Nutritional Regulation of Muscle Stem Cells in Exercise and Disease: The Role of Protein and Amino Acid Dietary Supplementation
Bibliographic record
Abstract
Human skeletal muscle is a remarkedly plastic tissue that has a high capacity to adapt in response to various stimuli. These adaptations are due in part to the function of muscle-resident stem/progenitor cells. Skeletal muscle regeneration and adaptation is facilitated by the activation and expansion of muscle stem cells (MuSCs). MuSC fate is regulated by signals released from cells in their niche, such as fibro-adipogenic progenitors (FAPs), as well as a variety of non-cellular niche components. Sufficient dietary protein consumption is critical for maximizing skeletal muscle adaptation to exercise and maintaining skeletal muscle in disease; however, the role of dietary protein in altering MuSC and FAP responses to exercise in healthy populations and skeletal muscle disease states requires more research. The present review provides an overview of this emerging field and suggestions for future directions. The current literature suggests that in response to resistance exercise, protein supplementation has been shown to increase MuSC content and the MuSC response to acute exercise. Similarly, protein supplementation augments the increase in MuSC content following resistance training. Endurance exercise, conversely, is an area of research that is sparse with respect to the interaction of protein supplementation and exercise on muscle stem/progenitor cell fate. Initial evidence suggests that protein supplementation augments the early myogenic response to acute endurance exercise but does not enhance the MuSC response to endurance training. Resistance training increases the number of proliferating FAPs with no additional effect of protein supplementation. Future research should continue to focus on the nutritional regulation of skeletal muscle stem/progenitor cell fate paired with studies examining the effects of exercise on a variety of human populations.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".