Abdominal and Gynoid Fat Mass Are Associated with Cardiovascular

ORIGINAL E n d o c r i n e

ARTICLE C a r e

Abdominal and Gynoid Fat Mass Are Associated with Cardiovascular Risk Factors in Men and Women Peder Wiklund, Fredrik Toss, Lars Weinehall, Go¨ran Hallmans, Paul W. Franks, Anna Nordstro¨m, and Peter Nordstro¨m Departments of Surgical and Perioperative Sciences (P.W., F.T., A.N., P.N.), Sports Medicine; Department of Community Medicine and Rehabilitation (P.W., F.T., A.N.), Rehabilitation Medicine; Department of Community Medicine and Rehabilitation (P.W., F.T., P.N.), Geriatric Medicine; Department of Public Health and Clinical Medicine (L.W.), Epidemiology and Public Health Sciences; Department of Public Health and Clinical Medicine (G.H.), Nutritional Research; and Department of Public Health and Clinical Medicine (P.W.F.), Medicine, Umeå University, 90185 Umeå, Sweden

Context: Abdominal obesity is an established risk factor for cardiovascular disease (CVD). However, the correlation of dual-energy x-ray absorptiometry (DEXA) measurements of regional fat mass with CVD risk factors has not been completely investigated. Objective: The aim of this study was to investigate the association of estimated regional fat mass, measured with DEXA and CVD risk factors. Design, Setting, and Participants: This was a cross-sectional study of 175 men and 417 women. DEXA measurements of regional fat mass were performed on all subjects, who subsequently participated in a community intervention program. Main Outcome Measures: Outcome measures included impaired glucose tolerance, hypercholesterolemia, hypertriglyceridemia, and hypertension. Results: We began by assessing the associations of the adipose measures with the cardiovascular outcomes. After adjustment for confounders, a SD unit increase in abdominal fat mass was the strongest predictor of most cardiovascular variables in men [odds ratio (OR) ⫽ 2.63–3.37; P ⬍ 0.05], whereas the ratio of abdominal to gynoid fat mass was the strongest predictor in women (OR ⫽ 1.48 –2.19; P ⬍ 0.05). Gynoid fat mass was positively associated with impaired glucose tolerance, hypertriglyceridemia, and hypertension in men (OR ⫽ 2.07–2.15; P ⬍ 0.05), whereas the ratio of gynoid to total fat mass showed a negative association with hypertriglyceridemia and hypertension (OR ⫽ 0.42– 0.62; P ⬍ 0.005). Conclusions: Abdominal fat mass is strongly independently associated with CVD risk factors in the present study. In contrast, gynoid fat mass was positively associated, whereas the ratio of gynoid to total fat mass was negatively associated with risk factors for CVD. (J Clin Endocrinol Metab 93: 4360 – 4366, 2008)

besity is a growing public health concern in the Western world that is caused by a combination of sedentary lifestyle and excessive caloric intake. The prevalence of obesity in Sweden has more than doubled during the last two decades, with approximately 45% of adults classified as overweight and 10% as obese in 2006 (1). The emerging prevalence of obesity is wor-

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risome, not least because it is a major risk factor for cardiovascular disease (CVD) and type 2 diabetes mellitus (2, 3). Male sex is a well-established risk factor for CVD. One reason for this may be that an android obesity profile, where adipose deposition around the abdomen predominates, significantly increases the risk of heart disease and insulin resistance (4). In

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Abbreviations: BMD, Bone mineral density; BMI, body mass index; CT, computed tomography; CVD, cardiovascular disease; DEXA, dual-energy x-ray absorptiometry; FPG, fasting plasma glucose; IGT, impaired glucose tolerance; OR, odds ratio; ROI, region of interest; VIP, Va¨sterbotten Intervention Program.

