Osteoarthritis, cerebrovascular dysfunction and the common denominator of inflammation: a narrative review
Bibliographic record
Abstract
OBJECTIVE: Population-based cohort studies suggest an association between osteoarthritis (OA) and cerebrovascular disease, yet the mechanisms underlying vascular comorbidities in OA remain unclear. The purpose of this narrative review is to discuss the literature examining inflammation in OA with a focus on physiological mechanisms, and whether overlapping mechanisms exist in cerebrovascular dysfunction. METHOD: A literature search was conducted in PubMed using combinations of search terms: osteoarthritis, cerebrovascular (disease/dysfunction/risk), cardiovascular (disease/dysfunction/risk), aging/ageing, inflammation, inflammatory mediators, cytokine, c-reactive protein, interleukin, advanced glycation end-products, metabolic syndrome, reactive oxidative species, cognitive impairment, (vascular-related) dementia, small cerebral vessel disease, endothelial function, blood-brain barrier, gender/sex, hypertension, peripheral vascular health, and physical activity. Reference lists of identified articles were also researched manually. RESULTS: Overlapping inflammatory factors that may contribute to onset and progression of both OA and cerebrovascular dysfunction are presented. We describe oxidative mechanisms involving pro-inflammatory cytokines and oxidative species, advanced glycation end-products, sex hormones, microvascular dysfunction and osteoprotegerin, and their specific roles in potentially contributing to OA and cerebrovascular dysfunction. CONCLUSION: Synthesis of the current literature suggests future investigations may benefit from directly testing cerebrovascular hemodynamics and cognitive function in individuals with or at risk of OA to elucidate common physiological mechanisms.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".