Li Feng, You Dongdong, Li Yun, Wang Xiaoyu, Lin Zhongdong, Shi Xulai, Li Zhongshan, Wu Jinyu, Liu Zhenwei
Department of Pediatric Neurology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Key Laboratory of Laboratory Medicine, Ministry of Education, Institute of Genomic Medicine, Wenzhou Medical University, Wenzhou, Zhejiang, China.
Front Microbiol. 2025 Sep 11;16:1630062. doi: 10.3389/fmicb.2025.1630062. eCollection 2025.
Epilepsy 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.
In 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.
Children with epilepsy (REP and CEP) exhibited distinct gut microbiota dysbiosis. Specifically, multivariable association modeling using MaAsLin 3 identified 13 discriminatory microbial taxa, with and 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.
These 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.
癫痫是一种发病机制不明的复杂神经系统疾病。新出现的证据表明，肠道微生物群失调和脑脊液（CSF）代谢改变通过肠-脑轴在癫痫进展中起关键作用。本研究旨在通过对匹配的粪便和脑脊液样本进行综合多组学分析，来表征小儿癫痫中的微生物和代谢紊乱，并识别潜在的诊断生物标志物。
在本研究中，我们对总共50名参与者的粪便样本进行了16S rRNA基因测序，其中包括17名常见癫痫（CEP）患者、23名难治性癫痫（REP）患者和10名非癫痫（NEP）患者，同时对来自REP和NEP组的24对脑脊液样本进行了非靶向代谢组学分析。应用多组学整合和随机森林模型来评估诊断性能，识别与癫痫相关的微生物和代谢物特征。
癫痫患儿（REP和CEP）表现出明显的肠道微生物群失调。具体而言，使用MaAsLin 3进行的多变量关联建模确定了13个具有鉴别性的微生物分类群，其中 和 在REP中富集程度最高。功能预测显示代谢途径存在显著差异，同时癫痫组之间的生态特征也受到破坏。此外，脑脊液代谢组学分析进一步揭示了REP和NEP之间的关键代谢变化，α-酮异己酸、α-酮异戊酸和乙酰-L-肉碱有明显改变，反映了癫痫中独特的代谢重编程。此外，相关性分析揭示了微生物群与代谢物之间的强烈关联，加强了肠-脑轴在癫痫发生中的作用。使用微生物属（AUC = 0.913，准确率 = 0.818）或代谢物（AUC = 0.875，准确率 = 0.833）的基于随机森林的独立诊断模型在区分REP和NEP方面表现出较高的分类准确率。值得注意的是，综合微生物群-代谢物分类模型在REP和NEP组中表现出卓越的诊断性能（AUC = 0.953，准确率 = 0.875），显著超过单个模型，突出了多组学整合在癫痫诊断中的潜力。
这些发现揭示了癫痫中同时存在的肠道微生物群失调和脑脊液代谢紊乱，强调了它们在癫痫发生中的相互关联作用，并加强了我们对微生物组-代谢组相互作用的理解。综合多组学模型表现出卓越的诊断性能，强调了其在癫痫分层和干预中发现精准生物标志物及临床应用的潜力。
