Urine metabolomic analysis for monitoring internal load in professional football players

Abstract

Introduction

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.

Objective

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.

Methods

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.

Results

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.

Conclusions

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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References

  1. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  2. 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.

    CAS  Article  PubMed  Google Scholar 

  3. 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.

    Article  PubMed  Google Scholar 

  4. 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.

    Article  Google Scholar 

  5. 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.

    Article  PubMed  Google Scholar 

  6. 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.

    Article  PubMed  Google Scholar 

  7. 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.

    Article  PubMed  Google Scholar 

  8. 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.

    Article  Google Scholar 

  9. 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.

    CAS  Article  Google Scholar 

  10. 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.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  11. 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.

    Article  Google Scholar 

  12. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  13. 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.

    CAS  Article  PubMed  Google Scholar 

  14. 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.

    CAS  Article  Google Scholar 

  15. Finaud, J., Lac, G., & Filaire, E. (2006). Oxidative stress: Relationship with exercise and training. Sports Medicine (Auckland, NZ),36(4), 327–358.

    Article  Google Scholar 

  16. 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.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. 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.

    CAS  Article  PubMed  Google Scholar 

  18. 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.

    PubMed  PubMed Central  Google Scholar 

  19. 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.

    Article  PubMed  Google Scholar 

  20. 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.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. 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.

    Article  PubMed  Google Scholar 

  22. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  23. 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.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  24. 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.

    CAS  Article  PubMed  Google Scholar 

  25. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  26. 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.

    Article  PubMed  PubMed Central  Google Scholar 

  27. 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.

    CAS  Article  Google Scholar 

  28. 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.

    CAS  Article  PubMed  Google Scholar 

  29. 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.

    CAS  Article  PubMed  Google Scholar 

  30. 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.

    CAS  Article  PubMed  Google Scholar 

  31. 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.

    CAS  Article  PubMed  Google Scholar 

  32. 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.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  33. 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.

    Article  PubMed  Google Scholar 

  34. 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.

    CAS  Article  PubMed  Google Scholar 

  35. 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.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  36. 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.

    CAS  Article  Google Scholar 

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Acknowledgements

The authors acknowledge the dedicated support from the FCB Medical Service and Barça Innovation Hub (https://barcainnovationhub.com/).

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Affiliations

Authors

Contributions

GR and GQ designed the experiments. GR, XV and MM carried out the sample and reference data collection. JDS-H and GQ carried out the sample analysis. GQ and XR carried out the chemometric analysis. GQ and GR wrote the manuscript and made the figures. All authors critically revised, read, and approved the final version of manuscript.

Corresponding authors

Correspondence to Guillermo Quintas or Gil Rodas.

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Conflict of interest

The authors declare that they have no conflicts of interest.

Ethical approval

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.

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Informed consent was obtained from all participants included in the study.

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Cite this article

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

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Keywords

  • External load
  • Internal load
  • Training
  • Metabolomics
  • EPTS
  • Football
  • Sports