Down-regulation of microRNA-873 attenuates insulin resistance and myocardial injury in rats with gestational diabetes mellitus by up-regulating IGFBP2.
MicroRNA-873 下调通过上调 igfbp2 减轻妊娠期糖尿病大鼠胰岛素抵抗和心肌损伤。
- 作者列表："Han N","Fang HY","Jiang JX","Xu Q
:Gestational diabetes mellitus (GDM) is a metabolic disorder characterized by insulin resistance, and patients with GDM have a higher risk of cardiovascular disease. Multiple microRNAs (miRNAs) are reported to be involved in the regulation of myocardial injury. Moreover, miR-873 was predicted to target insulin-like growth factor binding protein 2 (IGFBP2) through bioinformatic analysis, which was further confirmed using a luciferase assay. Thus, our objective was to assess whether microRNA-873 (miR-873) affects insulin resistance and myocardial injury in an established GDM rat model. The GDM rats were treated with miR-875 mimic or inhibitor, or IGFBP2 siRNA. The effects of miR-875 and IGFBP2 on the cardiac function, insulin resistance, and myocardial injury were evaluated by hemodynamic measurements, determination of biochemical indices of myocardium and serum, and insulin homeostatic model assessment. The results indicated that down-regulation of miR-873 up-regulated the expression of IGFBP2 and promoted the activation of PI3K/AKT/mTOR axis. With down-regulation of miR-873 in GDM rats, the cardiac function was improved, and the myocardial apoptosis was inhibited, coupled with elevated activity of superoxide dismutase, carbon monoxide synthase, and the nitric oxide content. Besides, the inhibition of miR-873 in GDM rats modulated the insulin resistance and reduced myocardial apoptosis. Overall, the data showed that inhibition of miR-873, by targeting IGFBP2, may regulate the insulin resistance and curtail myocardial injury in GDM rats through activating PI3K/AKT/mTOR axis, thus providing a potential means of impeding the progression of GDM.
: 妊娠期糖尿病 (GDM) 是一种以胰岛素抵抗为特征的代谢紊乱性疾病，GDM 患者发生心血管疾病的风险较高。多个 microRNAs (miRNAs) 被报道参与心肌损伤的调控。此外，通过生物信息学分析预测 miR-873 靶向胰岛素样生长因子结合蛋白 2 (IGFBP2)，用荧光素酶试验进一步证实了这一点。因此，我们的目的是在已建立的 GDM 大鼠模型中评估 microRNA-873 (miR-873) 是否影响胰岛素抵抗和心肌损伤。GDM 大鼠接受 miR-875 模拟物或抑制剂或 IGFBP2 siRNA 治疗。通过血流动力学测定、心肌和血清生化指标测定、胰岛素稳态模型评估 miR-875 和 IGFBP2 对心功能、胰岛素抵抗和心肌损伤的影响。结果表明，miR-873 下调上调 IGFBP2 的表达，促进 PI3K/AKT/mTOR 轴的激活。GDM 大鼠 miR-873 下调，心功能改善，心肌细胞凋亡受到抑制，再加上超氧化物歧化酶、一氧化碳合酶、和一氧化氮含量。此外，抑制 GDM 大鼠 miR-873 调节胰岛素抵抗，减少心肌细胞凋亡。总之，研究表明，通过靶向 IGFBP2 抑制 miR-873 可能通过激活 PI3K/AKT/mTOR 轴调节胰岛素抵抗，减轻 GDM 大鼠心肌损伤。从而提供了阻止 GDM 进展的潜在手段。
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.