Severe neonatal hypoglycaemia and intrapartum glycaemic control in pregnancies complicated by type 1, type 2 and gestational diabetes.
妊娠合并 1 型、 2 型和妊娠期糖尿病的严重新生儿低血糖和产时血糖控制。
- 作者列表："Yamamoto JM","Donovan LE","Mohammad K","Wood SL
AIMS:To determine if in-target intrapartum glucose control is associated with neonatal hypoglycaemia in women with type 1, type 2 or gestational diabetes. METHODS:This was a retrospective cohort study of pregnant women with diabetes and their neonates. The primary exposure was in-target glucose control, defined as all capillary glucose values within the range 3.5-6.5 mmol/l during the intrapartum period. The primary outcome, neonatal hypoglycaemia, was defined as treatment with intravenous dextrose therapy. Multiple logistic regression was used to examine the association between maternal intrapartum glycaemic control and neonatal hypoglycaemia, adjusting for covariates. RESULTS:Intrapartum glucose testing was available for 157 (86.3%), 267 (76.3%) and 3256 (52.4%) women with type 1, type 2 and gestational diabetes, respectively. In the univariate analysis, in-target glycaemic control was significantly associated with neonatal hypoglycaemia in women with gestational diabetes, but not in women with type 1 or 2 diabetes. However, after adjustment for important neonatal factors (large for gestational age, preterm delivery and infant sex), intrapartum in-target glycaemic control was not significantly associated with neonatal hypoglycaemia in women regardless of diabetes type [adjusted odds ratios 0.4 (95% CI 0.1, 1.4), 0.7 (95% CI 0.3, 1.3) and 0.7 (95% CI 0.5, 1.0) for women with type 1, type 2 and gestational diabetes, respectively]. CONCLUSIONS:There was no significant association between in-target glycaemic control and neonatal hypoglycaemia after adjustment for neonatal factors. Given the high risk of maternal hypoglycaemia and the resources required, future trials should consider whether more relaxed intrapartum glycaemic targets may be safer and yield similar neonatal outcomes.
目的: 确定目标产时血糖控制是否与 1 型、 2 型或妊娠期糖尿病妇女的新生儿低血糖相关。 方法: 这是一项针对糖尿病孕妇及其新生儿的回顾性队列研究。初次暴露为目标血糖控制，定义为产时所有毛细血管血糖值在 3.5-6.5 mmol/l 范围内。主要结局，新生儿低血糖，定义为静脉注射葡萄糖治疗。采用多元 logistic 回归分析母亲产时血糖控制与新生儿低血糖之间的关系，调整协变量。 结果: 分别有 157 (86.3%) 、 267 (76.3%) 和 3256 (52.4%) 的 1 型、 2 型和妊娠期糖尿病妇女可进行产时血糖检测。在单变量分析中，目标血糖控制与妊娠期糖尿病妇女新生儿低血糖显著相关，但与 1 型或 2 型糖尿病妇女无关。然而，在调整了重要的新生儿因素 (大胎龄、早产和婴儿性别) 后, 无论糖尿病类型如何，产时目标血糖控制与女性新生儿低血糖无显著相关性 [校正比值比 0.4 (95% CI 0.1，1.4)，0.7 (95% CI 0.3，1.3) 和 0.7(95% CI 0.5，1.0) 分别为 1 型、 2 型和妊娠期糖尿病妇女]。 结论: 在校正新生儿因素后，目标血糖控制与新生儿低血糖之间无显著相关性。鉴于产妇低血糖的高风险和所需的资源，未来的试验应考虑更宽松的产时血糖目标是否更安全，并产生类似的新生儿结局。
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.