| Obesity caused by excess body fat is becoming a worldwide noticeable problem. Common overall obesity and trunk obesity, which results from a complex interaction of environmental and genetic factors, is associated with an increased risk of metabolic diseases. Therefore, the early measurement of fat mass has potential importance to evaluate obesity. Significant anthropometric indices, such as body mass index (BMI) , waist circumference (WC) , hip circumference (HC) , waist-to-hip ratio (WHR) , conicity index (CD , are several surrogate phenotypes to assess body fat (BF) , percent body fat (BF%) and regional fat mass. Considering the characteristics of rapid, cheapness and validity, a number of researchers have used one or more above indices in obesity-related genetic and epidemiologic studies focus on Caucasians and various results were inconsistent about the accuracy of evaluating body fat by these indices. Additionally, the common genetic determination of BF and BMI used frequently in studies rarely is explored. The present study has two objectives: the first is to evaluate the correlation and prediction of trunk fat mass (FMtrunk) with five anthropometric indices by and principal component analysis (PCA) and multiple regression analyses in Chinese Han-ethnic females. The other is to determine genetic correlation ( P_g ) , environmental correlation ( P_e ) , and phenotypic correlation ( P_p ) between BMI and BF, BF% by the univariate variance and Bivariate variance decompositionanalyses in Caucasian nuclear families. 850 China Han-ethnic females aged 20-40 years and 512 Caucasian pedigrees, including 2667 females and 1822 males, were randomly recruited. Chinese female samples were divided into four age groups with 5-year range each group. Five anthropometric indices were measured using standard equipments or calculated. FMtrunk, BF and BF% in kg were measured using a dual-energy X-ray absorptiometry scanner. 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. Regression analyses showed that four PCs combined explained a greater variance (R2 = 45. 2-81. 6%) in FMtrunk than did each of the five indices alone (R2 = 2. 4-72. 2%) . we also developed 4 prediction equation by 5 anthropometric indices. Besides, univariate genetic analyses showed that the significant (/?<0.001) narrow-sense heritabilities (ff) forBMI, BF and BF% is 0. 39, 0. 38, 0. 43. The common household effect for BF was small and not significant and even near to zero for BF%. Bivariate quantitative genetic analyses indicated that Pe , Pg and Pp between BMI and BF, BF% also were extremely significant (/?<0. 001) and the corresponding values between BMI and BF and those between BF and BMI were 0. 859, 0. 900, 0. 884, and, 0. 695, 0. 829, 0. 773, respectively. 73. 8 percent of the genetic variation of BMI and BF and 48. 3 percent of those of BMI and BF% attributed to common genetic factors. Compared with those of BMI and BF, BF%, the shared degree of genetic variationof BF and BF% is higher (80.6%) .Those mentioned results in Chinese suggested that there is an increasing trend of FMtrunk and five anthropometric indices with aging;that age has the obvious effects on influencing the relationship of FMtrunk and the studied anthropometric indices;and that the accuracy of predicting the FMtrunk using five anthropometric indices combined is higher than using the five indices alone. The results in Caucasians disclosed that the mostly variation of BMI and BF, BF% due to the common set of genetic and environmental factors. Furthermore, the recognition and disentanglement of such genetic interactions could help us to understand better the relationship between the obesity phenotypes and BMI.
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