Printed in U.S.A. Copyright © 2008 by The Endocrine Society doi: 10.1210/jc.2008-0804 Received April 14, 2008. Accepted August 14, 2008. First Published Online August 26, 2008

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J Clin Endocrinol Metab. November 2008, 93(11):4360 – 4366

J Clin Endocrinol Metab, November 2008, 93(11):4360 – 4366

contrast, a gynoid obesity profile, where adipose tissue accumulates around the hips, is thought to protect against CVD (5, 6). An excess of abdominal fat is considered unfavorable, because visceral fat is thought to be more metabolically active, causing dysmetabolism of fatty acids and increased influx of free fatty acids into the splanchnic circulation (7, 8). Moreover, adipose tissue has the same features as endocrine organs in terms of secreting cytokines, and visceral adipocytes secrete greater quantities of proinflammatory cytokines than does sc adipose tissue (9, 10). Through these mechanisms, excess visceral obesity is hypothesized to cause insulin resistance and an atherogenic profile. Studies investigating body composition have used a number of different methods to quantify regional adiposity. Anthropological methods such as waist circumference, body mass index (BMI), waist-to-hip ratio, and skin fold measurements are widely used, because they are easily obtained and noninvasive, hence rendering them suitable for use in the epidemiological setting. Studies that have directly measured visceral adiposity often use computed tomography (CT) (11, 12), which is the reference standard for measuring visceral adiposity; however, its routine use in clinical practice and research is limited because of inaccessibility to equipment, the relatively high cost, and the exposure to ionizing radiation (13). Dual-energy x-ray absorptiometry (DEXA) provides an alternative to CT. DEXA can accurately assess total and abdominal fat mass (14 –17), and compared with CT, DEXA has the advantages of being a low-cost and relatively quick procedure and also involves much less exposure to ionizing radiation. Compared with anthropological methods, DEXA has the advantage of being able to measure both total body and regional fat mass. The purpose of this study was to compare the associations of abdominal fat mass, gynoid fat mass, and total fat mass, measured using DEXA, with cardiovascular risk factor levels in men and women.


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FIG. 1. Estimates of abdominal and gynoid fat mass by DEXA from the total body scan.

obtained. A total of 592 individuals whose data were registered in the BMD and fat mass database later participated in the VIP study.

Measurements of fat mass

Subjects and Methods The bone mineral density (BMD) and fat mass database Since 1991, DEXA has been used to measure fat mass and BMD at the Sports Medicine Unit, Umeå University, Sweden. By the end of 2006, DEXA scans had been performed on 4333 women and 2320 men. The reasons for admission were recorded for all subjects on whom DEXA measurements were performed during 2005 (n ⫽ 985). The most common reasons for admission were previous fracture (28%), previous or current oral corticoid steroid therapy (16%), or the patient’s concern of having osteoporosis (16%). About 19% of the patients were not admitted for medical reasons and underwent DEXA scans as part of research projects at the Sports Medicine Unit.

The Va¨sterbotten Intervention Program (VIP) The VIP is a community-based observational cohort study focusing on cerebrovascular disease and diabetes. The study began in 1985 in the county of Va¨sterbotten, Sweden, and has been described in detail previously (18). In brief, at ages 30, 40, 50, and 60 yr, all Va¨sterbotten residents are invited to receive a standardized health examination at their primary care centers. At the examination, information was gathered about lifestyle and psychosocial conditions, an oral glucose tolerance test was performed after an 8-h fast, and venous and capillary blood was

Fat mass was assessed using DEXA scans (GE Lunar, Madison, WI). Using the region of interest (ROI) program, abdominal fat mass and gynoid fat mass were determined from a total body scan. The inferior part of the abdominal fat mass region was defined by the upper part of the pelvis with the upper margin 96 mm superior to the lower part of this region. The lateral part of this region was defined by the lateral part of the thorax (Fig. 1). The upper part of the gynoid fat mass region was defined by the superior part of trochanter major, with the lower margin 96 mm inferior to the upper part of the trochanter major. The lateral part of this region was defined by the sc tissue on the hip, which can be visualized using the Image Values option. One investigator (P.W.) performed all of the analyses. DEXA has been validated previously in children, adults, and the elderly and has been found to be a reliable and valid method for measuring fat mass (14 –16). The coefficient of variation (i.e. SD from the mean) was evaluated in our laboratory by scanning one person (a male, 30 yr of age, 30% body fat, with normal weight and height) seven times in the same day, with repositioning between each scan. For this individual, the coefficient of variation was 2% for abdominal fat mass and total fat mass. The equipment was calibrated each day using a standardized phantom to detect drifts in measurements, and equipment servicing was performed regularly. Two different machines were used for the measurements. From 1991–1998, a Lunar DPX-L was used, and from 1998 –2006, a Lunar-IQ was used. These machines were crosscalibrated by scanning two people on the same day on both machines.

