Vitamins and Minerals for Migraine Prophylaxis: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To summarize the findings of randomized controlled trials (RCTs) on the efficacy and safety of vitamins and minerals for migraine prophylaxis. METHODS: We systematically searched bibliographic databases and relevant websites for parallel and crossover RCTs reporting efficacy and/or safety of vitamins and/or minerals for migraine prophylaxis. Our primary outcomes were migraine frequency (number of attacks) and duration (hours). Secondary outcomes were severity (intensity), days with migraine, and adverse events. Meta-analysis was conducted when analyzable data were available from at least two trials. RESULTS: Eighteen placebo-controlled trials met our eligibility criteria. Only coenzyme Q10 and magnesium contributed to meta-analyses. In adults, compared with placebo, coenzyme Q10 did not significantly decrease migraine frequency (mean difference (MD) -0.44 (-2.14 to 1.26); I2 53%; 2 trials; 97 participants; moderate strength of the evidence), duration (MD -1.97 (-4.82 to 0.87); I2 0%; 2 trials; 97 participants; moderate strength of the evidence), or severity (ratio of means (RoM) -0.05 (-0.20 to 0.11); I2 0%; 2 trials; 97 participants). In adults, compared with placebo, magnesium did not significantly decrease migraine severity (RoM -0.17 (-0.36 to 0.02); I2 48%; 3 trials; 226 participants; low strength of the evidence). Meta-analysis of other vitamins and minerals, and other outcomes were not feasible due to a lack of sufficiently reported data. CONCLUSIONS: Based on insufficient evidence, it is unknown if coenzyme Q10 and magnesium are effective for migraine prophylaxis in adults. High-quality, adequately powered RCTs are needed to fully evaluate the efficacy and safety of vitamins and minerals for migraine prophylaxis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".