|Year : 2020 | Volume
| Issue : 3 | Page : 118-129
Adverse effects of air pollutant exposure on blood lipid levels in adults: A systematic review and meta-analysis
Xin-Yu Zhang1, Si-Han Huang1, Hai-Yan Gong2, Xin-Yan Wang3, Ke-Yong Huang3, Xiang-Feng Lu3, Fang-Chao Liu3
1 Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou; Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
2 Department of Anesthesiology, Zhangqiu Maternal and Child Health Hospital, Jinan, China
3 Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
|Date of Submission||27-May-2020|
|Date of Acceptance||23-Jul-2020|
|Date of Web Publication||30-Sep-2020|
Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037
Source of Support: None, Conflict of Interest: None
Background: Air pollution is a crucial public health issue, but evidence on its association with blood lipids is still limited and inconsistent. Objectives: To systematically review and explore associations between major air pollutants (PM2.5, PM10, SO2, NO2, and O3) and blood lipid levels in long-, middle-, and short-term exposure durations. Data Source: Databases of PubMed, Embase, and Web of Science were searched for eligible articles published until August 16, 2019. Study Eligibility Criteria: English articles were explored for associations between air pollutants and blood lipids among adults using quantitative measures. Methods: Random-effect models were used to synthesize the association, and I2 value was used to evaluate the degree of heterogeneity. Results: Of the 15 studies that met the inclusion criteria, 11, 7, and 4 records were used to evaluate long-, middle-, and short-term effects. Generally, air pollutants had adverse influence on blood lipid levels, and results were robust across sensitivity analyses. For example, PM2.5 was significantly associated with increased total cholesterol and low-density lipoprotein-cholesterol levels, with a percentage change of 4.53 (0.12, 8.93) and 5.36 (0.37, 10.35) per 10 μg/m3 increment, respectively, in long-term exposure. However, associations with NO2, SO2, and O3 were still insufficient. Moreover, prospective evidence was considerably inadequate. Limitation: We only pooled the association of air pollution with major blood lipids. We were unable to clarify the health effects of chemical components or susceptible population because of limited studies. Conclusions: Ambient air pollutants have detrimental effects on blood lipid levels. Further prospective evidence is highly warranted to demonstrate these associations.
Keywords: Air pollutants; Lipids; Meta-analysis
|How to cite this article:|
Zhang XY, Huang SH, Gong HY, Wang XY, Huang KY, Lu XF, Liu FC. Adverse effects of air pollutant exposure on blood lipid levels in adults: A systematic review and meta-analysis. Cardiol Plus 2020;5:118-29
|How to cite this URL:|
Zhang XY, Huang SH, Gong HY, Wang XY, Huang KY, Lu XF, Liu FC. Adverse effects of air pollutant exposure on blood lipid levels in adults: A systematic review and meta-analysis. Cardiol Plus [serial online] 2020 [cited 2020 Oct 25];5:118-29. Available from: https://www.cardiologyplus.org/text.asp?2020/5/3/118/296819
| Introduction|| |
Dyslipidemia is a notable risk factor for cardiovascular disease morbidity and mortality worldwide. In past decades, the prevalence of dyslipidemia has gradually increased in China. The types of dyslipidemia mainly include high total cholesterol (TC), high low-density lipoprotein-cholesterol (LDL-C), low high-density lipoprotein-cholesterol (HDL-C), and hypertriglyceridemia (high triglycerides [TG]), and the corresponding prevalence rates in China were 6.9%, 8.1%, 20.4%, and 13.8% in 2013, respectively. Aside from the dietary effects (e.g., red meat, snack intake, and Mediterranean-style diet),, and unhealthy lifestyles (e.g., poor sleep quality, tobacco smoking, and alcohol intake),, environmental factors also have explicit influence on blood lipids.
Air pollution has become a crucial public health issue of global attention. Previous epidemiological studies have demonstrated that air pollution could induce cardiovascular disease. Simultaneously, some studies have revealed that air pollution is associated with numerous adverse influences on human health, such as glucose metabolism, hypertension, obesity, and lipid metabolism disorders. A comprehensive understanding between air pollution and dyslipidemia can further elucidate the health effects of air pollution.