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Fat Distribution and CVD Risk Factors


TABLE 1. Descriptive characteristics of the male and female part of the cohort Males n Age (yr) Weight (kg) Height (cm) BMI (kg/m2) Current smoker (%) Physical activity (index) Systolic BP (mm Hg) Diastolic BP (mm Hg) FPG (mmol/liter) Triglycerides (mmol/liter) Cholesterol (mmol/liter) Total fat mass (kg) Abdominal fat mass (grams) Gynoid fat mass (grams) Abdominal/gynoid fat mass Abdominal/total fat mass Gynoid/total fat mass

175 175 175 175 175 146 155 155 153 118 153 175 175 119 175 175 175

Mean ⴞ

SD

45.3 ⫾ 9.9 82.0 ⫾ 11.0c 179 ⫾ 6c 25.7 ⫾ 3.0 13 ⫾ 33 0.7 ⫾ 0.9a 129 ⫾ 18 80 ⫾ 13a 5.8 ⫾ 1.3b 1.4 ⫾ 0.7 5.5 ⫾ 1.0 20.0 ⫾ 7.6c 1508 ⫾ 477 1855 ⫾ 538c 0.811 ⫾ 0.132c 0.077 ⫾ 0.010c 0.096 ⫾ 0.011c

Females Range

n

25– 60 56 –120 163–198 18.7–34.3

417 417 417 417 417 357 377 377 377 339 378 417 417 417 417 417 417

0 –2 85–196 50 –178 4.1–16.9 0.1– 4.4 3.5– 8.3 5.5–51.3 420 –2961 758 –3578 0.48 –1.24 0.05– 0.11 0.07– 0.14

Mean ⴞ

SD

46.8 ⫾ 9.3 67.9 ⫾ 12.5 165 ⫾ 6 24.8 ⫾ 4.5 19 ⫾ 39 0.5 ⫾ 0.8 125 ⫾ 18 77 ⫾ 10 5.5 ⫾ 0.8 1.2 ⫾ 0.8 5.6 ⫾ 1.2 25.4 ⫾ 9.6 1562 ⫾ 626 2678 ⫾ 802 0.573 ⫾ 0.130 0.061 ⫾ 0.010 0.109 ⫾ 0.015

Range 20 –73 39 –107 140 –187 15.2– 41.5 0 –2 95–185 50 –110 3.1–13.3 0.8 – 6.9 0.7–10.5 4.9 –55.5 298 –3182 659 –5206 0.26 –1.10 0.03– 0.13 0.07– 0.17

P values are comparing the male and female cohort. BP, Blood pressure. a

P ⬍ 0.05.

b

P ⬍ 0.01.

c

P ⬍ 0.001.

Clinical measurements Blood pressure was measured using a mercury-gauge sphygmomanometer. Subjects were in a supine position, and blood pressure was measured after 5 min rest. Hypertension was defined as diastolic blood pressure greater than 90 mm Hg and/or systolic blood pressure greater than 140 mm Hg. An oral glucose tolerance test was performed on fasting volunteers using a 75-g oral glucose load (19). The plasma glucose (PG) concentration (millimoles per liter) in capillary plasma was measured 2 h after glucose administration using a Reflotron bench-top analyzer (Roche Molecular Biochemicals, GmbH, Mannheim, Germany). The glucose concentration was classified as normal glucose tolerance [fasting PG (FPG) ⬍7 and 2-h PG ⬍8.9 mmol/liter), impaired glucose tolerance (IGT) (FPG ⬍7 and 2-h PG ⫽ 8.9 –12.1 mmol/liter), or diabetes (FPG ⱖ 7 or 2-h PG ⱖ 12.1 mmol/liter). Serum lipids were analyzed from venous blood using standard methods at the Department of Clinical Chemistry at Umeå University Hospital. Hypertriglyceridemia was defined as a triglyceride concentration higher than 2 mmol/liter. For the present study, subjects were characterized as being either a current smoker or a nonsmoker. Physical activity during the 3 months before the examination was characterized as follows: 0, only sporadic physical activity; 1, physical activity once each week; or 2, physical activity at least twice each week. Weight and height were measured with the participant in light clothing using standardized equipment, and BMI was defined as weight (kilograms)/height (meters) squared. Obesity was defined as BMI of 30 kg/m2 or higher. Informed consent was given by all the participants, and the study protocol was approved by the Ethical Committee of the Medical Faculty, Umeå University, Umeå, Sweden.

