The association of vitamin D status and supplementation during pregnancy with gestational diabetes mellitus: a Chinese prospective birth cohort study.

妊娠期维生素d 状态和补充与妊娠期糖尿病的相关性: 一项中国前瞻性出生队列研究。

  • 影响因子:5.58
  • DOI:10.1093/ajcn/nqz260
  • 作者列表:"Yin WJ","Tao RX","Hu HL","Zhang Y","Jiang XM","Zhang MX","Jin D","Yao MN","Tao FB","Zhu P
  • 发表时间:2020-01-01

BACKGROUND:Previous studies have shown conflicting findings regarding the relation of vitamin D status and supplementation during pregnancy with gestational diabetes mellitus (GDM). Most of these studies hypothesized that 25-hydroxyvitamin D [25(OH)D] concentrations were associated with GDM risk and glucose metabolism based on linear association models. OBJECTIVES:We aimed to estimate the associations of 25(OH)D concentrations and vitamin D supplementation with GDM risk and glucose metabolism and determine the threshold concentrations of 25(OH)D that could significantly affect glucose metabolism and GDM risk. METHODS:In a prospective birth cohort study, we collected information about sociodemographic characteristics, health status, and lifestyle from 4984 pregnant women. Vitamin D supplementation and 25(OH)D concentrations were assessed in the second trimester. Data from the 75-g oral-glucose-tolerance test were obtained at 24-28 weeks of gestation. RESULTS:A total of 922 (18.5%) women were diagnosed with GDM. Compared with women with 25(OH)D concentrations 75 nmol/L (RR: 0.40; 95% CI: 0.22, 0.70). The curve-fitting models suggested a significant large reduction in GDM risk, fasting plasma glucose, and area under the curve of glucose with increasing 25(OH)D concentrations only for concentrations >50 nmol/L. Consistently, GDM risk was significantly reduced only in women who took 400-600 IU vitamin D/d (RR: 0.83; 95% CI: 0.70, 0.97) with a mean 25(OH)D concentration of 50 nmol/L but not in women taking vitamin D sometimes with a mean 25(OH)D concentration of 40 nmol/L. CONCLUSIONS:GDM risk was significantly reduced only in pregnant women with 25(OH)D concentrations >50 nmol/L. Pregnant women taking 400-600 IU vitamin D/d with mean 25(OH)D concentrations of 50 nmol/L had a lower risk of GDM.


背景: 先前的研究显示关于妊娠期糖尿病 (GDM) 与孕期维生素d 状态和补充之间的关系存在矛盾。这些研究中的大多数基于线性关联模型假设 25-羟基维生素d [25(OH)D] 浓度与 GDM 风险和糖代谢相关。 目的: 我们的目的是评估 25(OH)D 浓度和补充维生素 D 与 GDM 风险和糖代谢的相关性,并确定 25(OH) 的阈值浓度。D 可显著影响糖代谢和 GDM 风险。 方法: 在一项前瞻性出生队列研究中,我们收集了 4984 名孕妇的社会人口学特征、健康状况和生活方式信息。在孕中期评估维生素d 补充剂和 25(OH)D 浓度。在妊娠 24-28 周时获得 75g 口服葡萄糖耐量试验的数据。 结果: 共有 922 名 (18.5%) 女性被诊断为 GDM。与 25(OH)D 浓度为 75 nmol/L 的女性相比 (RR: 0.40; 95% CI: 0.22,0.70)。曲线拟合模型提示随着 25(OH) 的增加,GDM 风险、空腹血糖和血糖曲线下面积显著降低 D 浓度仅对于浓度> 50 nmol/L。一致地,只有服用 400-600 IU 维生素d/D 的女性 GDM 风险才显著降低 (RR: 0.83; 95% CI: 0.70,0.97),平均 25(OH)D 浓度为 50 nmol/L,但在服用维生素 D 的女性中没有,有时平均 25(OH)D 浓度为 40 nmol/L。 结论: 只有 25(OH)D 浓度> 50 nmol/L 的孕妇 GDM 风险显著降低。孕妇服用 400-600 IU 维生素d/D,平均 25(OH) d 浓度为 50 nmol/L,GDM 的风险较低。



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.