Integrating genetically predicted transcriptomic signatures with longitudinal real-world data enables scalable drug repurposing for Alzheimer's disease.

Drug repurposing offers a potential strategy to expand treatment options for conditions with limited therapies, but advancing repurposing candidates toward clinical implementation remains a challenge. Large-scale data, together with advanced genetic and epidemiological methods, may help address this gap. Here, we present an integrative digital medicine approach that combines genetically predicted transcriptomic signatures and perturbation screening for candidate identification with multi-cohort real-world validation for systematic evaluation of prioritized candidates. We applied this approach to Alzheimer's disease (AD), a disease with substantial unmet clinical need and persistent difficulty in developing effective therapies. We constructed AD signatures from genetically predicted expression changes across bulk tissues and microglia, then queried Connectivity Map profiles to identify compounds predicted to oppose these signatures. Aspirin emerged as a reproducible candidate across multiple signatures and underwent further evaluation. We then examined its association with incident AD in longitudinal electronic health record data from Vanderbilt University Medical Center and the NIH