Quantifying assumptions underlying peak oxygen consumption equations across the body mass spectrum.
Vincent BusqueJeffrey W ChristleKegan J MoneghettiNicholas CauwenberghsTatiana KouznetsovaYair BlumbergMatthew T WheelerEuan AshleyFrancois HaddadJonathan MyersPublished in: Clinical obesity (2024)
The goal of this study is to quantify the assumptions associated with the Wasserman-Hansen (WH) and Fitness Registry and the Importance of Exercise: A National Database (FRIEND) predictive peak oxygen consumption (pVO 2 ) equations across body mass index (BMI). Assumptions in pVO 2 for both equations were first determined using a simulation and then evaluated using exercise data from the Stanford Exercise Testing registry. We calculated percent-predicted VO 2 (ppVO 2 ) values for both equations and compared them using the Bland-Altman method. Assumptions associated with pVO 2 across BMI categories were quantified by comparing the slopes of age-adjusted VO 2 ratios (pVO 2 /pre-exercise VO 2 ) and ppVO 2 values for different BMI categories. The simulation revealed lower predicted fitness among adults with obesity using the FRIEND equation compared to the WH equations. In the clinical cohort, we evaluated 2471 patients (56.9% male, 22% with BMI >30 kg/m 2 , pVO 2 26.8 mlO 2 /kg/min). The Bland-Altman plot revealed an average relative difference of -1.7% (95% CI: -2.1 to -1.2%) between WH and FRIEND ppVO 2 values with greater differences among those with obesity. Analysis of the VO 2 ratio to ppVO 2 slopes across the BMI spectrum confirmed the assumption of lower fitness in those with obesity, and this trend was more pronounced using the FRIEND equation. Peak VO 2 estimations between the WH and FRIEND equations differed significantly among individuals with obesity. The FRIEND equation resulted in a greater attributable reduction in pVO 2 associated with obesity relative to the WH equations. The outlined relationships between BMI and predicted VO 2 may better inform the clinical interpretation of ppVO 2 values during cardiopulmonary exercise test evaluations.
Keyphrases
- weight gain
- body mass index
- physical activity
- insulin resistance
- weight loss
- metabolic syndrome
- high intensity
- high fat diet induced
- type diabetes
- resistance training
- body composition
- end stage renal disease
- ejection fraction
- chronic kidney disease
- skeletal muscle
- machine learning
- peritoneal dialysis
- adverse drug
- patient reported outcomes
- big data
- artificial intelligence
- prognostic factors