Objective: Detection the presumptive prevalence of silent celiac disease in patients with type 1 diabetes mellitus with determination of which gender more likely to be affected.
Methods: One hundred twenty asymptomatic patients [75 male , 45 female] with type 1 diabetes mellitus with mean age ± SD of 11.25 ± 2.85 year where included in the study . All subjects were serologically screened for the presence of anti-tissue transglutaminase IgA antibodies (anti-tTG antibodies) by Enzyme-Linked Immunosorbent Assay (ELISA) & total IgA was also measured for all using radial immunodiffusion plate . Anti-tissue transglutaminase IgG was selectively done for patients who were expressing negative anti-tissue transglutaminase IgA with low total IgA levels & results were compared to that obtained from healthy 60 persons with mean age ± SD for them was 15.25 ± 3.85 year . Al - Kindy Col Med J 2012 ; Vol .8 No. (2) p: 132
Results : Fourteen out of one hundred twenty (11.66 % ) diabetic patients had expressed positivity to anti-tissue transglutaminase IgA compared to 1/60 ( 1.66 %) of non diabetic patients who had expressed such positivity , P value equals to 0.0221 & it is considered to be statistically significant. Three out of one hundred twenty (2.5 % ) diabetic patients had expressed total IgA deficiency whereas all of non diabetic patients were expressing total IgA within the normal range , P value equals to 0.55 & it is considered to be not statistically significant. All of three diabetic patients with total IgA deficiency were not showing positivity to anti-tissue transglutaminase IgG . Six mals & Eight female of those with type 1 diabetes mellitus had expressed positivity to anti-tissue transglutaminase IgA , P value equals to 0.1426 & it is considered to be not statistically significant .
Conclusion : There is an increased prevalence of IgA antitissue transglutaminase antibodies ( 11.66 % ) in children & adolescent with type 1 diabetes mellitus in comparison with control group.
Scientific development has occupied a prominent place in the field of diagnosis, far from traditional procedures. Scientific progress and the development of cities have imposed diseases that have spread due to this development, perhaps the most prominent of which is diabetes for accurate diagnosis without examining blood samples and using image analysis by comparing two images of the affected person for no less than a period. Less than ten years ago they used artificial intelligence programs to analyze and prove the validity of this study by collecting samples of infected people and healthy people using one of the Python program libraries, which is (Open-CV) specialized in measuring changes to the human face, through which we can infer the
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