Objective: To design a dynamic prediction model for estimating the time of progression from a single glutamic acid decarboxylase autoantibody (GADA) to multiple islet autoantibodies and type 1 diabetes in children, exploring different longitudinally measured risk variables. Research Design and Methods: GADA‐positive children ( n = 379) participating in The Environmental Determinants of Diabetes in the Young (TEDDY) study were followed for the appearance of additional autoantibodies against either insulin autoantibody (IAA), insulinoma‐like 2 autoantibody (IA‐2A), or zinc transporter 8 antibody (ZnT8A) and type 1 diabetes. A dynamic prediction model was designed, including trajectories of longitudinal risk variables, autoantibody titers, and metabolic variables (C‐peptide, glucose, and HbA1c) together with time‐invariant variables (gender, age at GADA positivity, and high‐risk HLA genotypes). Results: Transition risk from GADA to multiple autoantibodies was increased by lower age ( p < 0.001) and by increased GADA titers during follow‐up ( p < 0.001), and was less likely in children with HLA DQ2/X but not DQ2/8 ( p = 0.004). The transition risk from multiple autoantibodies without IA‐2A to IA‐2A positivity was associated with increased levels of 2 h glucose following oral glucose tolerance test (OGTT) ( p < 0.001) and increased ZnT8A titers ( p < 0.001). Increasing HbA1c ( p < 0.001) and GADA titers ( p < 0.001) were associated with an increased risk of transition from GADA only to type 1 diabetes; while increasing HbA1c ( p < 0.001) was associated with the transition from multiple autoantibodies to type 1 diabetes. Risk of transition from multiple autoantibodies, including IA‐2A to type 1 diabetes was also associated with 2 h glucose level ( p < 0.001). Conclusion: The dynamic prediction model presented an individual time‐specific risk of transition from a single GADA to multiple autoantibodies and type 1 diabetes.
You et al. (Wed,) studied this question.