Identifying Alzheimer’s Disease Progression Subphenotypes via a Graph-based Framework using Electronic Health Records

This study developed a novel approach that combines a graph neural network (GNN)-based framework with time series clustering to characterize progression subphenotypes from MCI to AD. Applied to a real-world cohort of 2,525 patients (61.66% female; mean age 76 years), the model identified four distinct progression subphenotypes, each exhibiting characteristic clinical patterns, with average MCI-to-AD progression times ranging from 805 to 1,236 days. The findings indicate that AD does not follow a uniform progression trajectory but instead manifests heterogeneous pathways.