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Hu LW, Gong YC, Zou HX, Wang LB, Sun Y, Godinez A, Yang HY, Wu SH, Zhang S, Huang WZ, Gui ZH, Lin LZ, Zeng XW, Yang BY, Liu RQ, Chen G, Li S, Guo Y, Dong GH. Outdoor light at night is a modifiable environmental factor for metabolic syndrome: The 33 Communities Chinese Health Study (33CCHS). THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 954:176203. [PMID: 39270867 DOI: 10.1016/j.scitotenv.2024.176203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 09/04/2024] [Accepted: 09/09/2024] [Indexed: 09/15/2024]
Abstract
Metabolic syndrome (MetS) is a significant public health problem and presents an escalating clinical challenge globally. To combat this problem effectively, urgent measures including identify some modifiable environmental factors are necessary. Outdoor artificial light at night (LAN) exposure garnered much attention due to its impact on circadian rhythms and metabolic process. However, epidemiological evidence on the association between outdoor LAN exposure and MetS remains limited. To determine the relationship between outdoor LAN exposure and MetS, 15,477 adults participated the 33 Communities Chinese Health Study (33CCHS) in 2009 were evaluated. Annual levels of outdoor LAN exposure at participants' residential addresses were assessed using satellite data from the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS). Generalized linear mixed effect models were utilized to assess the association of LAN exposure with MetS and its components, including elevated waist circumference (WC), triglycerides (TG), blood pressure (BP), fasting blood glucose (FBG), and reduced high-density lipoprotein cholesterol (HDL-C). Effect modification by various social demographic and behavior factors was also examined. Overall, 4701 (30.37 %) participants were defined as MetS. The LAN exposure ranged from 6.03 to 175.00 nW/cm2/sr. The adjusted odds ratio (OR) of MetS each quartile increment of LAN exposure were 1.43 (95 % CI: 1.21-1.69), 1.44 (95 % CI: 1.19-1.74) and 1.52 (95 % CI: 1.11-2.08), respectively from Q2-Q4. Similar adverse associations were also found for the components of MetS, especially for elevated BP, TG and FBG. Interaction analyses indicated that the above associations were stronger in participants without habitual exercise compared with those with habitual exercise (e.g. OR were 1.52 [95 % CI: 1.28-1.82] vs. 1.27 [95 % CI, 1.04-1.55], P-interaction = 0.042 for MetS). These findings suggest that long-term exposure to LAN can have a significant deleterious effect on MetS, potentially making LAN an important modifiable environmental factor to target in future preventive strategies.
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Affiliation(s)
- Li-Wen Hu
- Joint International Research Laboratory of Environment and Health, Ministry of Education, 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
| | - Yan-Chen Gong
- Joint International Research Laboratory of Environment and Health, Ministry of Education, 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
| | - Hong-Xing Zou
- Joint International Research Laboratory of Environment and Health, Ministry of Education, 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
| | - Le-Bing Wang
- Joint International Research Laboratory of Environment and Health, Ministry of Education, 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
| | - Yanan Sun
- Department of Epidemiology & Biostatistics, College of Integrated Health Sciences, University at Albany, State University of New York, Rensselaer, NY, USA
| | - Alejandro Godinez
- Department of Epidemiology & Biostatistics, College of Integrated Health Sciences, University at Albany, State University of New York, Rensselaer, NY, USA
| | - Han-Yu Yang
- Joint International Research Laboratory of Environment and Health, Ministry of Education, 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
- Joint International Research Laboratory of Environment and Health, Ministry of Education, 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
| | - Shuo Zhang
- Joint International Research Laboratory of Environment and Health, Ministry of Education, 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
| | - Wen-Zhong Huang
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Zhao-Huan Gui
- Joint International Research Laboratory of Environment and Health, Ministry of Education, 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-Zi Lin
- Joint International Research Laboratory of Environment and Health, Ministry of Education, 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
- Joint International Research Laboratory of Environment and Health, Ministry of Education, 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
- Joint International Research Laboratory of Environment and Health, Ministry of Education, 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
- Joint International Research Laboratory of Environment and Health, Ministry of Education, 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
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Shanshan Li
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia
| | - Yuming Guo
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia.
| | - Guang-Hui Dong
- Joint International Research Laboratory of Environment and Health, Ministry of Education, 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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Zhang Y, Zheng P, Shi J, Ma Y, Chen Z, Wang T, Jia G. The modification effect of fasting blood glucose level on the associations between short-term ambient air pollution and blood lipids. INTERNATIONAL JOURNAL OF ENVIRONMENTAL HEALTH RESEARCH 2024; 34:2998-3010. [PMID: 37975287 DOI: 10.1080/09603123.2023.2283048] [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: 08/03/2023] [Accepted: 11/09/2023] [Indexed: 11/19/2023]
Abstract
The association between short-term ambient air pollution (AAP) exposure and blood lipids is inconsistent across populations. This study aimed to investigate the modifying effects of fasting blood glucose (FBG) levels on the associations between short-term AAP exposure and blood lipids in 110,637 male participants from Beijing, China. The results showed that FBG modified the association between short-term AAP exposure and blood lipids, especially low-density lipoprotein cholesterol (LDL-C). In the hyperglycemia group, a 10-μg/m3 increase in particles with diameters ≤ 2.5 μm (PM2.5), particles with diameters ≤ 10 μm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), or a 1-mg/m3 increase in carbon monoxide (CO) was associated with a 0.454%, 0.305%, 1.507%, 0.872%, or 3.961% increase in LDL-C, respectively. In the nonhyperglycemic group, short-term increases in air pollutants were even associated with small decreases in LDL-C. The findings demonstrate that lipids in hyperglycemic individuals are more vulnerable to short-term AAP exposure than those in normal populations.
