Identifying type 1 and 2 diabetes in research datasets where classification biomarkers are unavailable: assessing the accuracy of published approaches

Abstract

OBJECTIVE: We aimed to compare the performance of approaches for classifying insulin treated diabetes within research datasets without measured classification biomarkers, evaluated against two independent biological definitions of diabetes type. STUDY DESIGN AND SETTING: We compared accuracy of ten reported approaches for classifying insulin treated diabetes into type 1 (T1D) and type 2 (T2D) diabetes in two cohorts: UK Biobank (UKBB) n=26,399 and DARE n=1,296. Overall performance for classifying T1D and T2D were assessed using: a T1D genetic risk score and genetic stratification method (UKBB); C-peptide measured at >3 years diabetes duration (DARE). RESULTS: Approaches accuracy ranged from 71%-88% (UKBB) and 68%-88% (DARE). When classifying all participants, combining early insulin requirement with a T1D probability model (incorporating diagnosis age and BMI), and interview reported diabetes type (UKBB available in only 15%) consistently achieved high accuracy, (UKBB 87% and 87%, DARE 85% and 88% respectively). For identifying T1D with minimal misclassification, models with high thresholds or young diagnosis age (<20 years) had highest performance. Findings were incorporated into an online tool identifying optimum approaches based on variable availability. CONCLUSION: Models combining continuous features with early insulin requirement are the most accurate methods for classifying insulin treated diabetes in research datasets without measured classification biomarkers.