The interaction effects of smoking and polycyclic aromatic hydrocarbons exposure on the prevalence of metabolic syndrome in coke oven workers

Journal Pre-proof The interaction effects of smoking and polycyclic aromatic hydrocarbons exposure on the prevalence of metabolic syndrome in coke oven workers Bin Zhang, Baolong Pan, Xinyu Zhao, Ye Fu, Xuejing Li, Aimin Yang, Qiang Li, Jun Dong, Jisheng Nie, Jin Yang PII:

S0045-6535(20)30072-2

DOI:

https://doi.org/10.1016/j.chemosphere.2020.125880

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CHEM 125880

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Received Date: 18 November 2019 Revised Date:

8 January 2020

Accepted Date: 8 January 2020

Please cite this article as: Zhang, B., Pan, B., Zhao, X., Fu, Y., Li, X., Yang, A., Li, Q., Dong, J., Nie, J., Yang, J., The interaction effects of smoking and polycyclic aromatic hydrocarbons exposure on the prevalence of metabolic syndrome in coke oven workers, Chemosphere (2020), doi: https:// doi.org/10.1016/j.chemosphere.2020.125880. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

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The Interaction Effects of Smoking and Polycyclic Aromatic

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Hydrocarbons Exposure on the Prevalence of Metabolic Syndrome in

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Coke Oven Workers

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Bin Zhang1, Baolong Pan1, 2, Xinyu Zhao1, Ye Fu1, Xuejing Li1, Aimin Yang3, Qiang

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Li4, Jun Dong4, Jisheng Nie1, Jin Yang1*

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1

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University.

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2

General Hospital of Taiyuan Iron & Steel (Group) Co., Ltd.

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3

Hong Kong Institutes of Diabetes and Obesity, the Chinese University of Hong

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Kong.

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4

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Ltd.

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Bin Zhang and Baolong Pan contributed equally to this paper.

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* Corresponding author. Department of Occupational Health, School of Public Health,

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Shanxi Medical University, Taiyuan, China. Xinjiannan Road 56, Taiyuan 030001,

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Shanxi, China.

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Tel/fax: +86 (351) 4135 240

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E-mail: [email protected]

Department of Occupational Health, School of Public Health, Shanxi Medical

Center of Occupational Disease Prevention, Xishan Coal Electricity (Group) Co.,

1

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Abstract Introduction: Metabolic syndrome (MetS) is a cluster of interrelated risk factors,

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which increase the risk of cardiovascular disease (CVD) and cancer. The prevalence

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of MetS might be affected by environmental pollution and individual's poor lifestyles.

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Methods: In this cross-sectional study, we aimed to evaluate the interactions effect of

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PAHs exposure and poor lifestyles on MetS among coke oven workers. We measured

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the concentrations of 11 urinary PAH metabolites among 682 coke oven workers by

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HPLC-MS. China adult blood lipid abnormality prevention guide (2016) was

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employed for diagnosing MetS. An interaction effect was tested in the modified

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Poisson regression models. Results: The results showed that the urinary level of

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1-NAP (P for trend = 0.043) and 2-FLU (P for trend = 0.037) had significant

31

dose-response relationships with increased PR of MetS. For 1-NAP, statistically

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significant positive association was with low HDL among individuals (P for trend =

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0.003). Besides, smoking was associated with a significantly increased risk of

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prevalence ratio of MetS (PR = 1.07; 95% CI 1.00-1.13), high triglycerides (PR=1.13;

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95% CI 1.05-1.20) and low HDL (PR=1.07; 95% CI 1.01-1.14). Smokers who with

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high levels of 1-NAP and 2-FLU had higher prevalence ratio of MetS compared with

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non-smokers who with low levels of 1-NAP [PR (95% CI): 1.17 (1.06-1.29); P for

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trend = 0.002] and 2-FLU [PR (95% CI): 1.17 (1.06-1.29); P for trend = 0.004].

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Conclusions: Our findings suggested PAHs exposure increased the prevalence ratio of

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MetS and this effect can be modified by smoking status.

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Keywords: Polycyclic aromatic hydrocarbons; Smoking; Metabolic Syndrome; 2

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Interaction effects

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Abbreviations

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MetS: Metabolic syndrome

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BMI: Body Mass Index

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CVD: cardiovascular disease

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PAHs: Polycyclic aromatic hydrocarbons

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2-NAP: 2-hydroxynaphthalene

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1-NAP: 1-hydroxynaphthalene

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3-FLU: 3-hydroxyfluorene

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2-FLU: 2-hydroxyfluorene

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2-PHE: 2-hydroxyphenanthrene

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9-PHE: 9-hydroxyphenanthrene

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1-PHE: 1-hydroxyphenanthrene

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1-PYR: 1-hydroxypyrene

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3-CHR: 3-hydroxychrysene

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6-CHR: 6-hydroxychrysene

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9-BAP: 9-hydroxybenzpyrene

3

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1. Introduction Metabolic syndrome (MetS) is a cluster of interrelated risk factors (high blood

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pressure, dyslipidaemia, high glucose, and abdominal obesity), which increase the risk

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of cardiovascular disease (CVD) and cancer (Alberti et al., 2009). While excessive

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macronutrient intakes and physical inactivity have been identified as major

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contributors, the high burden of MetS remains not fully explained (Grundy, 2016).

