Crosstalk between skeletal muscle and the brain during physical activity - in search of epigenetic mechanisms
Bibliographic record
Abstract
Recent research highlights the crucial role of muscle-brain crosstalk in metabolic regulation, particularly in individuals with type 2 diabetes and obesity. Myokines, protein hormones secreted by skeletal muscle, play a crucial role in this communication, influencing brain functions such as neuroplasticity, memory, and mood. Specific myokines like cathepsin B, FNDC5/irisin and interleukin-6 have been identified as key players in this muscle-brain axis. Physical activity modulates the production of these molecular factors, enhancing muscle-brain crosstalk and influencing cellular interactions. Moreover, exercise training may lead to adaptive long-term changes in gene expression, mediated by epigenetic regulators. Metabolic pathways activated during exercise can directly impact epigenetic marks by modulating the availability of metabolic intermediates required for these modifications. In the present review, we summarize the latest findings on the association between obesity/diabetes and cognitive impairment due to hippocampal dysfunction, and elaborate on how exercise influences cognitive functions via the communication between skeletal muscle and the brain. We focus on the underlying mechanisms responsible for the muscle-brain crosstalk, emphasizing dynamic changes in the epigenome and epitranscriptome, which sheds light on novel preventive and therapeutic approaches to combat obesity and cognitive decline.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".