Changes in perinatal outcomes after implementation of IADPSG criteria for screening and diagnosis of gestational diabetes mellitus: A national survey.
妊娠期糖尿病筛查和诊断 IADPSG 标准实施后围产儿结局的变化: 一项全国性调查。
- 作者列表："Lucovnik M","Steblovnik L","Verdenik I","Premru-Srsen T","Tomazic M","Tul N
OBJECTIVE:To compare perinatal outcomes before and after implementation of the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria for testing of gestational diabetes mellitus (GDM). METHODS:A national, perinatal, registry-based cohort study of prospectively collected data was conducted. Patients with diabetes type 1 or 2 were excluded. Outcomes of 135 786 pregnancies before (January 1, 2004 to May 31, 2010) and 140 524 after (June 1, 2011 to December 31, 2017) the introduction of IADPSG criteria were compared using Student t test and χ2 test (P4500 g), Erb's palsy, and hypertensive disorders in pregnancy decreased despite increasing maternal age and pre-pregnancy obesity. Rates of cesarean delivery increased in both GDM and non-GDM groups, with a less pronounced increase in GDM mothers. Incidence of small-for-gestational age (SGA) increased in GDM but not in non-GDM group. CONCLUSION:Implementation of IADPSG criteria in a country with a relatively low prevalence of GDM did not result in higher rates of cesarean delivery and was associated with reductions in LGA and hypertensive disorders in pregnancy.
目的: 比较国际糖尿病与妊娠研究组 (IADPSG) 妊娠期糖尿病 (GDM) 检测标准实施前后的围产儿结局。 方法: 进行一项前瞻性收集数据的全国性、围产期、基于登记的队列研究。排除 1 型或 2 型糖尿病患者。135-786 次妊娠前 (2004年1月1日至 2010年5月31日) 和 140-524 次妊娠后 (2011年6月1日至 2017年12月31日) 的结局 IADPSG 标准的引入采用 Student t 检验和 χ 2 检验 (P4500 g) 进行比较，Erb 麻痹,和妊娠期高血压疾病下降，尽管增加了母亲的年龄和孕前肥胖。GDM 和非 GDM 组的剖宫产率均增加，GDM 母亲的增加不明显。GDM 组小于胎龄儿 (SGA) 发生率增加，而非 GDM 组无增加。 结论: 在 GDM 患病率相对较低的国家实施 IADPSG 标准并没有导致较高的剖宫产率，并且与妊娠期 LGA 和高血压疾病的减少相关。
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.
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.
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.