The relationship between long-term exposure to environmental NO2 and HDL levels among middle-aged and older adults in China

doi:10.21203/rs.3.rs-3921273/v1

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Research Article

The relationship between long-term exposure to environmental NO2 and HDL levels among middle-aged and older adults in China

https://doi.org/10.21203/rs.3.rs-3921273/v1

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Background

In recent years, an increasing number of studies have confirmed that air pollution mixtures can lead to abnormal levels of lipid proteins in the body. With the continuous deepening of research, the specific effects of different components in air pollution mixtures on human health have gradually begun to be explored. Nitrogen dioxide (NO₂), as an essential component of air pollutants, hurts human health. Normal levels of high-density lipoprotein (HDL) play a crucial role in preventing cardiovascular diseases. However, the effect of environmental NO₂ on HDL levels has yet to be further studied. Our study aims to explore the potential association between environmental NO₂ exposure and HDL levels in middle-aged and elderly populations aged 45 and above in China.

Methods

Our research subjects are the third-wave China Health and Retirement Longitudinal Study (CHARLS) participants. This cross-sectional study analyzed the personal data of 12519 participants aged 45 and above with venous blood samples. The participants' high-quality and high-resolution NO₂ exposure concentration data were obtained from the Chinese Air Pollutants (CHAP) dataset, and the HDL levels were obtained from the analysis results of venous blood samples. Multiple linear regression analysis, supplemented by subgroup analysis, was used to explore the correlation between NO₂ exposure concentration and HDL levels.

Results

This cross-sectional study found a negative correlation between long-term exposure to air NO₂ and HDL levels. The observed correlation between air NO₂ and HDL levels indicates that middle-aged and older adults living in areas with high NO₂ concentrations exhibit a decrease in HDL levels (Beta: -0.132; 95% CI: (-0.153, -0.111); P < 0.0001). This association still exists after considering various potential confounding factors (Beta=-0.103, 95% CI: -0.124, -0.083, p < 0.0001).

Conclusions

Our research results show a significant negative correlation between long-term exposure to high concentrations of NO₂ and HDL levels in the middle-aged and elderly population in China.

HDL

NO2

air pollution

China

middle-aged and older adults

Air pollution is a complex mixture of particles, gases, and chemicals, considered the most significant environmental factor causing global life loss and generating over 4.2 million deaths annually^[1]. However, hundreds of millions of people worldwide are continuously affected by high-level air pollution^[2], especially in developing countries such as China and India^[3]. Based on this, the World Health Organization has identified air pollution as the environmental problem with the most significant impact on health, which may pose a severe threat to global public health in the future. High-density lipoprotein(HDL) has anti-inflammatory and antioxidant properties[4], and normal levels of HDL play a core role in protecting cardiovascular system function and preventing cardiovascular events[5]. Many studies have found that exposure to air pollution may increase the incidence rate and mortality of cardiovascular diseases by reducing the level of HDL[6–12], especially for the elderly[13].

The unclear specific effects of different components in air pollution mixtures on the body hinder the improvement of air pollution prevention and control strategies[8]; Therefore, in recent years, many studies have begun to evaluate the effects of different components of air pollutants (such as PM₁₀, PM_2.5, and NO₂) on HDL levels[14]. Nitrogen dioxide (NO₂), a standard air pollutant, is formed by the combustion of fossil fuels in vehicle sources, power plants, and wildfires[15]. Its harmful effects may be more potent than PM_2.5 and PM₁₀[16], and increasing epidemiological evidence suggests that exposure to NO₂ can cause varying degrees of harm to human health. Multiple studies have found that short-term and long-term exposure to NO₂ increases the risk of neurodegenerative diseases, including cognitive impairment and dementia in the elderly [17–19]. Meanwhile, long-term exposure to high concentrations of NO₂ increases the risk of dyslipidemia in young people[10], leading to an increase in triglycerides[20] and serum total cholesterol levels in the body[21]. However, current research on the correlation between air NO₂ exposure and HDL levels shows differences and uncertainties[2, 22, 23]. In addition, most recent research focuses on the population of high-income countries, and there needs to be more research evidence on the population of middle and low-income countries with more severe air pollution[3, 24]. Previous studies have shown that as age increases, the body's ability to synthesize, degrade, and transport cholesterol decreases, and lipid levels also undergo adverse changes[25]; therefore, middle-aged and elderly populations exhibit a higher susceptibility to air pollution[22, 23]. Meanwhile, given the continuously increasing dietary habits and prevalence of dyslipidemia among the Chinese population[26, 27], it is necessary to study the impact of air NO₂ exposure on HDL indicators in the middle-aged and elderly population in China. It is of great significance to maintain the normal HDL levels of the middle-aged and elderly population in China, promote cardiovascular health, and reduce the social, economic, and medical burden.

