Study on non-targeted metabolomics of Alzheimer's disease based on machine learning
ZHANG Haowei
LI Xiaole
ZHANG Tiangui
LIU Ying
Abstract：To explore the relationships between metabolic disturbances in patients with Alzheimer's disease(AD)and different stages of the disease,and to study the potential of plasma in the prevention,diagnosis and treatment of AD,we employed three ma-chine learning methods to establish classification models for plasma and cerebrospinal fluid at different stages of the disease respective-ly,and conducted enrichment pathway analysis by screening relevant features as differential metabolites.The results indicated that ab-normalities in amino acid metabolism,lipid metabolism,energy metabolism and mitochondrial function were found in both body fluids of the patients,and these abnormalities occurred in the early stage of mild cognitive impairment.This research not only investigates the potential of plasma in the diagnosis of AD,but also explores the pathogenesis of different stages of AD from a metabolic perspective.
Keywords：Machine learningAlzheimer's diseaseMild cognitive impairmentMetabolomicsPlasmaCerebrospinal fluid
Publication Date：2025-06-30
Online Publishing Date：2026-09-11(First online date of this platform, not the publication date of the document)
Pages：8( 170-177 )