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Excessive Weight Gain Before and During Gestational Diabetes Mellitus Management: What Is the Impact?

妊娠期糖尿病管理前后体重过度增加: 有什么影响?

  • 影响因子:8.71
  • DOI:10.2337/dc19-0800
  • 作者列表:"Barnes RA","Wong T","Ross GP","Griffiths MM","Smart CE","Collins CE","MacDonald-Wicks L","Flack JR
  • 发表时间:2020-01-01

OBJECTIVE:Conventional gestational diabetes mellitus (GDM) management focuses on managing blood glucose in order to prevent adverse outcomes. We hypothesized that excessive weight gain at first presentation with GDM (excessive gestational weight gain [EGWG]) and continued EGWG (cEGWG) after commencing GDM management would increase the risk of adverse outcomes, despite treatment to optimize glycemia. RESEARCH DESIGN AND METHODS:Data collected prospectively from pregnant women with GDM at a single institution were analyzed. GDM was diagnosed on the basis of Australasian Diabetes in Pregnancy Society 1998 guidelines (1992-2015). EGWG means having exceeded the upper limit of the Institute of Medicine-recommended target ranges for the entire pregnancy, by GDM presentation. The relationship between EGWG and antenatal 75-g oral glucose tolerance test (oGTT) values and adverse outcomes was evaluated. Relationships were examined between cEGWG, insulin requirements, and large-for-gestational-age (LGA) infants. RESULTS:Of 3,281 pregnant women, 776 (23.6%) had EGWG. Women with EGWG had higher mean fasting plasma glucose (FPG) on oGTT (5.2 mmol/L [95% CI 5.1-5.3] vs. 5.0 mmol/L [95% CI 4.9-5.0]; P < 0.01), after adjusting for confounders, and more often received insulin therapy (47.0% vs. 33.6%; P < 0.0001), with an adjusted odds ratio (aOR) of 1.4 (95% CI 1.1-1.7; P < 0.01). aORs for each 2-kg increment of cEGWG were a 1.3-fold higher use of insulin therapy (95% CI 1.1-1.5; P < 0.001), an 8-unit increase in final daily insulin dose (95% CI 5.4-11.0; P < 0.0001), and a 1.4-fold increase in the rate of delivery of LGA infants (95% CI 1.2-1.7; P < 0.0001). CONCLUSIONS:The absence of EGWG and restricting cEGWG in GDM have a mitigating effect on oGTT-based FPG, the risk of having an LGA infant, and insulin requirements.


目的: 常规妊娠期糖尿病 (GDM) 管理的重点是血糖管理,以防止不良结局。我们假设 GDM 初次就诊时体重增加过多 (妊娠期体重增加过多 [EGWG]) 和开始 GDM 管理后继续 EGWG (cewg) 会增加不良结局的风险。尽管治疗优化血糖。 研究设计和方法: 前瞻性收集单个机构 GDM 孕妇的数据进行分析。GDM 诊断依据澳大利亚糖尿病妊娠学会 1998 指南 (1992-2015)。EGWG 表示通过 GDM 的介绍,已经超过了医学研究所推荐的整个妊娠目标范围的上限。评估 EGWG 和产前 75g 口服葡萄糖耐量试验 (oGTT) 值与不良结局的关系。检查 cewg 、胰岛素需求和大胎龄 (LGA) 婴儿之间的关系。 结果: 3,281 例孕妇中,776 例 (23.6%) 发生 EGWG。EGWG 女性在 oGTT 时平均空腹血糖 (FPG) 较高 (5.2 mmol/L [95% CI 5.1-5.3] vs。5.0 mmol/L [95% CI 4.9-5.0]; P <0.01),校正混杂因素后,更经常接受胰岛素治疗 (47.0% vs. 33.6%; P <0.0001),校正比值比(AOR) 为 1.4 (95% CI 1.1-1.7; P <0.01)。CEGWG 每增加 2 kg,aor 是胰岛素治疗使用的 1.3 倍 (95% CI 1.1-1.5; P <0.001),最终每日胰岛素剂量增加 8 个单位 (95% CI 5.4-11.0; P <0.0001),LGA 婴儿分娩率增加 1.4 倍 (95% CI 1.2-1.7;P <0.0001)。 结论: GDM 中没有 EGWG 和限制 cEGWG 对基于 oGTT 的 FPG 、 LGA 婴儿的风险和胰岛素需求有缓解作用。



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.