Premium
Predicting progression to type 1 diabetes from ages 3 to 6 in islet autoantibody positive TEDDY children
Author(s) -
Jacobsen Laura M.,
Larsson Helena E.,
Tamura Roy N.,
Vehik Kendra,
Clasen Joanna,
Sosenko Jay,
Hagopian William A.,
She JinXiong,
Steck Andrea K.,
Rewers Marian,
Simell Olli,
Toppari Jorma,
Veijola Riitta,
Ziegler Anette G.,
Krischer Jeffrey P.,
Akolkar Beena,
Haller Michael J.
Publication year - 2019
Publication title -
pediatric diabetes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.678
H-Index - 75
eISSN - 1399-5448
pISSN - 1399-543X
DOI - 10.1111/pedi.12812
Subject(s) - autoantibody , medicine , logistic regression , type 1 diabetes , receiver operating characteristic , body mass index , area under the curve , diabetes mellitus , oncology , immunology , endocrinology , antibody
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.