Type 1 Diabetes Risk Phenotypes Using Cluster Analysis

BackgroundAlthough statistical models for predicting type 1 diabetes risk have been developed, approaches that reveal clinically meaningful clusters in the at-risk population and allow for non-linear relationships between predictors are lacking. We aimed to identify and characterize clusters of islet autoantibody-positive individuals that share similar characteristics and type 1 diabetes risk. MethodsWe tested a novel outcome-guided clustering method in initially non-diabetic autoantibody-positive relatives of individuals with type 1 diabetes, using the TrialNet Pathway to Prevention (PTP) study data (n=1127). The outcome of the analysis was time to type 1 diabetes and variables in the model included demographics, genetics, metabolic factors and islet autoantibodies. An independent dataset (Diabetes Prevention Trial of Type 1 Diabetes, DPT-1 study) (n=704) was used for validation. FindingsThe analysis revealed 8 clusters with varying type 1 diabetes risks, categorized into three groups. Group A had three clusters with high glucose levels and high risk. Group B included four clusters with elevated autoantibody titers. Group C had three lower-risk clusters with lower autoantibody titers and glucose levels. Within the groups, the clusters exhibit variations in characteristics such as glucose levels, C-peptide levels, age, and genetic risk. A decision rule for assigning individuals to clusters was developed. The validation dataset confirms that the clusters can identify individuals with similar characteristics. InterpretationDemographic, metabolic, immunological, and genetic markers can be used to identify clusters of distinctive characteristics and different risks of progression to type 1 diabetes among autoantibody-positive individuals with a family history of type 1 diabetes. The results also revealed the heterogeneity in the population and complex interactions between variables. FundingNational Institute of Diabetes and Digestive and Kidney Diseases (R01DK121843), the National Institute of Allergy and Infectious Diseases, the Eunice Kennedy Shriver National Institute of Child Health and Human Development (cooperative agreements U01 DK061010, U01 DK061034, U01 DK061042, U01 DK061058, U01 DK085461, U01 DK085465, U01 DK085466, U01 DK085476, U01 DK085499, U01 DK085509, U01 DK103180, U01 DK103153, U01 DK103266, U01 DK103282, U01 DK106984, U01 DK106994, U01 DK107013, U01 DK107014, UC4 DK106993, UC4DK117009), and the Juvenile Diabetes Research Foundation. Research in ContextO_ST_ABSEvidence Before This StudyC_ST_ABSWe searched PubMed on June 12, 2023, using the keywords "cluster type 1 diabetes", "heterogeneity type 1 diabetes", and "endotypes type 1 diabetes" with no restrictions on publication date. Only articles published in English were considered. Existing literature suggests that individuals at risk of type 1 diabetes form a diverse population influenced by a combination of genetic and environmental factors. However, there is a scarcity of research focusing on defining clusters within this at-risk population; previous studies using clustering analysis to define diabetes subtypes have primarily utilized unsupervised clustering methods, which may not be as effective in capturing the critical variables that inform disease risks. Added Value of This StudyWe applied an outcome-guided clustering analysis to unravel the complexity and heterogeneity of type 1 diabetes. This study introduces a new method for identifying clusters of individuals based on key risk factors and the observed disease outcomes. Within each cluster, individuals will exhibit similar characteristics and diabetes risk, while the clusters themselves represent distinct levels of risk. Unlike previous approaches that employ regression models relying on linearity and additivity assumptions, this method provides advantages by uncovering underlying interactions and correlations among risk factors. Our results were validated in the DPT-1 study cohort. Implications of All the Available EvidenceOur findings suggest that demographic, metabolic, immunological, and genetic markers can be used to identify distinct clusters with varying risks of progression to type 1 diabetes within autoantibody-positive individuals with a family history of the disease. The results demonstrate how different combinations of these risk factors contribute to the risk and how clusters of similar risks can exhibit unique characteristics with regard to the risk factors, which highlights the heterogeneity in this population.