ArticlePediatric diabetes2019
Predicting progression to type 1 diabetes from ages 3 to 6 in islet autoantibody positive TEDDY children.
Article in Pediatric diabetes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Therapeutic potential of anti-thymocyte globulin in type 1 diabetes: A systematic review.PloS one · 2025Pooled it
- Long-Term Follow-Up Incidence of Type 1 Diabetes and Associated Autoimmune Diseases in First-Degree Relatives.Diabetes, obesity & metabolism · 2026Article
- Risk estimation and dynamic prediction using discrete-time joint models for longitudinal and multistate data with interval and state censoring.Biostatistics (Oxford, England) · 2026Article
- Associations of body weight and COVID-19 with autoimmunity in pediatric new-onset type 1 diabetes: results from the prospective DPV registry.BMJ open diabetes research & care · 2026Article
- 2. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes-2026.Diabetes care · 2026Review
- Article
- Current and Emerging Assays for Measuring Human T-Cell Responses Against Beta-Cell Antigens in Type 1 Diabetes.Biomolecules · 2025Review
- 2. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes-2025.Diabetes care · 2025Review
- Identification of type 1 diabetes risk phenotypes using an outcome-guided clustering analysis.Diabetologia · 2024Article
- Data-Driven Phenotyping of Presymptomatic Type 1 Diabetes Using Longitudinal Autoantibody Profiles.Diabetes care · 2024Article
- Islet autoantibodies as precision diagnostic tools to characterize heterogeneity in type 1 diabetes: a systematic review.Communications medicine · 2024Article
- Implementation of type 1 diabetes genetic risk screening in children in diverse communities: the Virginia PrIMeD project.Genome medicine · 2024Article
- 2. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes-2024.Diabetes care · 2024Review
- Type 1 Diabetes Risk Phenotypes Using Cluster Analysis.medRxiv : the preprint server for health sciences · 2023Article
- Refining the Definition of Stage 1 Type 1 Diabetes: An Ontology-Driven Analysis of the Heterogeneity of Multiple Islet Autoimmunity.Diabetes care · 2023Article
- Incomplete time-series gene expression in integrative study for islet autoimmunity prediction.Briefings in bioinformatics · 2023Article
- 2. Classification and Diagnosis of Diabetes: Standards of Care in Diabetes-2023.Diabetes care · 2023 · on this mapReview
- Predicting the development of T1D and identifying its Key Performance Indicators in children; a case-control study in Saudi Arabia.PloS one · 2023Article
- HbA1c as a time predictive biomarker for an additional islet autoantibody and type 1 diabetes in seroconverted TEDDY children.Pediatric diabetes · 2022Article
- Islet Autoantibody Levels Differentiate Progression Trajectories in Individuals With Presymptomatic Type 1 Diabetes.Diabetes · 2022Article
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Authors and funding
18 authors.
Funding
Abstract
objectiveThe 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.
methodsLogistic 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).
resultsA 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.
conclusionsThis 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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