Genetic‐Proteomic Integration Identifies Predictive Plasma Proteins for Multiple Sclerosis

作者：Yuan Ding, Dylan Hamitouche, Simon Thébault, Patrick Kearns, Ahmed Abdelhak, Adil Harroud · 发表于：Annals of Neurology · 年份：2026 · DOI：10.1002/ana.78256 · 研究领域：Multiple Sclerosis Research Studies、Genetic Associations and Epidemiology、Bioinformatics and Genomic Networks

Objective Multiple sclerosis (MS) develops after a prolonged preclinical phase. Identifying circulating biomarkers that capture this early biology can improve risk stratification and guide intervention. We aimed to identify plasma proteins driving MS susceptibility using large‐scale proteogenomic integration and to evaluate their prediagnostic predictive value and effects on severity. Methods We used cis‐acting protein quantitative trait loci (pQTL) for 2,545 plasma proteins (n = 80,824). Predicted protein levels were tested for association with MS risk in 14,802 cases and 26,703 controls using Mendelian randomization and colocalization. Splicing annotations were integrated to interpret platform‐specific discrepancies. We validated candidates as predictors of incident MS in prediagnostic United Kingdom Biobank samples (124 cases, 52,515 controls; median 5.9 years prediagnosis) and assessed associations with disease severity (n = 12,584). Results We identified 39 proteins associated with MS risk. Most formed a densely connected network enriched in immune regulatory pathways, B‐ and T‐cell costimulation, cytokine signaling, and Epstein–Barr virus‐related pathways. Transcriptomic enrichment was strongest in B‐cell subsets. Splicing data suggested that discordances between proteomic platforms reflect distinct proteoforms. Among 28 genetically implicated proteins measured in prediagnostic samples, 8 were associated with time to MS diagnosis, demonstrating enrichment beyond chance ...