Genetic Associations with Temporal Modeling of Alzheimer's Disease Progression Supports a Novel Paradigm for Disease...

A major challenge in Alzheimer's disease (AD) research is predicting who will develop AD, how it progresses, and how to slow, prevent, or reverse progression. Here, we apply a data-driven timeline inference framework to sparse longitudinal blood metabolomics data to reconstruct AD timelines and derive individual-specific timeline progression rates. Inferred temporal locations for each metabolomics sample along the AD timeline closely track clinical severity, while timeline progression rates capture inter-individual differences in the speed of pathophysiological progression. Genome-wide association studies of timeline progression rate identify novel loci distinct from those in AD case-control studies, notably showing no effect of the major risk locus APOE . These findings support a multidimensional paradigm of AD risk in which disease potential and progression act as partially independent factors. By explicitly modeling disease dynamics, this work reveals genetic contributions not captured by traditional approaches and provides a framework for studying AD and other progressive disorders.