Reduction of mitochondrial DNA copy number in peripheral blood is related to polycyclic aromatic hydrocarbons exposure in coke oven workers: Bayesian kernel machine regression

Environmental Pollution 260 (2020) 114026

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Reduction of mitochondrial DNA copy number in peripheral blood is related to polycyclic aromatic hydrocarbons exposure in coke oven workers: Bayesian kernel machine regression* Xinyu Zhao a, 1, Aimin Yang b, 1, Ye Fu a, Bin Zhang a, Xuejing Li a, Baolong Pan a, c, Qiang Li d, Jun Dong d, Jisheng Nie a, Jin Yang a, * a

Department of Occupational Health, School of Public Health, Shanxi Medical University, Taiyuan, 030001, Shanxi, China Hong Kong Institutes of Diabetes and Obesity, The Chinese University of Hong Kong, Taiyuan, 030001, Shanxi, China General Hospital of Taiyuan Iron & Steel (Group) Co., Ltd, Taiyuan, 030001, Shanxi, China d Center of Occupational Disease Prevention, Xishan Coal Electricity (Group) Co., Ltd, Taiyuan, 030001, Shanxi, China b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 11 October 2019 Received in revised form 4 January 2020 Accepted 19 January 2020 Available online 22 January 2020

Although association between polycyclic aromatic hydrocarbons (PAHs) exposure and mitochondrial DNA copy number (mtDNAcn) was researched by traditional linear model extensively, most of these studies analyzed independent effect of each PAHs metabolite and adjust for the confounding other metabolites concomitantly, without considering others interactions. As a complex organic pollutant, a reasonable statistical method is needed to study toxic effects of PAHs. Therefore, we aimed to conduct a novel statistical approach, Bayesian Kernel Machine Regression (BKMR), to explore the effect of PAHs exposure on mtDNAcn among coke oven workers. In this crosssectional study, the concentrations urinary of PAHs metabolites were measured using high performance liquid chromatography mass spectrometry (HPLC-MS). The mtDNAcn was measured using realtime quantitative polymerase chain reaction (RT-PCR) in peripheral blood of 696 Chinese coke oven workers. The relationship of urinary of PAHs metabolites and mtDNAcn were evaluated by BKMR model. And the results showed a significant negative effect of PAHs metabolites on mtDNAcn when PAHs metabolites concentrations were all above 35th percentile compared to the median and the statistically significant negative single-exposure effect of 2-OHNAP and 2-OHPHE on mtDNAcn when all of the other PAHs are fixed at a particular threshold (25th, 50th, 75th percentile). The changes in log 2-OHNAP and 2OHPHE from the 25th to the 75th percentile when other PAHs metabolites were at the 50th percentile were associated with change in mtDNAcn of
0.082 (
0.021,
0.124) and
0.048 (
0.021,
0.090) respectively. And evidence of a linear effect of urinary 2-OHNAP and 2-OHPHE were found. Finally, our findings suggested that PAHs cumulative exposures and particularly single-exposure of 2-OHNAP and 2OHPHE might compromise mitochondrial function by decreasing mtDNAcn in Chinese coke oven workers. © 2020 Elsevier Ltd. All rights reserved.

Keywords: Polycyclic aromatic hydrocarbons Mitochondrial DNA copy number Bayesian kernel machine regression

1. Introduction Polycyclic aromatic hydrocarbons (PAHs) are the main pollutants of coke oven emissions generated by incomplete combustion

* This paper has been recommended for acceptance by Payam Dadvand. * Corresponding author. Taiyuan, China. Xinjiannan Road 56, Taiyuan, 030001, Shanxi, China. E-mail address: [email protected] (J. Yang). 1 Xinyu Zhao and Aimin Yang contributed equally to this paper.

https://doi.org/10.1016/j.envpol.2020.114026 0269-7491/© 2020 Elsevier Ltd. All rights reserved.

in coking production. And PAHs may cause an elevation in reactive oxygen species (ROS) and which can affect DNA, lipids, or proteins directly, and even induce cancer (Kwack and Lee, 2000; Guo et al., 2014). Furthermore, growing evidence has shown that DNA alterations in the nucleus and the mitochondria are involved in agerelated disorders (Allen and Coombs, 1980; Kelly, 2011; DeBalsi et al., 2017). The ability of PAHs to damage nuclear DNA (nDNA) has been studied widely. However, compared with nDNA, mitochondria DNA (mtDNA) has diminished protective histones and DNA repair capacity. MtDNA is considered target of many

