The UK Biobank represents the largest well-characterised healthcare data set available to research. Due to its scale and the richness of data available, it is without peer. However, participants were recruited from England, Scotland and Wales and there are open questions about whether results will generalise to countries with different ethnicities and lifestyles. Diseases that affect the heart and arteries are the leading cause of ill-health worldwide. Our ability to predict diseases of the heart and arteries, including heart attacks and strokes, and to predict who will respond best to treatment is still relatively poor. We wish to use the data in the UK biobank along with data from patient groups in Italy, Serbia and Northern Ireland to explore and evaluate biomarkers that accurately predict the risk of diseases and predict the likely responses to treatment. We wish to explore how well these prediction algorithms translate between countries. This will have value as a tool for identifying the patients most at risk and for monitoring the progress of patients following intervention towards reducing risk.
Our aims are
1. To use the clinical measurements and data already measured from patient samples to identify and evaluate risk prediction formula for diseases of the heart and arteries and the likely success of treatment.
2. To explore how well these findings can be applied between patient groups from different countries, using patient data available from Italy, Serbia and Northern Ireland.
3. To identify what additional questions that can be asked of diseases that affect the heart and arteries and to determine which diseases can be targeted for the development of effective risk prediction.
In the first instance the project will run for 36 months and will align to a jointly funded EU-UK project, CardioSCOPE. We hope that the project will yield new insights into risk prediction for disease and treatment and how well this prediction transfers between countries. We also hope that the project will develop informatics skills in working with large population health data regionally and internationally and will drive participation in future large population health data projects.