Epidemiological linkage between migraine and diabetes mellitus: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background This study aimed to elucidate the nature and extent of the associations between diabetes mellitus (DM) and migraine through a systematic review and meta-analysis. Methods We searched the PubMed, Web of Science, and Scopus databases without a specified start date until June 2, 2024. Cross-sectional and cohort studies analyzing the risk of migraine in individuals with DM and vice versa were included. Studies without at least age and sex adjustments were excluded. Data were extracted to calculate odds ratios (ORs) and hazard ratios (HRs). Risk of bias was assessed using the Newcastle–Ottawa Quality Assessment Scale. Results Eight cross-sectional studies (131,361 patients with DM and 1,005,604 patients with migraine) and four cohort studies (103,205 patients with DM patients and 32,197 patients with migraine) were included. Meta-analyses of the cross-sectional studies showed no significant overall association between DM and migraine. Subgroup analyses revealed that type 1 diabetes reduced the odds of having migraine (OR 0.48, 95% confidence interval [CI] 0.30–0.77), while migraine without aura (MO) increased the odds of having DM (OR 1.19, 95% CI 1.02–1.39). The cohort studies indicated that DM decreased the risk of developing migraine (HR 0.83, 95% CI 0.76–0.90), and a history of migraine increased the risk of developing DM (HR 1.09, 95% CI 1.01–1.17). Conclusions DM, particularly type 1 diabetes, is negatively associated with migraine occurrence, whereas migraine, especially MO, is positively associated with DM occurrence. However, most of the results remained at a low or very low level of evidence, indicating the need for further research.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.029 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".