The association between sleep disorder and stroke risk: A meta-analysis of observational studies
Bibliographic record
Abstract
Background: Sleep disturbances, including insomnia and obstructive sleep apnea (OSA), have been increasingly linked to cardiovascular diseases. However, their relationship with stroke risk remains controversial. While some studies suggest a strong association, others show limited or no correlation. This meta-analysis synthesizes observational data to assess the association between sleep disorders and stroke risk, aiming to provide clearer insights into this critical public health concern. Methods: A systematic review and meta-analysis were conducted following PRISMA guidelines. A comprehensive search of PubMed, NIH, Scopus, and Google Scholar yielded 1,284 articles, of which 10 studies, including 514 participants, met the inclusion criteria. Studies included were cohort, case-control, or cross-sectional, with adequate statistical power to report risk estimates for stroke associated with sleep disorders. Two independent investigators performed data abstraction and quality assessment using the Newcastle-Ottawa Scale. The DerSimonian-Laird random-effects model was used to calculate pooled relative risks (RRs), with heterogeneity quantified using the I² statistic. Publication bias was assessed through funnel plots and Egger's test. Results: Sleep disorders were significantly associated with an increased risk of stroke, with a pooled RR of 1.82 (95% CI: 1.45-2.30, p < 0.001). Among subgroups, OSA posed the highest risk (RR: 2.17, 95% CI: 1.64-2.89), followed by insomnia (RR: 1.47, 95% CI: 1.12-1.92). High heterogeneity was observed across studies (I² = 76%), largely attributed to variations in study design, diagnostic criteria, and participant characteristics. Sensitivity analyses confirmed the robustness of the findings, and no significant publication bias was detected. Conclusions: This meta-analysis provides compelling evidence that sleep disorders, particularly OSA and insomnia, are strongly associated with an increased risk of stroke. Given the high prevalence of sleep disturbances, incorporating sleep disorder screenings into routine cardiovascular evaluations could significantly enhance stroke prevention strategies. Future research should explore the mechanisms underlying this association and evaluate the efficacy of targeted interventions in reducing stroke risk.
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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 | 0.025 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.064 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".