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Polycyclic aromatic hydrocarbons (PAHs) are the principal pollutant of coke

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oven emissions generated by incomplete combustion in coking production. PAHs

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have strong lipophilic properties making them quickly absorbed by the fatty tissues

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such as the kidney and liver within body and capable of being stored in fat cells and

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tissues containing fat, and easily accumulated through repeated and long-term

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exposures or bio-accumulate through the food chain (Simkhovich et al., 2008;

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Scinicariello and Buser, 2014). Recent studies also studying the adverse health effects

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of PAHs have focused on increasing risks of chronic diseases such as cardiovascular

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diseases (CVD) and a variety of cancers (Burstyn et al., 2005; Coogan et al., 2012).

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Previous studies also identified significant associations between PAH exposure and

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MetS and increased risks of cardiovascular diseases (Chen et al., 2008; Kuang et al.,

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2013; Liu et al., 2018). It is thus possible that PAHs exposure induce MetS by

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affecting the metabolic disorder of the body.

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Male workers account for large proportion in coke oven workers. Meanwhile,

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coke oven workers were more likely to have poor lifestyles, like smoking, drinking

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and fewer intakes of vegetables. And smoking was well-known risk factors for many 4

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metabolic diseases, including cancer, chronic inflammation and endothelial

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dysfunction (Lu et al., 2014; Lu et al., 2017). Cigarette smoking is also associated

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with abdominal obesity and dyslipidaemia (Slagter et al., 2013; Keith et al., 2016).

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Smoking and PAHs were both the common risk factors for coke oven workers.

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However, there are few studies on the interaction between PAHs and poor lifestyles

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on the prevalence of MetS yet. The present study aimed to evaluate the influence of

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the interaction between PAHs and poor lifestyles on risk of MetS, using a modified

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Poisson regression models from a population based cross sectional study in Chinese

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coke oven workers.

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2. Methods

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2.1 Study population

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The basic demographic data was collected from a coke oven plant in China using

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a cross-sectional survey in 2017. 859 workers participated in the study. We restricted

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our analyses to who had worked for more than 1 year. We excluded individuals who

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were missing with sufficient blood samples and sufficient urine samples (n = 76), or

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demographic characteristics (n = 83). We excluded individuals who were missing

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waist circumference measurements (n = 2), fasting blood-glucose (n = 6), blood

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pressure readings (n = 11), triglyceride measurements (n = 6), high-density

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lipoprotein (n = 5). Thus, our final analytic sample was 682 participants.

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Trained interviewers collected the information regarding sex, age, years of

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working, cooking fumes, smoking and drinking status, body mass index and eating

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habits (salt, vegetables and fruits) and occupational exposure history by a pre-tested 5

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questionnaire. Smokers were defined as those who smoked at least 1 cigarette every

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day and continuously more than six months, and drinkers were drank at least once a

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week on average and continuously more than six months. Venous blood (5 ml) and

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morning urine (20 ml) were provided by each participant. Every participant signed the

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informed consent and the study was approved by the Medical Ethics Committee of the

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Shanxi Medical University.

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2.2 Urine PAH metabolites

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According to the recommendation of the American Conference of Governmental

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Industrial Hygienists (ACGIH), the morning urine samples of end-of-work-week were

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obtained. All urine samples were freezed at -80°C until further processing. We used

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high performance liquid chromatography mass spectrometry (HPLC-MS) to detect

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urine biomarkers of PAHs exposure, including 2-hydroxynaphthalene (2-NAP),

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1-hydroxynaphthalene (1-NAP), 3-hydroxyfluorene (3-FLU), 2-hydroxyfluorene

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(2-FLU), 2-hydroxyphenanthrene (2-PHE), 9-hydroxyphenanthrene (9-PHE),

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1-hydroxyphenanthrene (1-PHE), 1-hydroxypyrene (1-PYR), 3-hydroxychrysene

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(3-CHR), 6-hydroxychrysene (6-CHR) and 9-hydroxybenzpyrene (9-BAP) levels.

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The linearity (expressing as R2), limit of detection (LOD), reproducibility (expressing

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as coefficient of variation (CV)) and mean recovery rate were 0.9989-1.000, 0.001 -

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0.014 ng/ml, 0.55%-3.48% and 71.44 %- 121.20 %, respectively (Table S1). The

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concentrations less than LOD were expressed with half a LOD value. Valid urine

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concentrations of PAH metabolites were adjusted using urine gravity.

