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Record W7117758853 · doi:10.1113/jp289684

Resistance training load does not determine resistance training‐induced hypertrophy across upper and lower limbs in healthy young males

2025· article· en· W7117758853 on OpenAlexafffund
Matthew Lees, Jonathan C. Mcleod, Robert W. Morton, Brad S. Currier, Matthew Fliss, Sean McKellar, Rajbir Sidhu, Ben N. Stansfield, Erin K. Webb, Chris McGlory, Jatin G. Burniston, Stuart M. Phillips

Bibliographic record

VenueThe Journal of Physiology · 2025
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsQueen's UniversityMcMaster University
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMuscle hypertrophyResistance trainingSkeletal muscleMyofibrilYoung adultMuscle massMuscle strengthEndogeny

Abstract

fetched live from OpenAlex

Resistance exercise training (RET) leads to marked interindividual heterogeneity in the hypertrophic response. Whether such heterogeneity is due to endogenous (i.e. inherent biological factors) or exogenous variables (i.e. external load) has not been firmly established. Twenty healthy young male participants completed thrice-weekly resistance exercise sessions for 10 weeks. Each participant had their legs and arms randomly assigned to perform unilateral bicep curls or knee extensions with either a higher (heavier) load (HL: 8-12 repetitions; ∼70%-80% of one-repetition maximum (1RM)) or, in the contralateral limb, lower load (LL: 20-25 repetitions at ∼30%-40% 1RM) for three sets to volitional fatigue during each session. Fat- and bone-free mass (dual-energy X-ray absorptiometry), muscle size (ultrasonography and muscle biopsies) and strength were measured pretraining and at 10 weeks. Skeletal muscle biopsies were obtained from the vastus lateralis, and we used ingested deuterated water to assess myofibrillar protein synthesis (MyoPS) at weeks 1 and 10 during training. Despite considerable interindividual variability in hypertrophic responses, we observed that muscle hypertrophy following RET was relatively well conserved within versus between subjects and was unaffected by load. Rates of MyoPS in weeks 1 and 10 of training were increased relative to rest (Week 1: Δ0.27 ± 0.11, P < 0.0001; Week 10: Δ0.10 ± 0.14%/d, P = 0.009); however, MyoPS was attenuated in week 10 versus week 1 (Δ0.16 ± 0.18%/d, P < 0.001). MyoPS rates were less heterogenous within versus between individuals. Variation in RET-induced muscle hypertrophy occurred independent of external load and was relatively well conserved (i.e., retention of the hypertrophic response) across different anatomical limbs within an individual. KEY POINTS: Considerable interindividual variability exists in resistance exercise training (RET)-induced muscle hypertrophy. However we observed that RET-induced muscle hypertrophy is relatively conserved within an individual (i.e. between the upper- and lower body) and is independent of external load when RET is performed to volitional fatigue. Changes in myofibrillar protein synthesis (MyoPS) rates are comparable with both higher and lower loads but are blunted following a period of RET despite progressive overload. There is negligible shared variance between RET-induced increases in muscle size and strength. Additionally, there are limited relationships between measures used to assess RET-induced muscle hypertrophy. We conclude that when effort is matched (i.e. working to volitional muscular fatigue), RET-induced hypertrophy is mediated to a far greater degree by inherent endogenous biological factors, which account for a large proportion of the heterogeneity between individuals.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.034
GPT teacher head0.315
Teacher spread0.281 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations3
Published2025
Admission routes2
Has abstractyes

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