2

X. Zhao et al. / Environmental Pollution 260 (2020) 114026

environmental toxicants, due to it is particularly susceptible to ROS-induced damage (Yakes and Van Houten, 1997; Lee et al., 2000). MtDNA copy number (mtDNAcn) is correlated positively with the size and number of mitochondria (Lee and Wei, 2000). Mitochondrial physiological states determined by mtDNAcn can sense environmental changes, and may be an indication of the mechanism of some disease (Taylor and Turnbull, 2005; Wallace, 2016). It has been shown that cells challenged by ROS can synthesize more mtDNAcn to compensate for damage (Lee and Wei, 2000). However, excessive oxidative stress may lead to decreased mtDNAcn, mitochondrial dysfunction, even cell necrosis and apoptosis (Golden and Melov, 2001; Byun and Baccarelli, 2014). The functional importance of mtDNAcn is confirmed by correlation between human or mouse mtDNA variation and a broad range of traits, including longevity, physical capacity, wide spectrum of metabolic diseases and cancer (Picard et al., 2015; Falah et al., 2016; Mishra et al., 2016). Decreasing sperm mtDNAcn among Chinese general college students exposed to PAHs was found by current epidemiologic study (Ling et al., 2017). And there’s still study to show that lower mtDNAcn in association with exposure to PAHs in house (Pieters et al., 2013). In addition, in a recent study of 46 Polish male coke oven workers, individuals exposed to PAHs exhibited higher mtDNAcn in peripheral blood than 44 matched controls (Pavanello et al., 2013). However, there is important that a crucial limitation in the current analysis of complex environmental pollutants are that exposure to multiple pollutants is rarely considered simultaneously. In a general way, multiple exposures have been studied via multivariable parametric regression that concomitantly adjust for the confounding effects of components and estimate the independent effect of each component, adjusting for the others. It’s worth noting that PAHs do act as a mixture, traditional approaches might be limited by both multicollinearity and model misspecification. Additionally, for general research methods, the selection of highly correlated environmental pollutants is limited and lacks high sensitivity. And for the study of environmental pollutants, we tend to evaluate the overall effect, interactions and nonlinear relationship of mixture. Therefore, our study contributes to new evidence on the effects of PAHs on mtDNAcn in Chinese coke oven workers by employing a novel statistical approach, Bayesian kernel machine regression (BKMR) (Bobb et al., 2015; Chiu et al., 2018), which has been used widely to evaluate the health effects of environmental pollutants (Coker et al., 2018; Minguez-Alarcon et al., 2019). The BKMR can not only utilize kernel function to estimate overall effect and accounting for possible relationship of non-linearity and interactions but also identify important mixture components through variable selection (Bobb et al., 2015; Bobb et al., 2018). In other word, BKMR method can be applied primely in epidemiological studies leading to scientific views that were not uncovered using general standard regression methods.

trained investigators to collect basic demographic data including age, sex, the status of smoking and drinking, education and exercise situation. Participants had drunk at least once a week for more than six months were defined as current drinkers. Those who had quitted over one year or more were defined as previous drinkers; others were defined as non-drinkers. Participants who had smoked at least one cigarette per day for six months or more were defined as current smokers. And participants who had quitted over for great than or equal to one year were defined as previous smoker; others were defined as non-smokers. Every participant signed the informed consent and this study was approved by the Medical Ethics Committee of the Shanxi Medical University. 2.2. Measurements of urinary PAHs metabolites According to the recommendation of the American Conference of Governmental Industrial Hygienists (ACGIH), the morning urine samples (20 mL) of end-of-work-week were obtained. All urine samples were freezed at
80  C until further processing. The urinary concentrations of eleven PAHs metabolites, including 2hydroxynaphthalene (2-OHNAP), 1-hydroxynaphthalene (1OHNAP), 3-hydroxyfluorene (3-OHFLU), 2-hydroxyfluorene (2OHFLU), 1-hydroxyphenanthrene (1-OHPHE), 2hydroxyphenanthrene (2-OHPHE), 9-hydroxyphenanthrene (9OHPHE), 1-hydroxypyrene (1-OHPYR), 3-hydroxychrysene (3OHCHR), 6-hydroxychrysene (6-OHCHR) and 9hydroxybenzpyrene (9-OHBAP), were determined by high performance liquid chromatography mass spectrometry (HPLC-MS (Shimadzu, Kyoto, Japan) (Onyemauwa et al., 2009; Nie et al., 2019)). In short, urine sample was thawed at room temperature first. Second, 1 mL urine was added with 4 mL sodium acetate buffer solution (PH ¼ 5.0), 3 mL deionized water, 50 mL internal standard solution, and 5 mL b-Glucuronidase/arylsulfatase. The mixture was incubated at 37  C for 12 h. Third, the solid phase extraction (SPE) tubes (sorbent C18) were conditioned with 5 mL methanol followed by 5 mL deionized water, then washing with 3 mL deionized water and 5 mL methanol after the loading of urine mixture. During these procedures, the flow rate was held lower than 1 mL/min. Finally, collected eluate were evaporated under the gentle stream of nitrogen and then dissolved in 500 mL of methanol. The methanol mixture transferred into the sample vials for the HPLC-MS analysis to determine PAHs metabolites levels. Quality control data for measurement of the urinary PAHs metabolites are presented in Table S1. Valid urinary concentrations of PAHs metabolites were adjusted using urine gravity (Herbstman et al., 2012). Concentrations of urinary PAHs metabolites below the limit of detections (LODs) were imputed as the limit of detection divided by 2. The detection rates of 9-OHBAP (50.7%), 3-OHCHR (25.1%) and 6OHCHR (41.6%) close to or below 50%, so those three metabolites were considered binary (above or below the LOD) in the analysis. 2.3. Measurements of mitochondrial DNA copy number

