Skeletal muscle growth to combat diabetes and obesity: the potential role of muscle‐secreted factors
Bibliographic record
Abstract
As the prevalence of obesity and metabolic disease continues to climb, the need for effective therapeutic interventions remains high. The growth of skeletal muscle (SkM) greatly influences systemic metabolism across the whole body, making this tissue an important therapeutic target to combat the rise of metabolic dysfunction. Transgenic rodent models of targeted SkM growth exhibit profound improvements in various remote tissues, including adipose tissue and the liver. It is currently unclear how selective stimulation of SkM growth alters the metabolism of distant tissues; however, evidence suggests that muscle-secreted factors may be involved. Here, we aim to provide basic biomedical researchers with a summary of the current knowledge regarding various muscle-secreted factors regulated by anabolic pathways and proteins in SkM, as well as their systemic metabolic effects, to implicate them in the whole-body metabolic effects of SkM growth. In this review, we also identify several knowledge gaps in this field, future directions of investigation, and implications for therapeutic interventions such as resistance exercise and pharmacology.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".