A Systematic Review of the Genetic Predisposition to Injury in Football
Bibliographic record
Abstract
Abstract Purpose Synthesise genetic association studies investigating injury involving football players to identify which genetic variants have the most empirical evidence to date. Methods A comprehensive search of the PubMed, SPORTDiscus, and MEDLINE databases until March 11th 2022 identified 34 studies. Inclusion criteria: primary investigations, included football players, examined the association of a genetic variant with injury, and were published in English. Risk of bias was assessed using the Newcastle–Ottawa Scale. A narrative synthesis summarised results. Results There were 33 candidate gene studies and one genome-wide study, with 9642 participants across all studies (range = 43–1311; median = 227). Ninety-nine polymorphisms were assessed within 63 genes. Forty-one polymorphisms were associated with injury once. Three polymorphisms had their specific allelic associations with injury replicated twice in independent cohorts: ACTN3 (rs1815739) XX genotype was associated with an increased susceptibility to non-contact muscle injuries, ACAN (rs1516797) G allele was associated with increased susceptibility to anterior cruciate ligament (ACL) injuries, and VEGFA (rs2010963) CC genotype was associated with an increased susceptibility to ACL and ligament or tendon injuries. However, several methodological issues (e.g., small sample sizes, cohort heterogeneity, and population stratification) are prevalent that limit the reliability and external validity of findings. Conclusion At present, the evidence base supporting the integration of genetic information as a prognostic or diagnosis tool for injury risk in football is weak. Future participation of organisations in international consortia is suggested to combat the current methodological issues and subsequently improve clarity concerning the underlying genetic contribution to injury susceptibility.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".