Multiple categories of polycyclic aromatic hydrocarbons in atmospheric PM2.5 associated with changes in lipid profiles: A longitudinal study in Beijing

Xue Wang Peking Union Medical College Hospital Ang Li Peking Union Medical College School of Basic Medicine: Chinese Academy of Medical Sciences and Peking Union Medical College Institute of Basic Medical Sciences Meiduo Zhao Peking Union Medical College School of Basic Medicine: Chinese Academy of Medical Sciences and Peking Union Medical College Institute of Basic Medical Sciences Jing Xu Peking Union Medical College School of Basic Medicine: Chinese Academy of Medical Sciences and Peking Union Medical College Institute of Basic Medical Sciences Yayuan Mei Peking Union Medical College School of Basic Medicine: Chinese Academy of Medical Sciences and Peking Union Medical College Institute of Basic Medical Sciences Qun Xu (  xuqun@ibms.cams.cn ) Institute of Basic Medical Sciences Chinese Academy of Medical Sciences https://orcid.org/00000003-0141-4427


Conclusion
Multiple categories of PAHs were signi cantly associated with altered lipid pro les. Although some PAHs are not carcinogenic, they may cause dyslipidemia, which in turn affects chronic diseases.
Signi cantly, abnormalities in lipid pro les have a particularly profound in uence on CVDs and represent around half of the population-attributable risk [10]. In fact, management of serum lipid levels has become a central objective in the effort to prevent cardiovascular events and the main target of therapy [11].
Although several traditional factors, such as age, sex, smoking [12] as well as alcohol consumption [13], have been closely related with alterations in blood lipid levels, recent studies suggested that new factors such as ambient air pollutants, especially particulate matter in aerodynamic diameter less than 2.5 µm (PM 2.5 ), were associated with altered lipid pro les [14][15][16]. Therefore, nding the key components of PM 2.5 that affect lipid levels is an urgent problem demanding prompt solution.
Polycyclic aromatic hydrocarbons (PAHs), composed of two or more fused aromatic (benzene) rings, have been considered as important components of primary organic aerosols [17]. Incomplete combustion of organic materials produces a large amount of PAHs, which diffuse through atmospheric transport, whereas PAHs are considered ubiquitous contaminants, detected in numerous environmental matrices such as air, fresh and groundwater, soil and sediments. [17]. PAHs have attracted worldwide attention because of their carcinogenicity and mutagenicity [18,19]. Environmental epidemiological studies have utilized carcinogenic PAHs as the basis of molecular epidemiological research among residents exposed to ambient air pollutants [20]. Several investigations have revealed that PAHs exposure may increase the risk of chronic non-infectious diseases such as artery diseases as well as CVDs [21,22], and oxidative stress may be one of the potential mechanisms [23,24]. However, there has been a lack of research on ambient PAHs exposure and lipid pro les at population level.
Recent research showed that airway exposure to benzo(a)pyrene, mostly emitted from fossil fuel, wood and coal combustion [25], may dysregulate lipid metabolism of mice [26]. The underlying mechanism may be that PAHs activate aryl hydrocarbon receptor (AhR) signaling pathway, thereby mediating the abnormal expression of cytochrome P450 and promoting the development and progression of dyslipidemia [26,27]. To our knowledge, there is a lack of studies comprehensively evaluating the effects of multiple categories of atmospheric PAHs on lipid pro les among general populations. This is essential for understanding the mechanism by which PAHs increase the risk of chronic non-infectious diseases.
Therefore, in this prospective follow-up study we focused on the lipidemic effects following exposure to PM 2.5 -bound PAHs, with repeated measurements conducted from 2016 to 2018.

