We report a comprehensive range of structural and functional phenotypes for the heart and aorta, quantified from cardiovascular magnetic resonance images from a population-based study, the UK Biobank, using an automated machine learning-based analysis pipeline. We explore the variations of these phenotypes with sex, age, major cardiovascular risk factors and other non-imaging phenotypes across 26,893 participants. Our study illustrates how population-based cardiac and aortic imaging phenotypes could be used to better define cardiovascular risks and heart-brain health interactions, highlighting new opportunities for studying disease mechanisms and developing image-based biomarkers.
Biobank Brain and Cardiac Mutual Risk Indexing (BBC MRI) study
The prevalence of both brain and cardiac disease rises with age and there are functional interactions between the two organs in health and disease (the heart-brain axis). However, cardiac and brain co-morbidities in later life are poorly explored and risk factors or markers that may suggest interacting pathophysiological mechanisms remain to be elucidated. The aim of our research is to investigate brain-heart axis using brain and cardiac MRI images and carotid IMT data in the context of clinical, genetic and lifestyle (e.g., smoking, alcohol use) data in UK Biobank. The results of our study will lead to better understanding of brain-heart axis and may improve the prevention, diagnosis and treatment of both brain and cardiac diseases in later life. We will perform analyses of the heart and brain images and review clinical histories of people in UK Biobank to identify signs of diseases such as stroke or signs of impaired heart or brain functions. We will work to understand relationships between these and how they may be influenced by a person's genes or other medical conditions or their lifestyle. We hope to understand how to better assess the risk of brain and cardiac disease in later life and how the health of the two organs is related. To have the greatest power for the range of nested analyses, data from the full cohort of subjects with imaging data available at the time of this application are requested (~5000 individuals anticipated). As future imaging data are able to be released, we would like to supplement the study group to test exploratory hypotheses generated from the initial (~5000 subject) data using a test dataset covering an additional 15,000 people.
|Lead investigator:||Professor Paul Matthews|
|Lead institution:||Imperial College London|
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