Prediction of fat-free mass in a multi-ethnic cohort of infants using bioelectrical impedance: Validation against the PEA POD - Lyons-Reid, Jaz; Ward, Leigh C.; Derraik, José G. B.; Tint, Mya-Thway; Monnard, Cathriona R.; Ramos Nieves, Jose M.; Albert, Benjamin B.; Kenealy, Timothy; Godfrey, Keith M.; Chan, Shiao-Yng; Cutfield, Wayne S. (Frontiers in Nutrition, 2022)
Background: Bioelectrical impedance analysis (BIA) is widely-used to measure body composition but has not been adequately evaluated in infancy. Prior studies have largely been of poor quality, and few included healthy term-born offspring, so it is unclear if BIA can accurately predict body composition at this age. Aims: This study evaluated impedance technology to predict fat-free mass (FFM) among a large multi-ethnic cohort of infants from the United Kingdom, Singapore, and New Zealand at ages 6 weeks and 6 months (n=292 and 212, respectively). Methods: Using air displacement plethysmography (PEA POD) as the reference, two impedance approaches were evaluated: 1) empirical prediction equations; 2) Cole modelling and mixture theory prediction. Sex-specific equations were developed among ~70% of the cohort. Equations were validated in the remaining ~30% and in an independent University of Queensland cohort. Mixture theory estimates of FFM were validated using the entire cohort at both ages. Results: Sex-specific equations based on weight and length explained 75–81% of FFM variance at 6 weeks but only 48–57% at 6 months. At both ages, the margin of error for these equations was 5–6% of mean FFM, as assessed by the root mean squared errors (RMSE). The stepwise addition of clinically-relevant covariates (i.e., gestational age, birthweight SDS, subscapular skinfold thickness, abdominal circumference) improved model accuracy (i.e., lowered RMSE). However, improvements in model accuracy were not consistently observed when impedance parameters (as the impedance index) were incorporated instead of length. The bioimpedance equations had mean absolute percentage errors (MAPE) <5% when validated. Limits of agreement analyses showed that biases were low (<100 g) and limits of agreement were narrower for bioimpedance-based than anthropometry-based equations, with no clear benefit following the addition of clinically-relevant variables. Estimates of FFM from BIS mixture theory prediction were inaccurate (MAPE 11–12%). Conclusions: The addition of the impedance index improved the accuracy of empirical FFM predictions. However, improvements were modest, so the benefits of using bioimpedance in the field remain unclear and require further investigation. Mixture theory prediction of FFM from BIS is inaccurate in infancy and cannot be recommended.