Myostatin Is a Skeletal Muscle Target of Growth Hormone Anabolic Action
Bibliographic record
Abstract
Myostatin is a cytokine that has recently been shown to selectively and potently inhibit myogenesis. To investigate the mechanisms of anabolic actions of GH on skeletal muscle growth, we examined the in vitro and in vivo effects of GH on myostatin regulation. Twelve GH-deficient hypopituitary adult subjects were treated with recombinant GH (5 micro g/kg.d) in a double-blind, placebo-controlled fashion. Body composition and physical function were assessed and skeletal muscle biopsies from the vastus lateralis performed at 6-monthly intervals during 18 months of treatment. Myostatin mRNA expression was significantly inhibited to 31 +/- 9% (P < 0.001) of control by GH but not by placebo administration (79 +/- 11%) as determined by quantitative real-time PCR normalized for the housekeeping glyceraldehyde-3-phosphate dehydrogenase gene. The inhibitory effect of GH on myostatin was sustained after 12 and 18 months of GH treatment. These effects were associated with increases in lean body mass and translated into enhanced aerobic performance as determined by maximal oxygen uptake and ventilation threshold. Parallel in vitro studies of skeletal muscle cells demonstrated significant reduction of myostatin expression by myotubes in response to GH, compared with vehicle treatment. Conversely, GH receptor antagonism resulted in up-regulation of myostatin in myoblasts. Given the potent catabolic actions of myostatin, our data suggest that myostatin represents a potential key target for GH-induced anabolism.
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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) | 0.001 | 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".