Abstract

Background: Body composition can be measured by several methods, each with specific benefits and disadvantages. Bioelectric impedance offers a favorable balance between accuracy, cost and ease of measurement in a range of settings. In this method, bioelectric measurements are converted to body composition measurements by prediction equations specific to age, population and bioimpedance device. Few prediction equations exist for populations in low-resource settings. We formed a prediction equation for total body water in Malawian adolescents using deuterium dilution as reference.

Methods: We studied 86 boys and 92 girls participating in the 11-14-year follow-up of the Lungwena Antenatal Intervention Study, a randomized trial of presumptive infection treatment among pregnant women. We measured body composition by Seca m515 bioimpedance analyser. Participants ingested a weight-standardized dose of deuterium oxide, after which we collected saliva at baseline, at 3 and 4 h post-ingestion, measured deuterium concentration using Fourier-transform infrared spectroscopy and calculated total body water. We formed predictive equations for total body water using anthropometrics plus resistance and reactance at a range of frequencies, applying multiple regression and repeated cross-validation in model building and in prediction error estimation.

Results: The best predictive model for percentage total body water (TBW %) was 100*(1.11373 + 0.0037049*height (cm)²/resistance(Ω) at 50 kHz– 0.25778*height(m)– 0.01812*BMI(kg/m²)– 0.02614*female sex). Calculation of absolute TBW (kg) by multiplying TBW (%) with body weight had better predictive power than a model directly constructed to predict absolute total body water (kg). This model explained 96.4% of variance in TBW (kg) and had a mean prediction error of 0.691 kg. Mean bias was 0.01 kg (95% limits of agreement -1.34, 1.36) for boys and -0.01 kg (1.41, 1.38) for girls.

Conclusions: Our equation provides an accurate, cost-effective and participant-friendly body composition prediction method among adolescents in clinic-based field studies in rural Africa, where electricity is available.