An Evidence-Based Narrative Review of Mechanisms of Resistance Exercise–Induced Human Skeletal Muscle Hypertrophy
Bibliographic record
Abstract
Skeletal muscle plays a critical role in physical function and metabolic health. Muscle is a highly adaptable tissue that responds to resistance exercise (RE; loading) by hypertrophying, or during muscle disuse, RE mitigates muscle loss. Resistance exercise training (RET)-induced skeletal muscle hypertrophy is a product of external (e.g., RE programming, diet, some supplements) and internal variables (e.g., mechanotransduction, ribosomes, gene expression, satellite cells activity). RE is undeniably the most potent nonpharmacological external variable to stimulate the activation/suppression of internal variables linked to muscular hypertrophy or countering disuse-induced muscle loss. Here, we posit that despite considerable research on the impact of external variables on RET and hypertrophy, internal variables (i.e., inherent skeletal muscle biology) are dominant in regulating the extent of hypertrophy in response to external stimuli. Thus, identifying the key internal skeletal muscle-derived variables that mediate the translation of external RE variables will be pivotal to determining the most effective strategies for skeletal muscle hypertrophy in healthy persons. Such work will aid in enhancing function in clinical populations, slowing functional decline, and promoting physical mobility. We provide up-to-date, evidence-based perspectives of the mechanisms regulating RET-induced skeletal muscle hypertrophy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".