Benchmark Dose Estimation Research Articles

Benchmark dose modeling for epidemiological dose-response assessment using case-control studies.

Following a previous article that focused on integrating epidemiological data from prospective cohort studies into toxicological risk assessment, this paper shifts the focus to case-control studies. Specifically, it utilizes the odds ratio (OR) as the main epidemiological measure, aligning it with the benchmark dose (BMD) methodology as the standard dose-response modeling approach to determine chemical toxicity values for regulatory risk assessment. A standardized BMD analysis framework has been established for toxicological data, including input data requirements, dose-response models, definitions of benchmark response, and consideration of model uncertainty. This framework has been enhanced by recent methods capable of handling both cohort and case-control studies using summary data that have been adjusted for confounders. The present study aims to investigate and compare the "effective count" based BMD modeling approach, merged with an algorithm used for converting odds ratio to relative risk in cohort studies with partial data information (i.e., the Wang algorithm), with the adjusted OR-based BMD analysis approach. The goal is to develop an adequate BMD modeling framework that can be generalized for analyzing published case-control study data. As in the previous study, these methods were applied to a database examining the association between bladder and lung cancer and inorganic arsenic exposure. The results indicate that estimated BMDs and BMDLs are relatively consistent across both methods. However, modeling adjusted OR values as continuous data for BMD estimation aligns better with established practices in toxicological BMD analysis, making it a more generalizable approach.

Metabolomics Simultaneously Derives Benchmark Dose Estimates and Discovers Metabolic Biotransformations in a Rat Bioassay.

Benchmark dose (BMD) modeling estimates the dose of a chemical that causes a perturbation from baseline. Transcriptional BMDs have been shown to be relatively consistent with apical end point BMDs, opening the door to using molecular BMDs to derive human health-based guidance values for chemical exposure. Metabolomics measures the responses of small-molecule endogenous metabolites to chemical exposure, complementing transcriptomics by characterizing downstream molecular phenotypes that are more closely associated with apical end points. The aim of this study was to apply BMD modeling to in vivo metabolomics data, to compare metabolic BMDs to both transcriptional and apical end point BMDs. This builds upon our previous application of transcriptomics and BMD modeling to a 5-day rat study of triphenyl phosphate (TPhP), applying metabolomics to the same archived tissues. Specifically, liver from rats exposed to five doses of TPhP was investigated using liquid chromatography-mass spectrometry and 1H nuclear magnetic resonance spectroscopy-based metabolomics. Following the application of BMDExpress2 software, 2903 endogenous metabolic features yielded viable dose-response models, confirming a perturbation to the liver metabolome. Metabolic BMD estimates were similarly sensitive to transcriptional BMDs, and more sensitive than both clinical chemistry and apical end point BMDs. Pathway analysis of the multiomics data sets revealed a major effect of TPhP exposure on cholesterol (and downstream) pathways, consistent with clinical chemistry measurements. Additionally, the transcriptomics data indicated that TPhP activated xenobiotic metabolism pathways, which was confirmed by using the underexploited capability of metabolomics to detect xenobiotic-related compounds. Eleven biotransformation products of TPhP were discovered, and their levels were highly correlated with multiple xenobiotic metabolism genes. This work provides a case study showing how metabolomics and transcriptomics can estimate mechanistically anchored points-of-departure. Furthermore, the study demonstrates how metabolomics can also discover biotransformation products, which could be of value within a regulatory setting, for example, as an enhancement of OECD Test Guideline 417 (toxicokinetics).

Bayesian benchmark dose risk assessment with mixed‐factor quantal data

AbstractBenchmark analysis is a general risk estimation strategy for identifying the benchmark dose (BMD) past which the risk of exhibiting an adverse environmental response exceeds a fixed, target value of benchmark response. Estimation of BMD and of its lower confidence limit (BMDL) is well understood for the case of an adverse response to a single stimulus. In many environmental settings, however, one or more additional, secondary, qualitative factor(s) may collude to affect the adverse outcome, such that the risk changes with differential levels of the secondary factor. Bayesian methods for estimation of the BMD and BMDL have grown in popularity, and a large variety of candidate dose–response models is available for applying these methods. This article applies Bayesian strategies to a mixed‐factor setting with a secondary qualitative factor possessing two levels to derive two‐factor Bayesian BMDs and BMDLs. We present reparameterized dose–response models that allow for explicit use of prior information on the target parameter of interest, the BMD. We also enhance our Bayesian estimation technique for BMD analysis by applying Bayesian model averaging to produce the BMDs and BMDLs, overcoming associated questions of model adequacy when multimodel uncertainty is present. An example from environmental carcinogenicity testing illustrates the calculations.

