ArticleEBioMedicine2026
Transcriptomics of type 1 diabetes progression: a validation study in newly diagnosed patients.
Article in EBioMedicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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17 authors.
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Abstract
backgroundType 1 diabetes is an autoimmune disease with significant long-term complications. Variability in the decline of insulin secretion after diagnosis complicates both the development of treatments and disease management. We previously reported that gene expression changes within the first year post-diagnosis were associated with C-peptide decline at two years in the first INNODIA cohort of patients with newly diagnosed type 1 diabetes. Here, we aimed to validate these findings in an independent follow-up cohort and to increase statistical power by combining the data from both cohorts.
methodsWe analysed transcriptomic data from a follow-up INNODIA cohort of 168 individuals with newly diagnosed type 1 diabetes to assess whether previously identified associations with disease progression could be replicated. We then combined data from the original and follow-up cohorts for integrated analysis. Longitudinal gene expression changes during the first year after diagnosis were examined in relation to disease progression, alongside age and estimated immune cell abundances.
findingsAnalysis of the follow-up cohort validated the previously observed longitudinal changes in gene expression during the first year after diagnosis. In the combined dataset, transcriptomic analysis identified a large number of genes that were differentially expressed during the first year after disease onset. More rapid disease progression was associated with younger age and a relative decrease in neutrophil abundance. In addition, changes in the expression of several genes were associated with the rate of disease progression.
interpretationThese findings support the existence of biological heterogeneity in disease progression after diagnosis of type 1 diabetes and contribute to an improved understanding of the molecular dynamics associated with disease progression. These findings may help future studies aiming to enable patient stratification and design of more targeted and personalised therapeutic approaches in type 1 diabetes.
fundingThis project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 115797 (INNODIA) and No 945268 (INNODIA HARVEST).
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