1. Data Resource

The data of this article is from the third wave of CHARLS; CHARLS (https://g2aging.org/) is a nationwide cohort study conducted by the National Development Research Institute of Peking University, using a multi-stage stratified probability sampling method to sample the middle-aged and elderly population of 150 municipalities directly under the central government in 28 provinces, municipalities, and autonomous regions across the country. This study started investigating and collecting data from 2011 to 2012 and has since collected data every two years. It provides longitudinal data on demographic indicators, family conditions, biomedical indicators, health status, and body functions of the middle-aged and elderly population in China. CHARLS has been approved by the Ethics Committee of Peking University School of Medicine, and the detailed investigation design plan has been described elsewhere[28].

The third wave of the CHARLS survey had a total of 20284 participants, excluding 571 participants under the age of 45 and further excluding 7194 participants who did not undergo venous blood collection; this study ultimately included 12519 participants. For details on excluding participants, please refer to Fig. 1.

2. Definition of Environmental NO2Exposure Concentration and HDL Level

The NO₂ exposure concentration in the participant's environment was obtained from the China Air Pollutants (CHAP) dataset^[29], which uses multi-source satellite remote sensing and artificial intelligence technology combined with rich ground observation data, satellite remote sensing products, atmospheric reanalysis, and model simulation to eliminate the spatiotemporal heterogeneity of air pollution. High-quality NO₂ data with a 1-kilometer resolution in China from 2000 to 2018 were obtained for recording. This study considers the participants' privacy issues in their residences. The NO₂ concentration in the city-level environment where the participants reside is taken as the personal level data of the participants, and the average value from the beginning of the study (2011) to the end of the study (2015) is taken as the exposure level of NO₂ in the participants' environment. The collected blood samples are analyzed for whole blood cell count (CBC) at the local health centre and then sent to the research headquarters for HDL measurement^[30].

3. Covariates

To eliminate any possible confounding variables, this study analyzed specific parameters that have been previously proven to affect HDL levels. Quantify age in years. BMI was calculated by dividing the weight in kilograms by the square of the height in meters. According to different levels of education, it is divided into two categories: secondary education and below and higher education. Residential locations are divided into rural and urban areas. Marital status is divided into two categories: married or with a partner, as well as separated, divorced, widowed or unmarried; Divided into different subgroups based on the subject's smoking and alcohol consumption; At the same time, participants were asked whether they had been diagnosed as the following diseases by doctors in the past: such as hypertension, diabetes, respiratory diseases, heart diseases, stroke, psychological diseases, arthritis, dyslipidemia, liver diseases, kidney diseases, digestive system diseases, etc. Missing covariates are inputted using multivariate input techniques that rely on predictive mean-matching methods.

4. Statistical analysis

Standard deviation and mean represent continuous variables in baseline features, while ratios and percentages indicate categorical variables. Kruskal Wallis test is used to determine the p-value of constant variables; Classification data analysis adopts the chi-square test. Fisher's exact test is used when the theoretical number is less than 10^[31].

