Machine learning model base on metabolomics and proteomics to predict cognitive impairment in Parkinson's disease.

ABSTRACT: There is an urgent need to identify predictive biomarkers of Parkinson's disease (PD) with cognitive impairment (PDCI) in order to individualize patient management, ensure timely intervention, and improve prognosis. The aim of this study was to screen for these biomarkers by comparing the plasma proteome and metabolome of PD patients with or without cognitive impairment. Proteomics and metabolomics analyses were performed on a discover cohort. A machine learning model was used to identify candidate protein and metabolite biomarkers of PDCI, which were validated in an independent cohort. The predictive ability of these biomarkers for PDCI was evaluated by plotting receiver operating characteristic curves and calculating the area under the curve (AUC). Moreover, we assessed the predictive ab

SUBMITTER: Yang B 

PROVIDER: S-EPMC11470017 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature