Biochemical impact of soccer: an analysis of hormonal, muscle damage, and redox markers during the season
Bibliographic record
Abstract
This study aimed to analyze changes in performance, muscle function, and stress-related biochemical markers in professional soccer players (n = 14) at 4 timepoints (3 for performance and 4 for stress-related biochemical markers) during the soccer season [Formula: see text] preseason (E1), midseason (E2), end of the season (E3) [Formula: see text] and after the end of the recovery period (E4). Performance in 5- and 30-m sprints, countermovement jump, and agility, and maximal isokinetic knee extension and knee flexion strength were measured (E1 to E3). We observed increased in-season levels of myoglobin (E2 > E1 and E4; p < 0.05), a higher testosterone/cortisol ratio (T/C), and increased levels of creatine kinase (CK), C-reactive protein, superoxide dismutase (SOD), protein sulfhydryls (-SH), and malondialdehyde (E2 and E3 > E1 and E4; p < 0.05). Lower cortisol concentrations (E3 < E1 and E4; p < 0.05) and glutathione reductase activity (E3 < E2 and E4; p < 0.05) were observed at the end of the season. T/C, CK, SOD, -SH, and malondialdehyde decreased during the off-season, and cortisol and glutathione reductase increased (E3 < E4; p < 0.05). Agility increased in E2 and E3 (p < 0.01). Significant correlations were found during the season between hormonal and muscle function parameters (r = 0.56-0.86; p < 0.05). In addition, in E2, significant associations were observed between match-accumulated time (MATE2; minutes played by each player during the competition period), performance, and hormonal and redox parameters (r = 0.456-0.615; p < 0.05). In conclusion, this study shows that soccer players face significant changes in biomarkers of physiologic strain (muscle damage and oxidative stress-related markers) during the season, but values return to normal during the off-season. Additionally, MAT influences physical, hormonal, and oxidative stress-related parameters in professional soccer players.
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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 | 0.000 |
| 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) | 0.001 | 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".