Central Fat Mass Versus Peripheral Fat and Lean Mass: Opposite ...

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The Journal of Clinical Endocrinology & Metabolism 89(6):2632–2639 Copyright © 2004 by The Endocrine Society doi: 10.1210/jc.2003-031619

Central Fat Mass Versus Peripheral Fat and Lean Mass: Opposite (Adverse Versus Favorable) Associations with Arterial Stiffness? The Amsterdam Growth and Health Longitudinal Study ISABEL FERREIRA, MARIEKE B. SNIJDER, JOS W. R. TWISK, WILLEM VAN MECHELEN, HAN C. G. KEMPER, JACOB C. SEIDELL, AND COEN D. A. STEHOUWER Institute for Research in Extramural Medicine (I.F., M.B.S., J.W.R.T., W.v.M., H.C.G.K., J.C.S., C.D.A.S.), Department of Clinical Epidemiology and Biostatistics (J.W.R.T.), Department of Social Medicine and Body@Work Research Centre for Physical Activity, Work and Health TNO-VU (W.v.M.),, and Department of Internal Medicine and the Institute for Cardiovascular Research (C.D.A.S.), VU University Medical Center, 1081 HV Amsterdam, The Netherlands; and Department for Nutrition and Health (J.C.S.), Faculty of Earth and Life Sciences, Vrije Universiteit, 1081 HV Amsterdam, The Netherlands Central and peripheral fatness seem to confer opposite (i.e. adverse vs. protective) effects on cardiovascular risk, but how this occurs is not clear. In addition, the role of peripheral lean mass needs to be elucidated. We therefore investigated, in 336 (175 women) 36-yr-old and apparently healthy adults, the relationship between trunk fat, peripheral fat, and peripheral lean mass on the one hand, and estimates of stiffness of three large arteries on the other. Body composition was assessed by dual-energy x-ray absorptiometry. Arterial properties were assessed by ultrasound imaging. We found that 1) trunk fat was positively (i.e. adversely) associated with stiffness of the carotid and femoral arteries, whereas peripheral fat was in-

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ODY FATNESS (1) and, in particular, central fatness (2–5) are major risk factors for cardiovascular disease. In contrast, peripheral fat seems to confer a protective effect on cardiovascular health, as suggested by the observations that the adverse effects of a high waist-to-hip ratio can be due not only to larger waist but also to smaller hip (6 –9) or thigh (9 –11) circumferences. However, this raises the question of whether the protective role of a larger hip or thigh circumference is due to a higher fat and/or a higher lean mass in the gluteal/femoral region. An answer to this question requires objective methods of body composition assessment such as dual-energy x-ray absorptiometry (DXA). In this line, some studies have investigated the opposite associations between trunk and peripheral fat in relation to several cardiovascular risk factors (4, 12–15), but none have considered the concomitant role of peripheral lean mass, which would have answered the question above more precisely (3, 16, 17). A possible mechanism by which body composition affects Abbreviations: AGAHLS, Amsterdam Growth and Health Longitudinal Study; ␤, standardized regression coefficient; BMI, body mass index; DXA, dual-energy x-ray absorptiometry; FFA, free fatty acids; HbA1c, glycated hemoglobin; HDL, high-density lipoprotein; IMT, intima-media thickness; PWV, pulse wave velocity. JCEM is published monthly by The Endocrine Society (http://www. endo-society.org), the foremost professional society serving the endocrine community.

versely (i.e. favorably) associated with stiffness of the brachial and the carotido-femoral segment; 2) peripheral lean mass was positively associated with arterial diameter and carotid compliance and inversely associated with stiffness of the carotido-femoral segment; and 3) after adjustment for the other body composition variables, the above-mentioned associations remained, but peripheral fat in addition became, if anything, favorably associated with stiffness of the femoral artery. We conclude that trunk fat is adversely associated with large artery stiffness, whereas some degree of protection is conferred by peripheral fat and lean mass. (J Clin Endocrinol Metab 89: 2632–2639, 2004)

cardiovascular health is through its association with atherosclerosis and arterial stiffness, both known causes of cardiovascular disease (18 –20). Several studies have shown central fat to have an adverse impact on arterial properties, such as carotid intima-media thickness (IMT) (21–23) and diverse estimates of local and regional arterial stiffness (24 –27). However, the concomitant role of peripheral fat and lean mass, in addition to that of central fat, on these arterial properties has never been addressed, and this therefore needs to be further investigated. In the Amsterdam Growth and Health Longitudinal Study (AGAHLS), measures of body composition (as assessed by DXA) and estimates of carotid atherosclerosis and large artery stiffness (as assessed by noninvasive ultrasound imaging) in a cohort of young and apparently healthy adults provide an opportunity to study these issues in detail. The purpose of the present study was therefore to investigate the associations between trunk fat mass and peripheral (i.e. in the limbs) fat and lean mass on the one hand, and large artery properties, such as carotid IMT and arterial stiffness of large arteries, on the other. Subjects and Methods Subjects and study design The AGAHLS is an observational longitudinal study that started in 1977 with a group of 450 boys and girls. Its initial goal was to describe