2. Material and methods 2.1. Study population The baseline population data were obtained from annual physical examination of a coke oven plant in China in 2017. A total of 859 coke oven workers who had been restricted for more than one year were included in the study. Excluding individuals with insufficient urine samples (n ¼ 76), and missing with demographic characteristics (n ¼ 85) and mtDNAcn measurements (n ¼ 2). Ultimately, the remaining 696 participants were included in the current study. All participants were administered face-to-face interview by

DNA was isolated from the whole blood samples by Magbead Blood DNA kit (CWbiotech, Beijing, China). In order to ensure the good quality of DNA samples, DNA integrity was tested by spectrophotometer (Eppendorf, Hamburg, Germany). And those with the ratio of the A260/280 ranged from 1.60 to 2.00 were considered to have a good DNA purity (Fu et al., 2018). Relative mtDNA and human beta globin (HBG) gene were measured based on real-time quantitative polymerase chain reaction (RT-PCR) and SYBR Green technology. And as previously described (Carugno et al., 2012), this assay is based on the ratio of copy number estimates of a mitochondrial gene (mtND1) to those of a nuclear gene (HBG). Copy numbers were determined by the cycle threshold (Ct) values at a

X. Zhao et al. / Environmental Pollution 260 (2020) 114026

constant fluorescence level. For each individual, the RT-PCR assays for mtDNA and HBG were performed on the same plate, and repeated two times. Ten percent of the total data was used to calculate interclass correlation coefficient (ICC), which determine the reliability (ICC > 0.8). Primers and conditions for mtDNAcn analysis and concentration and purity of DNA are provided in the supplemental material. 2.4. Statistical analysis Basic characteristics of 696 coke oven workers in the study were described by median (Med) and quartile, frequency and proportion. The unadjusted association for worker’s characteristics and mtDNAcn were determined using generalized linear regression model. Spearman’s correlation was used to explore the correlation of urinary PAHs metabolites. MtDNAcn and all urine hydroxyl metabolite were log10-transformed due to skewed data and the form of data was centered continuous variables. Firstly, the associations between urinary PAHs metabolites and mtDNAcn were examined by multivariable generalized linear regression model, with continuous mtDNAcn as the dependent variable, and continuous urinary level of PAHs metabolites as the independent variable, with adjustment for age (years), sex, smoking status (ever, now and non-smoking), drinking status (ever, now and non-drinking), the degree of exercise (never, mild and severe) and night shift (yes or no) (model 1) and further adjustment for other metabolites (9-OHBAP, 3-OHCHR and 6-OHCHR as binary, the rest of metabolites as continuous level) (model 2). Besides, to account for possible non-linearity and interactions among exposures, we implemented Bayesian Kernel Machine Regression (BKMR), a proposed as a novel strategy for assessing complex environmental contaminants (Bobb et al., 2015; Valeri et al., 2017). In the study, the BKMR model follows: Yi ¼ h (2-OHNAP, 1-OHNAP, 3-OHFLU, 2-OHFLU, 2-OHPHE, 9OHPHE, 1-OHPHE, 1-OHPYR) þ bTZi þ ei, where the function h () is an dose-response function, which contains nonlinear and/or interactions between components, and Z ¼ Z1, …, Zp are p potential confounders (confounders are consistent with model 2 of multivariable generalized linear regression model). There is Gaussian and Binomial possible choices for specifying the kernel function, and we used Gaussian kernel function, which has been applied in actual studies (Valeri et al., 2017). From the model fit, we also derived summary statistics that quantify scientifically relevant features of the exposureeresponse function in order to get insight on the cumulative effects of the mixture. And it’s worth noting that model also may allow visualizing different cross-sectional views of the surface. Specifically, we plotted the cumulative effects of the eight PAHs metabolites by comparing the estimate value of the exposure-response function when all of other exposures are at a particular quantile. And that we also summarized the singleexposure effects of each PAHs metabolite, where all of the other urinary PAHs metabolites are fixed to some particular quantile. In addition, we plotted a dose-response relationship of each PAHs metabolite with mtDNAcn while fixing the rest of PAHs metabolites at their 50th percentile. Finally, we can also visualize the bivariate exposure-response function for two metabolites, where all of the other PAHs metabolites are fixed at their median value, further indicated whether there is possible interaction of the two PAHs metabolites (Chiu et al., 2018). And we performed a sensitivity analyses, we assessed the robustness of our results by adjusting for work years instead of age. The multivariable generalized linear regression models were performed using SAS 9.4 (SAS institute Inc., Cary, NC) and BKMR (bkmr package) were carried out in Rstudio software (R version

