Plasma Proteomics for Parkinson’s Disease: Diagnostic Classification, Severity Association, and Therapeutic Hypotheses

Abstract Background Although there is no cure, early diagnosis of Parkinson’s disease allows effective management and symptomatic relief, potentially delaying the need for more potent medications. Blood-based biomarkers can facilitate early detection, symptom monitoring, and targeted therapy. Methods Gene expression and plasma proteomics data from two Parkinson’s disease cohorts were integrated. Machine learning models were trained to classify disease status using protein, gene expression, and combined datasets. A severity score was derived by regressing protein levels against clinical motor ratings and tested longitudinally. Enrichment and network analyses identified biological context, and drug perturbation databases were queried for candidate therapies. Results Proteomic models outperformed gene-based approaches and generalized well to external data. The severity score correlated with clinical burden and predicted future progression. Enriched pathways involved extracellular signaling, immune response, and post-translational regulation. Several compounds were identified as potential therapeutic candidates based on network targeting and reversal potential. Conclusions Peripheral proteomic signatures offer classification, progression, and therapeutic insights in Parkinson’s disease, supporting their biological relevance.