Applicability of a microRNA-based dynamic risk score for type 1 diabetes

Abstract

Identifying biomarkers of functional β-cell loss is critical in risk stratification for Type 1 Diabetes (T1D). We report a microRNA-based dynamic (responsive to environment) risk score developed using multi-center, multi-ethnic/country (“multi-context”) cohorts. Discovery (wet-lab and dry-lab) analysis identified 50 microRNAs that were measured across n=2,204 individuals from four contexts (4C=AUS/Australia, DNK/Denmark, HKG/Hong Kong SAR China, IND/India). A microRNA-based dynamic risk score (DRS) was generated (DRS4C), which effectively stratified individuals with/without T1D. Generative artificial intelligence (GAI) was used to create an enhanced (e)DRS4C that showed AUC >0.84 on an independent Validation set (n=313) from AUS, IND and NZL and predicted future exogenous-insulin requirement in islet transplantation recipients from Canada (CAN). In another T1D therapy, this microRNA signature stratified 1-year response to imatinib based on their profile at the study baseline. Utilizing machine learning and GAI, this study identified and validated a microRNA-based DRS for T1D stratification and treatment efficacy prediction.