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

Abstract

Background: Metabolic 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). Objective: Our 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. Methods: Serum 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 Shrinkage and Selection Operator (LASSO), Partial Least Squares (PLS), Random Forest, and XGBoost were trained to classify AD and HC. Repeated cross-validation was used to ensure performance evaluation. Results: The LASSO and PLS models showed the strongest classification performance on the test set, achieving area under the ROC curve (AUC) values of 0.84 and 0.90, respectively. A refined model based on only the top 5 metabolites maintained strong performance, and the inclusion of Apolipoprotein E (APOE) genotype information notably improved classification accuracy, particularly by reducing false negatives in AD cases. Conclusions: These results highlight important metabolic signatures that could help to reduce misdiagnosis and support the development of metabolomic panels to detect AD. The combination of multiple serum metabolic biomarkers and APOE genotyping can significantly improve classification accuracy and potentially assist in making non-invasive, cost-effective diagnostic approach.