Impact of occupational heat exposure on blood lipids among petrochemical workers: An analysis of 9-year longitudinal data

Impact of occupational heat exposure on blood lipids among petrochemical workers: An analysis of 9-year longitudinal data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of occupational heat exposure on blood lipids among petrochemical workers: An analysis of 9-year longitudinal data Yifeng Chen, Xiaoyun Li, Qingyu Li, Yan Yang, Zitong Zhang, Yilin Zhang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4446442/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Sep, 2023 Read the published version in ISEE Conference Abstracts → Version 1 posted You are reading this latest preprint version Abstract Objective: This study aims to assess the influence of occupational heat exposure on dyslipidemia among petrochemical workers and identify susceptible groups. Methods: A total of 30,847 workers’ occupational health examination data were collected from two petrochemical plants in Fujian Province from 2013 to 2021. The dataset included occupational exposure information and blood lipid test results, encompassing total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG) levels. A Generalized Estimating Equations model was used to analyze the impact of heat exposure alone or coupled with other occupational hazards on workers' blood lipids. Results: The one-factor exposure model shows that most of the individual occupational hazards were significantly negatively associated with dyslipidemia. In the two-factor exposure model, heat combined with gasoline exposure (Incidence Rate Ratio, IRR=1.267, 95% CI 1.117-1.437) and heat combined with hydrogen sulfide exposure (1.324, 1.166-1.505) significantly increased the risk of high TC. Stratified analysis showed that in the dual exposure model of high temperature combined with gasoline or hydrogen sulfide, women , individuals aged over 35, non-smoking , and non-alcohol drinking were more likely to have heat-related high TC. Conclusion: The impact of heat and other petrochemical factors on blood lipids may be affected by healthy worker effect. Heat exposure combined with gasoline or hydrogen sulfide can significantly increase the risk of dyslipidemia. Occupational health interventions should pay more attention to female workers aged over 35 years who do not smoke or drink alcohol. Occupational exposure Petrochemical industry Dyslipidemia High temperature Hydrogen sulfide Gasoline Figures Figure 1 1. Introduction The petrochemical industry converts raw materials from oil refining and gas processing into a wide range of products such as gasoline, diesel fuel, petroleum asphalt, alkynes, olefins, aromatics, synthetic fibers, rubber and resins ( 1 ). Currently, China stands as the world's second-largest petrochemical powerhouse, with production capacity and output of key products ranking prominently on the global stage. The sector comprises 26,271 enterprises, accounting for 11.6% of China’s overall operating revenue ( 2 ). The complex production process of petrochemical industry involves a wide variety of occupational hazards such as high temperature, noise, gasoline, hydrogen sulfide, carbon monoxide, sulfur dioxide, and benzene. Long-term low-dose exposure to these occupational hazards may have a detrimental impact on petrochemical workers’ health, safety, and wellbeing ( 3 – 5 ). Heat exposure is one of the most important occupational hazards for petrochemical workers, which may come from process-generated heat and/or outdoor high temperature ( 6 ). Heat sources in the workplace for petrochemical workers may include boilers, furnaces, high temperature reactors, compressors, and fractionation towers ( 7 ). Moreover, some workers may need to wear personal protective equipment (PPEs) such as overalls, glove, goggle, and helmet to protect themselves from toxic materials in the workplace, thereby increasing the risk of overheating and thermal strain as PPEs block heat dissipation ( 8 ). With the predicted increase in the frequency and intensity of extremely hot weather, heat exposure is posing a growing challenge for petrochemical workers’ health and safety particularly for those undertaking physically demanding outdoor tasks. Numerous studies have demonstrated that long-term heat exposure in the workplace can significantly increase the risk of cardiovascular diseases ( 9 – 12 ), while the potential mechanisms remain unclear. Dyslipidemia plays a pivotal role in the onset and progression of cardiovascular disease ( 13 ). In Bulgaria, male manufacturing workers with heat exposure showed a higher risk of dyslipidemia compared to those without heat exposure ( 14 ). A high prevalence of dyslipidemia (41.4%) was also reported in a large petrochemical plant in India ( 15 ). In addition to heat, other workplace hazards may also contribute to dyslipidemia. Vangelova et al. ( 16 ) found that the incidence of dyslipidemia increased significantly among middle-aged and older workers exposed to high temperatures combined with noise. Occupational exposure to xylene, organic solvents and silica has been reported as the risk factor for cardiovascular disease in petrochemical workers( 17 ). Chen et al. ( 18 ) found steel workers exposed to both heat and dust in China were at a higher risk of hyperuricemia compared to those solely exposed to heat. Hyperuricemia has been regarded as a significant and independent risk factor for hypertension and cardiovascular disease ( 19 ). Combined effects of heat exposure with environmental pollutants have also been observed in animal experiments. Song et al.( 20 ) found that cardiac damage of ApoE-/- mice was more significant when high temperature was combined with ozone exposure than heat exposure alone. Petrochemical workers are usually exposed to multiple occupational hazards in the workplace. Assessing the potential adverse health effects of a certain occupational hazard without taking possible interactions with other occupational exposures into account may not accurately reflect the true causes of disease ( 21 – 24 ). The purpose of this study was to explore the effects of occupational heat exposure combined with other occupational hazards on blood lipids among petrochemical workers. Findings of this study may provide implications for the etiological prevention of dyslipidemia, which is of great significance for early detection and control of cardiovascular diseases. 2. Materials and methods 2.1 Study population According to the requirements of the "Law of the People's Republic of China on the Prevention and Control of Occupational Diseases" and the "Technical Specifications for Occupational Health Surveillance" (GBZ188-2014), employers need to organize regular occupational health examinations for workers exposed to occupational disease hazards and establish occupational health files. The data for this research were sourced from two petrochemical companies in Fujian Province. During the period from 2013 to 2021, the workers of the two petrochemical enterprises underwent occupational physical examinations at Minnan Branch of the First Affiliated Hospital of Fujian Medical University, which is the only qualified local hospital carrying out occupational health examinations. The health examination items mainly include blood biochemical examination, blood routine, urine routine, upper abdominal color ultrasound and the collection of occupational information such as basic information, living and eating habits, and occupational exposure history. Occupational physicians conducted relevant inspections on petrochemical workers and assisted companies in establishing worker electronic health records. A total of 32,544 occupational health examination records were obtained, among which 1,697 records were excluded due to missing values of key information or multiple inspections in the same year. The data of workers who had multiple inspections in the same year were merged and only one inspection record was retained. Finally, 6,911 participants were included in the study, contributing a total of 30,847 records. Workers were required to fast from food and water after 20:00 on the night before the health examination, and fasting venous blood was collected from 8:00 to 10:00 on the day of the examination. Subsequently, the laboratory physician used a fully automatic biochemical analyzer (Cobas®8000 composed of Cobas C701 module and Cobas ISE module) to perform four blood lipid analysis of serum samples (Total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C)). This study has received approval from the Ethics Committee of Fujian Medical University (Fujian Ethics Examination No.111). All participants included in the study have signed informed consent. 2.2 Health outcomes The judgment of blood lipids is based on the diagnostic criteria for dyslipidemia in the Guidelines for the Prevention and Treatment of Dyslipidemia in Chinese Adults (Revised 2016): TC ≥ 6.2 mmol/L, TG ≥ 2.3mmol/L, LDL-C ≥ 4.1 mmol/L, HDL-C < 1.0 mmol/L. Dyslipidemia is determined if any one of these four indicators is abnormal( 25 ). Namely, if being diagnosed as any one or more of the following conditions (hypercholesterolemia, hypertriglyceridemia, hypo-high-density lipoproteinemia (LHDL-C), and hyper-low-density lipoproteinemia), it can be determined as dyslipidemia. Regular smoking was defined as smoking ≥ 1 cigarette per day for at least half a year in the last year, or ≥ 7 cigarettes per week. Occasional smoking is between regular and non-smoking. Alcohol consumption was defined as twice a week or more and an alcohol intake greater than 50 g for more than half a year. 2.3 Statistical analysis The measurement data is expressed by‾X ± S, and the enumeration data is expressed by N (%). Given the characteristics of longitudinal health examination data, we used generalized estimating equations (GEE) models with negative binominal distribution accounting for over-dispersion, a log link function and a first-order autocorrelation structure to quantify the effects of occupational hazard exposure on blood lipids on workers. Confounding factors were adjusted for, including age, sex, BMI, smoking, and alcohol consumption. The effects of high temperature alone and combined with other occupational hazards (gasoline, nitrogen oxides, sulfur dioxide, hydrogen sulfide, benzene, methanol, carbon monoxide) on the blood lipids were also analyzed. Stratified analyses were conducted on the basis of sex, age, BMI, smoking, and alcohol consumption. The statistical softwares StataSE.16 and SAS.9.4 were used to encode and clean the data and conduct statistical analysis. Results were considered statistically significant at a P value < 0.05. 3. Results 3.1 Comparison of the characteristics between high-temperature exposure and non-high-temperature exposure petrochemical workers Table 1 shows the distribution of sociodemographic characteristics and other occupational hazards in the workers with and without occupational heat exposure. “Overall” is the summary of all health examination records, “between” is the between-group statistic (between individuals), and “within” is within the group (the same individual in different periods) (26). The results showed that there were significant differences in gender, age, and smoking status among workers with and without heat exposure, while no significant differences were observed in BMI and alcohol consumption. Workers exposed to heat in the workplace had a significantly higher proportion of being exposed to gasoline, nitrogen oxides, sulfur dioxide, hydrogen sulfide, benzene, methanol, and carbon monoxide, compared to those without occupational heat exposure. Table 1. Characteristics of occupational heat exposure by demographic factors among petrochemical workers Variable Heat exposure Non-heat exposure P -value Overall n(%) Between n(%) Within % Overall n(%) Between n(%) Within % Gender Male 10090 (85.0) 4508 (83.6) 100.0 14130 (74.5) 4273 (78.5) 100.0 <0.001 Female 1782 (15.0) 882 (16.4) 100.0 4845 (25.5) 1172 (21.5) 100.0 Age group (years) ≦24 2351 (19.8) 1547 (28.7) 85.2 1354 (7.1) 914 (16.8) 78.2 <0.001 25-34 3865 (32.6) 2021 (37.5) 86.3 4127 (21.8) 1921 (35.3) 86.6 35-54 5267 (44.4) 2233 (41.4) 96.2 12256 (64.6) 2945 (54.1) 93.7 ≥55 387 (3.3) 205 (3.8) 83.9 1231 (6.5) 407 (7.5) 74.6 BMI (kg/m 2 ) <24 6451 (54.3) 3251 (60.3) 93.0 10316 (54.4) 3341 (61.4) 87.9 0.961 ≥24 5421 (45.7) 2588 (48.0) 91.4 8659 (45.6) 2919 (53.6) 85.9 Smoking No 6986 (67.0) 3097 (71.2) 96.4 10548 (66.0) 2927 (69.7) 96.0 0.008 Occasionally 1447 (13.9) 798 (18.3) 74.5 2132 (13.3) 753 (17.9) 73.7 Heavy 2002 (19.2) 883 (20.3) 87.6 3312 (20.7) 960 (22.9) 86.7 Drinking No 5220 (50.6) 2498 (58.2) 90.0 7885 (49.8) 2339 (56.5) 88.0 0.187 Yes 5091 (49.4) 2256 (52.6) 90.6 7951 (50.2) 2301 (55.6) 90.5 Gasoline Yes 7236 (61.0) 3666 (68.0) 92.2 5078 (26.8) 2426 (44.6) 77.7 <0.001 No 4636 (39.1) 2334 (43.3) 86.1 13897 (73.2) 4024 (73.9) 88.5 Oxynitrides Yes 1516 (12.8) 1128 (20.9) 72.6 620 (3.3) 526 (9.7) 63.1 <0.001 No 10356 (87.2) 4790 (88.9) 95.4 18355 (96.7) 5227 (96.0) 97.8 SO 2 Yes 1313 (11.1) 797 (14.8) 79.2 647 (3.4) 476 (8.7) 67.1 <0.001 No 10559 (88.9) 4886 (90.7) 97.4 18328 (96.6) 5220 (95.9) 98.2 H 2 S Yes 6713 (56.5) 3688 (68.4) 89.8 5248 (27.7) 2600 (47.8) 67.9 <0.001 No 5159 (43.5) 2426 (45.0) 85.7 13727 (72.3) 4326 (79.5) 85.1 Benzene Yes 3107 (26.2) 2093 (38.8) 81.4 3208 (16.9) 1579 (29.0) 71.6 <0.001 No 8765 (73.8) 3996 (74.1) 92.3 15767 (83.1) 4616 (84.8) 93.5 Methanol Yes 2336 (19.7) 1502 (27.9) 84.0 1459 (7.7) 664 (12.2) 70.3 <0.001 No 9536 (80.3) 4321 (80.2) 95.6 17516 (92.3) 5129 (94.2) 97.1 CO Yes 3967 (33.4) 2485 (46.1) 81.2 2225 (11.7) 1392 (25.6) 65.7 <0.001 No 7905 (66.6) 3756 (69.7) 89.8 16750 (88.3) 4824 (88.6) 93.9 Total 11872 (38.5) 5390 (78.0) 59.8 18975 (61.5) 5445 (78.8) 67.8 3.2 Comparison of dyslipidemia between heat exposure and non-heat exposure petrochemical workers Table 2 shows the distribution of the four types of dyslipidemia among petrochemical workers with/without heat exposure. Overall, the proportions of HTC, HTG, HLDL-C, and LHDL-C among heat exposure workers were 3.7%, 10.6%, 6.3%, and 6.3%, respectively. By contrast, the overall proportions of HTC (6.0%), HTG (19.3%), HLDL-C (9.4%), and LHDL-C (9.4%) in non-heat exposure workers were higher than their heat exposure workmates. Differences in TC, TG, LDL-C, and HDL-C between heat and non-heat exposure groups were statistically significant. As to the variation across individuals, the proportions of HTC (8.3% vs. 5.1%), HTG (19.9% vs. 12.4%), HLDL-C (12.8% vs. 9.2%), and LHDL-C (12.8% vs. 9.2) were also higher in the non-heat exposure workers than that in the heat exposure workers. Table 2. Blood lipid disorders by heat exposure among petrochemical workers Variable Heat exposure Non-heat exposure P -value Overall n (%) Between n (%) Within % Overall n (%) Between n (%) Within % High TC Yes 444 (3.7) 274 (5.1) 66.3 1135 (6.0) 453 (8.3) 45.8 <0.001 No 11428 (96.3) 5287 (98.1) 98.5 17840 (94.0) 5377 (98.8) 97.4 High TG Yes 1258 (10.6) 667 (12.4) 72.8 3662 (19.3) 1086 (19.9) 59.7 <0.001 No 10614 (89.4) 5068 (94.0) 96.8 15313 (80.7) 5139 (94.3) 93.3 High LDL-C Yes 752 (6.3) 494 (9.2) 62.9 1775 (9.4) 697 (12.8) 46.2 <0.001 No 11120 (93.7) 5228 (97.0) 97.2 17200 (90.7) 5350 (98.3) 95.8 Low HDL-C Yes 752 (6.3) 494 (9.2) 62.9 1775 (9.4) 697 (12.8) 46.2 <0.001 No 11120 (93.7) 5228 (97.0) 97.2 17200 (90.7) 5350 (98.3) 95.8 3.3 