Comparing individual and population differences in minute ventilation/carbon dioxide production slopes using centile growth curves and log-linear allometry

Identifying vulnerable groups and/or individuals’ cardiorespiratory fitness (CRF) is an important challenge for clinicians/researchers alike. To quantify CRF accurately, the assessment of several variables is now standard practice including maximal oxygen uptake (VʹCO2) and ventilatory efficiency, the latter assessed using the minute ventilation/carbon dioxide production (VʹE/VʹCO2) slope. Recently, reference values (centiles) for VʹE/VʹCO2 slopes for males and females aged 20 to 80 have been published, using cardiopulmonary exercise testing (CPX) data (treadmill protocol) from the Fitness Registry and the Importance of Exercise National Database (FRIEND Registry). In the current observational study we provide centile curves for the FRIEND Registry VʹE/VʹCO2 slopes, fitted using the generalised additive model for location, scale and shape (GAMLSS), to provide individuals with a more precise estimate of where their VʹE/VʹCO2 slopes fall within the population. We also confirm that by adopting allometric models (incorporating a log transformation), the resulting ANCOVAs provided more normal and homoscedastic residuals, with superior goodness-of-fit using the Akaike information criterion (AIC)=14 671 (compared with traditional ANCOVA's AIC=15 008) that confirms allometric models are vastly superior to traditional ANCOVA models. In conclusion, providing sex-by-age centile curves rather than referring to reference tables for ventilatory efficiency (VʹE/VʹCO2 slopes) will provide more accurate estimates of where an individual's particular VʹE/VʹCO2 slope falls within the population. Also, by adopting allometric models researchers are more likely to identify real and valid inferences when analysing population/group differences in VʹE/VʹCO2 slopes.
This article provides centile curves of VʹE/VʹCO2 slopes and demonstrates that by adopting log-linear models, more trustworthy inferences with group differences will also be found https://bit.ly/3uitACS