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Guo H, Wang M, Ye Y, Huang C, Wang S, Peng H, Wang X, Fan M, Hou T, Wu X, Huang X, Yan Y, Zheng K, Wu T, Li L. Short-Term Exposure to Nitrogen Dioxide Modifies Genetic Predisposition in Blood Lipid and Fasting Plasma Glucose: A Pedigree-Based Study. BIOLOGY 2023; 12:1470. [PMID: 38132296 PMCID: PMC10740487 DOI: 10.3390/biology12121470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 11/13/2023] [Accepted: 11/24/2023] [Indexed: 12/23/2023]
Abstract
(1) Background: Previous studies suggest that exposure to nitrogen dioxide (NO2) has a negative impact on health. But few studies have explored the association between NO2 and blood lipids or fasting plasma glucose (FPG), as well as gene-air pollution interactions. This study aims to fill this knowledge gap based on a pedigree cohort in southern China. (2) Methods: Employing a pedigree-based design, 1563 individuals from 452 families participated in this study. Serum levels of triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDLC), high-density lipoprotein cholesterol (HDLC), and FPG were measured. We investigated the associations between short-term NO2 exposure and lipid profiles or FPG using linear mixed regression models. The genotype-environment interaction (GenoXE) for each trait was estimated using variance component models. (3) Results: NO2 was inversely associated with HDLC but directly associated with TG and FPG. The results showed that each 1 μg/m3 increase in NO2 on day lag0 corresponded to a 1.926% (95%CI: 1.428-2.421%) decrease in HDLC and a 1.400% (95%CI: 0.341-2.470%) increase in FPG. Moreover, we observed a significant genotype-NO2 interaction with HDLC and FPG. (4) Conclusion: This study highlighted the association between NO2 exposure and blood lipid profiles or FPG. Additionally, our investigation suggested the presence of genotype-NO2 interactions in HDLC and FPG, indicating potential loci-specific interaction effects. These findings have the potential to inform and enhance the interpretation of studies that are focused on specific gene-environment interactions.
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Affiliation(s)
- Huangda Guo
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (H.G.)
| | - Mengying Wang
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
- Department of Nutrition and Food Hygiene, School of Public Health, Peking University, Beijing 100191, China
| | - Ying Ye
- Fujian Provincial Center for Disease Control and Prevention, Fuzhou 350012, China
| | - Chunlan Huang
- Department of Hygiene, Nanjing Country Center for Disease Control and Prevention, Nanjing 363600, China
| | - Siyue Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (H.G.)
| | - Hexiang Peng
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (H.G.)
| | - Xueheng Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (H.G.)
| | - Meng Fan
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (H.G.)
| | - Tianjiao Hou
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (H.G.)
| | - Xiaoling Wu
- Department of Hygiene, Nanjing Country Center for Disease Control and Prevention, Nanjing 363600, China
| | - Xiaoming Huang
- Department of Hygiene, Nanjing Country Center for Disease Control and Prevention, Nanjing 363600, China
| | - Yansheng Yan
- Fujian Provincial Center for Disease Control and Prevention, Fuzhou 350012, China
| | - Kuicheng Zheng
- Fujian Provincial Center for Disease Control and Prevention, Fuzhou 350012, China
| | - Tao Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (H.G.)
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
- Key Laboratory of Reproductive Health, Ministry of Health, Beijing 100191, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China; (H.G.)
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, China
- Center for Public Health and Epidemic Preparedness & Response, Peking University, Beijing 100191, China
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2
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Zhang Y, Shi J, Ma Y, Yu N, Zheng P, Chen Z, Wang T, Jia G. Association between Air Pollution and Lipid Profiles. TOXICS 2023; 11:894. [PMID: 37999546 PMCID: PMC10675150 DOI: 10.3390/toxics11110894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 09/30/2023] [Accepted: 10/28/2023] [Indexed: 11/25/2023]
Abstract
Dyslipidemia is a critical factor in the development of atherosclerosis and consequent cardiovascular disease. Numerous pieces of evidence demonstrate the association between air pollution and abnormal blood lipids. Although the results of epidemiological studies on the link between air pollution and blood lipids are unsettled due to different research methods and conditions, most of them corroborate the harmful effects of air pollution on blood lipids. Mechanism studies have revealed that air pollution may affect blood lipids via oxidative stress, inflammation, insulin resistance, mitochondrial dysfunction, and hypothalamic hormone and epigenetic changes. Moreover, there is a risk of metabolic diseases associated with air pollution, including fatty liver disease, diabetes mellitus, and obesity, which are often accompanied by dyslipidemia. Therefore, it is biologically plausible that air pollution affects blood lipids. The overall evidence supports that air pollution has a deleterious effect on blood lipid health. However, further research into susceptibility, indoor air pollution, and gaseous pollutants is required, and the issue of assessing the effects of mixtures of air pollutants remains an obstacle for the future.
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Affiliation(s)
- Yi Zhang
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing 100191, China; (Y.Z.); (J.S.); (Y.M.); (N.Y.); (P.Z.); (G.J.)
- Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, School of Public Health, Peking University, Beijing 100083, China
| | - Jiaqi Shi
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing 100191, China; (Y.Z.); (J.S.); (Y.M.); (N.Y.); (P.Z.); (G.J.)
- Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, School of Public Health, Peking University, Beijing 100083, China
| | - Ying Ma
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing 100191, China; (Y.Z.); (J.S.); (Y.M.); (N.Y.); (P.Z.); (G.J.)
- Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, School of Public Health, Peking University, Beijing 100083, China
| | - Nairui Yu
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing 100191, China; (Y.Z.); (J.S.); (Y.M.); (N.Y.); (P.Z.); (G.J.)
- Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, School of Public Health, Peking University, Beijing 100083, China
| | - Pai Zheng
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing 100191, China; (Y.Z.); (J.S.); (Y.M.); (N.Y.); (P.Z.); (G.J.)
- Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, School of Public Health, Peking University, Beijing 100083, China
| | - Zhangjian Chen
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing 100191, China; (Y.Z.); (J.S.); (Y.M.); (N.Y.); (P.Z.); (G.J.)
- Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, School of Public Health, Peking University, Beijing 100083, China
| | - Tiancheng Wang
- Department of Laboratory Medicine, Peking University Third Hospital, Beijing 100191, China;
| | - Guang Jia
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing 100191, China; (Y.Z.); (J.S.); (Y.M.); (N.Y.); (P.Z.); (G.J.)
- Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, School of Public Health, Peking University, Beijing 100083, China
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3
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Wang C, Meng XC, Huang C, Wang J, Liao YH, Huang Y, Liu R. Association between ambient air pollutants and lipid profile: A systematic review and meta-analysis. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 262:115140. [PMID: 37348216 DOI: 10.1016/j.ecoenv.2023.115140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 05/29/2023] [Accepted: 06/11/2023] [Indexed: 06/24/2023]
Abstract
BACKGROUND Studies of the effects of atmospheric pollutants on lipid profiles remain inconsistent and controversial. AIM The study was aimed to investigate the relationship between the exposure to ambient air pollutants and variations in the blood lipid profiles in the population. METHODS A comprehensive search of three different databases (PubMed, Web of Science, and the Cochrane Library) until December 17, 2022, yielded 17 origional studies fulfilling the inclusion criteria for a meta-analysis. Aggregate effect measures and 95% confidence intervals (95% CI) for the relevant ambient air pollutants were deduced employing random effects models. RESULTS The collective meta-analysis indicated that long-term exposure to PM1, PM2.5, PM10 and CO showed a substantial correlation with TC (PM1: β = 2.04, 95%CI = 0.15-3.94; PM2.5: β = 1.11, 95%CI = 0.39-1.84; PM10: β = 1.70, 95%CI = 0.67-2.73; CO: β = 0.08, 95%CI = 0.06-0.10), PM10 exhibited a significant association with TG (β = 0. 537,95% CI = 0.09-0.97), whereas HDL-C demonstrated notable relationships with PM1, PM10, SO2 and CO (PM1: β = -2.38, 95%CI = -4.00 to -2.76; PM10: β = -0.77, 95%CI = -1.33 to -0.21; SO2: β = -0.91, 95%CI = -1.73 to -0.10; CO: β = -0.03, 95%CI = -0.05 to 0.00). PM2.5, PM10 also showed significant associations with LDL-C (PM2.5: β = 1.44 95%CI = 0.48-2.40; PM10: β = 1.62 95%CI = 0.90-2.34). Subgroup analysis revealed significant or stronger correlations predominantly in cohort study designs, with higher male comparisons, and in regions exhibiting elevated contaminant levels. CONCLUSION In summary, the analysis substantiates that ambient air pollutants can be recognized as potent contributors to alterations in lipid profiles, particularly particulate pollutants which exert more obvious effects on lipid profiles.
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Affiliation(s)
- Chun Wang
- Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China
| | - Xing-Chen Meng
- Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China
| | - Chao Huang
- Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China
| | - Jia Wang
- Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China
| | - Ying-Hao Liao
- Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China
| | - Yang Huang
- Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China
| | - Ran Liu
- Key Laboratory of Environmental Medicine and Engineering of Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China.
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Peng H, Wang M, Wang S, Wang X, Fan M, Qin X, Wu Y, Chen D, Li J, Hu Y, Wu T. KCNQ1 rs2237892 polymorphism modify the association between short-term ambient particulate matter exposure and fasting blood glucose: A family-based study. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 876:162820. [PMID: 36921852 DOI: 10.1016/j.scitotenv.2023.162820] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 03/08/2023] [Accepted: 03/08/2023] [Indexed: 05/10/2023]
Abstract
BACKGROUND The association between particulate matter and fasting blood glucose (FBG) has shown conflicting results. Genome-wide association studies have shown that KCNQ1 rs2237892 polymorphism is associated with the risk of diabetes. Whether KCNQ1 rs2237892 polymorphism might modify the association between particulate matter and FBG is still uncertain. METHODS Data collected from a family-based cohort study in Northern China, were used to perform the analysis. A generalized additive Gaussian model was used to examine the short-term effects of air pollutants on FBG. We further conducted interaction analyses by including a cross-product term of air pollutants by rs2237892 within KCNQ1 gene. RESULTS A total of 4418 participants were included in the study. In the single pollutant model, the FBG level increased 0.0031 mmol/L with per 10 μg/m3 elevation in fine particular matter (PM2.5) for lag 0 day. After additional adjustments for nitrogen dioxide (NO2) and sulfur dioxide (SO2), similar results were observed for lag 0-2 days. As for particulate matter with particle size below 10 μm (PM10), the significant association between the daily average concentration of the pollutant and FBG level was observed for lag 0-3 days. Additionally, rs2237892 in KCNQ1 gene modified the association between PM and FBG level. The higher risk of FBG levels associated with elevations in PM10 and PM2.5 were more evident as the number of risk allele C increased. Individuals with a CC genotype had the highest risk of elevation in FBG levels. CONCLUSION Short-term exposures to PM2.5 and PM10 were associated with higher FBG levels. Additionally, rs2237892 in KCNQ1 gene might modify the association between the air pollutants and FBG levels.
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Affiliation(s)
- Hexiang Peng
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - Mengying Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - Siyue Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - Xueheng Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - Meng Fan
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - Xueying Qin
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - Yiqun Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - Dafang Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - Jing Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - Yonghua Hu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - Tao Wu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China.
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5
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Zhu K, Hou Z, Huang C, Xu M, Mu L, Yu G, Kaufman JD, Wang M, Lu B. Assessing the timing and the duration of exposure to air pollution on cardiometabolic biomarkers in patients suspected of coronary artery disease. ENVIRONMENTAL RESEARCH 2023:116334. [PMID: 37301499 DOI: 10.1016/j.envres.2023.116334] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 05/28/2023] [Accepted: 06/04/2023] [Indexed: 06/12/2023]
Abstract
Air pollution can affect cardiometabolic biomarkers in susceptible populations, but the most important exposure window (lag days) and exposure duration (length of averaging period) are not well understood. We investigated air pollution exposure across different time intervals on ten cardiometabolic biomarkers in 1550 patients suspected of coronary artery disease. Daily residential PM2.5 and NO2 were estimated using satellite-based spatiotemporal models and assigned to participants for up to one year before the blood collection. Distributed lag models and generalized linear models were used to examine the single-day-effects by variable lags and cumulative effects of exposures averaged over different periods before the blood draw. In single-day-effect models, PM2.5 was associated with lower apolipoprotein A (ApoA) in the first 22 lag days with the effect peaking on the first lag day; PM2.5 was also associated with elevated high-sensitivity C-reactive protein (hs-CRP) with significant exposure windows observed after the first 5 lag days. For the cumulative effects, short- and medium-term exposure was associated with lower ApoA (up to 30wk-average) and higher hs-CRP (up to 8wk-average), triglycerides and glucose (up to 6 d-average), but the associations were attenuated to null over the long term. The impacts of air pollution on inflammation, lipid, and glucose metabolism differ by the exposure timing and durations, which can inform our understanding of the cascade of underlying mechanisms among susceptible patients.
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Affiliation(s)
- Kexin Zhu
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Zhihui Hou
- Department of Radiology, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Conghong Huang
- College of Land Management, Nanjing Agricultural University, Nanjing, China; Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Muwu Xu
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Lina Mu
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, The State University of New York, Buffalo, NY, USA
| | - Guan Yu
- Department of Biostatistics, University of Pittsburgh, PA, USA
| | - Joel D Kaufman
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, USA
| | - Meng Wang
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, The State University of New York, Buffalo, NY, USA; Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, USA; Research and Education in Energy, Environment and Water Institute, University at Buffalo, Buffalo, NY, USA.
| | - Bin Lu
- Department of Radiology, Fuwai Hospital, Chinese Academy of Medical Sciences, Beijing, China.
