Construction of a risk assessment and prediction model for athlete doping use based on bioinformatics

  • Lu Zhang Institute of Physical Education, Baise University, Baise, Guangxi 533000, China
  • Hongtao Tian Institute of Physical Education, Baise University, Baise, Guangxi 533000, China
Keywords: doping prediction; bioinformatics; biomechanical performance; risk assessment; athlete behavior; genetic profiling; machine learning; sports integrity
Article ID: 446

Abstract

A suitable approach to identifying doping behavior among athletes is to use advanced techniques. Bioinformatics can analyze large biological databases. It has potential approaches for mapping out decision models. Doping substances can severely distort an athlete’s biomechanical performance. For example, stimulants may enhance short-term power output but disrupt the natural rhythm and coordination of muscle contractions, leading to imbalanced forces and increased risk of musculoskeletal injuries. This abnormal biomechanical loading can affect joint stability and movement efficiency. n training, doping gives a false impression of enhanced capacity. Athletes might overtrain, ignoring proper recovery periods. Their bodies, under the influence of doping, can’t follow the normal adaptive process of training, leading to a breakdown in the physiological systems. Recovery is also hampered. Doping can disrupt the body’s hormonal and metabolic balance, slowing down tissue repair and regeneration. Genetic predispositions, which might make an athlete more receptive to doping’s effects, along with lower recovery rates and high competitive stress levels, are identified as key doping risk factors. Bioinformatics collects multi-source data like genomic profiles, hormone levels, and metabolic markers. Advanced tools analyze these to expose patterns and correlations related to doping risks. Machine learning trains a prediction model using historical doping data and biological signatures. Validated via simulations and real-world tests, it predicts doping risks. Sports authorities can use the resulting risk matrix to detect potential dopers early, promoting clean sports.

References

1. Yan, J., & Bai, J. (2023). Reveal key genes and factors affecting athletes’ performance in endurance sports using bioinformatic technologies. BMC Genomic Data, 24(1), 10.

2. Houlihan, B., Hanstad, D. V., Loland, S., & Waddington, I. (2019). The World Anti-Doping Agency at 20: progress and challenges. International Journal of Sport Policy and Politics, 11(2), 193–201. https://doi.org/10.1080/19406940.2019.1617765

3. Sutehall, S., Malinsky, F., Voss, S., Chester, N., Xu, X., & Pitsiladis, Y. (2024). Practical steps to develop a transcriptomic test for blood doping. Translational Exercise Biomedicine, 1(2), 105–110.

4. Karanikolou, A. (2023). A transcriptomic approach to discovering novel biomarkers of blood doping and training (Doctoral dissertation, University of Brighton).

5. Lee, S., Park, J., Yoon, J., & Lee, J. (2023). A Validation Study of a Deep Learning-Based Doping Drug Text Recognition System to Ensure Safe Drug Use among Athletes. Healthcare, 11(12), 1769. https://doi.org/10.3390/healthcare11121769

6. Ao, Y., Li, H., Zhu, L., Ali, S., & Yang, Z. (2018). The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling. Journal of Petroleum Science and Engineering, 174, 776–789. https://doi.org/10.1016/j.petrol.2018.11.067

7. Wahi, A., Nagpal, R., Verma, S., Narula, A., Tonk, R. K., & Kumar, S. (2023). A comprehensive review of current analytical approaches used for the control of drug abuse in sports. Microchemical Journal, 191, 108834. https://doi.org/10.1016/j.microc.2023.108834

8. Acharjee, A., Larkman, J., Xu, Y., Cardoso, V. R., & Gkoutos, G. V. (2020). A random forest-based biomarker discovery and power analysis framework for diagnostics research. BMC Medical Genomics, 13(1). https://doi.org/10.1186/s12920-020-00826-6

9. World Anti-Doping Agency. (2022). The prohibited list. World Anti-Doping Agency. https://www.wada-ama.org/en/prohibited-list

10. Cadwallader, A. B., De La Torre, X., Tieri, A., & Botrè, F. (2019). The abuse of diuretics as performance‐enhancing drugs and masking agents in sport doping: pharmacology, toxicology and analysis. British Journal of Pharmacology, 161(1), 1–16. https://doi.org/10.1111/j.1476-5381.2010.00789.x

11. Kern, C., Klausch, T., & Kreuter, F. (2019, April 4). Tree-based machine learning methods for survey research. PubMed Central (PMC). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7425836/#:~:text=Random%20forests%20(Breiman%202001)%20represent,algorithm%20for%20growing%20individual%20trees.&text=Instead%20of%20building%20only%20one,trees%20into%20a%20robust%20ensemble

