| Title: | Risk factors for `less-frequent` cardiovascular diseases |
| Lead Institution: | University of Cambridge |
| Principal investigator: | Professor Emanuele Di Angelantonio |
Application 26865
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About
Previous studies on cardiovascular disease (CVD) have tended to focus on a limited number of specific diseases, such as coronary heart disease (CHD) or stroke. However, CHD and stroke currently represent only ~40% of incident cardiovascular events in UK, and other CVDs outcomes have received far less attention so far. The objectives of this project are therefore: 1)To identify potential genetic and non-genetic (biochemical, lifestyle and other characteristics) determinants of a range of ?less-frequent? CVDs. 2)To examine shared aetiological pathways underlying these CVD outcomes. 3)To develop risk prediction models integrating CHD, stroke and other ?less-frequent? CVDs. This project will use the power, detailed phenotypic information, and prospective nature of UK Biobank to improve our understanding of the determinants of ?less frequent? cardiovascular disease. This research is in the public interest considering population aging and the burden these diseases have on the adult population. Evidence concerning the determinants of these diseases could importantly inform public health priorities and the targeting of prevention efforts. We will analyse the genetics and non-genetic determinants of a range of `less-frequent` CVDs such as (but not limited to) venous thromboembolism (including deep vein thrombosis and pulmonary embolism), aortic aneurysms, peripheral arterial disease, atrial fibrillation and other arrhythmias, syncope, sudden cardiac death, and heart failure, and compare them with the determinants of CHD and stroke (including stroke subtypes). Furthermore, we will evaluate whether risk prediction models can be developed for CVDs either considering risk scores to predict each type of event separately or by using a single risk score model to predict the combined outcome of all such events. We will perform analyses in the full cohort.1 Return
| Return ID | App ID | Description | Archive Date |
|---|---|---|---|
| 2384 | 26865 | Genomic risk score offers predictive performance comparable to clinical risk factors for ischaemic stroke | 4 Sep 2020 |
7 Publications
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