Benchmark dose modeling for epidemiological dose-response assessment using prospective cohort studies.

Benchmark dose (BMD) methodology has been employed as a default dose-response modeling approach to determine the toxicity value of chemicals to support regulatory chemical risk assessment. Especially, a relatively standardized BMD analysis framework has been established for modeling toxicological data regarding the formats of input data, dose-response models, definitions of benchmark response, and model uncertainty consideration. However, the BMD approach has not been well developed for epidemiological data mainly because of the diverse designs of epidemiological studies and various formats of data reported in the literature. Although most of the epidemiological BMD analyses were developed to solve a particular question, the methods proposed in two recent studies are able to handle cohort and case-control studies using summary data with consideration of adjustments for confounders. Therefore, the purpose of the present study is to investigate and compare the "effective count"-based BMD modeling approach and adjusted relative risk (RR)-based BMD analysis approach to identify an appropriate BMD modeling framework that can be generalized for analyzing published data of prospective cohort studies for BMD analysis. The two methods were applied to the same set of studies that investigated the association between bladder and lung cancer and inorganic arsenic exposure for BMD estimation. The results suggest that estimated BMDs and BMDLs are relatively consistent; however, with the consideration of established common practice in BMD analysis, modeling adjusted RR values as continuous data for BMD estimation is a more generalizable approach harmonized with the BMD approach using toxicological data.

The Effect of Historical Data-Based Informative Prior on Benchmark Dose Estimation of Toxicogenomics.

High-throughput toxicogenomics as an advanced toolbox of Tox21 plays an increasingly important role in facilitating the toxicity assessment of environmental chemicals. However, toxicogenomic dose-response analyses are typically challenged by limited data, which may result in significant uncertainties in parameter and benchmark dose (BMD) estimation. Integrating historical data via prior distribution using a Bayesian method is a useful but not-well-studied strategy. The objective of this study is to evaluate the effectiveness of informative priors in genomic dose-response modeling and BMD estimation. Specifically, we aim to identify plausible informative priors and evaluate their effects on BMD estimates at both gene and pathway levels. A general informative prior and eight time-specific (from 3 h to 29 d) informative priors for seven commonly used continuous dose-response models were derived. Results suggest that the derived informative priors are sensitive to the specific data sets used for elicitation. Real data-based simulations indicate that BMD estimation with the time-specific informative priors can achieve increased or equivalent accuracy, significantly decreased uncertainty, and a slightly enhanced correlation with the points of departure estimated from apical end points than the counterparts with noninformative priors. Overall, our study systematically examined the effects of historical data-based informative priors on BMD estimates, highlighting the benefits of plausible information priors in advancing the practice of toxicogenomics.

Benchmark dose estimation based on oxidative damage in Chinese workers exposed to benzene series compounds

This study evaluated the effects of BTEX exposure on oxidative stress; it analyzed the correlation between oxidative stress and peripheral blood counts and estimated the benchmark dose (BMD) of BTEX compounds. This study recruited 247 exposed workers and 256 controls; physical examination data were collected and serum oxidative stress levels were measured. Relationships between BTEX exposure and biomarkers were analyzed using Mann-Whitney U, generalized linear model, and chi-square trend tests. Environmental Protection Agency Benchmark Dose Software was used to calculate the BMD and lower confidence limit of the BMD (BMDL) for BTEX exposure. The total antioxidant capacity (T-AOC) correlated positively with peripheral blood counts, and negatively with the cumulative exposure dose. On using T-AOC as the outcome variable, the estimated BMD and BMDL for BTEX exposure were 3.57 mg/m3 and 2.20 mg/m3, respectively. Based on T-AOC, the calculated occupational exposure limit of BTEX was 0.055 mg/m3.