Environmental NO2 exposure concentration and HDL level were treated as continuous variables. We stratified the baseline data by gender to examine potential differences among the middle-aged and elderly. Multiple linear regression analysis was used to study the relationship between environmental NO2 exposure concentration and high-density lipoprotein levels. To reduce the impact of confounding factors on the research results, models I-IV were set up to adjust for different covariates. Model I only adjusted for age, which is divided into four age groups based on the age of the subjects: 50 years old, 60 years old, 70 years old, and over 70 years old; Model II adjusted for the age, gender, education level, marital status, and place of residence of the subjects; Model III added variables such as smoking and alcohol consumption, BMI, etc. to Model II for additional variable adjustments; Model IV adds adjustments for previously diagnosed disease variables based on Model III. Using interaction analysis and covariate stratification to examine the heterogeneity of the association between environmental NO2 exposure concentration and HDL levels. Use stratified linear regression analysis for subgroup analysis and use a logarithmic likelihood ratio test to determine the p-value of interactions, which involves comparing models with and without covariate interactions. All statistical results of this study are considered statistically significant at a significance level of 0.05. All analyses were conducted using version R 4.2.2.

1.Baseline characteristics of investigators and the average level of NO2 concentration at the urban level in China

A total of 12519 participants were included in this study, and the baseline characteristics of the participants are detailed in Table 1. Analysis shows that female participants have higher HDL levels than male participants (52.30 ± 10.80 mg/dl vs 49.96 ± 12.38 mg/dl, p < 0.001). Meanwhile, the average age of female participants was lower than that of male participants (60.16 ± 9.74 vs 61.33 ± 9.73, p < 0.001). There were statistically significant differences (P < 0.05) between participants of different genders in terms of place of BMI, residence, education level, marital status, alcohol consumption, smoking, and comorbidities. The NO2 concentration at the city level where the participants are located is shown in Fig. 2.

Table 1

Baselines characteristics of the participants enrolled
		Gender
		male	female	P
		n = 5889	n = 6630
hdl (mean (SD))	mg/dl	49.96 (12.38)	52.30 (10.80)	< 0.001
NO2 (mean (SD))		30.14 (9.69)	30.32 (9.81)	0.287
age (mean (SD)	year	61.33 (9.73)	60.16 (9.74)	< 0.001
age_class (%)	< 50	744 (12.63)	989 (14.92)	< 0.001
	< 60	1840 (31.24)	2283 (34.43)	1
	< 70	2095 (35.57)	2194 (33.09)	1
	>=70	1210 (20.55)	1164 (17.56)	1
BMI (mean (SD))		23.84 (11.44)	25.04 (17.62)	< 0.001
residence (%)	urban	1163 (22.25)	1098 (18.51)	< 0.001
	rural	4064 (77.75)	4834 (81.49)	1
education_level (%)	Less than lower secondary	5761 (97.86)	6571 (99.14)	< 0.001
	tertiary	126 (2.14)	57 (0.86)	1
marital_status (%)	married or partnered	5293 (89.88)	5569 (84.00)	< 0.001
	separated divorced widowed or never married	596 (10.12)	1061 (16.00)	1
drinking_feq (%)	none	2473 (42.14)	5612 (84.95)	< 0.001
	less than once per day	1920 (32.72)	834 (12.62)	1
	once per day	800 (13.63)	108 (1.63)	1
	twice per day	490 (8.35)	45 (0.68)	1
	more than twice per day	185 (3.15)	7 (0.11)	1
smoking_feq (%)	none	2750 (47.23)	6285 (95.14)	< 0.001
	<=4	293 (5.03)	58 (0.88)	1
	<=10	763 (13.11)	139 (2.10)	1
	> 10	2016 (34.63)	124 (1.88)	1
hypertension (%)	no	3291 (65.56)	3664 (63.36)	0.018
	yes	1729 (34.44)	2119 (36.64)	1
diabetes (%)	no	4543 (90.86)	5069 (88.34)	< 0.001
	yes	457 (9.14)	669 (11.66)	1
lungdis (%)	no	4162 (82.63)	5107 (88.23)	< 0.001
	yes	875 (17.37)	681 (11.77)	1
heartdis (%)	no	4195 (83.58)	4532 (78.80)	< 0.001
	yes	824 (16.42)	1219 (21.20)	1
stroke (%)	no	4813 (95.51)	5621 (96.88)	< 0.001
	yes	226 (4.49)	181 (3.12)	1
psydis (%)	no	4958 (98.28)	5637 (97.39)	0.002
	yes	87 (1.72)	151 (2.61)	1
arthritis (%)	no	3072 (60.76)	2993 (51.93)	< 0.001
	yes	1984 (39.24)	2771 (48.07)	1
dyslipidemia (%)	no	3974 (80.85)	4428 (78.80)	0.01
	yes	941 (19.15)	1191 (21.20)	1
liverdis (%)	no	4638 (92.48)	5411 (93.99)	0.002
	yes	377 (7.52)	346 (6.01)	1
kidneydis (%)	no	4455 (88.69)	5260 (91.27)	< 0.001
	yes	568 (11.31)	503 (8.73)	1
digestdis (%)	no	3636 (71.97)	3792 (65.50)	< 0.001
	yes	1416 (28.03)	1997 (34.50)	1
asthma (%)	no	4674 (92.59)	5484 (94.83)	< 0.001
	yes	374 (7.41)	299 (5.17)	1

