Developing an Impedance Based Equation for Fat-Free Mass of Black Preadolescent South African Children

Nutrients. 2019 Aug 28;11(9):2021. doi: 10.3390/nu11092021.

Abstract

Bioelectrical impedance analysis (BIA) is a practical alternative to dual-energy X-ray absorptiometry (DXA) for determining body composition in children. Currently, there are no population specific equations available for predicting fat-free mass (FFM) in South African populations. We determined agreement between fat-free mass measured by DXA (FFMDXA) and FFM calculated from published multi-frequency bioelectrical impedance prediction equations (FFMBIA); and developed a new equation for predicting FFM for preadolescent black South African children. Cross-sectional data on a convenience sample of 84 children (mean age 8.5 ± 1.4 years; 44 {52%} girls) included body composition assessed using Dual X-ray Absorptiometry (FFMDXA) and impedance values obtained from the Seca mBCA 514 Medical Body Composition analyzer used to calculate FFM using 17 published prediction equations (FFMBIA). Only two equations yielded FFM estimates that were similar to the DXA readings (p > 0.05). According to the Bland-Altman analysis, the mean differences in FFM (kg) were 0.15 (LOA: -2.68; 2.37) and 0.01 (LOA: -2.68; 2.66). Our new prediction equation, F F M = 105.20 + 0.807 × S e x + 0.174 × W e i g h t + 0.01 × R e a c t a n c e + 15.71 × log ( R I ) , yielded an adjusted R2 = 0.9544. No statistical shrinkage was observed during cross-validation. A new equation enables the BIA-based prediction of FFM in the assessment of preadolescent black South African children.

Keywords: bioelectrical impedance analysis (BIA); body composition; dual energy X-ray absorptiometry (DXA); fat-free mass (FFM); preadolescent; prediction equations.

Publication types

  • Comparative Study
  • Validation Study

MeSH terms

  • Absorptiometry, Photon
  • Age Factors
  • Black People*
  • Body Composition*
  • Child
  • Cross-Sectional Studies
  • Electric Impedance
  • Female
  • Humans
  • Male
  • Models, Biological*
  • Predictive Value of Tests
  • Reproducibility of Results
  • South Africa