Objective: To investigate the prevalence of celiac disease in a large cohort of children and adolescents at the onset of type 1 diabetes and the occurrence of new cases during a 6-year follow-up.
Methods: We prospectively studied, by repeated serologic screening, 274 consecutive patients at the onset of type 1 diabetes (age [mean +/- standard deviation]: 8.28 +/- 4.65 years) for 6 subsequent years. One patient had a diagnosis of celiac disease before the onset of diabetes. The immunoglobulin A-antiendomysium antibody test was selected as the screening test; patients with positive results (++ or +++) or with 2 consecutive weak positive tests (+) were considered appropriate for the jejunal biopsy.
Results: At diabetes onset, 15 (5.5%) of 273 patients tested positive with the antiendomysium test; jejunal biopsy was performed in 10, and celiac disease was diagnosed in 9. The prevalence of biopsy-confirmed celiac disease at the manifestation of diabetes was 3.6% (10 of 274 patients). Twelve more patients with a negative antiendomysium antibody test at diabetes onset tested positive during the follow-up within 4 years; 10 of them had biopsies performed, and 7 had celiac disease. Therefore, the overall prevalence of biopsy-confirmed celiac disease in the entire cohort of patients was 6.2%. The age at diabetes onset in patients with and without celiac disease was not different (7.88 +/- 5.69 vs 8.3 +/- 4.58 years). The majority of cases of celiac disease were asymptomatic in their presentation, and no signs of overt malnutrition were documented.
Conclusions: The prevalence of celiac disease in patients with type 1 diabetes is approximately 20 times higher than in the general population. Sixty percent of cases are already present at diabetes onset, mostly undetected, but an additional 40% of patients develop celiac disease a few years after diabetes onset. Extending screening programs for celiac disease after the onset of type 1 diabetes is recommended, even in the absence of clinical symptoms.
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http://dx.doi.org/10.1542/peds.109.5.833 | DOI Listing |
Am J Gastroenterol
January 2025
Department of Internal Medicine and Medical Therapeutics, University of Pavia, Italy.
Introduction: Long-term prognosis of non-celiac enteropathies (NCEs) is poorly understood. We aimed to evaluate long-term outcomes and develop a prognostic score for NCEs.
Methods: NCEs patients from an international multicenter cohort (4 Italian centers,1 UK, 1 French,1 Norwegian,1 USA,1 Indian) followed-up over 30 years were enrolled.
Nat Rev Gastroenterol Hepatol
January 2025
Takeda Pharmaceuticals, Cambridge, MA, USA.
Coeliac disease is an autoimmune disease characterized by small intestinal villus atrophy and inflammation upon exposure to gluten. It has a global prevalence of approximately 1%. Although the gluten-free diet can be an effective treatment, this diet is burdensome with practical difficulties and frequent inadvertent gluten exposure.
View Article and Find Full Text PDFAm J Reprod Immunol
February 2025
Department of Gynecology and Obstetrics, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China.
Background: Observational studies suggested celiac disease (CD) possibly be a risk factor for premature ovarian failure (POF). However, causality remains unclear. And hypothyroidism and systemic lupus erythematosus may be the mediating factors.
View Article and Find Full Text PDFJ Hum Nutr Diet
February 2025
School of Life and Health Sciences, University of Roehampton, London, UK.
Introduction: A gluten-free (GF) diet, the only treatment for people living with coeliac disease (CD), is challenging, and international guidelines highlight the valuable role of healthcare professionals in enabling self-management. The study aimed to explore the acceptability of telephone and online video consultations for adults with CD.
Methods: A cross-sectional study consisting of an online and paper survey was promoted to adults with CD.
J Imaging Inform Med
January 2025
Department of Radiation Oncology, Henry Ford Health, Detroit, MI, USA.
Automatic segmentation of angiographic structures can aid in assessing vascular disease. While recent deep learning models promise automation, they lack validation on interventional angiographic data. This study investigates the feasibility of angiographic segmentation using in-context learning with the UniverSeg model, which is a cross-learning segmentation model that lacks inherent angiographic training.
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