Alterations in serum metabolomics predict drug-resistant epilepsy

Abstract Drug-resistant epilepsy (DRE) lacks a reliable early warning system. This prospective study developed an early predictive model for DRE based on baseline serum metabolite levels. Serum samples were collected from 151 prospectively recruited patients with epilepsy. Patients were categorized into the DRE and non-drug-refractory epilepsy (NDRE) groups after a four-year follow up. After propensity score matching of baseline data, including age and sex, 33 patients with DRE and 89 with NDRE were recruited and split into training and test sets at random in a 2:1 ratio. Nontargeted metabolomics of the training set identified 109 significantly altered metabolites, primarily those involved in dysregulated lipid metabolism. Compared with the NDRE group, 22 metabolites were upregulated and 87 were downregulated in the DRE group. Pathway enrichment analysis highlighted perturbations in choline metabolism in cancer and sphingolipid, glycerophospholipid, one-carbon pool by folate, and cholesterol metabolism. A support vector machine model incorporating 11 metabolites and one clinical characteristic achieved a cross-validated area under the curve (AUC) of 0.810 and an independent test set AUC of 0.794. This study provides a non-invasive, serum-based objective tool to identify the potential population of patients with DRE with good sensitivity and specificity and guide targeted metabolic therapies.