1125-P: Do Non-HLA Genes Contribute to Age of Type 1 Diabetes Onset in Monozygotic Twins?

Diabetes ◽  
2021 ◽  
Vol 70 (Supplement 1) ◽  
pp. 1125-P
Author(s):  
TAYLOR M. TRIOLO ◽  
FRAN DONG ◽  
HALI C. BRONCUCIA ◽  
SUNA ONENGUT-GUMUSCU ◽  
ANDREA STECK ◽  
...  
2019 ◽  
Author(s):  
Fujian Qin ◽  
Yanfeng Zhang ◽  
Kaiying Li ◽  
Huashan Gao ◽  
Qian Zhao ◽  
...  

2004 ◽  
Vol 27 (8) ◽  
pp. 728-732 ◽  
Author(s):  
K. Vondra ◽  
J. Vrbíková ◽  
I. Šterzl ◽  
R. Bílek ◽  
M. Vondrova ◽  
...  

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

2011 ◽  
Vol 27 (8) ◽  
pp. 899-905 ◽  
Author(s):  
Yong Gu ◽  
Mei Zhang ◽  
Heng Chen ◽  
Zhixiao Wang ◽  
Chunyan Xing ◽  
...  

Author(s):  
Fouzeyah OTHMAN ◽  
Dr. Fawzia Mandani ◽  
Dr. Zaidan Al-Mazidi ◽  
Dr. Khalid Al-Kandari

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).


2019 ◽  
Vol 104 (11) ◽  
pp. 5195-5204 ◽  
Author(s):  
Emma H Dahlström ◽  
Niina Sandholm ◽  
Carol M Forsblom ◽  
Lena M Thorn ◽  
Fanny J Jansson ◽  
...  

Abstract Context The relationship between body mass index (BMI) and mortality may differ between patients with type 1 diabetes and the general population; it is not known which clinical characteristics modify the relationship. Objective Our aim was to assess the relationship between BMI and mortality and the interaction with clinically meaningful factors. Design, Setting, and Participants This prospective study included 5836 individuals with type 1 diabetes from the FinnDiane study. Main Outcome Measure and Methods We retrieved death data for all participants on 31 December 2015. We estimated the effect of BMI on the risk of mortality using a Cox proportional hazards model with BMI as a restricted cubic spline as well as effect modification by adding interaction terms to the spline. Results During a median of 13.7 years, 876 individuals died. The relationship between baseline BMI and all-cause mortality was reverse J-shaped. When analyses were restricted to those with normal albumin excretion rate, the relationship was U-shaped. The nadir BMI (BMI with the lowest mortality) was in the normal weight region (24.3 to 24.8 kg/m2); however, among individuals with diabetic nephropathy, the nadir BMI was in the overweight region (25.9 to 26.1 kg/m2). Diabetic nephropathy, diabetes-onset age, and sex modified the relationship between BMI and mortality (Pinteraction < 0.05). Conclusions Normal weight is optimal for individuals with type 1 diabetes to delay mortality, whereas underweight might be an indication of underlying complications. Maintaining normal weight may translate into reduced risk of mortality in type 1 diabetes, particularly for individuals of male sex, later diabetes-onset age, and normal albumin excretion rate.


Bone ◽  
2019 ◽  
Vol 123 ◽  
pp. 260-264 ◽  
Author(s):  
Viral N. Shah ◽  
Prakriti Joshee ◽  
Rachel Sippl ◽  
Laura Pyle ◽  
Tim Vigers ◽  
...  

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