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2.3 Metabolic syndrome and component conditions 6

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China adult blood lipid abnormality prevention guide (2016) was employed for

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diagnosing MetS. MetS was defined according to the harmonized definition as the

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presence of at least 3 of following component conditions: 1) abdominal obesity (waist

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circumference of ≥ 85cm for women or ≥ 90cm for men); 2) high fasting blood

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glucose levels ( ≥ 6.10mmol/L or current use of medication to treat hyperglycaemia);

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3) high blood pressure (systolic blood pressure ≥ 130 mmHg, diastolic blood pressure

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≥ 85 mmHg, or current use of medication to treat high blood pressure); 4) high

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triglyceride levels ( ≥ 1.7mmol/L, or current use of medication to treat elevated

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triglycerides); or 5) low HDL cholesterol levels ( < 1.0mmol/L or current use of

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medication to treat reduced HDL) .

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Examinations and laboratory measures were conducted by trained nurse and

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physicians in Center of Occupational Disease Prevention of Xishan Coal Electricity

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(Group) Co., Ltd. Waist circumference measurements (cm) were taken at the level of

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the umbilicus in mid-respiration while the participant was standing. After sitting for

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10 min, systolic and diastolic blood pressures (mmHg) were measured twice within a

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5-minute interval, and the two measurements were averaged. Venous blood samples

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were obtained from each participant early in the morning, with at least 8 h of fasting.

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Laboratory measures included glucose, total serum cholesterol, low-density

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lipoprotein, high-density lipoprotein and triglycerides.

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2.4 Statistical analysis

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All statistical analyses were performed using SAS software version 9.4 (SAS Institute Inc., Cary, NC). We used the median (Med) and quartiles, frequency and 7

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proportion to describe the basic characteristics of participants by MetS among 682

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occupational workers. Crude data were compared by applying Chi-square test for

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categorical variables, and the Kruskal-Wallis H test for numerical variable. Spearman

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was used to assess the correlation of PAH metabolites.

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We tested for linear trends across tertiles of PAH metabolites by including the

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median of each tertile as a continuous variable in modified Poisson regression

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models. Multivariable regression analyses included adjustment for sex, age (< 32,

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32-46 and ≥ 46 years) and smoking status (yes or no), drinking status (yes or no)

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(Model 1), and further adjustment for cooking fumes (few or much), salt (light,

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medium and heavy), vegetables (< 2.5 and ≥ 2.5 kg/week), fruits (< 750 and ≥ 750

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g/week) and other PAH metabolites. BMI (kg/m2) was entered as an additional

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covariate, except for in models of abdominal obesity so as to avoid over adjustment

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(Model 2). The PAHs metabolites were detectable in the majority of participants in

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addition to 3-CHR, 6-CHR and 9-BAP. The percent of below LOD of 3-CHR and

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6-CHR were more than 50% (58.4% and 74.0%). So, 3-CHR and 6-CHR were not

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put in the regression analyses. And, 9-BAP was adjusted as a covariant in categorical

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variable (< LOD, 0.010- 0.024, ≥0.024), because the detectable rate of 9-BAP close

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to 50% (49.3%). In sensitivity analysis, we further performed additional 3-CHR and

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6-CHR in the analysis as covariates to explore the associations between PAH

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metabolites and the prevalence ratios of MetS. The dose-response relationship

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between PAH metabolites with prevalence ratio of MetS were plotted using natural

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cubic spline with three degree of freedom for PAH metabolites term in the fully 8

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adjusted modified Poisson regression models. Linearity was tested by comparing the

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model fit of the linear and the spline model using a log likelihood ratio Chi-square

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test. Modified Poisson regression models were performed to estimate the prevalence

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ratios of MetS for smoking status. To determine the interaction between smoking and

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PAH metabolites levels, an interaction term was tested in the modified Poisson

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regression models. Differences considered statistically significant for P values were

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<0.05.

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3. Results

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3.1 Main characteristics and PAHs exposure of study subjects

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Characteristics of the study population, stratified by MetS diagnosis, are depicted

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in Table 1. Among the 682 people included in this study, 14.67% (100/682) had MetS,

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and the mean age was 45.5 years old. Of these subjects, 61% and 38% were smokers

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and drinkers, respectively. As expected, the median of age in the MetS group were

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significantly older than the non-MetS group (P < 0.001), the group with MetS had

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higher BMI (P < 0.001). There was more male (89/100) with MetS, while people who

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have MetS were more likely to be exposed to cooking fumes (P < 0.05). And the

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levels of 2-FLU were higher among the MetS group (P < 0.001).

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Urine PAH metabolites were correlated with each other. The correlation ranged

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from 0.74 to -0.06. The correlation between 9-PHE and 1-PHE was stronger (r = 0.74),

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and there was a very slight negative correlation between 1-NAP and 9-BAP (r = -0.06)

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(Figure S1).

