Evaluating the correlation and prediction of trunk fat mass with five anthropometric indices in Chinese females aged 20–40 years

Nutrition, Metabolism & Cardiovascular Diseases (2007) 17, 676e683

www.elsevier.com/locate/nmcd

Evaluating the correlation and prediction of trunk fat mass with five anthropometric indices in Chinese females aged 20e40 years Cheng Jiang a,1, Shu-Feng Lei a,1, Man-Yuan Liu a, Su-Mei Xiao a, Xiang-Ding Chen a, Fei-Yan Deng a, Hong Xu a, Li-Jun Tan a, Yan-Jun Yang a, Yan-Bo Wang a, Xiao Sun a, Yan-Fang Guo a, Jing-Jing Guo a, Xue-Zhen Zhu a, Hong-Wen Deng a,b,* a

Laboratory of Molecular and Statistical Genetics and the Key Laboratory of Protein Chemistry and Developmental Biology of Ministry of Education, College of Life Sciences, Hunan Normal University, Changsha, Hunan 410081, P.R. China b Department of Orthopedic Surgery, School of Medicine, University of Missouri-Kansas City, 2411 Holmes Street, Kansas City, MO 64108, USA Received 9 December 2005; received in revised form 13 April 2006; accepted 26 April 2006

KEYWORDS Anthropometric index; Obesity; Trunk fat mass; Percent trunk fat mass; Principal component analysis

Abstract Background and aims: Obesity is a worldwide problem, and excess trunk fat mass (FMtrunk) has been associated with an increased risk of diseases. The early measurement of FMtrunk has potential importance to evaluate trunk obesity. We sought to evaluate the correlation and predication of FMtrunk with five anthropometric indices in Chinese females. Methods and results: A sample of 850 China females aged 20e40 years were recruited and divided into four age groups with a 5-year range in each group. Five anthropometric indices were measured or calculated. FMtrunk in kg was measured using a dual-energy X-ray absorptiometry scanner. Principal component analysis (PCA) and multiple regression analysis were performed to develop prediction equations. There was an increasing trend of FMtrunk and five anthropometric indices in successively older age groups. Four formed principal components (PCs) interpreted over 99% of the total variation of five relative anthropometric indices in all age groups. Regression analyses showed that four PCs combined explained a greater

* Corresponding author. Laboratory of Molecular and Statistical Genetics and the Key Laboratory of Protein Chemistry and Developmental Biology of Ministry of Education, College of Life Sciences, Hunan Normal University, Changsha, Hunan 410081, P.R. China Tel./fax: þ86 731 887 2791. E-mail address: [email protected] (H.-W. Deng). 1 These authors contribute equally to this article. 0939-4753/$ - see front matter ª 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.numecd.2006.04.007

The correlation and prediction of trunk fat mass with five anthropometric indices

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variance (R 2 ¼ 45.2e81.6%) in FM trunk than did each of the five indices alone (R2 ¼ 2.4e72.2%). Conclusions: Our results suggested that there is an increasing trend of FMtrunk and five anthropometric indices with aging; that age obviously influences the relationship of FMtrunk and the anthropometric indices studied; and that the accuracy of predicting the FMtrunk using five anthropometric indices combined is greater than using the five indices alone. ª 2006 Elsevier B.V. All rights reserved.

Introduction

Methods

Obesity has become a worldwide problem [1e3]. In China, over 40 million people suffer from obesity [4]. Reported studies show that excess trunk fat is associated with an increased risk of metabolic diseases [5e9]. In addition, there is specific distribution of body fat mass for the expenditure of energy and body building among various ethnic populations [10e13]. Therefore, the early measurement of trunk fat mass (FMtrunk, or, so-called, central fat mass) has the potential importance of evaluating adipose distribution in the trunk area, and to predict future health risk in the Chinese population. There are three major techniques, computed tomography (CT); magnetic resonance imaging (MRI); and dual-energy X-ray absorptiometry (DEXA), which are used to measure FMtrunk accurately. However, these techniques depend on complex techniques and are quite expensive. The indirect evaluation of FMtrunk by anthropometric indices is simpler and cheaper than that by DEXA, CT, and MRI, and is especially suitable for routine clinical use in developing countries for large-scale detection of trunk fat mass. A number of studies in Caucasians have used several anthropometric indices, such as body mass index (BMI), waist circumference (WC), hip circumference (HC), waist-to-hip ratio (WHR), conicity index (CI), as predictors of total fat mass and FMtrunk [14e18] and the results were inconsistent regarding the accuracy of prediction, due to the fact that the studies generally used only one, or not more than three, anthropometric indices to predict fat mass. To our knowledge, there are no data simultaneously using the above five anthropometric indices plus a DEXA scan, which is quick and safe and provides measurements of body composition (including fat mass, lean mass, and bone mass) with excellent precision compared to other methods [19,20]. Therefore, the objectives of the present study were: 1) to investigate the correlation between FMtrunk and anthropometric indices; 2) to evaluate the accuracy of FMtrunk predicted by the above five anthropometric indices; and 3) to develop four equations of predicting FMtrunk.

