Pioglitazone Reduces the Incidence of Alzheimer’s Disease: From Genetics to Population-Based Validation

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Alzheimer's & Dementia: The Journal of the Alzheimer's Association



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BACKGROUND: 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. METHOD: 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. RESULT: 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. CONCLUSION: 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.

Controlled Subject

Alzheimer's disease


Immunology and Infectious Disease

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