One-factor exposure model to analyze the association between occupational hazards and dyslipidemia Figure 1 shows the effects of heat, gasoline, nitrogen oxides, sulfur dioxide, hydrogen sulfide, benzene, methanol, and carbon monoxide on blood lipid levels in petrochemical workers. Results of univariate exposure models showed that gasoline (IRR=0.563, 95%CI 0.670-0.473), oxynitrides (IRR=0.595, 95%CI 0.439-0.807), sulfur dioxide (IRR=0.741, 95%CI 0.552-0.993), hydrogen sulfide (IRR=0.784, 95%CI 0.682-0.901), and methanol (IRR=0.774, 95%CI 0.611-0.980) were negatively associated with TC. Negative effects were also observed in TG, including heat exposure (IRR=0.787, 95%CI 0.835-0.742) and other seven occupational hazards. Similarly, LDL-C was negatively associated with heat exposure (IRR=0.863, 95%CI 0.795-0.937), gasoline (IRR=0.548, 95%CI 0.629-0.478), oxynitrides (IRR=0.281, 95%CI 0.186-0.423), sulfur dioxide (IRR=0.671, 95%CI 0.513-0.878), hydrogen sulfide (IRR=0.789, 95%CI 0.707-0.881), benzene (IRR=0.779, 95%CI 0.666-0.912) and methanol (IRR=0.746, 95%CI 0.604-0.922). By contrast, heat exposure (IRR=0.994, 95%CI 0.989-0.998), gasoline (IRR=0.994, 95%CI 0.989-0.998), oxynitrides (IRR=0.990, 95%CI 0.985-0.994), sulfur dioxide (IRR=0.993, 95%CI 0.987-0.999), methanol (IRR=0.993, 95%CI 0.986-0.999) and carbon monoxide (IRR=0.992, 95%CI 0.986-0.998) were negatively associated with HDL-C. 3.4 Two-factor exposure model to analyze the association between heat combined with other occupational hazards and dyslipidemia Table 3 shows that heat exposure coupled with gasoline could increase the risk of high cholesterol (IRR=1.267, 95%CI 1.117-1.437). Similarly, heat exposure coupled with hydrogen sulfide could increase the risk of high cholesterol (IRR=1.324, 95%CI 1.166-1.505). However, workplace heat exposure combined with nitrogen oxides, sulfur dioxide, benzene, methanol, carbon monoxide had no significant impact on the four types of dyslipidemia. Table 3. Effects of heat exposure on blood lipid levels among petrochemical workers, using multi-exposure models IRR (95%CI) TC TG LDL-C HDL-C Heat+gasoline Heat 1.267 (1.117-1.437) 0.847 (0.788-0.911) 1.077 (0.976-1.187) 0.997 (0.990-1.003) Gasoline 0.644 (0.529-0.785) 0.583 (0.527-0.645) 0.667 (0.578-0.769) 0.997 (0.989-1.006) Heat+Oxynitrides Heat 1.073 (0.962-1.198) 0.800 (0.753-0.851) 0.897 (0.824-0.977) 0.993 (0.989-0.998) Oxynitrides 0.807 (0.484-1.343) 0.798 (0.637-1.000) 0.414 (0.214-0.803) 0.983 (0.974-0.991) Heat+SO 2 Heat 1.077 (0.966-1.200) 0.812 (0.764-0.863) 0.918 (0.844-0.998) 0.993 (0.989-0.998) SO 2 1.002 (0.649-1.545) 1.041 (0.848-1.278) 1.307 (0.984-1.734) 0.988 (0.977-0.999) Heat+H 2 S Heat 1.324 (1.166-1.505) 0.865 (0.808-0.926) 1.076 (0.974-1.188) 0.994 (0.988-1.000) H 2 S 0.923 (0.792-1.076) 0.836 (0.775-0.901) 0.945 (0.842-1.060) 0.997 (0.989-1.004) Heat+Benzene Heat 1.059 (0.948-1.182) 0.782 (0.734-0.833) 0.863 (0.790-0.942) 0.993 (0.988-0.999) Benzene 1.004 (0.823-1.225) 0.861 (0.774-0.957) 0.777 (0.648-0.932) 0.994 (0.985-1.003) Heat+Methanol Heat 1.092 (0.979-1.217) 0.781 (0.733-0.831) 0.906 (0.831-0.986) 0.994 (0.989-0.998) Methanol 0.951 (0.709-1.277) 0.723 (0.609-0.857) 0.975 (0.772-1.232) 0.992 (0.981-1.003) Heat+CO Heat 1.119 (0.998-1.126) 0.798 (0.749-0.849) 0.920 (0.842-1.006) 0.992 (0.987-0.998) CO 1.126 (0.924-1.372) 0.888 (0.806-0.979) 1.068 (0.907-1.259) 0.989 (0.981-0.998) 3.5 Stratified analysis to identify vulnerable sub-groups To further analyze the effect of workplace heat exposure on high cholesterol among petrochemical workers and identify vulnerable sub-groups, stratified analyses by gender, age, smoking and drinking habits were conducted. As shown in Table 4, we found that female petrochemical workers (IRR=2.240, 95%CI 1.639-3.062), aged ≥35 years (IRR=1.317, 95%CI 1.158-1.497), without smoking (IRR=1.536, 95%CI 1.285-1.836) and drinking habits (IRR=1.587, 95%CI 1.276-1.973) were at high risk of high cholesterol if exposed to heat and gasoline in the workplace. Moreover, exposure to heat and hydrogen sulfide may increase the risk of high cholesterol among female petrochemical workers (IRR=2.347, 95%CI 1.736-3.173), aged ≥35 years (IRR=1.385, 95%CI 1.215-1.578), without smoking (IRR=1.602, 95%CI 1.344-1.911) and drinking habits (IRR=1.647, 95%CI 1.323-2.051). 4. Discussion Cardiovascular disease is one of the major health burden of petrochemical industry ( 27 ), and dyslipidemia plays an important role in the occurrence and development of cardiovascular disease ( 28 ). Therefore, regular occupational health examinations are of great importance to petrochemical workers in preventing cardiovascular diseases and minimizing its burden. To our best of knowledge, this is the first study investigated the impact of occupational heat exposure alone or combined with other occupational hazards on the blood lipid level of petrochemical workers. Using GEE regression models, a retrospective analysis of workers’ health examination records was conducted in this study to explore the relationship between occupational heat exposure and blood lipid levels. Our results show that workplace heat exposure combined with gasoline or hydrogen sulfide may significantly increase the risk of TC abnormalities. 4.1 The relationship between occupational heat exposure and dyslipidemia Due to the nature of petrochemical production, high temperature is one of the most important occupational hazards for petrochemical workers. Evidence has shown that heat exposure may increase the risk of cardiovascular disease by dyslipidemia ( 29 ). Vangelova et al. ( 14 ) investigated 102 male industrial workers with an average age of 37.4 years in ceramic foundries in Bulgaria and found that heat exposure increased the risk of high TC (OR:1.481 1.097–2.002) and LDL-C (OR:1.539 1.123–2.111). Another study including 545 Bulgarian male workers found higher rates of dyslipidemia among middle-aged and older workers in ceramic foundries exposed to heat and noise ( 16 ). However, there is still inconsistency in the temperature-dyslipidemia association. Results of this study found that heat exposure alone was negatively associated with HDC-C, LDC-C and TG. It is supported by one study which also found that workplace heat exposure had no effect on blood lipids or exerted a protective effect. Yamamoto et al. ( 30 ) conducted an experimental study to explore the relationship between short-term heat exposure and blood lipids in 13 ordinary healthy Japanese men with an average age of 22.3 years, and found that HDL-C was significantly increased at moderate temperatures (35.5 ± 0.2°C). However, at high temperatures (39.8 ± 0.1°C), the levels of TC, TG and LDL-C were decreased to some extents. Based on a 5-year Jinchang prospective cohort study, Shan et al. ( 31 ) analyzed the effect of high temperature on the blood lipid metabolism of workers in a large mining and metallurgical plant at high altitudes in northwestern China. After adjusting for confounders (e.g. age, sex, education, occupation, smoking, drinking), using a mixed-effects model analysis, they found that for every 5°C increase in mean temperature, TC, TG and LDL-C decreased by 1.82% (95% CI: 0.89% − 2.76%), 0.56% (95%CI: 0.11% − 1.00%) and 0.20% (95%CI: 0.01% − 0.40%), respectively. These studies are in line with the results of our study, indicating that there may be a complex regulatory mechanism for the effect of heat exposure on blood lipid levels. Lissarassa et al. ( 32 ) exposed ovariectomized Wistar adult rats to heat in a water bath (41°C) and found the weight of rats decreased and the level of HDL-C in serum increased. The behind mechanism may be related to serotonin by improving HSR in adipose tissue and reducing oxidative stress in skeletal muscle. 4.2 The possible impact of healthy worker effect on the relationship between occupational heat exposure and dyslipidemia Regarding the interpretation of the negative association between high temperature and dyslipidemia, some studies attributed it to a "healthy worker effect" phenomenon. Through a meta-analysis, Greenberg et al. ( 33 ) systematically evaluated the mortality rate of petrochemical workers in the U.S. and Western Europe, and found that the number of deaths from all-cause and cardiovascular diseases was lower than expected, which may be attributable to "healthy worker effect". Huebner et al. ( 34 ) investigated the mortality patterns and trends in a group of 49,705 women employed in U.S. oil company operations, and found that the overall mortality rate of female workers was 25% lower than that of the general U.S. females. Moreover, the death rate due to cardiovascular diseases reduced by 40%. Analysis of the reasons may be related to the impact of the "healthy worker effect"( 35 ). In this study, the negative association between high temperature and dyslipidemia may be due to the following reasons. First, the petrochemical industry has strict screening requirements on the physical fitness of workers engaged in high temperature operations. Second, workers undertaking high temperature operations are usually younger than their non-heat exposure counterparts. Third, workers are required to have regular occupational health examinations every year. Once diagnosed as dyslipidemia, they would be treated in a timely manner. In addition, the employers may rotate heat exposure workers to minimize the potential adverse heat effects. Therefore, the protective effect of heat exposure on dyslipidemia in this study may result from "healthy worker effect". 4.3 The impact of occupational heat combined with gasoline exposure on dyslipidemia Petrochemical workers are usually exposed to multiple occupational hazards more or less in the production operation process, even though preventive measure are in place. Therefore, we further analyzed the effects of heat combined with other occupational hazards on dyslipidemia. Gasoline is one of the main products of petroleum refining and one of the most common occupational hazards for petrochemical workers. In this study, we found that gasoline exposure alone was negatively associated with TC, LDC-C and TG. Interestingly, when workplace heat exposure combined with gasoline, it could significantly increase the risk of TC abnormalities. This is supported by animal experiments. Uboh et al. ( 36 ) found that serum TG levels were increased by 97% in Wistar rats after two weeks of gasoline fumes inhalation at different concentrations. Gasoline has a complex composition, including methyl tertiary butyl ether (MTBE) which is an additive used in gasoline to aid ignition. MTBE has been shown to affect glucose metabolism and cause dyslipidemia ( 37 ). In addition, high temperature may increase the risk of gasoline leak and evidence has shown that most crude oil and petroleum products (e.g., gasoline) evaporate at a logarithmic rate with respect to time and ambient temperature ( 38 ). This may be related to the lipophilicity and strong volatility of C4 ~ C12, cyclic hydrocarbons, aromatic hydrocarbons, and olefins, which are the main components of gasoline. 4.4 The impact of heat exposure combined with hydrogen sulfide on dyslipidemia The production process in petrochemical plants can generate large amounts of elemental sulfur, often in the form of hydrogen sulfide, due to unstable acid gas flow and high pressure variations ( 39 ). As a result, workers are often exposed to hydrogen sulfide along with heat in the workplace. In this study, we found that hydrogen sulfide exposure alone was negatively associated with TC, TG and LDL-C. An animal experimental study by Sun et al. ( 40 , 41 ) showed that exogenous sodium sulfide (NaHS, hydrogen sulfide donor) significantly reduced serum TG levels in male C57BL/6 mice fed a high-fat diet, with the possible mechanism being that hydrogen sulfide reduces serum TG levels by activating hepatic autophagy through the AMPK-mTOR pathway. Cystathione-γ-lyase (CSE) is an important endogenous hydrogen sulfide enzyme produced in the cardiovascular system and is found mainly in vascular smooth muscle and endothelial cells. A study by Mani et al. ( 42 ) found that treatment of CSE knockout mice with NaHS inhibited the progression of atherosclerosis and dyslipidemia, indicating that hydrogen sulfide may play a positive role in the regulation of lipid metabolism. However, we further analyzed the exposure to hydrogen sulfide in a high temperature environment and found that the regulation of lipid metabolism by hydrogen sulfide may be influenced by temperature. The results of this study suggest that co-exposure to heat and hydrogen sulfide can significantly increase the risk of TC abnormalities. Under normal circumstances, exogenous hydrogen sulfide is mostly absorbed through the respiratory tract ( 43 ). After entering the bloodstream, it is oxidized to sulfate and thiosulfate and excreted mainly in the urine ( 44 , 45 ). Ulutas et al. ( 46 )studied hydrogen sulfide emissions from a wastewater treatment plant in Istanbul, where the researchers collected samples of workplace and outdoor ambient air during three seasons: spring, summer and winter, and found that hydrogen sulfide concentrations increased with seasonal temperature and reached peak concentrations in summer. In hot or heat-exposed environments, the concentration of volatile hydrogen sulfide in the air increases, while workers sweat more and urinate less ( 47 ). This can lead to increased absorption of hydrogen sulfide through the respiratory tract and skin and decreased excretion through urine, which ultimately leads to accumulation of hydrogen sulfide in the body and thus promotes the development of dyslipidemia. The effects of hydrogen sulfide on dyslipidemia are intricate, and it has been suggested that hydrogen sulfide promotes lipid fractionation and causes dyslipidemia. In fly experiments, hydrogen sulfide supplementation was found to promote lipid hoarding, while knockdown of the CSE gene inhibited lipid hoarding in mice on a high-fat diet ( 48 ). In addition, it has also been suggested that hydrogen sulfide is an inhibitor of cytochrome oxidase, which enters the cell and binds to cytochrome oxidase in the mitochondria, blocking the endorespiration of the cell and causing tissue hypoxia. Hydrogen sulfide also inhibits monoamine oxidase and free radical damage. Therefore, it is likely that hydrogen sulfide-induced dyslipidemia in workers is associated with enhanced free radical formation and lipid peroxidation ( 49 , 50 ). As to the potential mechanism how hydrogen sulfide is related to lipids with or without heat exposure, further research is needed. 4.5 Stratified analysis of susceptible populations To identify the sub-groups who are more vulnerable to heat-attributed dyslipidemia, we conducted stratified analyses and found that women aged ≥ 35 years without drinking and smoking had a higher risk of TC-related dyslipidemia. A cross-sectional study by Li et al.