3

3.5.3) and with package “ggplot” for plotting the quantify and visualize results of BKMR model. 3. Results 3.1. Study population characteristics The unadjusted association between main characteristics of study participants and mtDNAcn were summarized in Table 1. Worker with night shift were associated with increased significantly mtDNAcn (P < 0.05). The results showed that 72.1% of the participants reported to nightshift, 69.5% reported never exercise, 42.5% reported to smoke and 29.4% reported to drink alcohol. And the participants were on median (25th - 75th) 38 (31e48) years old. There were no significantly differences in age, sex, education, drinking status, smoking status, exercise and working years (P > 0.05). Table S2 show the distribution of urinary PAHs metabolites and mtDNAcn. The levels of mtDNAcn among participants with tertile of urinary PAHs metabolites were shown in Fig. 1. Meanwhile, significantly differences in 2-OHNAP (P < 0.01), 2-OHPHE (P ¼ 0.02) and 9-OHPHE (P ¼ 0.02) were found in the study participants. Particularly, participants with the lower mtDNAcn were to be higher concentration of 2-OHNAP and 2-OHPHE. A majority of urinary PAHs metabolites were significantly correlated with each other (Fig. 2), indicating that there may be possible interactions among components in this study. 3.2. Multivariable generalized linear regression analyses Table 2 displays associations between urinary PAHs metabolites and mtDNAcn from multivariable generalized linear regression models. Linear regression analyses revealed a significant negative association of urinary 2-OHNAP and 2-OHPHE with mtDNAcn adjusting for age, sex, smoking status, drinking status, the degree of exercise, night shift and other PAHs metabolites. And each one-unit increase in log-transformed urinary 2-OHNAP and 2-OHPHE was significantly associated with a separated 0.12 and 0.14 decrease in log-transformed mtDNAcn (b 95% CI: 0.12 (
0.17,
0.06), P < 0.01; b 95% CI: 0.14 (
0.25,
0.02), P ¼ 0.02). 3.3. Bayesian Kernel Machine Regression analyses We display the visualization of the BKMR model. Firstly, we found a cumulative toxic effect of the mixture in the study. We can see that the mtDNAcn decreased with the increase of exposure. In particular, the overall effect was statistically significant when all metabolites were at or above their 35th percentile, as compared to when all metabolites were at their median values (Fig. 3A). We then sought to learn the single effect of metabolites by estimating univariate summaries of the change in the mtDNAcn associated with a change in single metabolites from its 25th percentile to 75th percentile, where all of the other metabolites are fixed at a particular threshold (25th, 50th, or 75th percentile). We found that urinary 2-OHNAP and 2-OHPHE displayed a significant negative effect. A change in urinary 2-OHNAP concentration from the 25th to the 75th percentile is associated with a significant decrease in mtDNAcn of 0.079 (
0.022,
0.123), 0.082 (
0.021,
0.124), and 0.083 (
0.023,
0.129) when other metabolites are fixed at the 25th, 50th, and 75th percentiles, respectively. In the same way, urinary 2-OHPHE concentration is associated with a significant decrease in mtDNAcn of 0.047 (
0.022,
0.090), 0.048 (
0.021,
0.090), and 0.048 (
0.022,
0.091), when other metabolites are fixed at the 25th, 50th, and 75th percentiles, respectively (Fig. 3B). To investigate potential nonlinearity of the

