Pioglitazone reduces the incidence of Alzheimer’s disease: From genetics to population‐based validation

Abstract

Genome-wide association studies (GWAS) have identified numerous susceptibility loci for Alzheimer's disease (AD). However, utilizing GWAS to identify high-confidence AD risk genes (ARGs) that can guide development of new therapeutics for patients suffering from AD has heretofore not been successful. To address this critical problem in the field, we have developed a genotype-informed, network-based methodology that interrogates pathogenesis to identify new therapeutics. When applied to AD, this approach integrates GWAS findings, multi-omics data from brain samples of AD patients and AD transgenic animal models, drug-target networks, and the human protein-protein interactome, along with large-scale patient database validation and in vitro mechanistic observations in human microglia cells. Through this approach, we identified 103 ARGs validated by various levels of pathobiological evidence in AD. Via network-based prediction and population-based validation, we then showed that three drugs are significantly associated with decreased risk of AD compared with matched control populations: pioglitazone (P = 0.005, hazard ratio (HR) = 0.916, 95% confidence interval [CI] 0.861-0.974), febuxostat (HR = 0.815, 95% CI 0.710-0.936, P = 0.004), and atenolol (HR = 0.949, 95% CI 0.923-0.976, P = 2.8 x 10-4 ). Pioglitazone is a peroxisome proliferator-activated receptor (PPAR) agonist used to treat type 2 diabetes, and propensity score matching cohort studies confirmed its association with reduced risk of AD in comparison to glipizide (HR =0.921, 95% CI 0.862-0.984, P = 0.0159), an insulin secretagogue that is also used to treat type 2 diabetes. In vitro experiments showed that pioglitazone downregulated glycogen synthase kinase 3 beta (GSK3β) and cyclin-dependent kinase (CDK5) in human microglia cells, supporting a possible mechanism-of-action for its beneficial effect in AD. In summary, we present an integrated, network-based methodology to rapidly translate GWAS findings and multi-omics data to genotype-informed therapeutic discovery in AD.