However, evidence on the relationship between air pollution and blood lipid levels is still insufficient and inconsistent.,,,, Thus, this systematic review and meta-analysis aimed to estimate pooled long-, middle-, and short-term health effects of major air pollutants on blood lipid levels and to provide valuable information for further epidemiological studies.
| Methods|| |
This meta-analysis was conducted in accordance with the statement of Meta-Analysis of Observational Studies in Epidemiology and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses [details in Supplementary Material].,
We included studies that met the following criteria: (1) studies explored associations between any air pollutant (PM2.5, PM10, SO2, NO2, and O3) and blood lipids; (2) study participants were adults (≥18 years); (3) full texts published in a peer-reviewed journal; (4) quantitative measures of associations between air pollution and blood lipids or data using which to calculate the associations were provided, along with their corresponding 95% confidence intervals (CIs); and (5) articles written in English.
Information source, search strategy, and study selection
Studies that reported the relationship between air pollution and blood lipid levels were identified by searching for articles in PubMed, Embase, and Web of Science up to August 16, 2019. The search strategy combined keywords of exposures (“air pollution,” “air pollutants,” “particulate matter, PM,” “nitrogen dioxide, NO2,” “sulfur dioxide, SO2” or “ozone, O3“) with outcomes (“lipid metabolism disorders,” “dyslipidemias,” “lipoproteins,” “cholesterol,” “serum lipids,” “TG,” “TC,” “HDL-C,” or “LDL-C”) and included filters of studies which were conducted in humans. Detailed search strategies are presented in Supplementary Method. All references from eligible articles, reviews, systematic reviews, and meta-analyses were also searched manually to identify potentially eligible articles.
Two investigators reviewed each article independently after removal of duplications. Titles and abstracts were screened for eligibility, and full texts of potentially relevant articles were further reviewed. Any discrepancies were solved by negotiating with the third investigator to achieve consensus.
Studies published before 2000 (due to an intensive concern about air pollution since 2000); investigated associations with other air pollutants (such as smoking, indoor air pollution, or traffic air pollution); conducted on pregnant women, children, or animals; involved human-controlled exposure; or reviews, editorials, or commentaries were not eligible in this meta-analysis.
Two researchers conducted data extraction independently, and any discrepancies were settled by discussion and consensus. The following data were extracted from the eligible studies: (1) study information ( first author, publication year, and countries); (2) study design; (3) sample size; (4) demographic characteristics of the study participants (sex, age, proportion of female patients, and health status); (5) air pollution conditions (duration, type, and measurement method); (6) effect estimates and their 95% CIs from the fully adjusted or main model; and (7) statistical models and adjusted covariates.
Study quality assessment
The Newcastle–Ottawa Scale (NOS), which included eight criteria, was applied to assess quality of cohort studies, longitudinal studies, and panel studies. The quality of cross-sectional studies was evaluated using an 11-item checklist, which was recommended by the Agency for Health-care Research and Quality (AHRQ). Concisely, an article was regarded as a high-quality study if the NOS score was ≥6 or the AHRQ score was ≥8. Details of study quality assessment are provided in [Supplementary Table S1] and [Supplementary Table S2].
Data synthesis and statistical analysis
In this study, we pooled the effect sizes of absolute changes or percentage changes in TC, LDL-C, TG, and HDL-C levels per 10 μg/m 3 increment of each air pollutant separately. Results from the fully adjusted model or “main model” assigned by researchers were used to estimate pooled effect sizes. Furthermore, results from single-pollutant model were used when results from both single-pollutant and multi-pollutant models were recorded. Any effect sizes associated with other scales of increment or units in air pollutants were standardized to a unified increment (per 10 μg/m 3). The absolute change and its standard error (SE) can be directly calculated using the following formula: β/SE (standardized) = β/SE (original) × increment (10)/increment (original). Percentage change was first supposed to back-transformed to log relative regression coefficient (β) and its SE, and we further calculated standardized percentage changes and their corresponding 95% CIs using the following formula: percentage change (standardized) = (exp [β (standardized)] − 1) ×100; 95% CIs = (exp [β (standardized) ±1.96 × SE (standardized)] − 1) ×100,, in which β/SE (standardized) was calculated using the above formula. Details of data conversion are provided in [Supplementary Table S3] and [Supplementary Table S4].
