Application of machine learning to blood-based biomarkers of Alzheimer's disease in Down syndrome.

INTRODUCTION

Blood-based biomarkers can improve Alzheimer's disease (AD) characterization in Down syndrome (DS). This study applied hierarchical clustering and machine learning-based feature selection to identify biomarkers associated with disease progression.

METHODS

Cross-sectional blood-based biomarkers were analyzed from 211 DS participants (n = 79 cognitively stable [CS]; n = 72 mild cognitive impairment [MCI]; n = 60 AD dementia [DS-AD]). These included markers of amyloid, tau, neurodegeneration, and inflammation. Clustering grouped biomarkers. Decision trees classified disease stage, and Shapley values identified the strongest predictors of disease stage.

RESULTS

The strongest predictors overall were neurofilament light chain (NfL), tau/amyloid beta (Aβ)40, Aβ42/Aβ40, alpha-2-macroglobulin (A2M), and interleukin (IL)-10. Within the CS group, NfL, tau/Aβ40, A2M, and IL-10 were strong predictors. In MCI, Aβ42/Aβ40, NfL, A2M, and IL-10 were strong predictors. In DS-AD, Aβ42/Aβ40, NfL, and tau/Aβ40 were the top predictors. Cluster membership varied based on disease stage.

DISCUSSION: These findings reveal evolving biomarker signatures and clustering patterns across cognitive stages, underscoring their potential for disease monitoring.