Benchmark dose estimation for benzene-exposed workers in China: Based on quantitative and multi-endpoint genotoxicity assessments

Based on previous exposure studies, benzene (BZ) has been classified as a human carcinogen and occupational exposure limit (OELs) for BZ has been set to be about 1 ppm around the world. However, health hazards have still been reported with exposure below the OEL. Thus, the OEL needs to be updated to reduce health risk. The overall aim of our study was therefore to generate new OEL for BZ via a benchmark dose (BMD) approach and based on quantitative and multi-endpoint genotoxicity assessments. Genotoxicities were determined using the novel human PIG-A gene mutation assay, the micronucleus (MN) test and the COMET assay in benzene-exposed workers. Among the 104 workers with below current OELs, they exhibited significantly higher PIG-A mutant frequencies (MFs) (15.96 ± 14.41 × 10−6) and MN frequencies (11.55 ± 6.83‰) than those among the controls (PIG-A MFs: 5.46 ± 4.56 × 10−6, MN frequencies: 4.51 ± 1.58 ‰), but no difference in the COMET assay. A significant association was also observed between BZ exposure doses and PIG-A MFs and MN frequencies (P < 0.001). Our results indicate that health hazards were induced among workers with below OEL exposures. Based on results from the PIG-A and MN assays, the lower confidence limit of the BMD (BMDL) were calculated to be 8.71 mg/m3-year and 0.44 mg/m3-year, respectively. Based on these calculations, the OEL for BZ was determined to be lower than 0.07 ppm. This value can be considered by regulatory agencies to set new exposure limits and to better protect workers.

Using Prior Toxicological Data to Support Dose-Response Assessment─Identifying Plausible Prior Distributions for Dichotomous Dose-Response Models.

The benchmark dose (BMD) methodology has significantly advanced the practice of dose-response analysis and created substantial opportunities to enhance the plausibility of BMD estimation by synthesizing dose-response information from different sources. Particularly, integrating existing toxicological information via prior distribution in a Bayesian framework is a promising but not well-studied strategy. The study objective is to identify a plausible way to incorporate toxicological information through informative prior to support BMD estimation using dichotomous data. There are four steps in this study: determine appropriate types of distribution for parameters in common dose-response models, estimate the parameters of the determined distributions, investigate the impact of alternative strategies of prior implementation, and derive endpoint-specific priors to examine how prior-eliciting data affect priors and BMD estimates. A plausible distribution was estimated for each parameter in the common dichotomous dose-response models using a general database. Alternative strategies for implementing informative prior have a limited impact on BMD estimation, but using informative prior can significantly reduce uncertainty in BMD estimation. Endpoint-specific informative priors are substantially different from the general one, highlighting the necessity for guidance on prior elicitation. The study developed a practical way to employ informative prior and laid a foundation for advanced Bayesian BMD modeling.

Guidance on the use of the benchmark dose approach in risk assessment.

The Scientific Committee (SC) reconfirms that the benchmark dose (BMD) approach is a scientifically more advanced method compared to the no‐observed‐adverse‐effect‐level (NOAEL) approach for deriving a Reference Point (RP). The major change compared to the previous Guidance (EFSA SC, 2017) concerns the Section 2.5, in which a change from the frequentist to the Bayesian paradigm is recommended. In the former, uncertainty about the unknown parameters is measured by confidence and significance levels, interpreted and calibrated under hypothetical repetition, while probability distributions are attached to the unknown parameters in the Bayesian approach, and the notion of probability is extended to reflect uncertainty of knowledge. In addition, the Bayesian approach can mimic a learning process and reflects the accumulation of knowledge over time. Model averaging is again recommended as the preferred method for estimating the BMD and calculating its credible interval. The set of default models to be used for BMD analysis has been reviewed and amended so that there is now a single set of models for quantal and continuous data. The flow chart guiding the reader step‐by‐step when performing a BMD analysis has also been updated, and a chapter comparing the frequentist to the Bayesian paradigm inserted. Also, when using Bayesian BMD modelling, the lower bound (BMDL) is to be considered as potential RP, and the upper bound (BMDU) is needed for establishing the BMDU/BMDL ratio reflecting the uncertainty in the BMD estimate. This updated guidance does not call for a general re‐evaluation of previous assessments where the NOAEL approach or the BMD approach as described in the 2009 or 2017 Guidance was used, in particular when the exposure is clearly lower (e.g. more than one order of magnitude) than the health‐based guidance value. Finally, the SC firmly reiterates to reconsider test guidelines given the wide application of the BMD approach.

Benchmark dose estimation based on Bayesian model averaging and its application to evaluation of cadmium exposure of population in Baiyin

Benchmark dose estimation based on Bayesian model averaging and its application to evaluation of cadmium exposure of population in Baiyin

Continuous Model Averaging for Benchmark Dose Analysis: Averaging Over Distributional Forms.