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Liu Q, Wang Z, Lu J, Li Z, Martinez L, Tao B, Wang C, Zhu L, Lu W, Zhu B, Pei X, Mao X. Effects of short-term PM 2.5 exposure on blood lipids among 197,957 people in eastern China. Sci Rep 2023; 13:4505. [PMID: 36934119 PMCID: PMC10024762 DOI: 10.1038/s41598-023-31513-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Accepted: 03/13/2023] [Indexed: 03/20/2023] Open
Abstract
Globally, air pollution is amongst the most significant causes of premature death. Nevertheless, studies on the relationship between fine particulate matter (PM2.5) exposure and blood lipids have typically not been population-based. In a large, community-based sample of residents in Yixing city, we assessed the relationship between short-term outdoor PM2.5 exposure and blood lipid concentrations. Participants who attended the physical examination were enrolled from Yixing People's hospital from 2015 to 2020. We collected general characteristics of participants, including gender and age, as well as test results of indicators of blood lipids. Data on daily meteorological factors were collected from the National Meteorological Data Sharing Center ( http://data.cma.cn/ ) and air pollutant concentrations were collected from the China Air Quality Online Monitoring and Analysis Platform ( https://www.aqistudy.cn/ ) during this period. We applied generalized additive models to estimate short-term effects of ambient PM2.5 exposure on each measured blood lipid-related indicators and converted these indicators into dichotomous variables (non- hyperlipidemia and hyperlipidemia) to calculate risks of hyperlipidemia associated with PM2.5 exposure. A total of 197,957 participants were included in the analysis with mean age 47.90 years (± SD, 14.28). The increase in PM2.5 was significantly associated with hyperlipidemia (odds ratio (OR) 1.003, 95% CI 1.001-1.004), and it was still significant in subgroups of males and age < 60 years. For every 10 μg/m3 increase in PM2.5, triglyceride levels decreased by 0.5447% (95% CI - 0.7873, - 0.3015), the low-density lipoprotein cholesterol concentration increased by 0.0127 mmol/L (95% CI 0.0099, 0.0156), the total cholesterol concentration increased by 0.0095 mmol/L (95% CI 0.0053, 0.0136), and no significant association was observed between PM2.5 and the high-density lipoprotein cholesterol concentration. After excluding people with abnormal blood lipid concentrations, the associations remained significant except for the high-density lipoprotein cholesterol concentration. PM2.5 was positively correlated with low-density lipoprotein cholesterol and total cholesterol, and negatively correlated with triglyceride, indicating PM2.5 can potentially affect health through blood lipid levels.
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Affiliation(s)
- Qiao Liu
- Department of Chronic Communicable Disease, Center for Disease Control and Prevention of Jiangsu Province, Nanjing, Jiangsu Province, People's Republic of China
| | - Zhan Wang
- Department of Chronic Communicable Disease, Center for Disease Control and Prevention of Jiangsu Province, Nanjing, Jiangsu Province, People's Republic of China
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China
| | - Junjie Lu
- Department of Critical Care Medicine, Affiliated Yixing People's Hospital, Jiangsu University, Wuxi, People's Republic of China
| | - Zhongqi Li
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China
| | - Leonardo Martinez
- Department of Epidemiology, School of Public Health, Boston University, Boston, MA, USA
| | - Bilin Tao
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People's Republic of China
| | - Chunlai Wang
- Department of Physical Examination Center, Affiliated Yixing People's Hospital, Jiangsu University, Wuxi, People's Republic of China
| | - Limei Zhu
- Department of Chronic Communicable Disease, Center for Disease Control and Prevention of Jiangsu Province, Nanjing, Jiangsu Province, People's Republic of China
| | - Wei Lu
- Department of Chronic Communicable Disease, Center for Disease Control and Prevention of Jiangsu Province, Nanjing, Jiangsu Province, People's Republic of China
| | - Baoli Zhu
- Department of Chronic Communicable Disease, Center for Disease Control and Prevention of Jiangsu Province, Nanjing, Jiangsu Province, People's Republic of China
| | - Xiaohua Pei
- Divison of Geriatric Nephrology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, People's Republic of China.
| | - Xuhua Mao
- Department of Clinical Laboratory, Affiliated Yixing People's Hospital, Jiangsu University, Wuxi, Jiangsu Province, People's Republic of China.
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7
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Li JM, Yang HY, Wu SH, Dharmage SC, Jalaludin B, Knibbs LD, Bloom MS, Guo Y, Morawska L, Heinrich J, Steve Hung Lam Y, Lin LZ, Zeng XW, Yang BY, Chen GB, Liu RQ, Dong GH, Hu LW. The associations of particulate matter short-term exposure and serum lipids are modified by vitamin D status: A panel study of young healthy adults. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 317:120686. [PMID: 36400145 DOI: 10.1016/j.envpol.2022.120686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 10/26/2022] [Accepted: 11/14/2022] [Indexed: 06/16/2023]
Abstract
Particulate matter (PM) exposure is associated to the adverse change in blood lipids. Vitamin D is beneficial to lipid metabolism, but whether vitamin D levels modifies the impact of air pollutants on lipids is unclear. The purpose of the study was to investigate if vitamin D modifies the associations of PM and serum lipids in young healthy people. From December 2017 to January 2018, a panel study with five once weekly follow-ups was conducted on 88 healthy adults aged 21.09 (1.08) (mean (SD)) years on average in Guangzhou, China. We measured serum lipids, serum 25-hydroxyvitamin D (25(OH)D) concentrations (440 blood samples in total), mass concentrations of particulate matter with diameters ≤2.5 μm (PM2.5), ≤1.0 μm (PM1.0), and ≤0.5 μm (PM0.5), and number concentrations of particulate matter with diameters ≤0.2 μm (PN0.2) and ≤0.1 μm (PN0.1) at each follow-up. Linear mixed-effect models were applied to assess the interaction of vitamin D and size-fractionated PM short-term exposure on four lipid metrics. We found the interactions between 25(OH)D and size-fractionated PM exposure on blood lipids in different lags (lag 3 days and 4 days). An interquartile range increase in PM2.5, PM1.0, PM0.5 were significantly associated with increments of 12.30%, 12.99%, and 13.66% in triglycerides (TGs) at lag 4 days at vitamin D levels <15 ng/mL group, respectively. Similar results were found for PN0.2, PN0.1 and low-density lipoprotein cholesterol (LDL-C). All the associations between size-fractionated PM and blood lipids were found null statistically significant in vitamin D levels ≥15 ng/mL group.
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Affiliation(s)
- Jia-Min Li
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Han-Yu Yang
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Si-Han Wu
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Shyamali C Dharmage
- Allergy and Lung Health Unit, Centre for Epidemiology and Biostatistics, School of Population & Global Health, The University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Bin Jalaludin
- Centre for Research, Evidence Management and Surveillance, South Western Sydney Local Health District, Liverpool, NSW, 2037, Australia; Ingham Institute for Applied Medical Research, Liverpool, NSW, 2170, Australia; School of Public Health and Community Medicine Sydney, University of New South Wales, Sydney, NSW, 2052, Australia
| | - Luke D Knibbs
- School of Public Health, The University of Sydney, NSW, 2006, Australia
| | - Michael S Bloom
- Department of Global and Community Health, George Mason University, Fairfax, VA, 22030, USA
| | - Yuming Guo
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia
| | - Lidia Morawska
- Queensland University of Technology, International Laboratory for Air Quality & Health, Brisbane, QLD, Australia; Queensland University of Technology, Science and Engineering Faculty, Brisbane, QLD, Australia
| | - Joachim Heinrich
- Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Ziemssenstraße 1, 80336, Munich, Germany; Comprehensive Pneumology Center Munich, German Center for Lung Research, Ziemssenstraße 1, 80336, Munich, Germany
| | - Yim Steve Hung Lam
- Asian School of the Environment, Nanyang Technological University, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Earth Observatory of Singapore, Nanyang Technological University, Singapore
| | - Li-Zi Lin
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Xiao-Wen Zeng
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Bo-Yi Yang
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Gong-Bo Chen
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Ru-Qing Liu
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Guang-Hui Dong
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China
| | - Li-Wen Hu
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China.
