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Can Haematological and Hormonal Biomarkers Predict Fitness Parameters in Youth Soccer Players? A Pilot Study

The study aimed to investigate the correlations among immune, haematological, endocrinological markers and fitness parameters, and assess if the physiological parameters could be a predictor of fitness values. Anthropometric, physical evaluations (countermovement jump—CMJ, 10 m sprint, VO(2)max, rep...

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Detalles Bibliográficos
Autores principales: Perroni, Fabrizio, Migliaccio, Silvia, Borrione, Paolo, Vetrano, Mario, Amatori, Stefano, Sisti, Davide, Rocchi, Marco B. L., Salerno, Gerardo, Vescovo, Riccardo Del, Cavarretta, Elena, Guidetti, Laura, Baldari, Carlo, Visco, Vincenzo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7503944/
https://www.ncbi.nlm.nih.gov/pubmed/32872427
http://dx.doi.org/10.3390/ijerph17176294
Descripción
Sumario:The study aimed to investigate the correlations among immune, haematological, endocrinological markers and fitness parameters, and assess if the physiological parameters could be a predictor of fitness values. Anthropometric, physical evaluations (countermovement jump—CMJ, 10 m sprint, VO(2)max, repeated sprint ability—RSA total time and index) and determination of blood (IL-6, IL-10, IL-17A and tumour necrosis factor) and salivary (testosterone and cortisol) samples parameters in 28 young male soccer players (age: 13.0 ± 0.2 years, body mass index (BMI): 19.5 ± 2.2 kg/m(2)) were analysed. To evaluate the dependence of the variables related to athletic performance, multiple linear regression with backward stepwise elimination was considered. A significant regression equation was found in CMJ (F((5,16)) = 9.86, p < 0.001, R(2) adjusted = 0.679) and in the RSA index (F((5,16)) = 15.39, p < 0.001, R(2) adjusted = 0.774) considering only five variables, in a 10 m sprint (F((4,17)) = 20.25, p < 0.001, R(2) adjusted = 0.786) and in the RSA total time (F((4,17)) = 15.31, p < 0.001, R(2) adjusted = 0.732) considering only four variables and in VO(2)max (F((9,12)) = 32.09, p < 0.001, R(2) adjusted = 0.930) considering nine variables. Our study suggests the use of regression equations to predict the fitness values of youth soccer players by blood and saliva samples, during different phases of the season, short periods of match congestion or recovery from an injury.