Nuclear‐localized androgen receptor content following resistance exercise training is associated with hypertrophy in males but not females
Bibliographic record
Abstract
Androgen receptor (AR) content has been implicated in the differential response between high and low responders following resistance exercise training (RET). However, the influence of AR expression on acute skeletal muscle damage and whether it may influence the adaptive response to RET in females is poorly understood. Thus, the purpose of this exploratory examination was to 1) investigate changes in AR content during skeletal muscle repair and 2) characterize AR-mediated sex-based differences following RET. A skeletal muscle biopsy from the vastus lateralis was obtained from 26 healthy young men (n = 13) and women (n = 13) at baseline and following 300 eccentric kicks. Subsequently, participants performed 10 weeks of full-body RET and a final muscle biopsy was collected. In the untrained state, AR mRNA expression was associated with paired box protein-7 (PAX7) mRNA in males. For the first time in human skeletal muscle, we quantified AR content in the myofiber and localized to the nucleus where AR has been shown to trigger cellular outcomes related to growth. Upon eccentric damage, nuclear-associated AR (nAR) content increased (p < .05) in males and not females. Males with the greatest increase in cross-sectional area (CSA) post-RET had more (p < .05) nAR content than females with the greatest gain CSA. Collectively, skeletal muscle damage and RET increased AR protein, and both gene and hypertrophy measures revealed sex differences in relation to AR. These findings suggest that AR content but more importantly, nuclear localization, is a factor that differentiates RET-induced hypertrophy between males and females.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".