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

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, remaining elusive in T1D. This study introduces an approach leveraging the multi-omics factor analysis (MOFA) strategy to explore endotypes through data integration. Analyzing data from 146 new-onset pediatric T1D patients, including circulating immunome, transcriptome, and serum metabolic hormones, we identify 12 factors explaining variability across the three data sets. Here we show that no clustering or direct association of these factors with clinical parameters, genetic predisposition and disease outcome are found, suggesting that a combination of clinical phenotypes is responsible for the differences across patients. These findings challenge the assumption that T1D heterogeneity reflects diverse developmental mechanisms, contributing substantially to the endotype discussion and impacting clinical trial design. Overall design: RNA-seq profiling of blood samples from pediatric patients with new-onset type 1 diabetes; The analysis involved selecting the top 5000 most variable genes, which were then used in MOFA for multi-omics analysis.