Annals of Biometrics and Biostatistics

Short-Term Exposure to Coarse Particles and Dyslipidemia Risk in Chengdu, China: A Time-Series Study

Research Article | Open Access | Volume 6 | Issue 1

  • 1. Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, China.
  • 2. Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR, China
  • 3. Department of Endocrinology, Fudan University, China
  • 4. Clinical Laboratory, University of Electronic Science and Technology of China, China.
  • 5. Department of Dermatology, Sichuan Provincial People’s Hospital, China
  • 6. Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, China.
+ Show More - Show Less
Corresponding Authors
Bin Cui, Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Rm1A13, 573 Xujiahui Road, Shanghai, China, 200025. Tel: +86-21-6437-0045; Fax: +86-21-6474-9885

Objectives: Dyslipidemia, as a major risk factor for atherosclerotic cardiovascular disease (CVD), contributed to a large number of deaths. Ambient air pollution was recognized as a significant risk factor for dyslipidemia. We designed the study to explore how short-term exposure to ambient particulate matter, especially PM2.5-10, affected the incidence of dyslipidemia.

Methods: We used lipid data from 309,654 persons provided by the Medical Examination Center of Sichuan Provincial People’s Hospital. Daily air pollutants and meteorological data derived from the nearest eight monitoring sites owned by the China Meteorological Administration. To evaluate the acute effects of ambient particulate matter on dyslipidemia in both the spatial and lag dimensions, we used a distributed lag non-linear model.

Results: We found that an increase of PM2.5-10 concentrations with an interquartile range (29.5 μg/m3) was positively associated with dyslipidemia with apparent lag effects cumulative effects at the lag of 0–7days. Furthermore, we discovered the unique role of PM2.5-10 on hypertriglyceridemia with a lag day of 1-3 days after adjusting the effect of PM2.5, and the cumulative impacts of PM2.5-10 peaked at a lag of 0–4 days (RR 1.045, 95%CI 1.005-1.087, p-value=0.05). Stratified analyses showed that younger, female or physically lighter individuals were potentially vulnerable groups.

Conclusions: Our study found that PM2.5-10 positively link with hypertriglyceridemia at lag days.


• Dyslipidemia

• Air pollution

• Distributed lag nonlinear models

• Coarse particles

• China


Jing R, Zheng X, Zhang Z, Su Y, Qi J, et al. (2023) Short-Term Exposure to Coarse Particles and Dyslipidemia Risk in Chengdu, China: A Time-Series Study. Ann Biom Biostat 6(1): 1039.


TC: Total Cholesterol; LDL-C: Low-density Lipoprotein Cholesterol; HDL: High-density Lipoprotein; TG: Triglycerides; CVD: Cardiovascular Disease; HyperLDL-C: Hyperbetalipoproteinemia; GBD: Global Burden of Disease Study; PM: Particulate Matter; HyperTC: Hypercholesterolemia; NO2 : Nitrogen Dioxide; SO2 : Sulfur Dioxide; O3 : Ozone; RH: Relative Humidity; DLNM: Distributed Lag Non-linear Model; GAM: Generalized Additive Model; ns: Natural Cubic Spline; Df: Degrees of Freedom; BMI: Body Mass Index; RR: Relative Risk; CI: Confidence Interval; IQR: Interquartile Range; WHO: World Health Organization; AQG; Air Quality Guideline; VLDL: Verylow-density Lipoproteins


Dyslipidemia, referring to the imbalance of lipids such as total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein (HDL), and triglycerides (TG), has been linked to a wide variety of adverse effects in cardiovascular disease (CVD)[1], pancreatitis [2] and non-alcoholic fatty liver disease [3]. Moreover, a large number of deaths attributed to dyslipidemia; for example, hyperbetalipoproteinemia (hyperLDL-C), a high risk for ischemic heart disease and ischemic stroke, contributed to about 4.40 million deaths globally in 2019, according to Global Burden of Disease Study (GBD) [4]. Changes in the prevalence of dyslipidemia show the same trend as socioeconomic development and are related to diet and unhealthy lifestyle habits [5]. As China’s social environment has shifted and its economic volume has skyrocketed in decades, the prevalence of dyslipidemia has rapidly and substantially increased to more than 34% [6]. Furthermore, rapid economic expansion causes vital environmental challenges alongside health problems.

Ambient particulate matter (PM) pollution strongly linked to social and economic development. And it represents one of the risk exposures with the largest increase from 2010 to 2019 [7], and is widely acknowledged as a significant risk factor for human health. Earlier studies have shown that prolonged exposure to PM significantly increased the mortality risk alongside various diseases, especially for CVD [8]. Dyslipidemia might be one of the possible mediators that mediate the effect of PM on mortality, as earlier research has established a link that prolonged PM exposure increased the risk of dyslipidemia [9]. However, the increased daily mortality and CVD risk associated with short-term PM exposure have raised considerable concern in recent years [10]. We then wanted to explore whether there was any link between short-term exposure to PM and dyslipidemia. As previous studies have not yielded consistent results, we previously estimated the effect of short-term exposure to PM2.5 (fine particles, diameter < 2.5 μm) on dyslipidemia where found that PM2.5 enhanced the relative risk of hyperTC (hypercholesterolemia), hyperLDL-C and hypertriglyceridemia (hyperTG) by a substantial amount [11]. In this article, we look more at the short-term role of PM2.5-10 (coarse particles, 2.5 μm < diameter < 10 μm).

