Load-induced human skeletal muscle hypertrophy: Mechanisms, myths, and misconceptions
Bibliographic record
Abstract
Mechanical tension is widely recognized as the primary stimulus underlying the molecular mechanisms that influence muscle hypertrophy induced by resistance training. Despite this, several outdated or overstated concepts continue to persist, both in the scientific literature and in the practical application of resistance training coaching and program design. Claims that acute hormonal responses, metabolic stress, cell swelling or "the pump" meaningfully contribute to hypertrophy are not supported by scientific evidence. Additionally, the concept of sarcoplasmic hypertrophy as a distinct and functionally meaningful contributor to hypertrophy lacks strong evidence. In this review, we critically evaluate several persistent misconceptions and contrast them with evidence-based mechanistic insights into load-induced hypertrophy. Specifically, we discuss the role (or lack thereof) of systemic hormones, metabolites, and cell swelling in promoting muscle hypertrophy. We also critically review the concept of sarcoplasmic hypertrophy and propose that it is not a meaningful contributor to muscle hypertrophy. Lastly, to translate knowledge for trainees and coaches, we discuss the upper limit of muscle hypertrophy and provide readers with evidence-based, reasonable expectations for muscle hypertrophy. We aimed, through this review, to use scientific evidence to enhance our understanding of what drives muscle hypertrophy and provide an evidence-based framework for resistance exercise training.
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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 | 0.018 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.016 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.001 | 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".