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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. Environ Geochem 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] [What about the content of this article? (0)] [Affiliation(s)] [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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Wang M, He Y, Zhao Y, Zhang L, Liu J, Zheng S, Bai Y. Exposure to PM 2.5 and its five constituents is associated with the incidence of type 2 diabetes mellitus: a prospective cohort study in northwest China. Environ Geochem Health 2024; 46:34. [PMID: 38227152 DOI: 10.1007/s10653-023-01794-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 10/31/2023] [Indexed: 01/17/2024]
Abstract
Studies have demonstrated that fine particulate matter (PM2.5) is an underlying risk factor for type 2 diabetes mellitus (T2DM), but evidence exploring the relationship between PM2.5 chemical components and T2DM was extremely limited, to investigate the effects of long-term exposure to PM2.5 and its five constituents (sulfate [SO42-], nitrate [NO3-], ammonium [NH4+]), organic matter [OM] and black carbon [BC]) on incidence of T2DM. Based on the "Jinchang Cohort" platform, a total of 19,884 participants were selected for analysis. Daily average concentrations of pollutants were gained from Tracking Air Pollution in China (TAP). Cox proportional hazards regression models were utilized to estimate the hazard ratios (HR) and 95% confidence interval (CI) in single-pollutant models, restricted cubic splines functions were used to examine the dose-response relationships, and quantile g-computation (QgC) was applied to evaluate the combined effect of PM2.5 compositions on T2DM. Stratification analysis was also considered. A total of 791 developed new cases of T2DM were observed during a follow-up period of 45254.16 person-years. The concentrations of PM2.5, NO3-, NH4+, OM and BC were significantly associated with incidence of T2DM (P-trend < 0.05), with the HRs in the highest quartiles of 2.16 (95% CI 1.79, 2.62), 1.43 (95% CI 1.16, 1.75), 1.75 (95% CI 1.45, 2.11), 1.31 (95% CI 1.08, 1.59) and 1.79 (95% CI 1.46, 2.21), respectively. Findings of QgC model showed a noticeably positive joint effect of one quartile increase in PM2.5 constituents on increased T2DM morbidity (HR 1.27, 95% CI 1.09, 1.49), and BC (32.7%) contributed the most to the overall effect. The drinkers, workers and subjects with hypertension, obesity, higher physical activity, and lower education and income were generally more susceptible to PM2.5 components hazards. Long-term exposure to PM2.5 and its components (i.e., NO3-, NH4+, OM, BC) was positively correlated with T2DM incidence. Moreover, BC may be the most responsible for the association between PM2.5 constituents and T2DM. In the future, more epidemiological and experimental studies are needed to identify the link and potential biological mechanisms.
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Affiliation(s)
- Minzhen Wang
- Department of Epidemiology and Statistics, School of Public Health, Lanzhou University, No. 199 Donggang West Road, Lanzhou, 730000, China
| | - Yingqian He
- Department of Epidemiology and Statistics, School of Public Health, Lanzhou University, No. 199 Donggang West Road, Lanzhou, 730000, China
| | - Yanan Zhao
- Department of Epidemiology and Statistics, School of Public Health, Lanzhou University, No. 199 Donggang West Road, Lanzhou, 730000, China
| | - Lulu Zhang
- Department of Epidemiology and Statistics, School of Public Health, Lanzhou University, No. 199 Donggang West Road, Lanzhou, 730000, China
| | - Jing Liu
- Department of Epidemiology and Statistics, School of Public Health, Lanzhou University, No. 199 Donggang West Road, Lanzhou, 730000, China
| | - Shan Zheng
- Department of Epidemiology and Statistics, School of Public Health, Lanzhou University, No. 199 Donggang West Road, Lanzhou, 730000, China.
| | - Yana Bai
- Department of Epidemiology and Statistics, School of Public Health, Lanzhou University, No. 199 Donggang West Road, Lanzhou, 730000, China
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Hang Y, Pu Q, Zhu Q, Meng X, Jin Z, Liang F, Tian H, Li T, Wang T, Cao J, Fu Q, Dey S, Li S, Huang K, Kan H, Shi X, Liu Y. Application of multi-angle spaceborne observations in characterizing the long-term particulate organic carbon pollution in China. Res Sq 2023:rs.3.rs-3734829. [PMID: 38168284 PMCID: PMC10760305 DOI: 10.21203/rs.3.rs-3734829/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Ambient PM2.5 pollution is recognized as a leading environmental risk factor, causing significant mortality and morbidity in China. However, the specific contributions of individual PM2.5 constituents remain unclear, primarily due to the lack of a comprehensive ground monitoring network for constituents. This issue is particularly critical for carbonaceous species such as organic carbon (OC) and elemental carbon (EC), which are known for their significant health impacts, and understanding the OC/EC ratio is crucial for identifying pollution sources. To address this, we developed a Super Learner model integrating Multi-angle Imaging SpectroRadiometer (MISR) retrievals to predict daily OC concentrations across China from 2003 to 2019 at a 10-km spatial resolution. Our model demonstrates robust predictive accuracy, as evidenced by a random cross-validation R2 of 0.84 and an RMSE of 4.9 μg/m3, at the daily level. Although MISR is a polar-orbiting instrument, its fractional aerosol data make a significant contribution to the OC exposure model. We then use the model to explore the spatiotemporal distributions of OC and further calculate the EC/OC ratio in China. We compared regional pollution discrepancies and source contributions of carbonaceous pollution over three selected regions: Beijing-Tianjin-Hebei, Fenwei Plain, and Yunnan Province. Our model observes that OC levels are elevated in Northern China due to industrial operations and central heating during the heating season, while in Yunnan, OC pollution is mainly contributed by local forest fires during fire seasons. Additionally, we found that OC pollution in China is likely influenced by climate phenomena such as the El Niño-Southern Oscillation. Considering that climate change is increasing the severity of OC concentrations with more frequent fire events, and its influence on OC formation and dispersion, we suggest emphasizing the role of climate change in future OC pollution control policies. We believe this study will contribute to future epidemiological studies on OC, aiding in refining public health guidelines and enhancing air quality management in China.