Statistical analysis Data are presented as the mean ⫾ SD unless indicated otherwise. Bivariate correlations were determined using Pearson’s coefficient of correlation. Differences between three or more groups were analyzed using ANOVA and Bonferroni’s post hoc test. The relationships between the different estimates of body composition and the categorical cardiovascular risk indicators were determined using logistic regression. A P value ⬍0.05 was considered statistically significant. SPSS for the PC (version 15.0) was used for statistical analyses.

Results The 175 male participants in the present study had a mean age of 45.3 ⫾ 9.9 yr at baseline, whereas the 417 females had a mean age of 46.8 ⫾ 9.3 yr (P ⬎ 0.05 for comparison). Physical characteristics, lifestyle factors, different estimates of fatness, and the significant differences between the male and female cohort are shown in Table 1. Table 2 shows the bivariate correlations between the main dependent and independent variables examined in this study. In both sexes, estimates of adiposity were generally positively correlated with diastolic and systolic blood pressure and serum triglycerides and negatively associated with physical activity (P ⬍ 0.05). Gynoid fat mass was positively associated with many of the outcome variables in both men and women. In contrast, the ratio of gynoid to total fat mass showed a negative correlation with blood pressure and triglycerides and a positive correlation with physical activity in both sexes (P ⬍ 0.05). As shown in Fig. 2, total fat mass and the estimates of regional fat mass were highly correlated. Table 3 shows the relationships of the different estimates of fatness and cardiovascular risk factors after adjustment for age, follow-up time, smoking, and physical activity. In men, abdominal fat mass was the best predictor of IGT, hypertriglyceridemia, and hypertension [odds ratio (OR) per SD increase ⫽ 2.63–3.37; P ⬍ 0.05). In women, the ratio of abdominal to gynoid fat mass was significantly related to IGT, triglyceridemia, and hypertension (OR per SD increase ⫽ 1.48 –2.19; P ⬍ 0.05). Gynoid fat mass was significantly related to all outcome variables but hypercholesterolemia in men (OR ⫽ 2.07–2.15; P ⬍ 0.05) and to hypertension in women (OR ⫽ 1.57; P ⫽ 0.003). In contrast, the ratio of



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TABLE 2. Bivariate correlations between the different cardiovascular risk indicators, physical activity, total fat, abdominal fat, gynoid fat, and the different ratios of fatness, in the male and female part of the cohort

Males Fasting plasma glucose 2-h blood glucose Systolic blood pressure Diastolic blood pressure Triglycerides Cholesterol Physical activity Females Fasting plasma glucose 2-h blood glucose Systolic blood pressure Diastolic blood pressure Triglycerides Cholesterol Physical activity a

Total fat

Abdominal fat

Gynoid fat

BMI

Abdominal fat/gynoid fat

Abdominal fat/total fat

Gynoid fat/total fat

0.15 0.08 0.18a 0.27b 0.25b -0.05 -0.19a

0.20a 0.11 0.27b 0.34b 0.31b 0.09 ⫺0.24b

0.14 0.08 0.17a 0.25b 0.24b ⫺0.07 ⫺0.20a

0.20a 0.00 0.20a 0.28b 0.24b ⫺0.07 ⫺0.21a

0.15 0.11 0.28b 0.27b 0.20a 0.32c ⫺0.14

0.03 0.11 0.17a 0.12 0.06 0.33c ⫺0.03

⫺0.18a ⫺0.08 ⫺0.22b ⫺0.28b ⫺0.23a ⫺0.09 0.19a

0.21c 0.12a 0.23c 0.14b 0.26c 0.01 -0.14a

0.22c 0.16b 0.27c 0.20c 0.33c 0.06 ⫺0.15a

0.20c 0.12a 0.23c 0.14b 0.22c 0.02 ⫺0.13a

0.24c 0.11a 0.20c 0.11a 0.22c ⫺0.02 ⫺0.08

0.18c 0.15b 0.21c 0.20c 0.32c 0.12a ⫺0.10a

0.09 0.13a 0.15c 0.17b 0.21c 0.14b ⫺0.06

⫺0.19c ⫺0.08 ⫺0.16c ⫺0.12a ⫺0.27c ⫺0.03 0.12a

P ⬍ 0.05.

