Altered Blood Biomarker Profiles in Athletes with a History of Repetitive Head Impacts
Bibliographic record
Abstract
The long-term health effects of concussion and sub-concussive impacts in sport are unknown. Growing evidence suggests both inflammation and neurodegeneration are pivotal to secondary injury processes and the etiology of neurodegenerative diseases. In the present study we characterized circulating brain injury and inflammatory mediators in healthy male and female athletes according to concussion history and collision sport participation. Eighty-seven university level athletes (male, n = 60; female, n = 27) were recruited before the start of the competitive season. Athletes were healthy at the time of the study (no medications, illness, concussion or musculoskeletal injuries). Dependent variables included 29 inflammatory and 10 neurological injury analytes assessed in the peripheral blood by immunoassay. Biomarkers were statistically evaluated using partial least squares multivariate analysis to identify possible relationships to self-reported previous concussion history, number of previous concussions and collision sport participation in male and female athletes. Multiple concussions were associated with increases in peripheral MCP-1 in females, and MCP-4 in males. Collision sport participation was associated with increases in tau levels in males. These results are consistent with previous experimental and clinical findings that suggest ongoing inflammatory and cerebral injury processes after repetitive mild head trauma. However, further validation is needed to correlate systemic biomarkers to repetitive brain impacts, as opposed to the extracranial effects common to an athletic population such as exercise and muscle damage.
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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) | 0.001 | 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".