Ambient particulate matter is a heterogeneous combination of solid and liquid particles floating in the air with two main components, PM2.5 and PM2.5-10, differ in terms of the impact on human health because of their diversity in composition and differential deposition in the body. A systematic review by Brunekreef, B.et al. proposed that fine and coarse particles are two different types of contaminants that must be examined separately in epidemiologic and research investigations [12]. Nevertheless, the health impacts of PM2.5-10 have received increased attention. The earlier studies discovered the unique role in PM2.5-10 in human health, including the mortality, morbidity, prevalence, or hospital admissions for various diseases [13,14]. Many studies explored inconsistent results about the relationship between PM2.5-10 and dyslipidemia. Few studies reported that PM2.5-10 correlated to changes in TG [15,16]. However, a study in Korea found no association between PM2.5-10 and lipid profiles [17].

Based on existing studies, the link between exposure to PM2.5-10 and dyslipidemia dosed not elucidated clearly. Our study attempted to explore the impact of short-term PM exposure on dyslipidemia, precisely the effect of PM2.5-10, among Chinese adults in both the spatial and lag dimensions. In addition, we investigated the possible moderating effects of age, gender, and BMI on these correlations.


Data collections

Lipids data: The lipid data collected from the Medical Examination Center of Sichuan Provincial People’s Hospital. Peripheral venous blood samples collected from 309,654 subjects aged 18-79year-old. These samples were collected by trained healthcare professionals using the automatic biochemical analyzer (Olympus AU5421, Japan) following an overnight fast, spanning the period from May 10, 2015, to May 10, 2017. The lipid data recorded after biochemical analysis of peripheral venous blood samples. Dyslipidemia is the occurrence of one or more of the following conditions: hypercholesterolemia (hyperTC, TC≥ 5.18 mmol/L), hyperbetalipoproteinemia (hyperLDL-C, LDL-C ≥ 3.4 mmol/L), hypoalphalipoproteinemia (hypoHDL-C, HDL-C ≤ 1.3 mmol/L) and hypertriglyceridemia (hyperTG, TG≥ 1.7 mmol/L), according to the National Lipid Association Recommendations[18]. The study did not need informed consent as the data were without any personal identifiable information. This study was approved by Sichuan Province Academy of Medical Sciences (Ethics Committee Approval Number: 2017- 156).

Air pollution and meteorological data: Air pollutants and meteorological data collected from eight environmental monitoring stations in Chengdu (http://www.cnemc.cn) from May 10, 2015, to May 10, 2017. Air pollutants data included 24- hour mean concentrations of PM2.5, PM10 (diameter < 10 μm), nitrogen dioxide (NO2 ), sulfur dioxide (SO2 ), and 8-hour mean concentrations of ozone (O3 ). Daily mean the concentration of PM2.5-10 were calculated by subtracting the daily mean concentrations of PM2.5 from PM10. Meteorological data included daily average temperature (°C) and day-to-day average relative humidity (RH). In the study there was no missing data.

Statistical analysis

Because distributed lag non-linear model (DLNM)[19], has been used to characterize relationships with both immediate and delayed impacts of environmental stressors, we applied a quasi-Poisson generalized additive model (GAM) with DLNM to estimate the influence of short-term PM exposure on the incidence of dyslipidemia, particularly in its lag dimension. First, the implementation of this model rests on the foundation of a cross-basis bi-dimensional function, one for the exposureresponse relationship and the other for the lag-response relationship. We fitted exposure-response associations using linear functions for air pollutants and non-linear function for temperature [20]. We fitted lag-exposure associations using polynomials function with degree of 3 and set the maximum lag to 7 days, which were typically short [21]. Second, as our interest was in short-term associations, we controlled the longterm patterns and seasonality by fitted time using natural cubic spline (ns) with 7 degrees of freedom (df) /year. We fitted the effect of RH using ns with 3 df and weekend days were also taken into account since exposure levels may be varied on weekdays and weekends. In addition, to avoid collinearity, PM10 and NO2 were not included in this multi-pollutant model. We set two models to further explore through the distinctive role of PM2.5-10. The models are crude model and the adjusted model. The study obtained from the crude model after adjusting the effect of PM2.5. Furthermore, the data stratified by sex (females and males), age (<45 years; ≥45 years) and BMI (<24 kg/m2 ; ≥24 kg/m2 ) for further analysis. We reported RRs with the 95% confidence interval (CI) of associations as the changes of the incidence of dyslipidemia for an increment of an interquartile range (IQR) concentration in three size-specific PM.

We performed sensitivity analyses by altering the df from 5 to 9 df/per year and from 3 to 5 df in the ns function for Time and RH, respectively, to see if we sufficiently controlled for long-term and the humidity trends. R (version. 4.0.3) and dlnm package[22] applied to analyze statistics and perform visualization through this study. The significance level employed in this research was 0.05.