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the natural development of growth, health, and lifestyle of adolescents and to investigate longitudinal relationships between biological and lifestyle variables. The mean age of the subjects at the beginning of the study was 13.1 (⫾0.8) yr. Since then, subjects have been measured two to eight times during a 23-yr follow-up period. At each measurement, anthropometrical (body height, weight, skinfolds, and circumferences), biological (serum lipoprotein levels, blood pressure, and physical fitness), lifestyle (nutritional habits, smoking behavior and daily physical activity), and psychological variables were assessed, as detailed elsewhere (28). In the most recent examination (in 2000), when the subjects’ mean age was 36.5 (⫾0.6) yr, total body and regional body composition, as assessed by DXA, and large artery properties, as assessed by noninvasive ultrasound imaging, were investigated for the first time. For the present study, data on the 2000 follow-up were analyzed. Large artery properties were assessed in 377 subjects. Of these, 41 subjects were not included because of missing DXA data (due to logistical reasons and/or pregnancy). Our final sample thus consisted of 336 subjects (175 women). The study was approved by the medical ethical committee of the VU University Medical Center, (Amsterdam, The Netherlands), and all subjects gave their written informed consent.

Body composition Regional (arms, legs, trunk, and head) body fat and body lean mass were measured with a whole-body DXA scanner (Hologic QDR-2000, software version V5.67A, Hologic Inc., Waltham, MA). The body regions were delineated with the use of specific anatomical landmarks as shown in Fig. 1. Peripheral fat was calculated by adding the fat mass of the legs to that of the arms, and peripheral lean mass was calculated by adding the lean mass of the legs to that of the arms.

Arterial properties All subjects had abstained from smoking and caffeine-containing beverages on the day the measurements were performed. At the time of measurements of arterial properties, subjects had been resting in a supine position for 15 min in a quiet, temperature-controlled room. Properties of the right common carotid, the common femoral, and the brachial arteries were obtained by two trained vascular sonographers with the use of an ultrasound scanner equipped with a 7.5-MHz linear array probe (Pie Medical, Maastricht, The Netherlands). The ultrasound scanner was connected to a personal computer equipped with an acquisition system and a vessel wall movement detector software system (Wall Track System 2, Pie Medical, Maastricht, The Netherlands). This integrated device enables measurements of arterial diameter, distension, pulse wave transit time, and IMT as described in detail elsewhere (29 – 32). The carotid artery was measured approximately 10 mm proximal to the beginning of the bulb, the femoral artery 20 mm proximal to the flow divider, and the brachial artery approximately 20 mm above the antecubital fossa. The mean diameter, distension, pulse wave transit time,

FIG. 1. Standard regions of a whole-body DXA scan: 1, head; 2, trunk; 3, arms; and 4, legs.

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and, for the carotid artery only, IMT of three consecutive measurements were used in the analyses. Throughout the entire period of ultrasound imaging, systolic, diastolic, and mean arterial pressure blood pressure were assessed in the left arm at 5-min intervals with an oscillometric device (Colin PressMate, Komaki-City, Japan; model BP-8800). Brachial artery pulse pressure was defined as systolic minus diastolic blood pressure, and pulse pressure at the common carotid and femoral arteries was calculated by calibration of the distension waveforms (33). Local arterial stiffness. The mean diameter (D), distension (⌬D), and local pulse pressure (PP) were used to estimate the distensibility coefficient (DC) and compliance coefficient (CC) as follows (32):

DC⫽共2⌬D䡠D ⫹ ⌬D2兲/共PP䡠D2兲, in 10⫺3/kPa CC ⫽ ␲ 䡠共2D䡠⌬D ⫹ ⌬D2兲/4PP, in mm2/kPa Distensibility reflects the elastic properties, whereas compliance reflects the buffering capacity of the artery. From carotid IMT, diameter, and distensibility coefficient of the carotid artery, we calculated Young’s elastic modulus (Einc), an estimate of the intrinsic elastic properties of the vessel wall:

Einc ⫽ D/(IMT䡠DC), in kPa Regional stiffness. We used the carotido-femoral travel time (in milliseconds), i.e. the carotid artery pulse wave transit time subtracted from that of the femoral artery, adjusted for sitting height [i.e. the carotido-femoral pulse wave velocity (PWV)], as an indicator of regional stiffness (24). To make data interpretation more convenient, we used 1/carotido-femoral PWV. For technical reasons, carotid and femoral PWV was measured only in 280 subjects (145 women).

