Bo-Yi Yang1, Michael S Bloom2, Iana Markevych3, Zhengmin Min Qian4, Michael G Vaughn5, Lenise A Cummings-Vaughn6, Shanshan Li7, Gongbo Chen7, Gayan Bowatte8, Jennifer L Perret9, Shyamali C Dharmage10, Joachim Heinrich11, Steve Hung-Lam Yim12, Shao Lin13, Linwei Tian14, Mo Yang1, Kang-Kang Liu1, Xiao-Wen Zeng1, Li-Wen Hu1, Yuming Guo15, Guang-Hui Dong16. 1. Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China. 2. Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China; Departments of Environmental Health Sciences and Epidemiology and Biostatics, University at Albany, State University of New York, Rensselaer, NY, USA. 3. Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich; Institute of Epidemiology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany. 4. Department of Epidemiology, College for Public Health and Social Justice, Saint Louis University, Saint Louis 63104, USA. 5. School of Social Work, College for Public Health and Social Justice, Saint Louis University, Saint Louis 63104, USA. 6. Division of Geriatrics and Nutritional Science, School of Medicine, Washington University-St. Louis, 4921 Parkview Place, St.Louis, MO 63110, USA. 7. Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne VIC 3004, Australia. 8. Allergy and Lung Health Unit, Centre for Epidemiology and Biostatistics, School of Population & Global Health, The University of Melbourne, Melbourne, Australia; National Institute of Fundamental Studies, Kandy, Sri Lanka. 9. Allergy and Lung Health Unit, Centre for Epidemiology and Biostatistics, School of Population & Global Health, The University of Melbourne, Melbourne, Australia. 10. Allergy and Lung Health Unit, Centre for Epidemiology and Biostatistics, School of Population & Global Health, The University of Melbourne, Melbourne, Australia; Murdoch Childrens Research Institute, Melbourne, Australia. 11. Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich; Comprehensive Pneumology Center Munich, German Center for Lung Research, Ziemssenstrasse 1, 80336 Muenchen, Germany. 12. Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, NT, Hong Kong, China; Stanley Ho Big Data Decision Analytics Research Centre, The Chinese University of Hong Kong, Shatin, NT, Hong Kong, China; Institute of Environment, Energy and Sustainability, The Chinese Uni-versity of Hong Kong, Shatin, NT, Hong Kong, China. 13. Departments of Environmental Health Sciences and Epidemiology and Biostatics, University at Albany, State University of New York, Rensselaer, NY, USA. 14. School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China. 15. Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne VIC 3004, Australia. Electronic address: yuming.guo@monash.edu. 16. Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China. Electronic address: donggh5@mail.sysu.edu.cn.
Abstract
BACKGROUND: Little information exists on the lipidemic effects of air pollution, particularly in developing countries. We aimed to investigate the associations of long-term exposure to ambient air pollutants with lipid levels and dyslipidemias in China. METHODS: In 2009, a total of 15,477 participants aged 18-74 years were recruited from the 33 Communities Chinese Health Study conducted in three Northeastern China cities. Total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were measured in participants' blood specimens. Three year (2006-08) average air pollution concentrations were assessed using data from 33 communities (particles with diameters ≤1.0 μm (PM1) and ≤2.5 μm (PM2.5) were predicted using a spatial statistical model) or 11 air monitoring stations (particles with diameters ≤10 μm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), and ozone (O3)). Associations were evaluated by two-level logistic and generalized linear regression models. RESULTS: We detected many significant associations between exposure to air pollutants (especially for PM1 and PM2.5) and blood lipid levels. Most of the associations suggested deleterious effects on blood lipid markers (e.g., a 10 μg/m3 increase in PM1 was associated with 1.6% (95% confidence interval (CI): 1.1, 2.0), 2.9% (95% CI: -3.3, 9.3), and 3.2% (95% CI: 2.6, 3.9) higher levels of TC, TG, and LDL-C, respectively, but 1.4% (95% CI: -1.8, -0.9) lower HDL-C levels), although beneficial associations were found for O3. In analysis with dyslipidemias, all the observed associations suggested deleterious lipidemic effects of air pollutants, and no significant beneficial association was observed for O3. Stratified analyses showed that the associations were stronger in overweight or obese participants; sex and age modified the associations, but the pattern of effects was mixed. CONCLUSIONS: Long-term ambient air pollution was associated with both altered lipid profiles and dyslipidemias, especially among overweight or obese participants.
BACKGROUND: Little information exists on the lipidemic effects of air pollution, particularly in developing countries. We aimed to investigate the associations of long-term exposure to ambient air pollutants with lipid levels and dyslipidemias in China. METHODS: In 2009, a total of 15,477 participants aged 18-74 years were recruited from the 33 Communities Chinese Health Study conducted in three Northeastern China cities. Total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were measured in participants' blood specimens. Three year (2006-08) average air pollution concentrations were assessed using data from 33 communities (particles with diameters ≤1.0 μm (PM1) and ≤2.5 μm (PM2.5) were predicted using a spatial statistical model) or 11 air monitoring stations (particles with diameters ≤10 μm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), and ozone (O3)). Associations were evaluated by two-level logistic and generalized linear regression models. RESULTS: We detected many significant associations between exposure to air pollutants (especially for PM1 and PM2.5) and blood lipid levels. Most of the associations suggested deleterious effects on blood lipid markers (e.g., a 10 μg/m3 increase in PM1 was associated with 1.6% (95% confidence interval (CI): 1.1, 2.0), 2.9% (95% CI: -3.3, 9.3), and 3.2% (95% CI: 2.6, 3.9) higher levels of TC, TG, and LDL-C, respectively, but 1.4% (95% CI: -1.8, -0.9) lower HDL-C levels), although beneficial associations were found for O3. In analysis with dyslipidemias, all the observed associations suggested deleterious lipidemic effects of air pollutants, and no significant beneficial association was observed for O3. Stratified analyses showed that the associations were stronger in overweight or obeseparticipants; sex and age modified the associations, but the pattern of effects was mixed. CONCLUSIONS: Long-term ambient air pollution was associated with both altered lipid profiles and dyslipidemias, especially among overweight or obeseparticipants.
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