Descriptive statistics

Table 1 displays a summary of the air pollutants, meteorological factors, and subjects’ profiles during the study period from 2015 to 2017. Daily mean values were 61.17μg/m3 for PM2.5, 102.47μg/m3 for PM10, 41.3μg/m3 for PM2.5-10, 53.61μg/ m3 for NO2 , 14.64μg/m3 SO2 , and 92.99μg/m3 for O3 . According to WHO global air quality guidelines, recommended short-term (24-hour) air quality guideline (AQG) levels were 15μg/m3 for PM2.5 and 45μg/m3 for PM10 (WHO, 2022). The daily mean levels of PM2.5 and PM10 were much higher than the AQG level. The daily mean temperature and relative humidity were 18.23±7.5°C and 77.2±11.61%, respectively. Our study population’s mean age and BMI were 42.91±13.54 years old and 23.35±3.30 kg/m2 . The mean serum levels of LDL-C, HDL-C, TC, and TG were 2.80±0.79, 1.30±0.32, 4.76±0.92, and 1.69±1.51 mmol/L, respectively. Our study computed the Pearson correlations between variables, and the results presented in (Figure S1).

Pearson correlations between air pollutants and meteorological factors

Figure S1: Pearson correlations between air pollutants and meteorological factors

The characteristics of subgroups are in [Table S1]. There was 309,654 samples, including 136,400 cases of females, 178,717 cases in the young group (age<45 years), and 184,161 cases with BMI<24 kg/m2 .

Associations between air pollutants and dyslipidemias

The associations between three size-specific PM and dyslipidemias presented in (Figure S2) and [Table S2].

Lagged and cumulative effects of PM2.5, PM2.5-10 and PM10 on dyslipidemia

Figure S2: Lagged and cumulative effects of PM2.5, PM2.5-10 and PM10 on dyslipidemia

In single day lag structures, the significant effects occurred to hyperLDL-C, hyper-TC, and hyperTG for PM2.5, and hyperLDL-C, and hyperTG for PM2.5-10. In cumulative lag structure, the significant effects occurred to hyperLDL-C, hyperTC, and hyper TG both for PM2.5 and PM2.5-10. We observed PM10, influenced by PM2.5 and PM2.5- 10, were significantly associated with hyperLDL-C, hyperTC, and hyperTG in both lag structures. The lagged and cumulative effects of PM2.5-10 on dyslipidemia using the crude and adjusted models displayed in (Figure1) and [Table 2].

Associations between an IQR increase of PM2.5-10 concentration (29.5 ?g/m3 ) and dyslipidemia

Figure 1: Associations between an IQR increase of PM2.5-10 concentration (29.5 μg/m3 ) and dyslipidemia

In the crude model, the risk of hyperLDL-C associated with PM2.5-10 exposure at lag0 (RR 1.030, 95%CI 1.002-1.058, p-value=0.05), and the risk of hyperTG was statistically significant with PM2.5-10 exposure at lag3(RR 1.009, 95%CI 1.001-1.017, p-value=0.05) and lag4 (RR 1.009, 95%CI 1.001-1.017, p-value=0.05). The cumulative effect of PM2.5-10 exposure reached the maximum at a lag of 0–7 days for hyperLDL-C (RR 1.055, 95%CI 1.002-1.018, p-value=0.05), a lag of 0-6 days for hyperTC (RR 1.046, 95%CI 1.009-1.085, p-value=0.05) and a lag of 0-6 days hyperTG (RR 1.042, 95%CI 1.009-1.077, p-value=0.05), respectively. In the adjusted model, only the risk of hyperTG was associated with PM2.5-10 exposure at lag1-3 and was higher than the risk without adjustment for PM2.5. As the lag days increased, the risk of hyperTG rose first and then decreased, reaching a maximum at lag2 (RR 1.018, 95%CI 1.005- 1.031, p-value=0.05). The cumulative effects of PM2.5-10 peaked at a lag of 0–4 days (RR 1.049, 95%CI 1.009-1.090, p-value=0.05) [Table 2]. Results consistently showed that an IQR increase of PM2.5-10 concentration (29.5 μg/m3) was positively associated with hyperTG with apparent lagged and cumulative effects.

In stratified analyses by sex, age, and BMI, Figure 2

Associations between an IQR increase of PM2.5-10 concentration (29.5 ?g/m3 ) and hypertriglyceridemia stratified by age, sex, and BMI

Figure 2: Associations between an IQR increase of PM2.5-10 concentration (29.5 μg/m3 ) and hypertriglyceridemia stratified by age, sex, and BMI

describes the results of the subgroup analyses with per IQR increase in PM2.5- 10 exposure on hypertriglyceridemia in different groups. These associations were only significant among the subjects who were female, younger than 45 years old and with BMI<24 kg/m2 . For example, the risk effects of PM2.5-10 with female were statistically significant at lag3 (RR 1.020, 95%CI 1.003-1.037, p-value=0.05) and lag4 (RR 1.017, 95%CI 1.001-1.033, p-value=0.05) and higher in the adjusted model at lag2 (RR 1.029, 95%CI 1.003- 1.057, p-value=0.05) and lag3 (RR 1.023, 95%CI 1.000-1.0457, p-value=0.05) [Table S3].

The associations of an IQR increase of PM2.5-10 exposure with dyslipidemias were robust when we altered the dfs for calendar time (5–9 df/year) (Figure S3) and altered the dfs for RH (3-6 df) (Figure S4).

