Researchers at the University of Exeter have developed tools which can help clinicians classify what type of diabetes a patient has to ensure they get the best treatment possible and reduce any complications.
Maturity Onset Diabetes of the Young (MODY), is a form of diabetes caused by single gene mutations that is difficult to recognise; often MODY patients are misdiagnosed.
University of Exeter researchers have shown that patients with MODY do not require insulin injections. Specific treatments are indicated depending on which MODY gene is found to be the cause. In one subtype (GCK) patients can discontinue pharmacological treatment without any deterioration in control of blood sugar whilst another subtype (HNF1A), patients can be successfully treated with sulphonylurea tablets.
Exeter specialises in a “precision diabetes” approach of prescribing the right treatment for the patient. To achieve this as early as possible, early diagnosis is key.
Estimates suggest that in the UK, over 75 per cent of MODY cases are misdiagnosed. It is less common than Type 1 or Type 2 diabetes, and many of the features overlap with Type 1 diabetes and young-onset Type 2 diabetes, making it harder to spot.
To improve detection, Dr Beverley Shields led the development and validation of a probability algorithm for MODY. To calculate the likelihood of patients having MODY, the tool incorporated clinical features such as a patient’s gender, BMI, glucose control, and age at diagnosis.
Combining information from multiple clinical features was highly effective in discriminating MODY from other forms of diabetes. The model was translated into an online calculator and app that helps clinicians decide whether to send for molecular genetic testing to confirm the diagnosis.
Exeter’s online MODY calculator can be accessed for free on computers and mobile devices worldwide. The online format enables patients and clinicians to input routinely available clinical characteristics and provides an immediate probability score. Since 2012 the website calculator has had been used over 150,000 times in 206 countries, with 65 per cent of users of the calculator being from outside the UK.
The calculator has become a ‘CE’ marked device, demonstrating its compliance with strict regulations. To date, 74 per cent of UK referrals state that they have used the calculator. The referrals that have used the calculator are more likely to receive a positive diagnosis of MODY than those who do not. The number of cases of MODY diagnosed in Exeter has increased threefold since the online calculator and app have been made available to clinicians, and 3,800 individuals have now received a genetic diagnosis.
A correct diagnosis of MODY has meant better treatment and clinical care for patients. It also allows for follow up with family members for genetic testing with 86 percent of family members receiving a genetic diagnosis for their own diabetes which means they can modify their treatment.
Finally, the MODY calculator could save the NHS £20-40 million over the lifetime of patients currently diagnosed with diabetes under 30. This is through the reduction of cost of treating raised blood glucose levels and avoiding additional expense associated with treating complications.
Building on the success of the MODY calculator, a second was developed to help identify whether a patient has type 1 or type 2 diabetes. Misdiagnosis of even these more recognised forms of diabetes is relatively common. For example, former Prime Minister Theresa May was initially diagnosed with type 2 diabetes and was only re-diagnosed with type 1 when tablet treatment failed to work.
The new calculator uses a model that considers available data about the patient, as well as blood test results. It can be used to identify if a person is likely to have type 1 diabetes, to reduce misdiagnosis.
Research at the University of Exeter has helped people receive a more accurate diagnosis of their diabetes and therefore better treatment. This precision diabetes approach will lead to better overall health of people who have diabetes whilst also saving the NHS millions of pounds in treatment costs.
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