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Affiliation(s)
- Yi Zhang
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China
- Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, School of Public Health, Peking University, Beijing, China
| | - Pai Zheng
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China
- Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, School of Public Health, Peking University, Beijing, China
| | - Jiaqi Shi
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China
- Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, School of Public Health, Peking University, Beijing, China
| | - Ying Ma
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China
- Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, School of Public Health, Peking University, Beijing, China
| | - Zhangjian Chen
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China
- Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, School of Public Health, Peking University, Beijing, China
| | - Tiancheng Wang
- Department of Laboratory Medicine, Peking University Third Hospital, Beijing, China
| | - Guang Jia
- Department of Occupational and Environmental Health Sciences, School of Public Health, Peking University, Beijing, China
- Beijing Key Laboratory of Toxicological Research and Risk Assessment for Food Safety, School of Public Health, Peking University, Beijing, China
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Chen J, Hart JE, Fisher NDL, Yanosky JD, Roscoe C, James P, Laden F. Multiple Environmental Exposures and the Development of Hypertension in a Prospective US-Based Cohort of Female Nurses: A Mixture Analysis. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024. [PMID: 39083359 DOI: 10.1021/acs.est.4c03722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/02/2024]
Abstract
We investigated the independent and joint associations between multiple environmental exposures and incident hypertension in a US nationwide prospective cohort of women: the Nurses' Health Study II. We followed 107,532 nonhypertensive participants from 1989 to diagnosis of hypertension, loss to follow-up, death, or end of follow-up in June 2019. We applied Cox proportional hazards models to assess associations of incident hypertension with time-varying residential exposure to air pollution, noise, surrounding greenness, temperature, and neighborhood socioeconomic status (nSES), adjusting for potential confounders and coexposures. We evaluated the joint association of simultaneous exposure using quantile g-computation. We observed 38,175 hypertension cases over 2,062,109 person-years. Increased hypertension incidence was consistently associated with lower nSES and higher levels of fine particles (PM2.5) and nighttime noise exposures: hazard ratio (HRs) and 95% confidence intervals (CIs) of 1.06 (1.04, 1.08), 1.04 (1.01, 1.07), and 1.01 (1.00, 1.03), respectively, per interquartile range change. Joint HR for a one-quartile change in simultaneous exposure to the mixture was 1.05 (95% CI: 1.02, 1.09), assuming additivity, or 1.13 (95% CI: 1.06, 1.20), considering potential interactions within the mixture. Hypertension prevention should focus on enhancing nSES and reducing PM2.5 and noise levels, recognizing that reducing the overall exposures may yield additional benefits.
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Affiliation(s)
- Jie Chen
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, Massachusetts 02115, United States
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, United States
| | - Jaime E Hart
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, Massachusetts 02115, United States
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, United States
| | - Naomi D L Fisher
- Division of Endocrinology, Diabetes and Hypertension, Brigham and Women's Hospital, 221 Longwood Avenue, Boston, Massachusetts 02115, United States
| | - Jeff D Yanosky
- Department of Public Health Sciences, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania 17033, United States
| | - Charlotte Roscoe
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, Massachusetts 02115, United States
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, United States
- Division of Population Sciences, Dana Faber Cancer Institute, Boston, Massachusetts 02215, United States
| | - Peter James
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, Massachusetts 02115, United States
- Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts 02215, United States
| | - Francine Laden
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, Massachusetts 02115, United States
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts 02115, United States
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts 02115, United States
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Altug H, Ogurtsova K, Breyer-Kohansal R, Schiffers C, Ofenheimer A, Tzivian L, Hartl S, Hoffmann B, Lucht S, Breyer MK. Associations of long-term exposure to air pollution and noise with body composition in children and adults: Results from the LEAD general population study. ENVIRONMENT INTERNATIONAL 2024; 189:108799. [PMID: 38865830 DOI: 10.1016/j.envint.2024.108799] [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: 12/15/2023] [Revised: 04/30/2024] [Accepted: 06/02/2024] [Indexed: 06/14/2024]
Abstract
BACKGROUND While long-term air pollution and noise exposure has been linked to increasing cardiometabolic disease risk, potential effects on body composition remains unclear. This study aimed to investigate the associations of long-term air pollution, noise and body composition. METHODS We used repeated data from the LEAD (Lung, hEart, sociAl, boDy) study conducted in Vienna, Austria. Body mass index (BMI; kg/m2), fat mass index (FMI; z-score), and lean mass index (LMI; z-score) were measured using dual-energy x-ray absorptiometry at the first (t0; 2011-ongoing) and second (t1; 2017-ongoing) examinations. Annual particulate matter (PM10) and nitrogen dioxide (NO2) concentrations were estimated with the GRAMM/GRAL model (2015-2021). Day-evening-night (Lden) and night-time (Lnight) noise levels from transportation were modeled for 2017 following the European Union Directive 2002/49/EC. Exposures were assigned to residential addresses. We performed analyses separately in children/adolescents and adults, using linear mixed-effects models with random participant intercepts and linear regression models for cross-sectional and longitudinal associations, respectively. Models were adjusted for co-exposure, lifestyle and sociodemographics. RESULTS A total of 19,202 observations (nt0 = 12,717, nt1 = 6,485) from participants aged 6-86 years (mean age at t0 = 41.0 years; 52.9 % female; mean PM10 = 21 µg/m3; mean follow-up time = 4.1 years) were analyzed. Among children and adolescents (age ≤ 18 years at first visit), higher PM10exposure was cross-sectionally associated with higher FMI z-scores (0.09 [95 % Confidence Interval (CI): 0.03, 0.16]) and lower LMI z-scores (-0.05 [95 % CI: -0.10, -0.002]) per 1.8 µg/m3. Adults showed similar trends in cross-sectional associations as children, though not reaching statistical significance. We observed no associations for noise exposures. Longitudinal analyses on body composition changes over time yielded positive associations for PM10, but not for other exposures. CONCLUSION Air pollution exposure, mainly PM10, was cross-sectionally and longitudinally associated with body composition in children/adolescents and adults. Railway/road-traffic noise exposures showed no associations in both cross-sectional and longitudinal analyses.
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Affiliation(s)
- Hicran Altug
- Institute for Occupational, Social and Environmental Medicine, Centre for Health and Society, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany.
| | - Katherine Ogurtsova
- Institute for Occupational, Social and Environmental Medicine, Centre for Health and Society, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany
| | - Robab Breyer-Kohansal
- Ludwig Boltzmann Institute for Lung Health, Vienna, Austria; Department of Respiratory and Pulmonary Diseases, Vienna Healthcare Group, Clinic Hietzing, Vienna, Austria
| | | | - Alina Ofenheimer
- Ludwig Boltzmann Institute for Lung Health, Vienna, Austria; NUTRIM, School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Lilian Tzivian
- Institute for Occupational, Social and Environmental Medicine, Centre for Health and Society, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany; Institute of Clinical and Preventive Medicine, University of Latvia, Riga, Latvia
| | - Sylvia Hartl
- Ludwig Boltzmann Institute for Lung Health, Vienna, Austria; Sigmund Freud University, Faculty of Medicine, Vienna, Austria
| | - Barbara Hoffmann
- Institute for Occupational, Social and Environmental Medicine, Centre for Health and Society, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany
| | - Sarah Lucht
- Institute for Occupational, Social and Environmental Medicine, Centre for Health and Society, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany; Cardinal Health, Dublin, OH, USA
| | - Marie-Kathrin Breyer
- Ludwig Boltzmann Institute for Lung Health, Vienna, Austria; Department of Respiratory and Pulmonary Diseases, Vienna Healthcare Group, Clinic Penzing, Vienna, Austria
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Zheng S, Jiang L, Qiu L. The effects of fine particulate matter on the blood-testis barrier and its potential mechanisms. REVIEWS ON ENVIRONMENTAL HEALTH 2024; 39:233-249. [PMID: 36863426 DOI: 10.1515/reveh-2022-0204] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 11/13/2022] [Indexed: 02/17/2024]
Abstract
With the rapid expansion of industrial scale, an increasing number of fine particulate matter (PM2.5) has bringing health concerns. Although exposure to PM2.5 has been clearly associated with male reproductive toxicity, the exact mechanisms are still unclear. Recent studies demonstrated that exposure to PM2.5 can disturb spermatogenesis through destroying the blood-testis barrier (BTB), consisting of different junction types, containing tight junctions (TJs), gap junctions (GJs), ectoplasmic specialization (ES) and desmosomes. The BTB is one of the tightest blood-tissue barriers among mammals, which isolating germ cells from hazardous substances and immune cell infiltration during spermatogenesis. Therefore, once the BTB is destroyed, hazardous substances and immune cells will enter seminiferous tubule and cause adversely reproductive effects. In addition, PM2.5 also has shown to cause cells and tissues injury via inducing autophagy, inflammation, sex hormones disorder, and oxidative stress. However, the exact mechanisms of the disruption of the BTB, induced by PM2.5, are still unclear. It is suggested that more research is required to identify the potential mechanisms. In this review, we aim to understand the adverse effects on the BTB after exposure to PM2.5 and explore its potential mechanisms, which provides novel insight into accounting for PM2.5-induced BTB injury.
