Unlocking the potential of exercise: harnessing myokines to delay musculoskeletal aging and improve cognitive health
Bibliographic record
Abstract
Objectives: This review aims to summarize the common physiological mechanisms associated with both mild cognitive impairment (MCI) and musculoskeletal aging while also examining the relevant literature on how exercise regulation influences the levels of shared myokines in these conditions. Methods: The literature search was conducted via databases such as PubMed (including MEDLINE), EMBASE, and the Cochrane Library of Systematic Reviews. The searches were limited to full-text articles published in English, with the most recent search conducted on 16 July 2024. The inclusion criteria for this review focused on the role of exercise and myokines in delaying musculoskeletal aging and enhancing cognitive health. The Newcastle‒Ottawa Scale (NOS) was utilized to assess the quality of nonrandomized studies, and only those studies with moderate to high quality scores, as per these criteria, were included in the final analysis. Data analysis was performed through narrative synthesis. Results: The primary outcome of this study was the evaluation of myokine expression, which included IL-6, IGF-1, BDNF, CTSB, irisin, and LIF. A total of 16 studies involving 633 older adults met the inclusion criteria. The current exercise modalities utilized in these studies primarily consisted of resistance training and moderate-to high-intensity cardiovascular exercise. The types of interventions included treadmill training, elastic band training, aquatic training, and Nordic walking training. The results indicated that both cardiovascular exercise and resistance exercise could delay musculoskeletal aging and enhance the cognitive functions of the brain. Additionally, different types and intensities of exercise exhibited varying effects on myokine expression. Conclusion: Current evidence suggests that exercise mediates the secretion of specific myokines, including IL-6, IGF-1, BDNF, CTSB, irisin, and LIF, which establish self-regulatory circuits between the brain and muscle. This interaction enhances cognitive function in the brain and improves skeletal muscle function. Future research should focus on elucidating the exact mechanisms that govern the release of myokines, the correlation between the intensity of exercise and the secretion of these myokines, and the distinct processes by which myokines influence the interaction between muscle and the brain.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".