipidemic syndrome of visceral obesity: Effect on
patient's risk. Obes.Res. 6: S8-S17, 1998.
8. Evans, E. M., R. E. Van Pelt, E. F. Binder, D. B. Williams, A. A. Ehsani, and W. M. Kohrt.
Effects of HRT and exercise training on insulin action, glucose tolerance, and body
composition in older women. J.Appl.Physiol. 90: 2033-2040, 2001.
9. Folsom, A., S. Kaye, T. Sellers, C.-P. Hong, J. Cerhan, J. Potter, and D. N. Proctor. Body fat
distribution and 5-year risk of death in older women. JAMA 269: 483-487, 1993.
13
10. Folsom, A. R., R. J. Prineas, S. A. Kaye, and R. G. Munger. Incidence of hypertension and
stroke in relation to body fat distribution and other risk factors in older women. Stroke 21: 701706, 1992.
11. Frayn, K. N. Visceral fat and insulin resistance - causative or correlative? Br.J.Nutr. 83: 71-77,
2000.
12. Friedewald, W., R. Levy, and D. Fredrickson. Estimation of the concentration of low-density
lipoprotein cholesterol in plasma, without the use of the preparative ultracentrifuge. Clin.Chem.
18: 499-502, 1972.
13. Goodpaster, B. H., F. L. Thaete, and D. E. Kelley. 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.
14. Jensen, M. D. Health consequences of fat distribution. Horm.Res. 48: 88-92, 1997.
15. Jensen, M. D. Lipolysis: Contribution from regional fat. Ann.Rev.Nutr. 17: 127-139, 1997.
16. Kissebah, A. H., N. Vydelingum, R. Murray, D. J. Evans, A. J. Hartz, R. K. Kalkhoff, and P. W.
Adams. Relation of body fat distribution to metabolic complications of obesity.
J.Clin.Endocrinol.Metab. 54: 254-260, 1982.
17. Kohrt, W. M., J. P. Kirwan, M. A. Staten, R. E. Bourey, D. S. King, and J. O. Holloszy. Insulin
resistance in aging is related to abdominal obesity. Diabetes 42: 273-281, 1993.
18. Krssak, M., Falk Peterson .K., A. Dresner, L. DiPietro, S. M. Vogel, D. L. Rothman, M. Roden,
and G. I. Shulman. Intramyocellular lipid concentrations are correlated with insulin sensitivity in
humans: a 1H NMR spectroscopy study. Diabetologia 42: 113-116, 1999.
19. Lemieux, S., D. Prud'homme, C. Bouchard, A. Tremblay, and J.-P. Després. 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.
20. Levine, R. and R. D. Hagan. Carbohydrate homeostasis. I. N.Engl.J.Med. 283: 175-183, 1970.
14
21. Ley, C. J., B. Lees, and J. C. Stevenson. Sex- and menopause-associated changes in bodyfat distribution. Am.J.Clin.Nutr. 55: 950-954, 1992.
22. Matsuda, M. and R. A. DeFronzo. Insulin sensitivity indices obtained from oral gluocse
tolerance testing. Diabetes Care 22: 1462-1470, 1999.
23. Montague, C. T. and S. O'Rahilly. The perils of portliness: Causes and consequences of
visceral adiposity. Diabetes 49: 883-888, 2000.
24. Morgan, D. R. and A. Lazarow. Immunoassay of insulin: two antibody system. Diabetes 12:
115-126, 1963.
25. Peiris, A. N., M. S. Sothmann, M. I. Hennes, M. B. Lee, C. R. Wilson, A. B. Gustafson, and A.
H. Kissebah. 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.
26. Peiris, A. N., M. S. Sothmann, R. G. Hoffmann, M. I. Hennes, C. R. Wilson, A. B. Gustafson,
and A. H. Kissebah. Adiposity, fat distribution, and cardiovascular risk. Ann.Int.Med. 110: 867872, 1989.