Subjects The project was approved by the Research Administration Departments of Hunan Normal University. The female subjects were from a large population that was recruited for genetic studies aimed at searching for the gene underlying peak bone mass variation in the Chinese population. Our sample size was initially determined by comparing similar studies. Three patterns of recruitment (internet, poster and interview) were used. Firstly, we placed an advertisement about nutrition and bone health on the homepage of Hunan Normal University. Secondly, we put posters around Hunan Normal University inviting volunteers to our laboratory for a free measurement. Thirdly, we randomly interviewed subjects in their homes. After the subject had signed the informed consent document, a questionnaire was administered to obtain information about her age, medical history, family history, physical activity, alcohol use, dietary habits and smoking history under the direction of a clinician. We also adopted the exclusion criteria detailed by Deng et al. [21] to screen and recruit ‘‘healthy’’ subjects. Briefly, subjects with chronic diseases and conditions that may affect bone mass or bone metabolism, were excluded from the study. We did not exclude subjects with an extremely low or high BMI. Finally, a total of 850 healthy Han Chinese females aged 20e40 years were recruited from Changsha city, the PR of China, and its surrounding region. In this sample, the number of subjects classified by BMI was 185 (BMI < 18.5), 644 (BMI ¼ 18.5e 24.9), 19 (BMI ¼ 25e29.9), 2 (BMI ¼ 30e35) respectively.

Measurements Height and weight were measured using standard altimeters and scales (made in China). BMI ¼ (kg/m2) was calculated as weight (kg) divided by square height (m2). WC and HC were measured with an anthropometric tape over light clothing,

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with measurement of waist and hip circumference at the minimum circumference between the iliac crest and the rib cage, and at the maximum protuberance of the buttocks. All the anthropometric measurements were taken in our laboratory. CI was calculated using the following formula: CI ¼ WC/[0.109  square root of (weight/height)] [16], where WC and height were measured in meters and weight was measured in kg. FMtrunk and trunk lean mass in kg was measured in the conventional DEXA trunk region using a Hologic QDR 2000 DEXA scanner (Hologic Corp., Waltham, MA). The trunk fat mass percentage (%FMtrunk) was calculated as [FMtrunk/(FMtrunk þ trunk lean mass)]. The coefficient of variation of FMtrunk, obtained from 30 individuals who were measured twice, of the DEXA measurements was 0.99%.

Statistical analysis All statistical analyses were performed with the SAS package (SAS Institute Inc., Cary, NC, USA). To investigate the effects of age, we divided the samples into four age groups with a 5-year (5-yr) range in each group (20e24-yr 25e29-yr, 30e34-yr and 35e39-yr). Pearson’s correlation coefficients were used to investigate the linear correlation of FMtrunk, %FMtrunk with five anthropometric indices. Because BMI, WC, HC, WHR and CI were five highly related anthropometric indices, to avoid the disturbance of colinearity when simultaneously modeling the five indices in multiple regression for predicting FMtrunk, a principal component (PC) analysis (PCA) was performed to form 4 PCs accounting for most variations in the five anthropometric indices [22]. Then the PC values were calculated by Eigenvalues of matrix and

Table 1

Eigenvectors, and were then used to estimate the regression coefficients and the proportions of the variance (R2) of FMtrunk predicted by these PCs by multiple regression analysis. Regression analyses were also conducted to determine whether four PCs combined explained a greater variance (R2) in FMtrunk than did BMI, WC, HC, WHR and CI alone and to investigate the correlation efficient of the measured FMtrunk and the predicted FMtrunk by four PCs or five anthropometric indices alone. According to the relationship of variables to each other in our analysis, we developed four simple equations to predict FMtrunk using measured values of five anthropometric indices.

