Development of Population-Specific Prediction Equations for Bioelectrical Impedance Analyses in Vietnamese Children. (29th May 2020) - Nguyen, Phuong; Young, Melissa; Khuong, Long; Martorell, Reynaldo; Ramakrishnan, Usha; Hoffman, Daniel
Abstract: Objectives: Bioelectrical impedance analysis (BIA) is an accurate, inexpensive and field-friendly methods to assess body composition, but there is limited information on its use and validity for children in low-middle income countries. Our aim was to develop and validate population-specific prediction equations for estimating total fat mass (FM) and fat free-mass (FFM) in Vietnamese children using reactance and resistance from BIA and anthropometric variables. Methods: We conducted a cross-sectional survey of 120 children in Thai Nguyen, Vietnam. Body composition was measured using dual energy x-ray absorptiometry (DXA), BIA and anthropometry (height, weight, triceps and subscapular skinfolds, and waist, hip, and mid upper arm circumferences). To develop prediction equations, we split the sample into development (70%) and validation datasets (30%). The model performance was evaluated using PRESS (Predicted residual error sum of squares), RMSE (Root mean squared error), MEA (Mean absolute error) and R, 2 . Results: %MCEPASTEBIN% The development of prediction equations for total FM resulted in seven models. We identified a top performing model with the least number of parameters (age, sex, weight and resistance index), low RMSE (178 and 164 for FM and FFM, respectively), low MAE (136 and 141 for FM and FFM, respectively), high R2 (>.90), and the least difference between predicted and actual values (FM 25 0.03 g and FFM 8 0.01 g). Conclusions: We developed valid and highly predictive equations to estimate FM and FFM in Vietnamese children using BIA. These findings have important implications for future research examining the risks associated with overweight and obesity in young children in resource-poor settings. Funding Sources: Rollins School of Public Health (RSPH) Dean's Pilot and Innovation Grant, the New Jersey Institute for Food, Nutrition, and Health, and the Nestle Foundation.