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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Vydáno v:	Pediatric diabetes Ročník 15; číslo 3; s. 162 - 174
Hlavní autoři:	Jaberi-Douraki, Majid, Liu, Shang Wan (Shalon), Pietropaolo, Massimo, Khadra, Anmar
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Former Munksgaard John Wiley & Sons A/S 01.05.2014
Témata:	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
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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.
Bibliografie:	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 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 ObjectType-Review-3 content type line 23 ObjectType-Article-2 ObjectType-Feature-1
ISSN:	1399-543X 1399-5448 1399-5448
DOI:	10.1111/pedi.12148