Department of Biostatistics and Epidemiology, Hudson College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA; Department of Medicine, Section of Epidemiology and Population Sciences, Baylor College of Medicine, Houston, TX, USA.
Department of Population Health and Biostatistics, School of Medicine, University of Texas Rio Grande Valley, Edinburg, TX, USA.
Environ Res. 2022 Nov;214(Pt 2):113897. doi: 10.1016/j.envres.2022.113897. Epub 2022 Jul 15.
Prior studies have identified the associations between environmental phenol and paraben exposures and increased risk of gestational diabetes mellitus (GDM), but no study addressed these exposures as mixtures. As methods have emerged to better assess exposures to multiple chemicals, our study aimed to apply Bayesian kernel machine regression (BKMR) to evaluate the association between phenol and paraben mixtures and GDM. This study included 64 GDM cases and 237 obstetric patient controls from the University of Oklahoma Medical Center. Mid-pregnancy spot urine samples were collected to quantify concentrations of bisphenol A (BPA), benzophenone-3, triclosan, 2,4-dichlorophenol, 2,5-dichlorophenol, butylparaben, methylparaben, and propylparaben. Multivariable logistic regression was used to evaluate the associations between individual chemical biomarkers and GDM while controlling for confounding. We used probit implementation of BKMR with hierarchical variable selection to estimate the mean difference in GDM probability for each component of the phenol and paraben mixtures while controlling for the correlation among the chemical biomarkers. When analyzing individual chemicals using logistic regression, benzophenone-3 was positively associated with GDM [adjusted odds ratio (aOR) per interquartile range (IQR) = 1.54, 95% confidence interval (CI) 1.15, 2.08], while BPA was negatively associated with GDM (aOR 0.61, 95% CI 0.37, 0.99). In probit-BKMR analysis, an increase in z-score transformed log urinary concentrations of benzophenone-3 from the 10th to 90th percentile was associated with an increase in the estimated difference in the probability of GDM (0.67, 95% Credible Interval 0.04, 1.30), holding other chemicals fixed at their medians. No associations were identified between other chemical biomarkers and GDM in the BKMR analyses. We observed that the association of BPA and GDM was attenuated when accounting for correlated phenols and parabens, suggesting the importance of addressing chemical mixtures in perinatal environmental exposure studies. Additional prospective investigations will increase the understanding of the relationship between benzophenone-3 exposure and GDM development.
先前的研究已经确定了环境酚和对羟基苯甲酸酯暴露与妊娠糖尿病(GDM)风险增加之间的关联,但没有研究将这些暴露作为混合物来探讨。随着更好地评估多种化学物质暴露的方法的出现,我们的研究旨在应用贝叶斯核机器回归(BKMR)来评估酚和对羟基苯甲酸酯混合物与 GDM 之间的关联。这项研究包括来自俄克拉荷马大学医学中心的 64 例 GDM 病例和 237 例产科患者对照。在妊娠中期采集了点尿样,以定量测定双酚 A(BPA)、二苯甲酮-3、三氯生、2,4-二氯苯酚、2,5-二氯苯酚、丁基对羟基苯甲酸酯、甲基对羟基苯甲酸酯和丙基对羟基苯甲酸酯的浓度。多变量逻辑回归用于评估个体化学生物标志物与 GDM 之间的关联,同时控制混杂因素。我们使用具有层次变量选择的 BKMR 的概率实现来估计酚和对羟基苯甲酸酯混合物中每个成分对 GDM 概率的平均差异,同时控制化学生物标志物之间的相关性。当使用逻辑回归分析个体化学物质时,二苯甲酮-3 与 GDM 呈正相关[每四分位距(IQR)的调整优势比(aOR)=1.54,95%置信区间(CI)1.15,2.08],而 BPA 与 GDM 呈负相关(aOR 0.61,95%CI 0.37,0.99)。在概率-BKMR 分析中,从第 10 到第 90 百分位的 z 分数转换后的尿液中二苯甲酮-3 的 log 浓度增加与 GDM 概率估计差异的增加相关(0.67,95%可信区间 0.04,1.30),同时将其他化学物质固定在中位数。在 BKMR 分析中,没有发现其他化学生物标志物与 GDM 之间存在关联。我们观察到,在考虑到相关的酚类和对羟基苯甲酸酯类时,BPA 与 GDM 的关联减弱,这表明在围产期环境暴露研究中解决化学混合物的重要性。进一步的前瞻性研究将增加对二苯甲酮-3 暴露与 GDM 发展之间关系的理解。