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

The TEDDY study group

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)263-270
Number of pages8
JournalPediatric Diabetes
Volume20
Issue number3
DOIs
StatePublished - May 1 2019

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Type 1 Diabetes Mellitus
Autoantibodies
Logistic Models
Insulinoma
Area Under Curve
Precision Medicine
HLA Antigens
ROC Curve
Single Nucleotide Polymorphism
Fasting
Hemoglobins
Body Mass Index
Insulin
Sensitivity and Specificity
Genes

Keywords

  • autoantibodies
  • metabolic
  • pediatric
  • prediction
  • type 1 diabetes

ASJC Scopus subject areas

  • Internal Medicine
  • Pediatrics, Perinatology, and Child Health
  • Endocrinology, Diabetes and Metabolism

Cite this

Predicting progression to type 1 diabetes from ages 3 to 6 in islet autoantibody positive TEDDY children. / The TEDDY study group.

In: Pediatric Diabetes, Vol. 20, No. 3, 01.05.2019, p. 263-270.

Research output: Contribution to journalArticle

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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.",
keywords = "autoantibodies, metabolic, pediatric, prediction, type 1 diabetes",
author = "{The TEDDY study group} and Jacobsen, {Laura M.} and Larsson, {Helena E.} and Tamura, {Roy N.} and Kendra Vehik and Joanna Clasen and Jay Sosenko and Hagopian, {William A.} and She, {Jin Xiong} and Jin-Xiong She and Marian Rewers and Olli Simell and Jorma Toppari and Riitta Veijola and Ziegler, {Anette G.} and Krischer, {Jeffrey P.} and Beena Akolkar and Haller, {Michael J.}",
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T1 - Predicting progression to type 1 diabetes from ages 3 to 6 in islet autoantibody positive TEDDY children

AU - The TEDDY study group

AU - Jacobsen, Laura M.

AU - Larsson, Helena E.

AU - Tamura, Roy N.

AU - Vehik, Kendra

AU - Clasen, Joanna

AU - Sosenko, Jay

AU - Hagopian, William A.

AU - She, Jin Xiong

AU - She, Jin-Xiong

AU - Rewers, Marian

AU - Simell, Olli

AU - Toppari, Jorma

AU - Veijola, Riitta

AU - Ziegler, Anette G.

AU - Krischer, Jeffrey P.

AU - Akolkar, Beena

AU - Haller, Michael J.

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N2 - 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.

AB - 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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KW - pediatric

KW - prediction

KW - type 1 diabetes

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