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Affiliation(s)
- Yun Hang
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, United States
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, 77030, United States
| | - Qiang Pu
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, United States
| | - Qiao Zhu
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, United States
| | - Xia Meng
- School of Public Health, Fudan University, Shanghai 200032, China
| | - Zhihao Jin
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, United States
| | - Fengchao Liang
- School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen, 518055, China
| | - Hezhong Tian
- State Key Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beiji ng, 100875, China
| | - Tiantian Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Tijian Wang
- School of Atmospheric Sciences, Nanjing University, Nanjing 210023, China
| | - Junji Cao
- Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100101, China
| | - Qingyan Fu
- State Ecologic Environmental Scientific Observation and Research Station at Dianshan Lake, Shanghai Environmental Monitoring Center, Shanghai 200235, China
| | - Sagnik Dey
- Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India
| | - Shenshen Li
- State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100101, China
| | - Kan Huang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention, Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Haidong Kan
- School of Public Health, Fudan University, Shanghai 200032, China
| | - Xiaoming Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Yang Liu
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, United States
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Lin L, Huang H, Lei F, Sun T, Chen Z, Qin K, Li M, Hu Y, Huang X, Zhang X, Zhang P, Zhang XJ, She ZG, Cai J, Yang S, Jia P, Li H. Long-Term Exposure to Fine Particulate Constituents and Vascular Damage in a Population with Metabolic Abnormality in China. J Atheroscler Thromb 2023; 30:1552-1567. [PMID: 37032101 PMCID: PMC10627764 DOI: 10.5551/jat.64062] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Accepted: 02/23/2023] [Indexed: 04/11/2023] Open
Abstract
AIM To date, PM2.5-associated vascular damage in metabolic abnormalities has remained controversial. We knew little about the vascular damage of PM2.5 constituents. Thus, this study aimed to investigate the relationship between long-term exposure to PM2.5 and its constituents and vascular damage in metabolic abnormalities. METHODS A total of 124,387 participants with metabolic abnormalities (defined as at least one metabolic disorder, such as obesity, elevated blood pressure, elevated triglyceride level, elevated fasting glucose level, or low HDL cholesterol level) were recruited in this study from 11 representative centers in China between January 2011 and December 2017. PM2.5 and its constituents (black carbon [BC], organic matter [OM], sulfate [SO42-], nitrate [NO3-], and ammonium salts [NH4+]) were extracted. Elevated brachial-ankle pulse wave velocity (baPWV) (≥ 1,400 cm/s) and declined ankle-brachial index (ABI) (<0.9) indicated vascular damage. Multivariable logistic regression and Quantile g-Computation models were utilized to explore the impact on outcomes. RESULTS Of the 124,387 participants (median age, 49 years), 87,870 (70.64%) were men. One-year lag exposure to PM2.5 and its constituents was significantly associated with vascular damage in single pollutant models. The adjusted odds ratios (OR) for each 1-µg/m3 increase in PM2.5 was 1.013 (95% CI, 1.012-1.015) and 1.031 (95% CI, 1.025-1.037) for elevated baPWV and decreased ABI, respectively. PM2.5 constituents were also associated with vascular damage in multi-pollutant models. Among the PM2.5 constituents, BC (47.17%), SO42- (33.59%), and NH4+ (19.23%) have the highest contribution to elevated baPWV and NO3- (47.89%) and BC (23.50%) to declined ABI. CONCLUSION Chronic exposure to PM2.5 and PM2.5 constituents was related to vascular damage in the abnormal metabolic population in China. The heterogeneous contribution of different PM2.5 constituents to vessel bed damage is worthy of attention when developing targeted strategies.
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Affiliation(s)
- Lijin Lin
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
- Institute of Model Animal, Wuhan University, Wuhan, China
- Medical Science Research Center, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Huxiang Huang
- Department of Respiratory and Critical Care Medicine, Huanggang central Hospital of Yangtze University, Huanggang, China
- Huanggang Institute of Translational Medicine, Huanggang, China
| | - Fang Lei
- Institute of Model Animal, Wuhan University, Wuhan, China
- School of Basic Medical Science, Wuhan University, Wuhan, China
| | - Tao Sun
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
- Institute of Model Animal, Wuhan University, Wuhan, China
| | - Ze Chen
- Institute of Model Animal, Wuhan University, Wuhan, China
- Department of Cardiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Kun Qin
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, China
- International Institute of Spatial Lifecourse Health (ISLE), Wuhan University, Wuhan, China
| | - Manyao Li
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, China
- International Institute of Spatial Lifecourse Health (ISLE), Wuhan University, Wuhan, China
| | - Yingying Hu
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
- Institute of Model Animal, Wuhan University, Wuhan, China
| | - Xuewei Huang
- Institute of Model Animal, Wuhan University, Wuhan, China
- Department of Cardiology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Xingyuan Zhang
- Institute of Model Animal, Wuhan University, Wuhan, China
- School of Basic Medical Science, Wuhan University, Wuhan, China
| | - Peng Zhang
- Institute of Model Animal, Wuhan University, Wuhan, China
- School of Basic Medical Science, Wuhan University, Wuhan, China
| | - Xiao-Jing Zhang
- Institute of Model Animal, Wuhan University, Wuhan, China
- School of Basic Medical Science, Wuhan University, Wuhan, China
| | - Zhi-Gang She
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
- Institute of Model Animal, Wuhan University, Wuhan, China
| | - Jingjing Cai
- Institute of Model Animal, Wuhan University, Wuhan, China
- Department of Cardiology, The Third Xiangya Hospital, Central South University, Changsha, China