When estimating a benchmark dose (BMD) from chemical toxicity experiments, model averaging is recommended by the National Institute for Occupational Safety and Health, World Health Organization and European Food Safety Authority. Though numerous studies exist for Model Average BMD estimation using dichotomous responses, fewer studies investigate it for BMD estimation using continuous response. In this setting, model averaging a BMD poses additional problems as the assumed distribution is essential to many BMD definitions, and distributional uncertainty is underestimated when one error distribution is chosen a priori. As model averaging combines full models, there is no reason one cannot include multiple error distributions. Consequently, we define a continuous model averaging approach over distributional models and show that it is superior to single distribution model averaging. To show the superiority of the approach, we apply the method to simulated and experimental response data.

A computational system for Bayesian benchmark dose estimation of genomic data in BBMD.

Background:Existing studies have revealed that the benchmark dose (BMD) estimates from short-term in vivo transcriptomics studies can approximate those from long-term guideline toxicity assessments. Existing software applications follow this trend by analyzing omics data through the maximum likelihood estimation and choosing the “best” model for BMD estimates. However, this practice ignores the model uncertainty and may result in over-confident inferences and predictions, leading to an inadequate decision.Objective:By generally following the National Toxicology Program Approach to Genomic Dose-Response Modeling, we developed a web-based dose–response modeling and BMD estimation system, Bayesian BMD (BBMD), for genomic data to quantitatively address uncertainty from various sources. The performances of BBMD are compared with BMDExpress.Methods:The system is primarily based on the previously developed BBMD system and further developed in a genomic perspective. Bayesian model averaging method is applied to BMD estimation and pathways analyses. Generally, the system is unique regarding the flexibility in preparing/storing data and in characterizing uncertainties.Results:This system was tested and validated versus 24 previously published in-vivo microarray dose–response datasets (GSE45892) and 64 molecules data from the Open TG-Gates database. Short term transcriptional BMD values for the median pathway in BBMD are highly correlated with the long-term apical BMD values (R = 0.78–0.91). The BMD estimates obtained by BBMD were compared to those by BMDExpress. The results indicate that BBMD provides more adequate results in terms of less extreme values and no failure in BMD and BMDL calculations. Also, the pathway analysis in BBMD provides a conservative estimate because a broader confidence interval is established.Discussion:Overall, this study demonstrates that dose–response modeling using genomic data can play a substantial role in support of chemical risk assessment. BBMD represents a robust and user-friendly alternative for genomic dose–response data analysis with outstanding functionalities to quantify uncertainty from various sources.

Quantifying uncertainty in dose–response screenings of nanoparticles: a Bayesian data analysis

Fitting theoretical models to experimental data for dose–response screenings of nanoparticles yields values of several hazard metrics that can support risk management. In this paper, we describe a Bayesian approach to the analysis of dose–response data for nanoparticles that takes into account multiple sources of uncertainty. Specifically, we develop a Bayesian model for the analysis of data for the cytotoxicity of ZnO nanoparticles that follow the log-logistic equation. This model reproduces the unequal variance across doses observed in the experimental data, incorporates information about the sensitivity of the cytotoxicity assay used (i.e. resazurin), and complements experimental data with historical information about the system. The model determines probability distributions for multiple values of toxicity potency (EC50), and exponential decay (the slope s); these distributions provide a direct measure of uncertainty in terms of probabilistic credibility intervals. By substituting these distributions in the log-logistic equation, we determine upper and lower limits of the benchmark dose (BMD), corresponding to upper and lower limits of credibility intervals with 95% probability given the experimental data, multiple sources of uncertainty, and historical information. In view of a reduction of costs and time of dose–response screenings, we use the Bayesian model for the cytotoxicity of ZnO nanoparticles to identify the experimental design that uses the minimum number of data while reducing uncertainty in the estimation of both fitting parameters and BMD.

Estimation of Benchmark Dose of Cumulative Cadmium Exposure for Renal Tubular Effect.

