Obesity Research & Clinical Practice (2012) 6, e340—e346
Value of dual-energy X-ray absorptiometry derived parameters vs anthropometric obesity indices in the assessment of early atherosclerosis in abdominally obese men Qiang Lu, Chun-Ming Ma, Rui Wang, Fu-Zai Yin, Chun-Mei Qin, Dong-Hui Lou, Bo Liu ∗ Department of Endocrinology, The First Hospital of Qinhuangdao, Hebei Medical University, No. 258 Wenhua Road, Qinhuangdao, 066000 Hebei Province, China Received 7 May 2011 ; received in revised form 27 July 2011; accepted 23 August 2011
KEYWORDS Abdominal obesity; Body fat distribution; Atherosclerosis
∗
Summary Objective: The purpose of this study was to evaluate the value of dual-energy X-ray absorptiometry (DEXA) derived parameters vs anthropometric obesity indices in the assessment of early atherosclerosis in abdominally obese men. Methods: This case—control study included 44 abdominally obese men (waist circumference ≥ 90 cm) and 30 non-abdominally obese men (waist circumference < 90 cm) who were between 20 and 50 years of age. All subjects were of the Han ethnicity. The carotid intima-media thickness (CIMT) was used as a surrogate marker of early atherosclerosis. In the first multiple linear regression model, body fat distribution was assessed by anthropometric obesity indices, while in the second one it was quantified by DEXA-derived parameters. Results: CIMT (0.74 ± 0.11 vs 0.67 ± 0.04 mm) were significantly higher in the abdominally obese men than in the non-abdominally obese men (P < 0.01). CIMT was positively correlated with anthropometric obesity indices (r: 0.352—0.488, P < 0.01) and the indices from DEXA(r: 0.244—0.482, P < 0.05). The correlation coefficients of anthropometric obesity indices and the indices from DEXA were highest for waist to height ratio and trunk fat mass, respectively. In model 1, 23.8% of the total variance of CIMT was due to waist to height ratio. In model 2, trunk fat mass explained 23.2% of the total variance of CIMT.
Corresponding author. Tel.: +86 335 5908368; fax: +86 335 3032042. E-mail address: [email protected] (B. Liu).
1871-403X/$ — see front matter © 2011 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.
doi:10.1016/j.orcp.2011.08.155
Body fat distribution and atherosclerosis
e341
Conclusions: The present study demonstrates the importance of characterizing body fat distribution in identifying early atherosclerosis. Body fat distribution evaluated by dualenergy X-ray absorptiometry was associated with CIMT, but was not superior to anthropometric measurements. © 2011 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.
Introduction
Methods
The epidemic of obesity sweeping developed nations is accompanied by an increase in atherosclerotic diseases. Atherosclerotic vascular changes and the pandemic of obesity are connected with the civilization process, in particular with diet modification and shortage of exercise and physical effort [1]. Obesity, primarily abdominal obesity, is a significant risk factor for atherosclerosis. Abdominal obesity increases the risk of clinical atherosclerotic diseases, and accelerates the progression of preclinical atherosclerosis [2]. Several methods were used to evaluate body fat distribution. Anthropometric measurements are easily obtained and noninvasive, hence rendering them suitable for use in the epidemiological setting. Body mass index (BMI), waist circumference (WC), waist to height ratio (WHtR) and waist to hip ratio (WHR) are easily measurable indices of obesity. Several studies have investigated the abilities of obesity anthropometric measures in predicting clinical atherosclerotic diseases and preclinical atherosclerosis [3—5]. Dual-energy X-ray absorptiometry (DEXA) is probably the most accurate and precise method available to study fat regional distribution and to directly measure total body fat and lean soft tissue mass. Body fat distribution evaluated by DEXA was associated with coronary atherosclerosis [6]. Recent study found that the predictive ability is slightly superior when obesity is measured using anthropometric measurements instead of DEXA measurements in predicting coronary artery disease [7]. However, only few studies have evaluated the relationship between DEXA and preclinical atherosclerosis; particularly, a comparison between a model including information coming from anthropometric measurements and a model in which fat is precisely measured by DEXA, is still lacking. Carotid intima-media thickness (CIMT) is considered a well-recognized marker of early atherosclerosis [8]. Therefore, we evaluated the relationship between early atherosclerosis assessed by CIMT and obesity in adult men using various markers of obesity by means of anthropometric measurements and DEXA.
Study design From May 2009 to May 2010, we selected 74 men who were between the ages of 20 and 50 years. All subjects were of the Han ethnicity. All subjects were health-examined individuals. No subjects had any known disease or treatment affecting fat metabolism. Abdominal obesity was defined as a man with a waist circumference (WC) ≥ 90 cm, according to the criteria for Asia subjects from the International Diabetes Federation [9]. The participants of the study were divided into two groups: non-abdominally obese group consisted of 30 nonabdominally obese men (WC 82.5 ± 5.6 cm) and abdominally obese group consisted of 44 abdominally obese men (WC 101.5 ± 8.0 cm). This study was approved by the ethics committee of the First Hospital of Qinhuangdao. All subjects provided written informed consent before study initiation.
Anthropometric measurements Anthropometric measurements, including height, weight, WC, hip circumference and blood pressure, were obtained while the subjects were in light clothing and not wearing shoes. WC was accurately measured at the level of midway between the lowest rib and the top of the iliac crest. Body mass index (BMI) was calculated by dividing weight (kg) by height squared (m2 ). Waist to height ratio (WHtR) was calculated by dividing waist circumference (cm) by height (cm). Waist to hip ratio (WHR) was calculated by dividing waist circumference (cm) by hip circumference (cm). Blood pressure was measured twice with a mercury sphygmomanometer after 10 min of rest while the subjects were seated, and the average of the two measurements was used for analysis.
Laboratory examinations All subjects underwent an oral glucose tolerance test (OGTT) with 75 g of oral anhydrous glucose that was initiated at 8:00 AM. Peripheral venous blood samples were taken at 0, 30, 60, 120, and
e342 180 min after glucose loading. Plasma glucose levels were measured using the glucose oxidase method, and serum lipid levels were measured using enzymatic assays with an autoanalyzer (Hitachi, Tokyo, Japan). Plasma concentrations of insulin were measured by radioimmunological assay using a commercially available kit (North Institute of Biological Technology, Beijing, China). The following equation was used to calculate the homeostasis model assessment (HOMA)-IR index: (fasting insulin level (IU/mL) × fasting glucose level (mmol/L))/22.5.
Body composition analysis All men had a total body scan with a Lunar Prodigy (GE Healthcare, Madison, WI, USA) to determine their total and regional body composition: total body bone mineral content (BMC), lean mass (LM), fat mass (FM), and fat percentage (%), as well as body fat mass in specific regions, i.e. arms, legs, trunk, android and gynoid. Total body bone mineral content (BMC), lean mass and fat mass were obtained from total-body scans. The trunk region was limited by vertical borders lateral to the ribs and a lower border by the iliac crest and an upper horizontal border below the neck cut. The arm region was limited by cuts that cross the arm sockets, as close to the body as possible, and separate the arms and hands from the body. The leg region is limited above by the oblique lines passing through the hip joint and cuts that separate the hands and forearms from the legs and a center leg cut which separates the right and left leg. Additional android and gynoid regions were defined using the software provided by the manufacturer. The android region has a lower boundary at the pelvis cut and the upper boundary above the pelvis cut by 20% of the distance between the pelvis and the neck cuts. The lateral boundaries are the arm cuts. The gynoid region has an upper boundary between the upper part of the greater trochanters and a lower boundary defined at a distance equal to twice the height of the android region. The lateral boundaries are the outer leg cuts. The android and gynoid fat mass were calculated from these measurements. Both the operation and scan analysis were performed and appraised by the same well-trained technologist. Daily quality assurance tests were performed with a calibration block supplied by the manufacturer. Repeated measurements on the calibration block had coefficients of variation ≤ 0.5%. In addition, a calibration aluminum phantom was measured weekly, with coefficients of variation ≤ 0.5%.
