Creatine supplementation and muscle-brain axis: a new possible mechanism?
Bibliographic record
Abstract
The brain and skeletal muscle have a high energy demand, of which creatine is an important regulator. Creatine acts as both a spatial and temporal energy buffer and reduces oxidative stress and inflammation. Creatine supplementation is well-recognized to enhance exercise performance, muscular strength and lean tissue mass, with emerging research showing benefits on cognitive function. Herein, we discuss the potential muscle-brain axis and the purported benefits of creatine supplementation on myokines, with a focus on brain-derived neurotrophic factor (BDNF). Myokines and the muscle-brain axis have been implicated in strength, endurance, neuroprotection, and cognitive performance, particularly in aging and clinical conditions. Creatine is a pleiotropic molecule and the mechanisms are multifactorial, however, they appear to be associated with improved bioenergetics, muscle hypertrophy, anti-inflammatory effects and on improved glucose metabolism. Despite the growing body of research on creatine, limitations such as variability in study designs, dosages, and individual responses need to be carefully interpreted. Further research is warranted to verify this hypothesis and to establish optimal supplementation protocols, particularly, in terms of its short-term and long-term implications for neuromuscular and cognitive performance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".