Covariates Anthropometry. Measurements of height and weight were performed in barefoot persons wearing underwear only. Height was measured with a Harpenden digital readout, wall-mounted stadiometer (Holtain, UK; van Rietschoten and Houwens, Rotterdam, The Netherlands) and recorded to the nearest 0.1 cm; body weight was measured on a spring balance (Van Vucht, Amsterdam, The Netherlands). The body mass index (BMI) was calculated as body weight divided by body height squared. Waist circumference was measured at the level midway between the lowest rib margin and the iliac crest, and the hip circumference was measured at the widest levels over the great trochanters. Lifestyle variables. Dietary intake was measured by a computer-assisted cross-check dietary history interview (34), in which all subjects were asked to recall their usual dietary intake during the previous month by reporting frequency, amounts, and methods of preparation of the foods consumed. The following nutrients were considered: 1) the intake of fat; 2) the intake of carbohydrates (both expressed as percentage of total energy intake); and 3) the Keys score, which combines the intake of saturated fatty acids, the intake of polyunsaturated fatty acids, and the intake of cholesterol. With the dietary history interview, alcohol consumption (expressed in grams per week) was also measured. Smoking behavior was measured by a separate questionnaire. The amount of tobacco smoked was combined with the duration of smoking and expressed in pack-years (35). Daily physical activity was measured by a structured interview in which the total time spent on physical activities in relation to work, organized and unorganized sports activities, other leisure time activities, transportation, etc. over the previous 3 months was registered. The time spent on these different activities was then combined with the intensity of the activities to calculate a total weighted activity score (expressed in metabolic equivalents per week) (36). Metabolic and other variables. Cardiopulmonary fitness was measured with a maximal running test on a treadmill (model 18-54, Quinton, Bothell, WA), with a speed of 8 km/h and increasing slope, and with direct measurements of oxygen uptake (Ergoanalyzer, Jaeger, Bunnik, The Netherlands) expressed as maximal oxygen uptake in ml/ min䡠kg2/3. Total and high-density lipoprotein (HDL) cholesterol and triglycerides (milligrams per deciliter) were measured by enzymatic techniques (Roche Diagnostics, Mannheim, Germany), and glycated

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hemoglobin (HbA1c; percentage) was determined by ion-exchange HPLC with a mono-S column (Pharmacia, Uppsala, Sweden). Resting heart rate (mean from 15 R-R intervals in the last 15 sec of the minute) was measured telemetrically (Telecust 36 and Sirecust BS1, Siemens, Amsterdam, The Netherlands).


the regression models to be disturbed by multicollinearity if the tolerance was less than 0.1 (37). All statistical analyses were carried out with the Statistical Package of Social Sciences, 10.1 for Windows (SPSS, Inc., Chicago, IL).

Results Statistical analysis We used multiple linear regression analyses to investigate the crosssectional association between body composition on the one hand and arterial properties on the other. Firstly, we investigated the associations of trunk fat, peripheral fat, and lean mass (determinants) with each of the arterial properties (outcomes). These relationships were adjusted for gender, height, and mean arterial pressure (the latter not in analyses with carotid IMT) (model 1). Secondly, we included the three body composition variables simultaneously in the regression model and investigated their associations (i.e. adjusted for each other) with each of the arterial properties (model 2). To investigate the potential confounding effect of lifestyle variables on the associations between body composition and large artery properties, we added those variables to the model 2 described above. To investigate the potential confounding and/or mediating effects of total/ HDL cholesterol, triglycerides, HbA1c, maximal oxygen uptake, and resting heart rate in the relationships between body composition and arterial properties, we added these variables to the same regression model (model 3). After we assessed the main effects, we added interaction terms between the determinants and gender to the regression models. When the interaction terms were significant (i.e. P ⬍ 0.05), stratified analyses were performed and data presented separately for men and women. Results of the regression analyses are reported as standardized regression coefficients (␤) and respective 95% confidence intervals to allow direct comparisons between the determinants. We considered the stability of

Table 1 shows the main characteristics of the study population concerning body composition, lifestyle, and metabolic and other covariates. BMI (kilograms per meter squared) ranged between 19.0 and 38.2 in men and between 16.7 and 38.2 in women: 67.8% of the subjects were lean (BMI ⬍ 25), 27.4% were overweight (BMI, 25–29.9), and 4.8% were obese (BMI ⱖ 30). Table 2 shows data on large artery properties. Body composition and large artery properties (Tables 3 and 4)

Trunk fat was inversely and significantly associated with the distensibility of the carotid and the femoral arteries and with the compliance of the femoral artery (Table 3; model 1), which resulted mainly from the inverse associations between trunk fat and distension of these arteries (Table 4; model 1). Trunk fat was also positively associated with the Young’s elastic modulus of the carotid artery. In contrast, peripheral fat was positively associated with the distensibility and compliance of the brachial artery, which again resulted mainly from the positive associations between peripheral fat and