( 51 ), which analyzed data from 13,354 general population lipid levels and environmental monitoring sites nationwide, found that women were at higher risk for dyslipidemia than men. Frenandez et al. ( 52 ) suggested that the increased risk of dyslipidemia in middle-aged and older women may be related to the decrease in estrogen levels after menopause. Frenandez thinks this may be because high estrogen levels may increase the rate of hepatic uptake of TC and LDL-C, promote HDL-C synthesis and facilitate bile acid secretion, and accelerate the clearance of cholesterol from the body. Evidence has shown that the risk of dyslipidemia is significantly increased in women with reduced estrogen levels or during menopause ( 53 ). Therefore, these studies suggested that women aged equal to or older than 35 years should examine their blood lipids regularly. In a study on the relationship between smoking and dyslipidemia, Craig et al. ( 54 ) analyzed 54 articles on the relationship between smoking and lipids in adults, suggesting that smokers had significantly higher levels of TC, TG, and LDL-C, and smokers had lower HDL-C than nonsmokers. In our study, we found a higher risk of abnormal TC in non-smoking female workers. Whether TC abnormalities are related to smoking and the mechanisms involved may need to be further investigated. In addition, we identified the sub-groups vulnerable to heat-attributed dyslipidemia, and found that most of them are mainly engaged in positions related to oil refining, catalytic reforming and laboratory oil testing. Generally, these positions are exposed to flammable and explosive products throughout the production process, and improper storage or negligence may cause explosions ( 55 , 56 ), so fire and smoking are strictly prohibited in the production plant and process, and workers need to be sober and alcohol-free at all times. This could also explain our findings from a new perspective. 4.6 Strengths and limitations of the study Based on a retrospective cohort study design with a 9-year quality occupational heat examination data and a large sample size, GEE models were used in this study to analyze the impact of high temperature alone or combined with other occupational hazards on dyslipidemia among petrochemical workers. Nevertheless, several limitations need to be addressed: ( 1 ) The occupational exposure and outcome variables were dichotomous, and the dose-response relationship could not be estimated. ( 2 ) In this study, we only considered process-generated heat exposure in the workplace and did not incorporate the possible effects of changes in local meteorological conditions on blood lipids( 57 ). ( 3 ) As information about workers’ history of lipid-lowering drug use and dietary intake is unavailable, these factors were not adjusted in the model analysis. ( 4 ) The workers of the two petrochemical plants had shift work, and we did not take the possible effect of shift work on blood lipid fluctuations into account( 58 ). 5.Conclusions The negative association between workplace heat exposure and dyslipidemia may be attributed to the healthy worker effect, however, occupational heat exposure combined with gasoline or hydrogen sulfide could significantly increase the risk of dyslipidemia. Health interventions in the petrochemical industry should pay more attention to female workers aged ≥ 35 years without smoking and drinking. Abbreviations TC Total cholesterol LDL-C Low-density lipoprotein cholesterol HDL-C High-density lipoprotein cholesterol TG Triglyceride IRR Incidence Rate Ratio HTC Hypercholesterolemia HTG Hypertriglyceridemia LHDL-C Hypo-high-density lipoproteinemia HLDL-C Hyper-low-density lipoproteinemia GEE Generalized estimating equations BMI Body Mass Index Declarations Competing interests The authors declare no competing interests. Ethics statement This study has received approval from the Ethics Committee of Fujian Medical University (Fujian Ethics Examination No.111). All participants included in the study have signed informed consent. Author contributions Yifeng Chen: Methodology, Data analysis, Writing – original draft, Data curation and management. Xiaoyun Li ： Data curation and data collection, Data analysis. Qingyu Li: Writing – review & editing, Methodology, Data analysis. Yan Yang, Zitong Zhang, Yilin Zhang ： Writing – review & editing, Methodology, Data curation. Shanshan Du, Fei He, Zihu Lv ： Supervision, Conceptualization, data collection and management. Weimin Ye, Wei Zheng, Jianjun Xiang: Conceptualization, Methodology, Writing – review & editing, Supervision, Formal analysis, Funding acquisition. Funding This work was supported by the Minjiang Scholar Start-up Research Fund of Fujian Province (Grant No. 2019-9202001001) and 2021 Natural Science Foundation of Fujian Province of China (2021J01722). Acknowledgements We thank all the participants who took part in the study. Data availability Article authors do not have the right to share data. Consent for publication Not applicable. References Alfares HK. Introduction to Petroleum and Petrochemical Industries. Cham: Springer, International P. 2023 2023. 1–23 p. (Alfares HK, editor. Applied Optimization in the Petroleum Industry). 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Shift work, and particularly permanent night shifts, promote dyslipidaemia: A systematic review and meta-analysis. Atherosclerosis. 2020;313:156–69. https://doi.org/10.1016/j.atherosclerosis.2020.08.015 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 17 Sep, 2023 Read the published version in ISEE Conference Abstracts → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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03:47:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4446442/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4446442/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1289/isee.2023.OP-373","type":"published","date":"2023-09-17T23:02:34+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":57953280,"identity":"0e5c13d5-f97c-4288-9405-30d0314f76ea","added_by":"auto","created_at":"2024-06-07 23:01:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":41168,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of heat exposure, gasoline, oxynitrides, SO\u003csub\u003e2\u003c/sub\u003e, H\u003csub\u003e2\u003c/sub\u003eS, benzene, methanol, and CO on blood lipid levels among petrochemical workers. Note: All the model adjusted for age, gender, body mass index, smoking, and alcohol drinking.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4446442/v1/d5f434185af13d8725dd8729.png"},{"id":57953656,"identity":"8b22bd9f-360c-4c3f-889d-9a839d17c328","added_by":"auto","created_at":"2024-06-07 23:09:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":949070,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4446442/v1/7274b78f-6ae2-4e18-bbc0-8f03782e8727.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of occupational heat exposure on blood lipids among petrochemical workers: An analysis of 9-year longitudinal data","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe petrochemical industry converts raw materials from oil refining and gas processing into a wide range of products such as gasoline, diesel fuel, petroleum asphalt, alkynes, olefins, aromatics, synthetic fibers, rubber and resins (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Currently, China stands as the world's second-largest petrochemical powerhouse, with production capacity and output of key products ranking prominently on the global stage. The sector comprises 26,271 enterprises, accounting for 11.6% of China\u0026rsquo;s overall operating revenue (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). The complex production process of petrochemical industry involves a wide variety of occupational hazards such as high temperature, noise, gasoline, hydrogen sulfide, carbon monoxide, sulfur dioxide, and benzene. Long-term low-dose exposure to these occupational hazards may have a detrimental impact on petrochemical workers\u0026rsquo; health, safety, and wellbeing (\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHeat exposure is one of the most important occupational hazards for petrochemical workers, which may come from process-generated heat and/or outdoor high temperature (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Heat sources in the workplace for petrochemical workers may include boilers, furnaces, high temperature reactors, compressors, and fractionation towers (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Moreover, some workers may need to wear personal protective equipment (PPEs) such as overalls, glove, goggle, and helmet to protect themselves from toxic materials in the workplace, thereby increasing the risk of overheating and thermal strain as PPEs block heat dissipation (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). With the predicted increase in the frequency and intensity of extremely hot weather, heat exposure is posing a growing challenge for petrochemical workers\u0026rsquo; health and safety particularly for those undertaking physically demanding outdoor tasks.\u003c/p\u003e \u003cp\u003eNumerous studies have demonstrated that long-term heat exposure in the workplace can significantly increase the risk of cardiovascular diseases (\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e), while the potential mechanisms remain unclear. Dyslipidemia plays a pivotal role in the onset and progression of cardiovascular disease (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). In Bulgaria, male manufacturing workers with heat exposure showed a higher risk of dyslipidemia compared to those without heat exposure (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). A high prevalence of dyslipidemia (41.4%) was also reported in a large petrochemical plant in India (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). In addition to heat, other workplace hazards may also contribute to dyslipidemia. Vangelova et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) found that the incidence of dyslipidemia increased significantly among middle-aged and older workers exposed to high temperatures combined with noise. Occupational exposure to xylene, organic solvents and silica has been reported as the risk factor for cardiovascular disease in petrochemical workers(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Chen et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) found steel workers exposed to both heat and dust in China were at a higher risk of hyperuricemia compared to those solely exposed to heat. Hyperuricemia has been regarded as a significant and independent risk factor for hypertension and cardiovascular disease (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Combined effects of heat exposure with environmental pollutants have also been observed in animal experiments. Song et al.(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) found that cardiac damage of ApoE-/- mice was more significant when high temperature was combined with ozone exposure than heat exposure alone.\u003c/p\u003e \u003cp\u003ePetrochemical workers are usually exposed to multiple occupational hazards in the workplace. Assessing the potential adverse health effects of a certain occupational hazard without taking possible interactions with other occupational exposures into account may not accurately reflect the true causes of disease (\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The purpose of this study was to explore the effects of occupational heat exposure combined with other occupational hazards on blood lipids among petrochemical workers. Findings of this study may provide implications for the etiological prevention of dyslipidemia, which is of great significance for early detection and control of cardiovascular diseases.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study population\u003c/h2\u003e \u003cp\u003eAccording to the requirements of the \"Law of the People's Republic of China on the Prevention and Control of Occupational Diseases\" and the \"Technical Specifications for Occupational Health Surveillance\" (GBZ188-2014), employers need to organize regular occupational health examinations for workers exposed to occupational disease hazards and establish occupational health files. The data for this research were sourced from two petrochemical companies in Fujian Province. During the period from 2013 to 2021, the workers of the two petrochemical enterprises underwent occupational physical examinations at Minnan Branch of the First Affiliated Hospital of Fujian Medical University, which is the only qualified local hospital carrying out occupational health examinations. The health examination items mainly include blood biochemical examination, blood routine, urine routine, upper abdominal color ultrasound and the collection of occupational information such as basic information, living and eating habits, and occupational exposure history. Occupational physicians conducted relevant inspections on petrochemical workers and assisted companies in establishing worker electronic health records. A total of 32,544 occupational health examination records were obtained, among which 1,697 records were excluded due to missing values of key information or multiple inspections in the same year. The data of workers who had multiple inspections in the same year were merged and only one inspection record was retained. Finally, 6,911 participants were included in the study, contributing a total of 30,847 records.\u003c/p\u003e \u003cp\u003eWorkers were required to fast from food and water after 20:00 on the night before the health examination, and fasting venous blood was collected from 8:00 to 10:00 on the day of the examination. Subsequently, the laboratory physician used a fully automatic biochemical analyzer (Cobas\u0026reg;8000 composed of Cobas C701 module and Cobas ISE module) to perform four blood lipid analysis of serum samples (Total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C)). This study has received approval from the Ethics Committee of Fujian Medical University (Fujian Ethics Examination No.111). All participants included in the study have signed informed consent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Health outcomes\u003c/h2\u003e \u003cp\u003eThe judgment of blood lipids is based on the diagnostic criteria for dyslipidemia in the Guidelines for the Prevention and Treatment of Dyslipidemia in Chinese Adults (Revised 2016): TC\u0026thinsp;\u0026ge;\u0026thinsp;6.2 mmol/L, TG\u0026thinsp;\u0026ge;\u0026thinsp;2.3mmol/L, LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;4.1 mmol/L, HDL-C\u0026thinsp;\u0026lt;\u0026thinsp;1.0 mmol/L. Dyslipidemia is determined if any one of these four indicators is abnormal(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Namely, if being diagnosed as any one or more of the following conditions (hypercholesterolemia, hypertriglyceridemia, hypo-high-density lipoproteinemia (LHDL-C), and hyper-low-density lipoproteinemia), it can be determined as dyslipidemia. Regular smoking was defined as smoking\u0026thinsp;\u0026ge;\u0026thinsp;1 cigarette per day for at least half a year in the last year, or \u0026ge;\u0026thinsp;7 cigarettes per week. Occasional smoking is between regular and non-smoking. Alcohol consumption was defined as twice a week or more and an alcohol intake greater than 50 g for more than half a year.