Abstract
Biological staging models are a key tool for assessing the severity of Alzheimer’s disease (AD), supporting personalized medicine and playing a critical role in clinical trial design. Recently, researchers have leveraged positron emission tomography (PET) to inform data-driven staging models of brain pathology related to AD. However, most approaches have focused on staging either amyloid or tau progressions separately, while both pathologies constitute defining factors of AD. Here, we aimed to derive a data-driven staging model which encompasses the spatial spread of both amyloid and tau. We assembled a large sample (n=3,293) of individuals with both amyloid and tau PET imaging stemming from 8 neuroimaging studies of AD and aging. We applied unsupervised machine learning to estimate brain areas which showed coordinated pathological accumulation across our sample, and we used these regions to inform a data-driven model for staging amyloid and tau. The resulting six stage model showed two stages of amyloid progression followed by four stages of tau spread, which were associated with cross-sectional and longitudinal assessments of cognitive decline. Comparison of our biological staging model with clinical disease stages recommended by the Alzheimer’s Association showed evidence of heterogenous symptom profiles. Replication of results in holdout data demonstrated the generalizability and prognostic value of our staging model. Together, these findings establish a comprehensive and rigorously validated biological staging model that jointly characterizes amyloid and tau progression, advances beyond global or anatomically predefined summaries, and provides a scalable framework for studying disease heterogeneity and progression in AD.
One Sentence Summary
Using PET imaging from a large sample of individuals (n=3,293), we derive a data-driven model for staging amyloid and tau pathology.
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