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Record W4413139975 · doi:10.1007/s11357-025-01833-0

Muscle-brain crosstalk as a driver of brain health in aging

2025· review· en· W4413139975 on OpenAlexafffundabout
Brendan McNeish, Iva Miljkovic, Teresa Liu‐Ambrose, Fabrisia Ambrosio, Karyn A. Esser, Margaret Fahnestock, Caterina Rosano

Bibliographic record

VenueGeroScience · 2025
Typereview
Languageen
FieldNeuroscience
TopicGenetic Neurodegenerative Diseases
Canadian institutionsMcMaster UniversityUniversity of British Columbia
FundersNational Institute on AgingCanadian Institutes of Health Research
KeywordsCrosstalkBrain agingNeuroscienceAging brainPsychologyComputer scienceEngineeringCognitionElectronic engineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.971
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.068
GPT teacher head0.394
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2025
Admission routes3
Has abstractyes

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