Exposure to air pollution up to 14 days was considered as short-term exposure, exposure between 2 weeks and 3 months as middle-term exposure, and exposure >3 months as long-term exposure. Estimates were pooled by inverse variance weighting method, using a random-effects model. The I2 value was used to evaluate the degree of heterogeneity and categorized as no heterogeneity (I2 < 25%), moderate heterogeneity (25% ≤ I2 < 50%), and substantial heterogeneity (I2 ≥ 50%). Sensitivity analyses were conducted to examine the influence of adding gray literature, including only “high-quality” articles or studies adjusting smoking or alcohol consumption and excluding studies measuring blood lipids using non-fasting blood samples or conducted in patients with specific diseases (not general healthy population), and pooling results from multi-pollutant models other than single-pollutant models. Because of the small number of studies for a specific exposure duration of each air pollutant, publication bias was evaluated for each lipid biomarker by including all related studies using funnel plots and Egger's test.
All statistical analyses were accomplished using STATA package version 12. 0 program (StataCorp LP, 4905 Lakeway Drive, College Station, Tx 77845, USA). All P values were two-tailed and values <0.05 were considered statistically significant.
| Results|| |
Study selection and characteristics
The initial combined search yielded 6895 articles. After the removal of duplicate articles (n = 416) and articles published before 2000 (n = 1370), titles and abstracts of 5119 articles were assessed and 199 articles were retained for full-text review. Finally, 15 articles that met the inclusion criteria were included [Figure 1].,,,,,,,,,,,,,,
The study characteristics are summarized in [Table 1]. The 15 articles were published since 2007 from five countries, with the sample size ranging from 12 to 93,277 participants. Among 15 articles, only two articles from one cohort study (SWAN) were identified (one reported effect sizes of PM2.5 and the other of PM10 and NO2), and 11 studies were performed among general populations. The associations of blood lipids with PM2.5, PM10, NO2, SO2, and O3 were examined in the included studies. In total, 11 records assessed long-term effects, seven middle-term effects, and four short-term effects, with the sample size of 208,617, 100,462, and 15,608, respectively. With respect to the study quality assessment, nine studies were recognized as having “high quality.”
As shown in [Table 2], the average age of the study population ranged from 23.3 to 69.1 years, and the proportion of female participants ranged from 29.68% to 100%. Nine studies assessed air pollution exposure based on central air monitoring stations or self-monitoring air pollution data, and the remaining six studies used model-derived assessment (e.g., land-use regression model, satellite-based exposure assessment model). Thirteen studies used fasting blood samples to determine lipid levels, except two studies conducted by Shanley et al. and Sorensen et al., Overall, 14 studies adjusted their analysis for age, sex, and body mass index (BMI); in addition, 12 studies further adjusted smoking, nine adjusted alcohol consumption, nine adjusted education, and seven adjusted temperature and humidity.
|Table 2: Information summary of included studies: Exposure assessment, outcome measurement, and adjusted covariates|
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Meta-analysis of studies that provided percentage change
We pooled estimates of percentage change associated with long-, middle-, and short-term air pollution exposure separately. Generally, high levels of air pollutants were associated with increased percentage changes in TC, LDL-C, and TG levels and decreased percentage change in HDL-C level [Figure 2], [Figure 3], [Figure 4]. PM2.5 exposure was significantly associated with increases in TC and LDL-C levels in long- and middle-term analyses. For example, the percentage changes in TC and LDL-C levels were 4.53 (0.12, 8.93) and 5.36 (0.37, 10.35) per 10 μg/m 3 increment of long-term exposure to PM2.5, respectively. In addition, long-term exposure to PM10 was significantly associated with increase in TG levels, with percentage change of 6.14 (2.44, 9.83) per 10 μg/m 3. Furthermore, a reduced percentage change in HDL-C levels was observed in middle-term exposure analyses to PM10. However, results from short-term exposure needed further confirmation due to the limited number of studies. We observed substantial heterogeneities among most of the analyses, except for middle-term analysis on PM2.5 and TC, whose heterogeneity was 48.5%.
