Injuries in professional sports have a significant impact on performance and also imply an economic cost for the teams that lose a player. One study has estimated that in the first half of the 2018-2019 season, the Premier League spent around £130 million on the salaries of injured players (JLT Specialty Limited, 2019). In an attempt to reduce injury incidence, new research is being conducted to identify the main risk factors.
A recent research study (Rodas et al., 2019) was carried out with the support of Progenika (GRIFOLS) which involved, among others, Gil Rodas and Ricard Pruna, doctors at FC Barcelona, and as well renowned researchers such as Alejandro Lucía. They analysed the association between the risk of tendinopathy and the variations in the genetic sequence (polymorphism) in team sports players. To do this, they extracted blood samples from 363 professional players from different sports areas of the club (football, basketball, handball, roller hockey and futsal) during the 2018-2019 pre-season, and analysed the incidence of tendinopathy in the previous ten years.
The researchers used the latest advances in statistical methodology and machine learning to identify complex patterns in high-dimensional environments, under the premise that “a prediction of complex features requires the use of a large number of DNA variants”. Training these highly parameterised methods requires large data sets, so a GWAS (genome-wide association study) was used to examine the association between 495,837 polymorphisms and the risk of tendinopathy. In other words, the researchers analysed the genome and looked for genetic markers that could be related to the injury. They then increased the set of polymorphisms to 1,419,69 by attributing synthetic variants using machine learning to create a predictive model. Although previous analysis has found an association with some of the genetic markers described above, this particular study identified two new genetic markers which were associated with an increase in the risk of tendinopathy (GJA1 rs11154027, and VAT1L rs4362400), while a third, CNTP2 rs1026302, was associated with a protective effect.
“Now we need to replicate our results in other clubs to increase the sample size to more than a 1,000 or 10,000 football players or team sports players.“ The opportunity to use cutting-edge data processing tools and to access a greater number of biomedical parameters (including hundreds of thousands of genotypes linked to phenotypes) will allow for unprecedented improvements in the field of preventive and personalised medicine”, says Gil Rodas.
At the same time, “future research projects should consider the role of epigenetics in the phenotypes of athletes. Different factors such as the diet, stress or training load can affect their genetic response”, comments Ricard Pruna.
This study is a valuable starting point in the processing of biomedical data to establish injury risk factors. However, it is clear that this must be a collaborative effort between different institutions in order to broaden the sample of participants and create more robust models that can be transferred to clinical practice. In this respect, emphasis must be placed on safeguarding the data protection law, as Gil Rodas explains, “the correct and rigorous use of it has been almost as challenging as the genetic and epidemiological analysis itself”.
The Barça Innovation Hub team
JLT Specialty Limited. (2019). Football injury analysis. (https://www.jlt.com/en-ca/industry/sports-media-and-entertainment-insurance/insights/premier-league-injury-analysis-2019)
Rodas, G., Osada, L., Arteta, D., Pruna, R., Fernández, D., & Lucía, A. (2019). Genomic Prediction of Tendinopathy Risk in Elite Team Sports. International Journal of Sports Physiology and Performance.