Table 1_Multi-omics integration reveals gut microbiota dysbiosis and metabolic alterations of cerebrospinal fluid in children with epilepsy.xlsx

IntroductionEpilepsy is a complex neurological disorder with an unclear pathogenesis. Emerging evidence suggests that gut microbiota dysbiosis and cerebrospinal fluid (CSF) metabolic alterations play a critical role in epilepsy progression through the gut–brain axis. This study aimed to characterize microbial and metabolic disturbances in pediatric epilepsy and identify potential diagnostic biomarkers through integrative multi-omics analysis of matched fecal and CSF samples.
MethodsIn this study, we conducted 16S rRNA gene sequencing on fecal samples from a total of 50 participants including 17 common epilepsy (CEP) patients, 23 refractory epilepsy (REP) patients, and 10 non-epilepsy (NEP) patients, along with untargeted metabolomic analysis on 24 paired CSF samples from REP and NEP groups. Multi-omics integration and a random forest model were applied to assess diagnostic performance, identifying microbial and metabolite signatures associated with epilepsy.
ResultsChildren with epilepsy (REP and CEP) exhibited distinct gut microbiota dysbiosis. Specifically, multivariable association modeling using MaAsLin 3 identified 13 discriminatory microbial taxa, with Clostridiales and Clostridiaceae ranking as the most enriched in REP. Functional predictions revealed significant differences in metabolic pathway, alongside disrupted ecological characteristics among epilepsy groups. In addition, CSF metabolomics analysis further revealed key metabolic shifts between REP and NEP, with notable alterations in alpha-Ketoisocaproic acid, alpha-Ketoisovaleric acid, and acetyl-L-carnitine, reflecting distinct metabolic reprogramming in epilepsy. Moreover, correlation analysis revealed strong microbiota-metabolite associations, reinforcing the involvement of the gut-brain axis in epileptogenesis. Independent random forest-based diagnostic models using microbial genera (AUC = 0.913, accuracy = 0.818) or metabolites (AUC = 0.875, accuracy = 0.833) demonstrated high classification accuracy in distinguishing REP from NEP. Notably, the integrated microbiota-metabolite classification model exhibited superior diagnostic performance in REP and NEP groups (AUC = 0.953, accuracy = 0.875), significantly surpassing individual models and highlighting the potential of multi-omics integration for epilepsy diagnostics.
ConclusionThese findings reveal concurrent gut microbiota dysbiosis and CSF metabolic disturbances in epilepsy, underscoring their interrelated roles in epileptogenesis and reinforcing our understanding of microbiome-metabolome crosstalk. The integrated multi-omics model demonstrated superior diagnostic performance, emphasizing its potential for precision biomarker discovery and clinical application in epilepsy stratification and intervention.