|Figure 2: Percentage change in TC (a), LDL-C (b), TG (c), and HDL-C (d) levels associated with long-term exposure* to each air pollutant. PM2.5: Particle with aerodynamic diameter ≤2.5 mm, PM10: Particle with aerodynamic diameter ≤10 mm, NO2: Nitrogen dioxide, SO2: Sulfur dioxide, O3: Ozone, TC: Total cholesterol, LDL-C: Low-density lipoprotein-cholesterol, TG: Triglycerides, HDL-C: High-density lipoprotein-cholesterol. *Long-term exposure was defined as exposure to air pollution >3 months|
Click here to view
|Figure 3: Percentage change in TC (a), LDL-C (b), TG (c), and HDL-C (d) levels associated with middle-term exposure* to each air pollutant. NO2: Nitrogen dioxide, O3: Ozone, PM10: Particle with aerodynamic diameter ≤10 mm, PM2.5: Particle with aerodynamic diameter ≤2.5 mm, SO2: Sulfur dioxide, TC: Total cholesterol, LDL-C: Low-density lipoprotein-cholesterol, TG: Triglycerides, HDL-C: High-density lipoprotein-cholesterol. *Middle-term exposure was defined as exposure to air pollution between 2 weeks and 3 months|
Click here to view
|Figure 4: Percentage change in TC (a), LDL-C (b), TG (c), and HDL-C (d) levels associated with short-term exposure* to each air pollutant. NO2, nitrogen dioxide, O3, ozone, PM10: Particle with aerodynamic diameter ≤10 mm, PM2.5: Particle with aerodynamic diameter ≤2.5 mm, SO2: Sulfur dioxide, TC: Total cholesterol, LDL-C: Low-density lipoprotein-cholesterol, TG: Triglycerides, HDL-C: High-density lipoprotein-cholesterol. *Short-term exposure was defined as exposure to air pollution <2 weeks|
Click here to view
Meta-analysis of studies that provided absolute change
Absolute changes in blood lipids associated with air pollution exposures are presented in [Supplementary Table S5]. Similar to the results of percentage change, high levels of air pollutants were related to increased TC and LDL-C levels and decreased HDL-C level. For instance, TC and LDL-C levels were significantly increased by 14.72 mg/dl (2.92, 26.52) and 11.02 mg/dl (0.40, 21.65) per 10 μg/m 3 increment in long-term exposure to PM2.5, respectively. In addition, an increment of 10 μg/m 3 in middle-term exposure to NO2 was associated with a −0.57 mg/dL (−1.12, −0.02) change in HDL-C level. Substantial heterogeneities were also noted, except in the middle-term analysis on NO2 [Supplementary Table S5].
Sensitivity analysis and publication bias
We conducted several sensitivity analyses, and the results are presented in [Supplementary Table S6] and [Supplementary Table S7]. Generally, among “high-quality” studies, studies conducted in general population or adjusted for smoking or alcohol consumption obviously had decreased heterogeneity. Meanwhile, the associations of PM2.5 and NO2 with blood lipids were more pronounced in these three analysis settings. For example, decreased HDL-C levels became significantly associated with the percentage change of −1.36 (−2.05, −0.67) per 10 μg/m 3 on long-term exposure to NO2. A significant association between the percentage change in NO2 and TG levels was identified, with an effect size of 2.80 (0.41, 5.18) per 10 μg/m 3.
