The design of training programs for football players is not straightforward due to intra- and inter-individual variability that leads to different physiological responses under similar training loads.
To study the association between the external load, defined by variables obtained using electronic performance tracking systems (EPTS), and the urinary metabolome as a surrogate of the metabolic adaptation to training.
Urine metabolic and EPTS data from 80 professional football players collected in an observational longitudinal study were analyzed by ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry and assessed by partial least squares (PLS) regression.
PLS models identified steroid hormone metabolites, hypoxanthine metabolites, acetylated amino acids, intermediates in phenylalanine metabolism, tyrosine, tryptophan metabolites, and riboflavin among the most relevant variables associated with external load. Metabolic network analysis identified enriched pathways including steroid hormone biosynthesis and metabolism of tyrosine and tryptophan. The ratio of players showing a deviation from the PLS model of adaptation to exercise was higher among those who suffered a muscular lesion compared to those who did not.
There was a significant association between the external load and the urinary metabolic profile, with alteration of biochemical 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 identify professional football players at risk of developing muscular injuries.
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Al-Khelaifi, F., Diboun, I., Donati, F., Botrè, F., Alsayrafi, M., Georgakopoulos, C., et al. (2018). A pilot study comparing the metabolic profiles of elite-level athletes from different sporting disciplines. Sports Medicine - Open. https://doi.org/10.1186/s40798-017-0114-z.
Badawy, A. A.-B., Lake, S. L., & Dougherty, D. M. (2014). Mechanisms of the pellagragenic effect of leucine: Stimulation of hepatic tryptophan oxidation by administration of branched-chain amino acids to healthy human volunteers and the role of plasma free tryptophan and total kynurenines. International Journal of Tryptophan Research: IJTR,7, 23–32. https://doi.org/10.4137/IJTR.S18231.
Bastida Castillo, A., Gómez Carmona, C. D., De la Cruz Sánchez, E., & Pino Ortega, J. (2018). Accuracy, intra- and inter-unit reliability, and comparison between GPS and UWB-based position-tracking systems used for time-motion analyses in soccer. European Journal of Sport Science,18(4), 450–457. https://doi.org/10.1080/17461391.2018.1427796.
Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological),57(1), 289–300.
Bouchard, C., & Rankinen, T. (2001). Individual differences in response to regular physical activity. Medicine and Science in Sports and Exercise. https://doi.org/10.1097/00005768-200106001-00013.
Bourdon, P. C., Cardinale, M., Murray, A., Gastin, P., Kellmann, M., Varley, M. C., et al. (2017). Monitoring athlete training loads: Consensus statement. International Journal of Sports Physiology and Performance,12(Suppl 2), S2161–S2170. https://doi.org/10.1123/IJSPP.2017-0208.
Cervenka, I., Agudelo, L. Z., & Ruas, J. L. (2017). Kynurenines: Tryptophan’s metabolites in exercise, inflammation, and mental health. Science. https://doi.org/10.1126/science.aaf9794.
Chang, C.-C., & Lin, C.-J. (2011). LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST),2(3), 27. https://doi.org/10.1145/1961189.1961199.
Chong, I.-G., & Jun, C.-H. (2005). Performance of some variable selection methods when multicollinearity is present. Chemometrics and Intelligent Laboratory Systems,78(1), 103–112. https://doi.org/10.1016/j.chemolab.2004.12.011.
Chong, J., Soufan, O., Li, C., Caraus, I., Li, S., Bourque, G., et al. (2018). MetaboAnalyst 4.0: Towards more transparent and integrative metabolomics analysis. Nucleic Acids Research,46(W1), W486–W494. https://doi.org/10.1093/nar/gky310.
Duft, R. G., Castro, A., ChaconMikahil, M. P. T., Cavaglieri, C. R., Duft, R. G., Castro, A., et al. (2017). Metabolomics and exercise: Possibilities and perspectives. Motriz: Revista de Educação Física. https://doi.org/10.1590/s1980-6574201700020010.
Dunstan, R. H., Sparkes, D. L., Macdonald, M. M., De Jonge, X. J., Dascombe, B. J., Gottfries, J., et al. (2017). Diverse characteristics of the urinary excretion of amino acids in humans and the use of amino acid supplementation to reduce fatigue and sub-health in adults. Nutrition Journal. https://doi.org/10.1186/s12937-017-0240-y.
Enea, C., Seguin, F., Petitpas-Mulliez, J., Boildieu, N., Boisseau, N., Delpech, N., et al. (2010). (1)H NMR-based metabolomics approach for exploring urinary metabolome modifications after acute and chronic physical exercise. Analytical and Bioanalytical Chemistry,396(3), 1167–1176. https://doi.org/10.1007/s00216-009-3289-4.
Ferraro, E., Giammarioli, A. M., Chiandotto, S., Spoletini, I., & Rosano, G. (2014). Exercise-induced skeletal muscle remodeling and metabolic adaptation: Redox signaling and role of autophagy. Antioxidants & Redox Signaling,21(1), 154–176. https://doi.org/10.1089/ars.2013.5773.
Finaud, J., Lac, G., & Filaire, E. (2006). Oxidative stress: Relationship with exercise and training. Sports Medicine (Auckland, NZ),36(4), 327–358.
Gorostiaga, E. M., Navarro-Amézqueta, I., Calbet, J. A. L., Hellsten, Y., Cusso, R., Guerrero, M., et al. (2012). Energy metabolism during repeated sets of leg press exercise leading to failure or not. PLoS ONE,7(7), e40621. https://doi.org/10.1371/journal.pone.0040621.
Gromski, P. S., Muhamadali, H., Ellis, D. I., Xu, Y., Correa, E., Turner, M. L., et al. (2015). A tutorial review: Metabolomics and partial least squares-discriminant analysis – a marriage of convenience or a shotgun wedding. Analytica Chimica Acta,879, 10–23. https://doi.org/10.1016/j.aca.2015.02.012.
Hackney, A. C., & Walz, E. A. (2013). Hormonal adaptation and the stress of exercise training: The role of glucocorticoids. Trends in sport sciences,20(4), 165–171.
Heaney, L. M., Deighton, K., & Suzuki, T. (2017). Non-targeted metabolomics in sport and exercise science. Journal of Sports Sciences. https://doi.org/10.1080/02640414.2017.1305122.
Hutchins, P. D., Russell, J. D., & Coon, J. J. (2018). LipiDex: An integrated software package for high-confidence lipid identification. Cell Systems,6(5), 621–625.e5. https://doi.org/10.1016/j.cels.2018.03.011.
Impellizzeri, F. M., Marcora, S. M., & Coutts, A. J. (2019). Internal and external training load: 15 years on. International Journal of Sports Physiology and Performance,14(2), 270–273. https://doi.org/10.1123/ijspp.2018-0935.
Kanehisa, M., Goto, S., Sato, Y., Furumichi, M., & Tanabe, M. (2012). KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Research. https://doi.org/10.1093/nar/gkr988.
Kim, Y.-N., Choi, J. Y., & Cho, Y.-O. (2015). Regular moderate exercise training can alter the urinary excretion of thiamin and riboflavin. Nutrition Research and Practice,9(1), 43–48. https://doi.org/10.4162/nrp.2015.9.1.43.
Kuligowski, J., Sánchez-Illana, Á., Sanjuán-Herráez, D., Vento, M., & Quintás, G. (2015). Intra-batch effect correction in liquid chromatography-mass spectrometry using quality control samples and support vector regression (QC-SVRC). The Analyst,140(22), 7810–7817. https://doi.org/10.1039/c5an01638j.
Kupr, B., & Handschin, C. (2015). Complex coordination of cell plasticity by a PGC-1α-controlled transcriptional network in skeletal muscle. Frontiers in Physiology. https://doi.org/10.3389/fphys.2015.00325.
Li, S., Park, Y., Duraisingham, S., Strobel, F. H., Khan, N., Soltow, Q. A., et al. (2013). Predicting network activity from high throughput metabolomics. PLoS Computational Biology. https://doi.org/10.1371/journal.pcbi.1003123.
Manaf, F. A., Lawler, N. G., Peiffer, J. J., Maker, G. L., Boyce, M. C., Fairchild, T. J., et al. (2018). Characterizing the plasma metabolome during and following a maximal exercise cycling test. Journal of Applied Physiology,125(4), 1193–1203. https://doi.org/10.1152/japplphysiol.00499.2018.
McGlory, C., Devries, M. C., & Phillips, S. M. (2017). Skeletal muscle and resistance exercise training; the role of protein synthesis in recovery and remodeling. Journal of Applied Physiology,122(3), 541–548. https://doi.org/10.1152/japplphysiol.00613.2016.
Neal, C. M., Hunter, A. M., Brennan, L., O’Sullivan, A., Hamilton, D., De Vito, G., et al. (2013). Six weeks of a polarized training-intensity distribution leads to greater physiological and performance adaptations than a threshold model in trained cyclists. Journal of Applied Physiology,114(4), 461–471. https://doi.org/10.1152/japplphysiol.00652.2012.
Pechlivanis, A., Kostidis, S., Saraslanidis, P., Petridou, A., Tsalis, G., Mougios, V., et al. (2010). (1)H NMR-based metabonomic investigation of the effect of two different exercise sessions on the metabolic fingerprint of human urine. Journal of Proteome Research,9(12), 6405–6416. https://doi.org/10.1021/pr100684t.
Pechlivanis, A., Papaioannou, K. G., Tsalis, G., Saraslanidis, P., Mougios, V., & Theodoridis, G. A. (2015). Monitoring the response of the human urinary metabolome to brief maximal exercise by a combination of RP-UPLC-MS and (1)H NMR spectroscopy. Journal of Proteome Research,14(11), 4610–4622. https://doi.org/10.1021/acs.jproteome.5b00470.
Rubingh, C. M., Bijlsma, S., Derks, E. P. P. A., Bobeldijk, I., Verheij, E. R., Kochhar, S., et al. (2006). Assessing the performance of statistical validation tools for megavariate metabolomics data. Metabolomics,2(2), 53–61. https://doi.org/10.1007/s11306-006-0022-6.
Sánchez-Illana, Á., Pérez-Guaita, D., Cuesta-García, D., Sanjuan-Herráez, J. D., Vento, M., Ruiz-Cerdá, J. L., et al. (2018). Model selection for within-batch effect correction in UPLC-MS metabolomics using quality control - support vector regression. Analytica Chimica Acta. https://doi.org/10.1016/j.aca.2018.04.055.
Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R., & Siuzdak, G. (2006). XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Analytical Chemistry,78(3), 779–787. https://doi.org/10.1021/ac051437y.
Strasser, B., Geiger, D., Schauer, M., Gatterer, H., Burtscher, M., & Fuchs, D. (2016). Effects of exhaustive aerobic exercise on tryptophan-kynurenine metabolism in trained athletes. PLoS ONE,11(4), e0153617. https://doi.org/10.1371/journal.pone.0153617.
Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: A basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems,58(2), 109–130. https://doi.org/10.1016/S0169-7439(01)00155-1.
The authors acknowledge the dedicated support from the FCB Medical Service and Barça Innovation Hub (https://barcainnovationhub.com/).
Conflict of interest
The authors declare that they have no conflicts of interest.
This study was conducted as an observational longitudinal study at the FCB. Institutional board approval for the study was obtained from the Ethics Commission of the Consell Català de l’Esport (Code 03/2019/CEICEGC, Generalitat de Catalunya, Barcelona, Spain). All procedures involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent was obtained from all participants included in the study.
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Quintas, G., Reche, X., Sanjuan-Herráez, J.D. et al. Urine metabolomic analysis for monitoring internal load in professional football players. Metabolomics 16, 45 (2020). https://doi.org/10.1007/s11306-020-01668-0
- External load
- Internal load