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Association of Estrogen Receptor α Gene Polymorphism and its Expression with Gestational Diabetes Mellitus.

雌激素受体 α 基因多态性及其表达与妊娠期糖尿病的相关性。

  • 影响因子:1.20
  • DOI:10.1159/000502378
  • 作者列表:"Li C","Qiao B","Zhou Y","Qi W","Ma C","Zheng L
  • 发表时间:2020-01-01
Abstract

BACKGROUND:The estrogen receptor α (ERα) gene is a potential candidate gene of gestational diabetes mellitus (GDM). OBJECTIVES:The purpose of the study was to investigate the relationship of ERα gene polymorphism (single nucleotide polymorphism [SNP]) and its expression in placental tissues with the development of GDM. METHODS:The SNPs of PvuII and Xba I in the ERα gene of 175 pregnant women with GDM and 240 healthy pregnant women were detected by polymerase chain reaction-restriction fragment length polymorphism. Immunohistochemistry and western blotting were used to analyze the expression of the ERα gene in placental tissues. RESULTS:The results showed that the frequency of the CC + CT genotype and the C allele frequency of PvuII in the GDM group was significantly higher than that of the control group (p < 0.05). There was no significant difference in the genotype distribution and allele frequency of Xba I between the GDM group and control group. The expression of ERα in placental tissues of pregnant women with GDM was higher than that in the control group (p < 0.05). The participants with the PvuII CC + CT genotype had elevated levels of fasting blood glucose, homeostasis model assessment of insulin resistance (IR), and ERα expression in placental tissues compared with those with the TT genotype in the GDM group (p < 0.05). The SNP of Xba I of ERα gene had no correlation with clinical biochemical indicators of GDM and the expression of ERα in placental tissues (p > 0.05). CONCLUSIONS:This study suggested that SNP of the ERα gene and abnormal expression of ERα in placenta tissues were associated with GDM. The C allele of PvuII may be associated with GDM. In addition, SNP of the PvuII site in pregnant women with GDM was related to the degree of IR and to the upregulation of ERα expression in placental tissues, which may play an important role in the pathogenesis of GDM.

摘要

背景: 雌激素受体 α (er α) 基因是妊娠期糖尿病 (GDM) 潜在的候选基因。 目的: 探讨 er α 基因多态性 (单核苷酸多态性 [SNP]) 及其在胎盘组织中的表达与 GDM 发病的关系。 方法: 采用聚合酶链反应-限制性片段长度多态性方法检测 175 例 GDM 孕妇和 240 例健康孕妇 er α 基因 PvuII 和 Xba I 的 SNPs。免疫组化和 western blotting 分析胎盘组织中 er α 基因的表达。 结果: 结果显示,GDM 组 PvuII 的 CC + CT 基因型频率和 C 等位基因频率明显高于对照组 (p 0.05)。 结论: er α 基因的 SNP 和胎盘组织中 er α 的异常表达与 GDM 的发病有关。PvuII 的 C 等位基因可能与 GDM 有关。此外,GDM 孕妇 PvuII 位点的 SNP 与 IR 程度和胎盘组织中 er α 表达上调有关, 这可能在 GDM 的发病机制中起重要作用。

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影响因子:2.86
发表时间:2020-01-08
来源期刊:Acta Diabetologica
DOI:10.1007/s00592-019-01469-5
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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翻译标题与摘要 下载文献
影响因子:19.14
发表时间:2020-01-01
来源期刊:Nature Medicine
DOI:10.1038/s41591-019-0724-8
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.

关键词: 暂无
翻译标题与摘要 下载文献
影响因子:4.34
发表时间:2020-01-27
DOI:10.1128/AAC.01777-19
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.

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