Study Participants and Design
From November 2016 to January 2018, we conducted a longitudinal study in order to investigate the health impacts of air pollution and its various chemical constituents on adults in Beijing. Based on the spatial distribution of the annual average PM 2.5 levels in 2015, ve communities were selected as sampling sites to represent different pollutant levels in Beijing. Two samplers were set up at each sampling site [28]. We included participants who have lived in those communities for more than ve years and will still be there in the next few years. Those who were unable to accomplish the follow-up studies were excluded. Participants who had severe cardiovascular diseases (stroke, congestive heart failure and myocardial infarction) and cancers were also excluded. Four repeated measurements were conducted. Visit 1 was conducted from November 2016 to December 2016, then followed by 3 followups: visit 2 in May 2017, visit 3 in November 2017, and visit 4 in January 2018. Health assessment questionnaires, physical examination and biological sample collection were performed during each visit. We included 98 participants who met the prespeci ed criteria. Among them, 97 completed all 4 visits, and 1 participant completed 3 visits. Thus, data from a total of 391 person-times was compiled and applied in this analysis. The ethics was approved by Institutional Review Board of Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences (IBMS, CAMS). Each participant completed a written informed consent. Ambient PM 2.5 sampling in the communities During the eld survey, we measured concentrations of PM 2.5 , the US Environmental Protection Agency's (USEPA) 16 priority PAHs, organic carbon (OC) and element carbon (EC) at xed samplers in each community. A total of ten xed samplers were set up in ve communities, including Fangshan, Dongcheng, Chaoyang, Liu Hegou and Qian Nantai, with no major roads or factories within a radius of 400, 150, 150, 150 and 160 meters, respectively. Medium ow samplers (TH-150C, Wuhan Tianhong, China) were used to measure PM 2.5 mass concentration. Each sampler was equipped with quartz-ber lters to determine the inorganic and organic constituents. Five daily successive samples were obtained prior to each visit.

Laboratory analysis for PAHs and other constituents
The PM 2.5 mass concentrations were determined following the standard operation procedures. Further details can be found elsewhere [28]. Quartz lters were analyzed by a thermal-optical carbon analyzer (Model 2001A, Atmoslytic Inc., USA) to determine organic (OC) and elemental carbon (EC) [28].
Given the cumulative lag effects of air pollutants [30], 1-to 5-days moving average (MA) concentrations of PAHs and other pollutants were examined. Speci cally, 1-day MA was de ned as the pollutant concentrations from 8 a.m. the day before clinic visit to 8 a.m. the day of the clinic visit, and 2-days MA was de ned as the average pollutant concentrations from 8 a.m. two days before clinic visit to the day of clinic visit, etc.
Quality control and quality assurance

Meteorological measurements
We obtained hourly temperature and relative humidity from the China Meteorological Administration for the entire study period. Daily averages of those meteorological parameters were calculated. 5-days moving average of temperature and relative humidity were calculated for further analysis.

