Effects of thermal interventions on skeletal muscle adaptations and regeneration: perspectives on epigenetics: a narrative review
Bibliographic record
Abstract
Recovery methods, such as thermal interventions, have been developed to promote optimal recovery and maximize long-term training adaptations. However, the beneficial effects of these recovery strategies remain a source of controversy. This narrative review aims to provide a detailed understanding of how cold and heat interventions impact long-term training adaptations. Emphasis is placed on skeletal muscle adaptations, particularly the involvement of signaling pathways regulating protein turnover, ribosome and mitochondrial biogenesis, as well as the critical role of satellite cells in promoting myofiber regeneration following atrophy. The current literature suggests that cold interventions can blunt molecular adaptations (e.g., protein synthesis and satellite cell activation) and oxi-inflammatory responses after resistance exercise, resulting in diminished exercise-induced hypertrophy and lower gains in isometric strength during training protocols. Conversely, heat interventions appear promising for mitigating skeletal muscle degradation during immobilization and atrophy. Indeed, heat treatments (e.g., passive interventions such as sauna-bathing or diathermy) can enhance protein turnover and improve the maintenance of muscle mass in atrophic conditions, although their effects on uninjured skeletal muscles in both humans and rodents remain controversial. Nonetheless, heat treatment may serve as an important tool for attenuating atrophy and preserving mitochondrial function in immobilized or injured athletes. Finally, the potential interplay between exercise, thermal interventions and epigenetics is discussed. Future studies must be encouraged to clarify how repeated thermal interventions (heat and cold) affect long-term exercise training adaptations and to determine the optimal modalities (i.e., method of application, temperature, duration, relative humidity, and timing).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".