Plasma proteomics identify biomarkers predicting Parkinson’s disease up to 7 years before symptom onset
Publication date: 18/06/2024
Authors: Hällqvist, J., Bartl, M., Dakna, M., et al.
Journal: Nature Communications, 15(1):4759
Commentary: In this study, Hällqvist et al. combined a state-of-the-art targeted multiplexed mass spectrometry assay with machine-learning models to characterise and discriminate the proteomic footprint of blood samples from de novo Parkinson’s disease (PD) patients and healthy controls, and to predict the phenoconversion rate to PD in a high-risk cohort composed of isolated REM sleep behavior disorder (iRBD) patients. Machine-learning models fully discriminated between PD patients and healthy controls by comparing the expression of eight blood proteins (GRN, MASP2, HSPA5, PTGDS, ICAM1, C3, DKK3, and SERPING1), known for their roles in inflammatory responses, protein folding, and WNT signaling. Furthermore, 79% of iRBD individuals were classified as PD up to seven years before motor onset. Notably, the expression of these biomarkers correlates with symptom severity. The study's findings are highly relevant for PD research as they open novel opportunities for early intervention and personalised treatment strategies by identifying at-risk individuals before clinical symptoms emerge.
Commented by: Marcello Serra (08/04/2025)
DOI: https://doi.org/10.1038/s41467-024-48961-3
