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Cord blood metabolic markers are strong mediators of the effect of maternal adiposity on fetal growth in pregnancies across the glucose tolerance spectrum: the PANDORA study.

脐带血代谢标志物是母体肥胖对妊娠期胎儿生长发育影响的强介质: PANDORA 研究。

  • 影响因子:5.79
  • DOI:10.1007/s00125-019-05079-2
  • 作者列表:"Lee IL","Barr ELM","Longmore D","Barzi F","Brown ADH","Connors C","Boyle JA","Kirkwood M","Hampton V","Lynch M","Lu ZX","O'Dea K","Oats J","McIntyre HD","Zimmet P","Shaw JE","Maple-Brown LJ","PANDORA study team.
  • 发表时间:2020-01-08

AIMS/HYPOTHESIS:We aimed to assess associations between cord blood metabolic markers and fetal overgrowth, and whether cord markers mediated the impact of maternal adiposity on neonatal anthropometric outcomes among children born to Indigenous and Non-Indigenous Australian women with normal glucose tolerance (NGT), gestational diabetes mellitus (GDM) and pregestational type 2 diabetes mellitus. METHODS:From the Pregnancy and Neonatal Outcomes in Remote Australia (PANDORA) study, an observational cohort of 1135 mother-baby pairs, venous cord blood was available for 645 singleton babies (49% Indigenous Australian) of women with NGT (n = 129), GDM (n = 419) and type 2 diabetes (n = 97). Cord glucose, triacylglycerol, HDL-cholesterol, C-reactive protein (CRP) and C-peptide were measured. Multivariable logistic and linear regression were used to assess the associations between cord blood metabolic markers and the outcomes of birthweight z score, sum of skinfold thickness (SSF), being large for gestational age (LGA) and percentage of body fat. Pathway analysis assessed whether cord markers mediated the associations between maternal and neonatal adiposity. RESULTS:Elevated cord C-peptide was significantly associated with increasing birthweight z score (β 0.57 [95% CI 0.42, 0.71]), SSF (β 0.83 [95% CI 0.41, 1.25]), percentage of body fat (β 1.20 [95% CI 0.69, 1.71]) and risk for LGA [OR 3.14 [95% CI 2.11, 4.68]), after adjusting for age, ethnicity and diabetes type. Cord triacylglycerol was negatively associated with birthweight z score for Indigenous Australian women only. No associations between cord glucose, HDL-cholesterol and CRP >0.3 mg/l (2.9 nmol/l) with neonatal outcomes were observed. C-peptide mediated 18% (95% CI 13, 36) of the association of maternal BMI with LGA and 11% (95% CI 8, 17) of the association with per cent neonatal fat. CONCLUSIONS/INTERPRETATION:Cord blood C-peptide is an important mediator of the association between maternal and infant adiposity, across the spectrum of maternal glucose tolerance.


目的/假设: 我们旨在评估脐带血代谢标志物与胎儿过度生长的相关性,以及脐带标志物是否介导了母亲肥胖对土著和非土著澳大利亚糖耐量正常 (NGT) 、妊娠期糖尿病 (GDM) 妇女所生儿童新生儿人体测量结局的影响和孕前 2 型糖尿病。 方法: 来自澳大利亚偏远地区 (PANDORA) 妊娠和新生儿结局研究,1135 对母婴的观察性队列,645 例单胎婴儿 (49% 例澳大利亚土著) 可获得静脉脐带血 NGT (n = 129) 、 GDM (n = 419) 和 2 型糖尿病 (n = 97) 妇女。检测脐带血糖、三酰甘油、 HDL-胆固醇、 C 反应蛋白 (CRP) 和 C-肽。采用多变量 logistic 回归和线性回归评估脐血代谢标志物与出生体重 z 评分、皮褶厚度总和 (SSF) 、胎龄大 (LGA) 结局的相关性和身体脂肪的百分比。通路分析评估脐带标志物是否介导了母亲和新生儿肥胖之间的关联。 结果: 脐带 c肽升高与出生体重增加显著相关 z 评分 (β 0.57 [95% CI 0.42,0.71]),SSF (β 0.83 [95% CI 0.41,1.25]),体脂百分比 (β 1.20 [95% CI 0.69,1.71]) 和 LGA 风险 [OR 3.14 [95% CI 2.11,4.68]),调整年龄、种族和糖尿病类型后。脐带三酰甘油仅与澳大利亚土著妇女的出生体重 z 评分呈负相关。未观察到脐带血糖、 HDL-胆固醇和 CRP >0.3 mg/l (2.9 nmol/l) 与新生儿结局之间的相关性。C肽介导了母亲 BMI 与 LGA 的 18% (95% CI 13,36) 和与新生儿脂肪百分比的 11% (95% CI 8,17)。 结论/解读: 脐血 c肽是母亲糖耐量谱中母亲和婴儿肥胖之间关联的重要介质。



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.