Results Age was significantly correlated with FMtrunk, %FMtrunk, and five anthropometric indices in all the females, but was not significantly associated with them in each 5-yr age group (data not shown). As shown in Table 1, there was an increasing trend of FMtrunk, %FMtrunk and five anthropometric indices in the successively older age groups. FMtrunk and %FMtrunk were significantly correlated with five anthropometric indices, with the correlation coefficients ranging from 0.11 to 0.85 (P ¼ 0.0001). However, the correlations between FMtrunk and five anthropometric indices were generally higher than those between %FMtrunk and the indices in each age group (Table 2), therefore our analyses focused on the prediction of FMtrunk with five anthropometric indices. Generally, the correlation coefficients between FMtrunk and BMI are higher than those between WC, HC, WHR, CI and FMtrunk in the same age group (e.g., the correlation coefficients between FMtrunk and BMI,

Basic characteristics of the sample

Age (yr) Height (cm) Weight (kg) BMI FMtrunk (kg) %FMtrunk WC (cm) HC (cm) WHR CI

20e24 yr (368)

25e29 yr (325)

30e34 yr (100)

35e39 yr (57)

23.6  1.08 159  5.26 49.8  5.77 19.7  1.98 5.66  1.84 24.2  5.38 64.9  5.04 88.1  4.53 0.74  0.04 1.16  0.06

26.8  1.34 158  4.95 50.1  5.65 20.1  2.01 5.74  1.80 24.3  6.01 66.0  5.22 88.3  4.60 0.75  0.06 1.17  0.07

32.3  1.53 157  4.97 52.3  6.59 21.1  2.41 6.71  2.50 26.3  6.49 69.7  6.43 89.9  4.63 0.77  0.05 1.21  0.07

37.2  1.55 157  4.98 55.2  6.17 22.4  2.51 7.80  2.25 29.3  5.92 72.1  7.30 92.0  4.90 0.78  0.05 1.22  0.07

Note: The values are presented as mean  standard deviation. The values presented in the parentheses indicate the number of individuals observed. Abbreviation: BMI-body mass index, FMtrunk-trunk fat mass, % FMtrunk-percentage of trunk fat mass, WCwaist circumference, HC-hip circumference, WHR-waist-to-hip ratio, CI-conicity index.

The correlation and prediction of trunk fat mass with five anthropometric indices Table 2 The correlations of FMtrunk, %FMtrunk with five anthropometric indices 20e24 yr 25e29 yr 30e34 yr 35e39 yr (368) (325) (100) (57) FMtrunk BMI WC HC WHR CI

0.79 0.74 0.64 0.42 0.29

0.60 0.53 0.50 0.17 0.15

0.85 0.80 0.70 0.60 0.38

0.80 0.85 0.66 0.76 0.71

%FMtrunk BMI WC HC WHR CI

0.69 0.60 0.49 0.37 0.25

0.42 0.35 0.31 0.11 0.11

0.76 0.71 0.55 0.60 0.38

0.57 0.64 0.44 0.62 0.62

Note: The values were presented as correlation coefficients. The values presented in the parentheses indicate the number of individuals observed. Abbreviation: BMI-body mass index, FMtrunk-trunk fat mass, %FMtrunk-percentage of trunk fat mass, WC-waist circumference, hip circumference (HC), waist-to-hip ratio (WHR), conicity index (CI). Above all p ¼ 0.0001.

WC, HC, WHR and CI in 20e24-yr group were 0.79, 0.74, 0.64, 0.42 and 0.29 respectively). Additionally, there were obvious differences of correlation coefficients among different age groups (e.g., Table 3

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in 25e29-yr group the correlation coefficients were smaller than those in other age groups). As shown in Table 3, the four PCs interpreted over 99.9% of the total variation of five relative anthropometric indices in the four age groups by PCA, with over 53.7% of the total variation accounted by PC1. Regression analyses (Table 4) showed that four PCs combined explained a greater variance of FMtrunk (R2 ¼ 45.2e81.6%) than did five indices alone (R2 ¼ 2.4e72.2%) (e.g., in the 20e24yr group, the proportions are 71.4%, 63.1%, 54.8%, 40.7%, 17.9% and 8.4% respectively by the four PCs, BMI, WC, HC, WHR, and CI). Although four PCs combined, or five indices alone, correlated strongly with FMtrunk, about 20e50% of the variance of FMtrunk remained to be explained. Fig. 1 demonstrates that the correlations of FMtrunk and the predicted FMtrunk by four PCs combined were higher than those by five indices alone, indicating that using the four PCs predicts more precisely FMtrunk than using five indices alone. Finally, we developed four prediction equations of FMtrunk according to age groups (Table 5).

