Global dementia cases are projected to exceed 80 million by 2040, with Alzheimer’s disease (AD) and cerebrovascular contributions representing major etiologies. Despite clinical co-occurrence, the genetic interplay and causal pathways between cerebrovascular disease (CeVD) and AD remain poorly characterized. Current evidence cannot distinguish causal relationships from shared risk factors, creating a critical knowledge gap for developing effective prevention strategies.
To address this gap, we will employ advanced genetic methodologies within the UK Biobank cohort. Our specific aims are to: (1) develop and validate polygenic risk scores for both diseases using a two-stage approach; (2) quantify shared genetic architecture via cross-trait PRS associations and pleiotropic locus identification; and (3) determine bidirectional causality using Mendelian randomization to establish evidence for causal pathways.
Genetic instruments enable causal inference in complex diseases. Polygenic risk scores integrate genetic susceptibility across variants to quantify disease risk, while Mendelian randomization utilizes these variants as natural experiments to establish causality and overcome confounding. Together, these methods provide powerful tools to elucidate disease etiology beyond conventional observational approaches. To solve this, our study will use genetic data as a powerful tool to uncover the true relationship. We will develop polygenic risk scores, which measure a person’s inherited risk for each disease. Using a method called Mendelian randomization, we will then treat these genetic scores as natural experiments to test if higher genetic risk for stroke actually causes a higher risk of Alzheimer’s, or if the reverse is true. The findings will provide crucial evidence on whether preventing or treating cerebrovascular disease can also reduce the risk of developing Alzheimer’s dementia, guiding future clinical strategies.
