Should women with gestational diabetes be screened at delivery hospitalization for type 2 diabetes?
妊娠糖尿病妇女是否应该在分娩住院时筛查 2 型糖尿病？
- 作者列表："Waters TP","Kim SY","Werner E","Dinglas C","Carter EB","Patel R","Sharma AJ","Catalano P
BACKGROUND:Less than one-half of women with gestational diabetes mellitus are screened for type 2 diabetes postpartum. Other approaches to postpartum screening need to be evaluated, including the role of screening during the delivery hospitalization. OBJECTIVE:To assess the performance of an oral glucose tolerance test administered during the delivery hospitalization compared with the oral glucose tolerance test administered at a 4- to 12-week postpartum visit. STUDY DESIGN:We conducted a combined analysis of patient-level data from 4 centers (6 clinical sites) assessing the utility of an immediate postpartum 75-g oral glucose tolerance test during the delivery hospitalization (PP1) for the diagnosis of type 2 diabetes compared with a routine 4- to 12-week postpartum oral glucose tolerance test (PP2). Eligible women underwent a 75-g oral glucose tolerance test at both PP1 and PP2. Sensitivity, specificity, and negative and positive predictive values of the PP1 test were estimated for diagnosis of type 2 diabetes, impaired fasting glucose, or impaired glucose tolerance. RESULTS:In total, 319 women completed a PP1 screening, with 152 (47.6%) lost to follow-up for the PP2 oral glucose tolerance test. None of the women with a normal PP1 oral glucose tolerance test (n=73) later tested as having type 2 diabetes at PP2. Overall, 12.6% of subjects (n=21) had a change from normal to impaired fasting glucose/impaired glucose tolerance or a change from impaired fasting glucose/impaired glucose tolerance to type 2 diabetes. The PP1 oral glucose tolerance test had 50% sensitivity (11.8-88.2), 95.7% specificity (91.3-98.2%) with a 98.1% (94.5-99.6%) negative predictive value and a 30% (95% confidence interval, 6.7-65.3) positive predictive value for type 2 diabetes vs normal/impaired fasting glucose/impaired glucose tolerance result. The negative predictive value of having type 2 diabetes at PP2 compared with a normal oral glucose tolerance test (excluding impaired fasting glucose/impaired glucose tolerance) at PP1 was 100% (95% confidence interval, 93.5-100) with a specificity of 96.5% (95% confidence interval, 87.9-99.6). CONCLUSION:A normal oral glucose tolerance test during the delivery hospitalization appears to exclude postpartum type 2 diabetes mellitus. However, the results of the immediate postpartum oral glucose tolerance test were mixed when including impaired fasting glucose or impaired glucose tolerance. As a majority of women do not return for postpartum diabetic screening, an oral glucose tolerance test during the delivery hospitalization may be of use in certain circumstances in which postpartum follow-up is challenging and resources could be focused on women with an abnormal screening immediately after the delivery hospitalization.
背景: 不到一半的妊娠期糖尿病妇女在产后接受 2 型糖尿病筛查。需要评估产后筛查的其他方法，包括分娩住院期间筛查的作用。 目的: 评估分娩住院期间进行的口服葡萄糖耐量试验与产后 4-12 周进行的口服葡萄糖耐量试验的性能。 研究设计: 我们对来自 4 个中心 (6 个临床中心) 的患者水平数据进行了综合分析在分娩住院期间评估产后即刻 75g 口服葡萄糖耐量试验的效用 (PP1) 对于 2 型糖尿病的诊断与常规产后 4-12 周口服葡萄糖耐量试验 (PP2) 比较。符合条件的女性在 PP1 和 pp2 都接受了 75g 口服葡萄糖耐量试验。评估 PP1 试验诊断 2 型糖尿病、空腹血糖受损或糖耐量受损的敏感性、特异性以及阴性和阳性预测值。 结果: 总共有 319 名女性完成了 PP1 筛查，152 名 (47.6%) 的女性失去了 PP2 口服葡萄糖耐量试验的随访。PP1 口服葡萄糖耐量试验正常的女性 (n = 73) 后来均未在 pp2 检测为 2 型糖尿病。总体而言，12.6% 的受试者 (n = 21) 从正常变为空腹血糖受损/糖耐量受损，或从空腹血糖受损/糖耐量受损变为 2 型糖尿病。PP1 口服葡萄糖耐量试验的敏感性为 50% (11.8-88.2)，特异性为 95.7% (91.3-98.2%)，特异性为 98.1% (94.5-99.6%) 2 型糖尿病与正常/空腹血糖受损/糖耐量受损结果的阴性预测值和 30% (95% 置信区间，6.7-65.3) 阳性预测值。与 PP1 时正常口服葡萄糖耐量试验 (不包括空腹血糖受损/糖耐量受损) 相比，PP2 时患 2 型糖尿病的阴性预测值为 100% (95% 置信区间, 93.5-100)，特异性为 96.5% (95% 可信区间，87.9-99.6)。 结论: 分娩住院期间正常的口服葡萄糖耐量试验似乎可以排除产后 2 型糖尿病。然而，当包括空腹血糖受损或糖耐量受损时，产后即刻口服葡萄糖耐量试验的结果是混合的。由于大多数妇女不回来进行产后糖尿病筛查, 分娩住院期间的口服葡萄糖耐量试验可能在产后随访具有挑战性的某些情况下有用，并且可以将资源集中在分娩住院后立即筛查异常的妇女身上。。
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.