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Record W4409699523 · doi:10.1186/s13293-025-00707-6

The role of androgens and global and tissue-specific androgen receptor expression on body composition, exercise adaptation, and performance

2025· review· en· W4409699523 on OpenAlexafffund
Sabrina Tzivia Barsky, D. Ashley Monks

Bibliographic record

VenueBiology of Sex Differences · 2025
Typereview
Languageen
FieldMedicine
TopicHormonal and reproductive studies
Canadian institutionsResearch CanadaUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAndrogen receptorEndocrinologyInternal medicineAndrogenSkeletal muscleAdipose tissueTestosterone (patch)AnabolismBiologyMedicineHormone

Abstract

fetched live from OpenAlex

Gonadal testosterone stimulates skeletal muscle anabolism and contributes to sexually differentiated adipose distribution through incompletely understood mechanisms. Observations in humans and animal models have indicated a major role for androgen receptor (AR) in mediating sex differences in body composition throughout the lifespan. Traditional surgical, genetic and pharmacological studies have tested systemic actions of circulating androgens, and more recent transgenic approaches have allowed for tests of AR gene function in specific androgen responsive niches contributing to body composition, including: skeletal muscle and surrounding interstitial cells, white and brown adipose, as well as trabecular and cortical bone. Less well understood is how these functions of gonadal androgens interact with exercise. Here, we summarize the understood mechanisms of action of AR and its interactions with exercise, specifically on outcomes of body composition and muscle function, and the global- and tissue-specific role of AR in regulating skeletal muscle, adipose, and bone morphology. Additionally, we describe the known effects of androgen and AR manipulation on female body composition, muscle morphology, and sport performance, while highlighting a need for greater inclusion of female subjects in human and animal muscle physiology and endocrinology research.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.032
GPT teacher head0.299
Teacher spread0.267 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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

Citations14
Published2025
Admission routes2
Has abstractyes

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