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.
This study seeks to understand why type 1 diabetes (T1D) affects individuals differently and whether specific subtypes of the disease, known as “endotypes,” can explain this variability to inform personalized treatment strategies. We analyze data from 146 children newly diagnosed with T1D, examining immune activity, gene expression, and hormone levels. We identify 12 factors that account for some variability among patients but find no clear associations between these factors and disease outcomes or genetic risk. These findings suggest that T1D is shaped by a complex interplay of factors rather than by distinct underlying mechanisms, highlighting the need for a more refined understanding of disease heterogeneity to guide effective treatments and the design of future clinical trials.
© 2025. The Author(s).