Q. Lu et al.
Carotid intima-media thickness Carotid sonography was performed using a single ultrasound machine (Hewlett-Packard Company, American) with a 7.5-MHz, sector-scan probe. Studies were performed in a standard fashion by a single physician who was specifically trained to perform the prescribed examination. The measurements were taken from the 1-cm segment proximal to the dilation of the carotid bulb, and the segments were always free of plaque. For each subject, three measurements were taken from both sides of the anterior, lateral, and posterior projection of the far wall, and the readings were then averaged [10].
Statistical analyses All analyses were performed using the SPSS 11.5 statistical software (SPSS 11.5 for Windows; SPSS, Inc., Chicago, IL). Values are expressed as mean with standard deviation. When not normally distributed, the data were ln-transformed for analysis and are expressed as medians with interquartile ranges. The two groups were compared using the Student ttest. A Pearson correlation coefficient was used to measure the strength of association between variables. Two multiple linear regression models were used to evaluate the relationships between body fat distribution and CIMT. In model 1, age, anthropometric obesity indices, systolic blood pressure (SBP), diastolic blood pressure (DBP), triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), fasting plasma glucose (FPG), 2-h plasma glucose level from the OGTT and HOMAIR were used as independent variables, and CIMT was used as the dependent variable. In model 2, anthropometric obesity indices were replaced by the indices from DEXA. P < 0.05 was considered statistically significant.
Results Age, as well as anthropometric, biochemical, and ultrasonographic data, are presented in Table 1. The ages of the two groups were similar (P > 0.05). BMI, WHtR, WHR, SBP, DBP, TG, 2-h plasma glucose levels from the OGTT, fasting insulin, HOMA-IR and CIMT were all significantly higher in the abdominally obese group than in the non-abdominally obese group (P < 0.05). HDL-C were significantly lower in the abdominally obese group than in the nonabdominally obese group (P < 0.01). The levels of
Body fat distribution and atherosclerosis Table 1
e343
Clinical and laboratory characteristics of the subjects in each group.
Variable
Non-abdominally obese group (n = 30)
Abdominally obese group (n = 44)
t
P
Age (y) mean (SD) BMI (kg/m2 ) mean (SD) WHtR mean (SD) WHR mean (SD) SBP (mmHg) mean (SD) DBP (mmHg) mean (SD) TG (mmol/L) median (IQR) TC (mmol/L) mean (SD) HDL-C (mmol/L) mean (SD) LDL-C (mmol/L) mean (SD) FPG (mmol/L) mean (SD) OGTT, 2-h plasma glucose (mmol/L) mean (SD) Fasting insulin (IU/mL) median (IQR) HOMA-IR median (IQR) CIMT (mm) mean (SD)
34.0(7.8) 23.4(2.1) 0.48(0.03) 0.87(0.05) 117.0(9.4) 78.0(6.1) 1.44(1.10) 4.61(0.81) 1.36(0.37) 2.40(0.86) 4.57(0.62) 5.32(1.53) 5.35(5.71) 1.12(1.29) 0.67(0.04)
35.8(6.8) 29.4(3.7) 0.58(0.04) 0.96(0.04) 130.1(9.9) 84.9(7.4) 2.34(2.91) 4.94(0.99) 1.06(0.29) 2.46(0.94) 4.78(0.72) 6.31(1.61) 10.88(8.22) 2.49(2.21) 0.74(0.11)
1.019 8.696 9.731 7.460 5.698 4.200 3.796 1.521 3.903 0.243 1.320 2.625 5.418 5.350 3.753
0.312 0.000 0.000 0.000 0.000 0.000 0.000 0.133 0.000 0.809 0.191 0.011 0.000 0.000 0.000
Values are expressed as mean (SD), and when not normally distributed, they were ln-transformed for analysis and are expressed as medians (IQR). SD: standard deviation; IQR: interquartile range; BMI: body mass index; WHtR: waist to height ratio; WHR: waist to hip ratio; SBP: systolic blood pressure; DBP: diastolic blood pressure; TG: triglyceride; TC: total cholesterol; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; FPG: fasting plasma glucose; OGTT: oral glucose tolerance test; HOMA: homeostasis model assessment; CIMT: carotid intima-media thickness.
TC, LDL-C and FPG were similar between the two groups (P ≥ 0.05). Table 2 shows the data from the DEXA. The abdominally obese group had a higher fat mass and fat percentage. Body fat mass in specific regions, i.e. arms, legs, trunk, android and gynoid were all significantly higher in the abdominally obese group than in the non-abdominally obese group (P < 0.01). The correlation coefficients between CIMT, anthropometric obesity indices, DEXA and the other variables for all of the subjects are shown in Table 3. CIMT was positively correlated with anthropometric obesity indices (r: 0.352—0.488, P < 0.01) and the indices from DEXA(r: 0.244—0.482, P < 0.05). In multiple linear regression model (model 1), CIMT was independently associated with WHtR (ˇ = 0.679, 95% CI: 0.365—0.993, P = 0.000) and TG (ˇ = 0.009, 95% CI: 0.002—0.015, P = 0.013) (Table 4). In model 2, CIMT was independently associated with trunk fat mass (ˇ = 0.007, 95% CI: 0.004—0.011, P = 0.000) and TG (ˇ = 0.009, 95% CI: 0.002—0.015, P = 0.015) (Table 5).
Discussion The results of the present study show that DEXA of trunk fat as a direct measure for obesity had a similar power compared with anthropometric obesity indices, particularly WHtR, in identifying early atherosclerosis. The correlation coefficients
of anthropometric obesity indices and the indices from DEXA were highest for WHtR and trunk fat mass, respectively. BMI is a marker of increases in overall adiposity. It fails to account for fat distribution and in particular abdominal fat. WC, WHtR and WHR reflect abdominal obesity. All aforementioned anthropometric obesity indices were positively correlated with CIMT. In multiple linear regression, we found that 23.8% of the total variance of CIMT was due to WHtR. Twenty-two prospective analyses showed that WHtR and WC were significant predictors of these cardiometabolic outcomes more often than BMI, with similar odds ratio but receiver operating characteristic curve studies showed WHtR may be a more power global screening tool than WC [11]. WHtR represents the best predictor of coronary artery disease than WC, WHR, and BMI [12,13] and closely correlated with CIMT, a measure of subclinical atherosclerosis [14]. WHtR has several key advantages: it is easy to calculate, is relatively constant across different age, sex or racial groups, and is a simple message that can be easily understood by clinicians and families alike [15,16]. Epidemiological survey found that WHtR was also an optimal predictor for cardiovascular risk factors in Chinese Adults [17,18]. A recent study has validated the suitability of WHtR to predict cardiovascular risk factors over direct body fat measures, such as using dual-energy X-ray absorptiometry scanning and bioelectrical impedance analysis [19].
