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

Authors :
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
Pieter Gillard
Kristina Casteels
Lutgart Overbergh
Chris Wallace
Mark Evans
Ajay Thankamony
Emile Hendriks
Loredana Marcoveccchio
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
Flemming Pociot
Jesper Johannesen
Peter Rossing
Cristina Legido Quigley
Roberto Mallone
Raphael Scharfmann
Christian Boitard
Timo Otonkoski
Riitta Veijola
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
Almut Nitsche
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
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
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
Source :
EBioMedicine, Vol 92, Iss , Pp 104625- (2023)
Publication Year :
2023
Publisher :
Elsevier, 2023.

Abstract

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.

Details

Language :
English
ISSN :
23523964
Volume :
92
Issue :
104625-
Database :
Directory of Open Access Journals
Journal :
EBioMedicine
Publication Type :
Academic Journal
Accession number :
edsdoj.2c41bdea112542e1901b5cf0742fc2e4
Document Type :
article
Full Text :
https://doi.org/10.1016/j.ebiom.2023.104625