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Dai Y, Yin J, Li S, Li J, Han X, Deji Q, Pengcuo C, Liu L, Yu Z, Chen L, Xie L, Guo B, Zhao X. Long-term exposure to fine particulate matter constituents in relation to chronic kidney disease: evidence from a large population-based study in China. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH 2024; 46:174. [PMID: 38592609 DOI: 10.1007/s10653-024-01949-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Accepted: 03/07/2024] [Indexed: 04/10/2024]
Abstract
The effects of long-term exposure to fine particulate matter (PM2.5) constituents on chronic kidney disease (CKD) are not fully known. This study sought to examine the association between long-term exposure to major PM2.5 constituents and CKD and look for potential constituents contributing substantially to CKD. This study included 81,137 adults from the 2018 to 2019 baseline survey of China Multi-Ethnic Cohort. CKD was defined by the estimated glomerular filtration rate. Exposure concentration data of 7 major PM2.5 constituents were assessed by satellite remote sensing. Logistic regression models were used to estimate the effect of each PM2.5 constituent exposure on CKD. The weighted quantile sum regression was used to estimate the effect of mixed exposure to all constituents. PM2.5 constituents had positive correlations with CKD (per standard deviation increase), with ORs (95% CIs) of 1.20 (1.02-1.41) for black carbon, 1.27 (1.07-1.51) for ammonium, 1.29 (1.08-1.55) for nitrate, 1.20 (1.01-1.43) for organic matter, 1.25 (1.06-1.46) for sulfate, 1.30 (1.11-1.54) for soil particles, and 1.63 (1.39-1.91) for sea salt. Mixed exposure to all constituents was positively associated with CKD (1.68, 1.32-2.11). Sea salt was the constituent with the largest weight (0.36), which suggested its importance in the PM2.5-CKD association, followed by nitrate (0.32), organic matter (0.18), soil particles (0.10), ammonium (0.03), BC (0.01). Sulfate had the least weight (< 0.01). Long-term exposure to PM2.5 sea salt and nitrate may contribute more than other constituents in increasing CKD risk, providing new evidence and insights for PM2.5-CKD mechanism research and air pollution control strategy.
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Affiliation(s)
- Yucen Dai
- West China School of Public Health and West China Fourth Hospital, Sichuan University, No.17, Section 3, South Renmin Road, Chengdu, 610041, Sichuan Province, China
| | - Jianzhong Yin
- School of Public Health, Kunming Medical University, Kunming, China
- Baoshan College of Traditional Chinese Medicine, Baoshan, China
| | - Sicheng Li
- West China School of Public Health and West China Fourth Hospital, Sichuan University, No.17, Section 3, South Renmin Road, Chengdu, 610041, Sichuan Province, China
| | - Jiawei Li
- West China School of Public Health and West China Fourth Hospital, Sichuan University, No.17, Section 3, South Renmin Road, Chengdu, 610041, Sichuan Province, China
| | - Xinyu Han
- West China School of Public Health and West China Fourth Hospital, Sichuan University, No.17, Section 3, South Renmin Road, Chengdu, 610041, Sichuan Province, China
| | | | - Ciren Pengcuo
- Tibet Center for Disease Control and Prevention CN, Lhasa, China
| | - Leilei Liu
- School of Public Health the Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, China
| | - Zhimiao Yu
- Chengdu Center for Disease Control and Prevention, Chengdu, China
| | - Liling Chen
- Chongqing Municipal Center for Disease Control and Prevention, Chongqing, China
| | - Linshen Xie
- West China School of Public Health and West China Fourth Hospital, Sichuan University, No.17, Section 3, South Renmin Road, Chengdu, 610041, Sichuan Province, China.
| | - Bing Guo
- West China School of Public Health and West China Fourth Hospital, Sichuan University, No.17, Section 3, South Renmin Road, Chengdu, 610041, Sichuan Province, China.