e344 Table 2
Q. Lu et al. Results from the dual-energy X-ray absorptiometry of the subjects in the different groups.
Variable
Non-abdominally obese group (n = 30)
Abdominally obese group (n = 44)
t
P
Fat mass (kg) Lean mass (kg) Bone mineral content (kg) Fat percentage (%) Arms fat mass (kg) Legs fat mass (kg) Trunk fat mass (kg) Android fat mass (kg) Gynoid fat mass (kg)
14.4(5.8) 49.6(4.4) 2.78(0.27) 22.1(7.8) 1.27(0.58) 4.39(1.84) 8.21(3.45) 1.47(0.63) 2.67(1.02)
27.8(6.6) 57.1(5.7) 3.06(0.34) 32.3(4.3) 2.47(0.69) 7.26(2.15) 17.21(4.03) 3.15(0.82) 4.23(1.05)
8.937 6.032 3.772 6.491 7.784 5.944 9.960 9.415 6.317
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Values are expressed as mean(standard deviation).
Obesity is primarily defined by excess of body fat. DEXA is useful clinical test for determining body fat mass and percentage. Body fat mass and percentage were all associated with CIMT. Moreover, there is a difference between the effects Table 3 Simple correlations between the carotid intima-media thickness and other variables in the study subjects. Variable
r
P
Age (y) Body mass index (kg/m2 ) Waist circumference (cm) Waist to height ratio Waist to hip ratio Systolic blood pressure (mmHg) Diastolic blood pressure (mmHg) Triglyceride (mmol/L) Total cholesterol (mmol/L) High density lipoprotein cholesterol (mmol/L) Low density lipoprotein cholesterol (mmol/L) Fasting plasma glucose (mmol/L) Oral glucose tolerance test, 2-h plasma glucose (mmol/L) Fasting insulin (IU/mL) HOMA-IR Fat mass (kg) Lean mass (kg) Bone mineral content (kg) Fat percentage (%) Arms fat mass (kg) Legs fat mass (kg) Trunk fat mass (kg) Android fat mass (kg) Gynoid fat mass (kg)
0.124 0.436 0.464 0.488 0.352 0.211
0.294 0.000 0.000 0.000 0.002 0.071
0.128
0.277
0.384 0.371 −0.255
0.001 0.001 0.028
0.097
0.412
0.081
0.495
0.201
0.086
0.328 0.324 0.447 0.379 0.244 0.372 0.394 0.336 0.482 0.470 0.365
0.004 0.005 0.000 0.001 0.036 0.001 0.001 0.003 0.000 0.000 0.001
of region-specific body fat in identifying early atherosclerosis. Comparing trunk fat mass with other region-specific body fat indices, none of the region-specific body fat indices was superior to trunk fat mass. Trunk fat mass is associated with arterial stiffening [20]. Hara et al. in their study described the trunk fat as a good marker of body fat distribution and their data on male revealed that trunk fat correlated positively with cardiovascular risk factors [6]. In a 12-weeks weight loss intervention, changes in trunk fat were positively correlated with small LDL and negatively correlated with large HDL in obese men [21]. In our study, trunk fat mass was independently associated with CIMT and explained 23.2% of the total variance of CIMT, but was not superior to WHtR. GE Healthcare developed a new DEXA application to standardize measurement of body fat at two regions using the total body data: the gynoid (hip) region, so named because of the tendency for higher values in women, and the android (waist) region where results are typically higher in men. Android type has greater frequency of cardiovascular and metabolic complications, as is occurrence of premature atherosclerosis [22]. In this study, android fat mass showed a significant increase in the abdominally obese group and was correlated in a positive way with CIMT. But once the effect of trunk fat mass is taken into account, android fat distribution has no relationships with CIMT. Leg fat correlates negatively with coronary atherosclerosis [6]. Gynoid fat deposition is associated with decreased risk of metabolic disease [23]. Leg fat mass and gynoid fat mass were positively correlated with CIMT in our study. But after adjustment for trunk fat mass, leg fat mass and gynoid fat mass becomes negatively associated with CIMT and were of borderline significance (leg fat mass r = −0.200; P = 0.089; gynoid fat mass r = −0.187; P = 0.112). This result is similar to previous studies.
Body fat distribution and atherosclerosis Table 4
e345
Multiple linear regression analyses for carotid intima-media thickness (method 1).
Model
Unstandardized coefficients ˇ
Std. error
(Constant) Waist to height ratio Triglyceride
0.326 0.679
0.085 0.157
0.009
0.003
t
P
95% CI
R2
0.436
3.831 4.309
0.000 0.000
0.156—0.495 0.365—0.993
0.238
0.257
2.538
0.013
0.002—0.015
0.301
Standardized coefficients ˇ
Dependent variable: carotid intima-media thickness.
Table 5
Multiple linear regression analyses for carotid intima-media thickness (method 2).
Model
Unstandardized coefficients ˇ
Std. error
(Constant) Trunk fat mass Triglyceride
0.594 0.007 0.009
0.026 0.002 0.003
Standardized coefficients ˇ 0.429 0.255
t
P
95% CI
R2
22.914 4.206 2.500
0.000 0.000 0.015
0.543—0.646 0.004—0.011 0.002—0.015
0.232 0.294
Dependent variable: carotid intima-media thickness.
Visceral fat has been shown to be correlated closer to cardiovascular disease than subcutaneous body fat [24]. Neither DEXA nor anthropometric obesity indices can distinguish between visceral and subcutaneous fat in the abdominal region. In men, WHtR is a good anthropometric obesity index which has a stronger correlation with the distribution of visceral and subcutaneous abdominal adipose tissue than other anthropometric obesity indices [25]. DEXA estimates of trunk fat mass were strongly associated with abdominal visceral fat. But DEXA does not offer a significant advantage over anthropometry for estimation of abdominal visceral fat [26]. So the power was similar between anthropometric obesity indices and DEXA in identifying early atherosclerosis. As shown in Table 1, abdominal obesity is associated with elevated blood pressure, dyslipidemia, and insulin resistance, which are the common risk factors in the development of atherosclerosis. In order to avoid the influence of these confounding factors, we used the multiple regression analysis. The importance of WHtR and the trunk fat mass remained significant after adjusting for these confounding factors. Our study has several limitations. One of the limitation of our study was that subjects were not scanned by computed tomography (CT). Second, unhealthy lifestyles (i.e. unhealthy eating habit and low physical activity) were associated with accelerated IMT progression [27]. Abdominal obese subjects usually have unhealthy lifestyles [28,29]. Healthy diet and physical activity can delay the progression of CIMT in abdominal obese subjects [30,31]. Regrettably, diet and physical activities was not evaluated in our study. Third, it
is important to clarify that this is a limited sample and, therefore, the present conclusions cannot be extrapolated to the general population. However, our findings open a window for future research. Further studies are needed to confirm these results in larger and diverse populations. In conclusion, the present study demonstrates the importance of characterizing body fat distribution in identifying early atherosclerosis. Body fat distribution evaluated by dualenergy X-ray absorptiometry was associated with CIMT, but was not superior to anthropometric obesity indices.
Funding This study was self-financed.
Conflict of interest We have no relevant conflicts of interest to disclose.