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe measurement data is expressed by\u0026oline;X\u0026thinsp;\u0026plusmn;\u0026thinsp;S, and the enumeration data is expressed by N (%). Given the characteristics of longitudinal health examination data, we used generalized estimating equations (GEE) models with negative binominal distribution accounting for over-dispersion, a log link function and a first-order autocorrelation structure to quantify the effects of occupational hazard exposure on blood lipids on workers. Confounding factors were adjusted for, including age, sex, BMI, smoking, and alcohol consumption. The effects of high temperature alone and combined with other occupational hazards (gasoline, nitrogen oxides, sulfur dioxide, hydrogen sulfide, benzene, methanol, carbon monoxide) on the blood lipids were also analyzed. Stratified analyses were conducted on the basis of sex, age, BMI, smoking, and alcohol consumption. The statistical softwares StataSE.16 and SAS.9.4 were used to encode and clean the data and conduct statistical analysis. Results were considered statistically significant at a P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1 Comparison of the characteristics between high-temperature exposure and non-high-temperature exposure petrochemical workers\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1 shows the distribution of sociodemographic characteristics and other occupational hazards in the workers with and without occupational heat exposure. \u0026ldquo;Overall\u0026rdquo; is the summary of all health examination records, \u0026ldquo;between\u0026rdquo; is the between-group statistic (between individuals), and \u0026ldquo;within\u0026rdquo; is within the group (the same individual in different periods) (26). The results showed that there were significant differences in gender, age, and smoking status among workers with and without heat exposure, while no significant differences were observed in BMI and alcohol consumption. Workers exposed to heat in the workplace had a significantly higher proportion of being exposed to gasoline, nitrogen oxides, sulfur dioxide, hydrogen sulfide, benzene, methanol, and carbon monoxide, compared to those without occupational heat exposure. \u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"731\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\"\u003e\n \u003cp\u003eTable 1. Characteristics of occupational heat exposure by demographic factors among petrochemical workers \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.82626538987688%\" rowspan=\"2\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.482900136798904%\" colspan=\"3\"\u003e\n \u003cp\u003eHeat exposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.79890560875513%\" colspan=\"3\"\u003e\n \u003cp\u003eNon-heat exposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.891928864569083%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.152854511970535%\"\u003e\n \u003cp\u003eOverall\u0026nbsp;\u003cbr\u003e\u0026nbsp;n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.968692449355434%\"\u003e\n \u003cp\u003eBetween\u003cbr\u003e\u0026nbsp;n(%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.338858195211786%\"\u003e\n \u003cp\u003eWithin\u003cbr\u003e\u0026nbsp;%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.626151012891345%\"\u003e\n \u003cp\u003eOverall\u0026nbsp;\u003cbr\u003e\u0026nbsp;n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.574585635359117%\"\u003e\n \u003cp\u003eBetween\u003cbr\u003e\u0026nbsp;n(%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.338858195211786%\"\u003e\n \u003cp\u003eWithin\u003cbr\u003e\u0026nbsp;%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.82626538987688%\" valign=\"bottom\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.227086183310533%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.090287277701778%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.321477428180575%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.311901504787961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.891928864569083%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.82626538987688%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.227086183310533%\" valign=\"bottom\"\u003e\n \u003cp\u003e10090 (85.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.090287277701778%\" valign=\"bottom\"\u003e\n \u003cp\u003e4508 (83.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.321477428180575%\" valign=\"bottom\"\u003e\n \u003cp\u003e14130 (74.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.311901504787961%\" valign=\"bottom\"\u003e\n \u003cp\u003e4273 (78.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.891928864569083%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46846846846847%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.615615615615615%\" valign=\"bottom\"\u003e\n \u003cp\u003e1782 (15.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.465465465465465%\" valign=\"bottom\"\u003e\n \u003cp\u003e882 (16.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.06006006006006%\" valign=\"bottom\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.816816816816818%\" valign=\"bottom\"\u003e\n \u003cp\u003e4845 (25.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.513513513513514%\" valign=\"bottom\"\u003e\n \u003cp\u003e1172 (21.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.06006006006006%\" valign=\"bottom\"\u003e\n \u003cp\u003e100.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.053351573187413%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eAge group (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.090287277701778%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.321477428180575%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.311901504787961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.891928864569083%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.82626538987688%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;≦24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.227086183310533%\" valign=\"bottom\"\u003e\n \u003cp\u003e2351 (19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.090287277701778%\" valign=\"bottom\"\u003e\n \u003cp\u003e1547 (28.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e85.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.321477428180575%\" valign=\"bottom\"\u003e\n \u003cp\u003e1354 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.311901504787961%\" valign=\"bottom\"\u003e\n \u003cp\u003e914 (16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e78.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.891928864569083%\" rowspan=\"4\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46846846846847%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;25-34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.615615615615615%\" valign=\"bottom\"\u003e\n \u003cp\u003e3865 (32.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.465465465465465%\" valign=\"bottom\"\u003e\n \u003cp\u003e2021 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.06006006006006%\" valign=\"bottom\"\u003e\n \u003cp\u003e86.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.816816816816818%\" valign=\"bottom\"\u003e\n \u003cp\u003e4127 (21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.513513513513514%\" valign=\"bottom\"\u003e\n \u003cp\u003e1921 (35.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.06006006006006%\" valign=\"bottom\"\u003e\n \u003cp\u003e86.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46846846846847%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;35-54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.615615615615615%\" valign=\"bottom\"\u003e\n \u003cp\u003e5267 (44.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.465465465465465%\" valign=\"bottom\"\u003e\n \u003cp\u003e2233 (41.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.06006006006006%\" valign=\"bottom\"\u003e\n \u003cp\u003e96.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.816816816816818%\" valign=\"bottom\"\u003e\n \u003cp\u003e12256 (64.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.513513513513514%\" valign=\"bottom\"\u003e\n \u003cp\u003e2945 (54.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.06006006006006%\" valign=\"bottom\"\u003e\n \u003cp\u003e93.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46846846846847%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026ge;55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.615615615615615%\" valign=\"bottom\"\u003e\n \u003cp\u003e387 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.465465465465465%\" valign=\"bottom\"\u003e\n \u003cp\u003e205 (3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.06006006006006%\" valign=\"bottom\"\u003e\n \u003cp\u003e83.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.816816816816818%\" valign=\"bottom\"\u003e\n \u003cp\u003e1231 (6.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.513513513513514%\" valign=\"bottom\"\u003e\n \u003cp\u003e407 (7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.06006006006006%\" valign=\"bottom\"\u003e\n \u003cp\u003e74.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.82626538987688%\" valign=\"bottom\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.227086183310533%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.090287277701778%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.321477428180575%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.311901504787961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.891928864569083%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.82626538987688%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026lt;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.227086183310533%\" valign=\"bottom\"\u003e\n \u003cp\u003e6451 (54.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.090287277701778%\" valign=\"bottom\"\u003e\n \u003cp\u003e3251 (60.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e93.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.321477428180575%\" valign=\"bottom\"\u003e\n \u003cp\u003e10316 (54.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.311901504787961%\" valign=\"bottom\"\u003e\n \u003cp\u003e3341 (61.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e87.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.891928864569083%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46846846846847%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026ge;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.615615615615615%\" valign=\"bottom\"\u003e\n \u003cp\u003e5421 (45.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.465465465465465%\" valign=\"bottom\"\u003e\n \u003cp\u003e2588 (48.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.06006006006006%\" valign=\"bottom\"\u003e\n \u003cp\u003e91.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.816816816816818%\" valign=\"bottom\"\u003e\n \u003cp\u003e8659 (45.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.513513513513514%\" valign=\"bottom\"\u003e\n \u003cp\u003e2919 (53.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.06006006006006%\" valign=\"bottom\"\u003e\n \u003cp\u003e85.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.82626538987688%\" valign=\"bottom\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.227086183310533%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.090287277701778%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.321477428180575%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.311901504787961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.891928864569083%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.82626538987688%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.227086183310533%\" valign=\"bottom\"\u003e\n \u003cp\u003e6986 (67.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.090287277701778%\" valign=\"bottom\"\u003e\n \u003cp\u003e3097 (71.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e96.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.321477428180575%\" valign=\"bottom\"\u003e\n \u003cp\u003e10548 (66.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.311901504787961%\" valign=\"bottom\"\u003e\n \u003cp\u003e2927 (69.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e96.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.891928864569083%\" rowspan=\"3\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46846846846847%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Occasionally\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.615615615615615%\" valign=\"bottom\"\u003e\n \u003cp\u003e1447 (13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.465465465465465%\" valign=\"bottom\"\u003e\n \u003cp\u003e798 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.06006006006006%\" valign=\"bottom\"\u003e\n \u003cp\u003e74.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.816816816816818%\" valign=\"bottom\"\u003e\n \u003cp\u003e2132 (13.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.513513513513514%\" valign=\"bottom\"\u003e\n \u003cp\u003e753 (17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.06006006006006%\" valign=\"bottom\"\u003e\n \u003cp\u003e73.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46846846846847%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Heavy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.615615615615615%\" valign=\"bottom\"\u003e\n \u003cp\u003e2002 (19.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.465465465465465%\" valign=\"bottom\"\u003e\n \u003cp\u003e883 (20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.06006006006006%\" valign=\"bottom\"\u003e\n \u003cp\u003e87.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.816816816816818%\" valign=\"bottom\"\u003e\n \u003cp\u003e3312 (20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.513513513513514%\" valign=\"bottom\"\u003e\n \u003cp\u003e960 (22.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.06006006006006%\" valign=\"bottom\"\u003e\n \u003cp\u003e86.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.82626538987688%\" valign=\"bottom\"\u003e\n \u003cp\u003eDrinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.227086183310533%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.090287277701778%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.321477428180575%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.311901504787961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.891928864569083%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.82626538987688%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.227086183310533%\" valign=\"bottom\"\u003e\n \u003cp\u003e5220 (50.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.090287277701778%\" valign=\"bottom\"\u003e\n \u003cp\u003e2498 (58.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e90.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.321477428180575%\" valign=\"bottom\"\u003e\n \u003cp\u003e7885 (49.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.311901504787961%\" valign=\"bottom\"\u003e\n \u003cp\u003e2339 (56.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e88.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.891928864569083%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46846846846847%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.615615615615615%\" valign=\"bottom\"\u003e\n \u003cp\u003e5091 (49.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.465465465465465%\" valign=\"bottom\"\u003e\n \u003cp\u003e2256 (52.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.06006006006006%\" valign=\"bottom\"\u003e\n \u003cp\u003e90.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.816816816816818%\" valign=\"bottom\"\u003e\n \u003cp\u003e7951 (50.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.513513513513514%\" valign=\"bottom\"\u003e\n \u003cp\u003e2301 (55.