| | - Xing Zhao
- West China School of Public Health and West China Fourth Hospital, Sichuan University, No.17, Section 3, South Renmin Road, Chengdu, 610041, Sichuan Province, China
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Yang C, Wang W, Wang F, Wang Y, Zhang F, Liang Z, Liang C, Wang J, Ma L, Li P, Li S, Zhang L. Ambient PM 2.5 components and prevalence of chronic kidney disease: a nationwide cross-sectional survey in China. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH 2024; 46:70. [PMID: 38353840 DOI: 10.1007/s10653-024-01867-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 01/10/2024] [Indexed: 02/16/2024]
Abstract
OBJECTIVES Chronic kidney disease (CKD) is a global public health concern, and accumulating evidence has indicated that air pollution increases the odds of CKD. However, a limited number of studies have examined the long-term effects of ambient fine particulate matter (PM2.5) components on the risk of CKD among general population; thus, major knowledge gaps remain. METHODS Using data from a nationwide representative cross-sectional survey in China and a validated PM2.5 composition dataset, we established generalized linear models to quantify the association between five major components of PM2.5 and CKD prevalence. RESULTS There were significant associations between long-term exposure to three PM2.5 components [including black carbon (BC), sulfate (SO42-), organic matter (OM)] and increased odds of CKD prevalence. Along with an interquartile range (IQR) increment in BC (3.3 μg/m3), SO42- (9.7 μg/m3), and OM (16.2 μg/m3) at a 4-year moving average, the odds ratios (ORs) for CKD prevalence were 1.28 (95% CI 1.07, 1.54), 1.23 (95% CI 1.03, 1.45), and 1.23 (95% CI 1.02, 1.47), respectively. We did not detect any significant association of the other two PM2.5 components [nitrate (NO3-) or ammonium (NH4+)] with CKD prevalence. Stratified analyses revealed no differences (P ≥ 0.05) in the effect estimates of subgroups based on administrative region, sex, age, and other demographic characteristics. For instance, along with an IQR increment in BC at a 4-year moving average, the ORs of CKD prevalence among males and females were 1.30 (95% CI 0.98, 1.73) and 1.29 (95% CI 1.01, 1.65), respectively. The odds of CKD were generally higher with increasing PM2.5 composition concentration. CONCLUSIONS Our study demonstrated that long-term exposure to specific PM2.5 components including BC, SO42-, and OM increased CKD risk in the general population. This study could provide new insights into source-directed PM2.5 control and CKD prevention.
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Affiliation(s)
- Chao Yang
- Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, 100034, China
- Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, 100034, China
- Advanced Institute of Information Technology, Peking University, Hangzhou, 311215, China
| | - Wanzhou Wang
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, 100191, China
- National Institute of Health Data Science at Peking University, Beijing, 100191, China
| | - Fulin Wang
- National Institute of Health Data Science at Peking University, Beijing, 100191, China
- Institute of Medical Technology, Peking University Health Science Center, Beijing, 100191, China
| | - Yueyao Wang
- Key Laboratory for Earth Surface Processes of the Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Feifei Zhang
- National Institute of Health Data Science at Peking University, Beijing, 100191, China
- Institute of Medical Technology, Peking University Health Science Center, Beijing, 100191, China
| | - Ze Liang
- Key Laboratory for Earth Surface Processes of the Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Chenyu Liang
- Key Laboratory for Earth Surface Processes of the Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Jinwei Wang
- Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, 100034, China
- Research Units of Diagnosis and Treatment of Immune-Mediated Kidney Diseases, Chinese Academy of Medical Sciences, Beijing, 100034, China
| | - Lin Ma
- Key Laboratory for Earth Surface Processes of the Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Pengfei Li
- Advanced Institute of Information Technology, Peking University, Hangzhou, 311215, China
| | - Shuangcheng Li
- Key Laboratory for Earth Surface Processes of the Ministry of Education, College of Urban and Environmental Sciences, Peking University, Beijing, 100871, China
| | - Luxia Zhang
- Renal Division, Department of Medicine, Peking University First Hospital, Peking University Institute of Nephrology, Beijing, 100034, China.
