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

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 risk. However, clinical diabetes can follow in weeks or only after decades, and is very difficult to predict. Ketoacidosis at onset remains common and is most severe in the very young, in whom it can be life threatening and difficult to treat. Autoantibody surveillance programs effectively prevent most ketoacidosis but require frequent evaluations whose expense limits public health adoption. Prevention therapies applied before onset, when greater islet mass remains, have rarely been feasible 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.