| | - Shujuan Yang
- International Institute of Spatial Lifecourse Health (ISLE), Wuhan University, Wuhan, China
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Peng Jia
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, China
- International Institute of Spatial Lifecourse Health (ISLE), Wuhan University, Wuhan, China
- Hubei Luojia Laboratory, Wuhan, China
- School of Public Health, Wuhan University, Wuhan, China
| | - Hongliang Li
- Department of Cardiology, Renmin Hospital of Wuhan University, Wuhan, China
- Institute of Model Animal, Wuhan University, Wuhan, China
- Huanggang Institute of Translational Medicine, Huanggang, China
- Medical Science Research Center, Zhongnan Hospital of Wuhan University, Wuhan, China
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Wang S, Zhao G, Zhang C, Kang N, Liao W, Wang C, Xie F. Association of Fine Particulate Matter Constituents with the Predicted 10-Year Atherosclerotic Cardiovascular Disease Risk: Evidence from a Large-Scale Cross-Sectional Study. Toxics 2023; 11:812. [PMID: 37888663 PMCID: PMC10611010 DOI: 10.3390/toxics11100812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 09/22/2023] [Accepted: 09/24/2023] [Indexed: 10/28/2023]
Abstract
Little is known concerning the associations of fine particulate matter (PM2.5) and its constituents with atherosclerotic cardiovascular disease (ASCVD). A total of 31,162 participants enrolled from the Henan Rural Cohort were used to specify associations of PM2.5 and its constituents with ASCVD. Hybrid machine learning was utilized to estimate the 3-year average concentration of PM2.5 and its constituents (black carbon [BC], nitrate [NO3-], ammonium [NH4+], inorganic sulfate [SO42-], organic matter [OM], and soil particles [SOIL]). Constituent concentration, proportion, and residual models were utilized to examine the associations of PM2.5 constituents with 10-year ASCVD risk and to identify the most hazardous constituent. The isochronous substitution model (ISM) was employed to analyze the substitution effect between PM2.5 constituents. We found that each 1 μg/m3 increase in PM2.5, BC, NH4+, NO3-, OM, SO42-, and SOIL was associated with a 3.5%, 49.3%, 19.4%, 10.5%, 21.4%, 14%, and 28.5% higher 10-year ASCVD risk, respectively (all p < 0.05). Comparable results were observed in proportion and residual models. The ISM found that replacing BC with other constituents will generate the greatest health benefits. The results indicated that long-term exposure to PM2.5 and its constituents were associated with increased risks of ASCVD, with BC being the most attributable constituent.
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Affiliation(s)
- Sheng Wang
- Key Laboratory of Tobacco Chemistry, Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou 450003, China; (S.W.); (G.Z.)
| | - Ge Zhao
- Key Laboratory of Tobacco Chemistry, Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou 450003, China; (S.W.); (G.Z.)
| | - Caiyun Zhang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China; (C.Z.); (N.K.); (W.L.)
| | - Ning Kang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China; (C.Z.); (N.K.); (W.L.)
| | - Wei Liao
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China; (C.Z.); (N.K.); (W.L.)
| | - Chongjian Wang
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China; (C.Z.); (N.K.); (W.L.)
| | - Fuwei Xie
- Key Laboratory of Tobacco Chemistry, Zhengzhou Tobacco Research Institute of CNTC, Zhengzhou 450003, China; (S.W.); (G.Z.)
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Li J, Tang W, Li S, He C, Dai Y, Feng S, Zeng C, Yang T, Meng Q, Meng J, Pan Y, Deji S, Zhang J, Xie L, Guo B, Lin H, Zhao X. Ambient PM2.5 and its components associated with 10-year atherosclerotic cardiovascular disease risk in Chinese adults. Ecotoxicol Environ Saf 2023; 263:115371. [PMID: 37643506 DOI: 10.1016/j.ecoenv.2023.115371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 08/11/2023] [Accepted: 08/12/2023] [Indexed: 08/31/2023]
Abstract
BACKGROUND Exposure to particulate matter with aerodynamic diameters less than 2.5 µm (PM2.5) may increase the risk of 10-year atherosclerotic cardiovascular disease (ASCVD) risk. While PM2.5 is comprised of various components, the evidence on the correlation of its components with 10-year ASCVD risk and which component contributes most remains limited. METHODS Data were derived from the baseline assessments of China Multi-Ethnic Cohort (CMEC). In total, 69,722 individuals aged 35-74 years were included into this study. The annual average concentration of PM2.5 and its components (black carbon, ammonium, nitrate, sulfate, organic matter, soil particles, and sea salt) were estimated by satellite remote sensing and chemical transport models. The ASCVD risk of individuals was calculated by the equations from the China-PAR Project (prediction for ASCVD risk in China). The relationship between single exposure to PM2.5 and its components and predicted 10-year ASCVD risk was assessed using the logistic regression model. The effect of joint exposure was estimated, and the most significant contributor was identified using the weighted quantile sum approach. RESULTS Totally 69,722 participants were included, of which 95.8 % and 4.2 % had low and high 10-year ASCVD risk, respectively. Per standard deviation increases in the 3-year average concentration of PM2.5 mass (odds ratio [OR] 1.23, 95 % confidence interval [CI]: 1.12-1.35), black carbon (1.21, 1.11-1.33), ammonium (1.21, 1.10-1.32), nitrate (1.25, 1.14-1.38), organic matter (1.29, 1.18-1.42), sulfate (1.17, 1.07-1.28), and soil particles (1.15, 1.04-1.26) were related to high 10-year ASCVD risk. The overall effect (1.19, 1.11-1.28) of the PM2.5 components was positively associated with 10-year ASCVD risk, and organic matter had the most contribution to this relationship. Female participants were more significantly impacted by PM2.5, black carbon, ammonium, nitrate, organic matter, sulfate, and soil particles compared to others. CONCLUSION Long-term exposure to PM2.5 mass, black carbon, ammonium, nitrate, organic matter, sulfate, and soil particles were positively associated with high 10-year ASCVD risk, while sea salt exhibited a protective effect. Moreover, the organic matter might take primary responsibility for the relationship between PM2.5 and 10-year ASCVD risk. Females were more susceptible to the adverse effect.