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.06006006006006%\" valign=\"bottom\"\u003e\n \u003cp\u003e90.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.82626538987688%\" valign=\"bottom\"\u003e\n \u003cp\u003eGasoline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.227086183310533%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.090287277701778%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.321477428180575%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.311901504787961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.891928864569083%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.82626538987688%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.227086183310533%\" valign=\"bottom\"\u003e\n \u003cp\u003e7236 (61.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.090287277701778%\" valign=\"bottom\"\u003e\n \u003cp\u003e3666 (68.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e92.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.321477428180575%\" valign=\"bottom\"\u003e\n \u003cp\u003e5078 (26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.311901504787961%\" valign=\"bottom\"\u003e\n \u003cp\u003e2426 (44.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e77.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.891928864569083%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46846846846847%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.615615615615615%\" valign=\"bottom\"\u003e\n \u003cp\u003e4636 (39.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.465465465465465%\" valign=\"bottom\"\u003e\n \u003cp\u003e2334 (43.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.06006006006006%\" valign=\"bottom\"\u003e\n \u003cp\u003e86.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.816816816816818%\" valign=\"bottom\"\u003e\n \u003cp\u003e13897 (73.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.513513513513514%\" valign=\"bottom\"\u003e\n \u003cp\u003e4024 (73.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.06006006006006%\" valign=\"bottom\"\u003e\n \u003cp\u003e88.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.82626538987688%\" valign=\"bottom\"\u003e\n \u003cp\u003eOxynitrides\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.227086183310533%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.090287277701778%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.321477428180575%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.311901504787961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.891928864569083%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.82626538987688%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.227086183310533%\" valign=\"bottom\"\u003e\n \u003cp\u003e1516 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.090287277701778%\" valign=\"bottom\"\u003e\n \u003cp\u003e1128 (20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e72.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.321477428180575%\" valign=\"bottom\"\u003e\n \u003cp\u003e620 (3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.311901504787961%\" valign=\"bottom\"\u003e\n \u003cp\u003e526 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e63.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.891928864569083%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46846846846847%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.615615615615615%\" valign=\"bottom\"\u003e\n \u003cp\u003e10356 (87.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.465465465465465%\" valign=\"bottom\"\u003e\n \u003cp\u003e4790 (88.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.06006006006006%\" valign=\"bottom\"\u003e\n \u003cp\u003e95.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.816816816816818%\" valign=\"bottom\"\u003e\n \u003cp\u003e18355 (96.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.513513513513514%\" valign=\"bottom\"\u003e\n \u003cp\u003e5227 (96.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.06006006006006%\" valign=\"bottom\"\u003e\n \u003cp\u003e97.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.82626538987688%\" valign=\"bottom\"\u003e\n \u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.227086183310533%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.090287277701778%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.321477428180575%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.311901504787961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.891928864569083%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.82626538987688%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.227086183310533%\" valign=\"bottom\"\u003e\n \u003cp\u003e1313 (11.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.090287277701778%\" valign=\"bottom\"\u003e\n \u003cp\u003e797 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e79.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.321477428180575%\" valign=\"bottom\"\u003e\n \u003cp\u003e647 (3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.311901504787961%\" valign=\"bottom\"\u003e\n \u003cp\u003e476 (8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e67.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.891928864569083%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46846846846847%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.615615615615615%\" valign=\"bottom\"\u003e\n \u003cp\u003e10559 (88.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.465465465465465%\" valign=\"bottom\"\u003e\n \u003cp\u003e4886 (90.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.06006006006006%\" valign=\"bottom\"\u003e\n \u003cp\u003e97.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.816816816816818%\" valign=\"bottom\"\u003e\n \u003cp\u003e18328 (96.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.513513513513514%\" valign=\"bottom\"\u003e\n \u003cp\u003e5220 (95.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.06006006006006%\" valign=\"bottom\"\u003e\n \u003cp\u003e98.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.82626538987688%\" valign=\"bottom\"\u003e\n \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003eS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.227086183310533%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.090287277701778%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.321477428180575%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.311901504787961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.891928864569083%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.82626538987688%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.227086183310533%\" valign=\"bottom\"\u003e\n \u003cp\u003e6713 (56.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.090287277701778%\" valign=\"bottom\"\u003e\n \u003cp\u003e3688 (68.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e89.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.321477428180575%\" valign=\"bottom\"\u003e\n \u003cp\u003e5248 (27.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.311901504787961%\" valign=\"bottom\"\u003e\n \u003cp\u003e2600 (47.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e67.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.891928864569083%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46846846846847%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.615615615615615%\" valign=\"bottom\"\u003e\n \u003cp\u003e5159 (43.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.465465465465465%\" valign=\"bottom\"\u003e\n \u003cp\u003e2426 (45.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.06006006006006%\" valign=\"bottom\"\u003e\n \u003cp\u003e85.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.816816816816818%\" valign=\"bottom\"\u003e\n \u003cp\u003e13727 (72.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.513513513513514%\" valign=\"bottom\"\u003e\n \u003cp\u003e4326 (79.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.06006006006006%\" valign=\"bottom\"\u003e\n \u003cp\u003e85.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.82626538987688%\" valign=\"bottom\"\u003e\n \u003cp\u003eBenzene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.227086183310533%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.090287277701778%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.321477428180575%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.311901504787961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.891928864569083%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.82626538987688%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.227086183310533%\" valign=\"bottom\"\u003e\n \u003cp\u003e3107 (26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.090287277701778%\" valign=\"bottom\"\u003e\n \u003cp\u003e2093 (38.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e81.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.321477428180575%\" valign=\"bottom\"\u003e\n \u003cp\u003e3208 (16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.311901504787961%\" valign=\"bottom\"\u003e\n \u003cp\u003e1579 (29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e71.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.891928864569083%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46846846846847%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.615615615615615%\" valign=\"bottom\"\u003e\n \u003cp\u003e8765 (73.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.465465465465465%\" valign=\"bottom\"\u003e\n \u003cp\u003e3996 (74.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.06006006006006%\" valign=\"bottom\"\u003e\n \u003cp\u003e92.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.816816816816818%\" valign=\"bottom\"\u003e\n \u003cp\u003e15767 (83.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.513513513513514%\" valign=\"bottom\"\u003e\n \u003cp\u003e4616 (84.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.06006006006006%\" valign=\"bottom\"\u003e\n \u003cp\u003e93.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.82626538987688%\" valign=\"bottom\"\u003e\n \u003cp\u003eMethanol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.227086183310533%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.090287277701778%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.321477428180575%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.311901504787961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.891928864569083%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.82626538987688%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.227086183310533%\" valign=\"bottom\"\u003e\n \u003cp\u003e2336 (19.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.090287277701778%\" valign=\"bottom\"\u003e\n \u003cp\u003e1502 (27.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e84.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.321477428180575%\" valign=\"bottom\"\u003e\n \u003cp\u003e1459 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.311901504787961%\" valign=\"bottom\"\u003e\n \u003cp\u003e664 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e70.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.891928864569083%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46846846846847%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.615615615615615%\" valign=\"bottom\"\u003e\n \u003cp\u003e9536 (80.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.465465465465465%\" valign=\"bottom\"\u003e\n \u003cp\u003e4321 (80.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.06006006006006%\" valign=\"bottom\"\u003e\n \u003cp\u003e95.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.816816816816818%\" valign=\"bottom\"\u003e\n \u003cp\u003e17516 (92.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.513513513513514%\" valign=\"bottom\"\u003e\n \u003cp\u003e5129 (94.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.06006006006006%\" valign=\"bottom\"\u003e\n \u003cp\u003e97.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.82626538987688%\" valign=\"bottom\"\u003e\n \u003cp\u003eCO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.227086183310533%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.090287277701778%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.321477428180575%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.311901504787961%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.891928864569083%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.82626538987688%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.227086183310533%\" valign=\"bottom\"\u003e\n \u003cp\u003e3967 (33.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.090287277701778%\" valign=\"bottom\"\u003e\n \u003cp\u003e2485 (46.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e81.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.321477428180575%\" valign=\"bottom\"\u003e\n \u003cp\u003e2225 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.311901504787961%\" valign=\"bottom\"\u003e\n \u003cp\u003e1392 (25.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.165526675786595%\" valign=\"bottom\"\u003e\n \u003cp\u003e65.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.891928864569083%\" rowspan=\"3\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46846846846847%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.615615615615615%\" valign=\"bottom\"\u003e\n \u003cp\u003e7905 (66.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.465465465465465%\" valign=\"bottom\"\u003e\n \u003cp\u003e3756 (69.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.06006006006006%\" valign=\"bottom\"\u003e\n \u003cp\u003e89.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.816816816816818%\" valign=\"bottom\"\u003e\n \u003cp\u003e16750 (88.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.513513513513514%\" valign=\"bottom\"\u003e\n \u003cp\u003e4824 (88.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.06006006006006%\" valign=\"bottom\"\u003e\n \u003cp\u003e93.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.46846846846847%\" valign=\"bottom\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.615615615615615%\" valign=\"bottom\"\u003e\n \u003cp\u003e11872 (38.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.465465465465465%\" valign=\"bottom\"\u003e\n \u003cp\u003e5390 (78.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.06006006006006%\" valign=\"bottom\"\u003e\n \u003cp\u003e59.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.816816816816818%\" valign=\"bottom\"\u003e\n \u003cp\u003e18975 (61.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.513513513513514%\" valign=\"bottom\"\u003e\n \u003cp\u003e5445 (78.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.06006006006006%\" valign=\"bottom\"\u003e\n \u003cp\u003e67.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e3.2 Comparison of dyslipidemia between heat exposure and non-heat exposure petrochemical workers\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2 shows the distribution of the four types of dyslipidemia among petrochemical workers with/without heat exposure. Overall, the proportions of HTC, HTG, HLDL-C, and LHDL-C among heat exposure workers were 3.7%, 10.6%, 6.3%, and 6.3%, respectively. By contrast, the overall proportions of HTC (6.0%), HTG (19.3%), HLDL-C (9.4%), and LHDL-C (9.4%) in non-heat exposure workers were higher than their heat exposure workmates. Differences in TC, TG, LDL-C, and HDL-C between heat and non-heat exposure groups were statistically significant. As to the variation across individuals, the proportions of HTC (8.3% vs. 5.1%), HTG (19.9% vs. 12.4%), HLDL-C (12.8% vs. 9.2%), and LHDL-C (12.8% vs. 9.2) were also higher in the non-heat exposure workers than that in the heat exposure workers.