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.

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

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

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.

EXTENDED SCOPE

We would like to extend the scope of this project to include analysis of genetic factors for a range of communicable and non-communicable diseases, in order to understand our findings on core genes of type 1 diabetes in context, and to facilitate discovery of core genes for other diseases.

The diseases of interest are detailed below, ordered by chapter in ICD-10:

ICD chapter i- severe COVID-19

ICD chapter ii- colorectal cancer, breast cancer, prostate cancer, pancreatic cancer, lung cancer, ovarian cancer, renal cell carcinoma, lymphoma

ICD chapter iii- pernicious anaemia

ICD chapter iv- autoimmune thyroid disease, thyrotoxicosis, Addison's disease, primary ovarian failure, type 1 diabetes, type 2 diabetes

ICD chapter v- bipolar affective disorder

ICD chapter vi- Alzheimer's disease, Alzheimer's disease before age 75, vascular dementia, migraine, epilepsy, amyotrophic lateral sclerosis, recurrent depressive disorder, alcohol-related behavioural problems, Parkinson's disease, multiple sclerosis

ICD chapter vii- iridocyclitis, glaucoma, age-related cataract, age-related macular degeneration, diabetic eye disease, diabetic retinopathy

ICD chapter viii- conductive and sensorineural hearing loss

ICD chapter ix- coronary artery disease, ischemic stroke, intracerebral hemorrhage, subarachnoid hemorrhage, atrial fibrillation, venous thrombosis or thromboembolism, hypertension before age 50, hypertensive end-organ damage

ICD chapter x- asthma, idiopathic pulmonary fibrosis, sarcoidosis

ICD chapter xi- ulcerative colitis, inflammatory bowel disease, Crohn's disease, primary biliary cholangitis, cholelithiasis or cholecystitis, non-alcoholic fatty liver disease, alcoholic liver disease, celiac disease

ICD chapter xii- vitiligo, alopecia areata, atopic dermatitis, psoriasis

ICD chapter xiii- giant-cell arteritis or polymyalgia rheumatica, polymyalgia rheumatica, giant-cell arteritis, systemic lupus erythematosus, rheumatoid arthritis, primary Sjogren's syndrome, spondyloarthritis, ankylosing spondylitis, psoriatic or enteropathic spondyloarthritis, osteoporosis, Paget's disease of bone

ICD chapter xiv- hypertensive renal disease, female infertility, nephritis, diabetic kidney disease, diabetic nephropathy, calculus of kidney and ureter, male infertility, prostatic hyperplasia

ICD chapter xxi- waist-hip ratio, body mass index