Plasma Proteomics for Parkinsons Disease: Diagnostic Classification, Severity Association, and Therapeutic Hypotheses

BackgroundAlthough there is no cure, early diagnosis of Parkinsons 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. MethodsGene expression and plasma proteomics data from two Parkinsons 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. ResultsProteomic 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. ConclusionsPeripheral proteomic signatures offer classification, progression, and therapeutic insights in Parkinsons disease, supporting their biological relevance.