4

X. Zhao et al. / Environmental Pollution 260 (2020) 114026 Table 1 Basic characteristics of participants (n ¼ 696). a

b

b

Characteristic

All participants

Age, Med (25th - 75th) Age, years < 32 32 - 46  46 Sex male female Working years (25th - 75th) Drinking status non-drinking now ever Smoking status non-smoking vnow ever Education, years 9 9 - 12 > 12 Exercise never mild severe Night Shift no yes

38 (31e48)


0.001 (
0.004, 0.001)

0.24

220 (23.2) 224 (30.6) 252 (46.2)

Reference
0.03 (
0.08, 0.03)
0.01 (
0.07, 0.04)

e 0.36 0.65

469 (68.9) 227 (31.1) 15.5 (9e28)

Reference 0.002 (
0.05, 0.05) 0.001 (
0.001, 0.003)

e 0.92 0.35

492 (68.3) 191 (29.4) 13 (2.3)

Reference 0.01 (
0.04, 0.06) 0.01 (
0.16, 0.18)

e 0.67 0.94

380 (53.1) 288 (42.5) 28 (4.4)

Reference 0.03 (
0.02, 0.08)
0.04 (
0.16, 0.08)

e 0.22 0.53

159 (27.9) 167 (25.8) 370 (46.3)

Reference
0.05 (
0.12, 0.02)
0.05 (
0.11, 0.01)

e 0.16 0.10

502 (69.5) 99 (17.0) 95 (13.5)

Reference
0.05 (
0.11, 0.02)
0.02 (
0.09, 0.05)

e 0.16 0.52

189 (27.9) 507 (72.1)

Reference 0.06 (0.01, 0.11)

e 0.02

a b

Estimate (95% CI)

P

Data were presented as n (%) or Med (25th - 75th). The unadjusted associations for the characteristics and mitochondrial DNA copy number were determined using generalized linear regression model.

Fig. 1. The violin plot of mtDNAcn. The violin plot combining a boxplot present the distributions of mitochondrial DNA copy number (mtDNAcn). T ¼ tertile. P-values were calculated from Kruskal-Wallis H test for numerical variables. The red line represents mean, the black line represents median, the light blue indicates the range from 25th percentile to 75th percentile, and the corn flower blue indicates the range from 5th percentile to 95th percentile. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

X. Zhao et al. / Environmental Pollution 260 (2020) 114026

Fig. 2. Correlation map of the urinary PAHs metabolites. Heat Map shows the Spearman correlation coefficients (r) between each PAHs metabolite.

Table 2 Associations between urinary PAHs metabolites and mitochondrial DNA copy number among the study population. PAHs metabolites (ng/mL)

2-OHNAP 1-OHNAP 3-OHFLU 2-OHFLU 2-OHPHE 9-OHPHE 1-OHPHE 1-OHPYR a b

Mitochondrial DNA copy number

b (95% CI)a

b (95% CI)b


0.09 (
0.13,-0.05)
0.03 (
0.07,0.01) 0.00 (
0.05,0.06) 0.01 (
0.05,0.06)
0.10 (
0.16,-0.03) 0.03 (
0.04,0.10)
0.02 (
0.08,0.03) 0.03 (
0.03,0.09)


0.12 (
0.17,-0.06) 0.05 (
0.01,0.11)
0.02 (
0.10,0.05) 0.05 (
0.03,0.13)
0.14 (
0.25,-0.02) 0.08 (
0.01,0.17)
0.04 (
0.13,0.04) 0.05 (
0.04,0.14)

P

<0.01 0.12 0.58 0.20 0.02 0.07 0.32 0.25

Adjusted for age, sex, exercise, drinking status, smoking status and night shift. Additionally adjusted for all the other urinary PAHs metabolites.

