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Predicting progression to type 1 diabetes from ages 3 to 6 in islet autoantibody positive TEDDY children

, ; Jacobsen, Laura M.; Larsson, Helena E. LU ; Tamura, Roy N.; Vehik, Kendra LU ; Clasen, Joanna; Sosenko, Jay; Hagopian, William A.; She, Jin Xiong and Steck, Andrea K., et al. (2019) In Pediatric 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... (More)

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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publication status
epub
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keywords
autoantibodies, metabolic, pediatric, prediction, type 1 diabetes
in
Pediatric Diabetes
publisher
Wiley-Blackwell
external identifiers
  • scopus:85060780097
ISSN
1399-543X
DOI
10.1111/pedi.12812
language
English
LU publication?
yes
id
d1bcb847-cd62-42da-9913-4ba2c1d9ad43
date added to LUP
2019-02-12 14:11:54
date last changed
2019-05-15 03:00:27
@article{d1bcb847-cd62-42da-9913-4ba2c1d9ad43,
  abstract     = {<p>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.</p>},
  author       = {,  and Jacobsen, Laura M. and Larsson, Helena E. and Tamura, Roy N. and Vehik, Kendra and Clasen, Joanna and Sosenko, Jay and Hagopian, William A. and She, Jin Xiong and Steck, Andrea K. and Rewers, Marian and Simell, Olli and Toppari, Jorma and Veijola, Riitta and Ziegler, Anette G. and Krischer, Jeffrey P. and Akolkar, Beena and Haller, Michael J.},
  issn         = {1399-543X},
  keyword      = {autoantibodies,metabolic,pediatric,prediction,type 1 diabetes},
  language     = {eng},
  month        = {01},
  publisher    = {Wiley-Blackwell},
  series       = {Pediatric Diabetes},
  title        = {Predicting progression to type 1 diabetes from ages 3 to 6 in islet autoantibody positive TEDDY children},
  url          = {http://dx.doi.org/10.1111/pedi.12812},
  year         = {2019},
}