妊娠糖尿病 22 年后的长期母体心脏代谢结局。
- 作者列表："Tutino GE","Tam CHT","Ozaki R","Yuen LY","So WY","Chan MHM","Ko GTC","Yang X","Chan JCN","Tam WH","Ma RCW
AIMS/INTRODUCTION:Women with gestational diabetes mellitus are at increased risk for type 2 diabetes. We characterized the association between maternal glycemia during pregnancy with long-term outcomes. METHODS AND METHODS:In this prospective nested case-cohort study, participants were recalled for follow up with detailed evaluation including oral glucose tolerance test at 8, 15 and 22 years. Logistic regression was used to estimate the risk of developing impaired glucose tolerance/type 2 diabetes and metabolic syndrome at follow up. The association between maternal glycemia at pregnancy and follow up was evaluated by linear regression. We also charted trajectory of β-cell function during follow up. RESULTS:The analysis included 121 women with a mean follow-up period of 22.5 years, and a mean age of 50.3 years. Gestational diabetes was associated with an adjusted odds ratio of 2.48 (95% confidence interval 1.03-5.99) for combined diabetes/impaired glucose tolerance at follow up (P = 0.04). Women with a pre-pregnancy body mass index ≥23 had an odds ratio of 5.43 (95% confidence interval 1.87-15.72) for metabolic syndrome at follow up, compared with those with body mass index <23 (P = 0.002). Both fasting and 2-h glucose during pregnancy were strongly associated with glycemic indices at follow up (P-value <0.001-0.016). Gestational diabetes was associated with impaired β-cell function that remained relatively stable after the index pregnancy. CONCLUSIONS:Chinese women with a history of gestational diabetes have a high prevalence of impaired glucose tolerance/type 2 diabetes at 22-year follow up. Glucose levels during mid-pregnancy are strongly associated with those of middle age.
目的/介绍: 妊娠期糖尿病妇女患 2 型糖尿病的风险增加。我们描述了妊娠期母体血糖与长期结局之间的相关性。 方法: 在这项前瞻性巢式病例队列研究中，召回参与者进行随访，详细评估包括 8 、 15 和 22 年的口服葡萄糖耐量试验。使用 Logistic 回归估计随访时发生糖耐量受损/2 型糖尿病和代谢综合征的风险。通过线性回归评估母亲妊娠血糖与随访之间的相关性。我们还绘制了随访期间 β 细胞功能的轨迹。 结果: 该分析包括 121 名女性，平均随访期为 22.5 年，平均年龄为 50.3 岁。妊娠糖尿病与随访时合并糖尿病/糖耐量异常的校正比值比为 2.48 (95% 置信区间 1.03-5.99) 相关 (P = 0.04)。孕前体重指数 ≥ 23 的女性在随访时代谢综合征的比值比为 5.43 (95% 置信区间 1.87-15.72),与体重指数 <23 者比较 (P = 0.002)。妊娠期间空腹和 2 h 血糖均与随访时的血糖指数强相关 (P 值 <0.001-0.016)。妊娠糖尿病与指数妊娠后保持相对稳定的 β 细胞功能受损有关。 结论: 在 22 年的随访中，有妊娠糖尿病史的中国女性糖耐量受损/2 型糖尿病的患病率较高。妊娠中期的血糖水平与中年的血糖水平密切相关。
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.