12. Mahawan, T., Luckett, T., Iza, A. M., Pornputtapong, N., & Gutiérrez, E. C. (2024). Robust and consistent biomarker candidate identification by a machine learning approach applied to pancreatic ductal adenocarcinoma metastasis. BMC Medical Informatics and Decision Making, 24(S4). https://doi.org/10.1186/s12911-024-02578-0

13. Matijašević, T., Antić, T., & Capuder, T. (2022). A systematic review of machine learning applications in the operation of smart distribution systems. Energy Reports, 8, 12379–12407. https://doi.org/10.1016/j.egyr.2022.09.068

14. Mareck, U., Geyer, H., Fußhöller, G., Schwenke, A., Haenelt, N., Piper, T., Thevis, M., & Schänzer, W. (2020). Reporting and managing elevated testosterone/epitestosterone ratios- Novice aspects after five years of experience. Drug Testing and Analysis, 2(11–12), 637–642. https://doi.org/10.1002/dta.234

15. Liu, D. (2024). Design of data mining system for sports training biochemical indicators based on artificial intelligence and association rules. International Journal of Data Mining and Bioinformatics, 28(3-4), 236–256.

16. Heuberger, J. a. a. C., Tervaert, J. M. C., Schepers, F. M. L., Vliegenthart, A. D. B., Rotmans, J. I., Daniels, J. M. A., Burggraaf, J., & Cohen, A. F. (2019). Erythropoietin is doping in cycling: lack of evidence for efficacy and a negative risk-benefit. British Journal of Clinical Pharmacology, 75(6), 1406–1421. https://doi.org/10.1111/bcp.12034

17. Ji, X., Li, Q., Liu, Z., Wu, W., Zhang, C., Sui, H., & Chen, M. (2024). Identification of Active Components for Sports Supplements: Machine Learning-Driven Classification and Cell-Based Validation. ACS omega, 9(10), 11347-11355.

18. Arioli, F., Gamberini, M. C., Pavlovic, R., Di Cesare, F., Draghi, S., Bussei, G., Mungiguerra, F., Casati, A., & Fidani, M. (2022). Testing cortisol and its metabolites in human urine by LC-MSn: applications in clinical diagnosis and anti-doping control. Analytical and Bioanalytical Chemistry, 414(23), 6841–6853. https://doi.org/10.1007/s00216-022-04249-3

19. Kvillemo, P., Strandberg, A. K., Elgán, T. H., & Gripenberg, J. (2022). Facilitators and barriers preventing doping among recreational athletes: A qualitative interview study among police officers. Frontiers in Public Health, 10. https://doi.org/10.3389/fpubh.2022.1017801

20. Andersen, A. B., Nordsborg, N. B., Bonne, T. C., & Bejder, J. (2022). Contemporary blood doping—Performance, mechanism, and detection. Scandinavian Journal of Medicine and Science in Sports, 34(1). https://doi.org/10.1111/sms.14243

21. Deventer, K., Pozo, O., Van Eenoo, P., & Delbeke, F. (2019). Qualitative detection of diuretics and acidic metabolites of other doping agents in human urine by high-performance liquid chromatography-tandem mass spectrometry. Journal of Chromatography A, 1216(31), 5819–5827. https://doi.org/10.1016/j.chroma.2009.06.003

22. Herzog W, Schappacher-Tilp G. Molecular mechanisms of muscle contraction: A historical perspective. J Biomech. 2023;155:111659. doi:10.1016/j.jbiomech.2023.111659

23. Henning A, McLean K, Andreasson J, Dimeo P. Risk and enabling environments in sport: Systematic doping as harm reduction. Int J Drug Policy. 2021;91:102897. doi:10.1016/j.drugpo.2020.102897

24. Smith ACT, Stavros C, Westberg K. Cognitive Enhancing Drugs in Sport: Current and Future Concerns. Subst Use Misuse. 2020;55(12):2064–2075. doi:10.1080/10826084.2020.1775652

25. Wilke J, Groneberg DA. Neurocognitive function and musculoskeletal injury risk in sports:A systematic review. J Sci Med Sport. 2022;25(1):41–45. doi:10.1016/j.jsams.2021.07.002

Published
2024-12-26
How to Cite
Zhang, L., & Tian, H. (2024). Construction of a risk assessment and prediction model for athlete doping use based on bioinformatics. Molecular & Cellular Biomechanics, 21(4), 446. https://doi.org/10.62617/mcb446
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Article