TABLE 1. Characteristics of the study population

Age (yr) Anthropometry Body height (cm) Sitting height (cm) Body weight (kg) BMI (kg/m2) Waist circumference (cm) Hip circumference (cm) Waist-to-hip ratio DXA Total fat mass (kg) Total lean mass (kg) Trunk fat mass (kg) Peripheral fat mass (kg) Peripheral lean mass (kg) Lifestyle variables Fat intake (% energy intake) Carbohydrates intake (% energy intake) Keys score Alcohol consumption (g/wk) Pack-years of smoking Daily physical activity (1000 METs/wk) Metabolic and other variables Cardiopulmonary fitness (ml/min/kg2/3) Systolic pressure (mm Hg) Diastolic pressure (mm Hg) Pulse pressure (mm Hg) Total cholesterol (mg/dl) HDL cholesterol (mg/dl) Total/HDL cholesterol (mg/dl) Triglycerides (mg/dl) HbA1c (%) Resting heart rate (bpm)

Men (n ⫽ 161)

Women (n ⫽ 175)

36.5 ⫾ 0.6

36.6 ⫾ 0.6

183.5 ⫾ 6.3 93.4 ⫾ 7.4 83.4 ⫾ 10.7 24.7 ⫾ 2.8 85.0 ⫾ 8.1 89.2 ⫾ 7.3 0.95 ⫾ 0.05

170.3 ⫾ 6.4 87.5 ⫾ 3.3 68.0 ⫾ 10.5 23.4 ⫾ 3.4 73.1 ⫾ 8.5 89.4 ⫾ 8.8 0.82 ⫾ 0.07

18.2 ⫾ 7.2 61.3 ⫾ 6.4 8.3 ⫾ 4.6 8.8 ⫾ 2.9 26.7 ⫾ 3.4

21.9 ⫾ 7.6 42.6 ⫾ 5.4 8.2 ⫾ 3.8 12.9 ⫾ 4.1 16.8 ⫾ 2.8

33.4 ⫾ 5.5 45.1 ⫾ 6.4 41.3 ⫾ 8.3 113.5 (39.7–196.2) 7.46 (1.08 –16.00) 3.89 (2.13–5.80)

33.6 ⫾ 4.6 45.7 ⫾ 6.2 42.7 ⫾ 8.3 75.0 (30.0 –145.6) 5.10 (1.43–11.31) 4.45 (3.03– 6.68)

222.1 ⫾ 30.0 121.7 ⫾ 10.3 67.0 ⫾ 6.8 54.7 ⫾ 6.0 200.2 ⫾ 38.6 47.2 ⫾ 11.0 4.48 ⫾ 1.38 139.2 (79.7–163.7) 5.32 ⫾ 0.78 70.9 ⫾ 11.8

166.4 ⫾ 22.6 111.5 ⫾ 10.6 62.9 ⫾ 6.9 48.6 ⫾ 5.8 184.6 ⫾ 30.3 60.3 ⫾ 12.8 3.20 ⫾ 0.92 82.7 (53.1–97.4) 5.21 ⫾ 0.39 70.4 ⫾ 10.9

Data are means ⫾ SD or median (interquartile range). SI conversion factors: to convert milligrams per deciliter to millimoles per liter, multiply by 0.0259 for cholesterol and by 0.0113 for triglycerides. MET, Metabolic equivalent; bpm, beats per minute.



TABLE 2. Large artery properties

Carotid artery IMT (mm) Diameter (mm) Distension (␮m) Local pulse pressure (mm Hg) Distensibility coefficient (10⫺3/kPa) Compliance coefficient (mm2/kPa) Young’s elastic modulus (103/kPa) Femoral artery Diameter (mm) Distension (␮m) Local pulse pressure (mm Hg) Distensibility coefficient (10⫺3/kPa) Compliance coefficient (mm2/kPa) Brachial artery Diameter (mm) Distension (␮m) Local pulse pressure (mm Hg) Distensibility coefficient (10⫺3/kPa) Compliance coefficient (mm2/kPa) Carotido-femoral PWVa 1/carotido-femoral transit time (1/sec)

Men (n ⫽ 161)

Women (n ⫽ 175)

0.62 ⫾ 0.10 7.19 ⫾ 0.51 625 ⫾ 140 52.7 ⫾ 7.8 26.1 ⫾ 5.3 1.06 ⫾ 0.27 0.47 ⫾ 0.12

0.63 ⫾ 0.10 6.60 ⫾ 0.52 516 ⫾ 120 45.6 ⫾ 7.9 27.3 ⫾ 6.8 0.94 ⫾ 0.26 0.42 ⫾ 0.12

10.6 ⫾ 1.03 217 ⫾ 95 54.1 ⫾ 9.1 5.9 ⫾ 2.8 0.51 ⫾ 0.24

9.0 ⫾ 1.1 229 ⫾ 98 49.3 ⫾ 9.8 8.3 ⫾ 4.3 0.51 ⫾ 0.24

4.49 ⫾ 0.53 193 ⫾ 99 55.0 ⫾ 6.3 12.7 ⫾ 8.5 0.19 ⫾ 0.09

3.55 ⫾ 0.46 174 ⫾ 83 48.5 ⫾ 6.4 16.2 ⫾ 8.9 0.16 ⫾ 0.07

13.0 ⫾ 2.4

13.2 ⫾ 2.8

Data are means ⫾ SD. a Data available on 280 subjects only (145 women). TABLE 3. Associations of trunk fat, peripheral fat, and lean mass with large artery stiffness indices Model