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8
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Zheng S, Zhang X, Zhang L, Shi G, Liu Y, Lv K, Zhang D, Yin C, Bai Y, Zhang Y, Wang M. Effects of short-term exposure to gaseous pollutants on metabolic health indicators of patients with metabolic syndrome in Northwest China. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 249:114438. [PMID: 38321659 DOI: 10.1016/j.ecoenv.2022.114438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 12/11/2022] [Accepted: 12/12/2022] [Indexed: 02/08/2024]
Abstract
Currently few studies have explored the relationship between exposure to gaseous pollutants and metabolic health indicators in patients, especially in patients with metabolic syndrome (Mets). This study collected 15,520 patients with Mets in a prospective cohort of nearly 50,000 people with 7 years of follow-up from 2011 to 2017, and matched air pollutants and meteorological data during the same period. The mixed effects model was used to analyze the relationship between different short exposure windows (1-week, 1-month, 2-month, and 3-month) of gaseous pollutants (SO2, NO2, and O3) and the metabolic health indicators of patients after controlled the confounding factors. Stratified analysis was performed by demographic characteristics and behavioral factors. The effects of gaseous pollutants on patients with different Met components were also analyzed. The results showed that the short-term exposure to SO2, NO2, and O3 had a certain effect on the metabolic health indicators of patients with Mets in different exposure windows, and with the extension of the exposure window period, the effects increased. The stratified analysis showed that gender, age, and life behaviors might modify these detrimental effects. In addition, the effects of gaseous pollutants on metabolic health indicators in G4 and G7 were more obvious than other Met components, and the effects of gaseous pollutants on the level of LDL-C were found to be statistically significant in most components. Therefore, patients with Mets should pay more attention to the influence of gaseous pollutants to take appropriate protection to reduce potential health risk.
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Affiliation(s)
- Shan Zheng
- Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou 73000, China
| | - Xiaofei Zhang
- Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou 73000, China
| | - Li Zhang
- Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou 73000, China
| | - Guoxiu Shi
- Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou 73000, China
| | - Yanli Liu
- Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou 73000, China
| | - Kang Lv
- Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou 73000, China
| | - Desheng Zhang
- Workers' Hospital of Jinchuan Group Co., Ltd., Jinchang 737103, China
| | - Chun Yin
- Workers' Hospital of Jinchuan Group Co., Ltd., Jinchang 737103, China
| | - Yana Bai
- Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou 73000, China
| | - Yaqun Zhang
- Gansu Academy of Eco-environmental Sciences, Lanzhou 730020, China.
| | - Minzhen Wang
- Institute of Epidemiology and Statistics, School of Public Health, Lanzhou University, Lanzhou 73000, China.
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9
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Sun J, Peng S, Li Z, Liu F, Wu C, Lu Y, Xiang H. Association of Short-Term Exposure to PM 2.5 with Blood Lipids and the Modification Effects of Insulin Resistance: A Panel Study in Wuhan. TOXICS 2022; 10:663. [PMID: 36355954 PMCID: PMC9698404 DOI: 10.3390/toxics10110663] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 10/31/2022] [Accepted: 11/02/2022] [Indexed: 06/16/2023]
Abstract
Results of previous studies about the acute effects of fine particulate matter (PM2.5) on blood lipids were inconsistent. This study aimed to quantify the short-term effects of PM2.5 on blood lipids and estimate the modifying role of insulin resistance, reflected by the homeostasis model assessment of insulin resistance (HOMA-IR). From September 2019 to January 2020, the study recruited 70 healthy adults from Wuhan University for a total of eight repeated data collections. At each visit, three consecutive days were monitored for personal exposure to PM2.5, and then a physical examination was carried out on the fourth day. The linear mixed-effect models were operated to investigate the impact of PM2.5 over diverse exposure windows on blood lipids. With the median of the HOMA-IR 1.820 as the cut-off point, participants were assigned to two groups for the interaction analyses. We found the overall mean level (standard deviation, SD) of PM2.5 was 38.34 (18.33) μg/m3. Additionally, with a 10 μg/m3 rise in PM2.5, the corresponding largest responses in triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), as well as high-density lipoprotein cholesterol (HDL-C), were −0.91% (95% confidence interval (CI): −1.63%, −0.18%), −0.33% (95% CI: −0.64%, −0.01%,), −0.94% (95% CI: −1.53%, −0.35%), and 0.67% (95% CI: 0.32%, 1.02%), respectively. The interaction analyses revealed that a significantly greater reduction in the four lipids corresponded to PM2.5 exposure when in the group with the lower HOMA-IR (<1.820). In conclusion, short-term PM2.5 exposure over specific time windows among healthy adults was associated with reduced TG, TC, as well as LDL-C levels, and elevated HDL-C. Additionally, the association of PM2.5−lipids may be modulated by insulin resistance.
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Affiliation(s)
- Jinhui Sun
- Department of Global Health, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan 430071, China
- Global Health Institute, Wuhan University, 115# Donghu Road, Wuhan 430071, China
| | - Shouxin Peng
- Department of Global Health, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan 430071, China
- Global Health Institute, Wuhan University, 115# Donghu Road, Wuhan 430071, China
| | - Zhaoyuan Li
- Department of Global Health, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan 430071, China
- Global Health Institute, Wuhan University, 115# Donghu Road, Wuhan 430071, China
| | - Feifei Liu
- Department of Global Health, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan 430071, China
- Global Health Institute, Wuhan University, 115# Donghu Road, Wuhan 430071, China
| | - Chuangxin Wu
- Department of Global Health, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan 430071, China
- Global Health Institute, Wuhan University, 115# Donghu Road, Wuhan 430071, China
| | - Yuanan Lu
- Environmental Health Laboratory, Department of Public Health Sciences, University of Hawaii at Manoa, 1960 East West Rd., Biomed Bldg D105, Honolulu, HI 96822, USA
| | - Hao Xiang
- Department of Global Health, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan 430071, China
- Global Health Institute, Wuhan University, 115# Donghu Road, Wuhan 430071, China
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10
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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. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:13160-13168. [PMID: 36043295 DOI: 10.1021/acs.est.2c01568] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [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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11
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Li H, Ge M, Pei Z, He J, Wang C. Nonlinear associations between environmental factors and lipid levels in middle-aged and elderly population in China: A national cross-sectional study. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 838:155962. [PMID: 35588809 DOI: 10.1016/j.scitotenv.2022.155962] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/04/2022] [Accepted: 05/11/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Blood lipid is an important factor affecting cardiovascular disease in middle-aged and elderly people. At present, the associations between environmental factors and blood lipid level in elderly people has been controversial, and the nonlinear effect of their relationship is lack of research. METHODS This study used data from a national cross-sectional survey of blood lipid levels in 13,354 subjects and data from environmental monitoring sites. Logistic regression was used to measure the relationship between the basic characteristics of the study population and blood lipid levels. After controlling the confounding factors, the nonlinear associations between environmental factors and blood lipid levels of middle-aged and elderly people in different geographical regions were studied by random forest model. RESULTS The risk of dyslipidemia is significantly higher in middle-aged women, obese people, elderly people, and urban people. Smoking and alcohol consumption increase the risk. The associations between environmental factors and lipid levels of middle-aged and elderly people are nonlinear, the correlation effect between air pollutants and blood lipid level is mainly shown in northern China, and the correlation between meteorological factors and blood lipid level is more obvious in southern China. CONCLUSIONS This study shows that the associations between environmental factors and lipid levels in middle-aged and elderly population are nonlinear and have regional differences. Therefore it should be considered in optimizing the allocation of public health resources and preventing and controlling environmental exposure of middle-aged and elderly population.
