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Distinct Metabolic Profile in Early Pregnancy of Overweight and Obese Women Developing Gestational Diabetes.


  • 影响因子:4.64
  • DOI:10.1093/jn/nxz220
  • 作者列表:"Mokkala K","Vahlberg T","Pellonperä O","Houttu N","Koivuniemi E","Laitinen K
  • 发表时间:2020-01-01

BACKGROUND:Reliable biomarkers for gestational diabetes mellitus (GDM) would be beneficial in the early prevention of adverse metabolic outcomes during pregnancy and beyond. OBJECTIVES:The objective of this study was to investigate whether the early pregnancy serum metabolic profile differs in women developing GDM from those remaining healthy. Furthermore, we evaluated the potential of these metabolites to act as predictive markers for GDM. METHODS:This was a prospective study investigating overweight and obese [prepregnancy BMI (in kg/m2) ≥25 and >30, respectively] pregnant women (prepregnancy median BMI: 28.5; IQR: 26.4-31.5; n = 357). Fasting serum samples were analyzed with a targeted NMR approach in early pregnancy (median: 14.3 weeks of gestation). GDM was diagnosed on the basis of a 2-h, 75-g oral-glucose-tolerance test at a median of 25.7 weeks of gestation. RESULTS:In early pregnancy, 78 lipid metabolites differed in women who later developed GDM (n = 82) compared with those who remained healthy (n = 275) (ANCOVA, adjusted for confounding factors and corrected for multiple comparisons; false discovery rate <0.05). Higher concentrations of several-sized VLDL particles and medium- and small-sized HDL particles, and lower concentrations of very large-sized HDL particles, were detected in women developing GDM. Furthermore, concentrations of amino acids including 2 branched-chain amino acids, isoleucine and leucine, and GlycA, a marker for low-grade inflammation, were higher in women who developed GDM. Receiver operating characteristic analysis revealed that the most predictive marker for GDM was a higher concentration of small-sized HDL particles (AUC: 0.71; 95% CI: 0.67, 0.77; P < 0.001). CONCLUSIONS:We identified a distinct early pregnancy metabolomic profile especially attributable to small HDL particles in women developing GDM. The aberrant metabolic profile could represent a novel way to allow early identification of this most common medical condition affecting pregnant women. This trial was registered at clinicaltrials.gov as NCT01922791.


背景: 妊娠期糖尿病 (GDM) 的可靠生物标志物将有利于早期预防妊娠期及以后的不良代谢结局。 目的: 本研究的目的是探讨发生 GDM 的妇女与保持健康的妇女的早期妊娠血清代谢特征是否不同。此外,我们评估了这些代谢物作为 GDM 预测标志物的潜力。 方法: 这是一项前瞻性研究,调查超重和肥胖 [孕前 BMI (单位: kg/m2) 分别 ≥ 25 和> 30] 孕妇 (孕前 BMI 中位数: 28.5; IQR: 26.4-31.5; n = 357)。在妊娠早期 (中位数: 妊娠 14.3 周) 用靶向 NMR 方法分析空腹血清样本。在中位妊娠 25.7 周时,根据 2-h,75g 口服葡萄糖耐量试验诊断 GDM。 结果: 在妊娠早期,78 种脂质代谢产物在后来发生 GDM 的妇女 (n = 82) 与保持健康的妇女 (n = 275) (ANCOVA,校正混杂因素并校正多重比较; 错误发现率 <0.05)。在发生 GDM 的妇女中检测到较高浓度的几种尺寸 VLDL 颗粒和中小型 HDL 颗粒,以及较低浓度的超大尺寸 HDL 颗粒。此外,在发生 GDM 的女性中,包括 2 个支链氨基酸、异亮氨酸和亮氨酸以及低度炎症标志物 GlycA 的氨基酸浓度较高。受试者工作特征分析显示,GDM 的最预测指标是较高浓度的小尺寸 HDL 颗粒 (AUC: 0.71; 95% CI: 0.67,0.77; P <0.001)。 结论: 我们发现了一个独特的早期妊娠代谢组学特征,尤其是在发生 GDM 的妇女中归因于小 HDL 颗粒。异常代谢特征可能代表了一种新的方式来早期识别这种影响孕妇的最常见的医疗状况。该试验在 clinicaltrials.gov 上注册为 nct01922791。



来源期刊:Acta Diabetologica
作者列表:["Benhalima, Katrien","Crombrugge, Paul","Moyson, Carolien","Verhaeghe, Johan","Vandeginste, Sofie","Verlaenen, Hilde","Vercammen, Chris","Maes, Toon","Dufraimont, Els","Block, Christophe","Jacquemyn, Yves","Mekahli, Farah","Clippel, Katrien","Den Bruel, Annick","Loccufier, Anne","Laenen, Annouschka","Minschart, Caro","Devlieger, Roland","Mathieu, Chantal"]

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.

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来源期刊:Nature Medicine
作者列表:["Artzi, Nitzan Shalom","Shilo, Smadar","Hadar, Eran","Rossman, Hagai","Barbash-Hazan, Shiri","Ben-Haroush, Avi","Balicer, Ran D.","Feldman, Becca","Wiznitzer, Arnon","Segal, Eran"]

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.

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作者列表:["Lowes DJ","Hevener KE","Peters BM"]

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.