biostudies-literatureUnknown108(2)Mukesha DBackgroundMetabolic 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 ShriJournal of Alzheimer's disease : JAD824-833https://www.ebi.ac.uk/biostudies/studies/S-EPMC12614907biostudies-literatureTargeted serum metabolomic profiling and machine learning approach in Alzheimer's disease using the Alzheimer's disease diagnostics clinical study (ADDIA) cohort.PMC12614907Pham-Van LDGurvit HKul SSellal FBlanc FBoutillier SGabelle AMarizzoni MSacco GDemiralp TDurand FDemonet JFBier JCIvanoiu APasquier FSarter MMukesha DFrisoni GBDubois BDavid RHalter DMagnin EDubray MFirat HfalseTargeted serum metabolomic profiling and machine learning approach in Alzheimer's disease using the Alzheimer's disease diagnostics clinical study (ADDIA) cohort.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 Shri2025-01-01T00:00:00Z2025 Nov2026-06-05T12:18:32.553Z2026-05-16T03:13:05.911ZS-EPMC126149074100462310.1177/13872877251378653