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Affiliation(s)
- Jiawei Li
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Wenge Tang
- Chongqing Center for Disease Control and Prevention, Chongqing 400042, China
| | - Sicheng Li
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Congyuan He
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Yucen Dai
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Shiyu Feng
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Chunmei Zeng
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Tingting Yang
- School of Public Health, the Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang 550025, China
| | - Qiong Meng
- Department of Epidemiology and Health Statistics, School of Public Health, Kunming Medical University, Kunming, Yunnan 850000, China
| | - Jiantong Meng
- Chengdu Center for Disease Control & Prevention, Chengdu, Sichuan 610041, China
| | | | - Suolang Deji
- Tibet Center for Disease Control and Prevention CN, Lhasa 850000, China
| | - Juying Zhang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
| | - Linshen Xie
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
| | - Bing Guo
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
| | - Hualiang Lin
- Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Xing Zhao
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
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Cao YY, Sun T, Wang ZP, Lei F, Lin LJ, Zhang XY, Song XH, Zhang XJ, Zhang P, She ZG, Cai JJ, Yang SJ, Jia P, Li J, Li HL. Association between one-year exposure to air pollution and the prevalence of pulmonary nodules in China. J Breath Res 2023; 17. [PMID: 37040740 DOI: 10.1088/1752-7163/accbe4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Accepted: 04/11/2023] [Indexed: 04/13/2023]
Abstract
BACKGROUND PM2.5 is a well-known airborne hazard to cause various diseases. Evidence suggests that air pollution exposure contributes to the occurrence of pulmonary nodules. Pulmonary nodules detected on the CT scans can be malignant or progress to malignant during follow-up. But the evidence of the association between PM2.5 exposure and pulmonary nodules was limited. 
Objective: To examine potential associations of exposures to PM2.5 and its major chemical constituents with the prevalence of pulmonary nodules.
Methods: 16865 participants were investigated from eight physical examination centers in China from 2014 to 2017. The daily concentrations of PM2.5 and its five components were estimated by high-resolution and high-quality spatiotemporal datasets of ground-level air pollutants in China. The logistic regression and the quantile-based g-computation models were used to assess the single and mixture impact of air pollutant PM2.5 and its components on the risk of pulmonary nodules, respectively. 
Results: Each 1mg/m3 increase in PM2.5 (OR 1.011 (95%CI: 1.007-1.014)) was positively associated with pulmonary nodules. Among five PM2.5 components, in single-pollutant effect models, every 1 μg/m3 increase in OM, BC, and NO3- elevated the risk of pulmonary nodule prevalence by 1.040 (95%CI: 1.025-1.055), 1.314 (95%CI: 1.209-1.407) and 1.021 (95%CI: 1.007-1.035) fold, respectively. In mixture-pollutant effect models, the joint effect of every quintile increase in PM2.5 components was 1.076 (95%CI: 1.023-1.133) fold. Notably, NO3- BC and OM contributed higher risks of pulmonary nodules than other PM2.5 components. And the NO3- particles were identified to have the highest contribution. The impacts of PM2.5 components on pulmonary nodules were consistent across gender and age.
Conclusion: These findings provide important evidence for the positive correlation between exposure to PM2.5 and pulmonary nodules in China and identify that NO3- particles have the highest contribution to the risk.
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Affiliation(s)
- Yuan Yuan Cao
- Institute of Model Animal, Wuhan University, No.115 Donghu Road, Wuhan, P.R. China, Wuhan, Hubei, 430072, CHINA
| | - Tao Sun
- Cardiology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Rd, Wuhan, China, Wuhan, 430060, CHINA
| | - Zhan Peng Wang
- School of Resource and Environmental Sciences, Wuhan University, No.299 Bayi Road, Wuchang District, Wuhan City, Hubei Province, Wuhan, Hubei, 430072, CHINA
| | - Fang Lei
- School of Basic Medical Science, Wuhan University, No.299 Bayi Road, Wuchang District, Wuhan City, Hubei Province, Wuhan, Hubei, 430072, CHINA
| | - Li Jin Lin
- Cardiology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Rd, Wuhan, China, Wuhan, Hubei, 430060, CHINA
| | - Xing Yuan Zhang
- School of Basic Medical Science, Wuhan University, No.299 Bayi Road, Wuchang District, Wuhan City, Hubei Province, Wuhan, Hubei, 430072, CHINA
| | - Xiao Hui Song
- Cardiology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Rd, Wuhan, China, Wuhan, 430060, CHINA
| | - Xiao Jing Zhang
- School of Basic Medical Science, Wuhan University, No.299 Bayi Road, Wuchang District, Wuhan City, Hubei Province, Wuhan, Hubei, 430072, CHINA
| | - Peng Zhang
- School of Basic Medical Science, Wuhan University, No.299 Bayi Road, Wuchang District, Wuhan City, Hubei Province, Wuhan, Hubei, 430072, CHINA
| | - Zhi Gang She
- Cardiology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Rd, Wuhan, China, Wuhan, 430060, CHINA
| | - Jing Jing Cai
- Department of Cardiology, The Third Xiangya Hospital, Central South University, NO.87 Xiangya Road, Changsha City, Changsha, Hunan, 410083, CHINA
| | - Shu Juan Yang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, No.16, Section 3, Renmin South Road, Chengdu City, Chengdu, Sichuan, 610065, CHINA
| | - Peng Jia
- School of Resource and Environmental Sciences, Wuhan University, No.299 Bayi Road, Wuchang District, Wuhan City, Hubei Province, Wuhan, Hubei, 430072, CHINA
| | - Jian Li
- Thoracic and cardiovascular surgery, Huanggang central Hospital, Yangtze University, No.11 Kaopeng Street, Huangzhou District, Huanggang City, Hubei Province, Jingzhou, Hubei, 434023, CHINA
| | - Hong Liang Li
- Cardiology, Renmin Hospital of Wuhan University, 99 Zhangzhidong Rd, Wuhan, China, Wuhan, 430060, CHINA
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8
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Li J, Song Y, Shi L, Jiang J, Wan X, Wang Y, Ma Y, Dong Y, Zou Z, Ma J. Long-term effects of ambient PM 2.5 constituents on metabolic syndrome in Chinese children and adolescents. Environ Res 2023; 220:115238. [PMID: 36621550 DOI: 10.1016/j.envres.2023.115238] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 01/04/2023] [Accepted: 01/05/2023] [Indexed: 06/17/2023]
Abstract
Metabolic syndrome (MetS) is considered a main public health issue as it remarkably adds the risk of cardiovascular disease, leading to a heavy burden of disease. There is growing evidence linking fine particulate matter (PM2.5) exposure to MetS. However, the influences of PM2.5 constituents, especially in children and adolescents, remain unclear. Our study was according to a national analysis among Chinese children and adolescents to examine the associations between long-term exposure to PM2.5 main constituents and MetS. A total of 10,066 children and adolescents aged 10-18 years were recruited in 7 provinces in China, with blood tests, health exams, and questionnaire surveys. We estimated long-term exposures to PM2.5 mass and its five constituents, containing black carbon (BC), organic matter (OM), inorganic nitrate (NO3-), sulfate (SO42-), and soil particles (SOIL) from multi-source data fusion models. Mixed-effects logistic regression models were used with the adjustment of a variety of covariates. In the surveyed populations, 2.9% were classified as MetS. From the single-pollutant models, we discovered that long-term exposures to PM2.5 mass, BC, OM, NO3-, as well as SO42-, were significantly associated with the prevalence of MetS, with odds ratios (ORs) per 1 μg/m3 that were 1.02 (95% confidence interval (CI): 1.01, 1.03) for PM2.5 mass, 1.24 (95% CI: 1.14, 1.35) for BC, 1.07 (95% CI: 1.04, 1.11) for OM, 1.09 (95% CI: 1.04, 1.13) for NO3-, and 1.14 (95% CI:1.04, 1.24) for SO42-. The influence of BC on the prevalence of MetS was robust in both the multi-pollutant model and the PM2.5-constituent joint model. The paper indicates long-term exposure to PM2.5 mass and specific PM2.5 constituents, particularly for BC, was significantly associated with a higher MetS prevalence among children and adolescents in China. Our results highlight the significance of establishing further regulations on PM2.5 constituents.
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Affiliation(s)
- Jing Li
- Institute of Child and Adolescent Health, School of Public Health, Peking University, Beijing, 100191, China
| | - Yi Song
- Institute of Child and Adolescent Health, School of Public Health, Peking University, Beijing, 100191, China
| | - Liuhua Shi
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, 30322, USA
| | - Jun Jiang
- Institute of Child and Adolescent Health, School of Public Health, Peking University, Beijing, 100191, China
| | - Xiaoyu Wan
- Institute of Child and Adolescent Health, School of Public Health, Peking University, Beijing, 100191, China
| | - Yaqi Wang
- Institute of Child and Adolescent Health, School of Public Health, Peking University, Beijing, 100191, China
| | - Yinghua Ma
- Institute of Child and Adolescent Health, School of Public Health, Peking University, Beijing, 100191, China
| | - Yanhui Dong
- Institute of Child and Adolescent Health, School of Public Health, Peking University, Beijing, 100191, China.
| | - Zhiyong Zou
- Institute of Child and Adolescent Health, School of Public Health, Peking University, Beijing, 100191, China.
| | - Jun Ma
- Institute of Child and Adolescent Health, School of Public Health, Peking University, Beijing, 100191, China
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9
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Shi L, Zhu Q, Wang Y, Hao H, Zhang H, Schwartz J, Amini H, van Donkelaar A, Martin RV, Steenland K, Sarnat JA, Caudle WM, Ma T, Li H, Chang HH, Liu JZ, Wingo T, Mao X, Russell AG, Weber RJ, Liu P. Incident dementia and long-term exposure to constituents of fine particle air pollution: A national cohort study in the United States. Proc Natl Acad Sci U S A 2023; 120:e2211282119. [PMID: 36574646 DOI: 10.1073/pnas.2211282119] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Growing evidence suggests that fine particulate matter (PM2.5) likely increases the risks of dementia, yet little is known about the relative contributions of different constituents. Here, we conducted a nationwide population-based cohort study (2000 to 2017) by integrating the Medicare Chronic Conditions Warehouse database and two independently sourced datasets of high-resolution PM2.5 major chemical composition, including black carbon (BC), organic matter (OM), nitrate (NO3-), sulfate (SO42-), ammonium (NH4+), and soil dust (DUST). To investigate the impact of long-term exposure to PM2.5 constituents on incident all-cause dementia and Alzheimer's disease (AD), hazard ratios for dementia and AD were estimated using Cox proportional hazards models, and penalized splines were used to evaluate potential nonlinear concentration-response (C-R) relationships. Results using two exposure datasets consistently indicated higher rates of incident dementia and AD for an increased exposure to PM2.5 and its major constituents. An interquartile range increase in PM2.5 mass was associated with a 6 to 7% increase in dementia incidence and a 9% increase in AD incidence. For different PM2.5 constituents, associations remained significant for BC, OM, SO42-, and NH4+ for both end points (even after adjustments of other constituents), among which BC and SO42- showed the strongest associations. All constituents had largely linear C-R relationships in the low exposure range, but most tailed off at higher exposure concentrations. Our findings suggest that long-term exposure to PM2.5 is significantly associated with higher rates of incident dementia and AD and that SO42-, BC, and OM related to traffic and fossil fuel combustion might drive the observed associations.
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10
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Zheng Y, Bian J, Hart J, Laden F, Soo-Tung Wen T, Zhao J, Qin H, Hu H. PM 2.5 Constituents and Onset of Gestational Diabetes Mellitus: Identifying Susceptible Exposure Windows. Atmos Environ (1994) 2022; 291:119409. [PMID: 37151750 PMCID: PMC10162772 DOI: 10.1016/j.atmosenv.2022.119409] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Fine particulate matter (PM2.5) has been linked to gestational diabetes mellitus (GDM). However, PM2.5 is a complex mixture with large spatiotemporal heterogeneities, and women with early-onset GDM (i.e., diagnosed before 24th gestation week) have distinct maternal characteristics and a higher risk of worse health outcomes compared with those with late-onset GDM (i.e., diagnosed in or after 24th gestation week). We aimed to examine differential impacts of PM2.5 and its constituents on early- vs. late-onset GDM, and to identify corresponding susceptible exposure windows. We leveraged statewide linked electronic health records and birth records data in Florida in 2012-2017. Exposures to PM2.5 and its constituents (i.e., sulfate [SO4 2-], ammonium [NH4 +], nitrate [NO3 -], organic matter [OM], black carbon [BC], mineral dust [DUST], and sea-salt [SS]) were spatiotemporally linked to pregnant women based on their residential histories. Cox proportional hazards models and multinomial logistic regression were used to examine the associations of PM2.5 and its constituents with GDM and its onsets. Distributed non-linear lag models were implemented to identify susceptible exposure windows. Exposures to PM2.5, SO4 2-, NH4 +, and BC were statistically significantly associated with higher hazards of GDM. Exposures to PM2.5 during weeks 1-12 of gestation were positively associated with GDM. Associations of early-onset GDM with PM2.5 in the 1st and 2nd trimesters, SO4 2- in the 1st and 2nd trimesters, and NO3 - in the preconception and 1st trimester were considerably stronger than observations for late-onset GDM. Our findings suggest there are differential associations of PM2.5 and its constituents with early- vs. late-onset GDM, with different susceptible exposure windows. This study helps better understand the impacts of air pollution on GDM accounting for its physiological heterogeneity.
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Affiliation(s)
- Yi Zheng
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Jaime Hart
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Francine Laden
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Tony Soo-Tung Wen
- Department of Obstetrics and Gynecology, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Jinying Zhao
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Huaizhen Qin
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Hui Hu
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
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11
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Zhou P, Hu J, Yu C, Bao J, Luo S, Shi Z, Yuan Y, Mo S, Yin Z, Zhang Y. Short-term exposure to fine particulate matter constituents and mortality: case-crossover evidence from 32 counties in China. Sci China Life Sci 2022; 65:2527-2538. [PMID: 35713841 DOI: 10.1007/s11427-021-2098-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 03/23/2022] [Indexed: 06/15/2023]
Abstract
A growing number of studies associated increased mortality with exposures to specific fine particulate (PM2.5) constituents, while great heterogeneity exists between locations. In China, evidence linking PM2.5 constituents and mortality was extensively sparse. This study primarily aimed to quantify short-term associations between PM2.5 constituents and non-accidental mortality among the Chinese population. We collected daily mortality records from 32 counties in China between January 1, 2011, and December 31, 2013. Daily concentrations of main PM2.5 constituents (organic carbon (OC), elemental carbon (EC), nitrate (NO3-), sulfate (SO42-), and ammonium (NH4+)) were estimated using the modified Community Multiscale Air Quality model. Time-stratified case-crossover design with conditional logistic regression models was adopted to estimate mortality risks associated with short-term exposures to PM2.5 mass and its constituents. Stratification analyses were done by sex, age, and season. A total of 116,959 non-accidental deaths were investigated. PM2.5 concentrations on the day of death were averaged at 75.7 µg m-3 (control day: 75.6 µg m-3), with an interquartile range (IQR) of 65.2 µg m-3. Per IQR rise in PM2.5, EC, OC, NO3-, SO42-, and NH4+ at lag-04 day was associated with an increase in non-accidental mortality of 2.4% (95% confidence interval, (1.0-3.7), 1.7% (0.8-2.7), 2.9% (1.6-4.3), 2.1% (0.4-3.9), 1.0% (0.2-1.9), and 1.6% (0.3-2.9), respectively. Both PM2.5 mass and its constituents were strongly associated with elevated cardiovascular mortality risks, but only PM2.5, EC, and OC were positively associated with respiratory mortality at lag-3 day. PM2.5 mass and its constituents associated effects on mortality varied among sex- and age-specific subpopulations. Differences in the seasonal pattern of associations exist among PM2.5 constituents, with stronger effects related to EC and NO3- in warm months but SO42- and NH4+ in cold months. Short-term exposures to PM2.5 compositions were positively associated with increased risks of mortality, particularly those constituents from combustion-related sources.
