BMJ open sport & exercise medicine · 2026
Should we use fat-free mass or body mass and percentage body fat as separate predictors to predict maximum oxygen uptake?
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Nevill Alan, Harber Matthew, Wyon Matthew, Myers Jonathan, Arena Ross, Myers Tony, Kaminsky Leonard A
Аннотация
BACKGROUND/OBJECTIVE: To assess whether fat-free mass (FFM) or body mass (M) is the more appropriate body size variable to predict maximum oxygen uptake (VO2max, L⋅min-1).
METHODS: Data (3930 cardiopulmonary exercise tests) were provided from the FRIEND registry. Our prediction equations adopted the well-known allometric/power function model VO2max (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.
RESULTS: Initially, when predicting Ln(VO2max) 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(VO2max) 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 VO2max using separate M and bodyfat% terms. The analysis identified an enlarged negative bodyfat% term that improved the prediction of VO2max, explained latterly by central adiposity (waist circumference). These final, more inclusive M and FFM exponents were estimated to be b=0.67, suggesting that VO2max should be normalised using VO2max (mL·FFM-2/3·min-1) or preferably VO2max (mL·M-2/3·min-1) rather than VO2max (mL·FFM-1·min-1). We also found that linear prediction models systematically under-estimate the VO2max of overweight and underweight individuals, but over-estimate the VO2max of average-weight individuals.
CONCLUSION: Incorporating FFM into equations to predict VO2max 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.
METHODS: Data (3930 cardiopulmonary exercise tests) were provided from the FRIEND registry. Our prediction equations adopted the well-known allometric/power function model VO2max (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.
RESULTS: Initially, when predicting Ln(VO2max) 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(VO2max) 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 VO2max using separate M and bodyfat% terms. The analysis identified an enlarged negative bodyfat% term that improved the prediction of VO2max, explained latterly by central adiposity (waist circumference). These final, more inclusive M and FFM exponents were estimated to be b=0.67, suggesting that VO2max should be normalised using VO2max (mL·FFM-2/3·min-1) or preferably VO2max (mL·M-2/3·min-1) rather than VO2max (mL·FFM-1·min-1). We also found that linear prediction models systematically under-estimate the VO2max of overweight and underweight individuals, but over-estimate the VO2max of average-weight individuals.
CONCLUSION: Incorporating FFM into equations to predict VO2max 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.