Plasma Proteomics for Parkinson’s Disease: Diagnostic Classification, Severity Association, and Therapeutic Hypotheses
Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Background
Parkinson’s disease lacks reliable early diagnostics and disease-modifying treatments. Blood-based biomarkers can facilitate early detection, symptom monitoring, and targeted therapy.
Methods
We integrated gene expression and plasma proteomics data from two Parkinson’s disease cohorts. 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.
