Extended Data Fig. 9: Usability of the eDRS4C risk score in different scenarios.

Plasma of a single individual was assessed for the PREDICT T1D microRNAs and autoantibodies from 14 months of age to 60 months of age (Clinical diagnosis of T1D). a) shows the PREDICT T1D microRNA-based eDRS4C at different timepoints during progression to T1D. The eDRS4C was already high (>60% T1D probability) at the first measurement (14 months), increasing to >75% T1D probability at 18 months of age, and remaining high (>60% probability) thereafter. Islet autoantibodies (b) increased in circulation at later time points (from week 39 onwards). Similarly, the PREDICT T1D microRNAs can be used in an anomaly detection algorithm to identify individuals within a cohort (first-degree T1D relatives from DNK) who could be further risk-stratified to T1D progressors and non-progressors. c) An isolation forest (anomaly detection) plot using existing biomarkers of T1D risk (GRS, autoantibodies and age), T1D progressors (n = 4), and non-progressor (n = 159) d) An isolation forest (anomaly detection) plot using the top 10 features (see Fig. 2d) of this microRNA-based T1D risk score. The red dots indicate siblings predicted to be at the highest risk of progression to T1D, while the blue dots represent those at lower risk of T1D. Four of these individuals (S1137, S1213, S1338, S3210) within this cohort progressed to T1D in 12 years from sample collection (T1D progressors n = 4, and non-progressors n = 288). Those labelled in a red-coloured font are correctly identified as progressors using the existing (GRS, autoantibodies, and age; c) or the top-10 features of the microRNA-based (d) risk scores.