(a): Association between PM2.5-10 and dyslipidemia when altering the degrees of freedom (5-9 df/year) for Time using the crude model. (b): Association  between PM2.5-10 and dyslipidemia when altering the degrees of freedom (5-9 df/year) for Time using the adjusted model

Figure S3 (a): Association between PM2.5-10 and dyslipidemia when altering the degrees of freedom (5-9 df/year) for Time using the crude model. (b): Association between PM2.5-10 and dyslipidemia when altering the degrees of freedom (5-9 df/year) for Time using the adjusted model

Association between air PM2.5-10 and dyslipidemia when altering the degrees of freedom (3-6 df/year) for RH

Figure S4: Association between air PM2.5-10 and dyslipidemia when altering the degrees of freedom (3-6 df/year) for RH


In this time-series investigation, we used DLNM to investigate the relationships between short-term exposure to size-specific particulate matters, including PM2.5, PM2.5-10, and PM10, and the prevalence of dyslipidemia in a developing nation. According to our findings, an IQR rise in PM concentrations had lag and cumulative effects on hyperLDL-C, hyperTC, and hyperTG in lag0- 7 days. Furthermore, we discovered the unique role of PM2.5-10 on hyperTG, and stratified analyses showed that the risk of PM2.5-10 exposure to hyperTG was generally higher among female, young and normal-weight participants.

Previous studies have associated lipid levels and dyslipidemia with PM exposure. Long-term exposure to PM2.5 has been associated with elevated TC, LDL-C, TG, and reduced HDL[23]. Regarding short-term exposure, several studies suggested that LDL-C, TC, and TG increased after exposure to a high level of PM2.5[24], consistent with our previous findings [11]. PM10 act similarly to PM2.5[25], partly because of the large proportion of PM2.5 in PM10 mass concentration [26]. The association between PM2.5-10 and blood lipids first proposed in a panel study published in 2007, which showed that PM2.5-10 is associated with increased serum TG in adults with asthma [15]. After that, a Chinese study found a significant correlation between long-term exposure to PM2.5-10 and the level of TG [16]. However, a research in Korea indicated no definitive link between PM2.5-10 exposure and lipid profile alterations [17]. Nevertheless, up to date, no more studies have explored the association between PM2.5-10 and dyslipidemia onsets, which restricted us from directly comparing our results with others.

This study explored the lagged effects of PM on dyslipidemia. We found that PM2.5 and PM2.5-10 had different impacts on dyslipidemia, and the effects of PM10 were mixed by both PM2.5 and PM2.5-10. We took a further step to investigate the effects of PM2.5-10 to reveal the unique role of PM2.5-10. Like mentioned above, the exposure of PM2.5 was strongly related to serum lipid levels and dyslipidemia. As a result, the impacts of PM2.5-10 were likely to confound with PM2.5 levels. We controlled the effect of PM2.5 and found that significant risks for hyperTG with an IQR increase of PM2.5-10 (29.5 μg/m3) that were noticed at lag1-3 and for cumulative effects, reached the maximum at a lag of 0–4 days (RR 1.049, 95%CI 1.009-1.092, p-value = 0.05). Even in the adjusted model, a large part of the effect of PM2.5-10 offset due to colinearity between PM2.5 and PM2.5-10, yet the higher risk suggested that the effect of PM2.5-10 on hyper-TG was still robust. These results are consistent with previous studies and more likely being an independent PM2.5-10 exposure.

The biochemical processes underlying the relationship between air pollutants and lipid metabolism are widely unknown. A number of biological routes have been suggested and shown that the impaired lipid metabolism seems related to oxidative, systemic inflammation [27], and DNA methylation [28], and the release of inflammatory factors can affect lipid metabolism [29]. On the other hand PM are tiny enough to pass through the respiratory tract and settle in the tracheobronchial tree, respiratory bronchioles, and alveoli, where gas exchange placed [30]. This might also explain why PM exposure has such a high cumulative impact. The potential mechanism of the unique effect on PM2.5-10 on hypertriglyceridemia may be related to its specific composition, especially endotoxin. Endotoxin-rich PM2.5-10 had a massive inflammatory potential and appeared more active than PM2.5[31], and endotoxin was associated with increases in serum triglycerides primarily by stimulating hepatic triglycerides production and very-low-density lipoproteins (VLDL) secretion [32].

In stratified analyses, the association between PM2.5-10 and hypertriglyceridemia modified by age, sex and weight. We found that the lag effect of PM2.5-10 has a vital impact on dyslipidemia only in people under 45 years old, which might be related to more outdoor activities and more air pollutant inhalation for younger people. Females were more sensitive to the effects of PM2.5-10. This gender gap could be due to estrogen. According to Huo et al. air pollution can operate as a probable xenoestrogen by causing reactive oxygen and oxidative stress, which can affect serum lipid levels [33]. Meanwhile, the overweight people were less vulnerable to the adverse effects caused by PM2.5-10, which was likely due to obesity’s association with chronic low-grade inflammation [34], and may be more tolerant of the inflammatory effects of short-term PM2.5-10 exposure. To describe the correlation between age, sex, and weight in changing air pollution and dyslipidemia, there is limited and inconsistent epidemiological evidence, therefore additional research is needed.