Discussion Our primary results in the present study show that there is an increasing trend of FMtrunk and five

Eigenvalues of the correlation matrix and eigenvectors in the PCA for five anthropometric indices Eigenvalues of matrix

Eigenvector

Eigenvalue

Proportion

BMI

WC

HC

WHR

CI

20e24 yr PC1 PC2 PC3 PC4

3.082 1.358 0.526 0.032

0.616 0.272 0.105 0.006

0.404 0.479 0.578 0.522

0.568 0.011 0.037 0.395

0.377 0.560 0.503 0.258

0.428 0.496 0.431 0.362

0.435 0.459 0.475 0.611

25e29 yr PC1 PC2 PC3 PC4

2.684 1.644 0.629 0.036

0.537 0.329 0.126 0.007

0.308 0.535 0.654 0.416

0.601 0.113 0.074 0.225

0.181 0.671 0.514 0.317

0.497 0.396 0.329 0.525

0.514 0.304 0.441 0.633

30e34 yr PC1 PC2 PC3 PC4

3.570 1.088 0.318 0.024

0.714 0.218 0.064 0.005

0.424 0.484 0.553 0.529

0.528 0.001 0.027 0.394

0.404 0.536 0.568 0.221

0.449 0.448 0.427 0.398

0.420 0.527 0.433 0.598

35e39 yr PC1 PC2 PC3 PC4

3.981 0.710 0.288 0.020

0.796 0.142 0.058 0.004

0.440 0.359 0.678 0.466

0.500 0.007 0.042 0.401

0.402 0.650 0.443 0.238

0.438 0.539 0.292 0.428

0.450 0.398 0.506 0.619

Note: PCs are five principal components derived from five anthropometric indices by principal component analysis (PCA).

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Table 4 The variation (R2) of FMtrunk by four PCs and five indices and the regression coefficients of four PCs 20e24 yr 25e29 yr 30e34 yr 35e39 yr R2a R2b R2c R2d R2e R2f

0.714 0.631 0.548 0.407 0.179 0.084

0.452 0.363 0.284 0.247 0.028 0.024

0.816 0.722 0.638 0.495 0.357 0.146

Regression coefficients PC1 0.770 0.508 PC2 0.547 0.660 PC3 0.541 0.247 PC4 1.035 0.816 Intercept 5.661 5.743

1.047 0.737 1.085 3.065 6.708

0.742 0.639 0.716 0.435 0.575 0.508 0.956 0.053* 0.585 0.743* 7.797

Note: R2aef is the proportion of the variance of FMtrunk explained by the four PCs, BMI, WC, HC, WHR, CI respectively. Above p value all are lower than 0.05 except for the data marked ‘‘*’’ (*: p > 0.05).

anthropometric indices in successively older age groups; that age has remarkable effects on the relationship of FMtrunk and the anthropometric indices studied; and that the accuracy of predicting the FMtrunk using five anthropometric indices is higher than using BMI, WC, HC, WHR, and CI alone. Because FMtrunk is a risk factor of metabolic disease, such as hypertension, hypertensive heart disease, coronary heart disease, diabetes and cardiovascular disease, etc. [23,24], our predicting equations may be applied to practical evaluation to health risk, especially in undeveloped regions and developing countries. Our results suggested that the predictions of FMtrunk using the five indices combined were more

