New Test May Predict Whether Babies Will Develop Type 1 Diabetes

Test combines multiple factors that influence whether a child is likely to develop type 1 diabetes.

Recent research published in Nature Medicine has found a new approach to predicting which babies will develop type 1 diabetes (T1D) by combining multiple factors that influence T1D risk, according to a press release.

Researchers followed 7798 children at high risk of developing T1D from birth over a course of 9 years from 7 international sites.

The study, known as TEDDY, provided enough data to allow researchers to develop a method of combining multiple factors that influence whether a child is likely to develop T1D. This combined risk score incorporates genetics, clinical factors such as family history of diabetes, and count of islet autoantibodies, which are biomarkers known to be implicated in T1D, according to the press release.

The researchers found that the combined approach significantly improved prediction of which children would develop T1D, potentially allowing better diabetes risk counseling of families, according to the study. Additionally, the new approach doubled the efficiency of programs to screen newborns to prevent ketoacidosis, in which insulin deficiency causes the blood to become too acidic.

“At the moment, 40% of children who are diagnosed with T1D have the severe complication of ketoacidosis,” said Lauric Ferrat, MD, in a press release. “For the very young this is life-threatening, resulting in long intensive hospitalizations and in some cases even paralysis or death. Using our new combined approach to identify which babies will develop diabetes can prevent these tragedies, and ensure children are on the right treatment pathway earlier in life, meaning better health.”

The researchers said the combined approach can be rolled out to predict the onset of other diseases with a strong genetic component that are identifiable in childhood, such as celiac disease.


New test better predicts which babies will develop type 1 diabetes. University of Exeter. Published August 7, 2020. Accessed August 10, 2020.

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