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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:...
Autores principales: | , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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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. |
format | Online Article Text |
id | pubmed-10277927 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
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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