Table 1. Baselines characteristics of the participants enrolled (SD: standard deviation, SD for continuous variables: P value was calculated by Kruskal Wallis rank-sum test, Number (%) for categorical variables: P value was calculated by chi-square test, HDL: high-density lipoprotein cholesterol)

2.The relationship between environmental NO₂ concentration and high-density lipoprotein HDL level

Table 2 shows the multivariate regression analysis results of the relationship between NO₂ concentration and HDL level in the participant's environment. In the rough model, the correlation between environmental NO₂ concentration and HDL level indicates that older adults with higher environmental NO₂ concentration levels have lower HDL levels (Beta: -0.132; 95% CI: (-0.153, -0.111); P < 0.0001). After controlling for age, the Beta value of Model I is -0.132 (95% CI: -0.153, -0.112; P < 0.0001). Even after considering several possible confounding factors, both Model II (Beta: -0.136; 95% CI: -0.156, -0.115; p < 0.0001) and Model III (Beta: -0.124; 95% CI: -0.144, -0.104, p < 0.0001) remained consistent. Model IV (Beta=-0.103, 95% CI: -0.124, -0.083, p < 0.0001) adjusted for all possible confounding variables, and the analysis results remained statistically significant. According to Model I in multivariate regression analysis, age may affect the correlation between environmental NO₂ concentration and HDL levels. From Model III, smoking, alcohol consumption, and BMI may contribute to this association. The research results of Model IV indicate that the presence of comorbidities significantly affects the overall correlation.

Table 2

Multivariate regression model of the relationship between NO₂ and the level of HDL
Supplement 2 Multivariate regression model of the relationship between NO2 and the level of HDL
Exposure	Crude model
Exposure	Beta (95%CI)	P-value
Crude model	-0.132 ( -0.153, -0.111 )	<0.0001
Model I	-0.132 ( -0.153, -0.112 )	<0.0001
Model II	-0.136 ( -0.156, -0.115 )	<0.0001
Model III	-0.124 ( -0.144, -0.104 )	<0.0001
Model IV	-0.103 ( -0.124, -0.083 )	<0.0001
Crude model adjust for none;
Model I adjust for: age
Model II adjust for: age; gender; education level; marital status; residence
Model III adjust for:age; gender; education level; marital status; residence; smoking condition; drinking condition; BMI
Model IV adjust for: age; gender; education level; marital status; residence; smoking condition; drinking condition; BMI; hypertension; diabetes; lung diseases; heart diseases; stroke; psych problems; arthritis; dyslipidemia; liver diseases; kidney diseases; digest diseases; asthma

Table 2. Multivariate liner regression was used to identify the association between ambient NO₂ and HDL level.; CI: confidence interval; BMI: body mass index.

3 Subgroup analysis

The results in Fig. 2 indicate a significant correlation between environmental NO₂ concentration and HDL levels across different age groups and genders. In addition, this correlation also remains consistent across other population groups, such as those living in rural areas, those who do not smoke or smoke more than four cigarettes per day, and those who do not drink alcohol or drink less than once a day. The results of the interaction test showed that age, gender, place of residence, smoking and drinking had a significant impact, and the p-value of the interaction was statistically significant.

