A targeted metabolic analysis of football players and its association to player load: Comparison between women and men profiles

Abstract

Professional athletes undertake a variety of training programs to enhance their physical performance, technical-tactical skills, while protecting their health and well-being. Regular exercise induces widespread changes in the whole body in an extremely complex network of signaling, and evidence indicates that phenotypical sex differences influence the physiological adaptations to player load of professional athletes. Despite that there remains an underrepresentation of women in clinical studies in sports, including football. The objectives of this study were twofold: to study the association between the external load (EPTS) and urinary metabolites as a surrogate of the adaptation to training, and to assess the effect of sex on the physiological adaptations to player load in professional football players. Targeted metabolic analysis of aminoacids, and tryptophan and phenylalanine metabolites detected progressive changes in the urinary metabolome associated with the external training load in men and women's football teams. Overrepresentation analysis and multivariate analysis of metabolic data showed significant differences of the effect of training on the metabolic profiles in the men and women teams analyzed. Collectively, our results demonstrate that the development of metabolic models of adaptation in professional football players can benefit from the separate analysis of women and men teams, providing more accurate insights into how adaptation to the external load is related to changes in the metabolic phenotypes. Furthermore, results support the use of metabolomics to understand changes in specific metabolic pathways provoked by the training process.

Keywords: EPTS; load analysis; metabolomics; training; women sports.

FIGURE 1

Scheme of the study.

FIGURE 2

Principal component analysis (PCA) of metabolomic data. (A) Scores plots of metabolomic data (male and female data sets). (B) Scores plots of metabolic data at each sample collection point.

FIGURE 3

Partial Least Squares (PLS) analysis. (A) cross-validation (CV) predicted values in the female and male teams. (B) Plot of the PLS regression (left) and VIP score (right) vectors in the PLS models built for the female and male teams.