exposure-response function, we then estimated the univariate relationship between each PAHs metabolite and mtDNAcn, where all of the rest of PAHs metabolites are fixed to 50th percentile. The plot shows a suggestion of linear effects of urinary 2-OHNAP and 2OHPHE, the levels urinary 2-OHNAP and 2-OHPHE increases and the mtDNAcn decrease significantly (Fig. 3C). To explore potential the relationship between urinary PAHs metabolites further, we plotted bivariate cross-sections of exposure-response function. Fig. 3D shows differences in mtDNAcn as a function of urinary 2OHPHE or 2-OHNAP, by moving urinary 2-OHNAP or 2-OHPHE concentrations from 25th to 50th and to 75th percentile (while fixing all other PAHs metabolites to their median). No evidence of interaction between urinary 2-OHPHE and 2-OHNAP was shown by parallel exposure - response relationships. We were not got evidence of interaction between other metabolites as well (Fig. S1). Finally, sensitivity analysis suggested that the associations of between urinary PAHs metabolites and mtDNAcn in the models adjust for work years were generally consistent with those of main analyses according to adjusting for age (Table S3 and Fig. S2). 4. Discussion In this cross-sectional study, we analyzed the relationship of PAHs and mtDNAcn by traditional multivariable generalized linear

5

regression model and a novel nonparametric BKMR model among coke oven workers. Traditional linear regression analyses showed that 2-OHNAP and 2-OHPHE were correlated negatively with mtDNAcn. And BKMR model indicated that the significant negative cumulative effect of urinary PAHs metabolites when concentrations are above the 35th percentile, the significant negative singleexposure effects of urinary 2-OHNAP and 2-OHPHE in mtDNAcn, and dose-response relationship of urinary 2-OHNAP and 2-OHPHE with mtDNAcn were evaluated. Since mtDNAcn plays an essential role in maintaining genomic integrity, cellular aging, and some chronic disease (Copeland and Longley, 2014; Pyle et al., 2016; Tin et al., 2016), these results provide potential explanations for environmental exposure and mtDNA-mediated disease. Previous studies suggested that increasing mtDNAcn is the early molecular events of human cells in response to endogenous or exogenous oxidative stress (Lee et al., 2000). A lot of evidences on environmental pollution and mtDNAcn have demonstrated that decreased peripheral blood mtDNAcn was associated with exposure to ambient particulate matter (PM) (Hou et al., 2013) and metals (Wu et al., 2019a), and increased peripheral blood mtDNAcn was associated with low dose benzene (Shen et al., 2008). These studies indicated that indicating mtDNAcn may be a vital target to explore the effects of environmental exposure. Studies have shown that low or even non-toxic dose of PAHs triggered a significant accumulation of ROS, caused oxidative DNA damage (Wilk et al., 2013). And higher mtDNAcn in association with increased level of oxidative stress among the healthy people (Liu et al., 2003). However, epidemiologic study have been showed that shorter time average PM 2.5 exposures were associated with increasing mtDNAcn and persistent average PM 2.5 exposures were associated with decreasing mtDNAcn (Pieters et al., 2016). It is suggested that the increasing of mtDNAcn may be an adaptations mechanism at the beginning of exposure to harmful pollutants. However, chronic exposure and more severe oxidative is suggested to alter the replication of mtDNA and cause a decrease in mtDNAcn (Golden and Melov, 2001; Lee and Wei, 2005; Byun and Baccarelli, 2014). Due to PAHs can be transported away from their original source through complex physical migration, chemical and biological conversion reactions and accumulate in various environmental matrices. For general population, the exposure sources to PAHs come from cooking fumes, air of vehicle exhausts and even soils (Jiang et al., 2015; Liu et al., 2015; Beriro et al., 2016). But PAHs levels are particularly high in coke oven emissions by incomplete combustion of coal, and with the features of high volatility. Coke oven workers are able to working with high-intensity, leading to their respiratory rate accelerated and more PAHs in the air are absorbed. As a result, that means coke oven workers were exposed actually to higher levels of PAHs than the general population. In the present study, we found that increased levels of urinary 2-OHNAP and 2-OHPHE were associated with decreased mtDNAcn in workers exposed to PAHs levels. However, we have not found that association between 1-OHPYR and mtDNAcn. The association between urinary 2-OHPHE and mtDNAcn was consistent with previous studies by Ling et al. (2017). But a previous cross-sectional study by Xu et al. also showed that a negative association between change in 1-OHPYR and change in mtDNAcn (Xu et al., 2018). 2-OHNAP and 2-OHPHE were defined as low molecular weight compounds because of consisting of fewer than four rings, and 1OHPYR was considered to be a high molecular weight compound. In fact, in ambient and indoor air, the concentrations of low molecular weight PAHs, which are predominantly in the gas phase, are significantly higher than high molecular weight PAHs, which are primarily in the particulate phase (Liu et al., 2001; Li et al., 2014). Table S2 also showed that concentration of urinary 2-OHNAP and 2OHPHE were higher than 1-OHPYR in present study. Although

