Pregnancy-associated plasma protein-A2 levels are increased in early-pregnancy gestational diabetes: a novel biomarker for early risk estimation.

妊娠早期妊娠糖尿病患者妊娠相关血浆 protein-A2 水平升高: 一种用于早期风险评估的新型生物标志物。

  • 影响因子:2.85
  • DOI:10.1111/dme.14088
  • 作者列表:"Dereke J","Nilsson C","Strevens H","Landin-Olsson M","Hillman M
  • 发表时间:2020-01-01

AIM:To determine whether pregnancy-associated plasma protein-A2 levels are increased in early pregnancies complicated by gestational diabetes and whether gestation age influences levels. The possible use of pregnancy-associated plasma protein-A2 as a pre-screening biomarker to reduce the need for performing oral glucose tolerance tests in pregnant women was also investigated. METHODS:Pregnant women were diagnosed with gestational diabetes in early pregnancy after a 2-hour 75 g oral glucose tolerance test in the catchment area of Skåne University Hospital, Lund, Sweden during 2011-2015 (n = 99). Age- and BMI-matched pregnant women without diabetes were recruited at similar gestational ages from maternal healthcare centres in the same geographical area during 2014-2015 to act as controls (n = 100). Circulating pregnancy-associated plasma protein-A2 was analysed in participant serum using commercially available enzyme-linked immunosorbent assay kits. RESULTS:Circulating pregnancy-associated plasma protein-A2 was increased in women diagnosed with gestational diabetes [13.5 (9.58-18.8) ng/ml] compared with controls [8.11 (5.74-11.3) ng/ml; P < 0.001]. Pregnancy-associated plasma protein-A2 was associated with gestational diabetes independent of age, BMI, C-peptide and adiponectin (P < 0.001). Pregnancy-associated plasma protein-A2 as a pre-screening biomarker to identify women at a decreased risk of gestational diabetes resulted in a negative predictive value of 99.7%, with a sensitivity of 96% and a specificity of 30% at a cut-off level of 6 ng/ml. CONCLUSIONS:This is the first study to show increased pregnancy-associated plasma protein-A2 levels in gestational diabetes. Pregnancy-associated plasma protein-A2 also shows promise as a pre-screening biomarker with the potential to reduce the need for performing oral glucose tolerance tests in early pregnancy. Future prospective cohort studies in a larger group of both high- and low-risk women are, however, needed to further confirm this observation.


目的: 探讨妊娠早期合并妊娠糖尿病患者妊娠相关血浆 protein-A2 水平是否升高及妊娠年龄是否影响其水平。还研究了妊娠相关血浆 protein-A2 作为筛查前生物标志物的可能性,以减少孕妇进行口服葡萄糖耐量试验的需要。 方法: 妊娠妇女在妊娠早期诊断为妊娠期糖尿病,在 sk?Ne 大学医院的汇水区进行 2 小时 75 g 口服葡萄糖耐量试验,瑞典在 2011-2015 (n = 99) 期间。在 2014-2015 年期间,从同一地理区域的孕产妇保健中心招募年龄和 BMI 匹配的无糖尿病的孕妇作为对照 (n = 100)。使用市售酶联免疫吸附测定试剂盒分析受试者血清中循环妊娠相关血浆 protein-A2。 结果: 与对照组 [13.5 (9.58-18.8)] 相比,诊断为妊娠糖尿病的妇女循环妊娠相关血浆 protein-A2 升高 [8.11 (5.74-11.3) ng/ml] ng/ml; P <0.001]。妊娠相关血浆 protein-A2 独立于年龄、 BMI 、 c肽和脂联素与妊娠期糖尿病相关 (P <0.001)。妊娠相关血浆 protein-A2 作为筛选前生物标志物,以确定妊娠糖尿病风险降低的妇女,其阴性预测值为 99.7%,在 6 ng/ml 的临界值下,敏感性为 96%,特异性为 30%。 结论: 本研究首次显示妊娠糖尿病患者血浆 protein-A2 水平升高。妊娠相关血浆 protein-A2 也有望成为筛选前的生物标志物,有可能减少妊娠早期进行口服葡萄糖耐量试验的需要。然而,未来在更大的高风险和低风险女性群体中的前瞻性队列研究需要进一步证实这一观察结果。



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.

关键词: 暂无
翻译标题与摘要 下载文献
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.

关键词: 暂无
翻译标题与摘要 下载文献
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.