3.5 Year MRC iCASE PhD Programme: Real-world assessment of glucose variability and hypoglycaemia in type 1 diabetes in Scotland

Chronic exposure to hyperglycaemia in type 1 diabetes (T1D) carries with it an increased risk of microvascular and macrovascular disease. Intensive insulin therapy (IIT) aimed at optimal glucose control can largely prevent or minimize these complications. However, limitations inherent in current insulin delivery systems as well as an impaired physiological ability to respond normally to hypoglycaemia in T1D, means that IIT is associated with increased glucose variability (GV) and severe hypoglycaemia. As a result, the majority of individuals with T1D do not appear to achieve recommended glycaemic targets. Novel technologies have been introduced to try and improve diabetes management and help people achieve glycaemic targets while also avoiding hypoglycaemia. One such technology is Continuous glucose monitoring (CGM) which provides information about the direction and rate of change of glucose and provides alarms to warn of impending hypoglycaemia. Clinical studies such as IN CONTROL (van Beers et al. Lancet Diab and Endocrinol 4:893-902, 2016) have shown that CGM use result in significant reductions in the rates of severe and biochemical hypoglycaemia in people with type 1 diabetes. Because of trials such as this CGM is licensed for use in Scotland for individuals at high risk of hypoglycaemia. Sci-Diabetes is a fully integrated shared electronic patient record to support treatment of NHS Scotland patients with Diabetes. Recent developments have enabled download of CGM data directly into Sci-Diabetes providing a unique access to ambulatory glucose recordings of people with type 1 diabetes using CGM in Scotland. This PhD project, working along with Dexcom will examine the effect of CGM on glucose variability and hypoglycaemia in subjects using CGM which can be linked through individual CHI number to phenotypic, biochemical data and hospital admissions (including ambulance call outs). We will also explore factors relating to persistence with CGM technology as well as exploring different methods of assessing glucose variability and hypoglycaemia exposure. The PhD student will learn about analysis and handling of large data sets as well as learn about data governance. There will also be the opportunity to model different aspects of glucose variability.