使用共暴露统计方法和基于生理学的优化毒物动力学模型计算基于妊娠糖尿病的二恶英基准剂量

Tao Ying, Xin Liu, Lei Zhang, Wencheng Cao, Sheng Wen, Yongning Wu, Gengsheng He* and Jingguang Li*, 
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引用次数: 0

摘要

二恶英是无处不在的干扰内分泌的物质,但在共同暴露的情况下确定其影响和基准剂量却极具挑战性。本研究的目的是评估二恶英与妊娠糖尿病(GDM)之间的关系,计算共同暴露情况下二恶英的基准剂量(BMD),并利用优化的生理学毒物动力学(PBTK)模型得出每日暴露阈值。根据一项巢式病例对照研究(包括 77 例 GDM 病例和 154 例对照病例),在妊娠 9-16 周时测定了 29 种二恶英类化合物 (DLC) 以及 10 种全氟烷基酸 (PFAAs)、7 种多溴联苯醚 (PBDE) 和 5 种非二恶英类多氯联苯 (ndl-PCB) 的血清水平。采用贝叶斯机器核回归(BKMR)来确定重要的化学物质,并使用 probit 和 logistic 模型来计算经重要化学物质调整后的 BMD。通过贝叶斯-蒙特卡洛马尔科夫链方法,利用多氟二苯并对二恶英和二苯并呋喃(PFDD/Fs)数据对基于生理学的毒物动力学(PBTK)模型进行了优化,并用于确定每日膳食暴露阈值。血清中二恶英总毒性当量(TEQ)的中位数为 7.72 pg TEQ/g脂肪。逻辑回归分析表明,与总毒性当量水平处于第一量级的人相比,总毒性当量水平处于第五量级的人患 GDM 的几率明显更高(OR, 8.87; 95% CI 3.19, 27.58)。BKMR 分析确定二恶英 TEQ 和 BDE-153 是影响最大的化合物。二元逻辑模型和 probit 模型显示,当考虑到 BDE-153 的共暴露达到 80% 的水平时,BMD10(相当于 10%额外风险的基准剂量)和 BMDL10(BMD10 的下限)分别为 3.71 和 3.46 pg TEQ/g(脂肪)。利用优化的 PBTK 模型和修正因子,估计每日暴露量应低于 4.34 皮克 TEQ kg-1 体重/周-1,才不会达到对 GDM 有害的血清浓度。进一步的研究应利用共暴露统计方法和生理药代动力学(PBTK)模型来计算参考剂量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Benchmark Dose for Dioxin Based on Gestational Diabetes Mellitus Using Coexposure Statistical Methods and an Optimized Physiologically Based Toxicokinetic Model

Benchmark Dose for Dioxin Based on Gestational Diabetes Mellitus Using Coexposure Statistical Methods and an Optimized Physiologically Based Toxicokinetic Model

Dioxins are ubiquitous endocrine-disrupting substances, but determining the effects and benchmark doses in situations of coexposure is highly challenging. The objective of this study was to assess the relationship between dioxin andgestational diabetes mellitus (GDM), calculate the benchmark dose (BMD) of dioxin in coexposure scenarios, and derive a daily exposure threshold using an optimized physiologically based toxicokinetic (PBTK) model. Based on a nested case-control study including 77 cases with GDM and 154 controls, serum levels of 29 dioxin-like compounds (DLCs) along with 10 perfluoroalkyl acids (PFAAs), seven polybrominated diphenyl ethers (PBDEs), and five non-dioxin-like polychlorinated biphenyls (ndl-PCBs) were measured at 9–16 weeks of gestation. Bayesian machine kernel regression (BKMR) was employed to identify significant chemicals, and probit and logistic models were used to calculate BMD adjusted for significant chemicals. A physiologically based toxicokinetic (PBTK) model was optimized using polyfluorinated dibenzo-p-dioxins and dibenzofurans (PFDD/Fs) data by the Bayesian–Monte Carlo Markov chain method and was used to determine the daily dietary exposure threshold. The median serum level of total dioxin toxic equivalent (TEQ) was 7.72 pg TEQ/g fat. Logistic regression analysis revealed that individuals in the fifth quantile of total TEQ level had significantly higher odds of developing GDM compared to those in the first quantile (OR, 8.87; 95% CI 3.19, 27.58). The BKMR analysis identified dioxin TEQ and BDE-153 as the compounds with the greatest influence. The binary logistic and probit models showed that the BMD10 (benchmark dose corresponding to a 10% extra risk) and BMDL10 (lower bound on the BMD10) were 3.71 and 3.46 pg TEQ/g fat, respectively, when accounting for coexposure to BDE-153 up to the 80% level. Using the optimized PBTK model and modifying factor, it was estimated that daily exposure should be below 4.34 pg TEQ kg–1 bw week–1 in order to not reach a harmful serum concentration for GDM. Further studies should utilize coexposure statistical methods and physiologically based pharmacokinetic (PBTK) models in reference dose calculation.

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来源期刊
Environment & Health
Environment & Health 环境科学、健康科学-
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期刊介绍: Environment & Health a peer-reviewed open access journal is committed to exploring the relationship between the environment and human health.As a premier journal for multidisciplinary research Environment & Health reports the health consequences for individuals and communities of changing and hazardous environmental factors. In supporting the UN Sustainable Development Goals the journal aims to help formulate policies to create a healthier world.Topics of interest include but are not limited to:Air water and soil pollutionExposomicsEnvironmental epidemiologyInnovative analytical methodology and instrumentation (multi-omics non-target analysis effect-directed analysis high-throughput screening etc.)Environmental toxicology (endocrine disrupting effect neurotoxicity alternative toxicology computational toxicology epigenetic toxicology etc.)Environmental microbiology pathogen and environmental transmission mechanisms of diseasesEnvironmental modeling bioinformatics and artificial intelligenceEmerging contaminants (including plastics engineered nanomaterials etc.)Climate change and related health effectHealth impacts of energy evolution and carbon neutralizationFood and drinking water safetyOccupational exposure and medicineInnovations in environmental technologies for better healthPolicies and international relations concerned with environmental health
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