Discovering Heterogeneous Causal Pathophysiology of Alzheimer's Disease

Project summary Alzheimer's disease (AD) represents a critical public health burden. Fortunately, recent disease- modifying treatments for AD have demonstrated promising outcomes based on the successful trials of Lecanemab and Donanemab. Nevertheless, how and when these Amyloid-beta- lowering medications most affect cognition is not known. This is clear from the evidence collected from both Lecanemab and Donanemab trials, where not all populations of participants showed significant beneficial effects of anti-Amyloid-beta (Abeta) plaque treatment on cognition. In this context, we aim to use existing datasets to understand AD pathophysiology progression in heterogeneous subpopulations comprehensively. The established insight will reveal optimal treatment windows for various populations, determining the precise timing and target groups for whom reducing Abeta or tau levels is most likely to yield cognitive benefits. In this proposal, we shall harness novel statistical theory and methodology to uncover the heterogeneous causal pathophysiology of Alzheimer's disease with four specific aims in this direction: (1) developing a new statistical method for heterogeneous causal discovery based on the Amyloid cascade hypothesis, (2) developing a new statistical method for data-driven causal discovery to uncover heterogeneous causal relationships among Abeta, tau accumulation, and cognition in various subpopulations, (3) applying the proposed methods in Aim 1-2 across diverse AD datasets and (4) developing software and dissemination research product.