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3.2 Associations between PAHs exposure and MetS risk 9

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We used the modified Poisson regression analyses to assess the association

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between PAH metabolites concentration and the prevalence ratio of MetS (Table 2). In

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model 2, after adjustment for demographic, lifestyles covariates (i.e. sex, age,

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smoking, drinking, cooking fumes, eating habits and BMI) and other PAH metabolites,

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the PRs (95% CI) of MetS for increasing tertiles of 2-FLU were 1.00 (reference), 1.07

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(1.01-1.12) and 1.10 (1.02-1.18), respectively (P for trend = 0.037). 1-NAP was

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positively related to the PR of MetS in a dose-dependent manner (3rd vs. 1st tertile:

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PR=1.08, 95% CI: 1.00-1.16; P for trend = 0.043). No association was observed

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between other PAHs metabolites and the prevalence of MetS. We also tested for linear

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trends across tertiles of PAH metabolites by including the median of each tertile as a

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continuous variable in logistic regression models (Table S2). The result was consistent

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with modified Poisson regression models.

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The associations between PAH metabolites and MetS components among the

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study population are provided in Figure 1. For 1-NAP, the only statistically significant

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positive association was with low HDL among individuals with the highest tertile

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(PR=1.11, 95% CI: 1.04-1.19; P for trend = 0.003). Urinary 2-PHE in the 3rd tertile

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were associated with an 8% (95% CI: 1.00-1.18; P for trend = 0.030) greater

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prevalence ratio of abdominal obesity. For 1-PYR, the only statistically significant

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positive association was with high glucose among individuals with the highest tertile

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(PR=1.08, 95% CI: 1.01-1.16; P for trend = 0.008). 3-FLU concentrations were

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negatively related to high triglycerides and abdominal obesity (P for trend < 0.05).

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Significant linear associations of 9-PHE (3rd vs. 1st tertile: PR=0.92, 95% CI: 10

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0.87-0.98; P for trend = 0.002) were observed for low HDL. And, significant linear

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associations of 9-PHE (3rd vs. 1st tertile: PR=0.91, 95% CI: 0.85-0.98; P for trend =

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0.021) was observed for high glucose.

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Comparison of the linear and spline models suggested the exposure-response

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relationship was essentially linear for the prevalence ratio of MetS (2-FLU: P = 0.877,

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1-NAP: P = 0.947) and the prevalence ratio of low HDL (P = 0.988) (Figure S2).

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Sensitivity analyses controlling for 3-CHR and 6-CHR did not appreciably alter

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the results. 1-NAP (P for trend =0.044) and 2-FLU (P for trend =0.038) were still

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positively related to MetS in a dose-dependent manner. 1-NAP concentrations were

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associated with lower HDL (P for trend =0.001). The associations between PAH

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metabolites and MetS components with additional adjustment for 3-CHR and 6-CHR

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are provided in Supplemental Table 3.

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3.3 Associations between lifestyles and MetS risk

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Adjusted prevalence ratios for MetS component conditions by lifestyles were

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assessed using the modified Poisson regression model in Figure 2. After adjusting for

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multiple confounding factors, smoking was associated with a significantly increased

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risk of prevalence ratio of MetS (PR=1.07; 95% CI 1.00-1.13), high triglycerides

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(PR=1.13; 95% CI 1.05-1.20) and low HDL (PR=1.07; 95% CI 1.01-1.14),

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respectively. Drinking (PR=1.22; 95% CI 1.02-1.46) and fruits intake (PR=1.20; 95%

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CI 1.02-1.41) were associated with high blood pressure. However, no significant

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association was observed between other lifestyles (salt and vegetables intake) and the

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prevalence of MetS or components. 11

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3.4 Effects of PAH metabolites and smoking on MetS components We tested the contribution rates of smoking on urine PAH metabolites

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(Supplemental Table 4), and found smoking accounted for 0.5% of the urinary 1-NAP

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variance, 0.1% of the urinary 2-FLU variance. There were the low contribution rates

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of smoking on 1-NAP and 2-FLU, so we can choose them as biomarkers of PAH

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exposure to explore the co-exposure effect of smoking and occupational PAH on the

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risk of MetS and components.

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The PRs for association of smoking and urine 1-NAP, 2-FLU co-exposure with

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high prevalence of MetS were presented in Figure 3(A and B). After adjusting

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covariates, we observed smokers who with high 1-NAP and 2-FLU levels had

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significantly higher prevalence of MetS compared with non-smokers who with low

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1-NAP [PR (95% CI): 1.17 (1.06-1.29); P for trend = 0.002] and 2-FLU levels [PR

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(95% CI): 1.17 (1.06-1.29); P for trend = 0.004]. Smokers, no matter exposed to low

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or high levels of urine 1-NAP and 2-FLU, had an increasing risk of high prevalence

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ratio of MetS compared with non-smokers.

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The PRs for association of smoking and urine 1-NAP co-exposure with low HDL

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were presented in Figure 3C. After adjusting for multiple confounding factors,

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smokers who with high 1-NAP levels had significantly low HDL compared with

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non-smokers who with low 1-NAP [PR (95% CI): 1.22 (1.10-1.34); P for trend =

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0.0001].

