Migraine and risk of all-cause mortality and specific cause mortality: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Migraine is a common neurological disorder associated with various comorbidities, however its impact on mortality remains controversial. This study aimed to systematically evaluate the associations between migraine and the risk of all-cause, cardiovascular disease (CVD), and suicide mortality. METHODS: A comprehensive literature search of PubMed, Embase, and the Cochrane Library was conducted up to July 1, 2025, to identify eligible cohort studies assessing mortality outcomes in individuals with migraine. Random- or fixed-effects models were used to calculate pooled hazard ratios (HRs) with 95% confidence intervals (CIs). Subgroup and meta-regression analyses were performed to explore the sources of heterogeneity. Study quality was assessed using the Newcastle-Ottawa Scale. RESULTS: Eighteen cohort studies, including 7,928,722 participants and 49,105 all-cause deaths, were included. No significant association was found between any migraine (AM) and all-cause mortality (HR = 0.93, 95% CI: 0.80–1.09, I² = 94.7%), CVD mortality (HR = 0.97, 95% CI: 0.80–1.18, I² = 80.6%), or suicide mortality (HR = 1.11, 95% CI: 0.98–1.26, I² = 15.5%). Considerable heterogeneity was observed across the studies for all-cause and CVD mortality. Subgroup analyses revealed a reduced risk of all-cause mortality in studies based on physician-diagnosed migraines and in those with a follow-up duration of less than 12 years. Meta-regression analyses identified the migraine diagnosis method and mean follow-up duration as potential sources of heterogeneity for all-cause mortality. However, the sex-stratified subgroup analysis demonstrated a higher CVD mortality risk with low heterogeneity in women with migraine (HR = 1.21, 95% CI: 1.08–1.34, I² = 0.0%), particularly in those with migraine with aura (MA) (HR = 1.28, 95% CI: 1.03–1.60, I² = 23.8%). Meta-regression analyses did not identify the sources of heterogeneity in the CVD mortality studies. CONCLUSION: This systematic review and meta-analysis found no evidence linking AM with all-cause or cause-specific mortality. However, significant heterogeneity across studies and indications of an elevated CVD mortality risk in women with AM, particularly MA, render these findings inconclusive. Further research is required to elucidate this relationship and its underlying 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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.039 |
| Bibliometrics | 0.007 | 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".