b

P ⬍ 0.01.

c

P ⬍ 0.001.

gynoid to total fat mass showed a negative correlation to triglyceridemia and hypertension (OR per SD increase⫽ 0.42– 0.62; P ⬍ 0.05) in both men and women. Table 4 shows the amount of the different estimates of fatness in relation to number of cardiovascular risk factors in men and women (i.e. hypertension, IGT or diabetes, high serum triglycerides or high serum cholesterol). Both men and women with zero risk factors for CVD had lower total fat mass, abdominal fat mass, and gynoid fat mass than subjects with three or more risk factors for CVD (P ⬍

0.05). In contrast, both men and women with zero risk factors for CVD had a higher ratio of gynoid to total fat mass than subjects with three or more risk factors for CVD (P ⬍ 0.05).

Discussion Several methods, which vary in accuracy and feasibility, are commonly used to assess obesity in humans. In the present study, we

FIG. 2. Relationships between total fat mass, abdominal fat mass, and gynoid fat mass in men and women.

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TABLE 3. OR for the risk of IGT (or antidiabetic treatment), hypercholesterolemia (or lipid-lowering treatment), triglyceridemia, and hypertension (or antihypertensive treatment) for every SD the explanatory variables change in the male and female part of the cohort Explanatory variables Males Abdominal fat mass Gynoid fat mass Total fat mass BMI Abdominal fat/gynoid fat Abdominal fat/total fat Gynoid fat/total fat Females Abdominal fat mass Gynoid fat mass Total fat mass BMI Abdominal fat/gynoid fat Abdominal fat/total fat Gynoid fat/total fat

IGT

Hypercholesterolemia

Triglyceridemia

Hypertension

2.69a 2.07a 2.15a 2.52a 1.33 0.90 0.52

1.25 1.10 1.06 1.24 1.23 1.28 1.00

3.37b 2.10a 2.22a 1.99 1.77a 1.04 0.42b

2.63b 2.15b 2.13b 3.05b 1.32 1.01 0.61a

1.25 1.04 1.20 1.32 1.48a 1.27 0.71

1.09 1.09 1.05 1.02 1.11 1.20 1.01

1.97b 1.41 1.66a 1.54a 2.19c 1.71b 0.49c

1.77c 1.57b 1.68c 1.64c 1.60c 1.36a 0.62c

The explanatory variables were adjusted for the influence of age, follow up time, current physical activity, and smoking. a

P ⬍ 0.05.

b

P ⬍ 0.01.

c

P ⬍ 0.001.

used DEXA to investigate the relationship between regional adiposity and cardiovascular risk factors in a large cohort of men and women. Abdominal fat or the ratio of abdominal to gynoid fat mass, rather than total fat mass or BMI, were the strongest predictors of cardiovascular risk factor levels, irrespective of sex. Interestingly, gynoid fat mass was positively associated with

many of the cardiovascular outcome variables studied, whereas the ratio of gynoid to total fat mass showed a negative correlation with the same risk factors. Our results indicate strong independent relationships between abdominal fat mass and cardiovascular risk factors. In comparison, total fat mass was generally less strongly related to

TABLE 4. Age, weight, height, and body composition measured by DEXA

Men n Age (yr) Weight (kg) Height (cm) BMI (kg/m2) Total fat mass Abdominal fat mass Gynoid fat mass Adominal/gynoid fat mass Adominal/total fat mass Gynoid/total fat mass Women n Age (yr) Weight (kg) Height (cm) BMI (kg/m2) Total fat mass Abdominal fat mass Gynoid fat mass Adominal/gynoid fat mass Adominal/total fat mass Gynoid/total fat mass

