Approved research

Understanding the Genetics of Type 1 Diabetes and Related Complications

University of Edinburgh

Lay summary

1) Our primary research aim is to use UK Biobank phenotype and genotype data to discover new genetic loci associated with type 1 diabetes and loci associated with diabetes complications (especially cardiovascular disease and renal disease) 2) Our secondary aim is to use UK Biobank biomarker and clinical data along with genetic data to build and evaluate predictive models of complications of type 1 diabetes 3) Our third aim is to compare and, as appropriate, to combine results of analyses with similar studies in other cohorts that include people with type 1 diabetes Type 1 diabetes is a major cause of death and disability. Not all the heritability of type 1 diabetes or its complications is explained by current genetic discoveries so additional attempts at genetic discovery are warranted. Current studies are hampered by small sample sizes even with international meta-analysis efforts. Genetic discoveries may yield important insight into causal pathways allowing development of preventive therapies targeting such pathways. Furthermore, combining genetic, clinical, and biomarker data for prediction allows better tailoring of treatments. Thus our research is aligned with UK Biobank's purpose to improve the prevention, diagnosis and treatment of disease. The research involves analysing data from UK Biobank and combining the results with similar analyses being conducted in other cohorts with type 1 diabetes. We are not seeking to conduct any analyses of biosamples. We will test for any differences in gene variant frequencies between people with and without type 1 diabetes in UK Biobank and for differences in gene frequencies among those with type 1 diabetes who do and don?t have complications. We will combine genetic data in statistical models with the available clinical and biomarker data to try to predict who has developed complications during follow up. We think it simplest to request genetic data and relevant phenotypic data from the entire cohort. There are 3176 persons at baseline in UK Biobank with a diagnosis of diabetes AND use of insulin within 1 year which we would consider as potential type 1 diabetes (T1DM) i.e. we will not exclude based on age of diagnosis. We also need to include those without T1DM as controls in the GWAS studies and, for the risk prediction studies relating to cardiovascular disease, we want to evaluate whether certain risk factors show markedly different relationships in those with versus without T1DM.

CURRENT SCOPE

2) Our secondary aim is to use UK Biobank biomarker and clinical data along with genetic data to build and evaluate predictive models of complications of type 1 diabetes

3) Our third aim is to compare and, as appropriate, to combine results of analyses with similar studies in other cohorts that include people with type 1 diabetes

In addition to our original proposal we were granted permission to perform additional research on retinal images. This research falls within the scope of the original research aims of investigating complications of diabetes. The extension work involves train deep learning algorithms on UK Biobank fundus photographs and optical coherence tomography images. Specifically, we will:

1) Use the OCT and fundus images to predict cardiovascular and renal disease in people with and without diabetes

2) Use the OCT images to derive ground truth labels (for instance retinal nerve fibre thickness) for training deep learning algorithms on fundus images

3) The models learned on Biobank data will be applied to a Scottish cohort of people with diabetes to predict progression of retinopathy and other complications of diabetes

EXTENDED SCOPE

We want to test if our findings in T1D are transferable to other autoimmune and common infectious diseases as well as COVID on the ground that these diseases are likely to have common pathways.