Resistance exercise‐induced increases in putative anabolic hormones do not enhance muscle protein synthesis or intracellular signalling in young men
Bibliographic record
Abstract
We aimed to determine whether exercise-induced elevations in systemic concentration of testosterone, growth hormone (GH) and insulin-like growth factor-1 (IGF-1) enhanced post-exercise myofibrillar protein synthesis (MPS) and phosphorylation of signalling proteins important in regulating mRNA translation. Eight young men (20 +/- 1.1 years, BMI = 26 +/- 3.5 kg m(-2)) completed two exercise protocols designed to maintain basal hormone concentrations (low hormone, LH) or elicit increases in endogenous hormones (high hormone, HH). In the LH protocol, participants performed a bout of unilateral resistance exercise with the elbow flexors. The HH protocol consisted of the same elbow flexor exercise with the contralateral arm followed immediately by high-volume leg resistance exercise. Participants consumed 25 g of protein after arm exercise to maximize MPS. Muscle biopsies and blood samples were taken as appropriate. There were no changes in serum testosterone, GH or IGF-1 after the LH protocol, whereas there were marked elevations after HH (testosterone, P < 0.001; GH, P < 0.001; IGF-1, P < 0.05). Exercise stimulated a rise in MPS in the biceps brachii (rest = 0.040 +/- 0.007, LH = 0.071 +/- 0.008, HH = 0.064 +/- 0.014% h(-1); P < 0.05) with no effect of elevated hormones (P = 0.72). Phosphorylation of the 70 kDa S6 protein kinase (p70S6K) also increased post-exercise (P < 0.05) with no differences between conditions. We conclude that the transient increases in endogenous purportedly anabolic hormones do not enhance fed-state anabolic signalling or MPS following resistance exercise. Local mechanisms are likely to be of predominant importance for the post-exercise increase in MPS.
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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".