In a recent study published in Nature Communications , researchers combined mass spectrometry-based proteomic phenotyping with machine learning to identify blood biomarkers in early Parkinson's disease (PD). PD is an increasingly prevalent neurological condition affecting the central nervous system, presenting with motor and non-motor symptoms due to alpha-synuclein aggregation in dopaminergic cells. The clinical heterogeneity of the disease and the lack of easily measurable biomarkers challenge current strategies.
Peripheral fluid biomarkers, such as the axonal marker neurofilament light chain (NfL), have shown longitudinal increases correlating with motor and cognitive PD progression. Developing disease-modifying and preventative measures requires an improved understanding of the initial events in PD molecular pathogenesis, objective and less-invasive peripheral fluid biomarkers, and biofluid assays. Biomarkers that can detect PD early might boost population-based screening for identifying at-risk patients, perhaps enrolling them in future preventative studies and enhancing disease monitoring.
In the present study, researchers developed a targeted multiplexed proteomic mass spectrometry assay to identify individuals at risk of Parkinson's disease. The exploratory cohort comprised drug-naïve PD patients (eight males, mean age of 67 years) and ten healthy controls (five men, mean age of 66 years) from the DeNoPa cohort. The validation cohort comprised 99 individuals with ne.
