Genetic tool helps uncover hidden type 1 diabetes in nearly one in five patients

Research led by experts at the Royal Devon and the University of Exeter could help identify hidden type 1 diabetes in young people referred for specialist genetic testing.

Incorporating a genetic risk score into standard testing for a genetic form of diabetes which affects young people could identify hidden type 1 diabetes in around one in five patients with a negative genetic test.

The team studied more than 1,100 people who were being treated with insulin and were referred for genetic testing for Maturity-Onset Diabetes of the Young (MODY). Often diagnosed before the age of 25, MODY is caused by a change in a single gene and has a strong inherited component. To be diagnosed, a blood sample is sent off to a lab and tested against a panel of genes known to cause MODY. However, around 80% of patients have a negative test and the cause of their diabetes is uncertain.

The study was funded by Diabetes UK and the Medical Research Council and supported by the NIHR Exeter Biomedical Research Centre and the NIHR Exeter Clinical Research Facility. Published in Diabetes Care, the researchers found that applying the type 1 genetic risk score, developed at Exeter, to people whose genetic test came back negative identified that 16 per cent (around 180 patients) do in fact have atypical type 1 diabetes.

The genetic risk score is a cheap, simple way to analyse risk of type 1 diabetes, which takes into account all known genetic risk factors.

Incorporating the score into routine testing could enable genetic laboratories to provide a diagnosis of type 1 diabetes for patients whose MODY test would previously have returned without an explanation. The approach will now be adopted as part of NHS testing, demonstrating the direct impact of the research on clinical practice and providing answers that were not previously possible.

The study was led by Kashyap Patel, Associate Professor at the University of Exeter, and Consultant Physician in Diabetes and Endocrinology. He said: “In clinic, it’s extremely challenging to distinguish between MODY and type 1 diabetes, yet getting the right diagnosis is crucial to getting the right treatment. We found that applying the type 1 genetic risk score to patients who tested negative for MODY is effective in identifying patients with type 1 diabetes, reducing the need for expensive further testing and providing answers to patients.”

The study was supported by Kevin Colclough, the lead clinical scientist for the Exeter MODY testing service at the Royal Devon University Healthcare NHS Foundation Trust. He said: “This study showcases the strong link between the University of Exeter and the Exeter Genomics Laboratory that enables rapid translation of research findings into improved patient care.  We’ve demonstrated how information on genetic risk can be incorporated into an established NHS genetic testing pathway to provide additional diagnostic information when testing is negative. This will be a significant quality improvement to our service, providing clinicians with a more informative result and helping to reduce diagnostic uncertainty for their patients.”

Dr Alison Evans, Consultant Physician, Diabetes and Endocrinology at Gloucestershire Hospitals NHS Foundation Trust. “The ability to differentiate between ‘straight forward type 1’ and other more uncommon forms of diabetes is invaluable in managing people with diabetes and to optimise and target their treatment on an individualised basis. Over the years I have been a clinician, there seem to be more grey areas and clinical uncertainties, and using risk scores is really helpful in focussing clinical care – this is a great example using results in the real world.”

Anna Morris, Assistant Director of Research at Diabetes UK, said: “An accurate diagnosis is crucial to ensure that people with diabetes get the right treatment and support. Yet for some people, existing tests do not clearly define the type of diabetes they have. This can leave them without clear answers and affect their care. This research shows how genetic approaches could help identify people with type 1 diabetes, reducing uncertainty for patients and clinicians and helping inform the

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