EBioMedicine (Jun 2023)

Gene expression signature predicts rate of type 1 diabetes progressionResearch in context

  • Tomi Suomi,
  • Inna Starskaia,
  • Ubaid Ullah Kalim,
  • Omid Rasool,
  • Maria K. Jaakkola,
  • Toni Grönroos,
  • Tommi Välikangas,
  • Caroline Brorsson,
  • Gianluca Mazzoni,
  • Sylvaine Bruggraber,
  • Lut Overbergh,
  • David Dunger,
  • Mark Peakman,
  • Piotr Chmura,
  • Søren Brunak,
  • Anke M. Schulte,
  • Chantal Mathieu,
  • Mikael Knip,
  • Riitta Lahesmaa,
  • Laura L. Elo,
  • Chantal Mathieu,
  • Pieter Gillard,
  • Kristina Casteels,
  • Lutgart Overbergh,
  • David Dunger,
  • Chris Wallace,
  • Mark Evans,
  • Ajay Thankamony,
  • Emile Hendriks,
  • Sylvaine Bruggraber,
  • Loredana Marcoveccchio,
  • Mark Peakman,
  • Timothy Tree,
  • Noel G. Morgan,
  • Sarah Richardson,
  • John A. Todd,
  • Linda Wicker,
  • Adrian Mander,
  • Colin Dayan,
  • Mohammad Alhadj Ali,
  • Thomas Pieber,
  • Decio L. Eizirik,
  • Myriam Cnop,
  • Søren Brunak,
  • Flemming Pociot,
  • Jesper Johannesen,
  • Peter Rossing,
  • Cristina Legido Quigley,
  • Roberto Mallone,
  • Raphael Scharfmann,
  • Christian Boitard,
  • Mikael Knip,
  • Timo Otonkoski,
  • Riitta Veijola,
  • Riitta Lahesmaa,
  • Matej Oresic,
  • Jorma Toppari,
  • Thomas Danne,
  • Anette G. Ziegler,
  • Peter Achenbach,
  • Teresa Rodriguez-Calvo,
  • Michele Solimena,
  • Ezio E. Bonifacio,
  • Stephan Speier,
  • Reinhard Holl,
  • Francesco Dotta,
  • Francesco Chiarelli,
  • Piero Marchetti,
  • Emanuele Bosi,
  • Stefano Cianfarani,
  • Paolo Ciampalini,
  • Carine De Beaufort,
  • Knut Dahl-Jørgensen,
  • Torild Skrivarhaug,
  • Geir Joner,
  • Lars Krogvold,
  • Przemka Jarosz-Chobot,
  • Tadej Battelino,
  • Bernard Thorens,
  • Martin Gotthardt,
  • Bart O. Roep,
  • Tanja Nikolic,
  • Arnaud Zaldumbide,
  • Ake Lernmark,
  • Marcus Lundgren,
  • Guillaume Costacalde,
  • Thorsten Strube,
  • Anke M. Schulte,
  • Almut Nitsche,
  • Mark Peakman,
  • Jose Vela,
  • Matthias Von Herrath,
  • Johnna Wesley,
  • Antonella Napolitano-Rosen,
  • Melissa Thomas,
  • Nanette Schloot,
  • Allison Goldfine,
  • Frank Waldron-Lynch,
  • Jill Kompa,
  • Aruna Vedala,
  • Nicole Hartmann,
  • Gwenaelle Nicolas,
  • Jean van Rampelbergh,
  • Nicolas Bovy,
  • Sanjoy Dutta,
  • Jeannette Soderberg,
  • Simi Ahmed,
  • Frank Martin,
  • Esther Latres,
  • Gina Agiostratidou,
  • Anne Koralova,
  • Ruben Willemsen,
  • Anne Smith,
  • Binu Anand,
  • Vipan Datta,
  • Vijith Puthi,
  • Sagen Zac-Varghese,
  • Renuka Dias,
  • Premkumar Sundaram,
  • Bijay Vaidya,
  • Catherine Patterson,
  • Katharine Owen,
  • Colin Dayan,
  • Barbara Piel,
  • Simon Heller,
  • Tabitha Randell,
  • Tasso Gazis,
  • Elise Bismuth Reismen,
  • Jean-Claude Carel,
  • Jean-Pierre Riveline,
  • Jean-Francoise Gautier,
  • Fabrizion Andreelli,
  • Florence Travert,
  • Emmanuel Cosson,
  • Alfred Penfornis,
  • Catherine Petit,
  • Bruno Feve,
  • Nadine Lucidarme,
  • Emmanuel Cosson,
  • Jean-Paul Beressi,
  • Catherina Ajzenman,
  • Alina Radu,
  • Stephanie Greteau-Hamoumou,
  • Cecile Bibal,
  • Thomas Meissner,
  • Bettina Heidtmann,
  • Sonia Toni,
  • Birgit Rami-Merhar,
  • Bart Eeckhout,
  • Bernard Peene,
  • N. Vantongerloo,
  • Toon Maes,
  • Leen Gommers

Journal volume & issue
Vol. 92
p. 104625

Abstract

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Summary: 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.

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