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Our research shows new genetic test better at spotting risk of Type 1 diabetes in newborns

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Our scientists have discovered a new way to find children at high risk of Type 1 diabetes that’s more effective than current methods.

Our research fellow, Dr Richard Oram, at the University of Exeter and scientists at Seattle University have developed a new ‘risk calculator’. The calculator uses information from genes linked to Type 1 diabetes to help find those at highest risk of developing the condition.  

Spotting babies at risk

In a study published today in Diabetes Care, the team used computer simulations to show that their new risk score (called the T1DGRS2) was nearly twice as effective as current methods at finding babies at high risk of Type 1 diabetes. 

Spotting children at high risk could help healthcare professionals to catch any signs of Type 1 diabetes as early as possible, reducing their risk of potentially life-threatening complications during diagnosis. 

It also means children at high risk can be offered opportunities to take part in cutting-edge research trials, hoping to find ways to delay or prevent Type 1 diabetes in the future. 

The power of genetics

To develop the test, the team looked at genetic information across the entire genome (a person’s complete set of DNA) in nearly 6,500 people with Type 1 diabetes and around 9,000 people without diabetes. They combined information about genetic differences strongly linked to Type 1 diabetes into a score. 

To test how well their new risk score worked, they wanted to see if it could spot people who already had Type 1 diabetes. So they tested it in a subset of people in the UK Biobank: a database of nearly half a million people, many of whom have Type 1 or Type 2 diabetes. 

Helping to reduce misdiagnosis

The team found that the new risk score was 50% better than current genetic scores at finding adults with Type 1 diabetes. Although these tests aren’t used currently, the findings show that as well as helping to spot children at high risk, the tests could be used to make sure people receive the right diabetes diagnosis in the future - and therefore the right treatments - as soon as possible.

Exeter scientists recently discovered that around half of all cases of Type 1 diabetes happen in adulthood. But it can sometimes be difficult for doctors to distinguish between Type 1 and Type 2 diabetes. 

Anna Morris, Assistant Director of Research Strategy and Partnership at Diabetes UK, said: 

"It's exciting to see the power of genetics being harnessed to help predict who might develop Type 1 diabetes in the future, particularly from a young age. If successful, this approach could help to reduce someone's risk of being misdiagnosed or developing complications during diagnosis. 

“In the future, this research could also open up new insights into what could be done to stop Type 1 diabetes from progressing."

Lead researcher Dr Richard Oram said: 

“Predicting which diseases we might get in the future is an important area of research, and Type 1 diabetes has a strong genetic element that we are now able to measure very well. 

“The Type 1 diabetes genetic risk score could help to predict who will develop the condition from early life, which in turn could inform the development of early life interventions, as well as helping to classify diabetes correctly at diagnosis.” 
 

Seth Sharp, PhD student working on the project with Dr Richard Oram, said:

"It has been fantastic working on my first research study with colleagues at Exeter and in Seattle with the support of Diabetes UK.

"Our study could help massively cut the costs involved in identifying babies who could go on to develop Type 1 diabetes. In turn, this would allow more cost-effective recruitment into studies that monitor and follow up those at high risk, which could be key to identifying what triggers Type 1 diabetes. 

"We are currently already studying how the environment combined with genetics can set off the chain of events leading to Type 1 diabetes and hope this study will lead to some exciting new findings."

 

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