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Krischer, Jeffrey P.; Liu, Xiang; Vehik, Kendra; Akolkar, Beena; Hagopian, William A.; Rewers, Marian J.; She, Jin-Xiong; Toppari, Jorma; Ziegler, Anette-G.; Lernmark, Åke; Rewers, Marian; Bautista, Kimberly; Baxter, Judith; Felipe-Morales, Daniel; Driscoll, Kimberly; Frohnert, Brigitte I.; Gallant, Marisa; Gesualdo, Patricia; Hoffman, Michelle; Karban, Rachel; Liu, Edwin; Norris, Jill; Steck, Andrea; Waugh, Kathleen; [...]

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

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  • Media type: E-Article
  • Title: Predicting Islet Cell Autoimmunity and Type 1 Diabetes: An 8-Year TEDDY Study Progress Report
  • Contributor: Krischer, Jeffrey P.; Liu, Xiang; Vehik, Kendra; Akolkar, Beena; Hagopian, William A.; Rewers, Marian J.; She, Jin-Xiong; Toppari, Jorma; Ziegler, Anette-G.; Lernmark, Åke; Rewers, Marian; Bautista, Kimberly; Baxter, Judith; Felipe-Morales, Daniel; Driscoll, Kimberly; Frohnert, Brigitte I.; Gallant, Marisa; Gesualdo, Patricia; Hoffman, Michelle; Karban, Rachel; Liu, Edwin; Norris, Jill; Steck, Andrea; Waugh, Kathleen; [...]
  • imprint: American Diabetes Association, 2019
  • Published in: Diabetes Care
  • Language: English
  • DOI: 10.2337/dc18-2282
  • ISSN: 0149-5992; 1935-5548
  • Keywords: Advanced and Specialized Nursing ; Endocrinology, Diabetes and Metabolism ; Internal Medicine
  • Origination:
  • Footnote:
  • Description: <jats:sec> <jats:title>OBJECTIVE</jats:title> <jats:p>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).</jats:p> </jats:sec> <jats:sec> <jats:title>RESEARCH DESIGN AND METHODS</jats:title> <jats:p>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.</jats:p> </jats:sec> <jats:sec> <jats:title>RESULTS</jats:title> <jats:p>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).</jats:p> </jats:sec> <jats:sec> <jats:title>CONCLUSIONS</jats:title> <jats:p>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.</jats:p> </jats:sec>
  • Access State: Open Access