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Affiliation(s)
- Shaokai Zheng
- School of Public Health, Nantong University, Nantong, P. R. China
| | - Lianlian Jiang
- School of Public Health, Nantong University, Nantong, P. R. China
| | - Lianglin Qiu
- School of Public Health, Nantong University, Nantong, P. R. China
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Tang JH, Jian HL, Chan TC. The impact of co-exposure to air and noise pollution on the incidence of metabolic syndrome from a health checkup cohort. Sci Rep 2024; 14:8841. [PMID: 38632465 PMCID: PMC11024131 DOI: 10.1038/s41598-024-59576-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 04/12/2024] [Indexed: 04/19/2024] Open
Abstract
Previous studies have found associations between the incidence of metabolic syndrome (MetS) and exposure to air pollution or road traffic noise. However, investigations on environmental co-exposures are limited. This study aimed to investigate the association between co-exposure to air pollution and road traffic noise and MetS and its subcomponents. Participants living in Taipei City who underwent at least two health checkups between 2010 and 2016 were included in the study. Data were sourced from the MJ Health database, a longitudinal, large-scale cohort in Taiwan. The monthly traffic noise exposure (Lden and Lnight) was computed using a dynamic noise map. Monthly fine particulate data at one kilometer resolution were computed from satellite imagery data. Cox proportional hazards regression models with month as the underlying time scale were used to estimate hazard ratios (HRs) for the impact of PM2.5 and road traffic noise exposure on the risk of developing MetS or its subcomponents. Data from 10,773 participants were included. We found significant positive associations between incident MetS and PM2.5 (HR: 1.88; 95% CI 1.67, 2.12), Lden (HR: 1.10; 95% CI 1.06, 1.15), and Lnight (HR: 1.07; 95% CI 1.02, 1.13) in single exposure models. Results further showed significant associations with an elevated risk of incident MetS in co-exposure models, with HRs of 1.91 (95% CI 1.69, 2.16) and 1.11 (95% CI 1.06, 1.16) for co-exposure to PM2.5 and Lden, and 1.90 (95% CI 1.68, 2.14) and 1.08 (95% CI 1.02, 1.13) for co-exposure to PM2.5 and Lnight. The HRs for the co-exposure models were higher than those for models with only a single exposure. This study provides evidence that PM2.5 and noise exposure may elevate the risk of incident MetS and its components in both single and co-exposure models. Therefore, preventive approaches to mitigate the risk of MetS and its subcomponents should consider reducing exposure to PM2.5 and noise pollution.
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Affiliation(s)
- Jia-Hong Tang
- Research Center for Humanities and Social Sciences, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 115, Taiwan
| | - Hong-Lian Jian
- Research Center for Humanities and Social Sciences, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 115, Taiwan
| | - Ta-Chien Chan
- Research Center for Humanities and Social Sciences, Academia Sinica, 128 Academia Road, Section 2, Nankang, Taipei, 115, Taiwan.
- Institute of Public Health, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan.
- Department of Public Health, College of Public Health, China Medical University, Taichung, Taiwan.
- School of Medicine, College of Medicine, National Sun Yat-Sen University, Kaohsiung, Taiwan.
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Badpa M, Schneider A, Schwettmann L, Thorand B, Wolf K, Peters A. Air pollution, traffic noise, greenness, and temperature and the risk of incident type 2 diabetes: Results from the KORA cohort study. Environ Epidemiol 2024; 8:e302. [PMID: 38617422 PMCID: PMC11008658 DOI: 10.1097/ee9.0000000000000302] [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: 10/17/2023] [Accepted: 02/12/2024] [Indexed: 04/16/2024] Open
Abstract
Introduction Type 2 diabetes (T2D) is a major public health concern, and various environmental factors have been associated with the development of this disease. This study aimed to investigate the longitudinal effects of multiple environmental exposures on the risk of incident T2D in a German population-based cohort. Methods We used data from the KORA cohort study (Augsburg, Germany) and assessed exposure to air pollutants, traffic noise, greenness, and temperature at the participants' residencies. Cox proportional hazard models were used to analyze the associations with incident T2D, adjusting for potential confounders. Results Of 7736 participants included in the analyses, 10.5% developed T2D during follow-up (mean: 15.0 years). We found weak or no association between environmental factors and the risk of T2D, with sex and education level significantly modifying the effects of air pollutants. Conclusion Our study contributes to the growing body of literature investigating the impact of environmental factors on T2D risks and suggests that the impact of environmental factors may be small.