Carotid artery Distensibility coefficient Compliance coefficient Young’s elastic modulus Femoral artery Distensibility coefficient Compliance coefficient Brachial artery Distensibility coefficient Compliance coefficient Carotido-femoral PWV

Trunk fat

Peripheral fat

Peripheral lean

␤

95% CI

␤

1 2 3 1 2 3 1 2 3

⫺0.12a ⫺0.20a ⫺0.12 ⫺0.07 ⫺0.12 ⫺0.05 0.11a 0.16a 0.14

⫺0.22; ⫺0.02 0.36; ⫺0.04 ⫺0.30; 0.06 ⫺0.19; 0.04 ⫺0.28; 0.03 ⫺0.23; 0.13 0.01; 0.22 0.00; 0.32 ⫺0.03; 0.33

⫺0.06 0.11 0.09 ⫺0.05 0.02 0.01 0.07 ⫺0.06 ⫺0.04

⫺0.17; 0.56 ⫺0.07; 0.29 ⫺0.09; 0.27 ⫺0.17; 0.07 ⫺0.16; 0.20 ⫺0.18; 0.19 ⫺0.05; 0.19 ⫺0.25; 0.12 ⫺0.22; 0.15

0.01 0.07 ⫺0.00 0.29a 0.35b 0.28b 0.00 ⫺0.06 ⫺0.06

⫺0.23; 0.24 ⫺0.17; 0.31 ⫺0.25; 0.25 0.06; 0.52 0.11; 0.54 0.03; 0.53 ⫺0.23; 0.24 ⫺0.30; 0.19 ⫺0.31; 0.19

1 2 3 1 2 3

⫺0.11a ⫺0.21b ⫺0.24a ⫺0.15b ⫺0.28c ⫺0.20a

⫺0.20; ⫺0.01 ⫺0.37; ⫺0.05 ⫺0.43; ⫺0.05 ⫺0.26; ⫺0.05 ⫺0.45; ⫺0.12 ⫺0.40; ⫺0.01

⫺0.03 0.16 0.16 ⫺0.06 0.17 0.15

⫺0.15; 0.09 ⫺0.02; 0.34 ⫺0.03; 0.35 ⫺0.18; 0.06 ⫺0.02; 0.36 ⫺0.04; 0.35

⫺0.10 ⫺0.05 ⫺0.03 0.09 0.18 0.11

⫺0.34; 0.14 ⫺0.29; 0.19 ⫺0.28; 0.23 ⫺0.16; 0.34 ⫺0.07; 0.43 ⫺0.16; 0.37

1 2 3 1 2 3 1 2 3

0.07 ⫺0.05 ⫺0.00 0.05 ⫺0.11 ⫺0.08 ⫺0.04 0.17 0.25a

⫺0.04; 0.17 ⫺0.21; 0.12 ⫺0.19; 0.19 ⫺0.06; 0.15 ⫺0.27; 0.05 ⫺0.20; 0.18 ⫺0.16; 0.08 ⫺0.01; 0.35 0.04; 0.46

0.13a 0.18a 0.12 0.13a 0.21a 0.16 ⫺0.16a ⫺0.24a ⫺0.28a

0.01; 0.26 0.00; 0.37 ⫺0.07; 0.32 0.01; 0.25 0.02; 0.40 ⫺0.03; 0.35 ⫺0.29; ⫺0.02 ⫺0.45; ⫺0.04 ⫺0.50; ⫺0.06

⫺0.02 ⫺0.06 ⫺0.00 0.19 0.16 0.16 ⫺0.41c ⫺0.37b ⫺0.38b

⫺0.26; 0.23 ⫺0.32; 0.19 ⫺0.27; 0.26 ⫺0.06; 0.43 ⫺0.09; 0.41 ⫺0.10; 0.42 ⫺0.64; ⫺0.17 ⫺0.62; ⫺0.13 ⫺0.63; ⫺0.12

95% CI

␤

95% CI

Model 1, Model adjusted for gender, body height (sitting height instead in analyses with carotido-femoral PWV), and mean arterial pressure; model 2, model 1 plus the other body composition variables; model 3, model 2 plus cardiopulmonary fitness, total/HDL cholesterol, triglycerides, HbA1c, and resting heart rate. CI, Confidence interval. a P ⬍ 0.05. b P ⬍ 0.01. c P ⬍ 0.001.

distension of this artery. Peripheral lean mass was positively and significantly associated with the diameter of the three arteries, but this led to positive and significant associations with the compliance of the carotid artery only. Both periph-

eral fat and lean mass were inversely associated with carotido-femoral PWV. After adjustment for the other body composition variables (model 2), trunk fat remained the strongest determinant of

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TABLE 4. Associations of trunk fat, peripheral fat, and lean mass with the individual elements of the arterial stiffness indices Model