The ageing problem in China is becoming increasingly severe, and the number of older adults aged 60 and above will increase significantly by 2031[32]. HDL, as a beneficial lipid index in the body, protects vascular wall function and prevents cardiovascular accidents. Therefore, normal HDL levels have crucial clinical significance[5]. This study is the first to explore the relationship between air NO₂ exposure and HDL levels in middle-aged and older adults aged 45 and above in China. Our cross-sectional study found that long-term exposure to environmental NO₂ is negatively correlated with HDL levels, and middle-aged and older adults with higher levels of ecological NO₂ exposure have lower HDL levels. Our study, based on previous research findings, found that this association remained consistent even after considering multiple possible confounding factors in the past.

Multiple studies have shown that exposure to different environmental air pollutants can hurt lipid metabolism in the general population[6, 12, 21, 33]. Exposure to air pollutants has a more significant adverse effect on the elderly, obese, and people with previous cardiovascular disease or diabetes[34]. But so far, most studies have focused on the adverse effects of PM_2.5 and PM₁₀ components in air pollution on blood lipid abnormalities[33], with only a few studies on the effects of NO₂, an air pollution component, on the HDL levels. In the study of Spanish adults[35] and Danish adults[36], a significant negative correlation was found between exposure to NO₂ and HDL levels, and our findings are consistent with previous studies. Therefore, because of previous studies that confirmed the critical role of HDL in preventing the development of atherosclerosis, new insights were provided for studying the vital mechanism of different air pollutants promoting cardiovascular diseases[2]. However, the exact mechanism of air pollution affecting lipid levels has yet to be entirely determined. Some studies suggest that air pollution can induce systemic and oxidative stress[37–39], leading to a decrease in lipid output capacity[40]. Another part of the research suggests that air pollution can lead to abnormal DNA methylation[41], altering specific genes related to lipid metabolism[42]; As a free radical, NO₂ can have an impact on health through oxidative stress and inflammatory reactions[43]. Previous studies have found that PM components in air pollution can cause local oxidative stress, and the presence of NO₂ can enhance oxidative stress[44]. This may be one of the mechanisms by which NO₂ affects the body's HDL levels.

Although the participants in this study were screened nationwide, there are some inevitable limitations:

This study is cross-sectional, and the causal relationship between air NO₂ exposure concentration and HDL has yet to be accurately determined.

The exclusion of various confounding factors in the study was based on the patient's self-report and did not yield objective examination results and diagnosis.

Considering the privacy issues of the respondents, only city-level NO₂ data can be obtained as a representative of personal-level data, which may have inevitable errors.

However, we expect more prospective and intervention studies in the future to provide more comprehensive explanations.

Our research findings indicate a significant correlation between long-term exposure to air NO₂ and HDL levels, and after considering potential confounding variables, long-term NO₂ exposure can also lead to a decrease in HDL levels in middle-aged and elderly population. Therefore, middle-aged and elderly population should pay more attention to taking protective measures to reduce NO₂ exposure. At the same time, considering the development trend of the ageing population in China, the results of this study also provide a reference for further research on the relationship between different components of environmental air pollution and HDL levels and provide a scientific basis for relevant departments to formulate policies, to reduce the burden of cardiovascular disease and protect the health of middle-aged and older adults through feasible and achievable targeted intervention measures. We look forward to more research in the future to elucidate this correlation and potential mechanisms of action.

Acknowledgments

The authors thank the National Development Research Institute of Peking University and the China Social Science Survey Center of Peking University for providing CHARLS data, participants, and researchers. At the same time, we also want to thank the Chinese Air Pollutants (CHAP) researchers.

Author Contributions

Conception and design FL. Data acquisition and analysis FL ZS. Drafting of the work LP. Revised and reviewed the article FL LP. All authors approved the final version of the manuscript.

Funding Statement

No funding

Data Availability

Publicly available datasets analyzed in this study can be found on the CHARLS website (https://charls.pku.edu.cn/) and the CHAP website (https://weijing-rs.github.io/index.html).

Ethics approval and consent to participate

All data used in this study were from the public domain.

Consent for publication

Not applicable.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Competing interests

The authors declare that they have no competing interests.

