てんかんにおける痙攣の血液バイオマーカー：機械学習が血漿プロテオームの変化を明らかに

Fluid biomarkers are emerging for diagnosis and monitoring in neurology. Epilepsy remains an exception, despite seizures being known to result in inflammation and altered levels of several brain proteins. We aimed to identify candidate biomarkers through a comprehensive proteomic analysis of proteins associated with brain disease or inflammation.

Cross-sectional analysis of plasma from adult participants in the PREDICT biobank study in Västra Götaland, Sweden. Participants aged 18-50 with epilepsy and seizures (n=88) were compared with those with epilepsy who had been seizure-free (n=88) for >1 year using four OlinkExplore384 panels, analysing 1447 proteins.

Two machine-learning models, one linear and one non-linear, identified protein expression differences through feature selection, resulting in 51 unique proteins between the models. Twenty-three proteins were considered differentially expressed after false discovery rate adjustment (p≤0.05), including neurofilament light and several cytokines. Protein-protein interaction (PPI) analysis identified clusters among 51 unique proteins, with the largest clusters primarily associated with inflammatory processes. In addition, machine learning-independent gene set enrichment analysis identified 34 gene sets, mainly related to immune and inflammatory processes, that were also enriched in participants with seizures.

Persons with epilepsy and seizures had different plasma protein profiles compared with those seizure-free, primarily suggesting altered inflammatory/immune processes. This fits well with growing evidence of inflammation as a key process in epilepsy. In addition to new therapeutic targets, immune processes need further exploration as candidate biomarkers for monitoring of treatment response in epilepsy.