ArticleEBioMedicine2023
Gene expression signature predicts rate of type 1 diabetes progression.
Article in EBioMedicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 18 citations in OpenAlex.
- Transcriptomics of type 1 diabetes progression: a validation study in newly diagnosed patients.EBioMedicine · 2026Article
- Integrative gene target mapping, RNA sequencing, in silico molecular docking, ADMET profiling and molecular dynamics simulation study of marine derived molecules for type 1 diabetes mellitus.Molecular diversity · 2026Article
- Review
- Immunogenetics and TCR expression patterns together predict future T1D onset from multi-omic datasets.Frontiers in genetics · 2026Article
- Imbalance of stem-like and effector T cell states in children with early type 1 diabetes across conventional and regulatory subsets.Nature communications · 2025Article
- Optimizing Type 1 Diabetes Screening in People With Family History: A German Perspective.Journal of diabetes science and technology · 2025Review
- Comparison of immunological and immunogenetic markers in recent-onset type 1 diabetes among children and adults.Scientific reports · 2025Article
- UHPLC-MS/MS-based plasma untargeted lipidomic analysis in patients with diabetes mellitus combined with hyperuricemia.Frontiers in molecular biosciences · 2025Article
- Integrating Omics into Functional Biomarkers of Type 1 Diabetes.Cold Spring Harbor perspectives in medicine · 2024Review
- Genetic and Epigenetic Aspects of Type 1 Diabetes Mellitus: Modern View on the Problem.Biomedicines · 2024Review
- Type 1 diabetes mellitus: a brave new world.Nature reviews. Endocrinology · 2024Article
- Footprint of pancreas infiltrating and circulating immune cells throughout type 1 diabetes development.Frontiers in endocrinology · 2023Article
- RNA-based diagnostic innovations: A new frontier in diabetes diagnosis and management.Diabetes & vascular disease researchReview
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Authors and funding
21 authors at 9 institutions in 6 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundType 1 diabetes is a complex heterogenous autoimmune disease without therapeutic interventions available to prevent or reverse the disease. This study aimed to identify transcriptional changes associated with the disease progression in patients with recent-onset type 1 diabetes.
methodsWhole-blood samples were collected as part of the INNODIA study at baseline and 12 months after diagnosis of type 1 diabetes. We used linear mixed-effects modelling on RNA-seq data to identify genes associated with age, sex, or disease progression. Cell-type proportions were estimated from the RNA-seq data using computational deconvolution. Associations to clinical variables were estimated using Pearson's or point-biserial correlation for continuous and dichotomous variables, respectively, using only complete pairs of observations.
findingsWe found that genes and pathways related to innate immunity were downregulated during the first year after diagnosis. Significant associations of the gene expression changes were found with ZnT8A autoantibody positivity. Rate of change in the expression of 16 genes between baseline and 12 months was found to predict the decline in C-peptide at 24 months. Interestingly and consistent with earlier reports, increased B cell levels and decreased neutrophil levels were associated with the rapid progression.
interpretationThere is considerable individual variation in the rate of progression from appearance of type 1 diabetes-specific autoantibodies to clinical disease. Patient stratification and prediction of disease progression can help in developing more personalised therapeutic strategies for different disease endotypes.
fundingA full list of funding bodies can be found under Acknowledgments.
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