PMID- 30628751 OWN - NLM STAT- MEDLINE DCOM- 20191028 LR - 20240422 IS - 1399-5448 (Electronic) IS - 1399-543X (Print) IS - 1399-543X (Linking) VI - 20 IP - 3 DP - 2019 May TI - Predicting progression to type 1 diabetes from ages 3 to 6 in islet autoantibody positive TEDDY children. PG - 263-270 LID - 10.1111/pedi.12812 [doi] 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. CI - (c) 2019 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd. FAU - Jacobsen, Laura M AU - Jacobsen LM AUID- ORCID: 0000-0002-5144-7836 AD - Department of Pediatrics, University of Florida, Gainesville, Florida. FAU - Larsson, Helena E AU - Larsson HE AUID- ORCID: 0000-0003-3306-1742 AD - Department of Clinical Sciences Malmo, Lund University, Skane University Hospital SUS, Malmo, Sweden. FAU - Tamura, Roy N AU - Tamura RN AD - Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, Florida. FAU - Vehik, Kendra AU - Vehik K AD - Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, Florida. FAU - Clasen, Joanna AU - Clasen J AD - Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, Florida. FAU - Sosenko, Jay AU - Sosenko J AD - Division of Endocrinology, University of Miami, Miami, Florida. FAU - Hagopian, William A AU - Hagopian WA AD - Pacific Northwest Diabetes Research Institute, Seattle, Washington. FAU - She, Jin-Xiong AU - She JX AD - Center for Biotechnology and Genomic Medicine, Medical College of Georgia, Augusta University, Augusta, Georgia. FAU - Steck, Andrea K AU - Steck AK AD - Barbara Davis Center for Childhood Diabetes, University of Colorado, Denver, Colorado. FAU - Rewers, Marian AU - Rewers M AD - Barbara Davis Center for Childhood Diabetes, University of Colorado, Denver, Colorado. FAU - Simell, Olli AU - Simell O AD - Department of Pediatrics, Turku University Hospital, Turku, Finland. FAU - Toppari, Jorma AU - Toppari J AD - Department of Pediatrics, Turku University Hospital, Turku, Finland. AD - Department of Physiology, Institute of Biomedicine, University of Turku, Turku, Finland. FAU - Veijola, Riitta AU - Veijola R AD - Department of Pediatrics, Medical Research Center, PEDEGO Research Unit, Oulu University Hospital and University of Oulu, Oulu, Finland. FAU - Ziegler, Anette G AU - Ziegler AG AD - Institute of Diabetes Research, Helmholtz Zentrum Munchen and Forschergruppe Diabetes e.V. Neuherberg, Neuherberg, Germany. FAU - Krischer, Jeffrey P AU - Krischer JP AD - Health Informatics Institute, Morsani College of Medicine, University of South Florida, Tampa, Florida. FAU - Akolkar, Beena AU - Akolkar B AD - Division of Diabetes, Endocrinology, and Metabolism, National Institute of Diabetes, Digestive, and Kidney Diseases, National Institutes of Health, Bethesda, Maryland. FAU - Haller, Michael J AU - Haller MJ AD - Department of Pediatrics, University of Florida, Gainesville, Florida. CN - TEDDY Study Group LA - eng GR - U01 DK063821/DK/NIDDK NIH HHS/United States GR - UC4 DK063863/DK/NIDDK NIH HHS/United States GR - UC4 DK112243/DK/NIDDK NIH HHS/United States GR - HHSN267200700014C/DK/NIDDK NIH HHS/United States GR - U01 DK063861/DK/NIDDK NIH HHS/United States GR - UL1 TR001427/TR/NCATS NIH HHS/United States GR - U01 DK063790/DK/NIDDK NIH HHS/United States GR - UL1 TR001082/TR/NCATS NIH HHS/United States GR - P30 DK017047/DK/NIDDK NIH HHS/United States GR - UL1 TR000064/TR/NCATS NIH HHS/United States GR - U01 DK063836/DK/NIDDK NIH HHS/United States GR - U01 DK063829/DK/NIDDK NIH HHS/United States GR - U01 DK063865/DK/NIDDK NIH HHS/United States GR - UC4 DK095300/DK/NIDDK NIH HHS/United States GR - UC4 DK063861/DK/NIDDK NIH HHS/United States GR - UC4 DK063829/DK/NIDDK NIH HHS/United States GR - UC4 DK063821/DK/NIDDK NIH HHS/United States GR - UC4 DK117483/DK/NIDDK NIH HHS/United States GR - UC4 DK063865/DK/NIDDK NIH HHS/United States GR - U01 DK063863/DK/NIDDK NIH HHS/United States GR - UC4 DK106955/DK/NIDDK NIH HHS/United States GR - UC4 DK100238/DK/NIDDK NIH HHS/United States PT - Journal Article PT - Research Support, N.I.H., Extramural PT - Research Support, Non-U.S. Gov't PT - Validation Study DEP - 20190129 PL - Denmark TA - Pediatr Diabetes JT - Pediatric diabetes JID - 100939345 RN - 0 (Autoantibodies) RN - 0 (HLA-DQ Antigens) SB - IM MH - Age Factors MH - Autoantibodies/analysis/*blood MH - Autoimmunity/genetics MH - Child MH - Child, Preschool MH - Cohort Studies MH - Diabetes Mellitus, Type 1/blood/*diagnosis/genetics/pathology MH - Disease Progression MH - Female MH - Genetic Predisposition to Disease MH - HLA-DQ Antigens/genetics MH - Humans MH - Islets of Langerhans/*immunology MH - Male MH - Polymorphism, Single Nucleotide MH - Prognosis PMC - PMC6456374 MID - NIHMS1008925 OTO - NOTNLM OT - autoantibodies OT - metabolic OT - pediatric OT - prediction OT - type 1 diabetes COIS- The authors have no relevant conflicts of interest to disclose. EDAT- 2019/01/11 06:00 MHDA- 2019/10/29 06:00 PMCR- 2020/05/01 CRDT- 2019/01/11 06:00 PHST- 2018/08/26 00:00 [received] PHST- 2018/11/11 00:00 [revised] PHST- 2019/01/04 00:00 [accepted] PHST- 2019/01/11 06:00 [pubmed] PHST- 2019/10/29 06:00 [medline] PHST- 2019/01/11 06:00 [entrez] PHST- 2020/05/01 00:00 [pmc-release] AID - 10.1111/pedi.12812 [doi] PST - ppublish SO - Pediatr Diabetes. 2019 May;20(3):263-270. doi: 10.1111/pedi.12812. Epub 2019 Jan 29.