MicroRNA-Based Risk Score Stratifies Type 1 Diabetes Risk Across Global Populations
Researchers developed a miRNA-based dynamic risk score using multicentre, multiethnic cohorts across four countries, identifying 50 miRNAs associated with functional beta-cell loss. Enhanced by generative AI, the risk score achieved an AUC of 0.84 for type 1 diabetes stratification in a separate validation set and accurately predicted insulin requirement after islet transplantation. Critically, the baseline miRNA signature distinguished drug responders from non-responders in an imatinib clinical trial, demonstrating utility as both a diagnostic and companion diagnostic biomarker.
The original study
A microRNA-based dynamic risk score for type 1 diabetes.
- Authors
- Joglekar MV, Wong WKM, Kunte PS, Hardikar HP, Kulkarni RA, Ahmed I, et al.
- Journal
- Nature medicine
- Type
- Journal Article, Multicenter Study
- PMID
- 40473952
Original abstract
Identifying individuals at high risk of type 1 diabetes (T1D) is crucial as disease-delaying medications are available. Here we report a microRNA (miRNA)-based dynamic (responsive to the environment) risk score developed using multicenter, multiethnic and multicountry ('multicontext') cohorts for T1D risk stratification. Discovery (wet and dry lab) analysis identified 50 miRNAs associated with functional β cell loss, which is a hallmark of T1D. These miRNAs measured across n = 2,204 individuals from four contexts (4C: Australia, Denmark, Hong Kong SAR People's Republic of China, India) led to a four-context, miRNA-based dynamic risk score (DRS) that effectively stratified individuals with and without T1D. Generative artificial intelligence was used to create an enhanced four-context, miRNA-based DRS, which offered good predictive power (area under the curve = 0.84) for T1D stratification in a separate multicontext validation dataset (n = 662), and accurately predicted future exogenous insulin requirement at 1 hour of islet transplantation. In a clinical trial assessing the imatinib drug therapy, baseline miRNA signature, rather than clinical characteristics, distinguished drug responders from nonresponders at 1 year. This study harnessed machine learning/generative artificial intelligence approaches, identifying and validating a miRNA-based DRS for T1D discrimination and treatment efficacy prediction.