References [1] Sucharda P. Obesity and atherosclerosis—–what’s the link? Vnitr Lek 2010;56:289—91. [2] Lakka TA, Lakka HM, Salonen R, Kaplan GA, Salonen JT. Abdominal obesity is associated with accelerated progression of carotid atherosclerosis in men. Atherosclerosis 2001;154:497—504. [3] Balkau B, Deanfield JE, Després JP, Bassand JP, Fox KA, Smith Jr SC, et al. International Day for the Evaluation of Abdominal Obesity (IDEA): a study of waist circumference, cardiovascular disease, and diabetes mellitus in
e346
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
Q. Lu et al.
168,000 primary care patients in 63 countries. Circulation 2007;116:1942—51. Yatsuya H, Yamagishi K, North KE, Brancati FL, Stevens J, Folsom AR, et al. Associations of obesity measures with subtypes of ischemic stroke in the ARIC Study. J Epidemiol 2010;20:347—54. Yan RT, Yan AT, Anderson TJ, Buithieu J, Charbonneau F, Title L, et al. The differential association between various anthropometric indices of obesity and subclinical atherosclerosis. Atherosclerosis 2009;207:232—8. Hara M, Saikawa T, Kurokawa M, Sakata T, Yoshimatsu H. Leg fat percentage correlates negatively with coronary atherosclerosis. Circ J 2004;68:1173—8. Foltran F, Berchialla P, Bigi R, Migliaretti G, Bestetti A, Gregori D. Understanding coronary atherosclerosis in relation to obesity: is getting the distribution of body fatness using dual-energy X-ray absorptiometry worth the effort? A novel perspective using Bayesian Networks. J Eval Clin Pract 2011;17:32—9. de Groot MR, Banga JD. Non invasive ultrasound measurement of intima-media thickness. A tool to measure early atherosclerosis. Eur J Vasc Surg 1994;8:257—63. Alberti KG, Zimmet P, Shaw J, IDF Epidemiology Task Force Consensus Group. The metabolic syndrome—–a new worldwide definition. Lancet 2005;366:1059—62. Esposito K, Ciotola M, Carleo D, Schisano B, Sardelli L, Di Tommaso D, et al. Post-meal glucose peaks at home associate with carotid intima-media thickness in type 2 diabetes. J Clin Endocrinol Metab 2008;93:1345—50. Browning LM, Hsieh SD, Ashwell M. A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes: 0.5 could be a suitable global boundary value. Nutr Res Rev 2010;23: 247—69. Tseng CH. Waist-to-height ratio and coronary artery disease in Taiwanese type 2 diabetic patients. Obesity (Silver Spring) 2008;16:2754—9. Schneider HJ, Friedrich N, Klotsche J, Pieper L, Nauck M, John U, et al. The predictive value of different measures of obesity for incident cardiovascular events and mortality. J Clin Endocrinol Metab 2010;95:1777—85. Srinivasan SR, Wang R, Chen W, Wei CY, Xu J, Berenson GS. Utility of waist-to-height ratio in detecting central obesity and related adverse cardiovascular risk profile among normal weight younger adults (from the Bogalusa Heart Study). Am J Cardiol 2009;104:721—4. Garnett SP, Baur LA, Cowell CT. Waist-to-height ratio: a simple option for determining excess central adiposity in young people. Int J Obes (Lond) 2008;32:1028—30. Mokha JS, Srinivasan SR, Dasmahapatra P, Fernandez C, Chen W, Xu J, et al. Utility of waist-to-height ratio in assessing the status of central obesity and related cardiometabolic risk profile among normal weight and overweight/obese children: the Bogalusa Heart Study. BMC Pediatr 2010;10:73. Shao J, Yu L, Shen X, Li D, Wang K. Waist-to-height ratio, an optimal predictor for obesity and metabolic syndrome in Chinese adults. J Nutr Health Aging 2010;14:782—5.
[18] Hong X, Li JQ, Liang YQ, Wang ZY, Xu F. The relationship between body mass index, waist circumference, waist-tohip ratio, waist-to-height ratio and hyperglycemia: a threeyear follow-up study in Nanjing. Zhonghua Liu Xing Bing Xue Za Zhi 2009;30:209—13. [19] Lee K, Song YM, Sung J. Which obesity indicators are better predictors of metabolic risk? Healthy twin study. Obesity (Silver Spring) 2008;16:834—40. [20] Inaba M, Tanaka K, Goto H, Sakai S, Yamada S, Naka H, et al. Independent association of increased trunk fat with increased arterial stiffening in postmenopausal patients with rheumatoid arthritis. J Rheumatol 2007;34:290—5. [21] Wood RJ, Volek JS, Liu Y, Shachter NS, Contois JH, Fernandez ML. Carbohydrate restriction alters lipoprotein metabolism by modifying VLDL, LDL, and HDL subfraction distribution and size in overweight men. J Nutr 2006;136:384—9. [22] Tounian P, Aggoun Y, Dubern B, Varille V, Guy-Grand B, Sidi D, et al. Presence of increased stiffness of the common carotid artery and endothelial dysfunction in severely obese children: a prospective study. Lancet 2001;358:1400—4. [23] Snijder MB, Visser M, Dekker JM, Goodpaster BH, Harris TB, Kritchevsky SB, et al. Low subcutaneous thigh fat is a risk factor for unfavourable glucose and lipid levels, independently of high abdominal fat. The Health ABC Study. Diabetologia 2005;48:301—8. [24] Matsuzawa Y, Nakamura T, Shimomura I, Kotani K. Visceral fat accumulation and cardiovascular disease. Obes Res 1995;3:645S—7S. [25] Wu HY, Xu SY, Chen LL, Zhang HF. Waist to height ratio as a predictor of abdominal fat distribution in men. Chin J Physiol 2009;52:441—5. [26] Clasey JL, Bouchard C, Teates CD, Riblett JE, Thorner MO, Hartman ML, et al. The use of anthropometric and dualenergy X-ray absorptiometry (DXA) measures to estimate total abdominal and abdominal visceral fat in men and women. Obes Res 1999;7:256—64. [27] Juonala M, Viikari JS, Kähönen M, Taittonen L, Laitinen T, Hutri-Kähönen N, et al. Life-time risk factors and progression of carotid atherosclerosis in young adults: the cardiovascular risk in Young Finns study. Eur Heart J 2010;31:1745—51. [28] Cavalcanti CB, Barros MV, Menêses AL, Santos CM, Azevedo AM, Guimarães FJ. Abdominal obesity in adolescents: prevalence and association with physical activity and eating habits. Arq Bras Cardiol 2010;94:350—6. [29] Halkjaer J, Tjønneland A, Overvad K, Sørensen TI. Dietary predictors of 5-year changes in waist circumference. J Am Diet Assoc 2009;109:1356—66. [30] Ferré R, Plana N, Merino J, Aragonès G, Girona J, Heras M, et al. Effects of therapeutic lifestyle changes on peripheral artery tonometry in patients with abdominal obesity. Nutr Metab Cardiovasc Dis 2010, doi:10.1016/j.numecd.2010.04.008 [Epub ahead of print, August 11] PMID: 20708393. [31] Woo KS, Chook P, Yu CW, Sung RY, Qiao M, Leung SS, et al. Effects of diet and exercise on obesity-related vascular dysfunction in children. Circulation 2004;109:1981—6.