\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"709\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"8\"\u003e\n \u003cp\u003eTable 2. Blood lipid disorders by heat exposure among petrochemical workers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.788732394366198%\" rowspan=\"2\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.46478873239437%\" colspan=\"3\"\u003e\n \u003cp\u003eHeat exposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.59154929577465%\" colspan=\"3\"\u003e\n \u003cp\u003eNon-heat exposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.154929577464788%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.29499072356215%\"\u003e\n \u003cp\u003eOverall\u0026nbsp;\u003cbr\u003e\u0026nbsp;n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.109461966604822%\"\u003e\n \u003cp\u003eBetween\u003cbr\u003e\u0026nbsp;n (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.760667903525047%\"\u003e\n \u003cp\u003eWithin\u003cbr\u003e\u0026nbsp;%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.77922077922078%\"\u003e\n \u003cp\u003eOverall\u0026nbsp;\u003cbr\u003e\u0026nbsp;n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.29499072356215%\"\u003e\n \u003cp\u003eBetween\u003cbr\u003e\u0026nbsp;n (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.760667903525047%\"\u003e\n \u003cp\u003eWithin\u003cbr\u003e\u0026nbsp;%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.809590973201692%\"\u003e\n \u003cp\u003eHigh TC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.668547249647391%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.527503526093088%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.180535966149506%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.796897038081806%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.668547249647391%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.180535966149506%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16784203102962%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.809590973201692%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.668547249647391%\" valign=\"bottom\"\u003e\n \u003cp\u003e444 (3.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.527503526093088%\" valign=\"bottom\"\u003e\n \u003cp\u003e274 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.180535966149506%\" valign=\"bottom\"\u003e\n \u003cp\u003e66.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.796897038081806%\" valign=\"bottom\"\u003e\n \u003cp\u003e1135 (6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.668547249647391%\" valign=\"bottom\"\u003e\n \u003cp\u003e453 (8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.180535966149506%\" valign=\"bottom\"\u003e\n \u003cp\u003e45.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16784203102962%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"bottom\"\u003e\n \u003cp\u003e11428 (96.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.993788819875776%\" valign=\"bottom\"\u003e\n \u003cp\u003e5287 (98.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.006211180124224%\" valign=\"bottom\"\u003e\n \u003cp\u003e98.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"bottom\"\u003e\n \u003cp\u003e17840 (94.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"bottom\"\u003e\n \u003cp\u003e5377 (98.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.006211180124224%\" valign=\"bottom\"\u003e\n \u003cp\u003e97.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.809590973201692%\"\u003e\n \u003cp\u003eHigh TG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.668547249647391%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.527503526093088%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.180535966149506%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.796897038081806%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.668547249647391%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.180535966149506%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16784203102962%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.809590973201692%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.668547249647391%\" valign=\"bottom\"\u003e\n \u003cp\u003e1258 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.527503526093088%\" valign=\"bottom\"\u003e\n \u003cp\u003e667 (12.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.180535966149506%\" valign=\"bottom\"\u003e\n \u003cp\u003e72.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.796897038081806%\" valign=\"bottom\"\u003e\n \u003cp\u003e3662 (19.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.668547249647391%\" valign=\"bottom\"\u003e\n \u003cp\u003e1086 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.180535966149506%\" valign=\"bottom\"\u003e\n \u003cp\u003e59.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16784203102962%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"bottom\"\u003e\n \u003cp\u003e10614 (89.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.993788819875776%\" valign=\"bottom\"\u003e\n \u003cp\u003e5068 (94.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.006211180124224%\" valign=\"bottom\"\u003e\n \u003cp\u003e96.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"bottom\"\u003e\n \u003cp\u003e15313 (80.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"bottom\"\u003e\n \u003cp\u003e5139 (94.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.006211180124224%\" valign=\"bottom\"\u003e\n \u003cp\u003e93.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.809590973201692%\"\u003e\n \u003cp\u003eHigh LDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.668547249647391%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.527503526093088%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.180535966149506%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.796897038081806%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.668547249647391%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.180535966149506%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16784203102962%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.809590973201692%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.668547249647391%\" valign=\"bottom\"\u003e\n \u003cp\u003e752 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.527503526093088%\" valign=\"bottom\"\u003e\n \u003cp\u003e494 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.180535966149506%\" valign=\"bottom\"\u003e\n \u003cp\u003e62.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.796897038081806%\" valign=\"bottom\"\u003e\n \u003cp\u003e1775 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.668547249647391%\" valign=\"bottom\"\u003e\n \u003cp\u003e697 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.180535966149506%\" valign=\"bottom\"\u003e\n \u003cp\u003e46.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16784203102962%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"bottom\"\u003e\n \u003cp\u003e11120 (93.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.993788819875776%\" valign=\"bottom\"\u003e\n \u003cp\u003e5228 (97.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.006211180124224%\" valign=\"bottom\"\u003e\n \u003cp\u003e97.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"bottom\"\u003e\n \u003cp\u003e17200 (90.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"bottom\"\u003e\n \u003cp\u003e5350 (98.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.006211180124224%\" valign=\"bottom\"\u003e\n \u003cp\u003e95.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.809590973201692%\"\u003e\n \u003cp\u003eLow HDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.668547249647391%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.527503526093088%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.180535966149506%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.796897038081806%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.668547249647391%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.180535966149506%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16784203102962%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.809590973201692%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; Yes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.668547249647391%\" valign=\"bottom\"\u003e\n \u003cp\u003e752 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.527503526093088%\" valign=\"bottom\"\u003e\n \u003cp\u003e494 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.180535966149506%\" valign=\"bottom\"\u003e\n \u003cp\u003e62.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.796897038081806%\" valign=\"bottom\"\u003e\n \u003cp\u003e1775 (9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.668547249647391%\" valign=\"bottom\"\u003e\n \u003cp\u003e697 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.180535966149506%\" valign=\"bottom\"\u003e\n \u003cp\u003e46.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.16784203102962%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.304347826086957%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; No\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"bottom\"\u003e\n \u003cp\u003e11120 (93.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.993788819875776%\" valign=\"bottom\"\u003e\n \u003cp\u003e5228 (97.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.006211180124224%\" valign=\"bottom\"\u003e\n \u003cp\u003e97.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.391304347826086%\" valign=\"bottom\"\u003e\n \u003cp\u003e17200 (90.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.149068322981368%\" valign=\"bottom\"\u003e\n \u003cp\u003e5350 (98.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.006211180124224%\" valign=\"bottom\"\u003e\n \u003cp\u003e95.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.3 One-factor exposure model to analyze the association between occupational hazards and dyslipidemia\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 1 shows the effects of heat, gasoline, nitrogen oxides, sulfur dioxide, hydrogen sulfide, benzene, methanol, and carbon monoxide on blood lipid levels in petrochemical workers. Results of univariate exposure models showed that gasoline (IRR=0.563, 95%CI 0.670-0.473), oxynitrides (IRR=0.595, 95%CI 0.439-0.807), sulfur dioxide (IRR=0.741, 95%CI 0.552-0.993), hydrogen sulfide (IRR=0.784, 95%CI 0.682-0.901), and methanol (IRR=0.774, 95%CI 0.611-0.980) were negatively associated with TC. Negative effects were also observed in TG, including heat exposure (IRR=0.787, 95%CI 0.835-0.742) and other seven occupational hazards. Similarly, LDL-C was negatively associated with heat exposure (IRR=0.863, 95%CI 0.795-0.937), gasoline (IRR=0.548, 95%CI 0.629-0.478), oxynitrides (IRR=0.281, 95%CI 0.186-0.423), sulfur dioxide (IRR=0.671, 95%CI 0.513-0.878), hydrogen sulfide (IRR=0.789, 95%CI 0.707-0.881), benzene (IRR=0.779, 95%CI 0.666-0.912) and methanol (IRR=0.746, 95%CI 0.604-0.922). By contrast, heat exposure (IRR=0.994, 95%CI 0.989-0.998), gasoline (IRR=0.994, 95%CI 0.989-0.998), oxynitrides (IRR=0.990, 95%CI 0.985-0.994), sulfur dioxide (IRR=0.993, 95%CI 0.987-0.999), methanol (IRR=0.993, 95%CI 0.986-0.999) and carbon monoxide (IRR=0.992, 95%CI 0.986-0.998) were negatively associated with HDL-C.\u003c/p\u003e\n\u003cp\u003e3.4 Two-factor exposure model to analyze the association between heat combined with other occupational hazards and dyslipidemia\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3 shows that heat exposure coupled with gasoline could increase the risk of high cholesterol (IRR=1.267, 95%CI 1.117-1.437). Similarly, heat exposure coupled with hydrogen sulfide could increase the risk of high cholesterol (IRR=1.324, 95%CI 1.166-1.505). However, workplace heat exposure combined with nitrogen oxides, sulfur dioxide, benzene, methanol, carbon monoxide had no significant impact on the four types of dyslipidemia.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"755\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"bottom\"\u003e\n \u003cp\u003eTable 3. Effects of heat exposure on blood lipid levels among petrochemical workers, using multi-exposure models\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.47019867549669%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"80.52980132450331%\" colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003eIRR (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.49602122015915%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.159151193633953%\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\"\u003e\n \u003cp\u003eLDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.29177718832891%\"\u003e\n \u003cp\u003eHDL-C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.49602122015915%\" valign=\"bottom\"\u003e\n \u003cp\u003eHeat+gasoline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.159151193633953%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.29177718832891%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.49602122015915%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Heat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.159151193633953%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.267 (1.117-1.437)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.847 (0.788-0.911)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.077 (0.976-1.187)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.29177718832891%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.997 (0.990-1.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.49602122015915%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Gasoline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.159151193633953%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.644 (0.529-0.785)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.583 (0.527-0.645)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.667 (0.578-0.769)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.29177718832891%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.997 (0.989-1.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.49602122015915%\" valign=\"bottom\"\u003e\n \u003cp\u003eHeat+Oxynitrides\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.159151193633953%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.29177718832891%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.49602122015915%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Heat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.159151193633953%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.073 (0.962-1.198)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.800 (0.753-0.851)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.897 (0.824-0.977)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.29177718832891%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.993 (0.989-0.998)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.49602122015915%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Oxynitrides\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.159151193633953%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.807 (0.484-1.343)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.798 (0.637-1.