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A new study has developed a prediction tool to help clinicians predict which newly diagnosed children and adolescents are unlikely to experience a period of remission.

The remission phase, also called the honeymoon phase, is the period of time after clinical diagnosis of T1D where the body can make just enough insulin (“endogenous” insulin) to control blood glucose levels either without needing insulin injections or with significantly lower doses. 

Residual endogenous secretion of insulin in people with T1D is associated with improved long-term glycaemic control, reduced risk of hypoglycaemia and reduced risk of long-term complications.

A significant proportion of children and adolescents diagnosed with T1D will not experience this remission phase, placing them at higher risk for both short and long term complications.

The predictors of non-remission in children with new onset T1D have not been adequately described previously, and preventing early dysglycaemia remains a significant therapeutic gap. A recent study published in PLOS One found that more than 50% of children and adolescents in the sample did not undergo remission, and aimed to determine if routinely collected clinical parameters predict non-remission in children and adolescents with new onset T1D.

The team collected data for the first 36 months of disease in 204 young people aged 2-14 years with new onset T1D. They found that non-remission occurred in 57.8% of people in this sample. The tool for prediction that was found to be 73% accurate was based on several measurements after diagnosis comprising of bicarbonate <15 mg/dL, age <5y, female sex, and >3 diabetes-associated autoantibodies.

The authors claim that this paper is the first to come up with a predictive model for non-remission. This presents the opportunity for clinicians to identify patients who are unlikely to remit, in order to begin intensive treatment early after diagnosis to ensure glucose levels are normal or as close to normal as possible.

Early identification of these non-remitters may guide the use of targeted therapy to limit dysglycemia and reduce the prevalence of long-term complications.

Due to the sample size, cross-sectional design and geographic isolation, this predictive model needs to be validated in larger populations around the world.

Regardless, this study highlights the importance of identifying those at risk of non-remission to ensure proper care in the initial phases to optimise blood glucose control and improve long term health outcomes.

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