No obvious publication bias was identified for the analyses on percentage changes in TC, HDL-C, and LDL-C levels, with P = 0.125, 0.412, and 0.708 for Egger's test, respectively, while potential publication bias existed in the analysis of TG (P = 0.001) [Supplementary Figure S1]. In addition, potential publication bias existed in absolute change analyses on TC (P = 0.028) and LDL-C (P = 0.009) [Supplementary Figure S2].
| Discussion|| |
To our knowledge, this is an elaborate systematic review and meta-analysis that explored the associations between ambient air pollution and blood lipids in adults. We found that exposure to high levels of ambient air pollutants was associated with adverse changes in blood lipid levels (i.e., higher TC, LDL-C, and TG and lower HDL-C), and findings were robust across our sensitivity analyses. However, evidence on the deleterious effects of air pollution on blood lipid levels is still insufficient, and the prospective evidence is extremely inadequate.
This study has important public health implications. With the acceleration of urbanization and the development of transportation infrastructure, air pollutant emissions are increasing cumulatively in China; moreover, the public is progressively aware of the harmful effects of air pollution on health., In addition, those with disorders of lipids, as a considerable risk factor for many diseases, especially for cardiovascular diseases, will be easily affected by the worsening air pollution. The potential biological mechanisms are likely to include triggering systematic inflammation and oxidative stress, altering fibrinolysis function, modifying DNA methylation, etc.,, The main findings of this study were based on PM2.5 and PM10. We observed significant increased TC levels associated with PM2.5 exposures and increased TG levels with PM10. In addition, both PM2.5 and PM10 elevated the levels of LDL-C and decreased the levels of HDL-C. However, further studies are indispensable to explore potential adverse effects of other air pollutants (i.e., NO2, SO2, and O3), due to the limited available evidence at present. In summary, comprehensive assessment of potential association between air pollution and blood lipids can provide some evidence for the risk assessment and prevention of cardiovascular disease.
This study provided valuable information for further research. The majority of the 15 included studies were cross-sectional studies, except for two studies from the same cohort (SWAN), thereby limiting the inference of direct causality between increasing air pollution and changes in blood lipid levels. One of the included cohort studies showed that elevated levels of LDL-C as well as reduced levels of HDL-C were associated with PM2.5 exposure, particularly with long-term exposure. Another cohort study found that long-term exposure to NO2 and SO2 was strongly associated with reduced HDL-related lipoproteins and SO2 exposure was also associated with increases in LDL levels. All of the above results are in line with our meta-analysis; nonetheless, findings from large population-based prospective studies are still highly needed for different populations, such as the Chinese population. Besides, few studies are investigating the associations of air pollution with dyslipidemia prevalence or incidence. Through our retrieval strategy, only three qualified population-based studies identified that exposure to high levels of air pollutants was associated with a higher prevalence or incidence of dyslipidemia, and results varied across difference air pollutants. Therefore, the health effects of air pollution on morbidity or the incidence of dyslipidemia should be further demonstrated by large-scale cohort studies in future.
In general, the results of our sensitivity analyses were in line with the overall findings. However, study quality, adjustment of related variables (such as smoking or alcohol), and study population with other diseases potentially influence the pooled results, and significant reductions on HDL-C level and increments on TG levels associated with long-term exposure to NO2 were observed in the sensitivity analyses. According to the results of our sensitivity analyses, smoking and drinking probably have confounded the effects. In addition, findings from “high-quality” studies were more reliable, which demonstrate the necessities of well-designed large-scale prospective cohort studies. Preexisting diseases may affect the susceptibility of the population, and results should be interpreted separately for general population and patients with specific diseases. Although most of the included studies only reported results from single-pollutant models, multi-pollutant models should be considered in the original studies due to the potential additive effects of multiple pollutants or the confounding effects of other air pollutants.
To our knowledge, this is the first meta-analysis that evaluated the relationships between different ambient air pollutants and diverse blood lipid biomarkers, with long-, middle-, and short-term exposure durations. The detailed illustration on associations of major air pollutants with blood lipid changes, either percentage change or absolute change, made the current study more valuable, and robust results from several sensitivity analyses made our results more reliable. Our research findings provided a comprehensive understanding about this topic and insightful suggestions for further studies.
