Type II (T2DM) is a chronic, multifactorial disease resulting in severe complications that lead to increased morbidity and even death. Due to the multifactorial pathology of the disease the interventions to treat T2DM and prevent complications may include lifestyle interventions, oral pharmacotherapy and self-injection with insulin. The incidence and prevalence of T2DM in the UK are expected to rise considerably in the coming years coinciding with an increase in the incidence of complications, such as cardiovascular disease. The expected costs that coincide with these increments are tremendous. Due to the many factors of influence and the variations in treatment response, personalized treatment is required to improve the current T2DM care provided. Therefore, it would be beneficial if high risk patients were to be identified so that their treatment can be adapted accordingly. Diabetes is an exemplar for the application of Big Data to disease management. Diabetes care has been at the forefront of patient-collected data and the ‘quantified self’ movement. Diabetes is a heterogeneous condition where improved nosology and stratification is likely to pay dividends through improved targeting of specific treatments. The potential cost savings and improvements in quality of life are considerable It is well recognized that there is substantial variation in how people with diabetes respond to particular treatments, beyond the distinction between insulin-dependent and non-insulin dependent diabetes. Type 2 diabetes is a polygenic disorder, which may be better understood as a group of diseases with the common manifestation of hyperglycaemia. The genetics are largely still unknown: there are a large number of genetic loci associated with a small increased risk of diabetes, and there are a number of metabolic pathways affected. This provides great potential for tailoring treatments to the individual patient (whether this is called personalised, precision, or stratified medicine. The aim of the project is to research and develop Big Data analytics and visualization tools that can support clinicians to identify the optimal treatment for an individual.
The project is in collaboration with the NHS as the clinical end user and the data provider. The work will form part of ongoing research activities at Kingston University in the area of Big data for healthcare.