TY - JOUR
T1 - Predicting islet cell autoimmunity and type 1 diabetes
T2 - An 8-year teddy study progress report
AU - TEDDY study group
AU - Krischer, Jeffrey P.
AU - Liu, Xiang
AU - Vehik, Kendra
AU - Akolkar, Beena
AU - Hagopian, William A.
AU - Rewers, Marian J.
AU - She, Jin Xiong
AU - She, Jin-Xiong
AU - Ziegler, Anette G.
AU - Lernmark, Ake
N1 - Funding Information:
Acknowledgments. The authors thank Sarah Austin-Gonzalez with the Health Informatics Institute at the University of South Florida for assistance with preparing the figures. Funding. The TEDDY Study is funded by the National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Allergy and Infectious Diseases, National Institute of Child Health and Human Development, National Institute of Environmental Health Sciences, JDRF, and the Centers for Disease Control and Prevention (grants U01 DK63829, U01 DK63861, U01 DK63821, U01 DK63865, U01 DK63863, U01 DK63836, U01 DK63790, UC4 DK63829, UC4 DK63861, UC4 DK63821, UC4 DK63865, UC4 DK63863, UC4 DK63836, UC4 DK95300, UC4 DK100238, UC4 DK106955, UC4 DK112243, and UC4 DK117483, and contract no. HHSN267200700014C). This work was supported in part by National Institutes of Health/National Center for Advancing Translational Sciences Clinical and Translational Science Awards to the University of Florida (UL1 TR000064) and the University of Colorado (UL1 TR001082). Duality of Interest. No potential conflicts of interest relevant to this article were reported. Author Contributions. J.P.K., X.L., K.V., B.A., W.A.H., M.J.R., J.-X.S., J.T., A.-G.Z., and Å.L. attest to meeting ICMJE (International Committee of Medical Journal Editors) uniform requirements for authorship by making substantial contributions to conception and design of this manuscript; acquisition, analysis, and interpretation of the data; drafting or revising the manuscript for intellectual content; and giving final approval of the published version. J.P.K. designed the study, proposed the analysis, interpreted the findings, and wrote the manuscript. X.L. performed the analysis and drafted/revised the manuscript. K.V. provided input on the analytical plan and interpretation of the results and drafted/revised the manuscript. B.A., W.A.H., M.J.R., J.-X.S., J.T., A.-G.Z., and Å.L. designed the study and reviewed/ edited the manuscript. J.P.K. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Prior Presentation. Parts of this study were presented in abstract form at the 78th Scientific Sessions of the American Diabetes Association, Orlando, FL, 22–26 June 2018.
Funding Information:
The TEDDY Study is funded by the National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Allergy and Infectious Diseases, National Institute of Child Health and Human Development, National Institute of Environmental Health Sciences, JDRF, and the Centers for Disease Control and Prevention (grants U01 DK63829, U01 DK63861, U01 DK63821, U01 DK63865, U01 DK63863, U01 DK63836, U01 DK63790, UC4 DK63829, UC4 DK63861, UC4 DK63821, UC4 DK63865, UC4 D K 6 3 8 6 3, UC4 DK63836, UC4 DK95300, UC4 DK100238, UC4 DK106955, UC4 DK112243, and UC4 DK117483, and contract no. HHSN267200700014C). This work was supported in part by National Institutes of Health/National Center for Advancing Translational Sciences Clinical and Translational Science Awards to the University of Florida (UL1 TR000064) and the University of Colorado (UL1 TR001082).
Publisher Copyright:
© 2019 by the American Diabetes Association.
PY - 2019/6/1
Y1 - 2019/6/1
N2 - 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.
AB - 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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U2 - 10.2337/dc18-2282
DO - 10.2337/dc18-2282
M3 - Article
C2 - 30967432
AN - SCOPUS:85066448751
SN - 0149-5992
VL - 42
SP - 1051
EP - 1060
JO - Diabetes Care
JF - Diabetes Care
IS - 6
ER -