Questionnaire, physical examinations and biomarker measurements
Our study used a standardized eld survey protocol, with a brief description as follows: Standardized questionnaires were designed to collect the following information: age, sex, education levels, hypertension, diabetes, alcohol consumption, smoking, and indoor smoking status; Physical examination was carried out for each resident, including the measurement of sitting blood pressure, height and weight, body mass index (BMI) was also estimated by the following equation: weight (kg) ÷ height 2 (m 2 ); Fasting venous blood of residents was collected at 8-9 a.m., and then blood glucose, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), total cholesterol (TC) and triglycerides (TG) were measured in the Department of Clinical Laboratory within the Peking Union Medical College Hospital.
According to the information provided by questionnaire, physical examinations and biomarker measurements, we rede ned diabetes and hypertension patients. Participants who reported physiciandiagnosed diabetes or fasting blood glucose ≥ 7.0 mmol/L were de ned as diabetes. Participants who reported physician-diagnosed hypertension or SBP ≥ 140 mmHg and/or DBP ≥ 90 mmHg were de ned as hypertension.
Additionally, in order to comprehensively evaluate the changes in lipid pro les and their potential effects, several lipoprotein ratios or "atherogenic indices" were calculated [33], which better predict cardiovascular diseases than the isolated lipid parameters [34][35][36]. To be speci c, the following equations were used: Three linear mixed-effects models were tted to evaluate the associations between multiple categories of PAHs and lipid pro les. In single-pollutant model (model 1), each category of PAHs with a speci c days MA was incorporated as the xed-effect term, and an unique identi cation (ID) for each participate was incorporated as the random-effect term. Several potential confounding factors were also included in this model: (1) individual characteristics, including age and BMI as continuous variables; sex, education, smoking and alcohol consumption as categorical variables; (2) meteorological factors were incorporated using natural splines with three degrees, including 5-days MA of mean temperature and relative humidity; (3) day of the week. We also tted two-pollutant models (model 2) to control any potential confounding caused by other pollutants, each of which included adjustment for PM 2.5 , OC, and EC on the same speci c days MA with PAHs, respectively. In order to control the collinearity among different pollutants, constituent-residual models (model 3) were tted. Residuals were obtained in a linear model containing both PM 2.5 and a speci c category of PAHs and then added into the main model replacing pollutants.
We performed strati ed analysis by cigarette smoking (yes, no), alcohol consumption, age (< 65y, ≥ 65y), BMI (< 25, ≥ 25), diabetes mellitus and hypertension. In addition, several sensitivity analyses were applied to test the robustness of the associations. (1) considering the potential associations between indoor smoking and indoor PAHs exposure [37], participants who were smokers or lived in smoker residency were excluded. (2) participates with diabetes or hypertension were excluded. (3) FLU was included in c-PAHs because of its relatively higher toxic equivalent factor compared to BghiP [38]. (4) the association between multiple categories of PAHs abundances per g of PM 2.5 and lipid pro les were evaluated.
All statistical analyses were performed with R software, version 3.5.0 (R Foundation for Statistical Computing), using "lme4" and "splines" packages. For the association between PAHs concentrations and lipid pro les, model estimates were calculated as per 10 ng/m 3 increase in PAHs exposure. For the association between PAHs abundances and lipid pro les, model estimates were calculated as per 1/1000 increase in PAHs abundances. A p-value less than 0.05 was considered statistically signi cant. Table 1 shows the demographic characteristics of 98 participants. Among them, 38 (38.8%) were male and 60 (61.2%) were female, with mean age of 62.7 ± 9.9 years, while 56 (57.1%) had a BMI of 25 kg/m 2 or more. Among them, 23 (23.5%) were current smokers, 24 (24.5%) were exposed to indoor smoking and 37 (37.8%) were regular drinkers, 34 (34.7%) had diabetes mellitus, and 65 (66.3%) had hypertension.   Table S1 shows the mean concentrations for 16 individual PAH. Table 3 summarizes lipid indices during each visit. HDL-C, CRI-I and AC levels were signi cantly different throughout a series of four measurements.   There were weak correlations between ΣPAHs and OC, EC as well as PM 2.5 , the correlation coe cients were 0.39, 0.34 and 0.29, respectively (p < 0.05 in all case; Table S2). ng/m 3 increase in exposure to all categories of PAHs from 1-to 5-days MA (Fig. 2). By contrast, the largest increases in TG levels by 4.08% (95%CI, 0.20 to 8.33%) to 17.35% (95%CI, 2.02 to 33.64%) were associated with a 10 ng/m 3 increase in LMW PAHs, nc-PAHs, or ΣPAHs from 2-to 5-days MA (Fig. 2). We also observed a marginally signi cant increase in TG levels by 6.18% (95%CI, -0.99 to 12.75%) to 8.33%
In subgroup analysis, elevated levels of LDL-C, TC, CRI-II and NHC were observed in smokers. Figure S9 shows the percentage changes in eight lipid indices strati ed by smoking status. Among smokers, we observed per 10 ng/m 3 increase in c-PAHs associated with 37.71% (95% CI, 9.42 to 73.33%), 49.18% (95% CI, 12.75 to 97.39%), 36.34% (95% CI, 13.88 to 64.87%) and 47.69% (95% CI, 9.42 to 99.37%) increments in LDL-C, TC. CRI-II and NHC at prior 2-days MA, respectively. However, signi cant decrease in HDL-C, signi cant increase in TG, CRI-I and AC were observed in non-smokers. Similar results can be observed in subgroup analysis by alcohol consumption ( Figure S10). Furthermore, we found multiple categories of PAHs were signi cantly associated with lipid indexes among participants ≥ 65 years old, BMI ≥ 25 kg/m 2 , non-diabetes participates, and hypertension participates ( Figure S11 to Figure S14).