This study has some strengths. Firstly, this is the first study to estimate lag and cumulative effects of exposure to three specificsize PM on dyslipidemia, including over 30 million samples from hospitals in China, by using distributed lag non-linear model (DLNM). Secondly, compared with the previous cross-sectional studies, this time series study may provide a more generalizable result because some factors (including diet, smoking, alcohol consumption which do not change from day to day) did not have an impact on our outcome. Finally, we discovered that short-term exposure to PM2.5-10 raised the likelihood of hypertriglyceridemia substantially after adjusting the effect of PM2.5, which concluded that PM2.5-10 did have its unique role in increasing the risk of dyslipidemia.

Our research has a few limitations. Firstly, daily average PM2.5-10 concentrations obtained by subtracting PM2.5 from PM10, like earlier research. This findings might result in more exposure misclassification of PM2.5-10 than PM2.5 or PM10, as a decreased capacity to detect severe PM2.5-10 consequences. Secondly, while utilizing the mean of the nine-site monitoring stations in Chengdu to reflect population exposure is a typical practice, it can lead to exposure measurement errors, which understate the consequences of air pollution. Finally, our data are based on only one city, Chengdu, and further multiple center studies are required to improve the generalizability of the results.


Our time-series study found the significant delayed effects of short-term PM exposure on dyslipidemia rates by using DLNM. PM2.5-10 originally reported to be strongly positively linked with hypertriglyceridemia at lag days, both with and without adjustment for PM2.5. Additionally, our study likely explains part of the rise in daily mortality that linked to air pollutants and serve as a reminder to policymakers to pay attention to the identification and control of coarse particulate matter, as well as the care of vulnerable populations, including younger, female, and physically lighter individuals.


We are pleased to the staff of University of Electronic Science and Technology of China and Shanghai Jiao Tong University School of Medicine Shanghai Jiao Tong University School of Medicine, for their support in data collection and analysis. We are very grateful to all participants; without them, this study was not having been possible.

Ethics statement

No informed consent was obtained in our study because any personally identifiable information was scrambled to protect privacy and the researchers were blinded to patient identities. This study was approved by Sichuan Province Academy of Medical Sciences (Ethics Committee Approval Number: 2017- 156).


1. Borén J, Chapman MJ, Krauss RM, Packard CJ, Bentzon JF, Binder CJ, et al. Low-density lipoproteins cause atherosclerotic cardiovascular disease: pathophysiological, genetic, and therapeutic insights: a consensus statement from the European Atherosclerosis Society Consensus Panel. Eur Heart J. 2020; 41: 2313-2330.

2. Pedersen SB, Langsted A, Nordestgaard BG. Nonfasting Mild-toModerate Hypertriglyceridemia and Risk of Acute Pancreatitis. JAMA Intern Med. 2016; 176: 1834-1842.

3. Sarkar S, Lipworth L, Kabagambe EK, Bian A, Stewart TG, Blot WJ, et al. A Description of Risk Factors for Non-alcoholic Fatty Liver Disease in the Southern Community Cohort Study: A Nested Case-Control Study. Front Nutr. 2020; 7: 71.

4. GBD Results. Institute for Health Metrics and Evaluation. 2022.

5. NCD Risk Factor Collaboration (NCD-RisC). Repositioning of the global epicentre of non-optimal cholesterol. Nature. 2020; 582: 73- 77.

6. Lu Y, Zhang H, Lu J, Ding Q, Li X, Wang X, et al. Prevalence of Dyslipidemia and Availability of Lipid-Lowering Medications Among Primary Health Care Settings in China. JAMA Netw Open. 2021; 4: e2127573.

7. GBD 2019 Risk Factors Collaborators Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020; 396: 1223-1249.

8. Beelen R, Raaschou-Nielsen O, Stafoggia M, Andersen ZJ, Weinmayr G, Hoffmann B, et al. Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project. Lancet. 2014; 383: 785-795.

9. Wang L, Chen G, Pan Y, Xia J, Chen L, Zhang X, et al. Association of long-term exposure to ambient air pollutants with blood lipids in Chinese adults: The China Multi-Ethnic Cohort study. Environ Res. 2021; 197: 111174.

10. Liu Y, Pan J, Fan C, Xu R, Wang Y, Xu C, et al. Short-Term Exposure to Ambient Air Pollution and Mortality From Myocardial Infarction. J Am Coll Cardiol. 2021; 77: 271-281.

11. Zhang Z, Su Y, Jing R, Qi J, Qi X, Xie Z, et al. Acute and lag effects of ambient fine particulate matter on the incidence of dyslipidemia in Chengdu, China: A time-series study. Environ Sci Pollut Res Int. 2022; 29: 37919-37929.

12. Brunekreef B, Forsberg B. Epidemiological evidence of effects of coarse airborne particles on health. Eur Respir J. 2005; 26: 309-318.

13. Yang L, Yang J, Liu M, Sun X, Li T, Guo Y, et al. Nonlinear effect of air pollution on adult pneumonia hospital visits in the coastal city of Qingdao, China: A time-series analysis. Environ Res. 2022; 209: 112754.

14. Tian F, Qi J, Wang L, Yin P, Qian Z Min, Ruan Z, et al. Differentiating the effects of ambient fine and coarse particles on mortality from cardiopulmonary diseases: A nationwide multicity study. Environ Int. 2020; 145: 106096.

