What are the potential mechanisms of fatigue-induced skeletal muscle hypertrophy with low-load resistance exercise training?
Bibliographic record
Abstract
High-load resistance exercise (>60% of 1-repetition maximum) is a well-known stimulus to enhance skeletal muscle hypertrophy with chronic training. However, studies have intriguingly shown that low-load resistance exercise training (RET) (≤60% of 1-repetition maximum) can lead to similar increases in skeletal muscle hypertrophy as compared with high-load RET. This has raised questions about the underlying mechanisms for eliciting the hypertrophic response with low-load RET. A key characteristic of low-load RET is performing resistance exercise to, or close to, task failure, thereby inducing muscle fatigue. The primary aim of this evidence-based narrative review is to explore whether muscle fatigue may act as an indirect or direct mechanism contributing to skeletal muscle hypertrophy during low-load RET. It has been proposed that muscle fatigue could indirectly stimulate muscle hypertrophy through increased muscle fiber recruitment, mechanical tension, ultrastructural muscle damage, the secretion of anabolic hormones, and/or alterations in the expression of specific proteins involved in muscle mass regulation (e.g., myostatin). Alternatively, it has been proposed that fatigue could directly stimulate muscle hypertrophy through the accumulation of metabolic by-products (e.g., lactate), and/or inflammation and oxidative stress. This review summarizes the existing literature eluding to the role of muscle fatigue as a stimulus for low-load RET-induced muscle hypertrophy and provides suggested avenues for future research to elucidate how muscle fatigue could mediate 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".