Imboden MT; Kaminsky LA; Peterman JE; Hutzler HL; Whaley MH; Fleenor BS; Harber MP;
Medicine And Science In Sports And Exercise [Med Sci Sports Exerc] 2020 Jan 24. Date of Electronic Publication: 2020 Jan 24.
Purpose: Cardiorespiratory fitness (CRF) is known to be directly related to fat-free mass (FFM), therefore it has been suggested that normalizing CRF to FFM (VO2peakFFM) may be the most accurate expression of CRF as related to exercise performance and cardiorespiratory function. However, the influence of VO2peakFFM (ml·kgFFMmin) on predicting mortality has been largely unexplored. This study aimed to primarily assess the relationship between VO2peakFFM and all-cause and disease-specific mortality risk in apparently healthy adults. Further, this study sought to compare the predictive ability of VO2peakFFM to VO2peak normalized to total body weight (VO2peakTBW) for mortality outcomes.
Methods: Participants included 2,905 adults (1,555 men, 1,350 women) who completed a cardiopulmonary exercise test (CPX) between 1970-2016 to determine CRF. Body composition was assessed using the skinfold method to estimate FFM. CRF was expressed as VO2peakTBW and VO2peakFFM. Participants were followed for 19.0 ± 11.7 years after their CPX for mortality outcomes. Cox-proportional hazard models were performed to determine the relationship of VO2peakFFM with mortality outcomes. Parameter estimates were assessed to compare the predictive ability of CRF expressed as VO2peakTBW and VO2peakFFM.
Results: Overall, VO2peakFFM was inversely related to all-cause, CVD, and cancer mortality, with a 16.2, 8.4, and 8.0% lower risk per 1 ml·kgFFM·min improvement, respectively (p<0.01). Further, assessment of the parameter estimates showed VO2peakFFM to be a significantly stronger predictor of all-cause mortality than VO2peakTBW (parameter estimates: -0.49 vs. -0.16).
Conclusion: Body composition is an important factor when considering the relationship between CRF and mortality risk. Clinicians should consider normalizing CRF to FFM when feasible, as it will strengthen the predictive power of the measure.