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Record W4404995906 · doi:10.1113/jp287342

Menstrual cycle phase does not influence muscle protein synthesis or whole‐body myofibrillar proteolysis in response to resistance exercise

2024· article· en· W4404995906 on OpenAlexafffund
Lauren M. Colenso‐Semple, James McKendry, Changhyun Lim, Philip J. Atherton, Daniel J. Wilkinson, Kenneth Smith, Stuart M. Phillips

Bibliographic record

VenueThe Journal of Physiology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsUniversity of British ColumbiaMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsLuteal phaseMenstrual cycleFollicular phaseMyofibrilEndocrinologyInternal medicineMedicineProtein catabolismBiologyChemistryHormoneBiochemistry

Abstract

fetched live from OpenAlex

Abstract It has been hypothesised that skeletal muscle protein turnover is affected by menstrual cycle phase with a more anabolic environment during the follicular vs. the luteal phase. We assessed the influence of menstrual cycle phase on muscle protein synthesis and myofibrillar protein breakdown in response to 6 days of controlled resistance exercise in young females during peak oestrogen and peak progesterone, using stable isotopes, unbiased metabolomics and muscle biopsies. We used comprehensive menstrual cycle phase‐detection methods, including cycle tracking, blood samples and urinary test kits, to classify menstrual phases. Participants (n = 12) completed two 6 day study phases in a randomised order: late follicular phase and mid‐luteal phase. Participants performed unilateral resistance exercise in each menstrual cycle phase, exercising the contralateral leg in each phase in a counterbalanced manner. Follicular phase myofibrillar protein synthesis (MPS) rates were 1.33 ± 0.27% d−1 in the control leg and 1.52 ± 0.27% d−1 in the exercise leg. Luteal phase MPS was 1.28 ± 0.27% d−1 in the control leg and 1.46 ± 0.25% d−1 in the exercise leg. We observed a significant effect of exercise (P < 0.001) but no effect of cycle phase or interaction. There was no significant effect of menstrual cycle phase on whole‐body myofibrillar protein breakdown (P = 0.24). Using unbiased metabolomics, we observed no notable phase‐specific changes in circulating blood metabolites associated with any particular menstrual cycle phase. Fluctuations in endogenous ovarian hormones influenced neither MPS, nor MPB in response to resistance exercise. Skeletal muscle is not more anabolically responsive to resistance exercise in a particular menstrual cycle phase. image Key points It has been hypothesised that the follicular (peak oestrogen) vs. the luteal (peak progesterone) phase of the menstrual cycle is more advantageous for skeletal muscle anabolism in response to resistance exercise. Using best practice methods to assess menstrual cycle status, we measured integrated (over 6 days) muscle protein synthesis (MPS) and myofibrillar protein breakdown (MPB) following resistance exercise in females (n = 12) in their follicular and luteal phases. We observed the expected differences in oestrogen and progesterone concentrations that confirmed our participants’ menstrual cycle phase; however, there were no notable metabolic pathway differences, as measured using metabolomics, between cycle phases. We observed that resistance exercise stimulated MPS, but there was no effect of menstrual cycle phase on either resting or exercise‐stimulated MPS or MPB. Our data show no greater anabolic effect of resistance exercise in the follicular vs. the luteal phase of the menstrual cycle.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

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)0.0010.000

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 head0.007
GPT teacher head0.268
Teacher spread0.261 · 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 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

Citations24
Published2024
Admission routes2
Has abstractyes

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