15. Yeatts K, Svendsen E, Creason J, Alexis N, Herbst M, Scott J, et al. Coarse Particulate Matter (PM2.5–10) Affects Heart Rate Variability, Blood Lipids, and Circulating Eosinophils in Adults with Asthma. Environ Health Perspect. 2007; 115: 709-714.

16. Yang BY, Bloom MS, Markevych I, Qian Z Min, Vaughn MG, CummingsVaughn LA, et al. Exposure to ambient air pollution and blood lipids in adults: The 33 Communities Chinese Health Study. Environ Int. 2018; 119: 485-492.

17. Shin W, Kim J, Lee G, Choi S, Kim SR, Hong YC, et al. Exposure to ambient fine particulate matter is associated with changes in fasting glucose and lipid profiles: a nationwide cohort study. BMC Public Health. 2020; 20: 430.

18. Jacobson TA, Ito MK, Maki KC, Orringer CE, Bays HE, Jones PH, et al. National Lipid Association Recommendations for Patient-Centered Management of Dyslipidemia: Part 1—Full Report. J Clin Lipidol. 2015; 9: 129-169.

19. Gasparrini A, Armstrong B, Kenward MG. Distributed lag non-linear models. Stat Med. 2010; 29: 2224-2234.

20. Zheng S, Zhu W, Shi Q, Wang M, Nie Y, Zhang D, et al. Effects of cold and hot temperature on metabolic indicators in adults from a prospective cohort study. Sci Total Environ. 2021; 772: 145046.

21. He Z-Z, Guo PY, Xu SL, Zhou Y, Jalaludin B, Leskinen A, et al. Associations of Particulate Matter Sizes and Chemical Constituents with Blood Lipids: A Panel Study in Guangzhou, China. Environ Sci Technol. 2021; 55: 5065-5075.

22. Gasparrini A. Distributed Lag Linear and Non-Linear Models in R?: The Package dlnm. J Stat Soft. 2011; 43: 1-20.

23. Lee S, Park H, Kim S, Lee E-K, Lee J, Hong YS, et al. Fine particulate matter and incidence of metabolic syndrome in non-CVD patients: A nationwide population-based cohort study. Int J Hyg Environ Health. 2019; 222: 533-540.

24. Wu Y, Tian Y, Wang M, Wang X, Wu J, Wang Z, et al. Short-term exposure to air pollution and its interaction effects with two ABO SNPs on blood lipid levels in northern China: A family-based study. Chemosphere. 2020; 249: 126120.

25. Wang M, Zheng S, Nie Y, Weng J, Cheng N, Hu X, et al. Association between Short-Term Exposure to Air Pollution and Dyslipidemias among Type 2 Diabetic Patients in Northwest China: A PopulationBased Study. Int J Environ Res Public Health. 2018; 15: 631.

26. Englert N. Fine particles and human health-a review of epidemiological studies. Toxicol Lett. 2004; 149: 235-242.

27. Vignal C, Pichavant M, Alleman LY, Djouina M, Dingreville F, Perdrix E, et al. Effects of urban coarse particles inhalation on oxidative and inflammatory parameters in the mouse lung and colon. Part Fibre Toxicol. 2017; 14: 46.

28. Chen R, Meng X, Zhao A, Wang C, Yang C, Li H, et al. DNA hypomethylation and its mediation in the effects of fine particulate air pollution on cardiovascular biomarkers: A randomized crossover trial. Environ Int. 2016; 94: 614-619.

29. Li J, Zhou C, Xu H, Brook RD, Liu S, Yi T, et al. Ambient Air Pollution is Associated With HDL (High-Density Lipoprotein) Dysfunction in Healthy Adults. Arterioscler Thromb Vasc Biol. 2019; 39: 513-522.

30. Kim K-H, Kabir E, Kabir S. A review on the human health impact of airborne particulate matter. Environ Int. 2015; 74: 136-143.

31. Alexis N, Lay J, Zeman K, Bennett W, Peden D, Soukup J, et al. Biological material on inhaled coarse fraction particulate matter activates airway phagocytes in vivo in healthy volunteers. J Allergy Clin Immunol. 2006; 117: 1396-1403.

32. Hudgins LC, Parker TS, Levine DM, Gordon BR, Saal SD, Jiang X, et al. A single intravenous dose of endotoxin rapidly alters serum lipoproteins and lipid transfer proteins in normal volunteers. J Lipid Res. 2003; 44: 1489-1498.

33. Huo Q, Zhang N, Wang X, Jiang L, Ma T, Yang Q, et al. Effects of Ambient Particulate Matter on Human Breast Cancer: Is Xenogenesis Responsible? PLoS One. 2013; 8: e76609.

34. Saltiel AR, Olefsky JM. Inflammatory mechanisms linking obesity and metabolic disease. J Clin Invest. 2017; 127: 1-4.

Jing R, Zheng X, Zhang Z, Su Y, Qi J, et al. (2023) Short-Term Exposure to Coarse Particles and Dyslipidemia Risk in Chengdu, China: A Time-Series Study. Ann Biom Biostat 6(1): 1039.

