Muscle-brain crosstalk as a driver of brain health in aging
Bibliographic record
Abstract
Cognitive impairment and dementia in older adults represent significant global health challenges. Although the bidirectional relationship between physical function and brain health is well established, the mechanistic drivers of this link remain poorly understood. Muscle function and quality are central to physical function, and muscle's secretome is increasingly recognized for its systemic health effects-supporting the potential for muscle-to-brain crosstalk. This concept was explored at the 3rd International Research Symposium on Brain Health, jointly hosted by Vancouver Coastal Health and the University of British Columbia. We present the findings of this symposium, which reviewed the current state of the literature on muscle-to-brain crosstalk from multiple perspectives, spanning population studies to preclinical models. A key focus was the muscle secretome, particularly myokines and extracellular vesicles, as potential messengers influencing brain health. The symposium also identified critical takeaways and proposed next steps to further elucidate the underlying mechanisms of muscle-to-brain crosstalk and explore how these pathways might be harnessed through exercise or pharmacologic interventions to promote brain health in older adults.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".