Background: Alzheimer's disease (AD) has so far proved refractory to intervention. However, disease incidence is variable across prior medication and observational measures. The present study adopted a data-driven approach to inform possible drug repurposing strategies in the light of concurrent prescription and biometric data. Methods and Results: A real-world dataset was harnessed to compare observational and prescription data for 250,000 individuals prior to AD diagnosis against an age-, sex-, and clinical practice-matched control cohort with no AD history. Observation data was shown to both explain the associations of classes of drug prescription with AD incidence and to bolster the repurposing potential of the GLP-1 agonist class of anti-diabetic drugs. Conclusion: This study provides insights into how drug repurposing based on prescription histories can be informed by concurrent observational data. These findings offer novel insights to be explored in future research on causal models for AD progression.

The authors have declared no competing interest.

This study did not receive any funding

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