Premium
Autoimmune responses in T1DM : quantitative methods to understand onset, progression, and prevention of disease
Author(s) -
JaberiDouraki Majid,
Liu Shang Wan Shalon,
Pietropaolo Massimo,
Khadra Anmar
Publication year - 2014
Publication title -
pediatric diabetes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.678
H-Index - 75
eISSN - 1399-5448
pISSN - 1399-543X
DOI - 10.1111/pedi.12148
Subject(s) - disease , medicine , autoantibody , type 1 diabetes , immunology , autoimmunity , autoimmune disease , diabetes mellitus , islet , mechanism (biology) , pancreatic islets , bioinformatics , neuroscience , biology , endocrinology , antibody , philosophy , epistemology
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.