This study validates a targeted mass spectrometry assay for identifying blood biomarkers predicting Parkinson's disease. Analyzing eight proteins in blood samples from recently diagnosed Parkinson's patients, pre-motor individuals with REM sleep behavior disorder, and healthy controls, a machine-learning model accurately identified all Parkinson's patients and 79% of pre-motor individuals up to 7 years before motor onset. This blood panel could help identify at-risk participants for clinical trials aimed at preventing Parkinson's disease.

Jenny Hällqvist, Michael Bartl, Mohammed Dakna, Sebastian Schade, Paolo Garagnani, Maria-Giulia Bacalini, Chiara Pirazzini, Kailash Bhatia, Sebastian Schreglmann, Mary Xylaki, Sandrina Weber, Marielle Ernst, Maria-Lucia Muntean, Friederike Sixel-Döring, Claudio Franceschi, Ivan Doykov, Justyna Śpiewak, Héloïse Vinette, Claudia Trenkwalder, Wendy E. Heywood, Kevin Mills, Brit Mollenhauer