Autoimmune responses in T1DM: quantitative methods to understand onset, progression, and prevention of disease

Understanding the physiological processes that underlie autoimmune disorders and identifying biomarkers to predict their onset are two pressing issues that need to be thoroughly sorted out by careful thought when analyzing these diseases. Type 1 diabetes (T1D) is a typical example of such diseases....

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Bibliographic Details
Published in:Pediatric diabetes Vol. 15; no. 3; pp. 162 - 174
Main Authors: Jaberi-Douraki, Majid, Liu, Shang Wan (Shalon), Pietropaolo, Massimo, Khadra, Anmar
Format: Journal Article
Language:English
Published: Former Munksgaard John Wiley & Sons A/S 01.05.2014
Subjects:
Animals
autoantibodies
Autoantibodies - analysis
Autoimmunity
avidity
B-cells
Biomarkers - metabolism
Cytotoxicity, Immunologic
Diabetes Mellitus, Type 1 - diagnosis
Diabetes Mellitus, Type 1 - immunology
Diabetes Mellitus, Type 1 - metabolism
Diabetes Mellitus, Type 1 - physiopathology
Disease Progression
Humans
Insulin - metabolism
Insulin Secretion
Insulin-Secreting Cells - immunology
Insulin-Secreting Cells - metabolism
Markov models
mathematical models
Models, Biological
predictive algorithms
Prognosis
T-cells
T1D
β-cells
T-cells
avidity
B-cells
autoimmunity
predictive algorithms
β-cells
T1D
mathematical models
Markov models
autoantibodies
ISSN:1399-543X, 1399-5448, 1399-5448
Online Access:Get full text
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Summary:Understanding the physiological processes that underlie autoimmune disorders and identifying biomarkers to predict their onset are two pressing issues that need to be thoroughly sorted out by careful thought when analyzing these diseases. Type 1 diabetes (T1D) is a typical example of such diseases. It is mediated by autoreactive cytotoxic CD4+ and CD8+ T‐cells that infiltrate the pancreatic islets of Langerhans and destroy insulin‐secreting β‐cells, leading to abnormal levels of glucose in affected individuals. The disease is also associated with a series of islet‐specific autoantibodies that appear in high‐risk subjects (HRS) several years prior to the onset of diabetes‐related symptoms. It has been suggested that T1D is relapsing‐remitting in nature and that islet‐specific autoantibodies released by lymphocytic B‐cells are detectable at different stages of the disease, depending on their binding affinity (the higher, the earlier they appear). The multifaceted nature of this disease and its intrinsic complexity make this disease very difficult to analyze experimentally as a whole. The use of quantitative methods, in the form of mathematical models and computational tools, to examine the disease has been a very powerful tool in providing predictions and insights about the underlying mechanism(s) regulating its onset and development. Furthermore, the models developed may have prognostic implications by aiding in the enrollment of HRS into trials for T1D prevention. In this review, we summarize recent advances made in determining T‐ and B‐cell involvement in T1D using these quantitative approaches and delineate areas where mathematical modeling can make further contributions in unraveling certain aspect of this disease.
Bibliography:istex:0FFA31EA1DDBCA752EF28DC9BCFDAE930FD0243C
Natural Sciences and Engineering Council of Canada
ArticleID:PEDI12148
National Institutes of Health - No. R01 DK53456; No. R01 DK56200; No. R21 DK073724
ark:/67375/WNG-60P5254R-H
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ISSN:1399-543X
1399-5448
1399-5448
DOI:10.1111/pedi.12148