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Affiliation(s)
- Peixuan Zhou
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University of Science and Technology, Wuhan, 430065, China
| | - Jianlin Hu
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Chuanhua Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, 430071, China
| | - Junzhe Bao
- Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, 450001, China
| | - Siqi Luo
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University of Science and Technology, Wuhan, 430065, China
| | - Zhihao Shi
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Yang Yuan
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University of Science and Technology, Wuhan, 430065, China
| | - Shaocai Mo
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University of Science and Technology, Wuhan, 430065, China
| | - Zhouxin Yin
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University of Science and Technology, Wuhan, 430065, China
| | - Yunquan Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University of Science and Technology, Wuhan, 430065, China.
- Hubei Province Key Laboratory of Occupational Hazard Identification and Control, Wuhan University of Science and Technology, Wuhan, 430065, China.
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12
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Niu R, Cheng J, Sun J, Li F, Fang H, Lei R, Shen Z, Hu H, Li J. Alveolar Type II Cell Damage and Nrf2-SOD1 Pathway Downregulation Are Involved in PM 2.5-Induced Lung Injury in Rats. Int J Environ Res Public Health 2022; 19:12893. [PMID: 36232201 PMCID: PMC9566353 DOI: 10.3390/ijerph191912893] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 09/24/2022] [Accepted: 10/03/2022] [Indexed: 06/16/2023]
Abstract
The general toxicity of fine particulate matter (PM2.5) has been intensively studied, but its pulmonary toxicities are still not fully understood. To investigate the changes of lung tissue after PM2.5 exposure and identify the potential mechanisms of pulmonary toxicity, PM2.5 samples were firstly collected and analyzed. Next, different doses of PM2.5 samples (5 mg/kg, 10 mg/kg, 20 mg/kg) were intratracheally instilled into rats to simulate lung inhalation of polluted air. After instillation for eight weeks, morphological alterations of the lung were examined, and the levels of oxidative stress were detected. The data indicated that the major contributors to PM2.5 mass were organic carbon, elemental carbon, sulfate, nitrate, and ammonium. Different concentrations of PM2.5 could trigger oxidative stress through increasing reactive oxygen species (ROS) and 8-hydroxy-2'-deoxyguanosine (8-OHdG) levels, and decreasing expression of antioxidant-related proteins (nuclear factor erythroid 2-related factor 2 (Nrf2), superoxide dismutase 1 (SOD1) and catalase). Histochemical staining and transmission electron microscopy displayed pulmonary inflammation, collagen deposition, mitochondrial swelling, and a decreasing number of multilamellar bodies in alveolar type II cells after PM2.5 exposure, which was related to PM2.5-induced oxidative stress. These results provide a basis for a better understanding of pulmonary impairment in response to PM2.5.
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Affiliation(s)
- Rui Niu
- Medical College, Xi’an Peihua University, Xi’an 710061, China
- Department of Pharmacology, School of Basic Medical Sciences, Health Science Center, Xi’an Jiaotong University, Xi’an 710061, China
| | - Jie Cheng
- Department of Pharmacology, School of Basic Medical Sciences, Health Science Center, Xi’an Jiaotong University, Xi’an 710061, China
| | - Jian Sun
- Department of Environmental Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China
| | - Fan Li
- Basic Medical Experiment Teaching Center, Health Science Center, Xi’an Jiaotong University, Xi’an 710061, China
| | - Huanle Fang
- Medical College, Xi’an Peihua University, Xi’an 710061, China
| | - Ronghui Lei
- School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an 710061, China
| | - Zhenxing Shen
- Department of Environmental Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China
| | - Hao Hu
- Department of Pharmacology, School of Basic Medical Sciences, Health Science Center, Xi’an Jiaotong University, Xi’an 710061, China
- Basic Medical Experiment Teaching Center, Health Science Center, Xi’an Jiaotong University, Xi’an 710061, China
- Key Laboratory of Environment and Genes Related to Diseases, Xi’an Jiaotong University, Ministry of Education of China, Xi’an 710061, China
| | - Jianjun Li
- Key Lab of Aerosol Chemistry & Physics, SKLLQG, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an 710061, China
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13
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Wang J, Li T, Fang J, Tang S, Zhang Y, Deng F, Shen C, Shi W, Liu Y, Chen C, Sun Q, Wang Y, Du Y, Dong H, Shi X. Associations between Individual Exposure to Fine Particulate Matter Elemental Constituent Mixtures and Blood Lipid Profiles: A Panel Study in Chinese People Aged 60-69 Years. Environ Sci Technol 2022; 56:13160-13168. [PMID: 36043295 DOI: 10.1021/acs.est.2c01568] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Dyslipidemia may be a potential mechanism linking fine particulate matter (PM2.5) to adverse cardiovascular outcomes. However, inconsistent associations between PM2.5 and blood lipids have resulted from the existing research, and the joint effect of PM2.5 elemental constituents on blood lipid profiles remains unclear. We aimed to explore the overall associations between PM2.5 elemental constituents and blood lipid profiles and to identify the significant PM2.5 elemental constituents in this association. Sixty-nine elderly people were recruited between September 2018 and January 2019. Each participant completed a survey questionnaire, 3 days of individual exposure monitoring, health examination, and biological sample collection at each follow-up visit. Bayesian kernel machine regression (BKMR) models were used to identify the joint effects of the 17 elemental constituents on blood lipid profiles. Total cholesterol, low-density lipoprotein cholesterol (LDL-C), and non-high-density lipoprotein cholesterol (non-HDL-C) levels were significantly increased in older adults when exposed to the mixture of PM2.5 elemental constituents. Copper and titanium had higher posterior inclusion probabilities than other constituents, ranging from 0.76 to 0.90 (Cu) and 0.74 to 0.94 (Ti). Copper and titanium in the PM2.5 elemental constituent mixture played an essential role in changes to blood lipid levels. This study highlights the importance of identifying critical hazardous PM2.5 constituents that may cause adverse cardiovascular outcomes in the future.
