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Gene expression signature predicts rate of type 1 diabetes progression

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:...

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Autores principales: Suomi, Tomi, Starskaia, Inna, Kalim, Ubaid Ullah, Rasool, Omid, Jaakkola, Maria K., Grönroos, Toni, Välikangas, Tommi, Brorsson, Caroline, Mazzoni, Gianluca, Bruggraber, Sylvaine, Overbergh, Lut, Dunger, David, Peakman, Mark, Chmura, Piotr, Brunak, Søren, Schulte, Anke M., Mathieu, Chantal, Knip, Mikael, Lahesmaa, Riitta, Elo, Laura L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10277927/
https://www.ncbi.nlm.nih.gov/pubmed/37224769
http://dx.doi.org/10.1016/j.ebiom.2023.104625
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author Suomi, Tomi
Starskaia, Inna
Kalim, Ubaid Ullah
Rasool, Omid
Jaakkola, Maria K.
Grönroos, Toni
Välikangas, Tommi
Brorsson, Caroline
Mazzoni, Gianluca
Bruggraber, Sylvaine
Overbergh, Lut
Dunger, David
Peakman, Mark
Chmura, Piotr
Brunak, Søren
Schulte, Anke M.
Mathieu, Chantal
Knip, Mikael
Lahesmaa, Riitta
Elo, Laura L.
author_facet Suomi, Tomi
Starskaia, Inna
Kalim, Ubaid Ullah
Rasool, Omid
Jaakkola, Maria K.
Grönroos, Toni
Välikangas, Tommi
Brorsson, Caroline
Mazzoni, Gianluca
Bruggraber, Sylvaine
Overbergh, Lut
Dunger, David
Peakman, Mark
Chmura, Piotr
Brunak, Søren
Schulte, Anke M.
Mathieu, Chantal
Knip, Mikael
Lahesmaa, Riitta
Elo, Laura L.
author_sort Suomi, Tomi
collection PubMed
description 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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spelling pubmed-102779272023-06-20 Gene expression signature predicts rate of type 1 diabetes progression Suomi, Tomi Starskaia, Inna Kalim, Ubaid Ullah Rasool, Omid Jaakkola, Maria K. Grönroos, Toni Välikangas, Tommi Brorsson, Caroline Mazzoni, Gianluca Bruggraber, Sylvaine Overbergh, Lut Dunger, David Peakman, Mark Chmura, Piotr Brunak, Søren Schulte, Anke M. Mathieu, Chantal Knip, Mikael Lahesmaa, Riitta Elo, Laura L. eBioMedicine Articles 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. Elsevier 2023-05-22 /pmc/articles/PMC10277927/ /pubmed/37224769 http://dx.doi.org/10.1016/j.ebiom.2023.104625 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Articles
Suomi, Tomi
Starskaia, Inna
Kalim, Ubaid Ullah
Rasool, Omid
Jaakkola, Maria K.
Grönroos, Toni
Välikangas, Tommi
Brorsson, Caroline
Mazzoni, Gianluca
Bruggraber, Sylvaine
Overbergh, Lut
Dunger, David
Peakman, Mark
Chmura, Piotr
Brunak, Søren
Schulte, Anke M.
Mathieu, Chantal
Knip, Mikael
Lahesmaa, Riitta
Elo, Laura L.
Gene expression signature predicts rate of type 1 diabetes progression
title Gene expression signature predicts rate of type 1 diabetes progression
title_full Gene expression signature predicts rate of type 1 diabetes progression
title_fullStr Gene expression signature predicts rate of type 1 diabetes progression
title_full_unstemmed Gene expression signature predicts rate of type 1 diabetes progression
title_short Gene expression signature predicts rate of type 1 diabetes progression
title_sort gene expression signature predicts rate of type 1 diabetes progression
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10277927/
https://www.ncbi.nlm.nih.gov/pubmed/37224769
http://dx.doi.org/10.1016/j.ebiom.2023.104625
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