Available online at www.sciencedirect.com
Value of dual-energy X-ray absorptiometry derived parameters vs anthropometric obesity indices in the assessment of early atherosclerosis in abdominally obese men Qiang Lu, Chun-Ming Ma, Rui Wang, Fu-Zai Yin, Chun-Mei Qin, Dong-Hui Lou, Bo Liu ∗ Department of Endocrinology, The First Hospital of Qinhuangdao, Hebei Medical University, No. 258 Wenhua Road, Qinhuangdao, 066000 Hebei Province, China Received 7 May 2011 ; received in revised form 27 July 2011; accepted 23 August 2011
KEYWORDS Abdominal obesity; Body fat distribution; Atherosclerosis
∗
Summary Objective: The purpose of this study was to evaluate the value of dual-energy X-ray absorptiometry (DEXA) derived parameters vs anthropometric obesity indices in the assessment of early atherosclerosis in abdominally obese men. Methods: This case—control study included 44 abdominally obese men (waist circumference ≥ 90 cm) and 30 non-abdominally obese men (waist circumference < 90 cm) who were between 20 and 50 years of age. All subjects were of the Han ethnicity. The carotid intima-media thickness (CIMT) was used as a surrogate marker of early atherosclerosis. In the first multiple linear regression model, body fat distribution was assessed by anthropometric obesity indices, while in the second one it was quantified by DEXA-derived parameters. Results: CIMT (0.74 ± 0.11 vs 0.67 ± 0.04 mm) were significantly higher in the abdominally obese men than in the non-abdominally obese men (P < 0.01). CIMT was positively correlated with anthropometric obesity indices (r: 0.352—0.488, P < 0.01) and the indices from DEXA(r: 0.244—0.482, P < 0.05). The correlation coefficients of anthropometric obesity indices and the indices from DEXA were highest for waist to height ratio and trunk fat mass, respectively. In model 1, 23.8% of the total variance of CIMT was due to waist to height ratio. In model 2, trunk fat mass explained 23.2% of the total variance of CIMT.
Corresponding author. Tel.: +86 335 5908368; fax: +86 335 3032042. E-mail address: [email protected] (B. Liu).
1871-403X/$ — see front matter © 2011 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.
doi:10.1016/j.orcp.2011.08.155
Body fat distribution and atherosclerosis
e341
Conclusions: The present study demonstrates the importance of characterizing body fat distribution in identifying early atherosclerosis. Body fat distribution evaluated by dualenergy X-ray absorptiometry was associated with CIMT, but was not superior to anthropometric measurements. © 2011 Asian Oceanian Association for the Study of Obesity. Published by Elsevier Ltd. All rights reserved.
Introduction
Methods
The epidemic of obesity sweeping developed nations is accompanied by an increase in atherosclerotic diseases. Atherosclerotic vascular changes and the pandemic of obesity are connected with the civilization process, in particular with diet modification and shortage of exercise and physical effort [1]. Obesity, primarily abdominal obesity, is a significant risk factor for atherosclerosis. Abdominal obesity increases the risk of clinical atherosclerotic diseases, and accelerates the progression of preclinical atherosclerosis [2]. Several methods were used to evaluate body fat distribution. Anthropometric measurements are easily obtained and noninvasive, hence rendering them suitable for use in the epidemiological setting. Body mass index (BMI), waist circumference (WC), waist to height ratio (WHtR) and waist to hip ratio (WHR) are easily measurable indices of obesity. Several studies have investigated the abilities of obesity anthropometric measures in predicting clinical atherosclerotic diseases and preclinical atherosclerosis [3—5]. Dual-energy X-ray absorptiometry (DEXA) is probably the most accurate and precise method available to study fat regional distribution and to directly measure total body fat and lean soft tissue mass. Body fat distribution evaluated by DEXA was associated with coronary atherosclerosis [6]. Recent study found that the predictive ability is slightly superior when obesity is measured using anthropometric measurements instead of DEXA measurements in predicting coronary artery disease [7]. However, only few studies have evaluated the relationship between DEXA and preclinical atherosclerosis; particularly, a comparison between a model including information coming from anthropometric measurements and a model in which fat is precisely measured by DEXA, is still lacking. Carotid intima-media thickness (CIMT) is considered a well-recognized marker of early atherosclerosis [8]. Therefore, we evaluated the relationship between early atherosclerosis assessed by CIMT and obesity in adult men using various markers of obesity by means of anthropometric measurements and DEXA.
Study design From May 2009 to May 2010, we selected 74 men who were between the ages of 20 and 50 years. All subjects were of the Han ethnicity. All subjects were health-examined individuals. No subjects had any known disease or treatment affecting fat metabolism. Abdominal obesity was defined as a man with a waist circumference (WC) ≥ 90 cm, according to the criteria for Asia subjects from the International Diabetes Federation [9]. The participants of the study were divided into two groups: non-abdominally obese group consisted of 30 nonabdominally obese men (WC 82.5 ± 5.6 cm) and abdominally obese group consisted of 44 abdominally obese men (WC 101.5 ± 8.0 cm). This study was approved by the ethics committee of the First Hospital of Qinhuangdao. All subjects provided written informed consent before study initiation.
Anthropometric measurements Anthropometric measurements, including height, weight, WC, hip circumference and blood pressure, were obtained while the subjects were in light clothing and not wearing shoes. WC was accurately measured at the level of midway between the lowest rib and the top of the iliac crest. Body mass index (BMI) was calculated by dividing weight (kg) by height squared (m2 ). Waist to height ratio (WHtR) was calculated by dividing waist circumference (cm) by height (cm). Waist to hip ratio (WHR) was calculated by dividing waist circumference (cm) by hip circumference (cm). Blood pressure was measured twice with a mercury sphygmomanometer after 10 min of rest while the subjects were seated, and the average of the two measurements was used for analysis.
Laboratory examinations All subjects underwent an oral glucose tolerance test (OGTT) with 75 g of oral anhydrous glucose that was initiated at 8:00 AM. Peripheral venous blood samples were taken at 0, 30, 60, 120, and
e342 180 min after glucose loading. Plasma glucose levels were measured using the glucose oxidase method, and serum lipid levels were measured using enzymatic assays with an autoanalyzer (Hitachi, Tokyo, Japan). Plasma concentrations of insulin were measured by radioimmunological assay using a commercially available kit (North Institute of Biological Technology, Beijing, China). The following equation was used to calculate the homeostasis model assessment (HOMA)-IR index: (fasting insulin level (IU/mL) × fasting glucose level (mmol/L))/22.5.
Body composition analysis All men had a total body scan with a Lunar Prodigy (GE Healthcare, Madison, WI, USA) to determine their total and regional body composition: total body bone mineral content (BMC), lean mass (LM), fat mass (FM), and fat percentage (%), as well as body fat mass in specific regions, i.e. arms, legs, trunk, android and gynoid. Total body bone mineral content (BMC), lean mass and fat mass were obtained from total-body scans. The trunk region was limited by vertical borders lateral to the ribs and a lower border by the iliac crest and an upper horizontal border below the neck cut. The arm region was limited by cuts that cross the arm sockets, as close to the body as possible, and separate the arms and hands from the body. The leg region is limited above by the oblique lines passing through the hip joint and cuts that separate the hands and forearms from the legs and a center leg cut which separates the right and left leg. Additional android and gynoid regions were defined using the software provided by the manufacturer. The android region has a lower boundary at the pelvis cut and the upper boundary above the pelvis cut by 20% of the distance between the pelvis and the neck cuts. The lateral boundaries are the arm cuts. The gynoid region has an upper boundary between the upper part of the greater trochanters and a lower boundary defined at a distance equal to twice the height of the android region. The lateral boundaries are the outer leg cuts. The android and gynoid fat mass were calculated from these measurements. Both the operation and scan analysis were performed and appraised by the same well-trained technologist. Daily quality assurance tests were performed with a calibration block supplied by the manufacturer. Repeated measurements on the calibration block had coefficients of variation ≤ 0.5%. In addition, a calibration aluminum phantom was measured weekly, with coefficients of variation ≤ 0.5%.