WHO Guidelines Approved by the Guidelines Review Committee. In: WHO global air quality guidelines: Particulate matter (PM(25) and PM(10)), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide. edn. Geneva: World Health Organization © World Health Organization 2021.; 2021.
Li JP, Zhou CP, Xu HB, Brook RD, Liu SC, Yi TC, Wang Y, Feng BH, Zhao MM, Wang X et al: Ambient Air Pollution Is Associated With HDL (High-Density Lipoprotein) Dysfunction in Healthy Adults. Arterioscl Throm Vas 2019, 39(3):513-522.
Murray CJL, Aravkin AY, Zheng P, Abbafati C, Abbas KM, Abbasi-Kangevari M, Abd-Allah F, Abdelalim A, Abdollahi M, Abdollahpour I et al: 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(10258):1223-1249.
Soran H, Schofield JD, Durrington PN: Antioxidant properties of HDL. Front Pharmacol 2015, 6.
Rosenson RS, Brewer HB, Ansell BJ, Bartert P, Chapman MJ, Heinecke JW, Kontush A, Tall AR, Webb NR: Dysfunctional HDL and atherosclerotic cardiovascular disease. Nat Rev Cardiol 2016, 13(1):48-60.
Mao SY, Chen GB, Liu FF, Li N, Wang CJ, Liu YS, Liu SY, Lu YA, Xiang H, Guo YM et al: Long-term effects of ambient air pollutants to blood lipids and dyslipidemias in a Chinese rural population. Environ Pollut 2020, 256.
Wu XM, Broadwin R, Basu R, Malig B, Ebisu K, Gold EB, Qi LH, Derby C, Park SK, Green S: Associations between fine particulate matter and changes in lipids/lipoproteins among midlife women. Sci Total Environ 2019, 654:1179-1186.
Zhang WL, Liu JX, Hu DY, Li LY, Cui LY, Xu JH, Wang WZ, Deng FR, Guo XB: Joint effect of multiple air pollutants on lipid profiles in obese and normal-weight young adults: The key role of ozone. Environ Pollut 2022, 292.
Gaio V, Roquette R, Dias CM, Nunes B: Ambient air pollution and lipid profile: Systematic review and meta-analysis. Environ Pollut 2019, 254.
Kim JS, Chen ZH, Alderete TL, Toledo-Corral C, Lurmann F, Berhane K, Gilliland FD: Associations of air pollution, obesity and cardiometabolic health in young adults: The Meta-AIR study. Environ Int 2019, 133.
McGuinn LA, Schneider A, McGarrah RW, Ward-Caviness C, Neas LM, Di Q, Schwartz J, Hauser ER, Kraus WE, Cascio WE et al: Association of long-term PM exposure with traditional and novel lipid measures related to cardiovascular disease risk. Environ Int 2019, 122:193-200.
Kim KN, Ha B, Seog W, Hwang IU: Long-term exposure to air pollution and the blood lipid levels of healthy young men. Environ Int 2022, 161.
Brook RD, Rajagopalan S, Pope CA, Brook JR, Bhatnagar A, Diez-Roux AV, Holguin F, Hong YL, Luepker RV, Mittleman MA et al: Particulate Matter Air Pollution and Cardiovascular Disease An Update to the Scientific Statement From the American Heart Association. Circulation 2010, 121(21):2331-2378.
Ossoli A, Favero C, Vigna L, Pesatori AC, Bollati V, Gomaraschi M: Body Mass Index Modulates the Impact of Short-Term Exposure to Air Particulate Matter on High-Density Lipoprotein Function. Antioxidants-Basel 2022, 11(10).
Chen H, Samet JM, Bromberg PA, Tong HY: Cardiovascular health impacts of wildfire smoke exposure. Part Fibre Toxicol 2021, 18(1).
Pan MN, Liu FF, Zhang K, Chen ZY, Tong JH, Wang XX, Zhou F, Xiang H: Independent and interactive associations between greenness and ambient pollutants on novel glycolipid metabolism biomarkers: A national repeated measurement study. Environmental Research 2023, 233.
Tzivian L, Winkler A, Dlugaj M, Schikowski T, Vossoughi M, Fuks K, Weinrnayr G, Hoffmann B: Effect of long-term outdoor air pollution and noise on cognitive and psychological functions in adults. Int J Hyg Envir Heal 2015, 218(1):1-11.
