TY - JOUR
T1 - Predicting Islet Cell Autoimmunity and Type 1 Diabetes
T2 - An 8-Year TEDDY Study Progress Report
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 - Toppari, Jorma
AU - Ziegler, Anette-G
AU - Lernmark, Åke
AU - TEDDY Study Group
A2 - Agardh, Daniel
A2 - Andrén Aronsson, Carin
A2 - Ask, Maria
A2 - Bremer, Jenny
A2 - Ericson-Hallström, Emelie
A2 - Björne Fors, Annika
A2 - Fransson, Lina
A2 - Gard, Thomas
A2 - Bennet, Rasmus
A2 - Hyberg, Susanne
A2 - Jisser, Hanna
A2 - Johansen, Fredrik
A2 - Jónsdóttir, Berglind
A2 - JOVIC, SILVIJA
A2 - Elding Larsson, Helena
A2 - Lindström, Marielle
A2 - Lundgren, Markus
A2 - Månsson Martinez, Maria
A2 - Markan, Maria
A2 - Melin, Marie Jessica
A2 - Mestan, Zeliha
A2 - Nilsson, Caroline N
A2 - Ottosson, Karin
A2 - Rahmati, Kobra
A2 - Ramelius, Anita
A2 - Salami, Falastin
A2 - Sjöberg, Anette
A2 - Sjöberg, Birgitta
A2 - Törn, Carina
A2 - Wallin, Anne
A2 - Wimar, Åsa
A2 - Åberg, Sofie
N1 - © 2019 by the American Diabetes Association.
PY - 2019/6
Y1 - 2019/6
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.
U2 - 10.2337/dc18-2282
DO - 10.2337/dc18-2282
M3 - Article
C2 - 30967432
VL - 42
SP - 1051
EP - 1060
JO - Diabetes Care
JF - Diabetes Care
SN - 1935-5548
IS - 6
ER -