27. Perusse, L., T. Rice, Y. C. Chagnon, J.-P. Despres, S. Lemieux, S. Roy, M. Lacaille, M. A. HoKim, M. A. Province, D. C. Rao, and C. Bouchard. A genome-wide scan for abdominal fat
assessed by computed tomography in the Quebec Family Study. Diabetes 50: 614-621, 2001.
28. Phillips, D. I., S. Caddy, V. Ilic, B. A. Fielding, K. N. Frayn, A. C. Borthwick, and R. Taylor.
Intramuscular triglyceride and muscle insulin sensitivity: evidence for a relationship in
nondiabetic subjects. Metabolism 45: 947-950, 1996.
29. Rexrode, K., V. Carey, C. Hennekens, E. Walters, G. Colditz, M. Stampfer, W. Willet, and J.
Manson. Abdominal adiposity and coronary heart disease in women. JAMA 280: 1843-1848,
1998.
15
30. Ryan, A. S. and B. J. Nicklas. 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.
31. Terry, R. B., M. L. Stefanick, W. L. Haskell, and P. D. Wood. Contribution of regional adipose
tissue depotes to plasma lipoprotein concentrations in overweight men and women: Possible
protective effects of thigh fat. Metabolism 40: 733-740, 1991.
32. Vaziri, S. M., J. C. Evans, M. G. Larson, and P. W. Wilson. The impact of female hormone
usage on the lipid profile. The Framingham Offspring Study. Arch.Int.Med. 153: 2200-2206,
1993.
33. Wahrenberg, H., F. Lönnqvist, and P. Arner. Mechanisms underlying regional differences in
lipolysis in human adipose tissue. J.Clin.Invest. 84: 458-467, 1989.
34. Warnick, G. R., J. Benderson, and J. J. Albers. Dextran sulfate-Mg2+ precipitation procedure
for quantification of high-density lipoprotein cholesterol. Clin.Chem. 28: 1379-1388, 1982.
35. Williams, M. J., G. R. Hunter, T. Kekes-Szabo, S. Snyder, and M. S. Treuth. Regional fat
distribution in women and risk of cardiovascular disease. Am.J.Clin.Nutr. 65: 855-860, 1997.
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Acknowledgments
This research was supported by the following awards from the National Institutes of Health:
Claude Pepper Older Americans Independence Center, AG13629; R01, AG18198; General
Clinical Research Center, RR00036; and Diabetes Research and Training Center, DK20579.
17
Table 1. Body composition and metabolic characteristics of the study cohort (n=166)
Variable
Mean ± SD
Variable
Weight (kg)
69.6 ± 13.2
Fasted glucose (mg/dL)
95 ± 14
BMI (kg/m2)
26.6 ± 4.7
GLUAUC (mg/dL.min.103)
24.4 ± 6.3
84 ± 11
Fasted insulin (µU/mL)
7.2 ± 4.0
Body fat (%)
42.1 ± 6.9
INSAUC (µU/mL.min.103)
8.8 ± 4.5
Trunk fat mass(kg)
14.0 ± 5.8
INSAUC x GLUAUC (units x 108)
Arm fat mass (kg)
3.7 ± 1.4
Total cholesterol (mg/dL)
212 ± 30
Leg fat mass (kg)
11.6 ± 3.5
HDL-cholesterol (mg/dL)
55 ± 14
Systolic BP (mmHg)
127 ± 17
LDL-cholesterol (mg/dL)
130 ± 27
Diastolic BP (mmHg)
80 ± 9
Triglycerides (mg/dL)
137 ± 75
Waist girth (cm)
Mean ± SD
2.23 ± 1.46
0.14
0.40*
0.41*
0.36*
0.46*
Leg fat mass (kg)
Arm fat mass(kg)
Body fat (%)
BMI (kg/m2)
Waist girth (cm)
0.51*
0.41*
0.41*
0.43*
0.16‡
0.53*
INSAUC x
GLUAUC
0.29*
0.23†
0.22†
0.14
-0.03
0.31*
TG
-0.36*
-0.32*
-0.29*
-0.26*
-0.12
-0.40*
HDL-C
0.15‡
0.18‡
0.18‡
0.12
0.11
0.18‡
TC
0.19‡
0.23†
0.22†
0.19‡
0.20‡
0.23†
LDL-C
0.38*
0.33*
0.22†
0.29*
0.15‡
0.35*
GLUAUC
SBP = systolic blood pressure (mmHg); DBP = diastolic blood pressure (mmHg); BMI = body mass index
0.34*
0.37*
0.35*
0.29*
0.32*
0.31*
SBP
Secondary dependent variables
Bolded values are the strongest predictors of each dependent variable. *p<0.001; †p<0.01; ‡ p<0.05.