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Affiliation(s)
- Mahnaz Badpa
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Faculty of Medicine, Pettenkofer School of Public Health, LMU Munich, Munich, Germany
| | - Alexandra Schneider
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany
| | - Lars Schwettmann
- Institute of Health Economics and Health Care Management, Helmholtz Zentrum München, Munich, Germany
- Department of Health Services Research, School of Medicine and Health Sciences, Carl von Ossietzky University of Oldenburg, Oldenburg, Germany
| | - Barbara Thorand
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Faculty of Medicine, Pettenkofer School of Public Health, LMU Munich, Munich, Germany
- German Center for Diabetes Research (DZD), Partner München-Neuherberg, Neuherberg, Germany
| | - Kathrin Wolf
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany
| | - Annette Peters
- Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany
- Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Faculty of Medicine, Pettenkofer School of Public Health, LMU Munich, Munich, Germany
- German Center for Diabetes Research (DZD), Partner München-Neuherberg, Neuherberg, Germany
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Fu L, Guo Y, Zhu Q, Chen Z, Yu S, Xu J, Tang W, Wu C, He G, Hu J, Zeng F, Dong X, Yang P, Lin Z, Wu F, Liu T, Ma W. Effects of long-term exposure to ambient fine particulate matter and its specific components on blood pressure and hypertension incidence. ENVIRONMENT INTERNATIONAL 2024; 184:108464. [PMID: 38324927 DOI: 10.1016/j.envint.2024.108464] [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: 10/17/2023] [Revised: 01/10/2024] [Accepted: 01/29/2024] [Indexed: 02/09/2024]
Abstract
BACKGROUND Epidemiological evidence on the association of PM2.5 (particulate matter with aerodynamic diameter ≤ 2.5 μm) and its specific components with hypertension and blood pressure is limited. METHODS We applied information of participants from the World Health Organization's (WHO) Study on Global Ageing and Adult Health (SAGE) to estimate the associations of long-term PM2.5 mass and its chemical components exposure with blood pressure (BP) and hypertension incidence in Chinese adults ≥ 50 years during 2007-2018. Generalized linear mixed model and Cox proportional hazard model were applied to investigate the effects of PM2.5 mass and its chemical components on the incidence of hypertension and BP, respectively. RESULTS Each interquartile range (IQR = 16.80 μg/m3) increase in the one-year average of PM2.5 mass concentration was associated with a 17 % increase in the risk of hypertension (HR = 1.17, 95 % CI: 1.10, 1.24), and the population attributable fraction (PAF) was 23.44 % (95 % CI: 14.69 %, 31.55 %). Each IQR μg/m3 increase in PM2.5 exposure was also related to increases of systolic blood pressure (SBP) by 2.54 mmHg (95 % CI:1.99, 3.10), and of diastolic blood pressure (DBP) by 1.36 mmHg (95 % CI: 1.04, 1.68). Additionally, the chemical components of SO42-, NO3-, NH4+, OM, and BC were also positively associated with an increased risk of hypertension incidence and elevated blood pressure. CONCLUSIONS These results indicate that long-term exposure to PM2.5 mass and its specific components may be major drivers of escalation in hypertension diseases.
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Affiliation(s)
- Li Fu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; Tianhe District Center for Disease Control and Prevention, Guangzhou 510655, China
| | - Yanfei Guo
- Shanghai Municipal Centre for Disease Control and Prevention, Shanghai 200336, China; General Practice/Family Medicine, School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Qijiong Zhu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou 510632, China
| | - Zhiqing Chen
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou 510632, China
| | - Siwen Yu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou 510632, China
| | - Jiahong Xu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou 510632, China
| | - Weiling Tang
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou 510632, China
| | - Cuiling Wu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou 510632, China
| | - Guanhao He
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou 510632, China
| | - Jianxiong Hu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou 510632, China
| | - Fangfang Zeng
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou 510632, China
| | - Xiaomei Dong
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou 510632, China
| | - Pan Yang
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou 510632, China
| | - Ziqiang Lin
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou 510632, China
| | - Fan Wu
- Shanghai Medical College, Fudan University, Shanghai 200032, China.
| | - Tao Liu
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou 510632, China.
| | - Wenjun Ma
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou 510632, China; China Greater Bay Area Research Center of Environmental Health, School of Medicine, Jinan University, Guangzhou 510632, China
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CHEN Q, YOU L, GUAN P, FANG C, QIN W, LIU X, XU G. [Risk analysis of serum chemical residues for metabolic associated fatty liver disease based on exposome-lipidome wide association study]. Se Pu 2024; 42:164-175. [PMID: 38374597 PMCID: PMC10877480 DOI: 10.3724/sp.j.1123.2023.12014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Indexed: 02/21/2024] Open
Abstract
Metabolic associated fatty liver disease (MAFLD) is a common liver disease with a prevalence of up to 25%; it not only adversely affects human health but also aggravates the economic burden of society. An increasing number of studies have suggested that the occurrence of chronic noncommunicable diseases is affected by both environmental exposures and genetic factors. Research has also shown that environmental pollution may increase the risk of MAFLD and promote its occurrence and development. However, the relationship between these concepts, as well as the underlying exposure effects and mechanism, remains incompletely understood. Lipidomics, a branch of metabolomics that studies lipid disorders, can help researchers investigate abnormal lipid metabolites in various disease states. Lipidome-exposome wide association studies are a promising paradigm for investigating the health effects of cumulative environmental exposures on biological responses, and could provide new ideas for determining the associations between metabolic and lipid changes and disease risk caused by chemical-pollutant exposure. Hence, in this study, targeted exposomics and nontargeted lipidomics studies based on ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) and ultra-high performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS) were used to characterize exogenous chemical pollutants and endogenous lipid metabolites in the sera of patients with MAFLD and healthy subjects. The results demonstrated that fipronil sulfone, malathion dicarboxylic acid, and monocyclohexyl phthalate may be positively associated with the disease risk of patients diagnosed as simple fatty liver disease (hereafter referred to as MAFLD(0)). Moreover, fipronil sulfone, acesulfame potassium, perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), perfluoroundecanoic acid (PFUnDA), 4-hydroxybenzophenone, and 3,5-di-tert-butyl-4-hydroxybenzoic acid (DBPOB) may be positively associated with the disease risk of patients diagnosed as fatty liver complicated by single or multiple metabolic disorders. Association analysis was carried out to explore the lipid metabolites induced by chemical residues. Triglyceride (TG) and diglyceride (DG) were significantly increased in MAFLD and MAFLD(0). The numbers of carbons of significantly changed DGs and TGs were mainly in the ranges of 32-40 and 35-60, respectively, and both were mainly characterized by changes in polyunsaturated lipids. Most of the lipid-effect markers were positively correlated with chemical residues and associated with increased disease risk. Our research provides a scientific basis for studies on the association and mechanism between serum chemical-pollutant residues and disease outcomes.
