학술논문

Gene expression signature predicts rate of type 1 diabetes progressionResearch in context
Document Type
article
Author
Tomi SuomiInna StarskaiaUbaid Ullah KalimOmid RasoolMaria K. JaakkolaToni GrönroosTommi VälikangasCaroline BrorssonGianluca MazzoniSylvaine BruggraberLut OverberghDavid DungerMark PeakmanPiotr ChmuraSøren BrunakAnke M. SchulteChantal MathieuMikael KnipRiitta LahesmaaLaura L. EloPieter GillardKristina CasteelsLutgart OverberghChris WallaceMark EvansAjay ThankamonyEmile HendriksLoredana MarcoveccchioTimothy TreeNoel G. MorganSarah RichardsonJohn A. ToddLinda WickerAdrian ManderColin DayanMohammad Alhadj AliThomas PieberDecio L. EizirikMyriam CnopFlemming PociotJesper JohannesenPeter RossingCristina Legido QuigleyRoberto MalloneRaphael ScharfmannChristian BoitardTimo OtonkoskiRiitta VeijolaMatej OresicJorma ToppariThomas DanneAnette G. ZieglerPeter AchenbachTeresa Rodriguez-CalvoMichele SolimenaEzio E. BonifacioStephan SpeierReinhard HollFrancesco DottaFrancesco ChiarelliPiero MarchettiEmanuele BosiStefano CianfaraniPaolo CiampaliniCarine De BeaufortKnut Dahl-JørgensenTorild SkrivarhaugGeir JonerLars KrogvoldPrzemka Jarosz-ChobotTadej BattelinoBernard ThorensMartin GotthardtBart O. RoepTanja NikolicArnaud ZaldumbideAke LernmarkMarcus LundgrenGuillaume CostacaldeThorsten StrubeAlmut NitscheJose VelaMatthias Von HerrathJohnna WesleyAntonella Napolitano-RosenMelissa ThomasNanette SchlootAllison GoldfineFrank Waldron-LynchJill KompaAruna VedalaNicole HartmannGwenaelle NicolasJean van RampelberghNicolas BovySanjoy DuttaJeannette SoderbergSimi AhmedFrank MartinEsther LatresGina AgiostratidouAnne KoralovaRuben WillemsenAnne SmithBinu AnandVipan DattaVijith PuthiSagen Zac-VargheseRenuka DiasPremkumar SundaramBijay VaidyaCatherine PattersonKatharine OwenBarbara PielSimon HellerTabitha RandellTasso GazisElise Bismuth ReismenJean-Claude CarelJean-Pierre RivelineJean-Francoise GautierFabrizion AndreelliFlorence TravertEmmanuel CossonAlfred PenfornisCatherine PetitBruno FeveNadine LucidarmeJean-Paul BeressiCatherina AjzenmanAlina RaduStephanie Greteau-HamoumouCecile BibalThomas MeissnerBettina HeidtmannSonia ToniBirgit Rami-MerharBart EeckhoutBernard PeeneN. VantongerlooToon MaesLeen Gommers
Source
EBioMedicine, Vol 92, Iss , Pp 104625- (2023)
Subject
Type 1 diabetes
Autoantibodies
RNA-seq
Gene expression signature
Predictive model
Medicine
Medicine (General)
R5-920
Language
English
ISSN
2352-3964
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.