scholarly journals Incomplete penetrance of susceptibility genes for MHC-determined immunoglobulin deficiencies in monozygotic twins discordant for type 1 diabetes

2006 ◽  
Vol 27 (2) ◽  
pp. 89-95 ◽  
Author(s):  
Chester A. Alper ◽  
Zaheed Husain ◽  
Charles E. Larsen ◽  
Devendra P. Dubey ◽  
Rosanne Stein ◽  
...  
2007 ◽  
Vol 123 ◽  
pp. S23-S24
Author(s):  
Caroline Brorsson ◽  
Elzbieta Swiergala ◽  
Kristoffer Rapacki ◽  
Regine Bergholdt ◽  
Shaun Purcell ◽  
...  

2016 ◽  
Vol 68 ◽  
pp. 23-29 ◽  
Author(s):  
Emon Elboudwarej ◽  
Michael Cole ◽  
Farren B.S. Briggs ◽  
Alexandra Fouts ◽  
Pamela R. Fain ◽  
...  

Author(s):  
David A. Savage ◽  
Stephen C. Bain

Type 1 diabetes, previously known as insulin-dependent diabetes mellitus, is a common chronic T-cell-mediated disease in which there is selective autoimmune destruction of the insulin-producing β‎ cells of the pancreas. Although the mechanisms underlying this process are not fully understood, type 1 diabetes occurs as a result of complex interactions between multiple genes (reviewed in references 1–3) and environmental influences, which may both promote and protect against disease. Type 1 diabetes clusters in some families, but with no distinct pattern of inheritance. The concordance rates in monozygotic twins for type 1 diabetes can reach 50%, compared to 6% for dizygotic twins. The sibling recurrence risk ratio (λ‎s) (risk to siblings ÷ risk to general population) value for type 1 diabetes is 15 (6.0 ÷ 0.4 or 6% ÷ 0.4%), and twin studies suggest that 80% to 85% of familial aggregation is accounted for by genes. Type 1 diabetes has been noted to coexist with other autoimmune diseases—notably, Graves’ disease and coeliac disease—in certain families, implying the involvement of common autoimmune pathways. Improved understanding of the so-called ‘allelic architecture’ (the identity of disease-associated gene variants, their frequencies, and size of the risk conferred by each variant) and biological pathways involved in type 1 diabetes is expected to facilitate the identification of new therapeutic targets for the development of new treatments. DNA biomarkers could also assist risk prediction at a population level. This is clinically relevant since individuals can survive with only 20% intact β‎-cell mass, and the time to reach this level of destruction can be considerably delayed in some individuals, offering a window of opportunity for intervention therapy. Furthermore, clinical trials should be improved by only focusing on those patients at highest risk of developing type 1 diabetes. Early prediction, improved treatments, and, ultimately, prevention of type 1 diabetes are major goals because incidence rates are increasing. A recent study by the EURODIAB Study Group, involving 20 population-based registries across 17 European countries, has assessed incidence trends in children diagnosed with type 1 diabetes under the age of 15 between 1989 and 2003: an overall increase of 3.9% per year was reported, and, in the under 5 age group, an increase of 5.4% per year was observed (4).


2007 ◽  
Vol 167 (2) ◽  
pp. 161-163
Author(s):  
Noriko Uchida ◽  
Yoshiro Amano ◽  
Yohei Akazawa ◽  
Shinichi Nakamura ◽  
Isaki Minami ◽  
...  

BMC Genetics ◽  
2007 ◽  
Vol 8 (1) ◽  
pp. 84 ◽  
Author(s):  
Qing Qiao ◽  
Anne-May Österholm ◽  
Bing He ◽  
Janne Pitkäniemi ◽  
Heather J Cordell ◽  
...  

10.1038/991 ◽  
1998 ◽  
Vol 19 (3) ◽  
pp. 297-300 ◽  
Author(s):  
CharlesA. Mein ◽  
Laura Esposito ◽  
Michael G. Dunn ◽  
Gillian C. L. Johnson ◽  
Andrew E. Timms ◽  
...  

Nature ◽  
1994 ◽  
Vol 371 (6493) ◽  
pp. 130-136 ◽  
Author(s):  
June L. Davies ◽  
Yoshihiko Kawaguchi ◽  
Simon T. Bennett ◽  
James B. Copeman ◽  
Heather J. Cordell ◽  
...  

2003 ◽  
Vol 64 (10) ◽  
pp. 951-959 ◽  
Author(s):  
Stefan Johansson ◽  
Benedicte A Lie ◽  
Anne Cambon-Thomsen ◽  
Flemming Pociot ◽  
Jørn Nerup ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document