ArticleCommunications medicine2025
A multi-omics integration approach relying on circulating factors does not discern subtypes of childhood type 1 diabetes.
Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Microbiome-Based Clustering Identifies Glycemic Control-Related Subtypes in Youth With Recent-Onset Type 1 Diabetes.MedComm · 2026Article
- Artificial intelligence-driven therapeutics for disease modification in type 1 diabetes: a digital public health and clinical translation framework.Frontiers in public health · 2026Review
- Immunogenetics and TCR expression patterns together predict future T1D onset from multi-omic datasets.Frontiers in genetics · 2026Article
- A multi-omics integration approach relying on circulating factors does not discern subtypes of childhood type 1 diabetes.Communications medicine · 2025Article
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Authors and funding
19 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundType 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.
methodsThis 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.
resultsHere 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.
conclusionsThese 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.
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Registered trials
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