Comparison of Hypoglycemia and Safety Outcomes With Long-Acting Insulins Versus Insulin NPH in Pregestational and Gestational Diabetes.
长效胰岛素与胰岛素 NPH 治疗孕前和妊娠糖尿病低血糖和安全性结局的比较。
- 作者列表："Sleeman A","Odom J","Schellinger M
:Background: Current guidelines from the American College of Obstetricians and Gynecologists recommend insulin as the standard therapy for treatment of pregestational and gestational diabetes (PGDM and GDM). However, the guidelines do not specify which type(s) of insulin to utilize. Additionally, there are limited published data regarding safety parameters of insulin in this population. Objective: To evaluate if insulin glargine or detemir (long-acting insulin) results in less hypoglycemia, hospitalizations, or delivery complications compared with intermediate-acting insulin neutral protamine Hagedorn (NPH) in PGDM and GDM. Methods: This single-center, retrospective, observational cohort study included pregnant women who were 18 years or older with PGDM or GDM and received insulin therapy during pregnancy at an outpatient obstetric clinic. The primary outcome was the frequency of hypoglycemia (BG < 60 mg/dL). Secondary outcomes included emergency department visits and hospitalizations, delivery complications, and the duration of time at glycemic targets during pregnancy. Results: A total of 63 patients were included for evaluation. There was no significant difference in the frequency of hypoglycemia between the long-acting and NPH groups (4.4 vs 6.2 events per patient, respectively; P = 0.361). Patients receiving long-acting insulin had significantly more encounters with diabetes education (10.6 vs 5.1 visits per patient, P = 0.002) and more consistently provided glucose readings at their appointments (8.3 vs 4.8, P = 0.043). There was no difference in hospitalizations or maternal and neonatal complications. Conclusion and Relevance: Long-acting insulins did not reduce the frequency of hypoglycemia compared with NPH. The results of this study confirm the need for additional investigations with larger populations.
背景: 美国妇产科医师学会目前的指南推荐胰岛素作为治疗孕前和妊娠糖尿病 (PGDM 和 GDM) 的标准疗法。然而，指南没有具体说明使用哪种类型的胰岛素。此外，关于胰岛素在该人群中的安全性参数的公开数据有限。目的: 评估与中效胰岛素中性鱼精蛋白 (NPH) 相比，甘精胰岛素或地特胰岛素 (长效胰岛素) 是否能减少低血糖、住院或分娩并发症在 PGDM 和 GDM。方法: 这项单中心、回顾性、观察性队列研究包括 18 岁或以上的 PGDM 或 GDM 孕妇，在产科门诊接受妊娠期胰岛素治疗。主要结局是低血糖的频率 (BG < 60 mg/dL)。次要结局包括急诊科就诊和住院、分娩并发症以及妊娠期血糖达标时间。结果: 共纳入 63 例患者进行评估。长效组和 NPH 组的低血糖频率无显著差异 (分别为每例患者 4.4 vs 6.2 次事件; P = 0.361)。接受长效胰岛素的患者接受糖尿病教育的次数明显更多 (每位患者 10.6 vs 5.1 次就诊，P = 0.002)，并且在预约时更一致地提供葡萄糖读数 (8.3 vs 4.8, P = 0.043)。住院或孕产妇和新生儿并发症无差异。结论和相关性: 与 NPH 相比，长效胰岛素并没有降低低血糖的频率。这项研究的结果证实了需要对更大的人群进行额外的调查。
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.