Perinatal outcomes for untreated women with gestational diabetes by IADPSG criteria: a population-based study.
IADPSG 标准未经治疗的妊娠期糖尿病妇女的围产期结局: 一项基于人群的研究。
- 作者列表："Shah BR","Sharifi F
OBJECTIVE:To estimate the risk for adverse perinatal outcomes for women who met the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria but not the two-step criteria for gestational diabetes mellitus (GDM). DESIGN:Population-level cross-sectional study. SETTING:Ontario, Canada. POPULATION:A total of 90 140 women who underwent a 75-g oral glucose tolerance test. METHODS:Women were divided into those who met the diagnostic thresholds for GDM by two-step criteria and were therefore treated, those who met only the IADPSG criteria for GDM and so were not treated, and those who did not have GDM by either criteria. MAIN OUTCOME MEASURES:Hypertensive disorders of pregnancy, preterm delivery, primary caesarean section, large-for-gestational-age, shoulder dystocia and neonatal intensive care unit admission. RESULTS:Women who met the IADPSG criteria had an increased risk for all adverse perinatal outcomes compared with women who did not have GDM. Women with GDM by two-step criteria also had an increased risk of most outcomes. However, their risk for large-for-gestational-age neonates and for shoulder dystocia was actually lower than that of women who met IADPSG criteria. CONCLUSION:Women who met IADPSG criteria but who were not diagnosed with GDM based on the current two-step diagnostic strategy, and were therefore not treated, had an increased risk for adverse perinatal outcomes compared with women who do not have GDM. The current strategy for diagnosing GDM may be leaving women who are at risk for adverse events without the dietary and pharmacological treatments that could improve their pregnancy outcomes. TWEETABLE ABSTRACT:Women who meet IADPSG criteria for GDM have an increased risk for adverse perinatal outcomes compared with women without GDM.
目的: 评估符合国际糖尿病和妊娠研究小组 (IADPSG) 的妇女的不良围产期结局风险标准，但不是妊娠期糖尿病 (GDM) 的两步标准。 设计: 人群水平横断面研究。 地点: 加拿大安大略省。 人群: 共 90 140 名接受 75g 口服葡萄糖耐量试验的女性。 方法: 根据两步法标准将女性分为符合 GDM 诊断阈值并因此接受治疗的女性，仅符合 GDM IADPSG 标准的女性不接受治疗, 和那些没有按照任何标准进行 GDM 的人。 主要结局指标: 妊娠期高血压疾病、早产、初次剖宫产、大于胎龄儿、肩难产和新生儿重症监护室入院。 结果: 与没有 GDM 的妇女相比，符合 IADPSG 标准的妇女发生所有不良围产期结局的风险增加。采用两步标准的 GDM 妇女也有增加大多数结局的风险。然而，他们对大于胎龄儿和肩难产的风险实际上低于符合 IADPSG 标准的妇女。 结论: 符合 IADPSG 标准但未根据目前的两步诊断策略诊断为 GDM 的妇女，因此未接受治疗, 与没有 GDM 的妇女相比，围产期不良结局的风险增加。目前诊断 GDM 的策略可能使有不良事件风险的妇女没有可以改善妊娠结局的饮食和药物治疗。 TWEETABLE 摘要: 与无 GDM 的妇女相比，符合 GDM IADPSG 标准的妇女发生不良围产结局的风险增加。
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.