Machine Learning Analysis of Circulating Alternative Spliced RNAs Reveal Segregation of Retained Intron Species with New-onset Type 1 Diabetes

Distinct patterns of RNA splicing differentiated participants with T1D from healthy unaffected controls. Notably, certain splicing events, particularly involving retained introns, showed significant association with T1D. Machine learning analysis using these splicing events as features from the training cohort demonstrated high accuracy in distinguishing between T1D subjects and controls in the validation cohort. Gene Ontology pathway enrichment analysis of the retained intron category showed evidence for a systemic viral response in T1D subjects. Examination of circulating alternative splicing patterns in subjects of T1D and matched healthy subjects