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Tsai HH, Tantoh DM, Lu WY, Chen CY, Liaw YP. Cigarette smoking and PM 2.5 might jointly exacerbate the risk of metabolic syndrome. Front Public Health 2024; 11:1234799. [PMID: 38288423 PMCID: PMC10822970 DOI: 10.3389/fpubh.2023.1234799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 12/27/2023] [Indexed: 01/31/2024] Open
Abstract
Background Cigarette smoking and particulate matter (PM) with aerodynamic diameter < 2.5 μm (PM2.5) are major preventable cardiovascular mortality and morbidity promoters. Their joint role in metabolic syndrome (MS) pathogenesis is unknown. We determined the risk of MS based on PM2.5 and cigarette smoking in Taiwanese adults. Methods The study included 126,366 Taiwanese between 30 and 70 years old with no personal history of cancer. The Taiwan Biobank (TWB) contained information on MS, cigarette smoking, and covariates, while the Environmental Protection Administration (EPA), Taiwan, contained the PM2.5 information. Individuals were categorized as current, former, and nonsmokers. PM2.5 levels were categorized into quartiles: PM2.5 ≤ Q1, Q1 < PM2.5 ≤ Q2, Q2 < PM2.5 ≤ Q3, and PM2.5 > Q3, corresponding to PM2.5 ≤ 27.137, 27.137 < PM2.5 ≤ 32.589, 32.589 < PM2.5 ≤ 38.205, and PM2.5 > 38.205 μg/m3. Results The prevalence of MS was significantly different according to PM2.5 exposure (p-value = 0.0280) and cigarette smoking (p-value < 0.0001). Higher PM2.5 levels were significantly associated with a higher risk of MS: odds ratio (OR); 95% confidence interval (CI) = 1.058; 1.014-1.104, 1.185; 1.134-1.238, and 1.149; 1.101-1.200 for 27.137 < PM2.5 ≤ 32.589, 32.589 < PM2.5 ≤ 38.205, and PM2.5 > 38.205 μg/m3, respectively. The risk of MS was significantly higher among former and current smokers with OR; 95% CI = 1.062; 1.008-1.118 and 1.531; 1.450-1.616, respectively, and a dose-dependent p-value < 0.0001. The interaction between both exposures regarding MS was significant (p-value = 0.0157). Stratification by cigarette smoking revealed a significant risk of MS due to PM2.5 exposure among nonsmokers: OR (95% CI) = 1.074 (1.022-1.128), 1.226 (1.166-1.290), and 1.187 (1.129-1.247) for 27.137 < PM2.5 ≤ 32.589, 32.589 < PM2.5 ≤ 38.205, and PM2.5 > 38.205 μg/m3, respectively. According to PM2.5 quartiles, current smokers had a higher risk of MS, regardless of PM2.5 levels (OR); 95% CI = 1.605; 1.444-1.785, 1.561; 1.409-1.728, 1.359; 1.211-1.524, and 1.585; 1.418-1.772 for PM2.5 ≤ 27.137, 27.137 < PM2.5 ≤ 32.589, 32.589 < PM2.5 ≤ 38.205, and PM2.5 > 38.205 μg/m3, respectively. After combining both exposures, the group, current smokers; PM2.5 > 38.205 μg/m3 had the highest odds (1.801; 95% CI =1.625-1.995). Conclusion PM2.5 and cigarette smoking were independently and jointly associated with a higher risk of MS. Stratified analyses revealed that cigarette smoking might have a much higher effect on MS than PM2.5. Nonetheless, exposure to both PM2.5 and cigarette smoking could compound the risk of MS.
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Affiliation(s)
- Hao-Hung Tsai
- Institute of Medicine, Chung Shan Medical University, Taichung City, Taiwan
- College of Medicine, Chung Shan Medical University, Taichung City, Taiwan
- Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung City, Taiwan
- Department of Medical Imaging, School of Medicine, Chung Shan Medical University, Taichung City, Taiwan
- Department of Medical Imaging and Radiological Sciences, Chung Shan Medical University, Taichung City, Taiwan
| | - Disline Manli Tantoh
- Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung City, Taiwan
- Department of Public Health and Institute of Public Health, Chung Shan Medical University, Taichung City, Taiwan
| | - Wen Yu Lu
- Department of Public Health and Institute of Public Health, Chung Shan Medical University, Taichung City, Taiwan
| | - Chih-Yi Chen
- Institute of Medicine, Chung Shan Medical University, Taichung City, Taiwan
- Division of Thoracic Surgery, Department of Surgery, Chung Shan Medical University Hospital, Taichung City, Taiwan
| | - Yung-Po Liaw
- Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung City, Taiwan
- Department of Public Health and Institute of Public Health, Chung Shan Medical University, Taichung City, Taiwan
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11
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Cai C, Chen Y, Feng C, Shao Y, Ye T, Yu B, Jia P, Yang S. Long-term effects of PM 2.5 constituents on metabolic syndrome and mediation effects of serum uric acid. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 341:122979. [PMID: 37989407 DOI: 10.1016/j.envpol.2023.122979] [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: 06/11/2023] [Revised: 11/14/2023] [Accepted: 11/15/2023] [Indexed: 11/23/2023]
Abstract
Exposure to particulate matter with aerodynamic diameter ≤2.5 μm (PM2.5) was associated with the risk for metabolic syndrome (MetS) in the general population, but the contributions of individual PM2.5 constituents to this association and the potential pathway between PM2.5 constituents and MetS risk are not well elaborated. This study aimed to investigate associations between PM2.5 constituents and MetS in general populations, relative importance of PM2.5 constituents to and mediation effects of serum uric acid (SUA) on those associations. The 48,148 participants from a provincially representative cohort established in southwest China were included. The 3-year average concentrations of PM2.5 and its constituents (nitrate [NO3-], sulfate [SO42-], ammonium [NH4+], organic matter [OM], and black carbon [BC]) were estimated using a series of machine-learning models. Multivariate logistic regression and weighted quantile sum regression were used to estimate effects of independent PM2.5 constituents on MetS and their contributions to the joint effect. Mediation analysis examined the potential mediation effects of SUA on the associations between PM2.5 constituents and MetS. Each interquartile range (IQR) increase in the concentration of PM2.5 constituents was all positively associated with the increased MetS odds, including SO42- (OR = 1.15 [1.11, 1.19]]), NO3- (OR = 1.12 [1.08, 1.16]), NH4+ (OR = 1.13 [1.09, 1.17]), OM (OR = 1.09 [1.06, 1.13]), and BC (OR = 1.09 [1.06, 1.13]). Their joint associations on MetS were mainly attributed to SO42- (weight=46.1%) and NH4+ (44.0%). The associations of PM2.5 constituents with abnormal MetS components were mainly attributed to NH4+ for elevated BP (51.6%) and reduced HDL-C (97.0%), SO42- for elevated FG (68.9%), NO3- for elevated TG (51.0%), and OM for elevated WC (63.0%). Percentages mediated by SUA for the associations of PM2.5, SO42-, NO3-, and BC with MetS were 13.6%, 13.1%, 10.6%, and 11.1%, respectively. Long-term exposure to PM2.5 constituents, mainly NH4+ and SO42-, was positively associated with MetS odds, partially mediated by SUA.
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Affiliation(s)
- Changwei Cai
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Yang Chen
- Yunnan Center for Disease Prevention and Control, Kunming, China; School of Public Health, Kunming Medical University, Kunming, China
| | - Chuanteng Feng
- Institute for Disaster Management and Reconstruction, Sichuan University-The Hong Kong Polytechnic University, Chengdu, China
| | - Ying Shao
- Yunnan Center for Disease Prevention and Control, Kunming, China
| | - Tingting Ye
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Bin Yu
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China; Institute for Disaster Management and Reconstruction, Sichuan University-The Hong Kong Polytechnic University, Chengdu, China
| | - Peng Jia
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, China; Hubei Luojia Laboratory, Wuhan, China; School of Public Health, Wuhan University, Wuhan, China; International Institute of Spatial Lifecourse Health (ISLE), Wuhan University, Wuhan, China
| | - Shujuan Yang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China; International Institute of Spatial Lifecourse Health (ISLE), Wuhan University, Wuhan, China; Department of Health Management Center, Clinical Medical College & Affiliated Hospital, Chengdu University, Chengdu, China.
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12
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Xu F, Wu Q, Yang Y, Zhang L, Yan Z, Li H, Li J, An Z, Wu H, Song J, Wu W. High temperature exacerbates ozone-induced airway inflammation: Implication of airway microbiota and metabolites. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 903:166795. [PMID: 37666337 DOI: 10.1016/j.scitotenv.2023.166795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 09/01/2023] [Accepted: 09/01/2023] [Indexed: 09/06/2023]
Abstract
Short-term exposure to ozone (O3) has been associated with airway inflammation. Given that high temperature (HT) accelerates O3 production, it is of significance to determine whether co-exposure to HT exacerbates O3-induced airway inflammation. The aim of this study was to examine the possible promotive effect of HT on O3-induced airway inflammation and underlying mechanisms. Forty-eight C57BL/6 N male mice were randomly divided into four groups: filtered air (control), O3, HT, and HT + O3 (co-exposure) groups. Mice in control and O3 groups were exposed to filtered air or 1 ppm O3 at 24 °C, respectively, while mice in HT and co-exposure groups were exposed to filtered air or 1 ppm O3 at 36 °C, respectively. The exposure scenario for four groups was 4 h/d for 5 consecutive days. Bronchoalveolar lavage fluids (BALF) were collected 24 h after the last exposure and subjected to examinations of oxidative stress and inflammation biomarkers, 16S rRNA sequencing, and metabolic profiling. Lung tissues were processed for H&E histological staining. The results showed that O3 inhalation triggered oxidative stress and inflammation in the airways, which was worsen by co-exposure to HT. Further studies revealed that co-exposure to HT strengthened O3-induced decline in Firmicutes and Allobaculum in airways. Moreover, co-exposure to HT promoted O3-induced airway metabolic disorder. Spearman correlation analysis revealed correlations among microbiota dysbiosis, metabolic disorder, oxidative stress and inflammation induced by co-exposure to HT and O3. Taken together, HT exposure aggravates O3-induced airway oxidative stress and inflammation, possibly through modulation of microbiota and metabolism of the airways.
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Affiliation(s)
- Fei Xu
- School of Public Health, Xinxiang Medical University, Xinxiang, China
| | - Qiong Wu
- School of Public Health, Xinxiang Medical University, Xinxiang, China
| | - Yishu Yang
- School of Public Health, Xinxiang Medical University, Xinxiang, China
| | - Ling Zhang
- School of Public Health, Xinxiang Medical University, Xinxiang, China
| | - Zhen Yan
- International School of Public Health and One Health, Hainan Medical University, Haikou, China
| | - Huijun Li
- School of Public Health, Xinxiang Medical University, Xinxiang, China
| | - Juan Li
- School of Public Health, Xinxiang Medical University, Xinxiang, China
| | - Zhen An
- School of Public Health, Xinxiang Medical University, Xinxiang, China
| | - Hui Wu
- School of Public Health, Xinxiang Medical University, Xinxiang, China
| | - Jie Song
- School of Public Health, Xinxiang Medical University, Xinxiang, China
| | - Weidong Wu
- School of Public Health, Xinxiang Medical University, Xinxiang, China.
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Paoin K, Pharino C, Vathesatogkit P, Phosri A, Buya S, Ueda K, Seposo XT, Ingviya T, Saranburut K, Thongmung N, Yingchoncharoen T, Sritara P. Associations between residential greenness and air pollution and the incident metabolic syndrome in a Thai worker cohort. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2023; 67:1965-1974. [PMID: 37735284 DOI: 10.1007/s00484-023-02554-9] [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: 05/05/2023] [Revised: 07/25/2023] [Accepted: 09/12/2023] [Indexed: 09/23/2023]
Abstract
Increasing air pollution and decreasing exposure to greenness may contribute to the metabolic syndrome (MetS). We examined associations between long-term exposure to residential greenness and air pollution and MetS incidence in the Bangkok Metropolitan Region, Thailand. Data from 1369 employees (aged 52-71 years) from the Electricity Generating Authority of Thailand cohort from 2002 to 2017 were analyzed. The greenness level within 500 m of each participant's residence was measured using the satellite-derived Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI). The kriging approach was used to generate the average concentration of each air pollutant (PM10, CO, SO2, NO2, and O3) at the sub-district level. The average long-term exposure to air pollution and greenness for each participant was calculated over the same period of person-time. Cox proportional hazards models were used to analyze the greenness-air pollution-MetS associations. The adjusted hazard ratio of MetS was 1.42 (95% confidence interval (CI): 1.32, 1.53), 1.22 (95% CI: 1.15, 1.30), and 2.0 (95% CI: 1.82, 2.20), per interquartile range increase in PM10 (9.5 μg/m3), SO2 (0.9 ppb), and CO (0.3 ppm), respectively. We found no clear association between NDVI or EVI and the incidence of MetS. On the contrary, the incident MetS was positively associated with NDVI and EVI for participants exposed to PM10 at concentrations more than 50 μg/m3. In summary, the incidence of MetS was positively associated with long-term exposure to air pollution. In areas with high levels of air pollution, green spaces may not benefit health outcomes.
