
Carin Andrén Aronsson
Head of unit

Predicting Islet Cell Autoimmunity and Type 1 Diabetes : An 8-Year TEDDY Study Progress Report
Author
Other contributions
- Daniel Agardh
- Carin Andrén Aronsson
- Maria Ask
- Jenny Bremer
- Emelie Ericson-Hallström
- Annika Björne Fors
- Lina Fransson
- Thomas Gard
- Rasmus Bennet
- Susanne Hyberg
- Hanna Jisser
- Fredrik Johansen
- Berglind Jónsdóttir
- SILVIJA JOVIC
- Helena Elding Larsson
- Marielle Lindström
- Markus Lundgren
- Maria Månsson Martinez
- Maria Markan
- Marie Jessica Melin
- Zeliha Mestan
- Caroline N Nilsson
- Karin Ottosson
- Kobra Rahmati
- Anita Ramelius
- Falastin Salami
- Anette Sjöberg
- Birgitta Sjöberg
- Carina Törn
- Anne Wallin
- Åsa Wimar
- Sofie Åberg
Summary, in English
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.
Department/s
- Celiac Disease and Diabetes Unit
- EXODIAB: Excellence of Diabetes Research in Sweden
- Transport and Roads
- Paediatric Endocrinology
Publishing year
2019-06
Language
English
Pages
1051-1060
Publication/Series
Diabetes Care
Volume
42
Issue
6
Document type
Journal article
Publisher
American Diabetes Association
Topic
- Endocrinology and Diabetes
Status
Published
Research group
- Celiac Disease and Diabetes Unit
- Paediatric Endocrinology
ISBN/ISSN/Other
- ISSN: 1935-5548