Background: A maturity-onset diabetes of the young (MODY) calculator has been described and validated for use in European Caucasians. This study evaluated its performance in Brazilians diagnosed with diabetes mellitus (DM) before 35 years of age.
Methods: The electronic records of 391 individuals were reviewed in 2020 at the diabetes clinic of a quaternary hospital in São Paulo were analyzed: 231 with type 1 DM (T1DM), 46 with type 2 (T2DM) and 114 with MODY. The MODY calculator was applied to the three groups. A receiver operating characteristic curve was calculated to obtain cut-off points for this population.
Results: The principal differences between the MODY and the T1DM and T2DM groups were body mass index, a positive family history of diabetes and mean HbA1c level. Age at diagnosis in the MODY group was only significantly different compared to the T2DM group. Specificity and sensitivity were good for the cut-off points of 40%, 50% and 60%, with the accuracy of the model for any of these cut-off points being > 95%.
Conclusion: The capacity of the calculator to identify Brazilian patients with MODY was good. Values ≥ 60% proved useful for selecting candidates for MODY genetic testing, with good sensitivity and specificity.
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http://dx.doi.org/10.1186/s13098-023-00985-3 | DOI Listing |
Zhong Nan Da Xue Xue Bao Yi Xue Ban
July 2024
Department of Nephrology, Third Xiangya Hospital, Central South University, Changsha 410013.
Objectives: Genetic factors play an important role in the pathogenesis of diabetic kidney disease (DKD). Studies have shown that gene polymorphism is associated with the pathogenesis of type 2 diabetes mellitus (T2DM), but its role in DKD remains unclear. This study aims to analyze the distribution of alleles and genotypes of gene in patients with T2DM, and investigate the association between genetic polymorphism and DKD susceptibility in T2DM patients, which may provide new ideas for the pathogenesis of DKD.
View Article and Find Full Text PDFDig Dis Sci
January 2025
Department of Gastroenterology and Hepatology, Amsterdam University Medical Centres, Location AMC, Meibergdreef 9, 1105AZ, Amsterdam, The Netherlands.
Aims: Duodenal Mucosal Resurfacing (DMR) is an endoscopic ablation technique aimed at improving glycemia in patients with type 2 diabetes mellitus (T2DM). Although the exact underlying mechanism is still unclear, it is postulated that the DMR-induced improvements are the result of changes in the duodenal mucosa. For this reason, we assessed macroscopic and microscopic changes in the duodenal mucosa induced by DMR + GLP-1RA.
View Article and Find Full Text PDFBMC Public Health
January 2025
Department of Prevention and Evaluation, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany.
Background: Exposure to nitrogen dioxide (NO) is associated with an increased risk of cardiovascular, respiratory, and other diseases and health outcomes. Although NO emissions have decreased in Germany, concentrations currently observed still pose a threat to population health. The aim of this study is to estimate the environmental burden of disease (EBD) resulting from long-term NO exposure in Germany from 2010 to 2021.
View Article and Find Full Text PDFNutrients
December 2024
Health Systems and Equity, Eastern Health Clinical School, Monash University, Level 2, 5 Arnold Street, Box Hill, VIC 3128, Australia.
: We aimed to review the effect of lifestyle interventions in women with a history of gestational diabetes mellitus (GDM) based on the participants and intervention characteristics. : We systematically searched seven databases for RCTs of lifestyle interventions published up to 24 July 2024. We included 30 studies that reported the incidence of type 2 diabetes mellitus (T2DM) or body weight.
View Article and Find Full Text PDFJ Clin Med
December 2024
Department of Medical Science, University of Turin, 10126 Torino, Italy.
: The use of artificial intelligence (AI) chatbots for obtaining healthcare advice is greatly increased in the general population. This study assessed the performance of general-purpose AI chatbots in giving nutritional advice for patients with obesity with or without multiple comorbidities. : The case of a 35-year-old male with obesity without comorbidities (Case 1), and the case of a 65-year-old female with obesity, type 2 diabetes mellitus, sarcopenia, and chronic kidney disease (Case 2) were submitted to 10 different AI chatbots on three consecutive days.
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