Targeted serum metabolomic profiling and machine learning approach in Alzheimer's disease using the Alzheimer's disease diagnostics clinical study (ADDIA) cohort.

ABSTRACT: BackgroundMetabolic biomarkers can potentially be used for early diagnosis, prognostic risk stratification and/or early treatment and prevention of individuals at risk to develop Alzheimer's disease (AD).ObjectiveOur goal was to evaluate changes in metabolite concentration levels associated with AD to identify biomarkers that could support early and accurate diagnosis and therapeutic interventions by using targeted mass spectrometry and machine learning approaches.MethodsSerum samples collected from a total of 107 individuals, including 55 individuals diagnosed with AD and 52 healthy controls (HC) enrolled previously to ADDIA cohort were analyzed using the biocrates AbsoluteIDQ^® p400 HR kit metabolite and lipid panel. Several machine learning models including Least Absolute Shri

SUBMITTER: Mukesha D 

PROVIDER: S-EPMC12614907 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature