Shortened telomeres and serum protein biomarker abnormalities in collision sport athletes regardless of concussion history and sex
Bibliographic record
Abstract
Mild brain injuries are frequent in athletes engaging in collision sports and have been linked to a range of long-term neurological abnormalities. There is a need to identify how these potential abnormalities manifest using objective measures; determine whether changes are due to concussive and/or sub-concussive injuries; and examine how biological sex affects outcomes. This study investigated cognitive, cellular, and molecular biomarkers in male and female amateur Australian footballers (i.e. Australia’s most participated collision sport). 95 Australian footballers (69 males, 26 females), both with and without a history of concussion, as well as 49 control athletes (28 males, 21 females) with no history of brain trauma or participation in collision sports were recruited to the study. Ocular motor assessment was used to examine cognitive function. Telomere length, a biomarker of cellular senescence and neurological health, was examined in saliva. Serum levels of tau, phosphorylated tau, neurofilament light chain, and 4-hydroxynonenal were used as markers to assess axonal injury and oxidative stress. Australian footballers had reduced telomere length (p = 0.031) and increased serum protein levels of 4-hydroxynonenal (p = 0.001), tau (p = 0.007), and phosphorylated tau (p = 0.036). These findings were independent of concussion history and sex. No significant ocular motor differences were found. Taken together, these findings suggest that engagement in collision sports, regardless of sex or a history of concussion, is associated with shortened telomeres, axonal injury, and oxidative stress. These saliva- and serum-based biomarkers may be useful to monitor neurological injury in collision sport athletes.
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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 | 0.001 | 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) | 0.002 | 0.000 |
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".