1 0.8

0.85

accurate than those using them separately. Previous evidence has shown that using the anthropometric indices independently has limits to predict fat mass, which may produce inconsistent results [15,16,25,26]. For example, BMI and WC were usually taken as proxies of overall fat mass and central fat mass, respectively [16,26]. However, in the present study, BMI is a better predictor of central fat mass than WC. The adipose distribution may partially affect the accuracy. Janssen et al. [14] and Ferland et al. [27] reported that in measurements such as BMI, WC and WHR, the relationship between different anthropometric indices and regional adipose tissue in the body was apparent. Therefore, the combination of five anthropometric indices (including HC and CI) can extract useful information. and thus improve the ability of predicting FMtrunk. Janssen et al. [14] has found that BMI and WC combined was a better predictor of abdominal fat than either variable alone. Additionally, those indices are highly related and thus cannot simply be molded in multiple regression analysis to predict FMtrunk. Hence, a principal component analysis was employed to extract the useful information of these related indices. It is obvious from Fig. 1 that the simple correlation coefficients are higher between the measured FMtrunk and the predicted FMtrunk by the four combined PCs than those by five anthropometric indices alone. Compared with Caucasians, relatively few data on the relationship between FMtrunk and anthropometric indices have been reported in the Chinese population. Until now, only few studies have predicted visceral adipose tissue, abdominal fat mass and body fat mass using anthropometric indices and DEXA data in Caucasians and Latinos [28e32], but there is no equation to evaluate

0.90 0.79

0.74

0.86 0.80 0.70

0.67

0.64

0.85

0.76

0.71

0.530.50

0.6 0.42

0.4

0.38 0.29 0.17 0.15

0.2 0

0.85 0.66

0.60

0.60

0.80

20-24y

25-29y

30-34y

35-39y

Simple correlation coefficients between trunk fat mass and the predicted trunk fat mass by four PCs Simple correlation coefficients between trunk fat mass and the predicted trunk fat mass by BMI Simple correlation coefficients between trunk fat mass and the predicted trunk fat mass by WC Simple correlation coefficients between trunk fat mass and the predicted trunk fat mass by HC Simple correlation coefficients between trunk fat mass and the predicted trunk fat mass by WHR Simple correlation coefficients between trunk fat mass and the predicted trunk fat mass by CI

Figure 1 Simple correlation coefficients between FMtrunk and the predicted FMtrunk by four PCs using multiple regression analysis.

The correlation and prediction of trunk fat mass with five anthropometric indices Table 5

20e24 yr 25e29 yr 30e34 yr 35e39 yr

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Predicted FMtrunk calculated by the measured values of five anthropometric indices using PCA Prediction equation

Parameters

The predicted FMtrunk (kg) ¼ 0.1588  BMI þ 0.1653  WC (cm) þ 0.1308  HC (cm) þ 16.6511  WHR  13.4376  CI  16.4485 The predicted FMtrunk (kg) ¼ 0.1647  BMI þ 0.1045  WC (cm) þ 0.1449  HC (cm) þ 8.3363  WHR  8.0802  CI  14.0519 The predicted FMtrunk (kg) ¼  0.0917  BMI þ 0.2691  WC (cm) þ 0.1899  HC (cm) þ 36.4305  WHR  32.186  CI  16.2969 The predicted FMtrunk (kg) ¼ 0.1954  BMI þ 0.1028  WC (cm) þ 0.0685  HC (cm) þ 17.5962  WHR  4.9591  CI  17.9763

R2 ¼ 0.714, RSD ¼ 0.989 R2 ¼ 0.452, RSD ¼ 1.344 R2 ¼ 0.816, RSD ¼ 1.094 R2 ¼ 0.742, RSD ¼ 1.183

Note: The predicted FMtrunk (kg) ¼ intercept þ RCPC1  PC1 (EPC1 BMI  SBMI þ EPC1 WC  SWC þ EPC1 HC  SHC þ EPC1 WHR  SWHR þ EPC1 CI  SCI) þ RCPC2  PC2 (EPC2 BMI  SBMI þ EPC2 WC  SWC þ EPC2 HC  SHC þ EPC2 WHR  SWHR þ EPC2 CI  SCI) þ RCPC3  PC3 (EPC3 BMI  SBMI þ EPC3 WC  SWC þ EPC3 HC  SHC þ EPC3 WHR  SWHR þ EPC3 CI  SCI) þ RCPC4  PC4 (EPC4 BMI  SBMI þ EPC4 WC  SWC þ EPC4 HC  SHC þ EPC4 WHR  SWHR þ EPC4 CI  SCI), where RCPCs are the regression coefficients for four PCs respectively; EPCs BMI, EPCs WC, EPCs HC, EPCs WHR and EPCs CI are the corresponding Eigenvectors of BMI, WC, HC, WHR and CI for four PCs; SBMI, SWC, SHCI, SWHR and SCI are the corresponding standard values [(measured value  mean measured value)/mean measured value] for five anthropometric index. R2 is the proportion of the variance of FMtrunk explained by the four PCs and RSD is residual standard deviation.