Q. Lu et al.
Carotid intima-media thickness Carotid sonography was performed using a single ultrasound machine (Hewlett-Packard Company, American) with a 7.5-MHz, sector-scan probe. Studies were performed in a standard fashion by a single physician who was specifically trained to perform the prescribed examination. The measurements were taken from the 1-cm segment proximal to the dilation of the carotid bulb, and the segments were always free of plaque. For each subject, three measurements were taken from both sides of the anterior, lateral, and posterior projection of the far wall, and the readings were then averaged [10].
Statistical analyses All analyses were performed using the SPSS 11.5 statistical software (SPSS 11.5 for Windows; SPSS, Inc., Chicago, IL). Values are expressed as mean with standard deviation. When not normally distributed, the data were ln-transformed for analysis and are expressed as medians with interquartile ranges. The two groups were compared using the Student ttest. A Pearson correlation coefficient was used to measure the strength of association between variables. Two multiple linear regression models were used to evaluate the relationships between body fat distribution and CIMT. In model 1, age, anthropometric obesity indices, systolic blood pressure (SBP), diastolic blood pressure (DBP), triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), fasting plasma glucose (FPG), 2-h plasma glucose level from the OGTT and HOMAIR were used as independent variables, and CIMT was used as the dependent variable. In model 2, anthropometric obesity indices were replaced by the indices from DEXA. P < 0.05 was considered statistically significant.
Results Age, as well as anthropometric, biochemical, and ultrasonographic data, are presented in Table 1. The ages of the two groups were similar (P > 0.05). BMI, WHtR, WHR, SBP, DBP, TG, 2-h plasma glucose levels from the OGTT, fasting insulin, HOMA-IR and CIMT were all significantly higher in the abdominally obese group than in the non-abdominally obese group (P < 0.05). HDL-C were significantly lower in the abdominally obese group than in the nonabdominally obese group (P < 0.01). The levels of
Body fat distribution and atherosclerosis Table 1
e343
Clinical and laboratory characteristics of the subjects in each group.
Variable
Non-abdominally obese group (n = 30)
Abdominally obese group (n = 44)
t
P
Age (y) mean (SD) BMI (kg/m2 ) mean (SD) WHtR mean (SD) WHR mean (SD) SBP (mmHg) mean (SD) DBP (mmHg) mean (SD) TG (mmol/L) median (IQR) TC (mmol/L) mean (SD) HDL-C (mmol/L) mean (SD) LDL-C (mmol/L) mean (SD) FPG (mmol/L) mean (SD) OGTT, 2-h plasma glucose (mmol/L) mean (SD) Fasting insulin (IU/mL) median (IQR) HOMA-IR median (IQR) CIMT (mm) mean (SD)
34.0(7.8) 23.4(2.1) 0.48(0.03) 0.87(0.05) 117.0(9.4) 78.0(6.1) 1.44(1.10) 4.61(0.81) 1.36(0.37) 2.40(0.86) 4.57(0.62) 5.32(1.53) 5.35(5.71) 1.12(1.29) 0.67(0.04)
35.8(6.8) 29.4(3.7) 0.58(0.04) 0.96(0.04) 130.1(9.9) 84.9(7.4) 2.34(2.91) 4.94(0.99) 1.06(0.29) 2.46(0.94) 4.78(0.72) 6.31(1.61) 10.88(8.22) 2.49(2.21) 0.74(0.11)
1.019 8.696 9.731 7.460 5.698 4.200 3.796 1.521 3.903 0.243 1.320 2.625 5.418 5.350 3.753
0.312 0.000 0.000 0.000 0.000 0.000 0.000 0.133 0.000 0.809 0.191 0.011 0.000 0.000 0.000
Values are expressed as mean (SD), and when not normally distributed, they were ln-transformed for analysis and are expressed as medians (IQR). SD: standard deviation; IQR: interquartile range; BMI: body mass index; WHtR: waist to height ratio; WHR: waist to hip ratio; SBP: systolic blood pressure; DBP: diastolic blood pressure; TG: triglyceride; TC: total cholesterol; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; FPG: fasting plasma glucose; OGTT: oral glucose tolerance test; HOMA: homeostasis model assessment; CIMT: carotid intima-media thickness.
TC, LDL-C and FPG were similar between the two groups (P ≥ 0.05). Table 2 shows the data from the DEXA. The abdominally obese group had a higher fat mass and fat percentage. Body fat mass in specific regions, i.e. arms, legs, trunk, android and gynoid were all significantly higher in the abdominally obese group than in the non-abdominally obese group (P < 0.01). The correlation coefficients between CIMT, anthropometric obesity indices, DEXA and the other variables for all of the subjects are shown in Table 3. CIMT was positively correlated with anthropometric obesity indices (r: 0.352—0.488, P < 0.01) and the indices from DEXA(r: 0.244—0.482, P < 0.05). In multiple linear regression model (model 1), CIMT was independently associated with WHtR (ˇ = 0.679, 95% CI: 0.365—0.993, P = 0.000) and TG (ˇ = 0.009, 95% CI: 0.002—0.015, P = 0.013) (Table 4). In model 2, CIMT was independently associated with trunk fat mass (ˇ = 0.007, 95% CI: 0.004—0.011, P = 0.000) and TG (ˇ = 0.009, 95% CI: 0.002—0.015, P = 0.015) (Table 5).
Discussion The results of the present study show that DEXA of trunk fat as a direct measure for obesity had a similar power compared with anthropometric obesity indices, particularly WHtR, in identifying early atherosclerosis. The correlation coefficients
of anthropometric obesity indices and the indices from DEXA were highest for WHtR and trunk fat mass, respectively. BMI is a marker of increases in overall adiposity. It fails to account for fat distribution and in particular abdominal fat. WC, WHtR and WHR reflect abdominal obesity. All aforementioned anthropometric obesity indices were positively correlated with CIMT. In multiple linear regression, we found that 23.8% of the total variance of CIMT was due to WHtR. Twenty-two prospective analyses showed that WHtR and WC were significant predictors of these cardiometabolic outcomes more often than BMI, with similar odds ratio but receiver operating characteristic curve studies showed WHtR may be a more power global screening tool than WC [11]. WHtR represents the best predictor of coronary artery disease than WC, WHR, and BMI [12,13] and closely correlated with CIMT, a measure of subclinical atherosclerosis [14]. WHtR has several key advantages: it is easy to calculate, is relatively constant across different age, sex or racial groups, and is a simple message that can be easily understood by clinicians and families alike [15,16]. Epidemiological survey found that WHtR was also an optimal predictor for cardiovascular risk factors in Chinese Adults [17,18]. A recent study has validated the suitability of WHtR to predict cardiovascular risk factors over direct body fat measures, such as using dual-energy X-ray absorptiometry scanning and bioelectrical impedance analysis [19].