0R

1R

2R

3 (or more) R

28 39.4 ⫾ 9.6a,b,c 77.7 ⫾ 8.8c 180 ⫾ 7 24.1 ⫾ 2.5c 16.6 ⫾ 5.4c 1251 ⫾ 407b,c 1623 ⫾ 466c 0.770 ⫾ 0.136b 0.076 ⫾ 0.010 0.100 ⫾ 0.011c

66 45.5 ⫾ 10.0 81.7 ⫾ 11.1e 179 ⫾ 6 25.4 ⫾ 2.7e 19.2 ⫾ 8.2e 1421 ⫾ 455e 1795 ⫾ 537e 0.790 ⫾ 0.131d 0.076 ⫾ 0.010 0.098 ⫾ 0.013e

38 47.6 ⫾ 8.7 81.6 ⫾ 9.4f 178 ⫾ 5 26.2 ⫾ 4.4f 25.6 ⫾ 2.5f 1595 ⫾ 363f 1868 ⫾ 426f 0.859 ⫾ 0.110 0.080 ⫾ 0.010 0.093 ⫾ 0.007

25 50.1 ⫾ 7.4 89.6 ⫾ 13.5 177 ⫾ 4 28.4 ⫾ 4.6 28.6 ⫾ 3.6 1943 ⫾ 487 2292 ⫾ 627 0.859 ⫾ 0.130 0.077 ⫾ 0.011 0.090 ⫾ 0.011

70 40.3 ⫾ 9.2a,b,c 64.2 ⫾ 8.5b,c 167 ⫾ 5c 23.1 ⫾ 2.6b,c 21.7 ⫾ 7.0b,c 1254 ⫾ 443b,c 2394 ⫾ 595b,c 0.516 ⫾ 0.110b,c 0.058 ⫾ 0.008b,c 0.114 ⫾ 0.140b,c

169 47.5 ⫾ 8.7e 65.7 ⫾ 11.9d,e 165 ⫾ 6 24.1 ⫾ 4.0d,e 23.6 ⫾ 9.2d,e 1446 ⫾ 607d,e 2551 ⫾ 773d,e 0.557 ⫾ 0.130d,e 0.061 ⫾ 0.011 0.112 ⫾ 0.143d,e

99 48.8 ⫾ 8.6 71.6 ⫾ 13.3 165 ⫾ 6 26.5 ⫾ 4.9 28.6 ⫾ 9.6f 1814 ⫾ 597f 2912 ⫾ 790f 0.619 ⫾ 109 0.064 ⫾ 0.008 0.105 ⫾ 0.140

46 52.5 ⫾ 6.4 76.0 ⫾ 14.9 164 ⫾ 6 28.3 ⫾ 5.1 33.7 ⫾ 9.8 2125 ⫾ 546 3310 ⫾ 908 0.654 ⫾ 0.137 0.064 ⫾ 0.008 0.100 ⫾ 0.114

Data are presented in the men and women according to number of risk factors (impaired FPG, hypertension, hyperlipidemia, and obesity) for CVD. Means, SD, and P