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X. Zhao et al. / Environmental Pollution 260 (2020) 114026

Fig. 3. Associations between urinary PAHs metabolites and mtDNAcn among the study population by BKMR model. The effect of the urinary PAHs metabolites on mitochondrial DNA copy number (mtDNAcn) by Bayesian Kernel Machine Regression (BKMR) model. Model adjust for age (years), sex, smoking status (ever, now and non-smoking), drinking status (ever, now and non-drinking), the degree of exercise (never, mild and severe) and night shift (yes or no) and further adjustment for other metabolites (9-OHBAP, 3-OHCHR and 6-OHCHR as binary, the rest of metabolites as continuous level). (A) The cumulative effect of the urinary PAHs metabolites (estimates and 95% credible intervals). PAHs metabolites are at a particular percentile (X-axis) compared to when exposures are all at 50th percentile. (B) The single-exposure effect (estimates and 95% credible intervals). (C) Univariate exposureeresponse functions and 95% confidence bands for each urinary PAHs metabolite with the other metabolites fixed at the median. (D) Bivariate exposureeresponse functions for: 2-OHNAP when 2-OHPHE fixed at either the 25th, 50th, or 75th percentile and the test of metabolites is fixed at the median (lower left panel); 2OHPHE when 2-OHNAP fixed at either the 25th, 50th, or 75th percentile and the test of metabolites is fixed at the median (top right panel).

these low molecular weight PAHs are considered to be acutely toxic and non-carcinogenic, naphthalene was classified as a possible carcinogen by the International Agency for Research on Cancer (IARC) in 2002 (WHO, 2002). Therefore, persistent and long-term PAHs exposure may be important risk factors of decreasing mtDNAcn among coke oven workers. And the lower mtDNAcn significant in association with PAHs exposure among coke oven workers were also consistent with the previously reported studies (Pieters et al., 2013; Ling et al., 2017). Humans are mainly exposed to PAHs through inhalation, dermal contact and ingestion (Ma and Harrad, 2015), then there are metabolized by a sequence of enzymes into hydroxy PAHs in the body, and excreted in the urine eventually (Bieniek, 1998; Lin et al.,

2016). Due to the significant correlations between urinary levels of corresponding metabolites and exposure to ambient PAHs, urinary PAHs metabolites often used as biomarkers for assessing environmental PAHs exposure (Scherer et al., 2000). Previous many studies, 1-OHPYR has been widely used as urinary biomarkers for PAHs exposure (Hu et al., 2012; Keir et al., 2017), but 1-OHPYR alone cannot truly reflect the actual cumulative exposure to PAHs in the environment. Therefore, we need more PAHs exposure biomarkers can improve the accuracy of exposure evaluation. In this study, we detected coke oven workers baseline urinary concentrations of eight hydroxyl PAHs metabolites successfully. But the correlations between the urinary PAHs metabolites are considered to be high (Fig. 1), so in traditional linear regression model, we have probably

