Objective:
To develop and validate multivariable clinical diagnostic models to assist
distinguishing between type 1 and type 2 diabetes in adults aged 18 to 50.
Design:
Multivariable logistic regression analysis was used to develop classification models
integrating five pre-specified predictor variables, including clinical features ...
Objective:
To develop and validate multivariable clinical diagnostic models to assist
distinguishing between type 1 and type 2 diabetes in adults aged 18 to 50.
Design:
Multivariable logistic regression analysis was used to develop classification models
integrating five pre-specified predictor variables, including clinical features (age of
diagnosis, BMI) 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.
Setting:
United Kingdom cohorts recruited from primary and secondary care.
Participants:
1,352 (model development) and 582 (external validation) participants diagnosed with
diabetes between the age of 18 and 50 years of white European origin.
Main outcome measures:
Type 1 diabetes was defined by rapid insulin requirement (within 3 years of
diagnosis) and severe endogenous insulin deficiency (C-peptide <200pmol/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 >600pmol/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.
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Results:
Type 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, 0.93] (clinical features
only) to 0.97 [0.96, 0.98] (all predictors) with low prediction error. Results were
consistent in external validation (clinical features and GADA ROC AUC 0.93 [0.90,
0.96]).
Conclusions:
Clinical 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.