About
Decades of epidemiological research has established many of the key associations between human biology and disease traits. For example, we know that smoking, a high body mass index, age, and sex (along with many other factirs) can modify one's risk of cardiovascular disease. Some of these risks are modifiable - for example one can quit smoking and attempt to lose weight - whereas some are not, for example age. Beyond these associations, however, it remains challenging to spot those at most risk of major disease events, ie. a heart attack or other potentially fatal organ failure, in a cohort where the vast majority would be considered at risk. This is the challenge many clinicians face when triaging patients at tertiary clinics for common chronic diseases such as diabetes and liver disease. The aim of this research project is to try and find biomarkers - either blood-based or genetic - that enable risk stratification of those that are already considered at a higher risk of a "major disease event" compared to 80-90% of the general population. The rationale behind this is clear, in that we hope to improve the prediction and prevention of disabling health-related outcomes, whilst improving the allocation of health resources to those who are most at risk. We expect the timeline of this project to be approximately 18 months, where the first phase will consist of data exploration, the second phase to be training and generating novel algorithms using the data, and the final phase to be validating these models using test data not previously seen by the algorithms and disseminating the results of the project.