ArticleBMJ open2019
Development and validation of multivariable clinical diagnostic models to identify type 1 diabetes requiring rapid insulin therapy in adults aged 18-50 years.
Article in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 2 of them syntheses that pooled it.
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Who cites it
39 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Accuracy of glutamic acid decarboxylase antibodies for the identification of adult-onset type 1 diabetes mellitus: a systematic review and meta-analysis.Frontiers in endocrinology · 2026Pooled it
- The Incidence of Adult-Onset Type 1 Diabetes: A Systematic Review From 32 Countries and Regions.Diabetes care · 2022Pooled it
- Article
- Differences in Clinical Manifestations and Islet Autoantibodies by Age in Adult-Onset Type 1 Diabetes.Diabetes care · 2026Article
- 2. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes-2026.Diabetes care · 2026Review
- Article
- Diabetes mellitus polygenic risk scores: heterogeneity and clinical translation.Nature reviews. Endocrinology · 2025Review
- Type 1 diabetes presenting in adults: Trends, diagnostic challenges and unique features.Diabetes, obesity & metabolism · 2025Review
- The epidemiology of type 1 diabetes mellitus in older adults.Nature reviews. Endocrinology · 2025Review
- Investigating misclassification of type 1 diabetes in a population-based cohort of British Pakistanis and Bangladeshis using polygenic risk scores.Scientific reports · 2025Article
- 2. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes-2025.Diabetes care · 2025Review
- Actionability of Genetic Variants in Diabetes: Core Aspects and Applied Examples.Diabetes spectrum : a publication of the American Diabetes Association · 2025Article
- Article
- Article
- Long-term TNF-alpha therapy for preserving beta cell function in new onset type 1 diabetes: a case report.Clinical diabetes and endocrinology · 2024Article
- Lessons and Applications of Omics Research in Diabetes Epidemiology.Current diabetes reports · 2024Review
- Diabetes Study of Children of Diverse Ethnicity and Race: Study design.Diabetes/metabolism research and reviews · 2024Observational
- 2. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes-2024.Diabetes care · 2024Review
- The challenges of identifying and studying type 1 diabetes in adults.Diabetologia · 2023Review
- A Review on Latent Autoimmune Diabetes in Adults.Cureus · 2023Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
objectiveTo develop and validate multivariable clinical diagnostic models to assist distinguishing between type 1 and type 2 diabetes in adults aged 18-50.
designMultivariable logistic regression analysis was used to develop classification models integrating five pre-specified predictor variables, including clinical features (age of diagnosis, body mass index) and clinical biomarkers (GADA and Islet Antigen 2 islet autoantibodies, Type 1 Diabetes Genetic Risk Score), to identify type 1 diabetes with rapid insulin requirement using data from existing cohorts.
settingUK cohorts recruited from primary and secondary care.
participants1352 (model development) and 582 (external validation) participants diagnosed with diabetes between the age of 18 and 50 years of white European origin.
main outcome measuresType 1 diabetes was defined by rapid insulin requirement (within 3 years of diagnosis) and severe endogenous insulin deficiency (C-peptide <200 pmol/L). Type 2 diabetes was defined by either a lack of rapid insulin requirement or, where insulin treated within 3 years, retained endogenous insulin secretion (C-peptide >600 pmol/L at ≥5 years diabetes duration). Model performance was assessed using area under the receiver operating characteristic curve (ROC AUC), and internal and external validation.
resultsType 1 diabetes was present in 13% of participants in the development cohort. All five predictor variables were discriminative and independent predictors of type 1 diabetes (p<0.001 for all) with individual ROC AUC ranging from 0.82 to 0.85. Model performance was high: ROC AUC range 0.90 (95% CI 0.88 to 0.93) (clinical features only) to 0.97 (95% CI 0.96 to 0.98) (all predictors) with low prediction error. Results were consistent in external validation (clinical features and GADA ROC AUC 0.93 (0.90 to 0.96)).
conclusionsClinical diagnostic models integrating clinical features with biomarkers have high accuracy for identifying type 1 diabetes with rapid insulin requirement, and could assist clinicians and researchers in accurately identifying patients with type 1 diabetes.
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