Expression and Function of Myostatin in Obesity, Diabetes, and Exercise Adaptation
Bibliographic record
Abstract
Myostatin is a member of the transforming growth factor-β/bone morphogenetic protein (TGF-β/BMP) superfamily of secreted factors that functions as a potent inhibitor of skeletal muscle growth. Moreover, considerable evidence has accumulated that myostatin also regulates metabolism and that its inhibition can significantly attenuate the progression of obesity and diabetes. Although at least part of these effects on metabolism can be attributable to myostatin's influence over skeletal muscle growth and therefore on the total volume of metabolically active lean body mass, there is mounting evidence that myostatin affects the growth and metabolic state of other tissues, including the adipose and the liver. In addition, recent work has explored the role of myostatin in substrate mobilization, uptake, and/or utilization of muscle independent of its effects on body composition. Finally, the effects of both endurance and resistance exercise on myostatin expression, as well as the potential role of myostatin in the beneficial metabolic adaptations occurring in response to exercise, have also begun to be delineated in greater detail. The purpose of this review was to summarize the work to date on the expression and function of myostatin in obesity, diabetes, and exercise adaptation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".