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Affiliation(s)
- Hao Li
- Institute of Healthy Geography, School of Geography and Tourism, Shaanxi Normal University, 620 West Chang'an Street, Chang'an District, Xi'an 710119, China
| | - Miao Ge
- Institute of Healthy Geography, School of Geography and Tourism, Shaanxi Normal University, 620 West Chang'an Street, Chang'an District, Xi'an 710119, China.
| | - Zehua Pei
- Institute of Healthy Geography, School of Geography and Tourism, Shaanxi Normal University, 620 West Chang'an Street, Chang'an District, Xi'an 710119, China
| | - Jinwei He
- Medical School, Yan'an University, 580 Shengdi Road, Yan'an 716000, China
| | - Congxia Wang
- Department of Cardiology, the Second Affiliated Hospital of Medical College, Xi'an Jiaotong University, No. 157, Xiwu Road, Xi'an 710004, China
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12
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Zhang Z, Su Y, Jing R, Qi J, Qi X, Xie Z, Cui B. Acute and lag effects of ambient fine particulate matter on the incidence of dyslipidemia in Chengdu, China: A time-series study. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:37919-37929. [PMID: 35072876 DOI: 10.1007/s11356-021-18400-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 12/25/2021] [Indexed: 06/14/2023]
Abstract
High levels of ambient fine particulate matter (PM2.5) might increase the risk of death due to cardiovascular diseases (CVDs). As a critical risk factor for CVDs, dyslipidemia can cause CVDs or exacerbate pre-existing ones. This study aimed to investigate whether a short-time exposure to PM2.5 leads to dyslipidemia (HyperTC, HyperLDL-C, HyperTG and HypoHDL-C) in adults. The serum lipid data were provided by the Sichuan Provincial People's Hospital Medical Examination Center. We included 309,654 subjects aged 18-79 between May 10, 2015, and May 10, 2017. An advanced distributed lag nonlinear model (DLNM) was applied to investigate the acute and lag effects of ambient PM2.5 on the risk of dyslipidemia. This study was also stratified by sex, age, BMI and season to examine potential effect modification. We observed that the associations between an interquartile increase in PM2.5 (43 μg/m3) and dyslipidemia were [relative risk (RR); 95% confidence interval (CI)]: 1.042 (1.013, 1.071) for HyperLDL-C and 1.027 (1.006, 1.049) for HyperTC at lag0 day. The lag effects were found at lag6 day for HyperLDL-C, in lag4-6 days for HyperTC and lag4-7 days for HyperTG. Short-term exposure to ambient PM2.5 was related to dyslipidemia and the effect modification was observed in the subgroup analysis. The female and normal-weight populations were more susceptible to the risks of PM2.5 on HyperLDL-C and HyperTC.
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Affiliation(s)
- Zizheng Zhang
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yang Su
- Clinical Laboratory, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
- Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China
| | - Renjie Jing
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jiying Qi
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaohui Qi
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhen Xie
- Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China
- Department of Dermatology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Bin Cui
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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13
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Kim KN, Ha B, Seog W, Hwang IU. Long-term exposure to air pollution and the blood lipid levels of healthy young men. ENVIRONMENT INTERNATIONAL 2022; 161:107119. [PMID: 35123376 DOI: 10.1016/j.envint.2022.107119] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 01/11/2022] [Accepted: 01/26/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND There is insufficient evidence of an association between long-term exposure to air pollution and changes in blood lipid levels, and assessments may be influenced by residual confounding factors, such as socioeconomic status. OBJECTIVES To investigate the associations between long-term exposure to air pollution and blood lipid profiles while controlling for the risk of residual confounding factors. METHODS We conducted a study involving conscripted Korean soldiers to assess the associations between air pollution and blood lipid levels. The soldiers, who were randomly distributed among military units throughout the country, led homogenous lives and were subjected to health checkups 8-12 months post-enlistment. We analyzed data pertaining to those who enlisted and underwent health checkups in 2019 (n = 12,778) using linear mixed models. Additionally, we evaluated quantile-specific associations using quantile regression models. We also assessed interactions based on body mass index (BMI) at the time of enlistment (≥25.0 vs. < 25.0 kg/m2). RESULTS The linear mixed models revealed that a 10-µg/m3 increase in fine particulate matter ≤ 2.5 μm (PM2.5) decreased high-density lipoprotein cholesterol (HDL-C) levels by -0.66% (95% confidence interval [CI]: -1.21, -0.10), and a 10-ppb increase in nitrogen dioxide (NO2) increased total cholesterol (TC) levels by 1.04% (95% CI: 0.24, 1.84). In the quantile regression models, associations were also found at specific deciles. PM2.5 exposure contributed to higher TC, NO2 resulted in higher triglycerides and lower HDL-C, and ozone (O3) led to lower HDL-C. The association between O3 and TC differed according to BMI (p-value for interaction = 0.03); among those with a BMI ≥ 25.0 kg/m2, a 10-ppb increase in O3 increased TC by 1.09% (95% CI: 0.20, 1.09). DISCUSSION These results shed new light on the importance of controlling air pollution, which can contribute to abnormal blood lipid levels, an independent risk factor for cardiovascular disease.
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Affiliation(s)
- Kyoung-Nam Kim
- Department of Preventive Medicine and Public Health, Ajou University School of Medicine, Suwon, Republic of Korea
| | - Beomman Ha
- The Republic of Korea Army Headquarter, Kyeryong, Republic of Korea
| | - Woong Seog
- The Armed Forces Capital Hospital, Seongnam, Republic of Korea
| | - Il-Ung Hwang
- Division of Public Health and Medical Care, Seoul National University Hospital, Seoul, Republic of Korea.
