Parkinson’s disease (PD) is increasing in prevalence, yet we lack readily available and non-invasive diagnostic biomarkers. An unbiased discovery proteomics study in plasma from PD patients and healthy controls (HC, phase 0) showed increased inflammation and decreased Wnt-signalling. To validate these findings, a targeted and multiplexed assay was developed and applied to a phase I cohort consisting of 99 de novo PD, 36 HC, and 18 subjects with polysomnography-confirmed isolated REM-sleep behaviour disorder (iRBD). In phase II, the multiplexed panel was refined and applied to a second cohort, focusing on the earliest stage of the disease by analysing 54 individuals with confirmed iRBD including longitudinal follow-up samples. A machine learning model, based on the expression of eight proteins, correctly identified all PD patients (phase I) and classified 79% of the iRBD as PD (phase II) up to 7 years before phenoconversion. Several of the identified biomarkers correlated with motor and non-motor symptom severity. This specific blood pattern was already evident in most iRBD cases, indicating pre-motor molecular events. This allows for the early identification of subjects at risk for PD in a blood test years before motor disease, which could support stratifying participants in future clinical trials aimed at preventing PD.

Targeted proteomics, using LC-MS MRM, applied to plasma from individuals newly diagnosed Parkinson's disease. Followed up with analysis and prediction as PD or control of new iRBD serum samples. Correlation between the expression of the putative biomarkers evaluated in plasma, serum and CSF.

Plasma, serum, CSF