Should we use fat-free mass or body mass and percentage body fat as separate predictors to predict maximum oxygen...

To assess whether fat-free mass (FFM) or body mass (M) is the more appropriate body size variable to predict maximum oxygen uptake (VO_2max, L⋅min^-1).

Data (3930 cardiopulmonary exercise tests) were provided from the FRIEND registry. Our prediction equations adopted the well-known allometric/power function model VO_2max (L⋅min^-1)=a·X ^b , using either FFM or M as the predictor variable (X). These models can be linearised with a log-transformation, and analysis of covariance (ANCOVA) can then be used to estimate the unknown parameters.

Initially, when predicting Ln(VO_2max) using only Ln(FFM) adjusted for age and sex, the explained variance was R²=0.718 (Akaike information criterion (AIC)=-1882.5), with the FFM exponent b=0.658. However, when predicting Ln(VO_2max) using M AND bodyfat% separately, the explained variance increased to R²=0.733 (AIC=-2077.4), with the M exponent b=0.636. The difference in R² and AICs confirmed the benefit of predicting VO_2max using separate M and bodyfat% terms. The analysis identified an enlarged negative bodyfat% term that improved the prediction of VO_2max, explained latterly by central adiposity (waist circumference). These final, more inclusive M and FFM exponents were estimated to be b=0.67, suggesting that VO_2max should be normalised using VO_2max (mL·FFM^-2/3·min^-1) or preferably VO_2max (mL·M^-2/3·min^-1) rather than VO_2max (mL·FFM^-1·min^-1). We also found that linear prediction models systematically under-estimate the VO_2max of overweight and underweight individuals, but over-estimate the VO_2max of average-weight individuals.

Incorporating FFM into equations to predict VO_2max fails to explain the negative effect of central adiposity. However, by incorporating M and percentage body fat (BF%) separately into the allometric models, a greater/enlarged negative BF% term explains this apparent omission/absence.