Migraine is a risk factor for dementia: a systematic review and meta-analysis of cohort studies
Bibliographic record
Abstract
BACKGROUND: Migraine affects more than one billion people worldwide, and there is growing concern about the burden of migraine. Migraine affects cognitive function during an attack, but reports are inconsistent on whether the effect of migraine on cognitive function persists and increases the risk of developing dementia. This systematic review and meta-analysis aimed to examine whether migraine is a risk factor for dementia. METHODS: We searched six databases and included cohort studies with participants without dementia and with migraine at baseline, the outcome of interest was the risk of dementia, expressed in adjusted hazard ratios and 95% confidence intervals. Subgroup analyses and meta-regression were used to explore the sources of heterogeneity. RESULTS: A total of 11 cohort studies containing 6,964,353 participants were included. Migraine increased the risk of all-cause dementia (HR = 1.26; 95% CI = 1.09–1.46), AD (HR = 1.32; 95% CI = 1.26–1.38), and VaD (HR = 1.28; 95% CI = 1.24–1.32). Subgroup analyses revealed migraine with aura had an increased risk of all-cause dementia compared to migraine without aura. The pooled results showed that migraine significantly increase the risk of all-cause dementia in studies with high quality and studies with sample sizes more than 2000. The results of meta-regression analyses revealed that region, migraine type, diagnostic criteria for dementia, gender, Newcastle-Ottawa Scale score, sample size, controls and mean follow-up time were not significant sources of study heterogeneity. CONCLUSIONS: This meta-analysis suggest migraine as a risk factor for dementia. Due to significant heterogeneity between studies, residual confounding factors and bias, the results should be cautiously interpreted.
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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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.034 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".