Predicting progression to type 1 diabetes from ages 3 to 6 in islet autoantibody positive TEDDY children

被引:35
作者
Jacobsen, Laura M. [1 ]
Larsson, Helena E. [2 ]
Tamura, Roy N. [3 ]
Vehik, Kendra [3 ,4 ,5 ,6 ,36 ]
Clasen, Joanna [3 ,36 ]
Sosenko, Jay [4 ]
Hagopian, William A. [1 ,3 ,4 ,5 ,6 ,12 ,13 ,34 ]
She, Jin-Xiong [1 ,3 ,4 ,6 ,12 ,13 ,25 ]
Steck, Andrea K. [7 ]
Rewers, Marian [1 ,4 ,5 ,6 ,7 ,10 ,11 ,12 ,13 ,15 ]
Simell, Olli [8 ]
Toppari, Jorma [1 ,4 ,8 ,9 ,12 ,13 ,16 ]
Veijola, Riitta [10 ,11 ,18 ,22 ]
Ziegler, Anette G. [1 ,3 ,4 ,12 ,13 ,28 ,29 ,30 ]
Krischer, Jeffrey P. [1 ,3 ,4 ,10 ,11 ,12 ,13 ,36 ]
Akolkar, Beena [1 ,3 ,4 ,5 ,6 ,10 ,11 ,12 ,13 ,14 ,37 ]
Haller, Michael J. [1 ]
Rewers, Marian [1 ,4 ,5 ,6 ,7 ,10 ,11 ,12 ,13 ,15 ]
Bautista, Kimberly [14 ,15 ]
Baxter, Judith [9 ,10 ,11 ,14 ,15 ]
Bedoy, Ruth [2 ,15 ]
Felipe-Morales, Daniel [15 ]
Driscoll, Kimberly [9 ,15 ]
Frohnert, Brigitte I. [2 ,15 ]
Gallant, Marisa [15 ]
Gesualdo, Patricia [2 ,6 ,14 ,15 ]
Hoffman, Michelle [14 ,15 ]
Karban, Rachel [14 ,15 ]
Liu, Edwin [15 ]
Norris, Jill [2 ,3 ,14 ,15 ]
Samper-Imaz, Adela [15 ]
Steck, Andrea [3 ,15 ]
Waugh, Kathleen [6 ,14 ,15 ]
Wright, Hali [14 ,15 ]
Toppari, Jorma [1 ,4 ,8 ,9 ,12 ,13 ,16 ]
Simell, Olli G.
Adamsson, Annika [14 ]
Ahonen, Suvi [17 ,21 ,23 ]
Hyoty, Heikki [6 ,17 ,21 ]
Ilonen, Jorma. [3 ,16 ,24 ]
Jokipuu, Sanna [19 ,20 ]
Kallio, Tiina [19 ,20 ]
Karlsson, Leena [19 ,20 ]
Kahonen, Miia [18 ,22 ]
Knip, Mikael [5 ,17 ,21 ]
Kovanen, Lea [17 ,21 ,23 ]
Koreasalo, Mirva [2 ,17 ,21 ,23 ]
Kurppa, Kalle [17 ,21 ]
Latva-aho, Tiina [18 ,22 ]
Lonnrot, Maria [6 ,17 ,21 ]
机构
[1] Univ Florida, Dept Pediat, Gainesville, FL USA
[2] Lund Univ, Skane Univ Hosp SUS, Dept Clin Sci Malmo, Malmo, Sweden
[3] Univ S Florida, Morsani Coll Med, Hlth Informat Inst, Tampa, FL USA
[4] Univ Miami, Div Endocrinol, Miami, FL USA
[5] Pacific Northwest Diabet Res Inst, Seattle, WA USA
[6] Augusta Univ, Med Coll Georgia, Ctr Biotechnol & Genom Med, Augusta, GA USA
[7] Univ Colorado, Barbara Davis Ctr Childhood Diabet, Denver, CO 80202 USA
[8] Turku Univ Hosp, Dept Pediat, Turku, Finland
[9] Univ Turku, Inst Biomed, Dept Physiol, Turku, Finland
[10] Oulu Univ Hosp, PEDEGO Res Unit, Med Res Ctr, Dept Pediat, Oulu, Finland
[11] Univ Oulu, Oulu, Finland
[12] Helmholtz Zentrum Munchen, Inst Diabet Res, Neuherberg, Germany
[13] Forschergrp Diabet eV Neuherberg, Neuherberg, Germany
[14] NIDDK, Div Diabet Endocrinol & Metab, NIH, Bethesda, MD 20892 USA
[15] Univ Colorado, Barbara Davis Ctr Childhood Diabet, Anschutz Med Campus, Aurora, CO USA
[16] Univ Turku, Turku, Finland
[17] Univ Tampere, Tampere, Finland
[18] Univ Oulu, Oulu, Finland
[19] Turku Univ Hosp, Turku, Finland
[20] Hosp Dist Southwest Finland, Turku, Finland
[21] Tampere Univ Hosp, Tampere, Finland
[22] Oulu Univ Hosp, Oulu, Finland
[23] Natl Inst Hlth & Welf, Helsinki, Finland
[24] Univ Kuopio, Kuopio, Finland
[25] Augusta Univ, Ctr Biotechnol & Genom Med, Augusta, GA USA
[26] Univ Florida, Gainesville, FL 32611 USA
[27] Pediat Endocrine Associates, Atlanta, GA USA
[28] Forschergruppe Diabet eV, Oberschleissheim, Germany
[29] Helmholtz Zentrum Munchen, Forschergrp Diabet, Diabet Res Inst, Oberschleissheim, Germany
[30] Tech Univ Munich, Klinikum Rechts Isar, Oberschleissheim, Germany
[31] Tech Univ Dresden, Ctr Regenerat Therapies, Dresden, Germany
[32] Ludwig Maximillians Univ Munich, Dept Gastroenterol, Dr Hauner Childrens Hosp, Munich, Germany
[33] Lund Univ, Lund, Sweden
[34] Pacific Northwest Res Inst, 720 Broadwayim, Seattle, WA 98122 USA
[35] UPMC, Childrens Hosp Pittsburgh, Pittsburgh, PA USA
[36] Univ S Florida, Tampa, FL USA
[37] NIDDK, Bethesda, MD 20892 USA
[38] Univ Colorado, Barbara Davis Ctr Childhood Diabet, Denver, CO 80202 USA
[39] Univ Bristol, Bristol Med Sch, Bristol, Avon, England
[40] Pacific Northwest Res Inst, 720 Broadway, Seattle, WA 98122 USA
[41] Childrens Hosp Oakland, Res Inst, Ctr Genet, Oakland, CA 94609 USA
[42] Univ Virginia, Ctr Publ Hlth Genom, Charlottesville, VA 22903 USA
[43] NIDDK, Biosample Repository Fisher BioServ, Bethesda, MD 20892 USA
[44] NIAID, 9000 Rockville Pike, Bethesda, MD 20892 USA
[45] Columbia Univ, New York, NY 10027 USA
[46] Florida State Univ, Tallahassee, FL 32306 USA
[47] Univ Florida, Gainesville, FL 32611 USA
关键词
autoantibodies; metabolic; pediatric; prediction; type; 1; diabetes; 1 RISK SCORE; GENETIC SUSCEPTIBILITY; IA-2; AUTOANTIBODIES; PREVENTION; AUTOIMMUNITY; DETERMINANTS; APPEARANCE; CHILDHOOD; DIAGNOSIS; HBA(1C);
D O I
10.1111/pedi.12812
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
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.
引用
收藏
页码:263 / 270
页数:8
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