MétaCan
Menu
Back to cohort
Record W4416624896 · doi:10.1016/j.jshs.2025.101104

Load-induced human skeletal muscle hypertrophy: Mechanisms, myths, and misconceptions

2025· article· en· W4416624896 on OpenAlexafffund
Derrick W. Van Every, Matthew Lees, Jeff Nippard, Stuart M. Phillips

Bibliographic record

VenueJournal of sport and health science/Journal of Sport and Health Science · 2025
Typearticle
Languageen
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsBombardier (Canada)McMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchNational Institutes of HealthCanada Research ChairsNutriciaNestlé Health ScienceDairy Farmers of Canada
KeywordsMuscle hypertrophySkeletal muscleResistance trainingMuscle contractionMyocyteHormoneMuscle disease

Abstract

fetched live from OpenAlex

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 Despite this, several outdated or overstated concepts continue to persist, both in the scientific literature and in the practical application of resistance training coaching and program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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.065
GPT teacher head0.397
Teacher spread0.332 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueJournal of sport and health science/Journal of Sport and Health ScienceSame topicExercise and Physiological ResponsesFrench-language works237,207