Association between phase angle and body composition: new equations to predict fat mass and skeletal muscle mass

Abstract

Objective

The aim of this cross-sectional study was to develop new regression equations for estimating fat mass (FM) and skeletal muscle mass (SMM) in a heterogeneous Caucasian population, using the phase angle (PhA) as a bioelectrical parameter and DXA as the reference method. We also aimed to cross-validate the new equations, and to compare them with the manufacturers’ equations.

Methods

The 212 healthy Caucasian participants aged 20–65 years were randomly distributed into two groups: development group (n = 141) and validation group (n = 71). Bioelectrical parameters were obtained with a 50 kHz foot-to-hand phase-sensitive body composition analyzer. The new FM percentage (FM%) and SMM percentage (SMM%) equations were developed by performing multiple forward regression analyses. Agreement between DXA and the different equations was assessed by mean differences, coefficient of determination, standard error of the estimate (SEE), concordance correlation coefficient (CCC), and Bland–Altman plots.

Results

The proposed equations explained 89.2% of the variance in the DXA-derived FM% and 91.8% in the DXA-derived SMM%, with low random errors (SEE = 3.04% and 1.92%, respectively), and a very strong agreement (CCC = 0.93 and 0.94, respectively). In addition, they demonstrated no fixed bias and a relatively low individual variability. However, the manufacturer's equations described a lower percentage of the variance, with higher random errors, obtained fixed bias of -5.77% for FM% and 4.91% for SMM%, as well as higher individual variability.

Conclusions

The new regression equations, which include the PhA as a bioelectrical parameter, can accurately predict DXA-derived FM% and SMM% in a heterogeneous Caucasian population, and are better options than the manufacturer's equations.