Evidence map›Paper›PMID 42846067›Full record

ArticleFrontiers in molecular biosciences2026

Plasma untargeted metabolomics reveals metabolic signatures associated with epilepsy and anti-seizure medication responsiveness.

Zijian Li, Zhen Liang, Juan Ren, Songyan Liu

Abstract read
In one paragraph

Article in Frontiers in molecular biosciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Zijian LiDepartment of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China.
Zhen LiangDepartment of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China.
Juan RenDepartment of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China.
Songyan LiuDepartment of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: 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. Methods: 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-metabolite-pathway network. Results: 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-palmitoylcarnitine, C18 (plasm)-18:1 PC, and 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine-effectively discriminated PWE from HCs. Between the DRE and DSE groups, 1,074 differential ion features were identified. Six key candidate metabolites-L-kynurenine, indolelactate, glycodeoxycholic acid, N-acetylcytidine, citric acid, and 5-hydroxyindoleacetate-showed exploratory discriminatory ability between the DRE and DSE groups. Conclusion: 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.

Indexed as

drug-resistant epilepsyepilepsymachine learningmetabolitestranscriptomicsuntargeted metabolomics

Identifiers

PMID42846067
PMCPMC13642817

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.