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.414 (0.214-0.803)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.29177718832891%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.983 (0.974-0.991)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.49602122015915%\" valign=\"bottom\"\u003e\n \u003cp\u003eHeat+SO\u003csub\u003e2\u003c/sub\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.159151193633953%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.29177718832891%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.49602122015915%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Heat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.159151193633953%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.077 (0.966-1.200)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.812 (0.764-0.863)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.918 (0.844-0.998)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.29177718832891%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.993 (0.989-0.998)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.49602122015915%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;SO\u003csub\u003e2\u0026nbsp;\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.159151193633953%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.002 (0.649-1.545)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.041 (0.848-1.278)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.307 (0.984-1.734)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.29177718832891%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.988 (0.977-0.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.49602122015915%\" valign=\"bottom\"\u003e\n \u003cp\u003eHeat+H\u003csub\u003e2\u003c/sub\u003eS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.159151193633953%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.29177718832891%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.49602122015915%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Heat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.159151193633953%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.324 (1.166-1.505)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.865 (0.808-0.926)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.076 (0.974-1.188)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.29177718832891%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.994 (0.988-1.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.49602122015915%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;H\u003csub\u003e2\u003c/sub\u003eS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.159151193633953%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.923 (0.792-1.076)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.836 (0.775-0.901)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.945 (0.842-1.060)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.29177718832891%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.997 (0.989-1.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.49602122015915%\" valign=\"bottom\"\u003e\n \u003cp\u003eHeat+Benzene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.159151193633953%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.29177718832891%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.49602122015915%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Heat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.159151193633953%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.059 (0.948-1.182)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.782 (0.734-0.833)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.863 (0.790-0.942)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.29177718832891%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.993 (0.988-0.999)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.49602122015915%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Benzene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.159151193633953%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.004 (0.823-1.225)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.861 (0.774-0.957)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.777 (0.648-0.932)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.29177718832891%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.994 (0.985-1.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.49602122015915%\" valign=\"bottom\"\u003e\n \u003cp\u003eHeat+Methanol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.159151193633953%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.29177718832891%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.49602122015915%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Heat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.159151193633953%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.092 (0.979-1.217)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.781 (0.733-0.831)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.906 (0.831-0.986)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.29177718832891%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.994 (0.989-0.998)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.49602122015915%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Methanol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.159151193633953%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.951 (0.709-1.277)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.723 (0.609-0.857)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.975 (0.772-1.232)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.29177718832891%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.992 (0.981-1.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.49602122015915%\" valign=\"bottom\"\u003e\n \u003cp\u003eHeat+CO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.159151193633953%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.29177718832891%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.49602122015915%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Heat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.159151193633953%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.119 (0.998-1.126)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.798 (0.749-0.849)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.920 (0.842-1.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.29177718832891%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.992 (0.987-0.998)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"19.49602122015915%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;CO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.159151193633953%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.126 (0.924-1.372)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.888 (0.806-0.979)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.02652519893899%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.068 (0.907-1.259)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.29177718832891%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.989 (0.981-0.998)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.5 Stratified analysis to identify vulnerable sub-groups\u003c/p\u003e\n\u003cp\u003eTo further analyze the effect of workplace heat exposure on high cholesterol among petrochemical workers and identify vulnerable sub-groups, stratified analyses by gender, age, smoking and drinking habits were conducted. As shown in Table 4, we found that female petrochemical workers (IRR=2.240, 95%CI 1.639-3.062), aged \u0026ge;35 years (IRR=1.317, 95%CI 1.158-1.497), without smoking (IRR=1.536, 95%CI 1.285-1.836) and drinking habits (IRR=1.587, 95%CI 1.276-1.973) were at high risk of high cholesterol if exposed to heat and gasoline in the workplace. Moreover, exposure to heat and hydrogen sulfide may increase the risk of high cholesterol among female petrochemical workers (IRR=2.347, 95%CI 1.736-3.173), aged \u0026ge;35 years (IRR=1.385, 95%CI 1.215-1.578), without smoking (IRR=1.602, 95%CI 1.344-1.911) and drinking habits (IRR=1.647, 95%CI 1.323-2.051).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cimg width=\"649\" 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\" alt=\"image\" height=\"317\"\u003e\u003c/strong\u003e\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eCardiovascular disease is one of the major health burden of petrochemical industry (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), and dyslipidemia plays an important role in the occurrence and development of cardiovascular disease (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Therefore, regular occupational health examinations are of great importance to petrochemical workers in preventing cardiovascular diseases and minimizing its burden. To our best of knowledge, this is the first study investigated the impact of occupational heat exposure alone or combined with other occupational hazards on the blood lipid level of petrochemical workers. Using GEE regression models, a retrospective analysis of workers\u0026rsquo; health examination records was conducted in this study to explore the relationship between occupational heat exposure and blood lipid levels. Our results show that workplace heat exposure combined with gasoline or hydrogen sulfide may significantly increase the risk of TC abnormalities.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1 The relationship between occupational heat exposure and dyslipidemia\u003c/h2\u003e \u003cp\u003eDue to the nature of petrochemical production, high temperature is one of the most important occupational hazards for petrochemical workers. Evidence has shown that heat exposure may increase the risk of cardiovascular disease by dyslipidemia (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Vangelova et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) investigated 102 male industrial workers with an average age of 37.4 years in ceramic foundries in Bulgaria and found that heat exposure increased the risk of high TC (OR:1.481 1.097\u0026ndash;2.002) and LDL-C (OR:1.539 1.123\u0026ndash;2.111). Another study including 545 Bulgarian male workers found higher rates of dyslipidemia among middle-aged and older workers in ceramic foundries exposed to heat and noise (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, there is still inconsistency in the temperature-dyslipidemia association. Results of this study found that heat exposure alone was negatively associated with HDC-C, LDC-C and TG. It is supported by one study which also found that workplace heat exposure had no effect on blood lipids or exerted a protective effect. Yamamoto et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) conducted an experimental study to explore the relationship between short-term heat exposure and blood lipids in 13 ordinary healthy Japanese men with an average age of 22.3 years, and found that HDL-C was significantly increased at moderate temperatures (35.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u0026deg;C). However, at high temperatures (39.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u0026deg;C), the levels of TC, TG and LDL-C were decreased to some extents. Based on a 5-year Jinchang prospective cohort study, Shan et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) analyzed the effect of high temperature on the blood lipid metabolism of workers in a large mining and metallurgical plant at high altitudes in northwestern China. After adjusting for confounders (e.g. age, sex, education, occupation, smoking, drinking), using a mixed-effects model analysis, they found that for every 5\u0026deg;C increase in mean temperature, TC, TG and LDL-C decreased by 1.82% (95% CI: 0.89% \u0026minus;\u0026thinsp;2.76%), 0.56% (95%CI: 0.11% \u0026minus;\u0026thinsp;1.00%) and 0.20% (95%CI: 0.01% \u0026minus;\u0026thinsp;0.40%), respectively. These studies are in line with the results of our study, indicating that there may be a complex regulatory mechanism for the effect of heat exposure on blood lipid levels. Lissarassa et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) exposed ovariectomized Wistar adult rats to heat in a water bath (41\u0026deg;C) and found the weight of rats decreased and the level of HDL-C in serum increased. The behind mechanism may be related to serotonin by improving HSR in adipose tissue and reducing oxidative stress in skeletal muscle.\u003c/p\u003e \u003cp\u003e4.2 The possible impact of healthy worker effect on the relationship between occupational heat exposure and dyslipidemia\u003c/p\u003e \u003cp\u003eRegarding the interpretation of the negative association between high temperature and dyslipidemia, some studies attributed it to a \"healthy worker effect\" phenomenon. Through a meta-analysis, Greenberg et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) systematically evaluated the mortality rate of petrochemical workers in the U.S. and Western Europe, and found that the number of deaths from all-cause and cardiovascular diseases was lower than expected, which may be attributable to \"healthy worker effect\". Huebner et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) investigated the mortality patterns and trends in a group of 49,705 women employed in U.S. oil company operations, and found that the overall mortality rate of female workers was 25% lower than that of the general U.S. females. Moreover, the death rate due to cardiovascular diseases reduced by 40%. Analysis of the reasons may be related to the impact of the \"healthy worker effect\"(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). In this study, the negative association between high temperature and dyslipidemia may be due to the following reasons. First, the petrochemical industry has strict screening requirements on the physical fitness of workers engaged in high temperature operations. Second, workers undertaking high temperature operations are usually younger than their non-heat exposure counterparts. Third, workers are required to have regular occupational health examinations every year. Once diagnosed as dyslipidemia, they would be treated in a timely manner. In addition, the employers may rotate heat exposure workers to minimize the potential adverse heat effects. Therefore, the protective effect of heat exposure on dyslipidemia in this study may result from \"healthy worker effect\".\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.3 The impact of occupational heat combined with gasoline exposure on dyslipidemia\u003c/h2\u003e \u003cp\u003ePetrochemical workers are usually exposed to multiple occupational hazards more or less in the production operation process, even though preventive measure are in place. Therefore, we further analyzed the effects of heat combined with other occupational hazards on dyslipidemia. Gasoline is one of the main products of petroleum refining and one of the most common occupational hazards for petrochemical workers. In this study, we found that gasoline exposure alone was negatively associated with TC, LDC-C and TG. Interestingly, when workplace heat exposure combined with gasoline, it could significantly increase the risk of TC abnormalities. This is supported by animal experiments. Uboh et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) found that serum TG levels were increased by 97% in Wistar rats after two weeks of gasoline fumes inhalation at different concentrations. Gasoline has a complex composition, including methyl tertiary butyl ether (MTBE) which is an additive used in gasoline to aid ignition. MTBE has been shown to affect glucose metabolism and cause dyslipidemia (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). In addition, high temperature may increase the risk of gasoline leak and evidence has shown that most crude oil and petroleum products (e.g., gasoline) evaporate at a logarithmic rate with respect to time and ambient temperature (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). This may be related to the lipophilicity and strong volatility of C4\u0026thinsp;~\u0026thinsp;C12, cyclic hydrocarbons, aromatic hydrocarbons, and olefins, which are the main components of gasoline.