{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T07:02:01Z","timestamp":1790146921987,"version":"4.0.1"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Mol. Biosci."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Epilepsy is a neurological disorder with heterogeneous anti-seizure medication responses and a lack of peripheral biomarkers for diagnosis and drug resistance assessment. This study aims to explore metabolic alterations and candidate metabolite panels associated with epilepsy and anti-seizure medication responsiveness.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Untargeted metabolomic analysis was performed on plasma samples from 42 patients with epilepsy (PWE), including 20 patients with drug-resistant epilepsy (DRE) and 22 patients with drug-sensitive epilepsy (DSE), as well as 20 healthy controls (HCs). Multivariate and univariate statistical analyses, pathway analysis, machine-learning-based feature selection, and logistic regression modeling were conducted, with internal validation by bootstrap resampling. Transcriptomic data were analyzed and intersected with metabolomic findings at the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway level, followed by construction of a gene\u2013metabolite\u2013pathway network.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Between the PWE and HC groups, 3,329 significantly different features were identified. KEGG enrichment analysis showed that the differential metabolites were mainly enriched in the cyclic adenosine monophosphate signaling pathway, hypoxia-inducible factor-1 signaling pathway, and dopaminergic synapse pathway, and these three pathways were also enriched in the transcriptomic dataset. Three key candidate metabolites\u2014palmitoylcarnitine, C18 (plasm)-18:1 PC, and 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine\u2014effectively discriminated PWE from HCs. Between the DRE and DSE groups, 1,074 differential ion features were identified. Six key candidate metabolites\u2014L-kynurenine, indolelactate, glycodeoxycholic acid, N-acetylcytidine, citric acid, and 5-hydroxyindoleacetate\u2014showed exploratory discriminatory ability between the DRE and DSE groups.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study revealed plasma metabolic signatures associated with epilepsy and anti-seizure medication responsiveness. The identified candidate metabolites and pathways provide preliminary metabolic evidence that requires further validation in independent cohorts and mechanistic studies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmolb.2026.1885513","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T06:20:59Z","timestamp":1790144459000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Plasma untargeted metabolomics reveals metabolic signatures associated with epilepsy and anti-seizure medication responsiveness"],"prefix":"10.3389","volume":"13","author":[{"given":"Zijian","family":"Li","sequence":"first","affiliation":[{"name":"Department of Neurology, China-Japan Union Hospital of Jilin University","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Liang","sequence":"additional","affiliation":[{"name":"Department of Neurology, China-Japan Union Hospital of Jilin University","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Ren","sequence":"additional","affiliation":[{"name":"Department of Neurology, China-Japan Union Hospital of Jilin University","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Songyan","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Neurology, China-Japan Union Hospital of Jilin University","place":["Changchun, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,23]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"65","DOI":"10.18926\/AMO\/57955","article-title":"Metabolic profiling of the cerebrospinal fluid in pediatric epilepsy","volume":"74","author":"Akiyama","year":"2020","journal-title":"Acta Med. 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