Carotid artery IMT Diameter

Distension Local pulse pressure

Local pulse pressure Brachial artery Diameter Distension Local pulse pressure

95% CI

Gender

Peripheral fat

␤

95% CI

0.05 ⫺0.01 ⫺0.02 0.19a ⫺0.10 0.18 ⫺0.28a 0.21 ⫺0.20 ⫺0.08 0.09 0.08 0.06 ⫺0.13 0.30a ⫺0.17 0.30a ⫺0.18

⫺0.08; 0.17 ⫺0.20; 0.19 ⫺0.22; 0.19 0.03; 0.35 ⫺0.25; 0.06 ⫺0.07; 0.44 ⫺0.47; ⫺0.01 ⫺0.02; 0.45 ⫺0.44; 0.04 ⫺0.19; 0.03 ⫺0.08; 0.26 ⫺0.10; 0.25 ⫺0.15; 0.26 ⫺0.28; 0.01 0.03; 0.56 ⫺0.42; 0.09 0.04; 0.56 ⫺0.42; 0.06

0.05 0.03 ⫺0.03 0.06

⫺0.06; 0.16 ⫺0.15; 0.20 ⫺0.23; 0.17 ⫺0.04; 0.15

2

0.07

⫺0.08; 0.21

3

0.04

⫺0.13; 0.20

1 2 3 1

⫺0.14a ⫺0.23b ⫺0.16 ⫺0.11 ⫺0.18 ⫺0.32a 0.06 ⫺0.32a 0.04

⫺0.23; ⫺0.04 ⫺0.37; ⫺0.08 ⫺0.33; 0.02 ⫺0.22; 0.09 ⫺0.24; 0.06 ⫺0.63; ⫺0.01 ⫺0.20; 0.32 ⫺0.57; ⫺0.06 ⫺0.20; 0.29

1 2 3 1 2 3 1 2 3

⫺0.03 ⫺0.10 0.11 ⫺0.17b ⫺0.27c ⫺0.25b ⫺0.08 0.01 0.01

⫺0.11; 0.06 ⫺0.23; 0.03 ⫺0.03; 0.25 ⫺0.28; ⫺0.06 ⫺0.44; ⫺0.10 ⫺0.44; ⫺0.04 ⫺0.18; 0.02 ⫺0.15; 0.16 ⫺0.17; 0.19

⫺0.00 0.04 0.01 ⫺0.09 0.14 0.12 ⫺0.12a ⫺0.13 ⫺0.13

⫺0.10; 0.10 0.12; 0.19 ⫺0.14; 0.15 ⫺0.22; 0.03 ⫺0.06; 0.33 ⫺0.08; 0.32 ⫺0.24; ⫺0.01 ⫺0.31; 0.05 ⫺0.31; 0.05

1 2 3 1 2 3 1

0.00 ⫺0.03 0.01 0.06 ⫺0.11 ⫺0.01 ⫺0.10 0.26c ⫺0.16 0.22 ⫺0.14 0.24a

⫺0.08; 0.08 ⫺0.15; 0.10 ⫺0.13; 0.15 ⫺0.05; 0.17 ⫺0.27; 0.07 ⫺0.21; 0.19 ⫺0.21; 0.02 0.13; 0.40 ⫺0.39; 0.08 ⫺0.01; 0.44 ⫺0.43; 0.14 0.00; 0.48

0.00 ⫺0.03 0.01 0.16a 0.24a 0.19 0.12a

3

Distension

Trunk fat

␤

1 2 3 1

2

Femoral artery Diameter

Gender

2 3

M W M W M W

M W M W M W

M W M W M W

M W M W M W

Peripheral lean

␤

95% CI

0.24 0.23 0.27 0.47c

⫺0.01; 0.49 ⫺0.03; 0.49 ⫺0.01; 0.54 0.26; 0.68

0.47c

0.25; 0.69

0.45c

0.22; 0.68

0.24a 0.33b 0.27a 0.08

0.02; 0.46 0.10; 0.55 0.03; 0.50 ⫺0.15; 0.31

0.12

⫺0.13; 0.37

0.12

⫺0.11; 0.36

0.43c 0.48c 0.33b ⫺0.01 0.09 0.03 ⫺0.05 ⫺0.00 ⫺0.08

0.24; 0.63 0.28; 0.68 0.14; 0.53 ⫺0.26; 0.25 ⫺0.17; 0.35 ⫺0.24; 0.30 ⫺0.29; 0.18 ⫺0.24; 0.24 ⫺0.32; 0.17

⫺0.10; 0.09 ⫺0.17; 0.11 ⫺0.13; 0.15 0.03; 0.29 0.05; 0.44 ⫺0.01; 0.38 0.02; 0.38

0.44c 0.47c 0.40c 0.08 0.04 0.07 0.11

0.26; 0.62 0.28; 0.65 0.21; 0.59 ⫺0.17; 0.34 ⫺0.22; 0.30 ⫺0.20; 0.34 ⫺0.10; 0.31

0.15

⫺0.01; 0.30

0.07

⫺0.14; 0.28

0.15

⫺0.02; 0.31

0.03

⫺0.19; 0.25

Model 1, Model adjusted for gender, body height, and mean arterial pressure (the latter not in analyses with carotid IMT); model 2, model 1 plus the other body composition variables; model 3, model 2 plus cardiopulmonary fitness, total/HDL cholesterol, triglycerides, HbA1c, and resting heart rate (and also local pulse pressure in analyses with carotid IMT). CI, Confidence interval; M, men; W, women. a P ⬍ 0.05. b P ⬍ 0.01. c P ⬍ 0.001.

