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Integration of metabolomics and proteomics to reveal the metabolic characteristics of high-intensity interval training.
Zhao, Jingjing; Wang, Yang; Zhao, Dan; Zhang, Lizhen; Chen, Peijie; Xu, Xin.
Afiliación
  • Zhao J; Shanghai anti-doping laboratory, Shanghai University of Sport, Changhai Road 399, Shanghai, 200438, China. chenpeijie@sus.edu.cn xxu2000@outlook.com.
Analyst ; 145(20): 6500-6510, 2020 Oct 21.
Article en En | MEDLINE | ID: mdl-32760941
ABSTRACT
High-intensity interval training (HIIT) can elicit a greater training stimulus to improve maximal aerobic capacity and is frequently applied in professional sports training. Although select metabolic pathways have been examined, the omics-scale molecular response to the HIIT has not been fully characterized. The longitudinal multi-omic profiling, including metabolome and proteome was performed on urine samples from 23 healthy young soccer players before and after the HIIT exercise. Metabolomics revealed the metabolomic changes during the HIIT, including steroid hormone metabolites, amino acid biosynthesis and relevant metabolites. Furthermore, changes in protein expression in metabolic pathways involved in energy metabolism, oxidative stress as well as immune pathways were found by proteomics, which provide the foundation for understanding metabolomic changes during the HIIT. There was a significant association between the HIIT and the urinary omic profiles, with the alteration in metabolic pathways associated with long-term adaptation to training. Future studies should focus on the validation of these findings and the development of metabolic models to monitor the training intensity and the adaptation in athletes.
Asunto(s)

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Entrenamiento de Intervalos de Alta Intensidad Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Analyst Año: 2020 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Entrenamiento de Intervalos de Alta Intensidad Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Analyst Año: 2020 Tipo del documento: Article