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Skeletal Muscle Neurovascular Coupling, Oxidative Capacity, and Microvascular Function with 'One Stop Shop' Near-infrared Spectroscopy
Published on: February 20, 2018
Optimizing the predictive validity of NIRS kinetic profiles to predict aerobic capacity from a resting skeletal
Heru Syarli Lesmana1,2, Kyohei Marume3, Justin S Lawley1
1Department of Sport Science, University of Innsbruck, Innsbruck, Austria.
Abstract:
Measuring and monitoring individual cardiorespiratory fitness through a valid and accessible non-exhaustive surrogate is required. Techniques measuring haemodynamics have shown promise, and this study aimed to optimize the predictive validity of these approaches alongside developing predictive equations. In a two-study design, 8 (pilot study) and 30 (confirmation study) healthy adults completed exercise testing to assess maximal oxygen consumption ( ) and an ischaemic occlusion test on the upper thigh to assess superficial femoral blood flow from ultrasonography and skeletal muscle oxygenation (SmO2) by near-infrared spectroscopy (NIRS) before, during and post-cuff release. In study 1, treadmill running and a 5-min 220 mmHg ischaemic cuff pressure were performed, whereas in study 2, cycling ergometry and a 3-min 300 mmHg cuff pressure were applied. In study 1 and study 2, abs and rel were correlated to peak blood flow post-cuff occlusion (r = 0.57-0.84, all P < 0.01). In study 2, several NIRS based metrics of post-occlusive reactive hyperaemia were strongly correlated with abs and rel (all P < 0.001). Moreover, the magnitude of oxygen desaturation during the cuff occlusion was highly significantly related to both abs and rel (all P < 0.001). As an example, the SmO2 desaturation slope was strongly associated with abs (r = -0.74, P < 0.001). Finally, intercorrelations between the rate of SmO2 desaturation during cuff occlusion and the rate of SmO2 reoxygenation and peak skeletal muscle blood flow post-cuff occlusion were observed (P < 0.01). An ischaemic-based test of skeletal muscle haemodynamic profiles could potentially be used to predict and estimate a person's fitness.
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