carotid distensibility (inversely) and Young’s elastic modulus (positively), and femoral distensibility and compliance (inversely). Similarly, peripheral fat was the strongest determinant of brachial distensibility and compliance (positively) and both peripheral fat and lean mass were the strongest determinants of the carotido-femoral PWV (inversely). In addition, peripheral fat was then, if anything, positively associated with distensibility and compliance of the femoral artery (P ⫽ 0.08 and P ⫽ 0.07, respectively). In other words, after adjustments for trunk fat and peripheral lean mass, peripheral fat was, if anything, favorably associated with stiffness estimates of femoral and brachial arteries. The opposite effects of trunk fat vs. peripheral fat and lean mass on large artery stiffness are further illustrated in Fig. 2.

Additional analyses

Adjustments of the associations between body composition and large artery properties for potential confounders, such as smoking habits, alcohol consumption, physical activity, and nutrient intake (i.e. the lifestyle variables), did not materially change any of the associations investigated (data not shown). We therefore dropped these variables from our models. Similarly, and despite the (significant) associations between body composition and cardiopulmonary fitness, total/HDL cholesterol ratio, triglycerides, HbA1c, and resting heart rate (data not shown), further adjustments for these variables did not change the strength of the associations between trunk fat and peripheral fat on the one hand, and femoral distensibility on the other; associations between



trunk fat and femoral compliance decreased to some extent but remained significant (model 3). The opposite associations of these variables with the carotido-femoral PWV became even stronger. However, the associations with carotid and brachial distensibility and compliance decreased and were no longer significant. Peripheral lean mass remained positively and significantly associated with the diameter of the three arteries and the compliance of the carotid artery and inversely associated with the carotido-femoral PWV. Discussion

To our knowledge, the present study is the first to evaluate the relationships between regional fat and lean mass and large artery properties of young and apparently healthy men and women. Our study clearly indicates that trunk fat is deleterious, whereas peripheral lean mass may contribute favorably to cardiovascular health. Furthermore, our study, like others (12–15), also suggests that some degree of protection may be provided by fat mass accumulated in the limbs. This derived from the observation that, for a given degree of truncal adiposity, greater peripheral adiposity (and also lean mass) is favorably associated with large artery stiffness. Changes in arterial stiffness may therefore, in part, mediate the association between body composition and cardiovascular risk. Our findings are consistent with the concept that adipose tissue accumulated preferentially in the trunk has more adverse effects on cardiovascular risk than does fat stored in peripheral depots (1, 3). Further discrimination of fat depots within these anatomical regions would have provided a better insight into the mechanisms. For instance, when considering truncal adiposity, the adipose tissue accumulated in the visceral region has been suggested to be more deleterious than that accumulated sc (38). However, others have also highlighted similar adverse effects of sc truncal fat (2, 4, 39). In this context, we have previously shown in the same cohort that truncal adiposity as estimated by skinfolds (i.e. sc truncal fat) and waist circumference (i.e. abdominal visceral and sc fat) were inversely associated with the distensibility and compliance of the carotid and femoral arteries (24). Therefore, not only visceral but also sc truncal adiposity seems to be related to increased arterial stiffness. With regard to limb composition, fat stored within the muscle may be more adverse than that accumulated in sc depots (40). However, the vast majority (i.e. ⬎90%) of the fat in the limbs is in sc regions (40), and our study therefore suggests that storage of (sc) fat in the limbs rather than in the trunk may be protective. Trunk fatness seemed to exert its deleterious effects on the distensibility and compliance of the elastic carotid and the muscular femoral arteries, whereas the opposing (i.e. protective) effects of peripheral fat were mainly visible with the distensibility and compliance of the muscular arteries. In general, proximal elastic arteries and peripheral muscular FIG. 2. Associations between trunk fat, peripheral fat, and lean fat mass adjusted for each other and for gender, body height, and mean arterial pressure, with distensibility and compliance of three large arteries and PWV of the carotido-femoral segment (A–C). CCA, Common carotid artery; CFA, common femoral artery; BA, brachial artery. *, P ⬍ 0.05; †, P ⬍ 0.01; ‡, P ⬍ 0.001.