- Advanced Institute of Information Technology, Peking University, Hangzhou, 311215, China.
- National Institute of Health Data Science at Peking University, Beijing, 100191, China.
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Feng Q, Liu H, Dai W, Cao Y, Shen M, Liu Y, Qi W, Chen Y, Guo X, Zhang Y, Li L, Zhou B, Li J. Comparison of chemical composition and acidity of size-resolved inorganic aerosols at the top and foot of Mt. Hua, Northwest China: The role of the gas-particle distribution of ammonia. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 905:166985. [PMID: 37704142 DOI: 10.1016/j.scitotenv.2023.166985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 09/04/2023] [Accepted: 09/09/2023] [Indexed: 09/15/2023]
Abstract
Aerosol pH is not only a diagnostic indicator of secondary aerosol formation, but also a key factor in the specific chemical reaction routes that produce sulfate and nitrate. To understand the characteristics of aerosol acidity in the Mt. Hua, the chemical fractions of water-soluble inorganic ions in the atmospheric PM2.5 and size-resolved particle at the top and foot of Mt. Hua in summer 2020 were studied. The results showed the mass concentrations of PM2.5 and water-soluble ions at the foot were 2.0-2.6 times higher than those at the top. The secondary inorganic ions, i.e., SO42-, NO3-, and NH4+ (SNA) were 56 %-61 % higher by day than by night. SO42- was mainly distributed in the fine particles (Dp < 2.1 μm). NO3- showed a unimodal size distribution (peaking at 0.7-1.1 μm) at the foot and a bimodal (0.7-1.1 μm and 4.7-5.8 μm) size distribution at the top. At the top site, the distribution of NO3- in coarse particles (> 2.1 μm) was mainly attributed to the gaseous HNO3 volatilized from fine particles reacting with cations in coarse particles to form non-volatile salts (such as Ca(NO3)2). The pH values of PM2.5 were 2.7 ± 1.3 and 3.3 ± 0.42 at the top and foot, respectively. NH4+/NH3(g) plays a decisive role in stabilizing aerosol acidity. In addition, the increase of the liquid water content (LWC) at the foot facilitates the gas-particle conversion of NH3, while the H+ concentration was diluted, resulting in a decrease in acidity at the foot. NH4+/NH3 had good linear correlations with SO42-, NO3-, and LWC during the daytime at both sites, indicating that SO42-, NO3-, and LWC together affect the gas-particle distribution of ammonia by day: however, the effect of LWC at night was not evident.
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Affiliation(s)
- Qiao Feng
- State Key Laboratory of Loess and Quaternary Geology, Key Lab of Aerosol Chemistry and Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China; College of Geography and Environment, Baoji University of Arts and Sciences, Shaanxi Key Laboratory of Disaster Monitoring and Mechanism Simulation, Baoji 721013, China
| | - Haijiao Liu
- Xi'an Institute for Innovative Earth Environment Research, Xi'an 710061, China
| | - Wenting Dai
- State Key Laboratory of Loess and Quaternary Geology, Key Lab of Aerosol Chemistry and Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China
| | - Yue Cao
- Xi'an Institute for Innovative Earth Environment Research, Xi'an 710061, China
| | - Minxia Shen
- State Key Laboratory of Loess and Quaternary Geology, Key Lab of Aerosol Chemistry and Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China; College of Geography and Environment, Baoji University of Arts and Sciences, Shaanxi Key Laboratory of Disaster Monitoring and Mechanism Simulation, Baoji 721013, China
| | - Yali Liu
- Xi'an Institute for Innovative Earth Environment Research, Xi'an 710061, China
| | - Weining Qi
- State Key Laboratory of Loess and Quaternary Geology, Key Lab of Aerosol Chemistry and Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China
| | - Yukun Chen
- State Key Laboratory of Loess and Quaternary Geology, Key Lab of Aerosol Chemistry and Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China; State Key Laboratory of Multiphase Flow in Power Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Xiao Guo
- State Key Laboratory of Loess and Quaternary Geology, Key Lab of Aerosol Chemistry and Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China
| | - Yifan Zhang
- Xi'an Institute for Innovative Earth Environment Research, Xi'an 710061, China
| | - Lu Li
- State Key Laboratory of Loess and Quaternary Geology, Key Lab of Aerosol Chemistry and Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China
| | - Bianhong Zhou
- State Key Laboratory of Loess and Quaternary Geology, Key Lab of Aerosol Chemistry and Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China; College of Geography and Environment, Baoji University of Arts and Sciences, Shaanxi Key Laboratory of Disaster Monitoring and Mechanism Simulation, Baoji 721013, China.
