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Maternal obesity and dyslipidemia associated with gestational diabetes mellitus (GDM).

妊娠期糖尿病 (GDM) 相关的母亲肥胖和血脂异常。

  • 影响因子:1.75
  • DOI:10.1016/j.ejogrb.2020.01.007
  • 作者列表:"O'Malley EG","Reynolds CME","Killalea A","O'Kelly R","Sheehan SR","Turner MJ
  • 发表时间:2020-01-13

OBJECTIVE:The association between gestational diabetes mellitus (GDM) and maternal dyslipidemia is well established, however, the role of obesity in this relationship is not well defined. We examined the relationship between maternal obesity at the first prenatal visit and fasting lipids measured at the time of the oral glucose tolerance test (OGTT) in women screened selectively for GDM. STUDY DESIGN:This prospective observational study was conducted in a large university maternity hospital. Women were recruited at the first prenatal visit following measurement of their weight and height. Clinical and sociodemographic details were recorded. Women with maternal risk factors for GDM were screened selectively with a one-step 75 g OGTT at 26-28 weeks gestation. GDM was diagnosed based on the World Health Organization (WHO) 2013 criteria. Fasting lipids were measured simultaneously. Maternal lipid levels and their relationship with GDM and obesity were analysed with linear and logistic models. RESULTS:Of the 275 women recruited at the first antenatal visit 202 attended for their OGTT at 26-28 weeks' and 53.5 % (108) had GDM based on the WHO criteria. The women with GDM were more likely to have obesity (70.4 % vs. 42.6 %, P 29.9kg/m2. Based on tertiles, women with GDM had a higher odds ratio of increased triglycerides (odds ratio 3.2 (95 % confidence interval; 1.4-6.9), P = 0.004) and lower HDL-Cholesterol (odds ratio 2.2, (95 % confidence interval; 1.1-4.7), P = 0.036) and an increased TG:HDL-cholesterol ratio (odds ratio 2.3, (95 % confidence interval; 1.1-4.9), P = 0.026), only if they had obesity. CONCLUSION:Our findings suggest that the epidemiological association between GDM and dyslipidemia is mediated through maternal obesity. Women with obesity alone or GDM alone did not have an elevated OR for dyslipidemia. Interventions designed to optimise maternal lipids should prioritise women with obesity and it may be preferable for these interventions to start prior to conception.


目的: 妊娠期糖尿病 (GDM) 和母亲血脂异常之间的关系已经确定,然而,肥胖在这种关系中的作用还没有很好的定义。我们在选择性筛查 GDM 的妇女中检测了首次产前访视时母亲肥胖与口服葡萄糖耐量试验 (OGTT) 时测量的空腹血脂之间的关系。 研究设计: 本前瞻性观察性研究在一家大型大学妇产医院进行。在测量体重和身高后的第一次产前访视时招募女性。记录临床和社会人口学细节。有 GDM 母体危险因素的妇女在孕 26-28 周选择性地进行一步 75g OGTT 筛查。根据世卫组织 2013 标准诊断 GDM。同时测定空腹血脂。用线性和 logistic 模型分析母亲血脂水平及其与 GDM 和肥胖的关系。 结果: 在首次产前访视时招募的 275 名妇女中,202 名在 26-28 周时参加了 OGTT,53.5% 名 (108) 根据 WHO 标准患有 GDM。GDM 妇女更容易出现肥胖 (70.4% vs.42.6%,p 29.9千克/m2)。基于 tertiles,GDM 女性甘油三酯升高的比值比较高 (比值比 3.2 (95% 置信区间; 1.4-6.9),p = 0.004) 降低 HDL-胆固醇 (比值比 2.2,(95% 置信区间; 1.1-4.7),p = 0.036) 和 TG: HDL-胆固醇比值增加 (比值比 2.3,(95% 置信区间; 1.1-4.9),p = 0.026),仅当他们有肥胖时。 结论: 我们的研究结果表明,GDM 与血脂异常之间的流行病学关联是通过母亲肥胖介导的。单独肥胖或单独 GDM 的妇女没有血脂异常或升高。旨在优化母体脂质的干预措施应优先考虑肥胖妇女,这些干预措施最好在受孕前开始。



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.