Discussion
This longitudinal study was conducted mainly to investigate the associations between multiple categories of PAHs and lipid pro les alternations. Our study shows that different categories of PAHs mixtures, including LMW (<4-rings), HMW (≥ 4-rings), carcinogenic and non-carcinogenic PAHs, could promote signi cant, but different patterns of alterations in blood lipids. We also found that PAHs may increase the level of blood lipid indices, such as the CRI-I and CRI-II, both of which are important indicators of vascular risk, and are more sensitive than the isolated lipid parameters. These ndings provide epidemiological evidence that PM 2.5 -associated PAHs might be a novel risk factor to CVDs, prompting that traditional risk factors cannot explain all of CVDs [39]. Our results also suggest that exposure to various PAHs, whether carcinogenic or not, might cause lipid disorders, thus increasing the risk of chronic diseases.
We explored the relationship between multi-categories of atmospheric PAHs and lipid pro les among general community populations for the rst time. Considering that cigarette smoking is one of the main indoor PAHs sources, we also assessed this relationship in participates from non-smoker residencies. Our ndings provide evidence that environmental PAHs may disturb proatherogenic lipid pro les, as evidenced by decreased HDL-C levels, increased TG levels, and increased atherosclerotic indices such as CRI-I, CRI-II and AC. This may further trigger several chronic diseases (e.g., CVDs) under a real-world exposure scenario. These ndings extended our current knowledge on population-based health outcomes following exposure to PM 2.5 constituents, which are novel and of great signi cance to public health.
Compositions of different PAHs can be used to indicate varying emission sources. In this study, we divided 16 priority PAHs into multiple categories based on their molecular weight and carcinogenicity.
LMW-PAHs are considered to be less carcinogenic than HMW-PAHs [40,41]. However, the former usually have a higher concentration in the urban and are more likely to produce toxic secondary pollutants. Coke production, wood and coal combustion and vehicular emissions are the main sources of LMW-PAHs, such as PHE and ANT [42]. HMW-PAHs exhibit nearly all the carcinogenic potential of total PAHs, which were considered to be the leading biologically active pollutants in the atmosphere [43]. Sources of HMW-PAHs include car exhausts (petrol and diesel), incinerators, wood combustion, oil burning, domestic coalstove emissions and tobacco smoke [41,42]. Our self-monitored pollutant data revealed that the concentrations of LMW-PAHs was highest in spring, with a higher LMW/HWM ratios, which means that petrochemical processes or fuel evaporation was the major sources of PAHs [42]. On the contrary, HMW-PAHs, especially 4-ring congeners, had the highest concentrations in winter, which might be due to increased emissions caused by heating, and reduced dispersion of pollutants caused by meteorological factors in cold environments [44]. The results of HMW-PAH concentrations are overall consistent with other reports in Beijing [42]. More importantly, the associations between PAHs exposure and lipid indices remained signi cant regardless of the molecular weight or the carcinogenicity. Our results provided environmental epidemiological evidence that PAHs exposure can lead to disruption of pre-clinical indicators, and even to increase the risk of chronic diseases such as CVDs. Therefore, atmospheric PAHs should be controlled either by source-directed measures like ltering pollution from relevant industries (e.g., incinerator) or limiting automobile emissions.
Although no studies have directly investigated the relationship between atmospheric PAHs and lipid pro les, some articles have explored the effect of urinary PAH metabolites on isolated lipid parameters. A cross-sectional Chinese study showed that exposure to 1-hydroxynaphthalene, 9-hydroxy uorene, 1hydroxyphenanthrene, 4-hydroxyphenanthrene, and 9-hydroxyphenanthrene may increase TC levels [45].
