702 MEP032 – Association of COL5A1 gene polymorphisms and knee ligament injuries in professional football (Soccer) players
Bibliographic record
Abstract
Background and Objective While investigating genetic risk factors aids in preventing knee injuries, limited evidence is available. This study aims to explore genetic factors for determining knee ligament injuries in professional soccer players. Study Design and Participants This study received approval from the Ethics Committee and included 94 professional soccer players (22.0 years). Genetic testing was conducted upon joining professional teams, examining players’ histories of ACL and MCL injuries, as well as subsequent injuries. Salivary DNA was collected using a DNA Genotek kit (ON, Canada), and TaqMan assays analyzed COL5A1 rs12722 C/T and rs10628678 AGGG/- (deletion) polymorphisms. Risk Assessment and Main Outcomes We assessed the impact of genetic variants on the risk of knee ligament injuries. Statistical analysis used SPSS version 26, with statistical significance at P-values <0.05. Results Among 94 players, 27 experienced knee ligament injuries. The rs12722 polymorphisms showed CC/CT/TT = 60/32/2 distribution, with CC variants trending toward higher injury risk (35%) but without statistical significance. For rs10628678 polymorphisms, frequencies were AGGG/AGGG = 24, AGGG/- = 53, and -/- = 17. Ligament injuries occurred in 8.3% of AGGG/AGGG, 35.8% of AGGG/-, and 41.2% of -/-, indicating higher injury frequency in AGGG/- or -/- variants compared to AGGG/AGGG (odds ratio [OR] = 6.5, 95% confidence interval [CI] = 1.4–21.9, P < 0.01, Fisher’s exact test). In logistic regression analysis, with knee ligament injury as the dependent variable and AGGG deletion/haploinsufficiency as the explanatory variable, while adjusting for age and the rs12722 variant, the OR was 6.0 (95% CI = 1.3–28.6, P < 0.05). Conclusions In the report by Alvarez-Romero and colleagues, only the COL5A1 rs10628678 -/- genotype was associated with ligament injuries in Japanese individuals, while it was not other ethnic groups. Unlike previous questionnaire-based surveys, this study, for the first time, revealed this association in actual clinical settings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".