Tao Ying, Xin Liu, Lei Zhang, Wencheng Cao, Sheng Wen, Yongning Wu, Gengsheng He* and Jingguang Li*,
{"title":"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","authors":"Tao Ying, Xin Liu, Lei Zhang, Wencheng Cao, Sheng Wen, Yongning Wu, Gengsheng He* and Jingguang Li*, ","doi":"10.1021/envhealth.4c0001710.1021/envhealth.4c00017","DOIUrl":null,"url":null,"abstract":"<p >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-<i>p</i>-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 BMD<sub>10</sub> (benchmark dose corresponding to a 10% extra risk) and BMDL<sub>10</sub> (lower bound on the BMD<sub>10</sub>) 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<sup>–1</sup> bw week<sup>–1</sup> 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.</p>","PeriodicalId":29795,"journal":{"name":"Environment & Health","volume":"2 9","pages":"661–671 661–671"},"PeriodicalIF":0.0000,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/envhealth.4c00017","citationCount":"0","resultStr":"{\"title\":\"Benchmark Dose for Dioxin Based on Gestational Diabetes Mellitus Using Coexposure Statistical Methods and an Optimized Physiologically Based Toxicokinetic Model\",\"authors\":\"Tao Ying, Xin Liu, Lei Zhang, Wencheng Cao, Sheng Wen, Yongning Wu, Gengsheng He* and Jingguang Li*, \",\"doi\":\"10.1021/envhealth.4c0001710.1021/envhealth.4c00017\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p >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-<i>p</i>-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 BMD<sub>10</sub> (benchmark dose corresponding to a 10% extra risk) and BMDL<sub>10</sub> (lower bound on the BMD<sub>10</sub>) 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<sup>–1</sup> bw week<sup>–1</sup> in order to not reach a harmful serum concentration for GDM. 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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.
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