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Vitamin D Receptor overexpression in β-cells ameliorates diabetes in mice.
维生素 D 受体在 β 细胞中的过表达可改善小鼠糖尿病。
- 影响因子:5.64
- DOI:10.2337/db19-0757
- 作者列表:"Morró M","Vilà L","Franckhauser S","Mallol C","Elias G","Ferré T","Molas M","Casana E","Rodó J","Pujol A","Téllez N","Bosch F","Casellas A
- 发表时间:2020-02-21
Abstract
:Vitamin D deficiency has been associated with increased incidence of diabetes, both in humans and animal models. In addition, association between vitamin D receptor (VDR) gene polymorphisms and diabetes has also been described. However, the involvement of VDR in the development of diabetes, specifically in pancreatic β-cell, has not been elucidated yet. Here we aimed to study the role of VDR in β-cell in the pathophysiology of diabetes. Our results indicate that Vdr expression was modulated by glucose in healthy islets and decreased in islets from both T1D and T2D mouse models. In addition, transgenic mice overexpressing VDR in β-cell were protected against STZ-induced diabetes, and presented a preserved β-cell mass and a reduction in islet inflammation. Altogether, these results suggest that sustained VDR levels in β-cells may preserve β-cell mass and β-cell function and protect against diabetes.
摘要
在人类和动物模型中,维生素d 缺乏与糖尿病发病率增加有关。此外,维生素d 受体 (VDR) 基因多态性与糖尿病之间的关联也被描述。然而,VDR 参与糖尿病的发展,特别是在胰腺 β 细胞中,尚未阐明。在此,我们旨在研究 β 细胞中 VDR 在糖尿病病理生理学中的作用。我们的结果表明,在健康胰岛中,Vdr 表达受葡萄糖调节,在 T1D 和 T2D 小鼠模型的胰岛中表达减少。此外,β 细胞中过表达 VDR 的转基因小鼠对 STZ 诱导的糖尿病有保护作用,并呈现保留的 β 细胞团和胰岛炎症的减少。总之,这些结果表明,β 细胞中持续的 VDR 水平可以保留 β 细胞质量和 β 细胞功能,并预防糖尿病。
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METHODS:Aims We aimed to develop a prediction model based on clinical and biochemical variables for gestational diabetes mellitus (GDM) based on the 2013 World Health Organization (WHO) criteria. Methods A total of 1843 women from a Belgian multi-centric prospective cohort study underwent universal screening for GDM. Using multivariable logistic regression analysis, a model to predict GDM was developed based on variables from early pregnancy. The performance of the model was assessed by receiver-operating characteristic (AUC) analysis. To account for over-optimism, an eightfold cross-validation was performed. The accuracy was compared with two validated models (van Leeuwen and Teede). Results A history with a first degree relative with diabetes, a history of smoking before pregnancy, a history of GDM, Asian origin, age, height and BMI were independent predictors for GDM with an AUC of 0.72 [95% confidence interval (CI) 0.69–0.76)]; after cross-validation, the AUC was 0.68 (95% CI 0.64–0.72). Adding biochemical variables, a history of a first degree relative with diabetes, a history of GDM, non-Caucasian origin, age, height, weight, fasting plasma glucose, triglycerides and HbA_1c were independent predictors for GDM, with an AUC of the model of 0.76 (95% CI 0.72–0.79); after cross-validation, the AUC was 0.72 (95% CI 0.66–0.78), compared to an AUC of 0.67 (95% CI 0.63–0.71) using the van Leeuwen model and an AUC of 0.66 (95% CI 0.62–0.70) using the Teede model. Conclusions A model based on easy to use variables in early pregnancy has a moderate accuracy to predict GDM based on the 2013 WHO criteria.
METHODS:Leveraging the availability of nationwide electronic health records from over 500,000 pregnancies in Israel, a machine-learning approach offers an alternative means of predicting gestational diabetes at high accuracy in the early stages of pregnancy. Gestational diabetes mellitus (GDM) poses increased risk of short- and long-term complications for mother and offspring^ 1 – 4 . GDM is typically diagnosed at 24–28 weeks of gestation, but earlier detection is desirable as this may prevent or considerably reduce the risk of adverse pregnancy outcomes^ 5 , 6 . Here we used a machine-learning approach to predict GDM on retrospective data of 588,622 pregnancies in Israel for which comprehensive electronic health records were available. Our models predict GDM with high accuracy even at pregnancy initiation (area under the receiver operating curve (auROC) = 0.85), substantially outperforming a baseline risk score (auROC = 0.68). We validated our results on both a future validation set and a geographical validation set from the most populated city in Israel, Jerusalem, thereby emulating real-world performance. Interrogating our model, we uncovered previously unreported risk factors, including results of previous pregnancy glucose challenge tests. Finally, we devised a simpler model based on just nine questions that a patient could answer, with only a modest reduction in accuracy (auROC = 0.80). Overall, our models may allow early-stage intervention in high-risk women, as well as a cost-effective screening approach that could avoid the need for glucose tolerance tests by identifying low-risk women. Future prospective studies and studies on additional populations are needed to assess the real-world clinical utility of the model.
METHODS::Repurposing of currently approved medications is an attractive option for the development of novel treatment strategies against physiological and infectious diseases. The antidiabetic sulfonylurea glyburide has demonstrated off-target capacity to inhibit activation of the NLRP3 inflammasome in a variety of disease models, including vaginal candidiasis, caused primarily by the fungal pathogen Candida albicans Therefore, we sought to determine which of the currently approved sulfonylurea drugs prevent the release of interleukin 1β (IL-1β), a major inflammasome effector, during C. albicans challenge of the human macrophage-like THP1 cell line. Findings revealed that the second-generation antidiabetics (glyburide, glisoxepide, gliquidone, and glimepiride), which exhibit greater antidiabetic efficacy than prior iterations, demonstrated anti-inflammatory effects with various degrees of potency as determined by calculation of 50% inhibitory concentrations (IC50s). These same compounds were also effective in reducing IL-1β release during noninfectious inflammasome activation (e.g., induced by lipopolysaccharide [LPS] plus ATP), suggesting that their anti-inflammatory activity is not specific to C. albicans challenge. Moreover, treatment with sulfonylurea drugs did not impact C. albicans growth and filamentation or THP1 viability. Finally, the use of ECE1 and Candidalysin deletion mutants, along with isogenic NLRP3-/- cells, demonstrated that both Candidalysin and NLRP3 are required for IL-1β secretion, further confirming that sulfonylureas suppress inflammasome signaling. Moreover, challenge of THP1 cells with synthetic Candidalysin peptide demonstrated that this toxin is sufficient to activate the inflammasome. Treatment with the experimental inflammasome inhibitor MCC950 led to similar blockade of IL-1β release, suggesting that Candidalysin-mediated inflammasome activation can be inhibited independently of potassium efflux. Together, these results demonstrate that the second-generation antidiabetic sulfonylureas retain anti-inflammatory activity and may be considered for repurposing against immunopathological diseases, including vaginal candidiasis.