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14
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Liu Q, Li G, Zhang L, Liu J, Du J, Shao B, Li Z. Effects of household cooking with clean energy on the risk for hypertension among women in Beijing. CHEMOSPHERE 2022; 289:133151. [PMID: 34871615 DOI: 10.1016/j.chemosphere.2021.133151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 11/09/2021] [Accepted: 12/01/2021] [Indexed: 06/13/2023]
Abstract
Outdoor air pollution and indoor burning of biomass fuel can cause high blood pressure. However, little is known about the effects of cooking with clean energy on hypertension. We thus explored whether cooking with clean energy is associated with the risk for hypertension. The study used baseline data from 12,349 women from a large population-based cohort study in Beijing, China. Information on cooking habits, health status, and other characteristics was collected by questionnaire and physical examination. Fasting blood samples were collected to measure total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and homocysteine (HCY). An index of cooking exposure was constructed. Log-binomial regression models were used to estimate the association between cooking exposure and risk for hypertension. The prevalence of hypertension was 26.7%. Any cooking exposure at all was associated with an increased risk for hypertension with an adjusted prevalence ratio (aPR) of 2.27 (95% confidence interval [CI]: 2.01, 2.57). The risk for hypertension increased with increases in cooking frequency, time spent cooking, and the cooking index, all showing a dose-effect relationship (P < 0.001). An increased risk for hypertension was associated with both cooking using mainly electricity (aPR: 1.75, 95% CI: 1.41, 2.17) and cooking using mainly natural gas (aPR: 2.30, 95% CI: 2.03, 2.60). The cooking index was positively correlated with plasma concentrations of TC, TG, LDL-C, and HCY and negatively correlated with HDL-C. Abnormal levels of all these biomarkers were associated with an increased prevalence of hypertension after adjustment for confounding factors. Cooking with clean energy, mainly cooking habit, may contribute to an increased risk for hypertension among female residents of Beijing. Abnormal metabolism of lipids or HCY may be an important mechanism involved in the development of cooking-related hypertension.
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Affiliation(s)
- Qingping Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, PR China; Beijing Center for Disease Prevention and Control, Beijing, 100013, PR China.
| | - Gang Li
- Beijing Center for Disease Prevention and Control, Beijing, 100013, PR China.
| | - Le Zhang
- Institute of Reproductive and Child Health, Peking University/ Key Laboratory of Reproductive Health, National Health Commission of the People's Republic of China, Beijing, 100191, PR China.
| | - Jufen Liu
- Institute of Reproductive and Child Health, Peking University/ Key Laboratory of Reproductive Health, National Health Commission of the People's Republic of China, Beijing, 100191, PR China.
| | - Jing Du
- Beijing Center for Disease Prevention and Control, Beijing, 100013, PR China.
| | - Bing Shao
- Beijing Center for Disease Prevention and Control, Beijing, 100013, PR China.
| | - Zhiwen Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, 100191, PR China; Institute of Reproductive and Child Health, Peking University/ Key Laboratory of Reproductive Health, National Health Commission of the People's Republic of China, Beijing, 100191, PR China.
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15
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Ni X, Bai C, Nie C, Qi L, Liu Y, Yuan H, Zhu X, Sun L, Zhou Q, Li Y, Zhen H, Su H, Li R, Lan R, Pang G, Lv Y, Zhang W, Yang F, Yao Y, Chen C, Wang Z, Gao D, Zhang N, Zhang S, Zhang L, Wu Z, Hu C, Zeng Y, Yang Z. Identification and replication of novel genetic variants of ABO gene to reduce the incidence of diseases and promote longevity by modulating lipid homeostasis. Aging (Albany NY) 2021; 13:24655-24674. [PMID: 34812738 PMCID: PMC8660604 DOI: 10.18632/aging.203700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 09/10/2021] [Indexed: 11/29/2022]
Abstract
Genes related to human longevity have not been studied so far, and need to be investigated thoroughly. This study aims to explore the relationship among ABO gene variants, lipid levels, and longevity phenotype in individuals (≥90yrs old) without adverse outcomes. A genotype-phenotype study was performed based on 5803 longevity subjects and 7026 younger controls from the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Four ABO gene variants associated with healthy longevity (rs8176719 C, rs687621 G, rs643434 A, and rs505922 C) were identified and replicated in the CLHLS GWAS data analysis and found significantly higher in longevity individuals than controls. The Bonferroni adjusted p-value and OR range were 0.013-0.020 and 1.126-1.151, respectively. According to the results of linkage disequilibrium (LD) analysis, the above four variants formed a block on the ABO gene (D’=1, r2range = 0.585-0.995). The carriers with genotypes rs687621 GG, rs643434 AX, or rs505922 CX (prange = 2.728 x 10-107-5.940 x 10-14; ORrange = 1.004-4.354) and haplotype CGAC/XGXX (p = 2.557 x 10-27; OR = 2.255) had a substantial connection with longevity, according to the results of genetic model analysis. Following the genotype and metabolic phenotype analysis, it has been shown that the longevity individuals with rs687621 GG, rs643434 AX, and rs505922 CX had a positive association with HDL-c, LDL-c, TC, TG (prange = 2.200 x 10-5-0.036, ORrange = 1.546-1.709), and BMI normal level (prange = 2.690 x 10-4-0.026, ORrange = 1.530-1.997). Finally, two pathways involving vWF/ADAMTS13 and the inflammatory markers (sE-selectin/ICAM1) that co-regulated lipid levels by glycosylation and effects on each other were speculated. In conclusion, the association between the identified longevity-associated ABO variants and better health lipid profile was elucidated, thus the findings can help in maintaining normal lipid metabolic phenotypes in the longevity population.