However, some limitations should be noted. First, only major blood lipid measures were included in this meta-analysis, and further studies are needed to investigate the health effects of air pollution on numerous other measures, such as apolipoprotein A1, apolipoprotein B, and other lipoproteins. Second, we cannot clarify the adverse effects of the chemical components of air pollutants on blood lipids, which may be helpful to identify the key trigger after air pollution exposure. Third, we are unable to conduct stratified analyses or discriminate susceptible population due to the limited number of studies.
| Conclusions|| |
This study is the first to report the pooled associations of air pollution with blood lipid levels under different exposure durations; the associations were more pronounced for PM2.5 and PM10. However, further evidence is still needed to demonstrate the adverse effects of other air pollutants, and prospective studies with multi-pollutant models are particularly warranted to improve our understanding of this association.
Financial support and sponsorship
This work was supported by National Natural Science Foundation of China (91643208, 91843302, 81600332) and Beijing Natural Science Foundation (7172145).
Conflicts of interest
There are no conflicts of interest.
Adverse Effect of Air Pollutants Exposure with Blood Lipids Levels in Adults: a Systematic Review and Meta-Analysis
Xinyu Zhang, MM1,2; Sihan Huang, MM1,2; Haiyan Gong, MB3; Xinyan Wang, MM2; Keyong Huang, PhD2; Xiangfeng Lu, PhD2; Fangchao Liu, PhD2
1Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, 100 Kexue Avenue, Zhengzhou 450001, Henan, PR China.
2Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, P. R. China.
3Department of Anesthesiology, Zhangqiu Maternal and Child Health Hospital, Jinan 250200, China.
List of files in Supplementary Material
1. Supplementary Methods: Search Strategies
2. Supplementary Tables:
Supplementary Table S1. Study quality assessment of cohort studies and panel studies: Newcastle-Ottawa Scale (NOS)*
Supplementary Table S2. Study quality assessment of cross sectional studies: Agency for Healthcare Research and Quality (AHRQ)*
Supplementary Table S3. Data transformed results of percentage changes of lipid-related biomarkers associated with air pollution
Supplementary Table S4. Data transformed results of absolute changes of lipid-related biomarkers associated with air pollution
Supplementary Table S5. Absolute changes of lipid-related biomarkers associated with air pollutions
Supplementary Table S6. Sensitivity analyses of percentage changes of lipid-related biomarkers associated with air pollutions
Supplementary Table S7. Sensitivity analyses of absolute changes of lipid-related biomarkers associated with air pollutions
3. Supplementary Figures:
Supplementary Figure S1. Funnel plot* for percentage changes of TC (a), LDL-C (b), TG (c), and HDL-C (d) associated associated with air pollutions.
Supplementary Figure S2. Funnel plot* for absolute changes of TC (a), LDL-C (b), TG (c), and HDL-C (d) associated with air pollutions.
4. PRISMA 2009 Checklist
5. Supplementary References
Search details for intervention
- “air pollution”[MeSH Terms] OR “air pollutions”[All Fields] OR “air quality”[All Fields]
- “air pollutants”[MeSH Terms] OR “air environmental pollutants”[All Fields] OR “environmental air pollutant”[All Fields]