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4. Discussion

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In this study, we used a cross-sectional study of Chinese coke oven workers to 12

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investigate the effects of PAHs exposure and smoking on the prevalence of MetS. The

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present study showed that status of smoking can modify the association between

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PAHs exposure and the prevalence of MetS. Smokers, no matter exposed to low or

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high levels of urine 1-NAP and 2-FLU, had an increasing risk of high prevalence ratio

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of MetS compared with non-smokers. We observed that smokers who with high

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1-NAP or 2-FLU levels had significantly higher prevalence ratio of MetS compared

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with non-smokers who with low 1-NAP or 2-FLU. And, smokers who with high

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1-NAP levels had significantly low HDL compared with non-smokers who with low

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1-NAP. These results provide evidence and potential explanations for the roles of

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environmental pollution and poor lifestyles interactions on increased risks of MetS.

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The etiology of MetS is very complex, and there may be obesity and adipose

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tissue disease, insulin resistance, environmental and genetic factors, etc. Other factors

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(such as age, pre-inflammatory state and hormone changes) also play a role. Recently,

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some scholars believe that industrial toxicants may be one of the pathogenic factors of

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MetS. The effects of PAHs exposure on lipids metabolism of mice have been

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observed in the previously experimental studies. Lipids profiles were significantly

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changed in mice serum after benzopyrene exposure (Li et al., 2019) and these results

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indicated that the lipid metabolism response to PAHs exposure may contribute to the

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MetS progression. In addition, other NHANES analyses have shown that increased

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concentrations of 2-FLU were significantly associated with a higher prevalence of the

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MetS (Hu et al., 2015) and fluorene metabolite also showed a marginally significant

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linear trend with self-report CVD (Xu et al., 2010; Xu et al., 2013). It was consistent 13

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with the results of our study. And the sensitivity analyses did not appreciably alter the

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results, it indicates that the statistical analysis in this study has a reasonable control

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over confounding factors and the analysis results are highly reliable. Meanwhile, the

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associations of urinary PAHs with obesity and the expression of a number of

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obesity-related cardiometabolic health risk factors have been reported in the literature

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(Ranjbar et al., 2015), the positive dose-dependent association between obesity and

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2-PHE was completely consistent with our results.

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Exposure to cigarette smoke has negative effects on lipid metabolism and

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oxidative stress status, even caused early glucose intolerance, the changes caused by

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cigarette smoke exposure can trigger the earlier onset of metabolic disorders

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associated with obesity, such as diabetes and metabolic syndrome (Damasceno et al.,

290

2013; Lietz et al., 2013). The evidence has been implicated the etiological role of

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NADPH oxidase (NOX) in smoking-induced CVD, the dysregulations of reactive

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oxygen species (ROS) generation and metabolism mainly contribute to the

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development of diverse CVDs (Kim et al., 2014). Experimental studies have shown

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that the obese rats exposed to tobacco cigarette smoke presented abnormal HDL-c

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levels and higher DNA damage, triglycerides, total cholesterol, free fatty acids,

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VLDL-c, and LDL-c levels compared to control and obese rats exposed to filtered air

297

(Damasceno et al., 2013). Our study found smoking status was associated with a

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significantly increased risk of prevalence ratio of MetS, high triglycerides and low

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HDL, which was consistent with the studies that cigarette smoking was associated

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with MetS risk factors (Titz et al., 2016; Cheng et al., 2019). In summary, the previous 14

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studies and ours have proved lipid metabolism and MetS were associated with PAHs

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and smoking status. Exposure to PAHs and tobacco could stimulate a chain of internal

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responses including xenobiotic metabolism, oxidative stress and DNA damages

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(Shimada and Fujii-Kuriyama, 2004; Kuang et al., 2013; Kim et al., 2014). A link

305

between PAHs concentrations and MetS has also been reported by a NHANES study

306

(Hu et al., 2015), and consistent results were observed in the subgroup analysis among

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non-smokers. These findings suggest that environmental exposure to PAHs

308

independent of cigarette smoking is associated with the increased prevalence of MetS.

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Consequently, the impacts of smoking and PAHs on MetS were important and both

310

increase the prevalence. Meanwhile, there were more male in coke oven workers

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whose lifestyles were correlated strongly with tobacco and alcohol. As expected, we

312

found there was an interaction effect of smoking and PAHs exposure on the

313

prevalence of MetS in this study and it’s consistent with the previously reported

314

studies.

315

The consistent inverse associations of 3-FLU with abdominal obesity and 9-PHE

316

with high glucose was unexpected. Meanwhile, 9-PHE was negative association with

317

lower level HDL. The underlying mechanisms for the association between PAHs

318

exposure and metabolic damage remain an open question. We have not found the

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relationship between 3-FLU, 9-PHE and MetS, and as their concentration increased,

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the change of related MetS components was marginally significant. The causes of

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MetS are complex. Lipid metabolism is not only related to environmental pollution

322

and living habits, but also has a great relationship with genetic factors, 15

323

insulin-resistant and inflammatory factors (Maintinguer Norde et al., 2018). Our

324

research should then take these factors into account and make a further exploration.

