Principal Investigator: Dr Loes Rutten-Jacobs
Department: University of Cambridge, Clinical Neurosciences, R3, Box 83, Cambridge Biomedical Campus, Cambridge, CB2 0QQ,
Institution: University of Cambridge
Lead Collaborators: 1) Professor Cathryn Lewis
Collaborating Institutions and Addresses: 1) King’s College London,SGDP Centre, Inst of Psychiatry,Psychology & Neuroscience, De Crespigny Park, London, SE5 8AF, United KingdomTags: 19463, 36509, causality, cerebrovascular, Dementia, genetics, MRI, Risk
1a: Cerebrovascular disease is a strong risk factor for cognitive decline and subsequently dementia. Vascular risk factors, genes and lifestyle have been associated with both cerebrovascular disease and cognitive decline, but the underlying pathophysiological mechanisms are complex. Our study aims to integrate and analyse the extensive clinical, epidemiological, genetic and imaging data available, in order to
– elucidate the biological pathways through which the vascular, (novel) genetic and lifestyle factors interact and are linked with acute cerebrovascular events, magnetic resonance imaging features of cerebrovascular disease, cognitive decline and dementia
– to what extend demographic characteristics and prescribed medication influence these relations
1b: An aim of UK Biobank is the reliable assessment of different causes of disease. The extensive clinical, epidemiological and genetic data available in UK Biobank provide the opportunity to increase our insight in the interplay and causality of different risk factors in the pathophysiological mechanisms underlying cerebrovascular disease or dementia. Previously, small sample sizes of typical cohort studies have limited the possibility to perform these analyses.
1c: We will test for associations between the risk We will test for associations between the risk factors of interests and the phenotypes under study.
We aim to identify novel genetic risk variants for the phenotypes under study by performing genome-wide association studies and statistically more efficient methods including pleiotropy-based methods. In addition we will do a comprehensive computational assessment of how the risk factors of interest interact in influencing the phenotypes under study and explore any mediating relationships.
1d: Data from the full cohort will be required (both males and females).