values are presented. R, Risk factor. a

0 R significantly different from 1 R; b 0 R significantly different from 2 R; c 0 R significantly different from 3 R.

d

1 R significantly different from 2 R; e 1 R significantly different from 3 R.

f

2 R significantly different from 3 R.


the different cardiovascular outcomes after adjusting for potential confounders in both sexes. This is of interest because, in our dataset, the ratio of total fat to abdominal fat was roughly 15:1. Thus, an increase of less than 1 kg of abdominal fat corresponded to an increase from no CVD risk factors to at least three CVD risk factors. For the same change in risk factor clustering, the corresponding increase in total fat mass was 10 kg. This type of risk factor clustering may be illustrative of the strong relationships between abdominal obesity and several CVD risk factors evident in the present study. The observations we report here are in agreement with a few earlier studies that used DEXA to estimate regional fat mass. Van Pelt et al. (20) assessed the correlation between abdominal obesity, dyslipidemia, blood pressure, and insulin resistance in 166 postmenopausal women. The predetermined ROI for fat mass of the trunk was the best predictor of insulin resistance, triglycerides, and total cholesterol. In another report, Wu et al. (21) studied 1088 men and women and showed that the ratio of android to gynoid fat mass and the waist-to-hip ratio were the best predictors for cardiovascular risk factors, consistent with the results of our study. Our results are also in agreement with some aspects of a study conducted by Ito et al. (22), who studied a cohort of 2728 men and women aged 20 –79 yr old. They concluded that regional obesity measured by DEXA was better than BMI or total fat mass in predicting blood pressure, dyslipidemia, and diabetes mellitus. Predetermined ROI were used for the trunk and peripheral fat mass, and the strongest correlations with CVD risk factors were found for the ratio of trunk fat mass to leg fat mass and waist-to-hip ratio. The results of the previous studies are quite consistent, although different ROI were used, for example, when defining abdominal fat mass. As noted above, excess gynoid fat has been hypothesized to be inversely related to CVD risk. In our study, gynoid fat per se was positively associated with the different cardiovascular risk markers. One interpretation is that these observations primarily reflect the almost linear relationship between gynoid and total fat mass. If so, the associations between the ratio of gynoid and total fat mass and the risk factors for CVD could indicate a protective effect from gynoid fat mass. Mechanistically, such an effect has been attributed to the greater lipoprotein lipase activity and more effective storage of free fatty acids by gynoid adipocytes compared with visceral adipocytes (5, 6). Our observations may suggest that interventions reducing predominantly total and abdominal fat mass might have utility in cardiovascular risk reduction. Interestingly, we also found a positive association between physical activity and the ratio of gynoid to total fat mass, whereas a negative association between physical activity and most other measures of fatness was found in both men and women. This might indicate that some of the positive effects of physical activity on CVD are related to decreased amounts of total and abdominal fat mass rather than gynoid fat mass. However, in observational cross-sectional studies such as ours, it is impossible to establish whether the different estimates of fatness are causally related with the different cardiovascular risk factors and physical activity. To our knowledge, only two previous studies have investigated the relationship between gynoid fat and risk factors for


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CVD. Caprio et al. (23) showed that in a cohort of 24 adolescent girls, gynoid adipose mass was inversely related to triglyceride and low-density lipoprotein cholesterol levels. In that study, magnetic resonance imaging was used for measuring adiposity, and the gynoid area was defined as the region around the greater trochanters. In the second study, Pouliot et al. (24) measured visceral and sc fat by CT in a cohort of 58 obese male cases and 29 nonobese male controls. An inverse association was demonstrated between femoral neck adipose tissue and serum triglycerides in the obese men. We cannot explain the difference between these findings and ours. This study has several limitations. Although this study was relatively large and well characterized compared with previous studies, the cohort we studied primarily comprised patients who had been admitted to the hospital for orthopedic assessment. Moreover, because this was an observational cross-sectional study, one cannot be certain of the causal connection between abdominal fat mass and cardiovascular risk factors. Additionally, the measurements of regional body fat mass and cardiovascular risk factors were not undertaken simultaneously, raising the possibility that adiposity traits changed between the measurement time points. Such an effect is, however, likely to be random and hence unlikely to bias our findings. Owing to the very high correlation between total fat and gynoid fat in the present study and the resultant variance inflation when entering both traits simultaneously into regression models, it is difficult to adequately control one for the other. As a compromise, we expressed these two variables as a ratio. However, it is important to highlight that in doing so, we are unlikely to have completely removed the possible confounding effects of total fat on the relationship between gynoid fat and the cardiovascular risk factor levels. Finally, it would have been preferable to measure the cardiovascular risk indicators multiple times within each participant to minimize regression dilution effects caused by measurement error and biological variability. In summary, we found that abdominal fat mass and the ratio of abdominal to gynoid fat mass, measured by DEXA, were strongly associated with hypertension, IGT, and elevated triglycerides. Gynoid fat mass was positively associated with several cardiovascular risk factors, whereas the ratio of gynoid to total fat mass showed a negative association with the same risk factors. Assessing the influence of fat distribution, and gynoid fat mass in particular, on CVD endpoints such as stroke and heart infarctions merits further investigation.

Acknowledgments Address all correspondence and requests for reprints to: Peter Nordstro¨m, M.D., Ph.D., Department of Community Medicine and Rehabilitation, Umeå University, 90185 Umeå, Sweden. E-mail: peter.nordstrom@idrott. umu.se. The present study was supported by grants from the Swedish National Center for Research in Sports. Disclosure Statement: The authors have nothing to disclose.

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