Myostatin Decreases with Aerobic Exercise and Associates with Insulin Resistance
Bibliographic record
Abstract
PURPOSE: There is mounting evidence that skeletal muscle produces and secretes biologically active proteins or "myokines" that facilitate metabolic cross talk between organ systems. The increased expression of myostatin, a secreted anabolic inhibitor of muscle growth and development, has been associated with obesity and insulin resistance. Despite these intriguing findings, there have been few studies linking myostatin and insulin resistance. METHODS: To explore this relationship in more detail, we quantified myostatin protein in muscle and plasma from 10 insulin-resistant, middle-aged (53.1 ± 5.5 yr) men before and after 6 months of moderate aerobic exercise training (1200 kcal·wk−¹ at 40%-55% VO2peak). To establish a cause-effect relationship, we also injected C57/Bl6 male mice with high physiological levels of recombinant myostatin protein. RESULTS: Myostatin protein levels were shown to decrease in muscle (37%, P = 0.042, n = 10) and matching plasma samples (from 28.7 ng·mL−¹ pretraining to 22.8 ng·mL−¹ posttraining, P = 0.003, n = 9) with aerobic exercise. Furthermore, the strong correlation between plasma myostatin levels and insulin sensitivity (R² = 0.82, P < 0.001, n = 9) suggested a cause-effect relationship that was subsequently confirmed by inducing insulin resistance in myostatin-injected mice. A modest increase (44%) in plasma myostatin levels was also associated with significant reductions in the insulin-stimulated phosphorylation of Akt (Thr308) in both muscle and liver of myostatin-treated animals. CONCLUSIONS: These findings indicate that both muscle and plasma myostatin protein levels are regulated by aerobic exercise and, furthermore, that myostatin is in the causal pathway of acquired insulin resistance with physical inactivity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".