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- Predicting progression to type 1 diabetes from ages 3 to 6 in islet autoantibody positive TEDDY children.
Predicting progression to type 1 diabetes from ages 3 to 6 in islet autoantibody positive TEDDY children.
Citation | “Predicting Progression To Type 1 Diabetes From Ages 3 To 6 In Islet Autoantibody Positive Teddy Children.”. Pediatric Diabetes, pp. 263-270. . |
Center | University of Washington |
Author | Laura M Jacobsen, Helena E Larsson, Roy N Tamura, Kendra Vehik, Joanna Clasen, Jay Sosenko, William A Hagopian, Jin-Xiong She, Andrea K Steck, Marian Rewers, Olli Simell, Jorma Toppari, Riitta Veijola, Anette G Ziegler, Jeffrey P Krischer, Beena Akolkar, Michael J Haller, Teddy Study Group |
Keywords | autoantibodies, metabolic, pediatric, prediction, type 1 diabetes |
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. |
Year of Publication |
2019
|
Journal |
Pediatric diabetes
|
Volume |
20
|
Issue |
3
|
Number of Pages |
263-270
|
Date Published |
12/2019
|
ISSN Number |
1399-5448
|
DOI |
10.1111/pedi.12812
|
Alternate Journal |
Pediatr Diabetes
|
PMID |
30628751
|
PMCID |
PMC6456374
|
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