Predicting VO2max using lung function and three-dimensional (3D) allometry provides new insights into the allometric cascade (M0.75).

Nevill, Alan M., Wyon, Matthew, Myers, Jonathan, Harber, Matthew, Arena, Ross, Myers, Tony D. and Kaminsky, Leonard A. (2025) Predicting VO2max using lung function and three-dimensional (3D) allometry provides new insights into the allometric cascade (M0.75). Sports Medicine. pp. 1-17. ISSN 1179-2035 (In Press)

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Abstract

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 second) that might provide a more dimensionally valid model (∝ M2/3) originally proposed by Åstrand and Rodahl[1] 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)·, (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 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.

Item Type:	Article
Divisions:	Faculty of of Arts, Society and Professional Studies > Department of Health and Behavioural Sciences
Depositing User:	Ms Hazel Barham
Date Deposited:	20 Mar 2025 13:31
Last Modified:	20 Mar 2025 13:31
URI:	https://newman.repository.guildhe.ac.uk/id/eprint/17383

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