- “particulate matter”[MeSH Terms] OR”airborne particulate matter”[All Fields] OR” particulate air pollutants”[All Fields] OR “ambient particulate matter”[All Fields] OR “fine particles”[All Fields] OR “fine particulate matter”[All Fields]
- “nitrogen dioxide”[MeSH Terms] OR “nitrogen oxides”[All Fields]
- “sulfur dioxide”[MeSH Terms] OR “sulphur dioxide”[All Fields] OR “sulfur oxides”[All Fields]
- “ozone”[MeSH Terms] OR “ground Level ozone”[All Fields]
- “PM10”[All Fields] OR “PM2.5”[All Fields] OR “PM1”[All Fields]
- “NO2”[All Fields]
- “SO2”[All Fields]
- “O3”[All Fields]
Search details for outcome
- “lipid metabolism disorders”[MeSH Terms] OR “lipid metabolism disorder”[All Fields]
- “dyslipidemias”[MeSH Terms] OR “dyslipidemia”[All Fields] OR “dyslipoproteinemias”[All Fields] OR “dyslipoproteinemia”[All Fields]
- “hyperlipidemias”[MeSH Terms] OR “hyperlipemia”[All Fields] OR “hyperlipemias”[All Fields] OR “hyperlipidemia”[All Fields] OR “lipidemia”[All Fields] OR “lipidemias”[All Fields] OR “lipemia”[All Fields] OR “lipemias”[All Fields]
- “hypercholesterolemia”[MeSH Terms] OR “hypercholesterolemias”[All Fields] OR “high cholesterol levels”[All Fields] OR “high cholesterol level”[All Fields] OR “elevated cholesterol”[All Fields] OR “elevated cholesterols”[All Fields] OR “hypercholesteremia”[All Fields] OR “hypercholesteremias”[All Fields]
- “hyperlipoproteinemias”[MeSH Terms] OR “hyperlipoproteinemia”[All Fields]
- “hypertriglyceridemia”[MeSH Terms] OR “hypertriglyceridemias”[All Fields]
- “hypertriglyceridemic waist”[MeSH Terms] OR “enlarged waist elevated triglycerides”[All Fields]
- “lipoproteins”[MeSH Terms] OR “lipoprotein”[All Fields] OR “circulating lipoproteins”[All Fields]
- “lipoproteins, HDL”[MeSH Terms] OR “HDL lipoproteins”[All Fields] OR “heavy lipoproteins”[All Fields] OR “high-density lipoproteins”[All Fields] OR “high density lipoproteins”[All Fields]
- “lipoproteins, IDL”[MeSH Terms] OR “IDL lipoproteins”[All Fields] OR “intermediate density lipoproteins”[All Fields] OR “intermediate-density lipoproteins”
- “lipoproteins, LDL”[MeSH Terms] OR “LDL lipoproteins”[All Fields] OR “low-density lipoproteins”[All Fields] OR “low density lipoproteins”[All Fields]
- “lipoproteins, VLDL”[MeSH Terms] OR “VLDL lipoproteins”[All Fields] OR “very-low-density lipoproteins”[All Fields] OR “very low density lipoproteins”[All Fields]
- “cholesterol”[MeSH Terms] OR “epicholesterol”[All Fields]
- “cholesterol, HDL”[MeSH Terms] OR “HDL cholesterol”[All Fields] OR “high density lipoprotein cholesterol”[All Fields]
- “cholesterol, LDL”[MeSH Terms] OR “LDL cholesterol”[All Fields] OR “low density lipoprotein cholesterol”[All Fields]
- “cholesterol, VLDL”[MeSH Terms] OR “VLDL cholesterol”[All Fields] OR “very low density lipoprotein cholesterol”[All Fields]
- “triglycerides”[MeSH Terms] OR “triacylglycerol”[All Fields] OR “triacylglycerols”[All Fields]
- “blood lipids”[All Fields]
- “total cholesterol”[All Fields]
- “blood fat”[All Fields]
Intervention (1 OR 2 OR 3 OR 4 OR 5 OR 6 OR 7 OR 8 OR 9 OR 1O) AND Outcome (1 OR 2 OR 3 OR 4 OR 5 OR 6 OR 7 OR 8 OR 9 OR 10 OR 11 OR 12 OR 13 OR 14OR 15 OR 16 OR 17 OR 18 OR 19 OR 20) Filters: Humans
Search details for intervention
- 'air pollution'/exp or 'air pollution'
- 'air pollutant'/exp or 'air pollutant'
- 'air quality'/exp or 'air quality'
- 'particulate matter'/exp or 'particulate matter'
- 'ambient air pollution'
- 'ambient particulate matter'
- 'fine particulate matter'
- 'nitrogen dioxide'/exp or 'nitrogen dioxide'
