Examining a role for irisin in treating cerebral ischemia
Bibliographic record
Abstract
Stroke is a leading cause of death and disability, with ischemic stroke representing most cases. Age is the most significant nonmodifiable risk factor for stroke, and with an aging population, there is an urgent need for effective prevention and treatment strategies. Physical inactivity is a strong risk factor for stroke, and exercise has long been held as a promising approach to improve poststroke outcomes. During exercise, the myokine irisin is released as a product of a type 1 membrane protein cleavage that is encoded by the fibronectin type III domain containing 5 (FNDC5) gene. This review summarizes recent literature on irisin's role in ischemic stroke, examining central effects, stroke risk, poststroke functional outcomes, and exogenous administration. Irisin has value as a prognostic marker for risk stratification. Low levels of irisin correlate with worse outcomes and higher mortality in patients with ischemic stroke. Irisin may also be a key to the benefits of exercise, particularly for high-intensity resistance training, which significantly increases irisin levels. Beyond exercise, exogenous irisin is neuroprotective in murine models, reducing brain edema, inflammation, and apoptosis, and increasing blood-brain barrier integrity and brain-derived neurotrophic factor levels. This underscores irisin's potential to mitigate ischemic damage and promote recovery. Human trials are necessary to validate these findings and explore the feasibility of irisin-based interventions in acute stroke care. This review lays a foundation for future research to clarify irisin's therapeutic benefits, establish optimal exercise protocols, and explore exogenous irisin as a novel intervention for ischemic stroke.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".