The clinical diagnosis of epilepsy is predominantly based on history taking, morbidity records, and imaging during seizures. The emergence of proteomics has enhanced disease marker detection and potential drug target identification. We perform a longitudinal survival analysis of 2,920 plasma proteins and epilepsy onset, utilizing plasma proteome data from 52,372 UK Biobank participants (440 incident cases). We identify 103 proteins with significant associations with epilepsy, with neurofilament light polypeptide (NEFL) (hazard ratio [HR] [95% confidence interval (CI)]: 2.13 [1.85-2.46]) and growth differentiation factor 1 (GDF15) (1.82 [1.60-2.07]) exhibiting the strongest correlations. Enrichment and network analyses uncovered the pivotal role of the immune response and pinpointed four central hubs. Furthermore, 103 screened proteins are significantly associated with brain regions implicated in epileptogenesis and show stronger correlation with stress-related events than genetic predisposition. We investigate the predictive ability of top-ranked proteins for future epilepsy risk and their potential as drug targets. These findings are crucial for identifying early biomarkers and optimizing therapeutic strategies.
Keywords: epilepsy; machine learning; plasma proteins; prediction.
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