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Affiliation(s)
- Jiaonan Wang
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Tiantian Li
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Jianlong Fang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Song Tang
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Yi Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Fuchang Deng
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Chong Shen
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
| | - Wanying Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Yuanyuan Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Chen Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Qinghua Sun
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Yanwen Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Yanjun Du
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Haoran Dong
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Xiaoming Shi
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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14
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Yi W, Zhao F, Pan R, Zhang Y, Xu Z, Song J, Sun Q, Du P, Fang J, Cheng J, Liu Y, Chen C, Lu Y, Li T, Su H, Shi X. Associations of Fine Particulate Matter Constituents with Metabolic Syndrome and the Mediating Role of Apolipoprotein B: A Multicenter Study in Middle-Aged and Elderly Chinese Adults. Environ Sci Technol 2022; 56:10161-10171. [PMID: 35802126 DOI: 10.1021/acs.est.1c08448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Fine particulate matter (PM2.5) was reported to be associated with metabolic syndrome (MetS), but how PM2.5 constituents affect MetS and the underlying mediators remains unclear. We aimed to investigate the associations of long-term exposure to 24 kinds of PM2.5 constituents with MetS (defined by five indicators) in middle-aged and elderly adults and to further explore the potential mediating role of apolipoprotein B (ApoB). A multicenter study was conducted by recruiting subjects (n = 2045) in the Beijing-Tianjin-Hebei region from the cohort of Sub-Clinical Outcomes of Polluted Air in China (SCOPA-China Cohort). Relationships among PM2.5 constituents, serum ApoB levels, and MetS were estimated by multiple logistic/linear regression models. Mediation analysis quantified the role of ApoB in "PM2.5 constituents-MetS" associations. Results indicated PM2.5 was significantly related to elevated MetS prevalence. The MetS odds increased after exposure to sulfate (SO42-), calcium ion (Ca2+), magnesium ion (Mg2+), Si, Zn, Ca, Mn, Ba, Cu, As, Cr, Ni, or Se (odds ratios ranged from 1.103 to 3.025 per interquartile range increase in each constituent). PM2.5 and some constituents (SO42-, Ca2+, Mg2+, Ca, and As) were positively related to serum ApoB levels. ApoB mediated 22.10% of the association between PM2.5 and MetS. Besides, ApoB mediated 24.59%, 50.17%, 12.70%, and 9.63% of the associations of SO42-, Ca2+, Ca, and As with MetS, respectively. Our findings suggest that ApoB partially mediates relationships between PM2.5 constituents and MetS risk in China.
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Affiliation(s)
- Weizhuo Yi
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, No. 81 Meishan Road, Shushan District, Hefei, Anhui 230031, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, No. 81 Meishan Road, Shushan District, Hefei, Anhui 230031, China
| | - Feng Zhao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No. 7 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Rubing Pan
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, No. 81 Meishan Road, Shushan District, Hefei, Anhui 230031, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, No. 81 Meishan Road, Shushan District, Hefei, Anhui 230031, China
| | - Yi Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No. 7 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Zhiwei Xu
- School of Public Health, Faculty of Medicine, University of Queensland, 288 Herston Road, Herston, Brisbane, 4006 Queensland, Australia
| | - Jian Song
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, No. 81 Meishan Road, Shushan District, Hefei, Anhui 230031, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, No. 81 Meishan Road, Shushan District, Hefei, Anhui 230031, China
| | - Qinghua Sun
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No. 7 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Peng Du
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No. 7 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Jianlong Fang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No. 7 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Jian Cheng
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, No. 81 Meishan Road, Shushan District, Hefei, Anhui 230031, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, No. 81 Meishan Road, Shushan District, Hefei, Anhui 230031, China
| | - Yingchun Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No. 7 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Chen Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No. 7 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Yifu Lu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No. 7 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Tiantian Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No. 7 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Hong Su
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, No. 81 Meishan Road, Shushan District, Hefei, Anhui 230031, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, No. 81 Meishan Road, Shushan District, Hefei, Anhui 230031, China
| | - Xiaoming Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No. 7 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
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Zhang Z, O’Neill MS, Sánchez BN. Using a latent variable model with non-constant factor loadings to examine PM 2.5 constituents related to secondary inorganic aerosols. STAT MODEL 2016; 16:91-113. [PMID: 27528825 PMCID: PMC4982519 DOI: 10.1177/1471082x15627004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Factor analysis is a commonly used method of modelling correlated multivariate exposure data. Typically, the measurement model is assumed to have constant factor loadings. However, from our preliminary analyses of the Environmental Protection Agency's (EPA's) PM2.5 fine speciation data, we have observed that the factor loadings for four constituents change considerably in stratified analyses. Since invariance of factor loadings is a prerequisite for valid comparison of the underlying latent variables, we propose a factor model that includes non-constant factor loadings that change over time and space using P-spline penalized with the generalized cross-validation (GCV) criterion. The model is implemented using the Expectation-Maximization (EM) algorithm and we select the multiple spline smoothing parameters by minimizing the GCV criterion with Newton's method during each iteration of the EM algorithm. The algorithm is applied to a one-factor model that includes four constituents. Through bootstrap confidence bands, we find that the factor loading for total nitrate changes across seasons and geographic regions.
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Affiliation(s)
- Zhenzhen Zhang
- Department of Biostatistics, University of Michigan, Ann Arbor, USA
| | - Marie S. O’Neill
- Department of Environmental Health Sciences, University of Michigan, Ann Arbor, USA
- Department of Epidemiology, University of Michigan, Ann Arbor, USA
| | - Brisa N. Sánchez
- Department of Biostatistics, University of Michigan, Ann Arbor, USA
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