FMtrunk in the Chinese population by simple anthropometric methods. Considering the effects of ethnic-population differences and anthropometric indices on FMtrunk, we performed the present study in a large Chinese female population to obtain corresponding predicting equations. Compared with established prediction equations in other populations [30e32], we involved more indices in the prediction equations in our study. For example, an equation to predict body fat (BF) in white women [32] was expressed by W/H (weight/ height) alone (BF ¼ 1.181  W/H  24.18). To decrease the effects of age, it was appropriate to divide our sample into four 5-yr age range groups, because age was significantly correlated with FMtrunk and five anthropometric indices in the whole sample, but was not associated with them in each age group. Consistent with previous studies [33,34], our results showed that there was an increasing trend of FMtrunk and five anthropometric indices in successively older age groups. Similar results were also reported in middle-aged Japanese women [35]. This tendency with aging may be partially explained by the decreased secretion of growth hormone (GH) [36e38]. For instance, Hoffman et al. [38] pointed out that GH diminished trunk adipose tissue in adults with GH deficiency syndrome. The variety of estrogen and declining physical activities with aging may also accelerate a more central body shape resulting from the higher proportion of FMtrunk [39e41]. Our results showed that age had potential effects on the correlations between FMtrunk and the anthropometric indices in four age groups studied. The difference in fat distribution in the different age groups may partially account for

this observation. For example, in the 25e30 yr group, the correlation coefficients between FMtrunk and anthropometric indices were smallest among all age groups. We presume that most Chinese females have the experience of procreating in this age phase, when the increasing level of estrogen may influence their fat distribution by controlling lipolysis [42]. Another study has shown that the estrogen-alpha gene polymorphisms were also associated with the distributions of regional fat in middle-aged women [43]. So, estrogen may largely affect the correlations between FMtrunk and the five anthropometric indices in 25e29 yr group through the related route of adipose tissue metabolism. Other differences in body shape and lifestyle in different age groups may also account for the differential correlations between FMtrunk and anthropometry [10]. Inevitably, the study has some questions that need to be explored further. Firstly, although five indices combined were strongly correlated with FMtrunk, about 20e50% of the variance of FMtrunk remained to be explained. It is necessary to find more underlying anthropometric indices for improving the accuracy of predicting FMtrunk. Secondly, the present results from ‘‘healthy’’ samples aged 20e40 years, according to our exclusion criterion, might be inappropriate for other individuals beyond our ‘‘healthy’’ criterion and be incapable of generalising to older or younger populations, including the individuals with extreme BMI because BMI in our samples were mostly normal. In summary, the current study has found a complex relationship between the five anthropometric indices and FMtrunk in a large cohort of Chinese adult females, with quite high validation of

682 predicting FMtrunk, and is the first to predict FMtrunk using the five anthropometric indices combined in Chinese females. These results add to our understanding of the relationship between these indices and trunk adiposity, as well as the effects that age may have on the relationship between the two.

Acknowledgments The study was partially supported by a key project grant (30230210), a general grant (30470534) from the National Science Foundation of China; three projects from Scientific Research Fund of Hunan Provincial Education Department (02A027, 03C226, 04B039); and a grant from the Natural Science Foundation of Hunan Province (04JJ1004). Investigator (HWD) was partially supported by grants from the Health Future Foundation of USA and grants from the National Health Institute (K01 AR0217001A2, R01 GM60402 and 5R01 AR050496-02).

