Applicability of a microRNA-based Dynamic Risk Score (DRS) for type 1 diabetes

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 4-context, microRNA-based dynamic risk score (DRS4C) was generated, which effectively stratified individuals with/without T1D. Generative artificial intelligence (GAI) was used to create an enhanced (e)DRS4C, that offered high AUC (0.84) on an independent multi-context Validation-set (n=662) and most accurately predicted future exogenous-insulin requirement at one-hour of islet transplantation in Canada (CAN) recipients. In a clinical trial assessing an emerging T1D therapy, baseline microRNA signature, but not the clinical characteristics, stratified 1-year response to Imatinib. This study harnessed ML and GAI approaches, identifying and validating a microRNA-based DRS for T1D stratification and treatment efficacy prediction. This capsule presents the code for stratifying controls and T1D study participants as well as for predicting outcomes of T1D therapy.