Predicting VO_2max Using Lung Function and Three-Dimensional (3D) Allometry Provides New Insights into the Allometric...

Area of Science:

• Physiology
• Biostatistics
• Epidemiology

Background:

• Directly measuring cardiorespiratory fitness (VO2max) in large populations is impractical.
• Predictive equations for VO2max often use additive models, which are less biologically interpretable than multiplicative models.
• Existing models may inflate mass exponents due to incomplete confounding variable inclusion.

Purpose of the Study:

• To develop multiplicative, allometric models for predicting VO2max.
• To incorporate key confounding variables including forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1).
• To achieve a dimensionally valid model (∝ M^2/3) as originally proposed.

Main Methods:

• A three-dimensional multiplicative allometric model was employed: VO2max = M^k1 * HT^k2 * WC^k3 * exp(a + b*age + c*age^2 + d*%fat) * ε.
• Model performance was compared using the Akaike information criterion (AIC) and residual diagnostics.
• Intercepts were adjusted for categorical factors like sex and physical inactivity.

Main Results:

• Significant predictors of VO2max included physical inactivity, body mass (M), waist circumference (WC), age^2, %fat, FVC, and FEV1.
• The body mass exponent was 0.695 (M^0.695), approximating M^2/3.
• Age^2 and physical inactivity were identified as the strongest predictors, with allometric models outperforming additive models in goodness-of-fit.

Conclusions:

• Multiplicative allometric models integrating FVC and FEV1 provide superior dimensional and theoretical prediction of VO2max compared to additive models.
• These models offer enhanced biological interpretability.
• Height (HT) can serve as a suitable surrogate if FVC and FEV1 data are unavailable.