References [1] Kaluski DN, Berry EM. Prevalence of obesity in Israel. Obes Rev 2005;6:115e6. [2] Kim DM, Ahn CW, Nam SY. Prevalence of obesity in Korea. Obes Rev 2005;6:117e21. [3] Milewicz A, Jedrzejuk D, Lwow F, Bialynicka AS, Lopatynski J, Mardarowicz G, et al. Prevalence of obesity in Poland. Obes Rev 2005;6:113e4. [4] Liu GL. An expert symposium. Obesity. Zhong guo shi yong nei ke za zhi (in Chinese) 2003;23:513e24. [5] Okosun IS, Chandra KM, Choi S, Christman J, Dever GE, Prewitt TE. Hypertension and Type 2 diabetes comorbidity in adults in the United States: risk of overall and regional adiposity. Obes Res 2001;9:1e9. [6] Lemieux S. Contribution of visceral obesity to the insulin resistance syndrome. Can J Appl Physiol 2001;26:273e90. [7] Kissebah AH, Krakower GR. Regional adiposity and morbidity. Physiol Rev 1994;74:761e811. [8] Krotkiewski M, Bjo ¨rntorp P, Sjo ¨stro ¨m L, Smith U. Impact of obesity on metabolism in men and women. Importance of regional adipose tissue distribution. J Clin Invest 1983;72: 1150e62. [9] Van Pelt RE, Evans EM, Schechtman KB, Ehsani AA, Kohrt WM. Contributions of total and regional fat mass to risk for cardiovascular disease in older women. Am J Physiol Endocrinol Metab 2002;282:E1023e8. [10] Deurenberg P, Yap M, van Staveren WA. Body mass index and percent body fat: a meta analysis among different ethnic groups. Int J Obes Relat Metab Disord 1998;22: 1164e71. [11] Duncan E, Schofield G, Duncan S, Kolt G, Rush E. Ethnicity and body fatness in New Zealanders. N Z Med J 2004;117: U913. [12] Conway JM, Yanovski SZ, Avila NA, Hubbard VS. Visceral adipose tissue differences in black and white women. Am J Clin Nutr 1995;61:765e71.

C. Jiang et al. [13] Lovejoy JC, de la Bretonne JA, Klemperer M, Tulley R. Abdominal fat distribution and metabolic risk factors: effects of race. Metabolism 1996;45:1119e24. [14] Janssen I, Heymsfield SB, Allison DB, Kotler DP, Ross R. Body mass index and waist circumference independently contribute to the prediction of non-abdominal, abdominal subcutaneous, and visceral fat. Am J Clin Nutr 2002;75: 683e8. [15] Taylor RW, Keil D, Gold EJ, Williams SM, Goulding A. Body mass index, waist girth, and waist-to-hip ratio as indices of total and regional adiposity in women: evaluation using receiver operating characteristic curves. Am J Clin Nutr 1998;67:44e9. [16] Taylor RW, Jones IE, Williams SM, Goulding A. Evaluation of waist circumference, waist-to-hip ratio, and the conicity index as screening tools for high trunk fat mass, as measured by dual-energy X-ray absorptiometry, in children aged 3e19 yr. Am J Clin Nutr 2000;72:490e5. [17] Xu WH, Matthews CE, Xiang YB, Zheng W, Ruan ZX, Cheng JR, et al. Effect of adiposity and fat distribution on endometrial cancer risk in Shanghai women. Am J Epidemiol 2005;161:939e47. [18] Czernichow S, Bertrais S, Oppert JM, Galan P, Blacher J, Ducimetie `re P, et al. Body composition and fat repartition in relation to structure and function of large arteries in middle-aged adults (the SU.VI.MAX study). Int J Obesity 2005;29:826e32. [19] Pritchard JE, Nowson CA, Strauss BJ, Carlson JS, Kaymakci B, Wark JD. Evaluation of dual energy X-ray absorptiometry as a method of measurement of body fat. Eur J Clin Nutr 1993;47:216e28. [20] Kopelman PG. Obesity as a medical problem. Nature 2000; 404:635e43. [21] Deng FY, Liu MY, Li MX, Lei SF, Qin YJ, Zhou Q, et al. Tests of linkage and association of the COL1A2 gene with bone phenotypes’ variation in Chinese nuclear families. Bone 2003;33:614e9. [22] Chapman KW, Lawless HT, Boor KJ. Quantitative descriptive analysis and principal component analysis for sensory characterization of ultrapasteurized milk. J Dairy Sci 2001; 84:12e20. [23] Chang CJ, Wu CH, Yao WJ, Yang YC, Wu JS, Lu FH. Relationships of age, menopause and central obesity on cardiovascular disease risk factors in Chinese women. Int J Obes Relat Metab Disord 2000;24:1699e704. [24] Gillum RF. The association of body fat distribution with hypertension, hypertensive heart disease, coronary heart disease, diabetes and cardiovascular risk factors in men and women aged 18e79 years. J Chron Dis 1987;40: 421e8. [25] Lemieux S, Prud’homme D, Bouchard C, Tremblay A, Despres JP. A single threshold value of waist girth identifies normal-weight and overweight subjects with excess visceral adipose tissue. Am J Clin Nutr 1996;64:685e93. [26] Lindsay RS, Hanson RL, Roumain J, Ravussin E, Knowler WC, Tataranni PA. Body mass index as a measure of adiposity in children and adolescents: relationship to adiposity by dual energy X-ray absorptiometry and to cardiovascular risk factors. J Clin Endocrinol Metab 2001;86: 4061e7. [27] Ferland M, Despres JP, Tremblay A, Pinault S, Nadeau A, Moorjani S, et al. Assessment of tissue distribution by computed tomography in obese women in association with body density and anthropometric measurement. Br J Nutr 1989;61:139e48. [28] Bertin E, Marcus C, Ruiz JC, Eschard JP, Leutenegger M. Measurement of visceral adipose tissue by DXA combined

