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Abstract
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.
Publisher
Nature Communications
Published On
Jun 18, 2024
Authors
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
Tags
Parkinson's disease
blood biomarkers
mass spectrometry
machine learning
early detection
clinical trials
REM sleep behavior disorder
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