Predicting progression to type 1 diabetes from ages 3 to 6 in islet autoantibody positive TEDDY children
Jacobsen, Laura M.; Larsson, Helena E.; Tamura, Roy N.; Vehik, Kendra; Clasen, Joanna; Sosenko, Jay; Hagopian, William A.; She, Jin‐Xiong; Steck, Andrea K.; Rewers, Marian; Simell, Olli; Toppari, Jorma; Veijola, Riitta; Ziegler, Anette G.; Krischer, Jeffrey P.; Akolkar, Beena; Haller, Michael J.; the TEDDY Study Group (2019-01-10)
Jacobsen, LM, Larsson, HE, Tamura, RN, et al. Predicting progression to type 1 diabetes from ages 3 to 6 in islet autoantibody positive TEDDY children. Pediatr Diabetes. 2019; 20: 263– 270. https://doi.org/10.1111/pedi.12812
© 2019 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd. This is the peer reviewed version of the following article: Jacobsen, LM, Larsson, HE, Tamura, RN, et al. Predicting progression to type 1 diabetes from ages 3 to 6 in islet autoantibody positive TEDDY children. Pediatr Diabetes. 2019; 20: 263– 270, which has been published in final form at https://doi.org/10.1111/pedi.12812. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
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https://urn.fi/URN:NBN:fi-fe2019042613357
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Abstract
Objective: The capacity to precisely predict progression to type 1 diabetes (T1D) in young children over a short time span is an unmet need. We sought to develop a risk algorithm to predict progression in children with high‐risk human leukocyte antigen (HLA) genes followed in The Environmental Determinants of Diabetes in the Young (TEDDY) study.
Methods: Logistic regression and 4‐fold cross‐validation examined 38 candidate predictors of risk from clinical, immunologic, metabolic, and genetic data. TEDDY subjects with at least one persistent, confirmed autoantibody at age 3 were analyzed with progression to T1D by age 6 serving as the primary endpoint. The logistic regression prediction model was compared to two non‐statistical predictors, multiple autoantibody status, and presence of insulinoma‐associated‐2 autoantibodies (IA‐2A).
Results: A total of 363 subjects had at least one autoantibody at age 3. Twenty‐one percent of subjects developed T1D by age 6. Logistic regression modeling identified 5 significant predictors ‐ IA‐2A status, hemoglobin A1c, body mass index Z‐score, single‐nucleotide polymorphism rs12708716_G, and a combination marker of autoantibody number plus fasting insulin level. The logistic model yielded a receiver operating characteristic area under the curve (AUC) of 0.80, higher than the two other predictors; however, the differences in AUC, sensitivity, and specificity were small across models.
Conclusions: This study highlights the application of precision medicine techniques to predict progression to diabetes over a 3‐year window in TEDDY subjects. This multifaceted model provides preliminary improvement in prediction over simpler prediction tools. Additional tools are needed to maximize the predictive value of these approaches.
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