title: Evaluating the correlation and prediction of trunk fat mass with five anthropometric indices in Chinese females aged 20-40 years.
abstract: OBJECTIVE
  Obesity is a worldwide problem, and excess trunk fat mass (FM(trunk)) has been associated with an increased risk of diseases. The early measurement of FM(trunk) has potential importance to evaluate trunk obesity. We sought to evaluate the correlation and predication of FM(trunk) with five anthropometric indices in Chinese females.
  
  
  RESULTS
  A sample of 850 China females aged 20-40 years were recruited and divided into four age groups with a 5-year range in each group. Five anthropometric indices were measured or calculated. FM(trunk) 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 FM(trunk) 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 variance (R(2)=45.2-81.6%) in FM(trunk) than did each of the five indices alone (R(2)=2.4-72.2%).
  
  
  CONCLUSIONS
  Our results suggested that there is an increasing trend of FM(trunk) and five anthropometric indices with aging; that age obviously influences the relationship of FM(trunk) and the anthropometric indices studied; and that the accuracy of predicting the FM(trunk) using five anthropometric indices combined is greater than using the five indices alone.
authors.affiliation: 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.
authors.forename: Cheng
authors.initials: C
authors.lastname: Jiang
authors.forename: Shu-Feng
authors.initials: SF
authors.lastname: Lei
authors.forename: Man-Yuan
authors.initials: MY
authors.lastname: Liu
authors.forename: Su-Mei
authors.initials: SM
authors.lastname: Xiao
authors.forename: Xiang-Ding
authors.initials: XD
authors.lastname: Chen
authors.forename: Fei-Yan
authors.initials: FY
authors.lastname: Deng
authors.forename: Hong
authors.initials: H
authors.lastname: Xu
authors.forename: Li-Jun
authors.initials: LJ
authors.lastname: Tan
authors.forename: Yan-Jun
authors.initials: YJ
authors.lastname: Yang
authors.forename: Yan-Bo
authors.initials: YB
authors.lastname: Wang
authors.forename: Xiao
authors.initials: X
authors.lastname: Sun
authors.forename: Yan-Fang
authors.initials: YF
authors.lastname: Guo
authors.forename: Jing-Jing
authors.initials: JJ
authors.lastname: Guo
authors.forename: Xue-Zhen
authors.initials: XZ
authors.lastname: Zhu
authors.forename: Hong-Wen
authors.initials: HW
authors.lastname: Deng
mesh_terms: D015502:Absorptiometry, Photon / Q000379:methods; D000273:Adipose Tissue / Q000033:anatomy & histology* / Q000378:metabolism*; D000328:Adult; D000367:Age Factors; D000375:Aging / Q000502:physiology*; D000886:Anthropometry; D001823:Body Composition / Q000502:physiology*; D015992:Body Mass Index; D002681:China / Q000453:epidemiology; D003430:Cross-Sectional Studies; D005260:Female; D006801:Humans; D009765:Obesity / Q000453:epidemiology* / Q000208:ethnology; D011237:Predictive Value of Tests; D025341:Principal Component Analysis / Q000379:methods; D050507:Subcutaneous Fat, Abdominal / Q000033:anatomy & histology; D049629:Waist-Hip Ratio