Creatine Supplementation and Skeletal Muscle Metabolism for Building Muscle Mass- Review of the Potential Mechanisms of Action
Bibliographic record
Abstract
Creatine, a very popular supplement among athletic populations, is of growing interest for clinical applications. Since over 90% of creatine is stored in skeletal muscle, the effect of creatine supplementation on muscle metabolism is a widely studied area. While numerous studies over the past few decades have shown that creatine supplementation has many favorable effects on skeletal muscle physiology and metabolism, including enhancing muscle mass (growth/hypertrophy); the underlying mechanisms are poorly understood. This report reviews studies addressing the mechanisms of action of creatine supplementation on skeletal muscle growth/hypertrophy. Early research proposed that the osmotic effect of creatine supplementation serves as a cellular stressor (osmosensing) that acts as an anabolic stimulus for protein synthesis signal pathways. Other reports indicated that creatine directly affects muscle protein synthesis via modulations of components in the mammalian target of rapamycin (mTOR) pathway. Creatine may also directly affect the myogenic process (formation of muscle tissue), by altering secretions of myokines, such as myostatin and insulin-like growth factor-1, and expressions of myogenic regulatory factors, resulting in enhanced satellite cells mitotic activities and differentiation into myofiber. Overall, there is still no clear understanding of the mechanisms of action regarding how creatine affects muscle mass/growth, but current evidence suggests it may exert its effects through multiple approaches, with converging impacts on protein synthesis and myogenesis.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".