A combined risk score enhances prediction of type 1 diabetes among susceptible children.

Abstract

Type 1 diabetes (T1D)-an autoimmune disease that destroys the pancreatic islets, resulting in insulin deficiency-often begins early in life when islet autoantibody appearance signals high risk¹. However, clinical diabetes can follow in weeks or only after decades, and is very difficult to predict. Ketoacidosis at onset remains common^2,3 and is most severe in the very young^4,5, in whom it can be life threatening and difficult to treat^6-9. Autoantibody surveillance programs effectively prevent most ketoacidosis^10-12 but require frequent evaluations whose expense limits public health adoption¹³. Prevention therapies applied before onset, when greater islet mass remains, have rarely been feasible¹⁴ because individuals at greatest risk of impending T1D are difficult to identify. To remedy this, we sought accurate, cost-effective estimation of future T1D risk by developing a combined risk score incorporating both fixed and variable factors (genetic, clinical and immunological) in 7,798 high-risk children followed closely from birth for 9.3 years. Compared with autoantibodies alone, the combined model dramatically improves T1D prediction at ≥2 years of age over horizons up to 8 years of age (area under the receiver operating characteristic curve ≥ 0.9), doubles the estimated efficiency of population-based newborn screening to prevent ketoacidosis, and enables individualized risk estimates for better prevention trial selection.