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Early gestational diabetes screening in obese women: a randomized controlled trial.

肥胖妇女早期妊娠糖尿病筛查: 一项随机对照试验。

  • 影响因子:4.22
  • DOI:10.1016/j.ajog.2019.12.021
  • 作者列表:"Harper LM","Jauk V","Longo S","Biggio JR","Szychowski JM","Tita AT
  • 发表时间:2020-01-09

BACKGROUND:Although in 2013 the American College of Obstetricians and Gynecologists recommended early screening for gestational diabetes in obese women, no studies demonstrate an improvement in perinatal outcomes with this strategy. OBJECTIVE:We sought to determine whether early screening for gestational diabetes improves perinatal outcomes in obese women. MATERIALS AND METHODS:Randomized controlled trial comparing early gestational diabetes screening (14-20 weeks) to routine screening (24-28 weeks) in obese women (body mass index ≥30 kg/m2) at 2 tertiary care centers in the United States. Screening was performed using a 50-g, 1-hour glucose challenge test followed by a 100-g, 3-hour glucose tolerance test if the initial screen was ≥135 mg/dL. Gestational diabetes was diagnosed using Carpenter-Coustan criteria. Women not diagnosed at 14 to 20 weeks were rescreened at 24 to 28 weeks. Exclusion criteria were pre-existing diabetes, major medical illness, bariatric surgery, and prior cesarean delivery. The primary outcome was a composite of macrosomia (>4000 g), primary cesarean delivery, hypertensive disease of pregnancy, shoulder dystocia, neonatal hyperbilirubinemia, and neonatal hypoglycemia (assessed within 48 hours of birth). RESULTS:A total of 962 women were randomized, and outcomes were available for 922. Of these 922 women, 459 (49.8%) were assigned to early screen and 463 (50.2%) to routine screen. Baseline characteristics were balanced between groups. In the early screening group, 69 (15.0%; 95% confidence interval, 11.9-18.6%) were diagnosed with gestational diabetes: 29 (6.3%; 95% confidence interval, 4.3-8.9%) at 24 weeks. Of those randomized to routine screening, 56 (12.1%; 95% confidence interval, 9.3-15.4%) had gestational diabetes. Early screening did not reduce the incidence of the primary outcome (56.9% in the early screen versus 50.8% in the routine screen, P = .07; relative risk, 1.12; 95% confidence interval, 0.99-1.26). CONCLUSION:Early screening for gestational diabetes in obese women did not reduce the composite perinatal outcome.


背景: 尽管 2013年美国妇产科医师学会推荐对肥胖妇女进行妊娠糖尿病的早期筛查,但没有研究证实该策略可改善围产期结局。 目的: 我们试图确定妊娠期糖尿病的早期筛查是否能改善肥胖妇女的围产期结局。 材料和方法: 比较肥胖妇女 (体重指数 ≥ 30千克 kg/m2) 早期妊娠糖尿病筛查 (14-20 周) 与常规筛查 (24-28 周) 的随机对照试验在美国的 2 个三级医疗中心。如果初始筛查 ≥ 100 mg/dL,则使用 50g 、 1 小时葡萄糖激发试验,然后进行 135g 、 3 小时葡萄糖耐量试验进行筛查。使用 Carpenter-Coustan 标准诊断妊娠糖尿病。14 ~ 20 周未确诊的女性在 24 ~ 28 周重新筛选。排除标准为预先存在的糖尿病、重大内科疾病、减肥手术和既往剖宫产。主要结局为巨大儿 (> 4000g) 、初次剖宫产、妊娠高血压疾病、肩难产、新生儿高胆红素血症、和新生儿低血糖 (出生 48 小时内评估)。 结果: 共有 962 名妇女进行了随机分组,结果为 922。在这 922 名女性中,459 名 (49.8%) 被分配到早期筛查,463 名 (50.2%) 被分配到常规筛查。基线特征在组间平衡。在早期筛查组中,69 例 (15.0%; 95% 置信区间,11.9-18.6%) 在 24 周时被诊断为妊娠糖尿病: 29 例 (6.3%; 95% 置信区间,4.3-8.9%)。在随机接受常规筛查的患者中,56 例 (12.1%; 95% 置信区间,9.3-15.4%) 患有妊娠糖尿病。早期筛查并没有降低主要结局的发生率 (早期筛查为 56.9%,而常规筛查为 50.8%,P =。 07; 相对危险度,1.12; 95% 可信区间,0.99-1.26)。 结论: 对肥胖妇女进行妊娠期糖尿病的早期筛查并不能降低复合围产结局。



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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翻译标题与摘要 下载文献
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.