Proteomics and machine learning identify a distinct biomarker panel to detect prodromal and early Parkinson’s disease
Research Square (Research Square)(2023)
摘要
Abstract Parkinson’s disease (PD) is an increasingly prevalent neurodegenerative disease for which readily available and non-invasive diagnostic biomarkers are scarce. Here, we present a panel of proteomic plasma biomarkers, capable of discriminating between PD and healthy controls with 100% accuracy in a machine learning model. We performed a discovery proteomics study on newly diagnosed PD patients and controls, followed by a multiplexed targeted proteomic assay applied to 99 de novo PD patients and 36 controls. The machine learning model correctly classified all patients, and multiple markers correlated with motor, non-motor symptom severity and cognitive decline. We also evaluated 18 prodromal subjects with iRBD and predicted 72 - 94% of the iRBD samples as PD. This figure matches the clinical conversion rate observed in PD, identifying a pattern already evident in iRBD and indicating pre-symptomatic molecular events. These findings may advance our understanding and supporting of future clinical trials.
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关键词
early parkinsons,distinct biomarker panel,prodromal
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