e344 Table 2
Q. Lu et al. Results from the dual-energy X-ray absorptiometry of the subjects in the different groups.
Variable
Non-abdominally obese group (n = 30)
Abdominally obese group (n = 44)
t
P
Fat mass (kg) Lean mass (kg) Bone mineral content (kg) Fat percentage (%) Arms fat mass (kg) Legs fat mass (kg) Trunk fat mass (kg) Android fat mass (kg) Gynoid fat mass (kg)
14.4(5.8) 49.6(4.4) 2.78(0.27) 22.1(7.8) 1.27(0.58) 4.39(1.84) 8.21(3.45) 1.47(0.63) 2.67(1.02)
27.8(6.6) 57.1(5.7) 3.06(0.34) 32.3(4.3) 2.47(0.69) 7.26(2.15) 17.21(4.03) 3.15(0.82) 4.23(1.05)
8.937 6.032 3.772 6.491 7.784 5.944 9.960 9.415 6.317
0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
Values are expressed as mean(standard deviation).
Obesity is primarily defined by excess of body fat. DEXA is useful clinical test for determining body fat mass and percentage. Body fat mass and percentage were all associated with CIMT. Moreover, there is a difference between the effects Table 3 Simple correlations between the carotid intima-media thickness and other variables in the study subjects. Variable
r
P
Age (y) Body mass index (kg/m2 ) Waist circumference (cm) Waist to height ratio Waist to hip ratio Systolic blood pressure (mmHg) Diastolic blood pressure (mmHg) Triglyceride (mmol/L) Total cholesterol (mmol/L) High density lipoprotein cholesterol (mmol/L) Low density lipoprotein cholesterol (mmol/L) Fasting plasma glucose (mmol/L) Oral glucose tolerance test, 2-h plasma glucose (mmol/L) Fasting insulin (IU/mL) HOMA-IR Fat mass (kg) Lean mass (kg) Bone mineral content (kg) Fat percentage (%) Arms fat mass (kg) Legs fat mass (kg) Trunk fat mass (kg) Android fat mass (kg) Gynoid fat mass (kg)
0.124 0.436 0.464 0.488 0.352 0.211
0.294 0.000 0.000 0.000 0.002 0.071
0.128
0.277
0.384 0.371 −0.255
0.001 0.001 0.028
0.097
0.412
0.081
0.495
0.201
0.086
0.328 0.324 0.447 0.379 0.244 0.372 0.394 0.336 0.482 0.470 0.365
0.004 0.005 0.000 0.001 0.036 0.001 0.001 0.003 0.000 0.000 0.001
of region-specific body fat in identifying early atherosclerosis. Comparing trunk fat mass with other region-specific body fat indices, none of the region-specific body fat indices was superior to trunk fat mass. Trunk fat mass is associated with arterial stiffening [20]. Hara et al. in their study described the trunk fat as a good marker of body fat distribution and their data on male revealed that trunk fat correlated positively with cardiovascular risk factors [6]. In a 12-weeks weight loss intervention, changes in trunk fat were positively correlated with small LDL and negatively correlated with large HDL in obese men [21]. In our study, trunk fat mass was independently associated with CIMT and explained 23.2% of the total variance of CIMT, but was not superior to WHtR. GE Healthcare developed a new DEXA application to standardize measurement of body fat at two regions using the total body data: the gynoid (hip) region, so named because of the tendency for higher values in women, and the android (waist) region where results are typically higher in men. Android type has greater frequency of cardiovascular and metabolic complications, as is occurrence of premature atherosclerosis [22]. In this study, android fat mass showed a significant increase in the abdominally obese group and was correlated in a positive way with CIMT. But once the effect of trunk fat mass is taken into account, android fat distribution has no relationships with CIMT. Leg fat correlates negatively with coronary atherosclerosis [6]. Gynoid fat deposition is associated with decreased risk of metabolic disease [23]. Leg fat mass and gynoid fat mass were positively correlated with CIMT in our study. But after adjustment for trunk fat mass, leg fat mass and gynoid fat mass becomes negatively associated with CIMT and were of borderline significance (leg fat mass r = −0.200; P = 0.089; gynoid fat mass r = −0.187; P = 0.112). This result is similar to previous studies.
Body fat distribution and atherosclerosis Table 4
e345
Multiple linear regression analyses for carotid intima-media thickness (method 1).
Model
Unstandardized coefficients ˇ
Std. error
(Constant) Waist to height ratio Triglyceride
0.326 0.679
0.085 0.157
0.009
0.003
t
P
95% CI
R2
0.436
3.831 4.309
0.000 0.000
0.156—0.495 0.365—0.993
0.238
0.257
2.538
0.013
0.002—0.015
0.301
Standardized coefficients ˇ
Dependent variable: carotid intima-media thickness.
Table 5
Multiple linear regression analyses for carotid intima-media thickness (method 2).
Model
Unstandardized coefficients ˇ
Std. error
(Constant) Trunk fat mass Triglyceride
0.594 0.007 0.009
0.026 0.002 0.003
Standardized coefficients ˇ 0.429 0.255
t
P
95% CI
R2
22.914 4.206 2.500
0.000 0.000 0.015
0.543—0.646 0.004—0.011 0.002—0.015
0.232 0.294
Dependent variable: carotid intima-media thickness.
Visceral fat has been shown to be correlated closer to cardiovascular disease than subcutaneous body fat [24]. Neither DEXA nor anthropometric obesity indices can distinguish between visceral and subcutaneous fat in the abdominal region. In men, WHtR is a good anthropometric obesity index which has a stronger correlation with the distribution of visceral and subcutaneous abdominal adipose tissue than other anthropometric obesity indices [25]. DEXA estimates of trunk fat mass were strongly associated with abdominal visceral fat. But DEXA does not offer a significant advantage over anthropometry for estimation of abdominal visceral fat [26]. So the power was similar between anthropometric obesity indices and DEXA in identifying early atherosclerosis. As shown in Table 1, abdominal obesity is associated with elevated blood pressure, dyslipidemia, and insulin resistance, which are the common risk factors in the development of atherosclerosis. In order to avoid the influence of these confounding factors, we used the multiple regression analysis. The importance of WHtR and the trunk fat mass remained significant after adjusting for these confounding factors. Our study has several limitations. One of the limitation of our study was that subjects were not scanned by computed tomography (CT). Second, unhealthy lifestyles (i.e. unhealthy eating habit and low physical activity) were associated with accelerated IMT progression [27]. Abdominal obese subjects usually have unhealthy lifestyles [28,29]. Healthy diet and physical activity can delay the progression of CIMT in abdominal obese subjects [30,31]. Regrettably, diet and physical activities was not evaluated in our study. Third, it
is important to clarify that this is a limited sample and, therefore, the present conclusions cannot be extrapolated to the general population. However, our findings open a window for future research. Further studies are needed to confirm these results in larger and diverse populations. In conclusion, the present study demonstrates the importance of characterizing body fat distribution in identifying early atherosclerosis. Body fat distribution evaluated by dualenergy X-ray absorptiometry was associated with CIMT, but was not superior to anthropometric obesity indices.
Funding This study was self-financed.
Conflict of interest We have no relevant conflicts of interest to disclose.
