Predicting VO2max Using Lung Function and Three-Dimensional (3D) Allometry Provides New Insights into the Allometric Cascade (M0.75).

BACKGROUND

Using directly measured cardiorespiratory fitness (i.e. VO2max) in epidemiological/population studies is rare due to practicality issues. As such, predicting VO2max is an attractive alternative. Most equations that predict VO2max adopt additive rather than multiplicative models despite evidence that the latter provides superior fits and more biologically interpretable models. Furthermore, incorporating some but not all confounding variables may lead to inflated mass exponents (∝ M0.75) as in the allometric cascade.

OBJECTIVE

Hence, the purpose of the current study was to develop multiplicative, allometric models to predict VO2max incorporating most well-known, but some less well-known confounding variables (FVC, forced vital capacity; FEV1, forced expiratory volume in 1 s) that might provide a more dimensionally valid model (∝ M2/3) originally proposed by Astrand and Rodahl.

METHODS

We adopted the following three-dimensional multiplicative allometric model for VO2max (l⋅min-1) = Mk1·HTk2·WCk3·exp(a + b·age + c·age2 + d·%fat)·ε, (M, body mass; HT, height; WC, waist circumference; %fat, percentage body fat). Model comparisons (goodness-of-fit) between the allometric and equivalent additive models was assessed using the Akaike information criterion plus residual diagnostics. Note that the intercept term 'a' was allowed to vary for categorical fixed factors such as sex and physical inactivity.

RESULTS

Analyses revealed that significant predictors of VO2max were physical inactivity, M, WC, age2, %fat, plus FVC, FEV1. The body-mass exponent was k1 = 0.695 (M0.695), approximately∝M2/3. However, the calculated effect-sizes identified age2 and physical inactivity, not mass, as the strongest predictors of VO2max. The quality-of-fit of the allometric models were superior to equivalent additive models.

CONCLUSIONS

Results provide compelling evidence that multiplicative allometric models incorporating FVC and FEV1 are dimensionally and theoretically superior at predicting VO2max(l⋅min-1) compared with additive models. If FVC and FEV1 are unavailable, a satisfactory model was obtained simply by using HT as a surrogate.