Carey IM, Anderson HR, Atkinson RW, Beevers SD, Cook DG, Strachan DP, Dajnak D, Gulliver J, Kelly FJ: Are noise and air pollution related to the incidence of dementia? A cohort study in London, England. BMJ Open 2018, 8(9):e022404.
Chen H, Kwong JC, Copes R, Hystad P, van Donkelaar A, Tu KR, Brook JR, Goldberg MS, Martin RV, Murray BJ et al: Exposure to ambient air pollution and the incidence of dementia: A population-based cohort study. Environ Int 2017, 108:271-277.
Patterson WB, Holzhausen E, Chalifour B, Goodrich J, Costello E, Lurmann F, Conti DV, Chen ZH, Chatzi L, Alderete TL: Exposure to ambient air pollutants, serum miRNA networks, lipid metabolism, and non-alcoholic fatty liver disease in young adults. Ecotox Environ Safe 2023, 264.
Gui ZH, Yang BY, Zou ZY, Ma J, Jing J, Wang HJ, Dong GH, Ma YH, Guo YM, Chen YJ: Exposure to ambient air pollution and blood lipids in children and adolescents: A national population based study in China. Environ Pollut 2020, 266.
Yang BY, Bloom MS, Markevych I, Qian ZM, Vaughn MG, Cummings-Vaughn LA, Li SS, Chen GB, Bowatte G, Perret JL et al: Exposure to ambient air pollution and blood lipids in adults: The 33 Communities Chinese Health Study. Environ Int 2018, 119:485-492.
Wang MZ, Zheng S, Nie YH, Weng J, Cheng N, Hu XB, Ren XW, Pei HB, Bai YN: CM-180906-1565386 Association between Short-Term Exposure to Air Pollution and Dyslipidemias among Type 2 Diabetic Patients in Northwest China: A Population-Based Study. International Journal of Environmental Research and Public Health 2018, 15(4).
Yusuf S, Joseph P, Rangarajan S, Islam S, Mente A, Hystad P, Brauer M, Kutty VR, Gupta R, Wielgosz A et al: Modifiable risk factors, cardiovascular disease, and mortality in 155 722 individuals from 21 high-income, middle-income, and low-income countries (PURE): a prospective cohort study. Lancet 2020, 395(10226):795-808.
Johnson AA, Stolzing A: The role of lipid metabolism in aging, lifespan regulation, and age-related disease. Aging Cell 2019, 18(6).
Pan L, Yang ZH, Wu Y, Yin RX, Liao YH, Wang JW, Gao BX, Zhang LX, Grp CKDW: The prevalence, awareness, treatment and control of dyslipidemia among adults in China. Atherosclerosis 2016, 248:2-9.
Peters SAE, Muntner P, Woodward M: Sex Differences in the Prevalence of, and Trends in, Cardiovascular Risk Factors, Treatment, and Control in the United States, 2001 to 2016. Circulation 2019, 139(8):1025-1035.
Zhao YH, Hu YS, Smith JP, Strauss J, Yang GH: Cohort Profile: The China Health and Retirement Longitudinal Study (CHARLS). Int J Epidemiol 2014, 43(1):61-68.
Wei J, Li ZQ, Lyapustin A, Sun L, Peng YR, Xue WH, Su TN, Cribb M: Reconstructing 1-km-resolution high-quality PM data records from 2000 to 2018 in China: spatiotemporal variations and policy implications. Remote Sens Environ 2021, 252.
Chen XX, Crimmins E, Hu PF, Kim JK, Meng QQ, Strauss J, Wang YF, Zeng JX, Zhang Y, Zhao YH: Venous Blood-Based Biomarkers in the China Health and Retirement Longitudinal Study: Rationale, Design, and Results From the 2015 Wave. Am J Epidemiol 2019, 188(11):1871-1877.
Ma K, Dong Q: Association between sleep quality and benign prostate hyperplasia among middle-aged and older men in India. Bmc Public Health 2023, 23(1).
Ye X, Wang M, Xia YQ, He P, Zheng XY: Direct economic burden attributable to age-related diseases in China: An econometric modelling study. J Glob Health 2023, 13.
Mao SY, Li SS, Wang CJ, Liu YS, Li N, Liu FF, Huang SQ, Liu SY, Lu YN, Mao ZX et al: Is long-term PM exposure associated with blood lipids and dyslipidemias in a Chinese rural population? Environ Int 2020, 138.