X. Zhao et al. / Environmental Pollution 260 (2020) 114026

face to the challenge of collinearity. Finally, in order to address the question, we assessed the effects of a mixture simultaneously in BKMR (Bobb et al., 2015) model. Although total PAHs exposure levels are thought sums of all detected metabolites in some previous studies (Fu et al., 2018), this approach may be not reliable. Because it is not known whether there are interactions between PAHs components. Negative cumulative effect of urinary PAHs metabolites among coke oven workers was found in this study by a novel BKMR model. However, the strength of BKMR is that it not only addresses the cumulative mixture effect, but can also deal with the effect of each component and dose-response relationships when other metabolites fixed a particular percentile (Valeri et al., 2017). The study found that the significant negative single effect of urinary 2-OHNAP and 2-OHPHE from its 25th percentile to 75th percentile, where all of the other components are fixed at a particular threshold (25th, 50th and 75th percentile). It is suggested that the association between lower mtDNAcn and urinary 2-OHNAP and 2-OHPHE is thought stable and not affected by other metabolites. However interestingly, the interaction of urinary PAHs metabolites was not found (Fig. S1) by BKMR model in this study. There also may be the reason why the results of multivariable generalized linear regression analysis are consistent with BKMR model. In addition, although a stronger correlation between urinary PAHs hydroxyl metabolites was observed, the collinearity problem is not fully explained. It is generally used to determine the collinearity between two variables and there may be errors in multiple variables (Charlotte and William, 1991). Therefore, in order to ensure the reliability of the results, it is particularly important to select the conservative BKMR model. In this study, although some results suggest a positive association between some urinary PAHs metabolites and mtDNAcn, there were not statistically significant. We believe this positive effect may be due to some unmeasured confounders. For example, the participants worked in a coke oven plant, where workers are had different of dietary habits and also may be exposed to PM and other harmful heavy metals. Studies have shown that increased mtDNAcn with fruit consumption and intakes of dietary flavanones were associated (Wu et al., 2019b). And higher mtDNAcn has been observed by some cross-sectional studies association with lower levels of PM (Iodice et al., 2018) and some harmful metals (Ameer et al., 2016; Liu et al., 2019). This may partly explain the large proportion of the positive association among our study participants. The advantages of present study is that we used a new and flexible statistical method (BKMR), and we can quantify and visualize the cumulative effect of the urinary PAHs metabolites and dose-response relationship with continuous variables, thus reducing measurement error bias. The key challenge in assessing the health effects of environmental chemical mixtures is the high degree of correlation between compounds. The use of BKMR model means that we can overcome the limitations of traditional analysis, such as single effect of PAHs and increased false discovery when fitting many regressions models. In the next place, two important methods are used for variable selection: hierarchical variable selection and component variable selection. Hierarchical variable selection will allow us to evaluate the effect of highly correlated environmental chemical mixtures. In other word, highly correlated PAHs metabolites cannot be recognized in general regression models (Chiu et al., 2018; Hou et al., 2019). Bobb et al. therefore propose BKMR with hierarchical variable selection of relative higher sensitivity, and BKMR produces estimates of the exposureresponse function and 95% credible intervals, which incorporate the uncertainty due to estimation of high dimensional exposures and multiple-testing penalty (Scott and Berger, 2010; Bobb et al.,

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2015; Valeri et al., 2017). It can be seen that this method has certain robustness. However, although the present study has sufficient statistical power, several limitations of the present study also warrant consideration. Firstly, despite we also accounted for some possible confounding factors, we cannot rule out that the effects of the PAHs on mtDNAcn are influenced by other confounding factors, such as harmful metals, dietary habits and PM. In the future, a larger sample of the population will be needed to study the effects of exposure, for instance, the effects of co-exposure of metals, living habits and PM on mtDNAcn. And the cross-sectional design limited this study ability to infer temporality between the PAHs exposure and the change of mitochondrial DNA copy number. Second, only one spot urine sample was collected in the study, it was relative shortcoming of the current study. Studies of multiple point urine sample collection should be applied to evaluate the individual long-term exposure to environmental PAHs in the future. However, the levels of PAHs exposures in our study population are thought to be relatively stable because of the operating post of workers was fixed. Meanwhile, a single time point urinary sample were also widespread used as determined PAHs metabolites in several studies (Zhou et al., 2018; Yang et al., 2018). Third, we only measured mtDNAcn in peripheral blood in the study, which may be different with mtDNAcn in different cells types and tissues However, the determinations of the cell specific mtDNAcn might be more helpful to evaluate mtDNAcn related disease. In addition, the association between mtDNAcn and PAHs metabolites was conducted in Chinese occupational workers, the results may not be generalized to populations with other ethnics and different PAHs exposure ranges. 5. Conclusions The result suggested that exposure to PAHs lead to mtDNAcn decreasing among coke oven workers by traditional multivariable generalized linear regression models and BKMR model. Our findings contribute new details for the association between PAHs exposure and mtDNAcn. Meanwhile, the results suggest that BKMR model may be advisable tool in larger-scale mixture studies in the future. Funding information This work was supported by National Natural Science Foundation of China [No. 81273041and 30901180] and Natural Science Foundation of Shanxi Province of China [No. 201701D121146]. Declaration of competing interest The authors declare that they have no actual or potential competing financial interest. CRediT authorship contribution statement Xinyu Zhao: Writing - original draft, Methodology, Formal analysis, Writing - review & editing. Aimin Yang: Writing - original draft, Methodology, Formal analysis, Writing - review & editing. Ye Fu: Investigation, Data curation, Resources, Writing - review & editing. Bin Zhang: Investigation, Data curation, Validation, Writing - review & editing. Xuejing Li: Investigation, Resources. Baolong Pan: Writing - review & editing, Software, Methodology. Qiang Li: Investigation, Writing - review & editing. Jun Dong: Investigation, Writing - review & editing. Jisheng Nie: Project administration, Writing - review & editing. Jin Yang: Funding acquisition, Conceptualization, Supervision, Project administration, Writing - review & editing.

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