Cross-sectional proteomic expression in Parkinson's disease-related proteins in drug-naïve patients vs healthy controls with longitudinal clinical follow-up.

ABSTRACT

There is an urgent need to find reliable and accessible blood-based biomarkers for early diagnosis of Parkinson's disease (PD) correlating with clinical symptoms and displaying predictive potential to improve future clinical trials. This led us to a conduct large-scale proteomics approach using an advanced high-throughput proteomics technology to create a proteomic profile for PD. Over 1300 proteins were measured in serum samples from a de novo Parkinson's (DeNoPa) cohort made up of 85 deep clinically phenotyped drug-naïve de novo PD patients and 93 matched healthy controls (HC) with longitudinal clinical follow-up available of up to 8 years. The analysis identified 73 differentially expressed proteins (DEPs) of which 14 proteins were confirmed as stable potential diagnostic markers using machine learning tools. Among the DEPs identified, eight proteins-ALCAM, contactin 1, CD36, DUS3, NEGR1, Notch1, TrkB, and BTK- significantly correlated with longitudinal clinical scores including motor and non-motor symptom scores, cognitive function and depression scales, indicating potential predictive values for progression in PD among various phenotypes. Known functions of these proteins and their possible relation to the pathophysiology or symptomatology of PD were discussed and presented with a particular emphasis on the potential biological mechanisms involved, such as cell adhesion, axonal guidance and neuroinflammation, and T-cell activation. In conclusion, with the use of advance multiplex proteomic technology, a blood-based protein signature profile was identified from serum samples of a well-characterized PD cohort capable of potentially differentiating PD from HC and predicting clinical disease progression of related motor and non-motor PD symptoms. We thereby highlight the need to validate and further investigate these markers in future prospective cohorts and assess their possible PD-related mechanisms.