Prediction of motor and non-motor Parkinson's disease symptoms using serum lipidomics and machine learning: a 2-year study.

ABSTRACT: Identifying biological factors which contribute to the clinical progression of heterogeneous motor and non-motor phenotypes in Parkinson's disease may help to better understand the disease process. Several lipid-related genetic risk factors for Parkinson's disease have been identified, and the serum lipid signature of Parkinson's disease patients is significantly distinguishable from controls. However, the extent to which lipid profiles are associated with clinical outcomes remains unclear. Untargeted high-performance liquid chromatography-tandem mass spectrometry identified >900 serum lipids in Parkinson's disease subjects at baseline (n = 122), and the potential for machine learning models using these lipids to predict motor and non-motor clinical scores after 2 years (n = 67) was assess

SUBMITTER: Galper J 

PROVIDER: S-EPMC11199659 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature