ArticleFrontiers in pharmacology2025
Assessment of drug-related migraine in a real-world large-scale database.
Article in Frontiers in pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
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4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Drug-induced migraine represents a clinically significant yet under-investigated subtype of migraine. This study aims to evaluate the risk of drug-related migraine based on real-world data from the FDA Adverse Event Reporting System (FAERS). Methods: A retrospective pharmacovigilance analysis was conducted using FAERS data from Q1 2004 to Q4 2024. Migraine cases were identified via standardized MedDRA (The Medical Dictionary for Regulatory Activities) terms. Only primary suspect drugs were included. Disproportionality analyses were performed using four algorithms: ROR, PRR, MGPS, and BCPNN. Drugs were classified by therapeutic indication and mechanism of action, and stratified by BCPNN values to assess risk levels. Results: A total of 20,886 migraine-related adverse events were identified, predominantly among females (77.4%) with a mean age of 45.7 years. Sixty-six drugs yielded positive signals, and after exclusion criteria, 39 remained for further analysis. The highest-risk agents included lorcaserin (BCPNN = 3.33), tasimelteon (3.20), and botulinum toxin type A (3.06). High-risk therapeutic classes included immunosuppressants, estrogens/progestogens, and sedative-hypnotics. Conclusion: This large-scale analysis identifies key drug categories and compounds associated with an elevated risk of migraine, providing actionable insights for clinicians. Especially lorcaserin, tasimelteon, and botulinum toxin as potential risk factors for migraine. Given the public health burden of migraine, pharmacovigilance efforts should incorporate such findings to mitigate iatrogenic risks. Further prospective studies are warranted to establish causal mechanisms and optimize therapeutic decision-making.
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