de Bont J, Jaganathan S, Dahlquist M, Persson A, Stafoggia M, Ljungman P: Ambient air pollution and cardiovascular diseases: An umbrella review of systematic reviews and meta-analyses. J Intern Med 2022, 291(6):779-800.
Valdés S, Doulatram-Gamgaram V, Maldonado-Araque C, Garcia-Escobar E, Garcia-Serrano S, Oualla-Bachiri W, Garcia-Vivanco M, Garrido JL, Gil V, Martin-Llorente F et al: Association between exposure to air pollution and blood lipids in the general population of Spain. Eur J Clin Invest 2023.
Roswall N, Poulsen AH, Hvidtfeldt UA, Hendriksen PF, Boll K, Halkjaer J, Ketzel M, Brandt J, Frohn LM, Christensen JH et al: Exposure to ambient air pollution and lipid levels and blood pressure in an adult, Danish cohort. Environmental Research 2023, 220.
Chen ST, Lin CC, Liu YS, Lin C, Hung PT, Jao CW, Lin PH: Airborne particulate collected from central Taiwan induces DNA strand breaks, Poly(ADP-ribose) polymerase-1 activation, and estrogen-disrupting activity in human breast carcinoma cell lines. J Environ Sci Heal A 2013, 48(2):173-181.
Chen T, Jia G, Wei YJ, Li JC: Beijing ambient particle exposure accelerates atherosclerosis in ApoE knockout mice. Toxicol Lett 2013, 223(2):146-153.
Li RS, Navab M, Pakbin P, Ning Z, Navab K, Hough G, Morgan TE, Finch CE, Araujo JA, Fogelman AM et al: Ambient ultrafine particles alter lipid metabolism and HDL anti-oxidant capacity in LDLR-null mice. J Lipid Res 2013, 54(6):1608-1615.
Zhang YZ, Zanotti I, Reilly MP, Glick JM, Rothblat GH, Rader DJ: Overexpression of apolipoprotein A-I promotes reverse transport of cholesterol from macrophages to feces in vivo. Circulation 2003, 108(6):661-663.
Li HC, Chen RJ, Cai J, Cui X, Huang N, Kan HD: Short-term exposure to fine particulate air pollution and genome-wide DNA methylation: A randomized, double-blind, crossover trial. Environ Int 2018, 120:130-136.
Bind MA, Lepeule J, Zanobetti A, Gasparrini A, Baccarelli A, Coull BA, Tarantini L, Vokonas PS, Koutrakis P, Schwartz J: Air pollution and gene-specific methylation in the Normative Aging Study: association, effect modification, and mediation analysis. Epigenetics 2014, 9(3):448-458.
WHO Guidelines Approved by the Guidelines Review Committee. In: WHO Guidelines for Indoor Air Quality: Selected Pollutants. edn. Geneva: World Health Organization Copyright © 2010, World Health Organization.; 2010.
Mudway IS, Kelly FJ: Ozone and the lung: a sensitive issue. Molecular aspects of medicine 2000, 21(1-2):1-48.

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The relationship between long-term exposure to environmental NO2 and HDL levels among middle-aged and older adults in China

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Methods

1. Data Resource

3. Covariates

4. Statistical analysis

Results

2.The relationship between environmental NO₂ concentration and high-density lipoprotein HDL level

3 Subgroup analysis

Discussion

Conclusions

Declarations

References

Additional Declarations

Status:

Version 1

The relationship between long-term exposure to environmental NO2 and HDL levels among middle-aged and older adults in China

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Methods

1. Data Resource

3. Covariates

4. Statistical analysis

Results

2.The relationship between environmental NO2 concentration and high-density lipoprotein HDL level

3 Subgroup analysis

Discussion

Conclusions

Declarations

References

Additional Declarations

Status:

Version 1

2.The relationship between environmental NO₂ concentration and high-density lipoprotein HDL level