Gene expression signature predicts rate of type 1 diabetes progression

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  • Tomi Suomi
  • Inna Starskaia
  • Ubaid Ullah Kalim
  • Omid Rasool
  • Maria K. Jaakkola
  • Toni Grönroos
  • Tommi Välikangas
  • Caroline Brorsson
  • Gianluca Mazzoni
  • Sylvaine Bruggraber
  • Lutgart Overbergh
  • David Dunger
  • Mark Peakman
  • Chmura, Piotr Jaroslaw
  • Brunak, Søren
  • Anke M. Schulte
  • Chantal Mathieu
  • Mikael Knip
  • Riitta Lahesmaa
  • Laura L. Elo
  • Pociot, Flemming (Member of author collaboration)
  • Johannesen, Jesper (Member of author collaboration)
  • Rossing, Peter (Member of author collaboration)
  • INNODIA consortium

Background: Type 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. Methods: Whole-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. Findings: We 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. Interpretation: There 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. Funding: A full list of funding bodies can be found under Acknowledgments.

Original languageEnglish
Article number104625
Number of pages18
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 The Authors

    Research areas

  • Autoantibodies, Gene expression signature, Predictive model, RNA-seq, Type 1 diabetes

ID: 357651850