Disrupting the clock: meta-analysis of irregular night shifts and migraine, proposing shift work migraine disorder with chronobiology strategies
Bibliographic record
Abstract
Background Migraine is linked to circadian rhythm disruptions, with morning attack peaks, circadian variations in trigeminal pain sensitivity, anterior hypothalamus involvement, and core circadian clock gene activity. Irregular night shift work, affecting up to 50% of the population, including new parents and students, causes significant circadian disruption. We hypothesize that irregular night shifts increase migraine prevalence compared to fixed schedules. Methods A systematic review and meta-analysis of observational studies up to March 27, 2025, assessed migraine prevalence in irregular versus fixed night shift workers, searching Web of Science and PubMed with terms like “shift work” and “migraine” (PRISMA/MOOSE-compliant, PROSPERO: CRD420250654865). Study quality was evaluated using the Newcastle–Ottawa Scale (NOS). A random-effects meta-analysis calculated weighted odds ratios (ORs) for migraine prevalence. Results From 203 records, 13 high-quality cross-sectional studies ( N = 38,798,271, 77% female, NOS 9–10) showed irregular night shifts significantly increased migraine odds (OR = 1.61, 95% CI: 1.27–2.04, p < 0.0001, I 2 = 73%), with females at higher odds (OR = 2.02–4.21). Meta-regression linked higher female representation to increased migraine odds ( β = 0.70, p = 0.0003, R 2 = 50%). Irregular night shifts showed no association with tension-type headache (OR = 0.79, 95% CI: 0.43–1.45). Conclusion Irregular night shifts disrupt circadian rhythms, elevating migraine odds but not tension-type headache, suggesting fixed schedules may reduce the burden. Chronobiology-informed management, including slow-rotating schedules (≥5 days with rest days), delay-directed rotations, timed light exposure, and ambient temperature regulation, needs testing to prevent ‘Shift Work Migraine Disorder,’ a proposed distinct migraine subgroup. Systematic review registration PROSPERO , CRD420250654865.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.043 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".