2638


arteries respond differently to aging, drugs, and other factors (19, 30), and the present study suggests that the effects of regional adiposity, also, are not uniform along the arterial tree. Peripheral lean mass was associated with greater arterial diameter. As muscle mass increases, so will the requirements for blood supply, resulting in a hyperdynamic circulation that may explain the size adaptation of the arteries (i.e. vascular remodeling) (31, 41). Peripheral lean mass was also the strongest determinant of carotid IMT, but this association disappeared after adjustment for carotid diameter [but, conversely, the association with carotid diameter was unchanged after adjustment for IMT (data not shown)]. This indicates that arterial thickening of the arterial wall might have been a consequence of an increased arterial diameter to maintain normal wall stress (Laplace’s Law) and therefore might not reflect atherosclerosis (42). The present study suggests that the beneficial contribution of lean mass to cardiovascular health (16, 39) might be, at least in part, due to its contributions to higher arterial compliance and lower aortic stiffness. Corticosteroids, growth and sex hormones, genetic factors, and intrauterine growth are all determinants of body composition (1, 3, 43). In addition, body composition and large artery properties may be influenced by behavioral characteristics such as smoking, alcohol consumption, and physical activity (44). In the present study, smoking behavior was positively associated with trunk fat, alcohol consumption was inversely associated with peripheral fat, and physical activity was positively associated with peripheral lean body mass (data not shown). Nevertheless, the associations between body composition and large artery properties were not confounded by these lifestyle variables. Associations between estimates of central and peripheral adiposity and stiffness of the carotid and brachial arteries may, however, have been confounded and/or mediated by the other cardiovascular risk factors considered (because further adjustments for these variables resulted in a decrease of the associations). Total/HDL cholesterol ratio and cardiopulmonary fitness were the main confounders or intermediates of these relationships, which suggests that these variables could constitute a pathway through which body composition affects arterial properties. Nevertheless, associations between central and peripheral fat with the muscular femoral artery and the carotido-femoral artery PWV (which reflects stiffness mainly of the descending aorta) were independent of adjustments for other risk factors. Other mechanisms must thus explain these direct and opposing effects. Differences in metabolism of adipose tissue depending on whether it is located centrally (i.e. in the trunk) or peripherally (i.e. in the limbs) are one possibility. Adipocytes from visceral abdominal regions are more sensitive to lipolytic activity and more resistant to suppression of lipolysis by insulin than are the adipocytes from the gluteal or femoral regions, whereas the metabolic characteristics of adipocytes from sc central regions tend to be intermediate (45). The higher lipolytic activity of the adipocytes in the trunk leads to overexposure of the liver to free fatty acids (FFA), resulting in insulin resistance and hyperinsulinemia (1, 3). In contrast, the gluteo/ femoral adipose tissue shows a higher lipoprotein lipase


activity and low fatty acid turnover. Therefore, adipose tissue in this region is more likely to take up FFA from the circulation and store them, thereby protecting other organs such as the liver, skeletal muscle, and pancreas from high FFA exposure (46). In addition, adipose tissue is an endocrine organ that produces many peptides, such as angiotensin, IL-6, TNF-␣, plasminogen activator inhibitor-1, leptin, and adiponectin (47), that in turn impact on vascular structure and function (21, 27, 48 –50). Regional differences in secretion of these peptides could thus potentially explain the direct and opposite effects of these fat depots on arterial properties. These possibilities require further investigation. Our study had several limitations. First, its cross-sectional design does not allow us to draw conclusions in terms of causality. Second, because there are strong associations between fat measures (i.e. trunk fat and peripheral fat), it can be argued that adjustment of these measurements for each other is not adequate, leading to overcorrected models. However, although the regression coefficients changed considerably after adjustments of body composition variables for each other (model 1 vs. model 2), our models were not disturbed by multicollinearity (i.e. tolerance was ⬎0.1. in all models). Third, our results were obtained in a young and apparently adult, white, and nonobese population, and therefore, inferences with regard to older individuals, other ethnicities, and high-risk populations (e.g. the obese) should be made with caution. We conclude that moderate degrees of regional adiposity differently influence arterial structural and functional properties that are related to cardiovascular risk. These data thus provide a pathophysiological framework for understanding the associations between body composition and arterial-stiffness-related complications, such as systolic hypertension, heart failure, and stroke. Acknowledgments We thank all the participants of the AGAHLS. Received September 16, 2003. Accepted January 27, 2004. Address all correspondence and requests for reprints to: Professor Dr. Coen D. A. Stehouwer, Department of Internal Medicine, VU University Medical Center, De Boelelaan 1117, P.O. Box 7057, 1081 HV Amsterdam, The Netherlands. E-mail: [email protected]. I.F. was supported by a research grant from the Foundation for Science and Technology—State Secretary of Science and Technology of Portugal, and the European Social Fund, within the Third European Community Framework Program (Grant PRAXIS XXI/BD/19760/99). The AGAHLS has, since 1974, been supported by major grants from the Foundation for Educational Research; the Dutch Prevention Fund; The Netherlands Heart Foundation; the Dutch Ministry of Public Health, Well Being and Sport; the Dairy Foundation on Nutrition and Health; The Netherlands Olympic Committee/Netherlands Sports Federation; Heineken BV; and Scientific Board of Smoking and Health.

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