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Affiliation(s)
- Kanawat Paoin
- Department of Environmental Engineering, Faculty of Engineering, Chulalongkorn University, Phayathai Rd., Wangmai, Pratumwan, Bangkok, 10330, Thailand.
| | - Chanathip Pharino
- Department of Environmental Engineering, Faculty of Engineering, Chulalongkorn University, Phayathai Rd., Wangmai, Pratumwan, Bangkok, 10330, Thailand.
| | - Prin Vathesatogkit
- Department of Internal Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Arthit Phosri
- Department of Environmental Health Sciences, Faculty of Public Health, Mahidol University, Bangkok, Thailand
| | - Suhaimee Buya
- School of Information, Computer and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Khlong Nueng, Pathum Thani, Thailand
- School of Knowledge Science, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan
| | - Kayo Ueda
- Department of Hygiene, Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
- Department of Environmental Engineering, Graduate School of Engineering, Kyoto University, Kyoto, Japan
- Graduate School of Global Environmental Sciences, Kyoto University, Kyoto, Japan
| | - Xerxes Tesoro Seposo
- Department of Hygiene, Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Thammasin Ingviya
- Air Pollution and Health Effect Research Center, Prince of Songkla University, Songkhla, Thailand
- Medical Data Center for Research and Innovation, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand
| | - Krittika Saranburut
- Cardiovascular and Metabolic Center, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Nisakron Thongmung
- Research Center, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Teerapat Yingchoncharoen
- Department of Internal Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
| | - Piyamitr Sritara
- Department of Internal Medicine, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand
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14
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Jeong Y, Park S, Kwon E, Hur YM, You YA, Kim SM, Lee G, Lee KA, Kim SJ, Cho GJ, Oh MJ, Na SH, Lee SJ, Bae JG, Kim YH, Lee SJ, Kim YH, Kim YJ. Personal exposure of PM 2.5 and metabolic syndrome markers of pregnant women in South Korea: APPO study. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:123893-123906. [PMID: 37996573 PMCID: PMC10746774 DOI: 10.1007/s11356-023-30921-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Accepted: 11/02/2023] [Indexed: 11/25/2023]
Abstract
We examined the association between exposure to PM2.5, focused on individual exposure level, and metabolic dysfunction during pregnancy. APPO study (Air Pollution on Pregnancy Outcome) was a prospective, multicenter, observational cohort study conducted from January 2021 to March 2023. Individual PM2.5 concentrations were calculated using a time-weighted average model. Metabolic dysfunction during pregnancy was assessed based on a modified definition of metabolic syndrome and its components, accounting for pregnancy-specific criteria. Exposure to PM2.5 during pregnancy was associated with worsened metabolic parameters especially glucose metabolism. In comparison to participants exposed to the low PM2.5 group, those exposed to high PM2.5 levels exhibited increased odds of gestational diabetes mellitus (GDM) after adjusting for confounding variables in different adjusted models. Specifically, in model 1, the adjusted odds ratio (aOR) was 3.117 with a 95% confidence interval (CI) of 1.234-7.870; in model 2, the aOR was 3.855 with a 95% CI of 1.255-11.844; in model 3, the aOR was 3.404 with a 95% CI of 1.206-9.607; and in model 4, the aOR was 2.741 with a 95% CI of 0.712-10.547. Exposure to higher levels of PM2.5 during pregnancy was associated with a tendency to worsen metabolic dysfunction markers specifically in glucose homeostasis. Further research is needed to investigate the mechanisms underlying the effects of ambient PM2.5 on metabolic dysfunction during pregnancy.
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Affiliation(s)
- Yeonseong Jeong
- Department of Obstetrics and Gynecology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea
| | - Sunwha Park
- Department of Obstetrics and Gynecology, College of Medicine, Ewha Medical Research Institute, Ewha Womans University Mokdong Hospital, Seoul, Korea
| | - Eunjin Kwon
- Department of Obstetrics and Gynecology, College of Medicine, Ewha Medical Research Institute, Ewha Womans University Mokdong Hospital, Seoul, Korea
| | - Young Min Hur
- Department of Obstetrics and Gynecology, College of Medicine, Ewha Medical Research Institute, Ewha Womans University Mokdong Hospital, Seoul, Korea
| | - Young-Ah You
- Department of Obstetrics and Gynecology, College of Medicine, Ewha Medical Research Institute, Ewha Womans University Mokdong Hospital, Seoul, Korea
| | - Soo Min Kim
- Department of Obstetrics and Gynecology, College of Medicine, Ewha Medical Research Institute, Ewha Womans University Mokdong Hospital, Seoul, Korea
- Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, Republic of Korea
| | - Gain Lee
- Department of Obstetrics and Gynecology, College of Medicine, Ewha Medical Research Institute, Ewha Womans University Mokdong Hospital, Seoul, Korea
- Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, Republic of Korea
| | - Kyung A Lee
- Department of Obstetrics and Gynecology, College of Medicine, Ewha Womans University Seoul Hospital, Seoul, Korea
| | - Soo Jung Kim
- Department of Obstetrics and Gynecology, College of Medicine, Ewha Womans University Seoul Hospital, Seoul, Korea
| | - Geum Joon Cho
- Department of Obstetrics and Gynecology, College of Medicine, Korea University Guro Hospital, Seoul, Korea
| | - Min-Jeong Oh
- Department of Obstetrics and Gynecology, College of Medicine, Korea University Guro Hospital, Seoul, Korea
| | - Sung Hun Na
- Department of Obstetrics and Gynecology, College of Medicine, Kangwon National University Hospital, Chuncheon, Korea
| | - Se Jin Lee
- Department of Obstetrics and Gynecology, College of Medicine, Kangwon National University Hospital, Chuncheon, Korea
| | - Jin-Gon Bae
- Department of Obstetrics and Gynecology, College of Medicine, Keimyung University Dongsan Medical Center, Daegu, Korea
| | - Yu-Hwan Kim
- Department of Obstetrics and Gynecology, College of Medicine, Keimyung University Dongsan Medical Center, Daegu, Korea
| | - Soo-Jeong Lee
- Department of Obstetrics and Gynecology, Ulsan university hospital, University of Ulsan College of Medicine Ulsan, Ulsan, Korea
| | - Young-Han Kim
- Department of Obstetrics and Gynecology, Institute of Women's Life Medical Science, Yonsei University College of Medicine, Yonsei University Health System, Seoul, Republic of Korea
| | - Young Ju Kim
- Department of Obstetrics and Gynecology, College of Medicine, Ewha Medical Research Institute, Ewha Womans University Mokdong Hospital, Seoul, Korea.
- Graduate Program in System Health Science and Engineering, Ewha Womans University, Seoul, Republic of Korea.