References [1] Sucharda P. Obesity and atherosclerosis—–what’s the link? Vnitr Lek 2010;56:289—91. [2] Lakka TA, Lakka HM, Salonen R, Kaplan GA, Salonen JT. Abdominal obesity is associated with accelerated progression of carotid atherosclerosis in men. Atherosclerosis 2001;154:497—504. [3] Balkau B, Deanfield JE, Després JP, Bassand JP, Fox KA, Smith Jr SC, et al. International Day for the Evaluation of Abdominal Obesity (IDEA): a study of waist circumference, cardiovascular disease, and diabetes mellitus in
e346
[4]
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
Q. Lu et al.
168,000 primary care patients in 63 countries. Circulation 2007;116:1942—51. Yatsuya H, Yamagishi K, North KE, Brancati FL, Stevens J, Folsom AR, et al. Associations of obesity measures with subtypes of ischemic stroke in the ARIC Study. J Epidemiol 2010;20:347—54. Yan RT, Yan AT, Anderson TJ, Buithieu J, Charbonneau F, Title L, et al. The differential association between various anthropometric indices of obesity and subclinical atherosclerosis. Atherosclerosis 2009;207:232—8. Hara M, Saikawa T, Kurokawa M, Sakata T, Yoshimatsu H. Leg fat percentage correlates negatively with coronary atherosclerosis. Circ J 2004;68:1173—8. Foltran F, Berchialla P, Bigi R, Migliaretti G, Bestetti A, Gregori D. Understanding coronary atherosclerosis in relation to obesity: is getting the distribution of body fatness using dual-energy X-ray absorptiometry worth the effort? A novel perspective using Bayesian Networks. J Eval Clin Pract 2011;17:32—9. de Groot MR, Banga JD. Non invasive ultrasound measurement of intima-media thickness. A tool to measure early atherosclerosis. Eur J Vasc Surg 1994;8:257—63. Alberti KG, Zimmet P, Shaw J, IDF Epidemiology Task Force Consensus Group. The metabolic syndrome—–a new worldwide definition. Lancet 2005;366:1059—62. Esposito K, Ciotola M, Carleo D, Schisano B, Sardelli L, Di Tommaso D, et al. Post-meal glucose peaks at home associate with carotid intima-media thickness in type 2 diabetes. J Clin Endocrinol Metab 2008;93:1345—50. Browning LM, Hsieh SD, Ashwell M. A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes: 0.5 could be a suitable global boundary value. Nutr Res Rev 2010;23: 247—69. Tseng CH. Waist-to-height ratio and coronary artery disease in Taiwanese type 2 diabetic patients. Obesity (Silver Spring) 2008;16:2754—9. Schneider HJ, Friedrich N, Klotsche J, Pieper L, Nauck M, John U, et al. The predictive value of different measures of obesity for incident cardiovascular events and mortality. J Clin Endocrinol Metab 2010;95:1777—85. Srinivasan SR, Wang R, Chen W, Wei CY, Xu J, Berenson GS. Utility of waist-to-height ratio in detecting central obesity and related adverse cardiovascular risk profile among normal weight younger adults (from the Bogalusa Heart Study). Am J Cardiol 2009;104:721—4. Garnett SP, Baur LA, Cowell CT. Waist-to-height ratio: a simple option for determining excess central adiposity in young people. Int J Obes (Lond) 2008;32:1028—30. Mokha JS, Srinivasan SR, Dasmahapatra P, Fernandez C, Chen W, Xu J, et al. Utility of waist-to-height ratio in assessing the status of central obesity and related cardiometabolic risk profile among normal weight and overweight/obese children: the Bogalusa Heart Study. BMC Pediatr 2010;10:73. Shao J, Yu L, Shen X, Li D, Wang K. Waist-to-height ratio, an optimal predictor for obesity and metabolic syndrome in Chinese adults. J Nutr Health Aging 2010;14:782—5.
[18] Hong X, Li JQ, Liang YQ, Wang ZY, Xu F. The relationship between body mass index, waist circumference, waist-tohip ratio, waist-to-height ratio and hyperglycemia: a threeyear follow-up study in Nanjing. Zhonghua Liu Xing Bing Xue Za Zhi 2009;30:209—13. [19] Lee K, Song YM, Sung J. Which obesity indicators are better predictors of metabolic risk? Healthy twin study. Obesity (Silver Spring) 2008;16:834—40. [20] Inaba M, Tanaka K, Goto H, Sakai S, Yamada S, Naka H, et al. Independent association of increased trunk fat with increased arterial stiffening in postmenopausal patients with rheumatoid arthritis. J Rheumatol 2007;34:290—5. [21] Wood RJ, Volek JS, Liu Y, Shachter NS, Contois JH, Fernandez ML. Carbohydrate restriction alters lipoprotein metabolism by modifying VLDL, LDL, and HDL subfraction distribution and size in overweight men. J Nutr 2006;136:384—9. [22] Tounian P, Aggoun Y, Dubern B, Varille V, Guy-Grand B, Sidi D, et al. Presence of increased stiffness of the common carotid artery and endothelial dysfunction in severely obese children: a prospective study. Lancet 2001;358:1400—4. [23] Snijder MB, Visser M, Dekker JM, Goodpaster BH, Harris TB, Kritchevsky SB, et al. Low subcutaneous thigh fat is a risk factor for unfavourable glucose and lipid levels, independently of high abdominal fat. The Health ABC Study. Diabetologia 2005;48:301—8. [24] Matsuzawa Y, Nakamura T, Shimomura I, Kotani K. Visceral fat accumulation and cardiovascular disease. Obes Res 1995;3:645S—7S. [25] Wu HY, Xu SY, Chen LL, Zhang HF. Waist to height ratio as a predictor of abdominal fat distribution in men. Chin J Physiol 2009;52:441—5. [26] Clasey JL, Bouchard C, Teates CD, Riblett JE, Thorner MO, Hartman ML, et al. The use of anthropometric and dualenergy X-ray absorptiometry (DXA) measures to estimate total abdominal and abdominal visceral fat in men and women. Obes Res 1999;7:256—64. [27] Juonala M, Viikari JS, Kähönen M, Taittonen L, Laitinen T, Hutri-Kähönen N, et al. Life-time risk factors and progression of carotid atherosclerosis in young adults: the cardiovascular risk in Young Finns study. Eur Heart J 2010;31:1745—51. [28] Cavalcanti CB, Barros MV, Menêses AL, Santos CM, Azevedo AM, Guimarães FJ. Abdominal obesity in adolescents: prevalence and association with physical activity and eating habits. Arq Bras Cardiol 2010;94:350—6. [29] Halkjaer J, Tjønneland A, Overvad K, Sørensen TI. Dietary predictors of 5-year changes in waist circumference. J Am Diet Assoc 2009;109:1356—66. [30] Ferré R, Plana N, Merino J, Aragonès G, Girona J, Heras M, et al. Effects of therapeutic lifestyle changes on peripheral artery tonometry in patients with abdominal obesity. Nutr Metab Cardiovasc Dis 2010, doi:10.1016/j.numecd.2010.04.008 [Epub ahead of print, August 11] PMID: 20708393. [31] Woo KS, Chook P, Yu CW, Sung RY, Qiao M, Leung SS, et al. Effects of diet and exercise on obesity-related vascular dysfunction in children. Circulation 2004;109:1981—6.
Available online at www.sciencedirect.com
