The association between adipocytokines and glycemic control in women with gestational diabetes mellitus.
- 作者列表："Aviram A","Shtaif B","Gat-Yablonski G","Yogev Y
:Objective: To evaluate the relationship between adipocytokines and glycemic control.Study design: Prospective observational trial of gestations with gestational diabetes mellitus (GDM). Fasting glucose (FG), insulin, adiponectin, leptin, chemerin, retinol-binding protein 4 (RBP-4), osteocalcin, and resistin were measured. HomeOstasis model assessment of insulin resistance (HOMA-IR) and QUantitative insulin sensitivity ChecK Index (QUICKI) were calculated. Women who required medications for glycemic control were compared to women using nutritional therapy only.Results: Overall, 75 women were included -26 (34.7%) required medications to achieve good glycemic control. Factors associated with poor control are as follows: low resistin (aOR 0.84), HOMA-IR (aOR 1.96), QUICKI (aOR 0.62), first trimester FG (aOR 1.43), and maternal age (aOR 1.26). HOMA-IR and QUICKI performed highest for prediction. Resistin, first trimester FG, maternal age, and QUICKI had an AUC of 0.878, sensitivity and specificity of 87.5% for the prediction of the need for medications.Conclusions: Low resistin is associated with poor control. A model utilizing maternal age, first trimester fasting glucose, and first visit QUICKI yields good predictability.
目的: 探讨脂肪细胞因子与血糖控制的关系。研究设计: 妊娠期糖尿病 (GDM) 的前瞻性观察研究。测定空腹血糖 (FG) 、胰岛素、脂联素、瘦素、趋化素、视黄醇结合蛋白 4 (RBP-4) 、骨钙素和抵抗素。计算稳态模型评估胰岛素抵抗 (HOMA-IR) 和定量胰岛素敏感性检查指数 (QUICKI)。将需要药物控制血糖的女性与仅使用营养治疗的女性进行比较。结果: 总体上，75 名女性被纳入-26 例 (34.7%) 需要药物来实现良好的血糖控制。与控制不良相关的因素如下: 低抵抗素 (aOR 0.84) 、 HOMA-IR (aOR 1.96) 、 QUICKI (aOR 0.62) 、孕早期 FG (aOR 1.43) 、和母亲年龄 (aOR 1.26)。HOMA-IR 和 QUICKI 的预测表现最高。抵抗素、孕早期 FG 、母亲年龄和 QUICKI 对预测药物需求的 AUC 为 0.878，敏感性和特异性为 87.5%。结论: 低抵抗素与控制不良有关。利用母亲年龄、孕早期空腹血糖和首次访视 QUICKI 的模型产生良好的可预测性。
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.