Development of population-specific prediction equations for bioelectrical impedance analyses in Vietnamese children
2020
Nguyen, Phuong Hong | Young, Melissa F. | Khuong, Long | Ramakrishnan, Usha | Martorell, Reynaldo | Hoffman, Daniel J.
There is a need for accurate, inexpensive and field-friendly methods to assess body composition in children. Bioelectrical impedance analyzer (BIA) is a promising approach; however, there has been limited validation and use among young children in resource poor settings. We aim to develop and validate population-specific prediction equations for estimating total fat mass (FM), fat free-mass (FFM), and percent body fat (PBF) in Vietnamese children (4-7 years) using reactance and resistance from BIA, anthropometric variables and demographic information. We conducted a cross-sectional survey of 120 children. Body composition was measured using dual energy x-ray absorptiometry (DXA), BIA and anthropometry. To develop prediction equations, we split all data 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 R2. We identified a top performing model with the least number of parameters (age, sex, weight and resistance index or resistance and height), low RMSE (FM 0.70, FFM 0.74, PBF 3.10), low MAE (FM 0.55, FFM 0.62, PBF 2.49), high R2 (FM 0.95, FFM 0.92, PBF 0.82), and the least difference between predicted values and actual values from DEXA (FM 0.03kg or 0.01SD, FFM 0.06kg or 0.02SD, PBF 0.27% or 0.04SD). In conclusion, we developed the first valid and highly predictive equations to estimate FM, FFM and PBF in Vietnamese children using BIA. These findings have important implications for future research on the double burden of disease and risks associated with overweight and obesity in young children.
Afficher plus [+] Moins [-]Mots clés AGROVOC
Informations bibliographiques
Cette notice bibliographique a été fournie par International Livestock Research Institute
Découvrez la collection de ce fournisseur de données dans AGRIS