| Title: | Identification of the shared biological and sociodemographic factors underlying cardiovascular disease and dementia risk |
| Lead Institution: | McMaster University |
| Principal investigator: | Professor Guillaume Pare |
Application 15255
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About
Our goal is to construct a comprehensive picture of the genetic, biological and sociodemographic risk factors underlying the observed link between cardiovascular disease (CVD) and dementia. Our research program comprises three projects: 1) identify genetic variation associated with risk of CVD, dementia and their associated risk factors; 2) identify biological risk factors with a causal role in the development of CVD and dementia, and elucidate the biological pathways connecting these conditions; and 3) identify sociodemographic factors with a causal role in CVD and dementia, and describe how sociodemographic factors interact with biological risk. CVD and dementia contribute significant health burden in the UK, particularly as the population ages. There is evidence that cardiometabolic factors are associated with risk of both CVD and dementia ? and that risk varies by sociodemographic status ? but the causal relationship between these conditions and their shared risk factors is not known. Consistent with the aims of the UK Biobank, our research will contribute to the prevention, diagnosis, and treatment of both conditions. Specifically, our research will inform the development of clinical screening programs and identification of high-risk groups, and facilitate early and effective intervention on disease risk. Primary analyses will be conducted using clinical, genetic and sociodemographic data from the UK Biobank. With this dataset, we will identify novel genetic variants and biomarkers associated with CVD and dementia, and elucidate the biological pathways underlying these conditions. Simultaneously, we will investigate sociodemographic risk factors and disparities in CVD and dementia risk. Finally, we will investigate in the UK Biobank interactions between biological and sociodemographic risk factors for CVD and dementia, and then replicate and validate our findings in other available datasets. We are requesting data from all available participants. Data from the whole cohort is essential in order to facilitate investigation of gene-environment interactions, which requires large sample sizes to ensure sufficient statistical power. We have the experience and computational capacity to analyze large datasets and genomic data.5 Publications
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 8760 | A method to estimate the contribution of rare coding variants to complex trait heritability | Nazia Pathan (+9) | 2024 | Nature Communications |
| 9706 | A versatile, fast and unbiased method for estimation of gene-by-environment interaction effects on biobank-scale datasets | Matteo Di Scipio (+12) | 2023 | Nature Communications |
| 7620 | Calibrated rare variant genetic risk scores for complex disease prediction using large exome sequence repositories | Ricky Lali (+6) | 2021 | Nature Communications |
| 7567 | Cost-Effectiveness of Polygenic Risk Scores to Guide Statin Therapy for Cardiovascular Disease Prevention. | Michel Kiflen (+6) | 2022 | Circulation Genomic and Precision Medicine |
| 9632 | Surrogate Adiposity Markers and Mortality | Irfan Khan (+12) | 2023 | JAMA Network Open |
Enabling scientific discoveries that improve human health