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Affiliation(s)
- Xiaolin Ni
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, P.R. China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Science, Beijing 100730, P.R. China
| | - Chen Bai
- Center for the Study of Aging and Human Development and Geriatrics Division, Medical School of Duke University, Durham, NC 27708, USA.,Center for Healthy Aging and Development Studies, National School of Development, Peking University, Beijing 100871, P.R. China
| | - Chao Nie
- BGI-Shenzhen, Shenzhen, Guangdong 518083, P.R. China
| | - Liping Qi
- College of Science and Technology, Hebei Agricultural University, Cangzhou 061100, Hebei, P.R. China
| | - Yifang Liu
- Joint Graduate Program of Peking-Tsinghua-NIBS, School of Life Sciences, Tsinghua University, Beijing 100084, P.R. China
| | - Huiping Yuan
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, P.R. China
| | - Xiaoquan Zhu
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, P.R. China
| | - Liang Sun
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, P.R. China
| | - Qi Zhou
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, P.R. China
| | - Yan Li
- BGI-Shenzhen, Shenzhen, Guangdong 518083, P.R. China
| | - Hefu Zhen
- BGI-Shenzhen, Shenzhen, Guangdong 518083, P.R. China
| | - Huabing Su
- Jiangbin Hospital, Guangxi Zhuang Autonomous Region, Nanning 530021, P.R. China
| | - Rongqiao Li
- Jiangbin Hospital, Guangxi Zhuang Autonomous Region, Nanning 530021, P.R. China
| | - Rushu Lan
- Jiangbin Hospital, Guangxi Zhuang Autonomous Region, Nanning 530021, P.R. China
| | - Guofang Pang
- Jiangbin Hospital, Guangxi Zhuang Autonomous Region, Nanning 530021, P.R. China
| | - Yuan Lv
- Jiangbin Hospital, Guangxi Zhuang Autonomous Region, Nanning 530021, P.R. China
| | - Wei Zhang
- Jiangbin Hospital, Guangxi Zhuang Autonomous Region, Nanning 530021, P.R. China
| | - Fan Yang
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, P.R. China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Science, Beijing 100730, P.R. China
| | - Yao Yao
- Center for the Study of Aging and Human Development and Geriatrics Division, Medical School of Duke University, Durham, NC 27708, USA.,Center for Healthy Aging and Development Studies, National School of Development, Peking University, Beijing 100871, P.R. China
| | - Chen Chen
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, P.R. China
| | - Zhaoping Wang
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, P.R. China
| | - Danni Gao
- Peking University Fifth School of Clinical Medicine, Beijing 100191, P.R. China
| | - Nan Zhang
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, P.R. China
| | - Shenqi Zhang
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, P.R. China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Science, Beijing 100730, P.R. China
| | - Li Zhang
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, P.R. China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Science, Beijing 100730, P.R. China
| | - Zhu Wu
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, P.R. China
| | - Caiyou Hu
- Jiangbin Hospital, Guangxi Zhuang Autonomous Region, Nanning 530021, P.R. China
| | - Yi Zeng
- Center for the Study of Aging and Human Development and Geriatrics Division, Medical School of Duke University, Durham, NC 27708, USA.,Center for Healthy Aging and Development Studies, National School of Development, Peking University, Beijing 100871, P.R. China
| | - Ze Yang
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100730, P.R. China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Science, Beijing 100730, P.R. China
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16
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Bentley AR, Chen G, Doumatey AP, Shriner D, Meeks KAC, Gouveia MH, Ekoru K, Zhou J, Adeyemo A, Rotimi CN. GWAS in Africans identifies novel lipids loci and demonstrates heterogenous association within Africa. Hum Mol Genet 2021; 30:2205-2214. [PMID: 34196372 PMCID: PMC8561421 DOI: 10.1093/hmg/ddab174] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 06/22/2021] [Accepted: 06/23/2021] [Indexed: 01/11/2023] Open
Abstract
Serum lipids are biomarkers of cardiometabolic disease risk, and understanding genomic factors contributing to their distribution is of interest. Studies of lipids in Africans are rare, though it is expected that such studies could identify novel loci. We conducted a GWAS of 4317 Africans enrolled from Nigeria, Ghana and Kenya. We evaluated linear mixed models of high-density lipoprotein cholesterol (HDLC), low-density lipoprotein cholesterol (LDLC), total cholesterol (CHOL), triglycerides (TG) and TG/HDLC. Replication was attempted in 9542 African Americans (AA). In our main analysis, we identified 28 novel associations in Africans. Of the 18 of these that could be tested in AA, three associations replicated (GPNMB-TG, ENPP1-TG and SMARCA4-LDLC). Five additional novel loci were discovered upon meta-analysis with AA (rs138282551-TG, PGBD5-HDLC, CD80-TG/HDLC, SLC44A1-CHOL and TLL2-CHOL). Analyses considering only those with predominantly West African ancestry (Nigeria, Ghana and AA) yielded new insights: ORC5-LDLC and chr20:60973327-CHOL. Among our novel findings are some loci with known connections to lipids pathways. For instance, rs147706369 (TLL2) alters a regulatory motif for sterol regulatory element-binding proteins, a family of transcription factors that control the expression of a range of enzymes involved in cholesterol, fatty acid and TG synthesis, and rs115749422 (SMARCA4), an independent association near the known LDLR locus that is rare or absent in populations without African ancestry. These findings demonstrate the utility of conducting genomic analyses in Africans for discovering novel loci and provide some preliminary evidence for caution against treating 'African ancestry' as a monolithic category.
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Affiliation(s)
- Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Guanjie Chen
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Ayo P Doumatey
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Daniel Shriner
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Karlijn A C Meeks
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Mateus H Gouveia
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Kenneth Ekoru
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Jie Zhou
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Adebowale Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
| | - Charles N Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA
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17
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He ZZ, Guo PY, Xu SL, Zhou Y, Jalaludin B, Leskinen A, Knibbs LD, Heinrich J, Morawska L, Yim SHL, Bui D, Komppula M, Roponen M, Hu L, Chen G, Zeng XW, Yu Y, Yang BY, Dong G. Associations of Particulate Matter Sizes and Chemical Constituents with Blood Lipids: A Panel Study in Guangzhou, China. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2021; 55:5065-5075. [PMID: 33764049 DOI: 10.1021/acs.est.0c06974] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Existing evidence is scarce concerning the various effects of different PM sizes and chemical constituents on blood lipids. A panel study that involved 88 healthy college students with five repeated measurements (440 blood samples in total) was performed. We measured mass concentrations of particulate matter with diameters ≤ 2.5 μm (PM2.5), ≤1.0 μm (PM1.0), and ≤0.5 μm (PM0.5) as well as number concentrations of particulate matter with diameters ≤ 0.2 μm (PN0.2) and ≤0.1 μm (PN0.1). We applied linear mixed-effect models to assess the associations between short-term exposure to different PM size fractions and PM2.5 constituents and seven lipid metrics. We found significant associations of greater concentrations of PM in different size fractions within 5 days before blood collection with lower high-density lipoprotein cholesterol (HDL-C) and apolipoprotein A (ApoA1) levels, higher apolipoprotein B (ApoB) levels, and lower ApoA1/ApoB ratios. Among the PM2.5 constituents, we observed that higher concentrations of tin and lead were significantly associated with decreased HDL-C levels, and higher concentrations of nickel were associated with higher HDL-C levels. Our results suggest that short-term exposure to PM in different sizes was deleteriously associated with blood lipids. Some constituents, especially metals, might be the major contributors to the detrimental effects.