325

Moreover, we monitored the environmental levels of PAH exposure and found that

326

the sum of PAH in the air of the worker places was dramatically lower than another

327

study (Kuang et al., 2013) in coke oven workers (0.38 mg/m3 vs. 1.13 mg/m3 for the

328

non-coke-oven workers, 1.45 mg/m3 for the coke-oven workers vs. 11.08 mg/m3 for

329

workers in the side and bottom of the coke oven, and 90.30 mg/m3 for workers at the

330

top of the coke oven). The concentration of this study is also lower than some of the

331

other coke-oven plant (Fu et al., 2018) and chimney sweeps workers (Alhamdow et al.,

332

2019). The difference in urinary PAH metabolites may be caused by lifestyle

333

behaviors, air pollution, laboratory methods and regional differences. In addition,

334

Table S1 showed that good quality control data for measurement of the urinary PAH

335

metabolites in present study.

336

Several limitations and strengths should be acknowledged in the current study.

337

First, a cross sectional study can show associations between PAHs exposure, smoking

338

status and the prevalence of MetS, but cannot assess the time order. Future studies

339

should consider repeatedly measuring PAHs exposure to better elucidate longitudinal

340

associations with MetS. Second, other pollutants, such as metals, nitrogen dioxide,

341

and ozone, are also present in the coke oven emissions and can increase the risk of

342

cardiometabolic health. Likewise, measurement of tobacco metabolites in urine as

343

better long-term markers of tobacco exposures, a more accurate picture of tobacco

344

exposure may be obtained. Then, we could further analyse the dose-response effects 16

345

of tobacco metabolites and PAH metabolites on MetS. And, we found no significant

346

effect of eating habits on MetS in this study, perhaps some workers suffer from

347

chronic metabolic diseases had changed their inherent eating habits. In addition, the

348

association between MetS and PAH metabolites was conducted in China occupational

349

workers, the results may not be generalized to populations with other ethnics and

350

different PAH exposure ranges. Despite these drawbacks, the current analysis has

351

several strengths. The modified Poisson regression models were used to analyse the

352

association between PAHs, smoking status and MetS (Thompson et al., 1998; Zou,

353

2004; Zou and Donner, 2013). If there are many covariate strata, the results for

354

estimating the PR by logistic regression may be quite cumbersome. Not only will this

355

conversion method provide invalid confidence limits, but it will also produce

356

inconsistent estimates for the relative risk. However, proportional hazards regression

357

to directly estimate the PR, but with wider or less precise interval estimates. Poisson

358

regression is usually regarded as an appropriate approach for analyzing rare events.

359

When Poisson regression is applied to binomial data, the error for the estimated

360

relative risk will be overestimated. However, this problem may be rectified by using a

361

robust error variance procedure known as sandwich estimation, thus leading to a

362

technique that I refer to as modified Poisson regression. Compared with application of

363

binomial regression, the modified Poisson regression procedure has no difficulty with

364

converging, and it provides results very similar to those obtained by using the

365

Mantel-Haenszel procedure when the covariate of interest is categorical. We also

366

tested the results in Logistic regression models, and the results were consistent with 17

367

modified Poisson regression models. However, the modified Poisson regression

368

models were more suitable for studying MetS. As we know, the etiology of MetS is

369

very complex. Meanwhile, there were 68.7% male workers in our study, 61.1% of

370

them were smokers, and the proportion of male in patients with MetS accounts for

371

88.8%. However, there was little study to evaluate environmental pollution and poor

372

lifestyles interactions on MetS among coke oven workers. In summary, this work

373

provides insights on PAHs exposures and tobacco exposure with adverse lipid

374

metabolic health in Chinese coke oven workers. Future prospective studies are needed

375

to confirm whether these findings represent causal associations.

376

5. Conclusions

377

Elevated exposure to PAHs can increase the prevalence ratio of MetS and this

378

effect can be modified by smoking status. These results may add potential evidence

379

for environment-lifestyle interactions on MetS.

380

Funding information

381

This work was supported by National Nature Science Foundation of China [No.

382

81273041 and 30901180] and Natural Science Foundation of Shanxi Province of

383

China [No. 201701D121146].

384

Conflicts of interest

385

The authors declare that they have no actual or potential competing financial

386

interest.

387

Acknowledgements

388

We are grateful for General Hospital of Taiyuan Iron & Steel (Group) Co., 18

389

Ltd.and Center of Occupational Disease Prevention of Xishan Coal Electricity (Group)

390

Co., Ltd. for their helps in collecting biological sample and interviewing the study

391

population participants.