The correlation and prediction of trunk fat mass with five anthropometric indices

[29]

[30]

[31]

[32]

[33]

[34]

[35]

with anthropometry in obese humans. Int J Obes Relat Metab Disord 2000;24:263e70. Jensen MD, Kanaley JA, Reed JE, Sheedy PF. Measurement of abdominal and visceral fat with computed tomography and dual-energy X-ray absorptiometry. Am J Clin Nutr 1995;61:274e8. Huang TT, Watkins MP, Goran MI. Predicting total body fat from anthropometry in Latino children. Obes Res 2003;11: 1192e9. Pongchaiyakul C, Kosulwat V, Rojroongwasinkul N, Charoenkiatkul S, Thepsuthammarat K, Laopaiboon M, et al. Prediction of percentage body fat in rural Thai population using simple anthropometric measurements. Obes Res 2005;13:729e38. Larsson I, Henning B, Lindroos AK, Naslund I, Sjostrom CD, Sjostrom L. Optimized predictions of absolute and relative amounts of body fat from weight, height, other anthropometric predictors, and age. Am J Clin Nutr 2006;83:252e9. Kotani K, Tokunaga K, Fujioka S, Kobatake T, Keno Y, Yoshida S, et al. Sexual dimorphism of age related changes in whole-body fat distribution in the obese. Int J Obes 1994;18:207e12. Zamboni M, Armellini F, Harris T, Turcato E, Micciolo R, Bergamo-Andreis IA, et al. Effects of age on body fat distribution and cardiovascular risk factors in women. Am J Clin Nutr 1997;66:111e5. Ito H, Ohshima A, Ohto N, Ogasawara M, Tsuzuki M, Takao K, et al. Relation between body composition and age in healthy Japanese subjects. Eur J Clin Nutr 2001; 55:462e70.

683

[36] Bjorntorp P. The regulation of adipose tissue distribution in humans. Int J Obes Relat Metab Disord 1996;20:291e302. [37] Corpas E, Harman SM, Blackman MR. Human growth hormone and human aging. Endocr Rev 1993;14:20e39. [38] Hoffman AR, Biller BM, Cook D, Baptista J, Silverman BL, Dao L, et al. Genentech Adult Growth Hormone Deficiency Study Group. Efficacy of a long-acting growth hormone (GH) preparation in patients with adult GH deficiency. J Clin Endocrinol Metab 2005;90:6431e40. [39] Guo SS, Zeller C, Chumlea WC, Siervogel RM. Aging, body composition, and lifestyle: the Fels Longitudinal Study. Am J Clin Nutr 1999;70:405e11. [40] Mitchell D, Haan MN, Steinberg FM, Visser M. Body composition in the elderly: the influence of nutritional factors and physical activity. J Nutr Health Aging 2003;7: 130e9. [41] Parsons TJ, Power C, Manor O. Physical activity, television viewing and body mass index: a cross-sectional analysis from childhood to adulthood in the 1958 British cohort. Int J Obes (Lond) 2005;29:1212e21. [42] Pedersen SB, Kristensen K, Hermann PA, Katzenellenbogen JA, Richelsen B. Estrogen controls lipolysis by up-regulating alpha2A-adrenergic receptors directly in human adipose tissue through the estrogen receptor alpha. Implications for the female fat distribution. J Clin Endocrinol Metab 2004;89:1869e78. [43] Okura T, Koda M, Ando F, Niino N, Ohta S, Shimokata H. Association of polymorphisms in the estrogen receptor alpha gene with body fat distribution. Int J Obes Relat Metab Disord 2003;27:1020e7.