Received : 09 Sep 2023
Accepted : 16 Oct 2023
Published : 17 Oct 2023
Annals of Otolaryngology and Rhinology
ISSN : 2379-948X
Launched : 2014
JSM Schizophrenia
Launched : 2016
Journal of Nausea
Launched : 2020
JSM Internal Medicine
Launched : 2016
JSM Hepatitis
Launched : 2016
JSM Oro Facial Surgeries
ISSN : 2578-3211
Launched : 2016
Journal of Human Nutrition and Food Science
ISSN : 2333-6706
Launched : 2013
JSM Regenerative Medicine and Bioengineering
ISSN : 2379-0490
Launched : 2013
JSM Spine
ISSN : 2578-3181
Launched : 2016
Archives of Palliative Care
ISSN : 2573-1165
Launched : 2016
JSM Nutritional Disorders
ISSN : 2578-3203
Launched : 2017
Annals of Neurodegenerative Disorders
ISSN : 2476-2032
Launched : 2016
Journal of Fever
ISSN : 2641-7782
Launched : 2017
JSM Bone Marrow Research
ISSN : 2578-3351
Launched : 2016
JSM Mathematics and Statistics
ISSN : 2578-3173
Launched : 2014
Journal of Autoimmunity and Research
ISSN : 2573-1173
Launched : 2014
JSM Arthritis
ISSN : 2475-9155
Launched : 2016
JSM Head and Neck Cancer-Cases and Reviews
ISSN : 2573-1610
Launched : 2016
JSM General Surgery Cases and Images
ISSN : 2573-1564
Launched : 2016
JSM Anatomy and Physiology
ISSN : 2573-1262
Launched : 2016
JSM Dental Surgery
ISSN : 2573-1548
Launched : 2016
Annals of Emergency Surgery
ISSN : 2573-1017
Launched : 2016
Annals of Mens Health and Wellness
ISSN : 2641-7707
Launched : 2017
Journal of Preventive Medicine and Health Care
ISSN : 2576-0084
Launched : 2018
Journal of Chronic Diseases and Management
ISSN : 2573-1300
Launched : 2016
Annals of Vaccines and Immunization
ISSN : 2378-9379
Launched : 2014
JSM Heart Surgery Cases and Images
ISSN : 2578-3157
Launched : 2016
Annals of Reproductive Medicine and Treatment
ISSN : 2573-1092
Launched : 2016
JSM Brain Science
ISSN : 2573-1289
Launched : 2016
JSM Biomarkers
ISSN : 2578-3815
Launched : 2014
JSM Biology
ISSN : 2475-9392
Launched : 2016
Archives of Stem Cell and Research
ISSN : 2578-3580
Launched : 2014
Annals of Clinical and Medical Microbiology
ISSN : 2578-3629
Launched : 2014
JSM Pediatric Surgery
ISSN : 2578-3149
Launched : 2017
Journal of Memory Disorder and Rehabilitation
ISSN : 2578-319X
Launched : 2016
JSM Tropical Medicine and Research
ISSN : 2578-3165
Launched : 2016
JSM Head and Face Medicine
ISSN : 2578-3793
Launched : 2016
JSM Cardiothoracic Surgery
ISSN : 2573-1297
Launched : 2016
JSM Bone and Joint Diseases
ISSN : 2578-3351
Launched : 2017
JSM Bioavailability and Bioequivalence
ISSN : 2641-7812
Launched : 2017
JSM Atherosclerosis
ISSN : 2573-1270
Launched : 2016
Journal of Genitourinary Disorders
ISSN : 2641-7790
Launched : 2017
Journal of Fractures and Sprains
ISSN : 2578-3831
Launched : 2016
Journal of Autism and Epilepsy
ISSN : 2641-7774
Launched : 2016
Annals of Marine Biology and Research
ISSN : 2573-105X
Launched : 2014
JSM Health Education & Primary Health Care
ISSN : 2578-3777
Launched : 2016
JSM Communication Disorders
ISSN : 2578-3807
Launched : 2016
Annals of Musculoskeletal Disorders
ISSN : 2578-3599
Launched : 2016
Annals of Virology and Research
ISSN : 2573-1122
Launched : 2014
JSM Renal Medicine
ISSN : 2573-1637
Launched : 2016
Journal of Muscle Health
ISSN : 2578-3823
Launched : 2016
JSM Genetics and Genomics
ISSN : 2334-1823
Launched : 2013
JSM Anxiety and Depression
ISSN : 2475-9139
Launched : 2016
Clinical Journal of Heart Diseases
ISSN : 2641-7766
Launched : 2016
Annals of Medicinal Chemistry and Research
ISSN : 2378-9336
Launched : 2014
JSM Pain and Management
ISSN : 2578-3378
Launched : 2016
JSM Women's Health
ISSN : 2578-3696
Launched : 2016
Clinical Research in HIV or AIDS
ISSN : 2374-0094
Launched : 2013
Journal of Endocrinology, Diabetes and Obesity
ISSN : 2333-6692
Launched : 2013
Journal of Substance Abuse and Alcoholism
ISSN : 2373-9363
Launched : 2013
JSM Neurosurgery and Spine
ISSN : 2373-9479
Launched : 2013
Journal of Liver and Clinical Research
ISSN : 2379-0830
Launched : 2014
Journal of Drug Design and Research
ISSN : 2379-089X
Launched : 2014
JSM Clinical Oncology and Research
ISSN : 2373-938X
Launched : 2013
JSM Bioinformatics, Genomics and Proteomics
