Predicting progression of Alzheimer's disease using blood-based multi-omics data

Early prediction of Alzheimer's disease (AD) progression from mild cognitive impairment (MCI) remains a major challenge, particularly when relying on non-invasive biomarkers. Identifying individuals with progressive MCI (pMCI) before conversion to AD could improve intervention strategies and clinical management. In this study, we developed a machine learning (ML) framework integrating blood-based multi-omics and demographic data to distinguish pMCI from stable MCI (sMCI).

We trained ML models using combinations of single nucleotide polymorphism (SNP), DNA methylation, gene expression, lipid, and bile acid metabolite data using both early and late data integration strategies. Late integration consistently outperformed early integration, with the L1-regularized logistic regression model achieving the highest F1 score of 90.7% when combining SNP and lipid data. Feature interpretability analyses using LIME and SHAP identified reproducible biomarkers across omic layers, including SNPs in MYH11, FOXP1, MAPK10, and SYN3; methylation changes in CDX2 and DHX58; and altered ceramide lipid CER.D19.1.24.0 levels, all previously associated with AD-related pathology. These findings demonstrate that combining multi-omics and demographic data can improve early prediction of AD progression and support the feasibility of blood-based, interpretable biomarkers for precision diagnostics.

The data analyzed in this study were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (https://adni.loni.usc.edu/) and are available to qualified researchers upon application. The code used in this study, together with instructions and a toy dataset, is available at: https://github.com/bozdaglab/AD_sMCI_vs_pMCI.