Title: Integrating clinical features and biomarkers to improve diabetes classification and treatment in young adults
Funder: Department of Health and Social Care

Abstract: Background Correct classification of Type 1 and 2 diabetes is fundamental to appropriate management. Rapid beta cell failure in Type 1 diabetes means these patients require early and physiological insulin replacement. In contrast patients with Type 2 produce substantial endogenous insulin for many years after diagnosis and may therefore be treated with non-insulin therapies. As population obesity has increased classification of young adult diabetes has become increasingly difficult, with Type 2 diabetes occurring in the young and many people with Type 1 diabetes being obese. Misclassification is therefore common, occurring in 26% of adults treated as Type 1. We have systematically reviewed the utility of clinical features in classifying diabetes and found no papers assessing young adult onset diabetes, and only one assessing features in combination. While biomarkers such as islet autoantibodies may assist classification there is a lack of evidence supporting their use. Aims To systematically assess the utility of clinical features and biomarkers in classification of young adult onset diabetes and integrate these into a validated clinical probability model. Objectives To develop a clinical probability model for diabetes subtype from presenting clinical features, using three existing cohorts To assess diagnostic accuracy internally on the same three datasets and externally on three independent datasets To assess the frequency and implication of discordance between the model and clinical management using a large population dataset To determine the utility of adding established and new biomarkers to the model in a prospective study Plan of investigation: Development and validation of clinical probability model We will develop a clinical probability model for diabetes subtype using cross sectional datasets with recorded clinical features and measured endogenous insulin (C-peptide), n=6350. Diabetes subtype will be defined by early insulin requirement and beta cell failure (Type 1 = insulin treatment and severe insulin deficiency at 3 years diabetes duration, Type 2 = lack of insulin requirement (non insulin treated or high C-peptide) at 3 years diabetes duration). We will perform external model validation in a further 3 datasets (n=5800). We will assess potential clinical impact of the probability model in >150000 cases in CPRD, by comparing early outcomes where model diagnosis is concordant with clinicians initial treatment with those where clinical probability model and clinical management are discordant.Prospective study of newly diagnosed young adult onset diabetes To determine utility of biomarkers in improving diagnosis in young adult diabetes we will recruit a prospective cohort of 800 adults age 18-45 diagnosed with diabetes within the previous 6 months. Participants will be recruited through the NIHR Clinical Research Network. At baseline blood will be taken for biomarker assessment and biobanking, and clinical features recorded. We will follow participants for 3 years with annual telephone interview and postal urine C-peptide. A further face to face visit with blood sampling will be performed at 3 years. We will assess the utility of adding biomarkers to clinical features in diagnosing diabetes subtype (defined by insulin requirement at 3 years as above), and where utility is shown integrate these into our probability model. Potential benefits of research This research will produce a robustly developed clinical probability model that will for the first time integrate validated clinical features and biomarkers to allow clinicians to accurately assess classification and need for insulin treatment at diabetes diagnosis. This will improve patient experience, outcomes and reduce NHS costs by reducing inappropriate insulin treatment (where Type 2 diabetes is misdiagnosed as Type 1), and improving early glycaemic control and healthcare utilisation (Where Type 1 diabetes is misdiagnosed as Type 2). The model will be freely available to clinicians and patients through a website and smartphone App.

Lay abstract: The treatment of Type 1 and Type 2 diabetes is very different. People with Type 1 diabetes rapidly stop making their own insulin, so need insulin injections from diagnosis. People with Type 2 diabetes can keep making their own insulin but it may not work as well as it should, so they can be treated with diet or tablets. While they may eventually need insulin treatment it is usually not until many years after diagnosis. It is often difficult for doctors to tell which kind of diabetes a person has, particularly in younger adults where both Type 1 and Type 2 diabetes are common. Because of this, sometimes (in about 10% of cases overall, and 26% of adults treated as Type 1 diabetes) people are given the wrong diagnosis. This can have a huge impact as it means they could receive the wrong treatment. A person incorrectly diagnosed with Type 1 diabetes will be prescribed unnecessary insulin injections and miss out on other helpful therapies. A person incorrectly diagnosed with Type 2 diabetes may develop severely high glucose and become unwell with a condition called Diabetic Ketoacidosis if they do not receive insulin treatment. We aim to improve this situation by helping doctors more accurately tell the type of diabetes a person has when they are first diagnosed. We will use information from previous studies to develop a calculator (called a clinical probability model) that will for the first time allow doctors to combine different clinical features to generate a more accurate diagnosis of Type 1 or Type 2 diabetes. We will use data collected from over 150000 patients with diabetes to assess whether this calculator is likely to improve the early care of patients with diabetes.It is likely that in many cases we will not be able to accurately diagnose whether a person has Type 1 or Type 2 diabetes from clinical features alone. A number of blood tests have been proposed for helping doctors diagnose type of diabetes but it is not clear if and when these tests should be used. These include tests for markers of the autoimmune process that is associated with Type 1 diabetes, measuring a person's own insulin secretion at diagnosis, and genetic tests. In order to tell if these tests are useful in younger adults we will recruit 800 participants who have recently been diagnosed with diabetes between the ages of 18 and 45. We will measure blood tests for diabetes type at diagnosis and follow participants for 3 years to see whether they stop producing their own insulin and need insulin treatment, which confirms Type 1 diabetes. We will assess whether these blood tests can help us tell if a patient needs rapid insulin treatment and should be treated as Type 1 or Type 2 diabetes. We will combine results from these studies to produce a calculator, called a clinical probability model, that will allow doctors and patients to combine information from clinical features and blood tests to accurately diagnose what type of diabetes a person has and therefore give the correct treatment. This will be freely available to doctors and patients as a website calculator and smartphone app.

Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES
Health categories: METABOLIC AND ENDOCRINE