ISSN : 2576-1102
Launched : 2014
JSM Chemistry
ISSN : 2334-1831
Launched : 2013
Journal of Trauma and Care
ISSN : 2573-1246
Launched : 2014
JSM Surgical Oncology and Research
ISSN : 2578-3688
Launched : 2016
Annals of Food Processing and Preservation
ISSN : 2573-1033
Launched : 2016
Journal of Radiology and Radiation Therapy
ISSN : 2333-7095
Launched : 2013
JSM Physical Medicine and Rehabilitation
ISSN : 2578-3572
Launched : 2016
Annals of Clinical Pathology
ISSN : 2373-9282
Launched : 2013
Annals of Cardiovascular Diseases
ISSN : 2641-7731
Launched : 2016
Journal of Behavior
ISSN : 2576-0076
Launched : 2016
Annals of Clinical and Experimental Metabolism
ISSN : 2572-2492
Launched : 2016
Clinical Research in Infectious Diseases
ISSN : 2379-0636
Launched : 2013
JSM Microbiology
ISSN : 2333-6455
Launched : 2013
Journal of Urology and Research
ISSN : 2379-951X
Launched : 2014
Journal of Family Medicine and Community Health
ISSN : 2379-0547
Launched : 2013
Annals of Pregnancy and Care
ISSN : 2578-336X
Launched : 2017
JSM Cell and Developmental Biology
ISSN : 2379-061X
Launched : 2013
Annals of Aquaculture and Research
ISSN : 2379-0881
Launched : 2014
Clinical Research in Pulmonology
ISSN : 2333-6625
Launched : 2013
Journal of Immunology and Clinical Research
ISSN : 2333-6714
Launched : 2013
Annals of Forensic Research and Analysis
ISSN : 2378-9476
Launched : 2014
JSM Biochemistry and Molecular Biology
ISSN : 2333-7109
Launched : 2013
Annals of Breast Cancer Research
ISSN : 2641-7685
Launched : 2016
Annals of Gerontology and Geriatric Research
ISSN : 2378-9409
Launched : 2014
Journal of Sleep Medicine and Disorders
ISSN : 2379-0822
Launched : 2014
JSM Burns and Trauma
ISSN : 2475-9406
Launched : 2016
Chemical Engineering and Process Techniques
ISSN : 2333-6633
Launched : 2013
Annals of Clinical Cytology and Pathology
ISSN : 2475-9430
Launched : 2014
JSM Allergy and Asthma
ISSN : 2573-1254
Launched : 2016
Journal of Neurological Disorders and Stroke
ISSN : 2334-2307
Launched : 2013
Annals of Sports Medicine and Research
ISSN : 2379-0571
Launched : 2014
JSM Sexual Medicine
ISSN : 2578-3718
Launched : 2016
Annals of Vascular Medicine and Research
ISSN : 2378-9344
Launched : 2014
JSM Biotechnology and Biomedical Engineering
ISSN : 2333-7117
Launched : 2013
Journal of Hematology and Transfusion
ISSN : 2333-6684
Launched : 2013
JSM Environmental Science and Ecology
ISSN : 2333-7141
Launched : 2013
Journal of Cardiology and Clinical Research
ISSN : 2333-6676
Launched : 2013
JSM Nanotechnology and Nanomedicine
ISSN : 2334-1815
Launched : 2013
Journal of Ear, Nose and Throat Disorders
ISSN : 2475-9473
Launched : 2016
JSM Ophthalmology
ISSN : 2333-6447
Launched : 2013
Journal of Pharmacology and Clinical Toxicology
ISSN : 2333-7079
Launched : 2013
Annals of Psychiatry and Mental Health
ISSN : 2374-0124
Launched : 2013
Medical Journal of Obstetrics and Gynecology
ISSN : 2333-6439
Launched : 2013
Annals of Pediatrics and Child Health
ISSN : 2373-9312
Launched : 2013
JSM Clinical Pharmaceutics
ISSN : 2379-9498
Launched : 2014
JSM Foot and Ankle
ISSN : 2475-9112
Launched : 2016
JSM Alzheimer's Disease and Related Dementia
ISSN : 2378-9565
Launched : 2014
Journal of Addiction Medicine and Therapy
ISSN : 2333-665X
Launched : 2013
Journal of Veterinary Medicine and Research
ISSN : 2378-931X
Launched : 2013
Annals of Public Health and Research
ISSN : 2378-9328
Launched : 2014
Annals of Orthopedics and Rheumatology
ISSN : 2373-9290
Launched : 2013
Journal of Clinical Nephrology and Research
ISSN : 2379-0652
Launched : 2014
Annals of Community Medicine and Practice
ISSN : 2475-9465
Launched : 2014
JSM Clinical Case Reports
ISSN : 2373-9819
Launched : 2013
Journal of Cancer Biology and Research
ISSN : 2373-9436
Launched : 2013
Journal of Surgery and Transplantation Science
ISSN : 2379-0911
Launched : 2013
Journal of Dermatology and Clinical Research
ISSN : 2373-9371
Launched : 2013
JSM Gastroenterology and Hepatology
ISSN : 2373-9487
Launched : 2013
Annals of Nursing and Practice
ISSN : 2379-9501
Launched : 2014
JSM Dentistry
ISSN : 2333-7133
Launched : 2013
Author Information X