Models for Predicting Type 1 Diabetes in Siblings of Affected Children

Abstract

OBJECTIVE—To generate predictive models for the assessment of risk of type 1 diabetes and age at diagnosis in siblings of children with newly diagnosed type 1 diabetes. RESEARCH DESIGN AND METHODS—Cox regression analysis was used to assess the risk of progression to type 1 diabetes, and multiple regression analysis was used to estimate the age at disease presentation in 701 siblings of affected children. Sociodemographic, genetic, and immunological variables were included in the analyses. Subanalyses were performed in a group of 77 autoantibody-positive siblings with additional metabolic data. RESULTS—A total of 47 siblings (6.7%) presented with type 1 diabetes during the 15-year observation period. Young age, an increasing number of detectable diabetes-associated autoantibodies at initial sampling and of affected first-degree relatives, and HLA DR–conferred disease susceptibility predicted progression to type 1 diabetes. In the subgroup of 77 autoantibody-positive siblings, young age, HLA DR–conferred susceptibility, an increasing number of autoantibodies, a reduced first-phase insulin response, and decreased insulin sensitivity in relation to first-phase insulin response were associated with increased risk of progression to type 1 diabetes. Age at diagnosis was predicted by age, insulinoma-associated protein 2 antibody levels, and number of autoantibodies at initial sampling (R2 = 0.76; P < 0.001). In the smaller cohort of autoantibody-positive subjects, first-phase insulin response and HLA DR–conferred susceptibility were additional predictors of age at diagnosis. CONCLUSIONS—Information on autoantibody status and levels, HLA-conferred disease susceptibility, and insulin secretion and sensitivity seems to be useful in addition to age and family history of type 1 diabetes when assessing risk of progression to type 1 diabetes and time to diagnosis in siblings of children with newly diagnosed type 1 diabetes.