392

19

393

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25

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Figure Legends

510

Figure 1. The associations between PAH metabolites and MetS components

511

among the study population (n=682)

512

T = tertile

513

Adjusted prevalence ratios (95%) for metabolic syndrome and components conditions

514

by PAH metabolites

515

Adjusted for sex, age, smoking, drinking, cooking fumes, eating habits, BMI (except

516

for abdominal obesity), 2-NAP, 1-NAP, 3-FLU, 2-FLU, 2-PHE, 9-PHE, 1-PHE,

517

1-PYR and 9-BAP

518

Figure 2. The associations between lifestyles and MetS and components among

519

the study population (n=682)

520

Adjusted prevalence ratios (95%) for metabolic syndrome component conditions by

521

lifestyles (smoking, drinking and eating habits)

522

Adjusted for sex, age, smoking, drinking, cooking fumes, eating habits, BMI (except

523

for abdominal obesity), 2-NAP, 1-NAP, 3-FLU, 2-FLU, 2-PHE, 9-PHE, 1-PHE,

524

1-PYR and 9-BAP

525

Figure 3. Adjusted prevalence ratios of MetS and low HDL for different

526

combinations of smoking status and the levels of PAH metabolites (n=682)

527

T = tertile

528

Data were presented as prevalence ratio (PR)

529

Adjusted for sex, age, smoking, drinking, cooking fumes, eating habits, BMI, 2-NAP,

530

1-NAP, 3-FLU, 2-FLU, 2-PHE, 9-PHE, 1-PHE, 1-PYR and 9-BAP 26

531

(A) MetS & 1-NAP; (B) MetS & 2-FLU; (C) low HDL & 1-NAP

27

Table 1. Basic characteristics of participants by Metabolic Syndrome among 682 occupational workersa Variable

Metabolic Syndrome Total (n=682)

Yes (n=100)

No (n=582)

38 (31 - 48)

45.5 (33 - 50)

35 (30 - 47)

< 32

218 (23.5)

197 (25.3)

21 (14.3)

32 - 46

222 (31.0)

193 (31.9)

29 (26.4)

≥ 46

242 (45.4)

192 (42.8)

50 (59.2)

male

458 (68.7)

89 (88.8)

369 (64.9)

female

224 (31.3)

11 (11.2)

213 (35.1)

no

401 (57.7)

39 (40.3)

362 (61.0)

yes

281 (42.3)

61 (59.7)

220 (39.0)

no

494 (70.5)

62 (61.5)

432 (72.2)

yes

188 (29.5)

38 (38.5)

150 (27.8)

few

604 (87.7)

80 (78.9)

524 (89.3)

much

77 (12.2)

20 (21.1)

57 (10.5)

≤ 25

373 (54.2)

13 (12.4)

360 (62.1)

> 25

309 (45.8)

87 (87.6)

222 (37.9)

light

152 (22.5)

24 (24.8)

128 (22.0)

medium

316 (44.5)

40 (39.2)

276 (45.5)

heavy

214 (33.1)

36 (36.0)

178 (32.5)

< 2.5

223 (32.4)

31 (29.2)

192 (33)

≥ 2.5

459 (67.6)

69 (70.8)

390 (67)

Age (years)

P valueb

<0.001

Age (years)

0.003

Sex <0.001

Smoking <0.001

Drinking 0.012

Cooking fumes 0.003

BMI <0.001

Eating habits Salt

0.374

Vegetables (kg/week)

Fruits (g/week)

0.695

never

10 (1.7)

-

10 (2)

< 750

294 (43.6)

53 (51.8)

241 (42.1)

≥ 750

378 (54.6)

47 (48.2)

331 (55.9)

2-NAP

0.30 (0.13 - 0.69)

0.31 (0.15 - 1.04)

0.29 (0.13 - 0.68)

0.275

1-NAP

0.03 (0.01 - 0.06)

0.03 (0.01 - 0.07)

0.03 (0.01 - 0.06)

0.065

3-FLU

0.03 (0.01 - 0.06)

0.04 (0.02 - 0.06)

0.03 (0.01 - 0.05)

0.264

2-FLU

0.15 (0.07 - 0.29)

0.20 (0.12 - 0.34)

0.14 (0.07 - 0.28)

0.002

2-PHE

0.20 (0.12 - 0.32)

0.22 (0.12 - 0.42)

0.19 (0.12 - 0.32)

0.369

9-PHE

0.11 (0.07 - 0.18)

0.13 (0.07 - 0.18)

0.11 (0.07 - 0.18)

0.275

1-PHE

0.06 (0.03 - 0.11)

0.06 (0.03 - 0.12)

0.06 (0.03 - 0.11)

0.644

1-PYR

0.09 (0.06 - 0.16)

0.11 (0.06 - 0.22)

0.09 (0.06 - 0.16)

0.080

9-BAP

0.011 (0.003 - 0.029) 0.011 (0.004 - 0.029) 0.011 (0.003 - 0.029)

0.053

PAHs internal exposure biomarker (ng/ml)

0.727

Concentration unit of 2-NAP, 1-NAP 3-FLU, 2-FLU, 2-PHE, 9-PHE, 1-PHE, 1-PYE and 9-BAP is ng/ml. a : Data were presented as n (%) or Med (25th - 75th). b : P-values were calculated from Chi-square test for categorical variables and Kruskal-Wallis H test for numerical variables.

