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Jacobsen, L.M.* ; Elding Larsson, H.* ; Tamura, R.N.* ; Vehik, K.* ; Clasen, J.* ; Sosenko, J.M.* ; Hagopian, W.A.* ; She, J.X.* ; Steck, A.K.* ; Rewers, M.* ; Simell, O.* ; Toppari, J.* ; Veijola, R.* ; Ziegler, A.-G. ; Krischer, J.P.* ; Akolkar, B.* ; Haller, M.J.* ; The Teddy Study Group*

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

Pediatr. Diabetes 20, 263-270 (2019)
Postprint DOI PMC
Open Access Green
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.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Autoantibodies ; Metabolic ; Pediatric ; Prediction ; Type 1 Diabetes; 1 Risk Score; Genetic Susceptibility; Ia-2 Autoantibodies; Prevention; Autoimmunity; Determinants; Appearance; Childhood; Diagnosis; Hba(1c)
Language english
Publication Year 2019
HGF-reported in Year 2019
ISSN (print) / ISBN 1399-543X
e-ISSN 1399-5448
Journal Pediatric Diabetes
Quellenangaben Volume: 20, Issue: 3, Pages: 263-270 Article Number: , Supplement: ,
Publisher Wiley
Publishing Place 111 River St, Hoboken 07030-5774, Nj Usa
Reviewing status Peer reviewed
Institute(s) Institute of Diabetes Research (IDF)
POF-Topic(s) 30201 - Metabolic Health
Research field(s) Helmholtz Diabetes Center
PSP Element(s) G-502100-001
DOI 10.1111/pedi.12812
WOS ID WOS:000464382400004
Scopus ID 85060780097
PubMed ID 30628751
Erfassungsdatum 2019-03-11
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