Gene expression signature predicts rate of type 1 diabetes progression

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Beitragende

  • University of Turku
  • Novo Nordisk Foundation
  • University of Cambridge
  • KU Leuven
  • Sanofi-Aventis U.S. LLC
  • Sanofi-Aventis
  • University of Helsinki
  • Tampere University

Abstract

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.

Details

OriginalspracheEnglisch
Aufsatznummer104625
Seiten (von - bis)1-12
Seitenumfang12
FachzeitschriftEBioMedicine
Jahrgang92
Ausgabenummer2023
PublikationsstatusVeröffentlicht - Juni 2023
Peer-Review-StatusJa

Externe IDs

PubMed 37224769
ORCID /0000-0002-8704-4713/work/148603445

Schlagworte

Ziele für nachhaltige Entwicklung

Schlagwörter

  • Autoantibodies, Gene expression signature, Predictive model, RNA-seq, Type 1 diabetes, Humans, Autoimmune Diseases, Transcriptome, Diabetes Mellitus, Type 1, Disease Progression

Bibliotheksschlagworte