A multi-omics integration approach relying on circulating factors does not discern subtypes of childhood type 1 diabetes

Background: Type 1 Diabetes (T1D) exhibits considerable heterogeneity, impacting prediction, prevention, diagnosis, and treatment. Precision medicine aims to tailor treatments using 'endotypes'-subtypes of disease with distinct pathophysiological mechanisms. However, proposed endotypes often lack mechanistic associations with clinical outcomes for accurately identifying T1D cases. Methods: This study introduces an approach leveraging the multi-omics factor analysis (MOFA) strategy, a computational method for unsupervised integration analysis, to explore endotypes. Analyzing data from 146 new-onset children with T1D (54 females, 92 males; age range 5-18 years), including circulating immunome, transcriptome, and serum metabolic hormones, we identify 12 factors explaining variability across the three data sets. Results: Here we find no associations, either direct or through clustering, between these 12 factors and clinical parameters, genetic predisposition, or disease outcome. These results suggest that a combination of clinical phenotypes might be responsible for the differences across T1D cases. Conclusions: These findings challenge the assumption that T1D heterogeneity reflects diverse developmental mechanisms. These results add to the ongoing debate on endotypes and carry important implications for clinical trial design-particularly in how treatments are evaluated for their effectiveness across broad and diverse patient populations.