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.4 The impact of heat exposure combined with hydrogen sulfide on dyslipidemia\u003c/h2\u003e \u003cp\u003eThe production process in petrochemical plants can generate large amounts of elemental sulfur, often in the form of hydrogen sulfide, due to unstable acid gas flow and high pressure variations (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). As a result, workers are often exposed to hydrogen sulfide along with heat in the workplace. In this study, we found that hydrogen sulfide exposure alone was negatively associated with TC, TG and LDL-C. An animal experimental study by Sun et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e) showed that exogenous sodium sulfide (NaHS, hydrogen sulfide donor) significantly reduced serum TG levels in male C57BL/6 mice fed a high-fat diet, with the possible mechanism being that hydrogen sulfide reduces serum TG levels by activating hepatic autophagy through the AMPK-mTOR pathway. Cystathione-γ-lyase (CSE) is an important endogenous hydrogen sulfide enzyme produced in the cardiovascular system and is found mainly in vascular smooth muscle and endothelial cells. A study by Mani et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e) found that treatment of CSE knockout mice with NaHS inhibited the progression of atherosclerosis and dyslipidemia, indicating that hydrogen sulfide may play a positive role in the regulation of lipid metabolism. However, we further analyzed the exposure to hydrogen sulfide in a high temperature environment and found that the regulation of lipid metabolism by hydrogen sulfide may be influenced by temperature. The results of this study suggest that co-exposure to heat and hydrogen sulfide can significantly increase the risk of TC abnormalities. Under normal circumstances, exogenous hydrogen sulfide is mostly absorbed through the respiratory tract (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). After entering the bloodstream, it is oxidized to sulfate and thiosulfate and excreted mainly in the urine (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Ulutas et al. (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e)studied hydrogen sulfide emissions from a wastewater treatment plant in Istanbul, where the researchers collected samples of workplace and outdoor ambient air during three seasons: spring, summer and winter, and found that hydrogen sulfide concentrations increased with seasonal temperature and reached peak concentrations in summer. In hot or heat-exposed environments, the concentration of volatile hydrogen sulfide in the air increases, while workers sweat more and urinate less (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). This can lead to increased absorption of hydrogen sulfide through the respiratory tract and skin and decreased excretion through urine, which ultimately leads to accumulation of hydrogen sulfide in the body and thus promotes the development of dyslipidemia.\u003c/p\u003e \u003cp\u003eThe effects of hydrogen sulfide on dyslipidemia are intricate, and it has been suggested that hydrogen sulfide promotes lipid fractionation and causes dyslipidemia. In fly experiments, hydrogen sulfide supplementation was found to promote lipid hoarding, while knockdown of the CSE gene inhibited lipid hoarding in mice on a high-fat diet (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). In addition, it has also been suggested that hydrogen sulfide is an inhibitor of cytochrome oxidase, which enters the cell and binds to cytochrome oxidase in the mitochondria, blocking the endorespiration of the cell and causing tissue hypoxia. Hydrogen sulfide also inhibits monoamine oxidase and free radical damage. Therefore, it is likely that hydrogen sulfide-induced dyslipidemia in workers is associated with enhanced free radical formation and lipid peroxidation (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). As to the potential mechanism how hydrogen sulfide is related to lipids with or without heat exposure, further research is needed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Stratified analysis of susceptible populations\u003c/h2\u003e \u003cp\u003eTo identify the sub-groups who are more vulnerable to heat-attributed dyslipidemia, we conducted stratified analyses and found that women aged\u0026thinsp;\u0026ge;\u0026thinsp;35 years without drinking and smoking had a higher risk of TC-related dyslipidemia. A cross-sectional study by Li et al.(\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e), which analyzed data from 13,354 general population lipid levels and environmental monitoring sites nationwide, found that women were at higher risk for dyslipidemia than men. Frenandez et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e) suggested that the increased risk of dyslipidemia in middle-aged and older women may be related to the decrease in estrogen levels after menopause. Frenandez thinks this may be because high estrogen levels may increase the rate of hepatic uptake of TC and LDL-C, promote HDL-C synthesis and facilitate bile acid secretion, and accelerate the clearance of cholesterol from the body. Evidence has shown that the risk of dyslipidemia is significantly increased in women with reduced estrogen levels or during menopause (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Therefore, these studies suggested that women aged equal to or older than 35 years should examine their blood lipids regularly.\u003c/p\u003e \u003cp\u003eIn a study on the relationship between smoking and dyslipidemia, Craig et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e) analyzed 54 articles on the relationship between smoking and lipids in adults, suggesting that smokers had significantly higher levels of TC, TG, and LDL-C, and smokers had lower HDL-C than nonsmokers. In our study, we found a higher risk of abnormal TC in non-smoking female workers. Whether TC abnormalities are related to smoking and the mechanisms involved may need to be further investigated.\u003c/p\u003e \u003cp\u003eIn addition, we identified the sub-groups vulnerable to heat-attributed dyslipidemia, and found that most of them are mainly engaged in positions related to oil refining, catalytic reforming and laboratory oil testing. Generally, these positions are exposed to flammable and explosive products throughout the production process, and improper storage or negligence may cause explosions (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e), so fire and smoking are strictly prohibited in the production plant and process, and workers need to be sober and alcohol-free at all times. This could also explain our findings from a new perspective.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Strengths and limitations of the study\u003c/h2\u003e \u003cp\u003eBased on a retrospective cohort study design with a 9-year quality occupational heat examination data and a large sample size, GEE models were used in this study to analyze the impact of high temperature alone or combined with other occupational hazards on dyslipidemia among petrochemical workers. Nevertheless, several limitations need to be addressed: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) The occupational exposure and outcome variables were dichotomous, and the dose-response relationship could not be estimated. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) In this study, we only considered process-generated heat exposure in the workplace and did not incorporate the possible effects of changes in local meteorological conditions on blood lipids(\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) As information about workers\u0026rsquo; history of lipid-lowering drug use and dietary intake is unavailable, these factors were not adjusted in the model analysis. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) The workers of the two petrochemical plants had shift work, and we did not take the possible effect of shift work on blood lipid fluctuations into account(\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"5.Conclusions","content":"\u003cp\u003eThe negative association between workplace heat exposure and dyslipidemia may be attributed to the healthy worker effect, however, occupational heat exposure combined with gasoline or hydrogen sulfide could significantly increase the risk of dyslipidemia. Health interventions in the petrochemical industry should pay more attention to female workers aged\u0026thinsp;\u0026ge;\u0026thinsp;35 years without smoking and drinking.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTC \u0026nbsp; \u0026nbsp; \u0026nbsp;Total cholesterol\u003c/p\u003e\n\u003cp\u003eLDL-C \u0026nbsp; Low-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eHDL-C \u0026nbsp; High-density lipoprotein cholesterol\u003c/p\u003e\n\u003cp\u003eTG \u0026nbsp; \u0026nbsp; \u0026nbsp;Triglyceride\u003c/p\u003e\n\u003cp\u003eIRR \u0026nbsp; \u0026nbsp; \u0026nbsp;Incidence Rate Ratio\u003c/p\u003e\n\u003cp\u003eHTC \u0026nbsp; \u0026nbsp; Hypercholesterolemia\u003c/p\u003e\n\u003cp\u003eHTG \u0026nbsp; \u0026nbsp; Hypertriglyceridemia\u003c/p\u003e\n\u003cp\u003eLHDL-C \u0026nbsp;Hypo-high-density lipoproteinemia\u003c/p\u003e\n\u003cp\u003eHLDL-C \u0026nbsp;Hyper-low-density lipoproteinemia\u003c/p\u003e\n\u003cp\u003eGEE \u0026nbsp; \u0026nbsp; Generalized estimating equations\u003c/p\u003e\n\u003cp\u003eBMI \u0026nbsp; \u0026nbsp; Body Mass Index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has received approval from the Ethics Committee of Fujian Medical University (Fujian Ethics Examination No.111). All participants included in the study have signed informed consent.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYifeng Chen:\u003c/strong\u003e Methodology, Data analysis, Writing \u0026ndash; original draft, Data curation and management. \u003cstrong\u003eXiaoyun Li\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eData curation and data collection, Data analysis. \u003cstrong\u003eQingyu Li:\u003c/strong\u003e Writing \u0026ndash; review \u0026amp; editing, Methodology, Data analysis.\u003cstrong\u003e\u0026nbsp;Yan Yang, Zitong Zhang, Yilin Zhang\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eWriting \u0026ndash; review \u0026amp; editing, Methodology, Data curation. \u003cstrong\u003eShanshan Du, Fei He, Zihu Lv\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eSupervision, Conceptualization, data collection and management.\u003cstrong\u003e\u0026nbsp;Weimin Ye, Wei Zheng, Jianjun Xiang:\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology, Writing \u0026ndash; review \u0026amp; editing, Supervision, Formal analysis, Funding acquisition.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Minjiang Scholar Start-up Research Fund of Fujian Province (Grant No. 2019-9202001001) and 2021 Natural Science Foundation of Fujian Province of China (2021J01722).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all the participants who took part in the study.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eArticle authors do not have the right to share data.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlfares HK. 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Atherosclerosis. 2020;313:156\u0026ndash;69. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.atherosclerosis.2020.08.015\u003c/span\u003e\u003cspan address=\"10.1016/j.atherosclerosis.2020.08.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Occupational exposure, Petrochemical industry, Dyslipidemia, High temperature, Hydrogen sulfide, Gasoline","lastPublishedDoi":"10.21203/rs.3.rs-4446442/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4446442/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e This study aims to assess the influence of occupational heat exposure on dyslipidemia among petrochemical workers and identify susceptible groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A total of 30,847 workers’ occupational health examination data were collected from two petrochemical plants in Fujian Province from 2013 to 2021. The dataset included occupational exposure information and blood lipid test results, encompassing total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride (TG) levels. A Generalized Estimating Equations model was used to analyze the impact of heat exposure alone or coupled with other occupational hazards on workers' blood lipids.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The one-factor exposure model shows that most of the individual occupational hazards were significantly negatively associated with dyslipidemia. In the two-factor exposure model, heat combined with gasoline exposure (Incidence Rate Ratio, IRR=1.267, 95% CI 1.117-1.437) and heat combined with hydrogen sulfide exposure (1.324, 1.166-1.505) significantly increased the risk of high TC. Stratified analysis showed that in the dual exposure model of high temperature combined with gasoline or hydrogen sulfide, women , individuals aged over 35, non-smoking , and non-alcohol drinking were more likely to have heat-related high TC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e The impact of heat and other petrochemical factors on blood lipids may be affected by healthy worker effect. Heat exposure combined with gasoline or hydrogen sulfide can significantly increase the risk of dyslipidemia. Occupational health interventions should pay more attention to female workers aged over 35 years who do not smoke or drink alcohol.\u003c/p\u003e","manuscriptTitle":"Impact of occupational heat exposure on blood lipids among petrochemical workers: An analysis of 9-year longitudinal data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-07 23:01:17","doi":"10.21203/rs.3.rs-4446442/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b487b502-ac06-4a31-b261-db41931be843","owner":[],"postedDate":"June 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-06-07T23:02:34+00:00","versionOfRecord":{"articleIdentity":"rs-4446442","link":"https://doi.org/10.1289/isee.2023.OP-373","journal":{"identity":"isee-conference-abstracts","isVorOnly":true,"title":"ISEE Conference Abstracts"},"publishedOn":"2023-09-17 23:02:34","publishedOnDateReadable":"September 17th, 2023"},"versionCreatedAt":"2024-06-07 23:01:17","video":"","vorDoi":"10.1289/isee.2023.OP-373","vorDoiUrl":"https://doi.org/10.1289/isee.2023.OP-373","workflowStages":[]},"version":"v1","identity":"rs-4446442","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4446442","identity":"rs-4446442","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}