Table 2. The associations between PAH metabolites and Metabolic Syndrome among the study population (n=682). PAH metabolites (ng/ml) 2-NAP 1st tertile (< 0.17) 2nd tertile (0.17 - 0.50) 3rd tertile (≥ 0.50)

Metabolic Syndrome (n=100) PR* (95% CI) Unadjusted

Model1a

Model2b

1.00 ( Reference) 1.02 (0.96 - 1.08) 1.02 (0.96 - 1.08)

1.00 ( Reference) 1.00 (0.94 - 1.06) 0.96 (0.89 - 1.03)

1.00 ( Reference) 1.00 (0.94 - 1.05) 0.96 (0.90 - 1.03) 0.352

Ptrend 1-NAP 1st tertile (< 0.01) 2nd tertile (0.01 - 0.05) 3rd tertile (≥ 0.05)

1.00 ( Reference) 1.02 (0.97 - 1.08) 1.06 (1.00 - 1.12)

1.00 ( Reference) 1.02 (0.96 - 1.09) 1.08 (1.01 - 1.17)

Ptrend 3-FLU 1st tertile (< 0.02) 2nd tertile (0.02 - 0.04) 3rd tertile (≥ 0.04)

0.026 1.00 ( Reference) 1.03 (0.97 - 1.09) 1.02 (0.97 - 1.08)

1.00 ( Reference) 0.98 (0.92 - 1.04) 0.91 (0.85 - 0.99)

Ptrend 2-FLU 1st tertile (< 0.09) 2nd tertile (0.09 - 0.22) 3rd tertile (≥ 0.22)

1.00 ( Reference) 1.06 (1.01 - 1.12) 1.10 (1.04 - 1.16)

1.00 ( Reference) 1.06 (1.01 - 1.12) 1.12 (1.04 - 1.21)

1.00 ( Reference) 0.99 (0.94 - 1.05) 1.01 (0.96 - 1.07)

1.00 ( Reference) 0.97 (0.92 - 1.03) 0.97 (0.89 - 1.05)

Ptrend

1.00 ( Reference) 0.98 (0.92 - 1.04) 0.96 (0.89 - 1.04) 0.289

1.00 ( Reference) 0.98 (0.93 - 1.03) 1.03 (0.97 - 1.09)

1.00 ( Reference) 0.96 (0.90 - 1.02) 0.99 (0.91 - 1.06)

Ptrend 1-PHE 1st tertile (< 0.04) 2nd tertile (0.04 - 0.09) 3rd tertile (≥ 0.09)

1.00 ( Reference) 1.05 (1.00 - 1.11) 1.10 (1.02 - 1.18) 0.042

Ptrend 9-PHE 1st tertile (< 0.08) 2nd tertile (0.08 - 0.15) 3rd tertile (≥ 0.15)

1.00 ( Reference) 0.98 (0.93 - 1.04) 0.94 (0.87 - 1.01) 0.063

Ptrend 2-PHE 1st tertile (< 0.14) 2nd tertile (0.14 - 0.27) 3rd tertile (≥ 0.27)

1.00 ( Reference) 1.03 (0.97 - 1.09) 1.09 (1.01 - 1.17)

1.00 ( Reference) 0.96 (0.91 - 1.02) 0.99 (0.92 - 1.06) 0.859

1.00 ( Reference) 0.97 (0.92 - 1.03) 0.99 (0.94 - 1.05)

1.00 ( Reference) 0.98 (0.92 - 1.05) 0.99 (0.91 - 1.07)

1.00 ( Reference) 0.97 (0.91 - 1.03) 0.99 (0.92 - 1.07) 0.981

1-PYR 1st tertile (< 0.07) 2nd tertile (0.07 - 0.14) 3rd tertile (≥ 0.14)

1.00 ( Reference) 0.98 (0.93 - 1.03) 1.04 (0.98 - 1.10)

1.00 ( Reference) 0.97 (0.91 - 1.03) 0.99 (0.92 - 1.07)

1.00 ( Reference) 0.97 (0.92 - 1.03) 1.00 (0.93 - 1.07) 0.829

Ptrend

* A modified Poisson Regression was conducted. a : adjusted for sex, age, education. b : additional adjust for smoking, drinking, physical activities, eating habits, BMI, 2-NAP, 1-NAP, 3-FLU, 2-FLU, 2-PHE, 9-PHE, 1-PHE, 1-PYR, 9-BAP.

Highlights:


Relationship of PAH exposure and MetS was analysed by modified Poisson regression.




PAHs exposure can increase the prevalence ratio of MetS in coke oven workers.




Smoking modifies the prevalence ratio of MetS induced by PAHs exposure.

Jin Yang: Funding acquisition, Conceptualization, Supervision, Project administration Bin Zhang: Writing- Original draft, Methodology and Formal analysis. Baolong Pan: Writing- Original draft, Methodology and Formal analysis. Xinyu Zhao: Investigation, Data Curation, Resources Ye Fu: Investigation, Data Curation, Validation Xuejing Li: Investigation, Resources Aimin Yang: Writing - Review & Editing, Software and Methodology Qiang Li: Investigation Jun Dong: Investigation Jisheng Nie: Project administration All authors have read and approved the final text.

Declaration of Interest Statement The authors declare that they have no actual or potential competing financial interest.