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Predicting Islet Cell Autoimmunity and Type 1 Diabetes : An 8-Year TEDDY Study Progress Report

, ; Krischer, Jeffrey P; Liu, Xiang; Vehik, Kendra LU ; Akolkar, Beena; Hagopian, William A; Rewers, Marian J; She, Jin-Xiong; Toppari, Jorma and Ziegler, Anette-G, et al. (2019) In Diabetes Care 42(6). p.1051-1060
Abstract

OBJECTIVE: Assessment of the predictive power of The Environmental Determinants of Diabetes in the Young (TEDDY)-identified risk factors for islet autoimmunity (IA), the type of autoantibody appearing first, and type 1 diabetes (T1D).

RESEARCH DESIGN AND METHODS: A total of 7,777 children were followed from birth to a median of 9.1 years of age for the development of islet autoantibodies and progression to T1D. Time-dependent sensitivity, specificity, and receiver operating characteristic (ROC) curves were calculated to provide estimates of their individual and collective ability to predict IA and T1D.

RESULTS: HLA genotype (DR3/4 vs. others) was the best predictor for IA (Youden's index J = 0.117) and single nucleotide... (More)

OBJECTIVE: Assessment of the predictive power of The Environmental Determinants of Diabetes in the Young (TEDDY)-identified risk factors for islet autoimmunity (IA), the type of autoantibody appearing first, and type 1 diabetes (T1D).

RESEARCH DESIGN AND METHODS: A total of 7,777 children were followed from birth to a median of 9.1 years of age for the development of islet autoantibodies and progression to T1D. Time-dependent sensitivity, specificity, and receiver operating characteristic (ROC) curves were calculated to provide estimates of their individual and collective ability to predict IA and T1D.

RESULTS: HLA genotype (DR3/4 vs. others) was the best predictor for IA (Youden's index J = 0.117) and single nucleotide polymorphism rs2476601, in PTPN22, was the best predictor for insulin autoantibodies (IAA) appearing first (IAA-first) (J = 0.123). For GAD autoantibodies (GADA)-first, weight at 1 year was the best predictor (J = 0.114). In a multivariate model, the area under the ROC curve (AUC) was 0.678 (95% CI 0.655, 0.701), 0.707 (95% CI 0.676, 0.739), and 0.686 (95% CI 0.651, 0.722) for IA, IAA-first, and GADA-first, respectively, at 6 years. The AUC of the prediction model for T1D at 3 years after the appearance of multiple autoantibodies reached 0.706 (95% CI 0.649, 0.762).

CONCLUSIONS: Prediction modeling statistics are valuable tools, when applied in a time-until-event setting, to evaluate the ability of risk factors to discriminate between those who will and those who will not get disease. Although significantly associated with IA and T1D, the TEDDY risk factors individually contribute little to prediction. However, in combination, these factors increased IA and T1D prediction substantially.

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published
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Diabetes Care
volume
42
issue
6
pages
10 pages
publisher
American Diabetes Association
external identifiers
  • scopus:85066448751
ISSN
1935-5548
DOI
10.2337/dc18-2282
language
English
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yes
id
d4a847df-2957-40fc-ad4a-d9c751d6e20d
date added to LUP
2019-06-25 13:10:02
date last changed
2019-11-13 05:37:16
@article{d4a847df-2957-40fc-ad4a-d9c751d6e20d,
  abstract     = {<p>OBJECTIVE: Assessment of the predictive power of The Environmental Determinants of Diabetes in the Young (TEDDY)-identified risk factors for islet autoimmunity (IA), the type of autoantibody appearing first, and type 1 diabetes (T1D).</p><p>RESEARCH DESIGN AND METHODS: A total of 7,777 children were followed from birth to a median of 9.1 years of age for the development of islet autoantibodies and progression to T1D. Time-dependent sensitivity, specificity, and receiver operating characteristic (ROC) curves were calculated to provide estimates of their individual and collective ability to predict IA and T1D.</p><p>RESULTS: HLA genotype (DR3/4 vs. others) was the best predictor for IA (Youden's index J = 0.117) and single nucleotide polymorphism rs2476601, in PTPN22, was the best predictor for insulin autoantibodies (IAA) appearing first (IAA-first) (J = 0.123). For GAD autoantibodies (GADA)-first, weight at 1 year was the best predictor (J = 0.114). In a multivariate model, the area under the ROC curve (AUC) was 0.678 (95% CI 0.655, 0.701), 0.707 (95% CI 0.676, 0.739), and 0.686 (95% CI 0.651, 0.722) for IA, IAA-first, and GADA-first, respectively, at 6 years. The AUC of the prediction model for T1D at 3 years after the appearance of multiple autoantibodies reached 0.706 (95% CI 0.649, 0.762).</p><p>CONCLUSIONS: Prediction modeling statistics are valuable tools, when applied in a time-until-event setting, to evaluate the ability of risk factors to discriminate between those who will and those who will not get disease. Although significantly associated with IA and T1D, the TEDDY risk factors individually contribute little to prediction. However, in combination, these factors increased IA and T1D prediction substantially.</p>},
  author       = {,  and Krischer, Jeffrey P and Liu, Xiang and Vehik, Kendra and Akolkar, Beena and Hagopian, William A and Rewers, Marian J and She, Jin-Xiong and Toppari, Jorma and Ziegler, Anette-G and Lernmark, Åke},
  issn         = {1935-5548},
  language     = {eng},
  number       = {6},
  pages        = {1051--1060},
  publisher    = {American Diabetes Association},
  series       = {Diabetes Care},
  title        = {Predicting Islet Cell Autoimmunity and Type 1 Diabetes : An 8-Year TEDDY Study Progress Report},
  url          = {http://dx.doi.org/10.2337/dc18-2282},
  volume       = {42},
  year         = {2019},
}