Allison Diry1,2, Sébastien Ratel3, Joffrey Bardin2, Neil Armstrong4, Quentin De Larochelambert5, Claire Thomas6, Hugo Maciejewski7,8. 1. French Rowing Federation, 17, boulevard de la Marne, 94130, Nogent-sur-Marne, France. 2. Laboratory Sport, Expertise, and Performance - EA 7370, Research Department, French Institute of Sport (INSEP), Paris, France. 3. AME2P - EA 3533, Clermont-Auvergne University, Clermont-Ferrand, France. 4. Children's Health and Exercise Research Centre, University of Exeter, Exeter, UK. 5. French Institute of Sport (INSEP), IRMES (Institut de Recherche bioMédicale et d'Épidémiologie du Sport) - EA 7329, Paris, France. 6. LBEPS - University of Évry Val d'Essonne, IRBA - Université Paris Saclay, Évry, France. 7. French Rowing Federation, 17, boulevard de la Marne, 94130, Nogent-sur-Marne, France. hugo.maciejewski@ffaviron.fr. 8. LBEPS - University of Évry Val d'Essonne, IRBA - Université Paris Saclay, Évry, France. hugo.maciejewski@ffaviron.fr.
Abstract
PURPOSE: The aim of the present study was to investigate (i) how glycolytic metabolism assessed by accumulated oxygen deficit (AODgly) and blood metabolic responses (lactate and pH) resulting from high-intensity exercise change during growth, and (ii) how lean body mass (LBM) influences AODgly and its relationship with blood markers. METHODS: Thirty-six 11- to 17-year olds performed a 60-s all-out test on a rowing ergometer. Allometric modelling was used to investigate the influence of LBM and LBM + maturity offset (MO) on AODgly and its relationship with the extreme post-exercise blood values of lactate ([La]max) and pH (pHmin) obtained during the recovery period. RESULTS: AODgly and [La]max increased while pHmin decreased linearly with LBM and MO (r2 = 0.46 to 0.72, p < 0.001). Moreover, AODgly was positively correlated with [La]max (r2 = 0.75, p < 0.001) and negatively correlated with pHmin (r2 = 0.77, p < 0.001). When AODgly was scaled for LBM, the coefficients of the relationships with blood markers drastically decreased by three to four times ([La]max: r2 = 0.24, p = 0.002; pHmin: r2 = 0.30, p < 0.001). Furthermore, by scaling AODgly for LBM + MO, the correlation coefficients with blood markers became even lower ([La]max: r2 = 0.12, p = 0.037; pHmin: r2 = 0.18, p = 0.009). However, MO-related additional changes accounted much less than LBM for the relationships between AODgly and blood markers. CONCLUSION: The results challenge previous reports of maturation-related differences in glycolytic energy turnover and suggest that changes in lean body mass are a more powerful influence than maturity status on glycolytic metabolism during growth.
PURPOSE: The aim of the present study was to investigate (i) how glycolytic metabolism assessed by accumulated oxygen deficit (AODgly) and blood metabolic responses (lactate and pH) resulting from high-intensity exercise change during growth, and (ii) how lean body mass (LBM) influences AODgly and its relationship with blood markers. METHODS: Thirty-six 11- to 17-year olds performed a 60-s all-out test on a rowing ergometer. Allometric modelling was used to investigate the influence of LBM and LBM + maturity offset (MO) on AODgly and its relationship with the extreme post-exercise blood values of lactate ([La]max) and pH (pHmin) obtained during the recovery period. RESULTS: AODgly and [La]max increased while pHmin decreased linearly with LBM and MO (r2 = 0.46 to 0.72, p < 0.001). Moreover, AODgly was positively correlated with [La]max (r2 = 0.75, p < 0.001) and negatively correlated with pHmin (r2 = 0.77, p < 0.001). When AODgly was scaled for LBM, the coefficients of the relationships with blood markers drastically decreased by three to four times ([La]max: r2 = 0.24, p = 0.002; pHmin: r2 = 0.30, p < 0.001). Furthermore, by scaling AODgly for LBM + MO, the correlation coefficients with blood markers became even lower ([La]max: r2 = 0.12, p = 0.037; pHmin: r2 = 0.18, p = 0.009). However, MO-related additional changes accounted much less than LBM for the relationships between AODgly and blood markers. CONCLUSION: The results challenge previous reports of maturation-related differences in glycolytic energy turnover and suggest that changes in lean body mass are a more powerful influence than maturity status on glycolytic metabolism during growth.
Authors: Paulo Francisco de Almeida-Neto; Luiz Felipe Da Silva; Bianca Miarka; Jason Azevedo De Medeiros; Rafaela Catherine da Silva Cunha de Medeiros; Rafael Pereira Azevedo Teixeira; Felipe J Aidar; Breno Guilherme De Araujo Tinoco Cabral; Paulo Moreira Silva Dantas Journal: Front Physiol Date: 2022-05-17 Impact factor: 4.755