Loss of endothelin type B receptor function improves insulin sensitivity in rats.

内皮素 B 型受体功能丧失改善大鼠胰岛素敏感性。

  • 影响因子:2.06
  • DOI:10.1139/cjpp-2019-0666
  • 作者列表:"Rivera-Gonzalez OJ","Kasztan M","Johnston JG","Hyndman KA","Speed JS
  • 发表时间:2020-02-21

:High salt intake (HS) is associated with obesity and insulin resistance. ET-1, a peptide released in response to HS, inhibits the actions of insulin on cultured adipocytes through ET-1 type B (ETB) receptors; however, the in vivo implications of ETB receptor activation on lipid metabolism and insulin resistance is unknown. We hypothesized that activation of ETB receptors in response to HS intake promotes dyslipidemia and insulin resistance. In normal salt (NS) fed rats, no significant difference in body weight or epidydimal fat mass was observed between control and ETB deficient rats. After 2 weeks of HS, ETB def rats had significantly lower body weight and epidydimal fat mass compared to controls. Non-fasting plasma glucose was not different between genotypes, however plasma insulin concentration was significantly lower in ETB deficient rats compared to controls suggesting improved insulin sensitivity. In addition, ETB deficient rats had higher circulating free fatty acids in both NS and HS groups, with no difference in plasma triglycerides between genotypes. In a separate experiment, ETB deficient rats had significantly lower fasting blood glucose and improved glucose and insulin tolerance compared to controls. These data suggest that ET-1 promotes adipose deposition and insulin resistance via the ETB receptor.


: 高盐摄入 (HS) 与肥胖和胰岛素抵抗有关。ET-1,一种响应于 HS 释放的肽,通过 B 型 (ETB) 受体抑制胰岛素对培养脂肪细胞 ET-1 作用; 然而, ETB 受体激活对脂质代谢和胰岛素抵抗的体内影响尚不清楚。我们假设对 HS 摄入的反应中 ETB 受体的激活促进了血脂异常和胰岛素抵抗。在正常盐 (NS) 喂养的大鼠中,未观察到对照组和 ETB 缺乏大鼠之间的体重或表皮脂肪量存在显著差异。HS 2 周后,与对照组相比,ETB def 大鼠的体重和脂肪量显著降低。非空腹血糖在基因型之间无差异,但与对照组相比,ETB 缺陷大鼠血浆胰岛素浓度显著降低,提示胰岛素敏感性改善。此外,ETB 缺陷大鼠在 NS 和 HS 组中均有较高的循环游离脂肪酸,基因型间血浆甘油三酯无差异。在另一项实验中,与对照组相比,ETB 缺陷大鼠的空腹血糖显著降低,葡萄糖和胰岛素耐受性改善。这些数据表明,ET-1 通过 ETB 受体促进脂肪沉积和胰岛素抵抗。



来源期刊:Acta Diabetologica
作者列表:["Benhalima, Katrien","Crombrugge, Paul","Moyson, Carolien","Verhaeghe, Johan","Vandeginste, Sofie","Verlaenen, Hilde","Vercammen, Chris","Maes, Toon","Dufraimont, Els","Block, Christophe","Jacquemyn, Yves","Mekahli, Farah","Clippel, Katrien","Den Bruel, Annick","Loccufier, Anne","Laenen, Annouschka","Minschart, Caro","Devlieger, Roland","Mathieu, Chantal"]

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.

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来源期刊:Nature Medicine
作者列表:["Artzi, Nitzan Shalom","Shilo, Smadar","Hadar, Eran","Rossman, Hagai","Barbash-Hazan, Shiri","Ben-Haroush, Avi","Balicer, Ran D.","Feldman, Becca","Wiznitzer, Arnon","Segal, Eran"]

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.

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作者列表:["Lowes DJ","Hevener KE","Peters BM"]

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.