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Affiliation(s)
- Zhi-Zhou He
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Peng-Yue Guo
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Shu-Li Xu
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Yang Zhou
- State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Environmental Protection, Guangzhou 510655, China
| | - Bin Jalaludin
- Centre for Air Quality and Health Research and Evaluation, Glebe, NSW 2037, Australia
- Population Health, South Western Sydney Local Health District, Liverpool, NSW 2170, Australia
- Ingham Institute for Applied Medical Research, Liverpool, NSW 2170, Australia
- School of Public Health and Community Medicine, The University of New South Wales, Kensington, NSW 2052, Australia
| | - Ari Leskinen
- Finnish Meteorological Institute, Kuopio 70211, Finland
- Department of Applied Physics, University of Eastern Finland, Kuopio 70211, Finland
| | - Luke D Knibbs
- School of Public Health, The University of Queensland, Herston, Queensland 4006, Australia
| | - Joachim Heinrich
- Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Munich 80336, Germany
- Comprehensive Pneumology Center Munich, German Center for Lung Research, Munich 80336, Germany
| | - Lidia Morawska
- International Laboratory for Air Quality and Health, Queensland University of Technology (QUT), GP.O. Box 2434, Brisbane, Queensland 4001, Australia
| | - Steve Hung-Lam Yim
- Department of Geography and Resource Management, Stanley Ho Big Data Decision Analytics Research Centre, Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Shatin, China
| | - Dinh Bui
- Allergy and Lung Health Unit, Centre for Epidemiology and Biostatistics, School of Population & Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia
| | - Mika Komppula
- Finnish Meteorological Institute, Kuopio 70211, Finland
| | - Marjut Roponen
- Department of Environmental and Biological Sciences, University of Eastern Finland, Kuopio, FI 70211, Finland
| | - Liwen Hu
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Gongbo Chen
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Xiao-Wen Zeng
- Guangdong Provincial Engineering Technology Research Center of Environmental Pollution and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Yunjiang Yu
- State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Environmental Protection, Guangzhou 510655, China
| | - Bo-Yi Yang
- State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Environmental Protection, Guangzhou 510655, China
| | - Guanghui Dong
- State Environmental Protection Key Laboratory of Environmental Pollution Health Risk Assessment, South China Institute of Environmental Sciences, Ministry of Environmental Protection, Guangzhou 510655, China
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18
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Chu H, Xin J, Yuan Q, Wu Y, Du M, Zheng R, Liu H, Wu S, Zhang Z, Wang M. A prospective study of the associations among fine particulate matter, genetic variants, and the risk of colorectal cancer. ENVIRONMENT INTERNATIONAL 2021; 147:106309. [PMID: 33338681 DOI: 10.1016/j.envint.2020.106309] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Revised: 11/17/2020] [Accepted: 11/26/2020] [Indexed: 05/25/2023]
Abstract
BACKGROUND Fine particulate matter (PM2.5) is suspected to increase the risk of colorectal cancer, but the mechanism remains unknown. We aimed to investigate the association between PM2.5 exposure, genetic variants and colorectal cancer risk in the Prostate, Lung, Colon and Ovarian (PLCO) Cancer Screening trial. METHODS We included a prospective cohort of 139,534 cancer-free individuals from 10 United States research centers with over ten years of follow-up. We used a Cox regression model to assess the association between PM2.5 exposure and colorectal cancer incidence by calculating the hazard ratio (HR) and 95% confidence interval (CI) with adjustment for potential confounders. The polygenic risk score (PRS) and genome-wide interaction analysis (GWIA) were used to evaluate the multiplicative interaction between PM2.5 exposure and genetic variants in regard to colorectal cancer risk. RESULTS After a median of 10.43 years of follow-up, 1,666 participants had been diagnosed with colorectal cancer. PM2.5 exposure was significantly associated with an increased risk of colorectal cancer (HR = 1.27; 95% CI = 1.17-1.37 per 5 μg/m3 increase). Five independent susceptibility loci reached statistical significance at P < 1.22 × 10-8 in the interaction analysis. Furthermore, a joint interaction was observed between PM2.5 exposure and the PRS based on these five loci with colorectal cancer risk (P = 3.11 × 10-29). The Gene Ontology analysis showed that the vascular endothelial growth factor (VEGF) receptor signaling pathway was involved in the biological process of colorectal cancer. CONCLUSIONS Our large-scale analysis has shown for the first time that long-term PM2.5 exposure potential increases colorectal cancer risk, which might be modified by genetic variants.
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Affiliation(s)
- Haiyan Chu
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Junyi Xin
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Qi Yuan
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Nanjing Municipal Center for Disease Control and Prevention, Nanjing, China
| | - Yanling Wu
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Mulong Du
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Rui Zheng
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Hanting Liu
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
| | - Shaowei Wu
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China
| | - Zhengdong Zhang
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
| | - Meilin Wang
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China; Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
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19
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Zhang K, Wang H, He W, Chen G, Lu P, Xu R, Yu P, Ye T, Guo S, Li S, Xie Y, Hao Z, Wang H, Guo Y. The association between ambient air pollution and blood lipids: A longitudinal study in Shijiazhuang, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 752:141648. [PMID: 32889259 DOI: 10.1016/j.scitotenv.2020.141648] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Revised: 07/20/2020] [Accepted: 08/10/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Few studies have explored the associations between ambient air pollution and blood lipid levels. This study aimed to fill this knowledge gap based on a routine health examination cohort in Shijiazhuang, China. METHODS We included 7063 participants who took the routine health examination for 2-3 times at Hebei General Hospital from January 2016 to December 2018. Individual serum levels of cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) were measured. Their three-month average exposure to air pollution prior to the routine health examinations was estimated using inverse distance weighted method. We used linear mixed-effects regression models to examine the associations between air pollution and levels of blood lipids while controlling for age, gender, body mass index (BMI), smoking, alcohol drinking, temperature, humidity, with a random effect for each individual. RESULTS Particles with diameters ≤2.5 μm and ≤10 μm (PM2.5 and PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2) and ozone (O3) were all positively associated with TC, TG, and LDL-C and negatively associated with HDL-C, in single pollutant models. Each 10 μg/m3 increment of 3-month average PM2.5 was associated with 0.65% [95% confidence interval (CI): 0.03%-1.28%], 0.56% (95%CI: 0.33%-0.79%) and 0.63% (95%CI: 0.35%-0.91%) increment in TG, TC, and LDL-C, and 0.91% (95%CI: 0.68%-1.13%) decrease in HDL-C. In two-pollutant models, the effects of gaseous pollutants on blood lipids were weakened, while those of PMs were strengthened. Stronger associations were presented in the elderly (≥60 years) and overweight/obese (BMI ≥ 24) participants. CONCLUSIONS Ambient air pollution had significantly adverse effects on blood lipid levels, especially in overweight/obese and elderly individuals. CAPSULE Significant associations between increased air pollution and worse blood lipid levels were found, especially in overweight/obese and elderly individuals.
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Affiliation(s)
- Kaihua Zhang
- Hebei Medical University, Shijiazhuang, Hebei, China; Department of Neurology, Hebei General Hospital, Shijiazhuang, Hebei, China
| | - Haoyuan Wang
- Hebei Medical University, Shijiazhuang, Hebei, China
| | - Weiliang He
- Department of Neurology, Hebei General Hospital, Shijiazhuang, Hebei, China
| | - Gongbo Chen
- Department of Global Health, School of Health Sciences, Wuhan University, Wuhan, China
| | - Peng Lu
- Department of Epidemiology, School of Public Health and Management, Binzhou Medical University, Yantai, Shandong, China
| | - Rongbin Xu
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Pei Yu
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Tingting Ye
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Suying Guo
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Shanshan Li
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Yinyu Xie
- Department of Global Health, School of Health Sciences, Wuhan University, Wuhan, China
| | - Zhihua Hao
- Physical Examination Center of Hebei General Hospital, Shijiazhuang, Hebei, China
| | - Hebo Wang
- Hebei Medical University, Shijiazhuang, Hebei, China; Department of Neurology, Hebei General Hospital, Shijiazhuang, Hebei, China.
| | - Yuming Guo
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
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