- 'ozone'/exp or 'ozone'
- 'sulfur dioxide'/exp or 'sulfur dioxide'
Search details for outcome
- 'blood lipids'/exp or 'blood lipids'
- 'lipid blood level'/exp or 'lipid blood level'
- 'cholesterol blood level'/exp or 'cholesterol blood level'
- 'lipoprotein blood level'/exp or 'lipoprotein blood level'
- 'triacylglycerol blood level'/exp or 'triacylglycerol blood level'
- 'disorders of lipid and lipoprotein metabolism'/exp or 'disorders of lipid and lipoprotein metabolism'
- 'dyslipidemia'/exp or 'dyslipidemia'
- 'disorders of cholesterol metabolism'/exp or 'disorders of cholesterol metabolism'
- 'hypercholesterolemia'/exp or 'hypercholesterolemia'
- 'disorders of lipid metabolism'/exp or 'disorders of lipid metabolism'
- 'disorders of lipoprotein metabolism'/exp or 'disorders of lipoprotein metabolism'
- 'hyperlipoproteinemia'/exp or 'hyperlipoproteinemia'
- 'hyperlipidemia'/exp or 'hyperlipidemia'
- 'hypercholesterolemia'/exp or 'hypercholesterolemia'
- 'hypertriglyceridemia'/exp or 'hypertriglyceridemia'
- 'hypertriglyceridemic waist'/exp or 'hypertriglyceridemic waist'
- 'cholesterol ester'/exp or 'cholesterol ester'
- 'high density lipoprotein cholesterol'/exp or 'high density lipoprotein cholesterol'
- 'low density lipoprotein cholesterol'/exp or 'low density lipoprotein cholesterol'
- 'very low density lipoprotein cholesterol'/exp or 'very low density lipoprotein cholesterol'
- 'metabolic syndrome X'/exp or 'metabolic syndrome X'
Intervention (1 OR 2 OR 3 OR 4 OR 5 OR 6 OR 7 OR 8 OR 9 OR 10 OR 11 OR 12 OR 13 OR 14 OR 15 OR 16) AND Outcome (1 OR 2 OR 3 OR 4 OR 5 OR 6 OR 7 OR 8 OR 9 OR 10 OR 11 OR 12 OR 13 OR 14 OR 15 OR 16 OR 17 OR 18 OR 19 OR 20 OR 21) AND [humans]/lim
Web of Science
Search details for intervention
- Topic=(air pollution)
- Topic=(air pollutants)
- Topic=(particulate matter)
- Topic=(nitrogen dioxide)
- Topic=(sulfur dioxide)
Search details for outcome
- Topic=(lipid metabolism disorders)
- Topic=(hypertriglyceridemic waist)
- Topic=(lipoproteins, HDL)
- Topic=(lipoproteins, IDL)
- Topic=(lipoproteins, LDL)
- Topic=(lipoproteins, VLDL)
- Topic=(cholesterol, HDL)
- Topic=(cholesterol, LDL)
- Topic=(cholesterol, VLDL)
- Topic=(blood lipids)
- Topic=(total cholesterol)
- Topic=(blood fat)
Intervention (1 OR 2 OR 3 OR 4 OR 5 OR 6 OR 7 OR 8 OR 9 OR 10 OR 11 OR 12) AND Outcome (1 OR 2 OR 3 OR 4 OR 5 OR 6 OR 7 OR 8 OR 9 OR 10 OR 11 OR 12 OR 13 OR 14 OR 15 OR 16 OR 17 OR 18 OR 19 OR 20)
Databases= WOS Timespan=All years Search language=English
PRISMA 2009 Checklist
- Yang Y, Zhang D, Feng N, Chen G, Liu J, Chen G, et al. Increased intake of vegetables, but not fruit, reduces risk for hepatocellular carcinoma: a meta-analysis. Gastroenterology. 2014; 147: 1031-1042.
- Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur. J. Epidemiol. 2010; 25: 603-605.
- Hu J, Dong Y, Chen X, Liu Y, Ma D, Liu X, et al. Prevalence of suicide attempts among Chinese adolescents: A meta-analysis of cross-sectional studies. Compr. Psychiatry. 2015; 61: 78-89.
- Li J, Liu C, Cheng Y, Guo S, Sun Q, Kan L, et al. Association between ambient particulate matter air pollution and ST-elevation myocardial infarction: A case-crossover study in a Chinese city. Chemosphere. 2019; 219: 724-729.
- Yang BY, Bloom MS, Markevych I, Qian ZM, Vaughn MG, Cummings-Vaughn 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.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4]
[Table 1], [Table 2]