Elevated fasting vs post-load glucose levels and pregnancy outcomes in gestational diabetes: a population-based study.

空腹与负荷后血糖水平升高与妊娠期糖尿病的妊娠结局: 一项基于人群的研究。

  • 影响因子:2.85
  • DOI:10.1111/dme.14173
  • 作者列表:"Ryan EA","Savu A","Yeung RO","Moore LE","Bowker SL","Kaul P
  • 发表时间:2020-01-01

AIMS:To examine the relative association between fasting plasma glucose vs post-load (1-h and 2-h) glucose levels based on the oral glucose tolerance test in pregnancy and large-for-gestational-age and hypertensive disorders of pregnancy outcomes. METHODS:All live singleton births between October 2008 and December 2014 in Alberta, Canada were included. Gestational diabetes mellitus was diagnosed using Diabetes Canada criteria. Logistic regression models were used to examine the association between fasting plasma glucose vs post-load values and large-for-gestational-age infants and hypertensive disorders of pregnancy after adjusting for maternal characteristics and pharmaceutical intervention in gestational diabetes pregnancies. RESULTS:Among 257 547 pregnancies, 208 344 (80.9%) had negative 50-g glucose challenge tests, 36 261 (14.1%) had negative 75-g oral glucose tolerance tests, and 12 942 (5.0%) had gestational diabetes based on either elevated fasting plasma glucose (n=4130, 1.6%) or elevated 1-h and/or 2-h oral glucose tolerance test values (n=8812, 3.4%). Large-for-gestational-age and hypertensive disorders of pregnancy rates were 8.1% and 5.1% in negative glucose challenge test pregnancies, 11.0% and 7.0% in negative oral glucose tolerance test pregnancies, 22.4% and 11.9% in gestational diabetes pregnancies with elevated fasting plasma glucose, and 9.1% and 8% in gestational diabetes pregnancies with elevated post-load levels, respectively. Among gestational diabetes pregnancies, those with elevated fasting plasma glucose were at higher risk of large-for-gestational age (adjusted odds ratio 2.66, 95% CI 2.39-2.96) and hypertensive disorders of pregnancy (adjusted odds ratio 1.51, 95% CI 1.33-1.72) outcomes relative to pregnancies with post-load glucose elevations only. Fasting plasma glucose remained significantly associated with adverse outcomes in gestational diabetes pregnancies with and without pharmacological intervention. CONCLUSIONS:Elevated fasting plasma glucose in women with gestational diabetes is a stronger predictor of large-for-gestational-age and hypertensive disorders of pregnancy outcomes than elevated post-load glucose.


目的: 研究空腹血糖与负荷后 (1-h 和 2-h) 之间的相对相关性基于口服葡萄糖耐量试验的血糖水平与妊娠结局的大于胎龄儿和高血压疾病。 方法: 纳入加拿大艾伯塔省 2008年10月至 2014年12月期间的所有活单胎分娩。使用加拿大糖尿病标准诊断妊娠期糖尿病。在调整母亲特征和药物干预后,采用 Logistic 回归模型研究空腹血糖和负荷后值与大于胎龄儿和妊娠期高血压疾病之间的相关性妊娠期糖尿病妊娠。 结果: 在 257 547 例妊娠中,50-g 葡萄糖激发试验阴性的有 208 344 例 (80.9%),75-g 口服葡萄糖耐量试验阴性的有 36 261 例 (14.1%), 12 942 (5.0%) 患有妊娠糖尿病 (基于空腹血糖升高) (n = 4130,1.6%)或升高 1-h 和/或 2-h 口服葡萄糖耐量试验值 (n = 8812,3.4%)。大于胎龄儿和高血压疾病的妊娠率在葡萄糖激发试验阴性妊娠中为 8.1% 和 5.1%,在口服葡萄糖耐量试验阴性妊娠中为 11.0% 和 7.0%, 空腹血糖升高的妊娠期糖尿病孕妇分别为 22.4% 和 11.9%,负荷后水平升高的妊娠期糖尿病孕妇分别为 9.1% 和 8%。在妊娠糖尿病孕妇中,空腹血糖升高的孕妇发生大于胎龄儿的风险更高 (校正比值比 2.66,95% CI 2.39-2.96) 和妊娠期高血压疾病 (校正比值比 1.51,95% CI 1.33-1.72) 结局相对于仅负荷后血糖升高的妊娠。无论药物干预与否,空腹血糖均与妊娠期糖尿病妊娠的不良结局显著相关。 结论: 妊娠糖尿病妇女空腹血糖升高比负荷后血糖升高更能预测妊娠结局的大于胎龄儿和高血压疾病。



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.