In addition, exposure to 1-hydroxynaphthalene may also increase LDL-C levels. However, these PAH metabolites may not change hyper-TG levels [45]. Ranjbar et al. reported that exposure to high levels of PAH metabolites were related to a greater likelihood of dyslipidemia in a dose-dependent manner [46]. However, a research from southern Sweden did not nd signi cant association of urinary PAH metabolites on HDL-C or TC in chimney sweepers [47]. These inconsistent ndings might be the result of differences in the demographic characteristics of the participants and PAH exposure sources. This may indicate that the effects of different PAH chemicals on lipid metabolism are heterogeneous.
The underlying mechanisms of PAH impact on lipid metabolism are not fully understood, but the activated aryl hydrocarbon receptor (AhR) signaling pathway may be involved in this process. Metabolic pro le of PAHs could accelerate by inducing microsomal cytochrome P450, which is mediated by binding to a cytosolic receptor protein, the AhR. Alexander et al. showed that the activation of AhR signaling pathway may promote TG accumulation and ultimately cause lipid metabolism disorders [48]. In addition, because of PAHs' distribution and metabolism characteristics, the highest extent of metabolism usually occurs in the liver, while adipose tissue contains higher PAHs concentrations compared with other tissues. As is well documented, apolipoprotein A and apolipoprotein B are synthesized in the liver and are the major structural component of HDL-C and LDL-C, respectively. It is not clear whether PAHs could directly affect apolipoprotein production in the liver or whether it could be distributed in adipose tissue to alter lipid deposition.
There are several advantages in this longitudinal study. First, this study has a relatively large sample size, along with repeated measurements. All participants lived in their community for more than ve years and without occupational exposure to PAHs. Most of them were older and more sensitive to the harmful impacts of atmospheric PAHs. Second, the original sixteen priority PM 2.5 -bound PAHs were detected in this study, which re ects the unique characteristics of particulate PAHs in the community's environment. Third, we used a comprehensive lipoprotein ratio or "atherogenic indices" instead of isolated lipid parameters, which optimized its predictive value for CVDs.
Several study limitations should also be noted. First, more sampling sites should be added to better re ect the distribution of PAHs in Beijing. Second, individual exposure assessment was based on the community levels, so that there was a lack of accuracy on the individual level, which could be advanced in further work. Third, indoor environments are also key factors in health risk assessment in residencies. Indoor smoking, heating and incense burning are main sources of indoor PAHs [37]. In this study, indoor PAHs concentrations were not monitored given the limitation in equipment and human hands, which should be improved in further studies. However, in order to reduce the potential effects caused by indoor PAHs. We collected smoking status and indoor smoking and excluded potential smokers in sensitivity analysis. The results were almost consistent with our main ndings. In addition, participants in this study used central heating and did not burn incense indoors.

Conclusion
This study reveals for the rst time that exposure to multiple categories of PM 2.5 -bound PAHs was associated with signi cant alterations of lipid pro les among a general community population in Beijing, suggesting that atmospheric PAH exposure might be a novel risk factor for changing of lipid pro les and even afterwards cardiovascular diseases. Among blood lipids, HDL-C and TG were signi cantly associated with multiple categories of PAHs. Among lipid indices, CRI-I, CRI-II and AC were signi cantly associated with multiple categories of PAHs. Our results also highlighted that even for the noncarcinogenic PAHs, their adverse effects on lipid metabolism and chronic diseases should not be overlooked. These ndings are novel and of great signi cance to public health by addressing the knowledge gap between atmospheric PAHs exposure and lipid pro les disruption.

Availability of data and materials
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.  Changes in lipid indices associated with a 10 ng/m3 increase in different categories of PAHs.

Figure 4
Changes in blood lipid associated with PAHs among participants from non-smoker residencies.

Figure 5
Changes in lipid indices associated with PAHs among participants from non-smoker residencies.

Supplementary Files
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