Anterior cruciate ligament (ACL) injuries represent a highly relevant issue in both grassroots and professional sports, with a particularly high incidence in young and female populations. The objective of this project is to develop and validate a multiscale predictive algorithm for ACL injury risk in athletes from Villarreal CF (aged 10-45), integrating biomechanical, physiological, genetic, and gut microbiome biomarkers.
The study, with a prospective and longitudinal design (4 years), will include a cohort of 200-250 players from the academy and first team. The following assessments will be conducted: biomechanical analysis of jumps using force platforms (instrumented LESS), physiological monitoring through resting heart rate and nocturnal heart rate variability (HRV), genotyping from saliva samples, and characterization of the gut microbiome 16 rRNA sequencing. The systematic recording of training, exposures, and injuries will follow OSTRC criteria and will be supervised by the club's medical team.
The expected outcome is a multivariate predictive model, validated in a professional sports setting, capable of identifying individual risk profiles and generating a personalized score to guide preventive interventions (exercise, strength training, nutritional or probiotic strategies). This approach aims to reduce the incidence of ACL injuries, optimize performance, and translate biomedical knowledge into clinical and sports practice.