Batterson 2020 Physiol Rep

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Batterson PM, Norton MR, Hetz SE, Rohilla S, Lindsay KG, Subudhi AW, Jacobs RA (2020) Improving biologic predictors of cycling endurance performance with near-infrared spectroscopy derived measures of skeletal muscle respiration: E Pluribus Unum. Physiol Rep 8:e14342.

» PMID: 31960629 Open Access

Batterson Philip M, Norton Michael R, Hetz Sarah E, Rohilla Sachi, Lindsay Keston G, Subudhi Andrew W, Jacobs Robert A (2020) Physiol Rep

Abstract: The study aim was to compare the predictive validity of the often referenced traditional model of human endurance performance (i.e. oxygen consumption, VO2, or power at maximal effort, fatigue threshold values, and indices of exercise efficiency) versus measures of skeletal muscle oxidative potential in relation to endurance cycling performance. We hypothesized that skeletal muscle oxidative potential would more completely explain endurance performance than the traditional model, which has never been collectively verified with cycling. Accordingly, we obtained nine measures of VO2 or power at maximal efforts, 20 measures reflective of various fatigue threshold values, 14 indices of cycling efficiency, and near-infrared spectroscopy-derived measures reflecting in vivo skeletal muscle oxidative potential. Forward regression modeling identified variable combinations that best explained 25-km time trial time-to-completion (TTC) across a group of trained male participants (n = 24). The time constant for skeletal muscle oxygen consumption recovery, a validated measure of maximal skeletal muscle respiration, explained 92.7% of TTC variance by itself (Adj R2= .927, F = 294.2, SEE = 71.2, p < .001). Alternatively, the best complete traditional model of performance, including VO2max (L·min-1 ), %VO2max determined by the ventilatory equivalents method, and cycling economy at 50 W, only explained 76.2% of TTC variance (Adj R2= .762, F = 25.6, SEE = 128.7, p < .001). These results confirm our hypothesis by demonstrating that maximal rates of skeletal muscle respiration more completely explain cycling endurance performance than even the best combination of traditional variables long postulated to predict human endurance performance. Keywords: Exercise, Human performance, Mitochondria, Sport Bioblast editor: Plangger M O2k-Network Lab: US CO Colorado Springs Jacobs RA


Labels: MiParea: Respiration 





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2020-06