Objectives: The aim of this study was to determine the no observed adverse effect level (NOAEL), the lowest observed adverse effect level (LOAEL) and the benchmark dose low (BMDL) of cadmium exposure by re-evaluation of the dose–response relationship between cumulative cadmium exposure and renal tubular damage reported previously. Methods: The participants were workers (326 men and 114 women) employed for at least three months between 1931 and 1982. Blood cadmium (Cd-B) and air cadmium (Cd-A) were collected at regular intervals with urinary β2-microglobulin as the tubular effect marker. Cumulative Cd-A and Cd-B were estimated by multiplying concentration and working period. The BMDL was calculated using Benchmark Dose Software (version 3.1.2). The benchmark response (BMR) was set at 5% or 10%. Results: By logistic regression, the NOAEL of mean cumulative Cd-B was 7122 months nmol/L. The LOAEL of cumulative Cd-A and least-squares cumulative Cd-B was 691 yrs μg/m3 and 8586 months nmol/L, respectively. Among various models for dose–response relationships, a probit model was adopted as the best fitting model. The obtained BMDLs of cumulative Cd-A were 272.3 yrs µg/m3 (BMR5%) and 707.5 yrs µg/m3 (BMR10%). The BMDLs of mean cumulative Cd-B were 3967.2 months nmol/L (BMR5%) and 7798.1 months nmol/L (BMR10%). The BMDLs of least-squares cumulative Cd-B were 3588.6 months nmol/L (BMR5%) and 8616.3 months nmol/L (BMR10%). Assuming a working period of 40 years, the BMDLs for BMR10% corresponded to 17.7 µg/m3 (Cd-A) and 1.8~2.0 µg/L (Cd-B). Discussion: This study provides new valuable information to enhance the reliability of limit values and thereby make a significant contribution to preventing the health effects of Cd in exposed workers.

Bayesian benchmark dose analysis for inorganic arsenic in drinking water associated with bladder and lung cancer using epidemiological data

Abundant epidemiological evidence has shown that there is a strong causal relationship between long-term exposure to inorganic arsenic (iAs) through drinking water and a few types of cancer (e.g., lung and bladder cancer). Traditionally, a linear low-dose extrapolation assumption was applied in risk assessment for iAs which resulted in a relatively conservative cancer risk estimate. Growing biological evidence suggests that the mode of action of iAs-induced cancer follows a threshold process (e.g., sufficient concentration of trivalent arsenic is required to disrupt normal cellular function). In this study, we applied the benchmark dose (BMD) methodology to model the relationship between the relative risk of bladder and lung cancer and the iAs concentration in drinking water using the high-quality epidemiological data reported in recently published papers, with a special focus on the low exposure range (i.e., <150 μg/L). Because of its biological plausibility and statistical flexibility, the Hill model has been chosen to model the data under a Bayesian framework. A Bayesian hierarchal model together with a bootstrap method for exposure estimation were applied to quantify uncertainty from various sources, including the within-study, between-study, and exposure uncertainties. Dose-response assessment results obtained from a number of alternative model structures and methods consistently demonstrate a threshold type dose-response curve with a threshold in the range between 40–60 μg/L of iAs concentration in drinking water. The BMD for iAs in drinking water associated with 0.1 % increase in relative risk of bladder cancer is 42.2 μg/L (BMDL 39.2 μg/L); for 0.05 % increase, the BMD is 41.6 μg/L (BMDL 38.6 μg/L). For lung cancer, the two counterpart BMD estimates are 57.0 μg/L (BMDL 43.6 μg/L) and 55.7 μg/L (BMDL 42.5 μg/L) for 0.1 % and 0.05 % increase, respectively. These analyses provide additional statistical support for a non-linear dose response for cancer risk from inorganic arsenic which may have important policy implications.

Benchmark dose risk analysis with mixed-factor quantal data in environmental risk assessment.

Benchmark analysis is a general risk estimation strategy for identifying the benchmark dose (BMD) past which the risk of exhibiting an adverse environmental response exceeds a fixed, target value of benchmark response (BMR). Estimation of BMD and of its lower confidence limit (BMDL) is well understood for the case of an adverse response to a single stimulus. In many environmental settings, however, one or more additional, secondary, qualitative factor(s) may collude to affect the adverse outcome, such that the risk changes with differential levels of the secondary factor. This paper extends the single-dose BMD paradigm to a mixed-factor setting with a secondary qualitative factor possessing two levels. With focus on quantal-response data and using a generalized linear model with a complementary-log link function, we derive expressions for BMD and BMDL. We study the operating characteristics of six different multiplicity-adjusted approaches to calculate the BMDL, using Monte Carlo evaluations. We illustrate the calculations via an example data set from environmental carcinogenicity testing.

