Cardiovascular Diseases

In the UK, 7M people live with cardiovascular disease (CVD), 100,000 are hospitalised and 450 die daily. Annual healthcare cost is £9 billion. Our theme aims to transform the outlook for patients with cardiovascular disease.

Our partnership with the University provides our theme a strong foundation in fundamental science, but we also work closely with patients and volunteers, including very large national longitudinal studies, international consortia and national health records data.

We also work closely with other BRC specialist themes because the heart and blood vessels influence the development and progress of many related diseases.

For the next 5 years our theme has identified the following key strategic and scientific aims:

• Harness ‘omics, electrophysiology, imaging, and artificial intelligence (AI) expertise we aim to better predict, prevent, diagnose, and treat CVD in the context of multimorbidity and socioeconomic and demographic drivers
• Interrogate genotype-phenotype relationships to generate predictive markers; validate therapeutic targets for drug development and repurposing;
• Develop diagnostic and interventional devices to support patient care.
• Tackle high failure rates in R&D; transition robust innovations from research to care settings; address needs of an ageing population; and support development of future research leaders.

These aims will be delivered through three sub-themes:

1. Prediction, prevention and screening; developing better risk prediction models using electronics health records, genomics and other ‘omics; improving screening for rare inherited disorders.
2. Precision diagnostics and disease stratification; using next generation imaging methods to characterise disease; harnessing AI to improve throughput, diagnostic accuracy and support risk prediction; linking molecular pathology with images to create new diagnostics, to translate and disseminate globally via partnership with industry and SMEs.
3. De-risking cardiovascular drug and device development through genomics, engineering, imaging, and computational modelling.