Frontiers in Cardiovascular Medicine (Dec 2024)
A generalized equation for predicting peak oxygen consumption during treadmill exercise testing: mitigating the bias from total body mass scaling
Abstract
BackgroundIndexing peak oxygen uptake (VO2peak) to total body mass can underestimate cardiorespiratory fitness (CRF) in women, older adults, and individuals with obesity. The primary objective of this multicenter study was to derive and validate a body size-independent scaling metric for VO2peak. This metric was termed exercise body mass (EBM).MethodIn a cohort of apparently healthy individuals from the Fitness Registry and the Importance of Exercise National Database, we derived EBM using multivariable log-normal regression analysis. Subsequently, we developed a novel workload (WL) equation based on speed (Sp), fractional grade (fGr), and heart rate reserve (HRR). The generalized equation for VO2peak can be expressed as VO2peak = Cst × EBM × WL, where Cst is a constant representing the VO2peak equivalent of one metabolic equivalent of task. This generalized equation was externally validated using the Stanford exercise testing (SET) dataset.ResultsA total of 5,618 apparently healthy individuals with a respiratory exchange ratio >1.0 (57% men, mean age 44 ± 13 years) were included. The EBM was expressed as Mass (kg)0.63 × Height (m)0.53 × 1.16 (if a man) × exp (−0.39 × 10−4 × age2), which was also approximated using simple sex-specific additive equations. Unlike total body mass, EBM provided body size-independent scaling across both sexes and WL categories. The generalized VO2peak equation was expressed as 11 × EBM × [2 + Sp (in mph) × (1.06 + 5.22 × fGr) + 0.019 × HRR] and had an R2 of 0.83, p < 0.001. This generalized equation mitigated bias in VO2peak estimations across age, sex, and body mass index subgroups and was validated in the SET registry, achieving an R2 of 0.84 (p < 0.001).ConclusionsWe derived a generalized equation for measuring VO2peak during treadmill exercise testing using a novel body size-independent scaling metric. This approach significantly reduced biases in CRF estimates across age, sex, and body composition.
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