Integrating Metabolomics and Machine Learning Reveals Metabolic Signatures and Candidate Biomarkers for Vestibular Migraine.

Vestibular migraine (VM) is an underrecognized disorder characterized by episodic vertigo, often accompanied by headache. Despite its substantial prevalence and disease burden, its metabolic features remain poorly characterized, and serum biomarkers for diagnosis or activity assessment are lacking. We performed untargeted metabolomics in discovery (33 VM vs 30 healthy controls [HCs]) and replication (37 VM vs 27 HCs) cohorts, applying machine learning solely in the discovery cohort for marker selection. In the discovery cohort, 510 metabolites exhibited differential abundance (197 upregulated, 313 downregulated). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed enrichment trends for upregulated metabolites in cysteine/methionine, histidine, and phenylalanine metabolism. Machine learning identified three candidate metabolites─phenylethylamine, N-acetylserotonin, and cysteine-S-sulfate─each achieving an area under the curve > 0.9. In the discovery cohort, cysteine-S-sulfate correlated with vertigo Visual Analog Scale scores, and phenylethylamine correlated with Headache Impact Test-6 and Generalized Anxiety Disorder-7 (GAD-7) scores. In the replication cohort, all three markers maintained differential expression and discriminative performance; additionally, cysteine-S-sulfate correlated with Dizziness Handicap Inventory scores, and phenylethylamine with GAD-7 scores. This study elucidates the metabolic landscape of VM and identifies promising serum biomarkers, though these findings warrant further validation in larger, multicenter cohorts before clinical translation.