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Meta-Immunological Profiling of Children With Type 1 Diabetes Identifies New Biomarkers to Monitor Disease Progression

Galgani, Mario ; Nugnes, Rosa ; Bruzzese, Dario ; Perna, Francesco ; De Rosa, Veronica ; Procaccini, Claudio ; Mozzillo, Enza ; Cilio, Corrado LU ; Larsson, Helena LU and Lernmark, Åke LU orcid , et al. (2013) In Diabetes 62(7). p.2481-2491
Abstract
Type 1 diabetes is characterized by autoimmurte destruction of pancreatic beta-cells in genetically susceptible individuals. Triggers of islet autoimmunity, time course, and the precise mechanisms responsible for the progressive beta-cell failure are not completely understood. The recent escalation of obesity in affluent countries has been suggested to contribute to the increased incidence of type 1 diabetes. Understanding the link between metabolism and immune tolerance could lead to the identification of new markers for the monitoring of disease onset and progression. We studied several immune cell subsets and factors with high metabolic impact as markers associated with disease progression in high-risk subjects and type 1 diabetic... (More)
Type 1 diabetes is characterized by autoimmurte destruction of pancreatic beta-cells in genetically susceptible individuals. Triggers of islet autoimmunity, time course, and the precise mechanisms responsible for the progressive beta-cell failure are not completely understood. The recent escalation of obesity in affluent countries has been suggested to contribute to the increased incidence of type 1 diabetes. Understanding the link between metabolism and immune tolerance could lead to the identification of new markers for the monitoring of disease onset and progression. We studied several immune cell subsets and factors with high metabolic impact as markers associated with disease progression in high-risk subjects and type 1 diabetic patients at onset and at 12 and 24 months after diagnosis. A multiple correlation matrix among different parameters was evaluated statistically and assessed visually on two-dimensional graphs. Markers to predict residual beta-cell function up to 1 year after diagnosis were identified in multivariate logistic regression models. The meta-immunological profile changed significantly over time in patients, and a specific signature that was associated with worsening disease was identified. A multivariate logistic regression model measuring age, BMI, fasting C-peptide, number of circulating CD3(+)CD16(+)CD56(+) cells, and the percentage of CD1c(+)CD19(-)CD14(-)CD303(-) type 1 myeloid dendritic cells at disease onset had a significant predictive value. The identification of a specific meta-immunological profile associated with disease status may contribute to our understanding of the basis of diabetes progression. (Less)
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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Diabetes
volume
62
issue
7
pages
2481 - 2491
publisher
American Diabetes Association Inc.
external identifiers
  • wos:000321077200037
  • scopus:84881071841
  • pmid:23396400
ISSN
1939-327X
DOI
10.2337/db12-1273
language
English
LU publication?
yes
id
42139ebf-ec2f-418b-8c94-0be2db47dd4d (old id 3979612)
date added to LUP
2016-04-01 13:00:14
date last changed
2022-03-29 05:00:56
@article{42139ebf-ec2f-418b-8c94-0be2db47dd4d,
  abstract     = {{Type 1 diabetes is characterized by autoimmurte destruction of pancreatic beta-cells in genetically susceptible individuals. Triggers of islet autoimmunity, time course, and the precise mechanisms responsible for the progressive beta-cell failure are not completely understood. The recent escalation of obesity in affluent countries has been suggested to contribute to the increased incidence of type 1 diabetes. Understanding the link between metabolism and immune tolerance could lead to the identification of new markers for the monitoring of disease onset and progression. We studied several immune cell subsets and factors with high metabolic impact as markers associated with disease progression in high-risk subjects and type 1 diabetic patients at onset and at 12 and 24 months after diagnosis. A multiple correlation matrix among different parameters was evaluated statistically and assessed visually on two-dimensional graphs. Markers to predict residual beta-cell function up to 1 year after diagnosis were identified in multivariate logistic regression models. The meta-immunological profile changed significantly over time in patients, and a specific signature that was associated with worsening disease was identified. A multivariate logistic regression model measuring age, BMI, fasting C-peptide, number of circulating CD3(+)CD16(+)CD56(+) cells, and the percentage of CD1c(+)CD19(-)CD14(-)CD303(-) type 1 myeloid dendritic cells at disease onset had a significant predictive value. The identification of a specific meta-immunological profile associated with disease status may contribute to our understanding of the basis of diabetes progression.}},
  author       = {{Galgani, Mario and Nugnes, Rosa and Bruzzese, Dario and Perna, Francesco and De Rosa, Veronica and Procaccini, Claudio and Mozzillo, Enza and Cilio, Corrado and Larsson, Helena and Lernmark, Åke and La Cava, Antonio and Franzese, Adriana and Matarese, Giuseppe}},
  issn         = {{1939-327X}},
  language     = {{eng}},
  number       = {{7}},
  pages        = {{2481--2491}},
  publisher    = {{American Diabetes Association Inc.}},
  series       = {{Diabetes}},
  title        = {{Meta-Immunological Profiling of Children With Type 1 Diabetes Identifies New Biomarkers to Monitor Disease Progression}},
  url          = {{http://dx.doi.org/10.2337/db12-1273}},
  doi          = {{10.2337/db12-1273}},
  volume       = {{62}},
  year         = {{2013}},
}