Intramuscular mechanisms of overtraining
Bibliographic record
Abstract
Strenuous exercise is a potent stimulus to induce beneficial skeletal muscle adaptations, ranging from increased endurance due to mitochondrial biogenesis and angiogenesis, to increased strength from hypertrophy. While exercise is necessary to trigger and stimulate muscle adaptations, the post-exercise recovery period is equally critical in providing sufficient time for metabolic and structural adaptations to occur within skeletal muscle. These cyclical periods between exhausting exercise and recovery form the basis of any effective exercise training prescription to improve muscle endurance and strength. However, imbalance between the fatigue induced from intense training/competitions, and inadequate post-exercise/competition recovery periods can lead to a decline in physical performance. In fact, prolonged periods of this imbalance may eventually lead to extended periods of performance impairment, referred to as the state of overreaching that may progress into overtraining syndrome (OTS). OTS may have devastating implications on an athlete's career and the purpose of this review is to discuss potential underlying mechanisms that may contribute to exercise-induced OTS in skeletal muscle. First, we discuss the conditions that lead to OTS, and their potential contributions to impaired skeletal muscle function. Then we assess the evidence to support or refute the major proposed mechanisms underlying skeletal muscle weakness in OTS: 1) glycogen depletion hypothesis, 2) muscle damage hypothesis, 3) inflammation hypothesis, and 4) the oxidative stress hypothesis. Current data implicates reactive oxygen and nitrogen species (ROS) and inflammatory pathways as the most likely mechanisms contributing to OTS in skeletal muscle. Finally, we allude to potential interventions that can mitigate OTS in skeletal muscle.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".