Prediction Tool Assesses Newborn Risk of Type 1 Diabetes Mellitus Development - European Medical Journal

Prediction Tool Assesses Newborn Risk of Type 1 Diabetes Mellitus Development

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THE ENVIRONMENTAL Determinants of Diabetes in the Young (TEDDY) study data have shown that multiple factors influence whether a child will go on to develop Type 1 diabetes mellitus.

The study followed 7,798 children who were at high risk of developing Type 1 diabetes mellitus over a period of 9 years. The researchers at the University of Exeter, Exeter, UK, and the Pacific Northwest Research Institute, Seattle, Washington, USA, used the study’s data to find that a combined risk score can be calculated by incorporating genetics, family history of diabetes, and islet autoantibody count (a biomarker known to be implicated in Type 1 diabetes mellitus).

The combined approach was shown to drastically improve the accuracy of predicting which children would develop Type 1 diabetes mellitus, the result of which is two-fold: ketoacidosis, an insulin deficiency disorder that causes the blood to become acidic, could be prevented, and families could be given better diabetes risk counselling.

Dr Lauric Ferrat, University of Exeter, highlighted the prevalence of this condition: “At the moment, 40% of children who are diagnosed with Type 1 diabetes have the severe complication of ketoacidosis. For the very young this is life-threatening, resulting in long, intensive hospitalisations and in some cases even paralysis or death.”

It is therefore hoped that earlier intervention will be possible, and babies known to be at risk of developing Type 1 diabetes mellitus can be managed with the appropriate treatment earlier in life, ultimately leading to improved health outcomes.

Prof William Hagopian, the Pacific Northwest Research Institute, expressed his excitement at these findings: “They suggest that the routine heel prick testing of babies done at birth could go a long way towards preventing early sickness, as well as predicting which children will get Type 1 diabetes years later.” Furthermore, the approach could be used to predict the onset of other conditions with a strong genetic component, such as coeliac disease.

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