Development and cross-validation of prediction equations for body composition in adult cancer survivors from the Korean National Health and Nutrition Examination Survey (KNHANES).

Epidemiological studies frequently use indices of adiposity related to mortality. However, no studies have validated prediction equations for body composition in adult cancer survivors. We aimed to develop and cross-validate prediction equations for body fat mass (BFM), lean body mass (LBM), trunk fat mass (TFM), and appendicular lean mass (ALM) in adult cancer survivors using sociodemographic, anthropometric, and laboratory test data. This study included adult cancer survivors from the Korean National Health and Nutrition Examination Survey 2008-2011 with complete data on Dual-energy X-ray absorptiometry (DXA) measurements. A total of 310 participants were randomly divided into development and cross-validation groups (5:5 ratio). Age, height, weight, waist circumference, serum creatinine levels, and lifestyle factors were included as independent variables The predictive equations were developed using a multiple linear regression and their predictive performances were primarily evaluated with R2 and Concordance Correlation Coefficient (CCC). The initial equations, which included age, height, weight, and waist circumference, showed different predictive abilities based on sex for BFM (total: R2 = 0.810, standard error of estimate [SEE] = 3.072 kg, CCC = 0.897; men: R2 = 0.848, SEE = 2.217 kg CCC = 0.855; women: R2 = 0.791, SEE = 2.194 kg, CCC = 0.840), LBM (total: R2 = 0.736, SEE = 3.321 kg, CCC = 0.838; men: R2 = 0.703, SEE = 2.450 kg, CCC = 0.774; women: R2 = 0.854, SEE = 2.234 kg, CCC = 0.902), TFM (total: R2 = 0.758, SEE = 1.932 kg, CCC = 0.844; men: R2 = 0.650, SEE = 1.745 kg, CCC = 0.794; women: R2 = 0.852, SEE = 1.504 kg, CCC = 0.890), and ALM (total: R2 = 0.775, SEE = 1.726 kg, CCC = 0.876; men: R2 = 0.805, SEE = 1.320 kg, CCC = 0.817; women: R2 = 0.726, SEE = 1.198 kg, CCC = 0.802). When additional factors, such as creatinine, smoking, alcohol consumption, and physically inactive were included in the initial equations the predictive performance of the equations were generally improved. The prediction equations for body composition derived from this study suggest a potential application in epidemiological investigations on adult cancer survivors.