Objective: To replicate existing and identify novel protein biomarkers for PD diagnosis and progression.

Background: Parkinson’s disease (PD) lacks reliable biomarkers for early diagnosis and progression monitoring and prognosis, limiting timely intervention and clinical trial design. Proteomic approaches may identify novel markers reflecting underlying pathophysiology across disease stages.

Method: This study was conducted using two longitudinal datasets. The first consisted of existing clinical, demographic, and plasma proteomic data from the PPMI study (https://www.ppmi-info.org). The second consisted of clinical, demographic, and novel serum proteomic data from a cohort of PD patients recruited by the Medical University of Graz (N = 169). Samples from this cohort were run on Olink’s Explore HT panel, Alamar’s Inflammation and CNS panels, Octave’s custom MSDA panel, and Quanterix’s Neuro 3- and 4-Plex panels. Our quality control and covariate correction approach included the following steps: removal of dubious markers and samples, imputation of extreme values, age, sex, and plate correction, PC correction, and removal of highly non-normal markers. Ratios of post-translational modifications to total protein were calculated and included where available. We performed univariate biomarker association analyses for the following endpoints: PD diagnosis, prodromal diagnosis, PD vs. prodromal, and several disease progression constructs, with correction for the effective number of tests.

Results: Across all endpoints and biomarkers, 124 associations of 101 unique biomarkers survived multiple testing correction. We performed a comprehensive literature review to identify previously-associated PD biomarkers, leaving 67 putatively novel biomarkers.

Conclusion: This multi-platform proteomic analysis successfully replicated known and identified 67 novel protein biomarkers associated with PD diagnosis and progression. These findings enhance our understanding of disease mechanisms and provide candidates for clinical application in early detection, patient stratification, and monitoring. Further validation in independent cohorts is warranted to establish their utility as reliable biomarkers. This project was funded by The Michael J. Fox Foundation (MJFF-024499). PPMI – a public-private partnership – is funded by The Michael J. Fox Foundation for Parkinson’s Research, and funding partners.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/comprehensive-multi-platform-proteomic-analysis-identifies-novel-biomarkers-for-parkinsons-disease-diagnosis-and-progression-monitoring/