The association of elevated serum ferritin concentration in early pregnancy with gestational diabetes mellitus: a prospective observational study
- 作者列表："Cheng, Yan","Li, Tingting","He, Mulan","Liu, Junxiu","Wu, Kui","Liu, Shuangping","Ma, Ziwen","Lu, Jingbo","Zhang, Qingying","Cheng, Haidong
Background/Objectives The results linking body iron stores to the risk of gestational diabetes mellitus (GDM) are conflicting. We aimed to measure the serum ferritin level of women in early pregnancy and evaluate the risk of GDM in a Chinese urban population. Subjects/Methods In total, 851 pregnant women between 10 and 20 weeks of gestation took part in the prospective, observational study conducted. The women were divided into four groups by quartiles of serum ferritin levels (Q1–4). Their blood samples were collected and assayed for several biochemical variables at the beginning of the study, and the women were followed up with a 75-g oral glucose tolerance test at 24–28 weeks of gestation. Results The participants had an average serum ferritin concentration of 65.67 μg/L. GDM prevalence within each serum ferritin quartile was 9.4%, 14.6%, 18.8% and 19.3%, respectively, ( P = 0.016). The odds ratio for GDM in the ferritin Q2–4 was 1.64 (CI: 0.90–2.99), 2.23 (CI: 1.26–3.96) and 2.31 (CI: 1.30–4.10), compared with Q1, respectively. This association persisted after adjusting for potential confounders factors. In addition, in Q4, pregnant women with a pre-pregnancy body mass index ≥24 kg/m^2, maternal age ≤35 years old or haemoglobin≥ 110 g/L did have an increased risk of developing GDM. Conclusions Elevated serum ferritin concentrations in early gestation are associated with an increased risk of GDM, especially in pregnant women who have a high baseline iron storage status with no anaemia or who are overweight/obese. Individual iron supplementation should be considered to minimize the risk of GDM.
背景/目的将体内铁储存与妊娠期糖尿病 (GDM) 风险联系起来的结果是相互矛盾的。我们旨在测量孕早期妇女的血清铁蛋白水平，并评估中国城市人群 GDM 的风险。受试者/方法总共有 851 名妊娠 10 至 20 周的孕妇参加了这项前瞻性、观察性研究。根据血清铁蛋白水平分为四组 (Q1-4)。在研究开始时，收集他们的血液样本并分析几个生化变量, 在妊娠 24-28 周时对这些妇女进行 75g 口服葡萄糖耐量试验随访。结果受试者血清铁蛋白平均浓度为 65.67 μ g/L。各血清铁蛋白四分位数的 GDM 患病率分别为 9.4% 、 14.6% 、 18.8% 和 19.3% (P = 0.016)。铁蛋白 Q2-4 GDM 的比值比为 1.64 (CI: 0.90-2.99) 、 2.23 (CI: 1.26-3.96) 和 2.31 (CI: 1.30-4.10)。分别与 Q1 相比。调整潜在混杂因素后，这种关联持续存在。此外，在第 4 季度，孕前体重指数 ≥ 24 千克/米 ^ 2 的孕妇, 母亲年龄 ≤ 35 岁或血红蛋白 ≥ 110g/L 确实发生 GDM 的风险增加。结论妊娠早期血清铁蛋白浓度升高与 GDM 风险增加相关，尤其是在基线铁储存状态较高、无贫血或超重/肥胖的孕妇中。应考虑单独补铁，以尽量减少 GDM 的风险。
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.