The urgent need for safe and effective therapies for Alzheimer’s disease (AD) has spurred a growing interest in repurposing existing drugs to treat or prevent AD. In this study, we combined multi-omics and clinical data to investigate possible repurposing opportunities for AD. We performed transcriptome-wide association studies (TWAS) to construct gene expression signatures of AD from publicly available GWAS summary statistics, using both transcriptome prediction models for 49 tissues from the Genotype-Tissue Expression (GTEx) project and microglia-specific models trained on eQTL data from the Microglia Genomic Atlas (MiGA). We then identified compounds capable of reversing the AD-associated changes in gene expression observed in these signatures by querying the Connectivity Map (CMap) drug perturbation database. Out of >2,000 small-molecule compounds in CMap, aspirin emerged as the most promising AD repurposing candidate. To investigate the longitudinal effects of aspirin use on AD, we collected drug exposure and AD coded diagnoses from three independent sources of real-world data: electronic health records (EHRs) from Vanderbilt University Medical Center (VUMC) and the National Institutes of Health All of Us Research Program, along with national healthcare claims from the MarketScan Research Databases. In meta-analysis of EHR data from VUMC and All of Us, we found that aspirin use before age 65 was associated with decreased risk of incident AD (hazard ratio=0.76, 95% confidence interval [CI]: 0.64-0.89, P=0.001). Consistent with the findings utilizing EHR data, analysis of claims data from MarketScan revealed significantly lower odds of aspirin exposure among AD cases compared to matched controls (odds ratio=0.32, 95% CI: 0.28-0.38, P<0.001). Our results demonstrate the value of integrating genetic and clinical data for drug repurposing studies and highlight aspirin as a promising repurposing candidate for AD, warranting further investigation in clinical trials.

The authors have declared no competing interest.

This study was supported by the National Institute of Aging of the National Institutes of Health under award numbers R01AG069900, F30AG080885.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This study was conducted with approval from the VUMC Institutional Review Board and the NIH All of Us Research Program. All EHR data from VUMC and All of Us are de-identified; use of these data is considered non-human subjects research.

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All data generated or analyzed during this study are included in this published article and its Supplementary Information. The microglia eQTL summary statistics from the Microglia Genomic Atlas used in this study can be downloaded from the NIAGADS Data Sharing Service using accession number NG00105.v3. The AD GWAS summary statistics used in this study are available at https://cncr.nl/research/summary_statistics/. The MASHR GTEx v8 transcriptome prediction models can be downloaded from PredictDB (https://predictdb.org/categories/downloads/). Access to VUMC EHR’s database requires institutional approval and compliance with a data use agreement. Data from the All of Us Research Program can be accessed through the Researcher Workbench (https://workbench.researchallofus.org). The MarketScan claims data used in this study can be requested from Merative®.