Impact of Disturbed Glucose Homeostasis Regulated by AMPK in Endometrium on Embryo Implantation in Diabetes Mice.
子宫内膜 AMPK 调节的葡萄糖稳态紊乱对糖尿病小鼠胚胎着床的影响。
- 作者列表："Zhang XQ","Zhao D","Ma YD","Wang YC","Zhang LX","Guo WJ","Zhang JH","Nie L","Yue LM
:The incidence of diabetes in women of childbearing age has been increasing recently and implantation failure and early abortion are important reasons for infertility in diabetic women. Glycogen synthesis and decomposition are the cores of glucose homeostasis in endometrium and AMPK is activated when cellular energy consumption increases. Embryo implantation is a complex process required huge energy. Yet the changes of glucose metabolism in endometrium and its impact on embryo implantation in diabetic women are still unclear. In this research, we established diabetic pregnancy mice model by intraperitoneal injecting streptozotocin on pregnant day 1. We first tested the changes of endometrial glucose homeostasis and embryo implantation. Next, we demonstrated abnormal activation of AMPK in the endometrium of diabetic mice and its affecting endometrial glucose homeostasis. Finally, we compared the endometrial glucose homeostasis and embryo implantation outcome in diabetic pregnant mice treated with insulin or insulin combined with metformin. The results indicated that there was disturbed glucose homeostasis associated with excessive activation of AMPK in endometrium of diabetic pregnant mice. AMPK inhibitor improved the over-activation of AMPK pathway in the endometrium, meanwhile, partially corrected the abnormal glycogen metabolism and improved the implantation. Insulin improved the disorder of endometrial glucose homeostasis and implantation of diabetic mice. Our research explores the causes of high abortion and infertility rate in diabetic women which is to provide a therapeutic reference for patients with diabetes complicated with infertility and early abortion.
: 近年来，育龄期妇女糖尿病发病率呈上升趋势，种植失败和早期流产是糖尿病妇女不孕的重要原因。糖原合成和分解是子宫内膜葡萄糖稳态的核心，当细胞能量消耗增加时，AMPK 被激活。胚胎着床是一个复杂的过程，需要巨大的能量。然而，糖尿病妇女子宫内膜糖代谢的变化及其对胚胎着床的影响仍不清楚。本研究通过在妊娠第 1 天腹腔注射链脲佐菌素建立糖尿病妊娠小鼠模型，首先检测子宫内膜葡萄糖稳态和胚胎着床的变化。接下来，我们证明了 AMPK 在糖尿病小鼠子宫内膜中的异常激活及其影响子宫内膜葡萄糖稳态。最后，我们比较了胰岛素或胰岛素联合二甲双胍处理的糖尿病妊娠小鼠的子宫内膜葡萄糖稳态和胚胎着床结局。结果表明，糖尿病妊娠小鼠子宫内膜中存在与 AMPK 过度激活相关的葡萄糖稳态紊乱。AMPK 抑制剂改善了子宫内膜 AMPK 通路的过度激活，同时部分纠正了糖原代谢异常，改善了着床。胰岛素改善糖尿病小鼠子宫内膜葡萄糖稳态紊乱和着床。本研究探讨了糖尿病妇女流产及不孕率高的原因，旨在为糖尿病合并不孕及早期流产患者提供治疗参考。
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.