Use of AI-driven technologies to determine a differential and prognostic signature of autoimmune diabetes subtypes

The immune system and biology of the immune system is key to understanding how and why disease occurs. Diabetes is one of the top 10 leading causes of death and disability, with 529 million men, women, and children of all ages living with the disease worldwide. By 2050, this number is projected to double to 1.3 billion people, with cases increasing in every country.

Autoimmune diabetes makes up 18% (~95 million) of cases worldwide, which can be separated into subtypes associated with islet autoantibodies which include type 1 diabetes (T1D), latent autoimmune diabetes of adults (LADA), latent autoimmune diabetes in the young (LADY) and latent autoimmune diabetes in children (LADC). Because T1D, LADA, LADY and LADC are seen to be immunologically similar, the choice of insulin as the treatment is the same.

However current research suggests significant variations between the different forms and underlying causes. It has been suggested that autoimmune diabetes is a heterogeneous group of diseases, due to the autoantibodies to islet beta cells in T1D, LADA, LADY and LADC varying dramatically, as well as the rate of immune destruction.

The key aim of project is to fully analyse and define the molecular and immunological signatures to understand the development of different subtypes of T1D. 1.What are the underlying biological pathways in different subtypes of Type 1 diabetes? 2.What are the molecular biomarkers that determine the prognosis of the disease?

3.What are the immune signatures associates with the disease subtype?