A Nonmechanistic Parametric Modeling Approach for Benchmark Dose Estimation of Event-Time Data.

We propose benchmark dose estimation for event-time data, using a two-step approach. This approach avoids estimation of complex models and has been previously shown to give robust results for summarizing relevant parameters for risk assessment. In the first step, the probability of the event of interest to occur (in a certain time interval) is described as a function of time, resulting in an event-time model; such a model is fitted allowing an individual curve for each dose, and relevant estimates are extracted. In the second step, a dose-response model is fitted to the estimates of t50 obtained from the event-time model in the first step. Given a predefined benchmark response, the benchmark dose is then estimated from the resulting model. This novel approach is demonstrated in two examples. Our application of the time-to-event model showed a gain in power compared to the traditional analysis of end-of-study summary data.

Bmd: an R package for benchmark dose estimation.

The benchmark dose (BMD) methodology is used to derive a hazard characterization measure for risk assessment in toxicology or ecotoxicology. The present paper’s objective is to introduce the R extension package bmd, which facilitates the estimation of BMD and the benchmark dose lower limit for a wide range of dose-response models via the popular package drc. It allows using the most current statistical methods for BMD estimation, including model averaging. The package bmd can be used for BMD estimation for binomial, continuous, and count data in a simple set up or from complex hierarchical designs and is introduced using four examples. While there are other stand-alone software solutions available to estimate BMDs, the package bmd facilitates easy estimation within the established and flexible statistical environment R. It allows the rapid implementation of available, novel, and future statistical methods and the integration of other statistical analyses.

Benchmark dose estimation for coke oven emissions based on oxidative damage in Chinese exposed workers

Coke oven emissions (COEs) can cause oxidative stress of the body, which in turn induces the occupational lung disease and also increases the risk of other diseases. COEs are the major occupational hazard factors for coke oven workers. The aim of the study is to explore the influences of COEs exposure on oxidative damage and estimate the benchmark dose (BMD) of COEs. A group of 542 workers exposed to COEs and 237 healthy controls from the same city were recruited in this study. The corresponding measuring kits were used to determine the plasma biomarkers of oxidative damage level. Generalized linear models and trend tests were used to analyze the relationship between COEs exposure and biomarkers. EPA Benchmark Dose Software was performed to calculate BMD and the lower confidence limit of the benchmark dose (BMDL) of COEs exposure. The results showed the significant dose-response relationship was observed between COEs exposure and oxidative damage with T-AOC as a biomarker. The BMD of COEs exposure were 2.83 mg/m3 and 1.39 mg/m3 in males and females, respectively, and the corresponding BMDL were 1.47 mg/m3 and 0.75 mg/m3 in males and females, respectively. Our results suggested the exposure level of COEs below the current national occupational exposure limits (OELs) would induce oxidative damage, and the OEL of COEs based on the T-AOC damage was suggested at 0.03 mg/m3 in this study.Keywords: Coke oven emissions, polycyclic aromatic hydrocarbons, oxidative damage, plasma total antioxidant capacity, benchmark dose, occupational exposure limits

Benchmark dose estimation for coke oven emissions based on oxidative damage in Chinese exposed workers

Coke oven emissions (COEs) can cause oxidative stress of the body, which in turn induces the occupational lung disease and also increases the risk of other diseases. COEs are the major occupational hazard factors for coke oven workers. The aim of the study is to explore the influences of COEs exposure on oxidative damage and estimate the benchmark dose (BMD) of COEs. A group of 542 workers exposed to COEs and 237 healthy controls from the same city were recruited in this study. The corresponding measuring kits were used to determine the plasma biomarkers of oxidative damage level. Generalized linear models and trend tests were used to analyze the relationship between COEs exposure and biomarkers. EPA Benchmark Dose Software was performed to calculate BMD and the lower confidence limit of the benchmark dose (BMDL) of COEs exposure. A significant association was observed between COEs exposure and oxidative damage with T-AOC as a biomarker. The BMD of COEs exposure were 2.83 mg/m3 and 1.39 mg/m3 for males and females, respectively, and the corresponding BMDL were 1.47 mg/m3 and 0.75 mg/m3, respectively. Our results suggested that the exposure level of COEs below the current national occupational exposure limits (OELs) would induce oxidative damage, and the OEL of COEs based on the T-AOC damage was suggested at 0.03 mg/m3 in this study.