| | - Jianjun Li
- State Key Laboratory of Loess and Quaternary Geology, Key Lab of Aerosol Chemistry and Physics, Institute of Earth Environment, Chinese Academy of Sciences, Xi'an 710061, China; National Observation and Research Station of Regional Ecological Environment Change and Comprehensive Management in the Guanzhong Plain, Shaanxi, China.
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Wen F, Xie Y, Li B, Li P, Qi H, Zhang F, Sun Y, Zhang L. Combined effects of ambient air pollution and PM 2.5 components on renal function and the potential mediation effects of metabolic risk factors in China. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 259:115039. [PMID: 37235899 DOI: 10.1016/j.ecoenv.2023.115039] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 05/16/2023] [Accepted: 05/17/2023] [Indexed: 05/28/2023]
Abstract
Growing evidence links long-term air pollution exposure with renal function. However, little research has been conducted on the combined effects of air pollutant mixture on renal function and multiple mediation effects of metabolic risk factors. This study enrolled 8996 adults without chronic kidney disease (CKD) at baseline from the CHCN-BTH cohort study. Three-year exposure to air pollutants [particulate matter ≤ 2.5 µm (PM2.5), PM10, PM1, ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2) and carbon monoxide (CO)] and PM2.5 components [black carbon (BC), ammonium (NH4+), nitrate (NO3-), sulfate (SO42-) and organic matter (OM)] were assessed using well-validated machine learning methods. Linear mixed models were applied to investigate the associations between air pollutants and estimated glomerular filtration rate (eGFR). Quantile G-computation was used to assess the combined effects of pollutant mixtures. Causal mediation analysis and Bayesian mediation analysis were employed to estimate the mediation effects of metabolic risk factors. An interquartile range increases in BC (-0.256, 95 %CI: -0.331, -0.180) and OM (-0.603, 95 %CI: -0.810, -0.397) were significantly associated with eGFR decline; while O3 (1.151, 95 %CI: 0.813, 1.489), PM10 (0.721, 95 %CI: 0.309, 1.133), NH4+ (0.990, 95 %CI: 0.638, 1.342), and NO3- (0.610, 95 %CI: 0.405, 0.815) were associated with higher eGFR. The combined effect of the PM2.5 component mixture was found to be associated with lower eGFR (-1.147, 95 % CI: -1.456, -0.839), with OM contributing 72.4 % of the negative effect. Univariate mediation analyses showed that high-density lipoprotein (HDL) mediated 7.1 %, 6.9 %, and 6.1 % effects of O3, BC, and OM, respectively. However, these mediation effects were not significant in Bayesian mediation analysis. These findings suggest the effect of the PM2.5 component mixture on eGFR decline and the strong contribution of OM. Metabolic risk factors may not mediate the effects of air pollutants. Further study is warranted to clarify the potential mechanisms involved.
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Affiliation(s)
- Fuyuan Wen
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, and Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Yunyi Xie
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, and Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Bingxiao Li
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, and Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Pandi Li
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, and Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Han Qi
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, and Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China; The National Clinical Research Center for Mental Disorders and Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital and the Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Fengxu Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, and Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Yuan Sun
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, and Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China
| | - Ling Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, and Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China.
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