0.49*
INSAUC
Primary dependent variables
Trunk fat mass (kg)
Predictors
Table 2. Pearson correlation coefficients between predictors and dependent variables
0.33*
0.34*
0.29*
0.30*
0.28*
0.30*
DBP
18
0.376
0.0173
0.0822
#
0.2771
INSAUC x
GLUAUC
0.210
0.1121
#
0.0982
TG
0.204
0.0472
0.1571
#
HDL-C
0.034
0.034
TC
0.053
0.053
LDL-C
0.148
0.148
GLUAUC
0.132
0.132
SBP
Secondary dependent variables
0.116
0.116
DBP
subscript indicates order of entry into regression model.
Indicates negative correlation; values are the independent contributions to R2 for each predictor variable in the model; the
#
Total R2
Waist girth (cm)
0.369
0.0263
Body fat (%)
BMI (kg/m2)
0.0194
0.0802
#
0.2421
INSAUC
Primary dependent variables
Arm fat mass (kg)
Leg fat mass (kg)
Trunk fat mass(kg)
Predictors
analyses included predictors from Table 2 and retained variables with p values less than 0.10.
Table 3. Independent predictors of dependent variables resulting from multiple stepwise linear regression analysis. The
19
20
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.
Adjusted variable
Predictor
Trunk fat mass
Arm fat mass
Leg fat mass
*p<0.01; †p<0.05.
Dependent variable
None
Leg fat
Arm fat
Trunk fat
INSAUC
0.492*
0.557*
0.310*
INSAUC x GLUAUC
0.526*
0.585*
0.342*
TG
0.312*
0.458*
0.352*
HDL-C
-0.397*
-0.439*
-0.330*
INSAUC
0.402*
-0.011
INSAUC x GLUAUC
0.426*
-0.019
TG
0.141
-0.220*
HDL-C
-0.263*
0.126
INSAUC
0.136
-0.326*
INSAUC x GLUAUC
0.158†
-0.336*
TG
0.025
-0.353*
HDL-C
-0.119
0.236*
21
Table 5. Characteristics of women in the middle tertile of trunk fat mass (10.8 to 15.4
kg, n=58), categorized by tertile of leg fat mass.
Tertile of Leg Fat Mass
Low
Middle
High
(6.3 to 9.8 kg) (10.0 to 12.8 kg) (13.0 to 17.2 kg) p-value
n
17
26
15
INSAUC (µU/mL.min.103)
9.4 ± 3.6
8.6 ± 2.3
7.7 ± 5.4
0.40
INSAUC x GLUAUC
2.5 ± 1.1
1.9 ± 0.7
1.8 ± 1.3
0.02
HDL-C (mg/dL)
49 ± 13
58 ± 17
60 ± 13
0.07
TG (mg/dL)
192 ± 72
149 ± 92
98 ± 46
<0.05
Trunk fat mass (kg)
12.8 ± 1.3
13.1 ± 1.3
13.1 ± 1.4
0.67
Leg fat mass (kg)
8.5 ± 1.1
11.2 ± 0.9
14.4 ± 1.3
<0.001
Body fat (%)
25.0 ± 2.0
28.4 ± 2.0
32.3 ± 2.0
<0.001
82 ± 5
83 ± 6
84 ± 5
0.55
Waist girth (cm)
Mean ± SD; one-way ANOVA p-values are presented.