Objective: To characterise Parkinson’s disease (PD)-related proteomic profiles in plasma and cerebrospinal fluid (CSF), to assess differential expression between PD and healthy controls (HC), along with evaluating longitudinal biomarker trajectories over two years and identifying biomarker-defined PD subgroups.
Background: PD is biologically heterogeneous and current clinical classifications do not fully capture underlying pathophysiology. High-dimensional proteomic technologies enable measurement of proteins involved in neurodegeneration, inflammation and synaptic biology, which may help identify biologically meaningful disease subtypes.
Method: Plasma and CSF samples from PD and age-matched HC participants were analysed using the NULISA™ CNS Diseases Panel. The plasma cohort included 34 PD and 22 HC participants; CSF analyses were available in a subgroup (16 PD, 13 HC). Baseline differential expression was assessed using parametric or non-parametric tests with false discovery rate (FDR) correction. Longitudinal trajectories over two years were evaluated using linear mixed-effects models testing group×time interactions. Exploratory clustering using principal component analysis followed by k-means clustering was performed in PD participants at baseline using plasma proteomic profiles.
Results: Baseline plasma analyses showed nominal differences between PD and HC in proteins related to synaptic signalling (DDC, BDNF, SOD1), inflammatory pathways (IL1B, CXCL1, CD40LG), and neurodegeneration-related processes (APOE, TIMP2) (all p<0.05), while NPTX1 and UBB were higher in HC. In CSF, DDC was increased in PD and remained significant after FDR correction (pFDR<0.05). Longitudinal analyses identified group×time interactions for plasma biomarkers including SNCA, oligomeric SNCA, CD40LG, CCL13, TNF, SOD1, PRDX6 and MDH1, and for CSF biomarkers including Aβ42, Aβ40, GFAP, SQSTM1 and GDF15 (all p<0.05). Clustering of baseline plasma proteomic profiles identified three PD subgroups with distinct biological signatures, with the subgroup exhibiting the lowest biomarker levels showing greater non-motor symptom burden.
Conclusion: High-dimensional proteomic profiling used in this study identified molecular signatures associated with PD and biologically defined patient subgroups linked to clinical heterogeneity, supporting proteomic approaches for disease stratification.
References: Feng, W., Beer, J.C., Hao, Q. et al. NULISA: a proteomic liquid biopsy platform with attomolar sensitivity and high multiplexing. Nat Commun 14, 7238 (2023). https://doi.org/10.1038/s41467-023-42834-x
To cite this abstract in AMA style:
S. Rota, B. Batzu, M. Veronese, J. Shubert, A. Heslegrave, B. Fernandes Gomes, A. Reith, C. Parker, S. Lee, N. Barwick, S. Williams. High-dimensional proteomic profiling in plasma and CSF identifies molecular signatures in Parkinson’s disease [abstract]. Mov Disord. 2026; 41 (suppl 1). https://www.mdsabstracts.org/abstract/high-dimensional-proteomic-profiling-in-plasma-and-csf-identifies-molecular-signatures-in-parkinsons-disease/. Accessed October 1, 2026.« Back to 2026 International Congress
MDS Abstracts - https://www.mdsabstracts.org/abstract/high-dimensional-proteomic-profiling-in-plasma-and-csf-identifies-molecular-signatures-in-parkinsons-disease/