Associations between exposomic phenotypes, glycemic variability and complications in people with type 1 diabetes

Glycemic variability (GV) is an important determinant of diabetes-related micro- and macrovascular complications and is a source of diabetes distress and poor quality of life in people with type 1 diabetes (T1D). Lifestyle factors are drivers of GV, but little is known on other potential risk factors. The PhD project will investigate the influence of environmental factors on GV and the risk of complications in people with type 1 diabetes.

Using data from the large SFDT1 cohort study augmented by innovative voice-based data collection, the PhD student will develop machine learning based approaches (including, but not restricted to, DDR-Tree and network analyses) to study the cocktail effect of multiple environmental factors (air quality, pollen, noise pollution, water quality, UV and weather) on diabetes-related outcomes. This will allow identifying exposomic phenotypes relevant in T1D care and defining personalised exposome-based interventions to prevent or delay diabetes-related complications.