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15
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Xiao Y, Liu C, Lei R, Wang Z, Wang X, Tian H, Xue B, Zhou E, Zhang K, Hu J, Luo B. Associations of PM 2.5 composition and green space with metabolic syndrome in a Chinese essential hypertensive population. CHEMOSPHERE 2023; 343:140243. [PMID: 37742756 DOI: 10.1016/j.chemosphere.2023.140243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 08/16/2023] [Accepted: 09/20/2023] [Indexed: 09/26/2023]
Abstract
BACKGROUND Metabolic syndrome (MetS) has emerged as a significant global public health concern. While environmental factors, including PM2.5, have been identified as important risk factors for MetS in the general population, limited studies have investigated their impact on individuals with essential hypertension. Therefore, our study aims to explore the relationship between PM2.5 composition, green space, and their combined effects on MetS among a Chinese essential hypertensive population. METHOD A total of 20,131 participants diagnosed with essential hypertension from 10 provinces in China were included in this study. Individual level exposure to various environmental factors (including PM2.5, PM2.5 composition, green space and temperature) were evaluated using spatiotemporal models based on satellites data. Participants were defined as MetS according to the definition issued by the International Diabetes Federation. Generalized additive mixed models were used to analyze the individual air pollutants, green space and their interaction on MetS. RESULT The prevalence of MetS in this population was 44.33%. The adjusted odd ratio (OR) of MetS, with each one unit increase in SO42-, BC and NO3- were 1.077 (1.049, 1.106), 1.126 (1.077, 1.177) and 0.977 (0.958, 0.996) respectively. Additionally, each unit increase of the Normalized Difference Vegetation Index (NDVI) was associated with a decreased risk of MetS (OR: 0.988, 95% CI: 0.984-0.993). In particular, green space was found to mitigate the adverse impacts of PM2.5 on MetS (OR: 0.988, 95% CI: 0.984-0.993). CONCLUSION Our results suggested that there was a positive association between PM2.5 and its composition (SO42-, BC) with MetS in the essential hypertensive population, while green space might play a protective role. Moreover, green space could effectively weaken the positive relationship between air pollutants and MetS, especially in males and participants younger than 60 years old.
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Affiliation(s)
- Ya Xiao
- Institute of Occupational Health and Environmental Health, School of Public Health, Lanzhou University, Lanzhou, Gansu, 730000, China
| | - Ce Liu
- Institute of Occupational Health and Environmental Health, School of Public Health, Lanzhou University, Lanzhou, Gansu, 730000, China
| | - Ruoyi Lei
- Institute of Occupational Health and Environmental Health, School of Public Health, Lanzhou University, Lanzhou, Gansu, 730000, China
| | - Zengwu Wang
- Division of Prevention and Community Health, National Center for Cardiovascular Disease, Fuwai Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing, 100037, China
| | - Xin Wang
- Division of Prevention and Community Health, National Center for Cardiovascular Disease, Fuwai Hospital, Peking Union Medical College & Chinese Academy of Medical Sciences, Beijing, 100037, China
| | - Hezhong Tian
- State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing, 100875, China
| | - Baode Xue
- Institute of Occupational Health and Environmental Health, School of Public Health, Lanzhou University, Lanzhou, Gansu, 730000, China
| | - Erkai Zhou
- Institute of Occupational Health and Environmental Health, School of Public Health, Lanzhou University, Lanzhou, Gansu, 730000, China
| | - Kai Zhang
- Department of Environmental Health Sciences, School of Public Health, University at Albany, State University of New York, One University Place, Rensselaer, NY, 12144, USA.
| | - Jihong Hu
- School of Public Health, Gansu University of Chinese Medicine, Lanzhou, Gansu, 730000, China.
| | - Bin Luo
- Institute of Occupational Health and Environmental Health, School of Public Health, Lanzhou University, Lanzhou, Gansu, 730000, China.
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Zhang T, Ni M, Jia J, Deng Y, Sun X, Wang X, Chen Y, Fang L, Zhao H, Xu S, Ma Y, Zhu J, Pan F. Research on the relationship between common metabolic syndrome and meteorological factors in Wuhu, a subtropical humid city of China. BMC Public Health 2023; 23:2363. [PMID: 38031031 PMCID: PMC10685562 DOI: 10.1186/s12889-023-17299-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 11/22/2023] [Indexed: 12/01/2023] Open
Abstract
As climate conditions deteriorate, human health faces a broader range of threats. This study aimed to determine the risk of death from metabolic syndrome (MetS) due to meteorological factors. We collected daily data from 2014 to 2020 in Wuhu City, including meteorological factors, environmental pollutants and death data of common MetS (hypertension, hyperlipidemia and diabetes), as well as a total number of 15,272 MetS deaths. To examine the relationship between meteorological factors, air pollutants, and MetS mortality, we used a generalized additive model (GAM) combined with a distributed delay nonlinear model (DLNM) for time series analysis. The relationship between the above factors and death outcomes was preliminarily evaluated using Spearman analysis and structural equation modeling (SEM). As per out discovery, diurnal temperature range (DTR) and daily mean temperature (T mean) increased the MetS mortality risk notably. The ultra low DTR raised the MetS mortality risk upon the general people, with the highest RR value of 1.033 (95% CI: 1.002, 1.065) at lag day 14. In addition, T mean was also significantly associated with MetS death. The highest risk of ultra low and ultra high T mean occured on the same day (lag 14), RR values were 1.043 (95% CI: 1.010, 1.077) and 1.032 (95% CI: 1.003, 1.061) respectively. Stratified analysis's result showed lower DTR had a more pronounced effect on women and the elderly, and ultra low and high T mean was a risk factor for MetS mortality in women and men. The elderly need to take extra note of temperature changes, and different levels of T mean will increase the risk of death. In warm seasons, ultra high RH and T mean can increase the mortality rate of MetS patients.
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Affiliation(s)
- Tao Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
- The Key Laboratory of Major Autoimmune Diseases, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
| | - Man Ni
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
- The Key Laboratory of Major Autoimmune Diseases, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
| | - Juan Jia
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
- The Key Laboratory of Major Autoimmune Diseases, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
| | - Yujie Deng
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
- Department of Hospital Management Research, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230032, China
| | - Xiaoya Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
- Department of Hospital Management Research, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230032, China
| | - Xinqi Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
- Department of Hospital Management Research, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230032, China
| | - Yuting Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
- The Key Laboratory of Major Autoimmune Diseases, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
| | - Lanlan Fang
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
- The Key Laboratory of Major Autoimmune Diseases, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
| | - Hui Zhao
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
- Department of Hospital Management Research, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230032, China
| | - Shanshan Xu
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
- The Key Laboratory of Major Autoimmune Diseases, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
| | - Yubo Ma
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
- The Key Laboratory of Major Autoimmune Diseases, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China
| | - Jiansheng Zhu
- Wuhu center for disease control and prevention, Wuhu, Anhui, China
| | - Faming Pan
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China.
- The Key Laboratory of Major Autoimmune Diseases, Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China.
- Anhui Medical University, 81 Meishan Road, Hefei, Anhui, 230032, China.
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Sun Y, Zhang M, Wu W, Liu R, Zhang Y, Su S, Zhang E, Sun L, Yue W, Wu Q, Chen G, Zhang W, Yin C. Ambient cold exposure amplifies the effect of ambient PM 1 on blood pressure and hypertensive disorders of pregnancy among Chinese pregnant women: A nationwide cohort study. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 897:165234. [PMID: 37400028 DOI: 10.1016/j.scitotenv.2023.165234] [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: 09/03/2022] [Revised: 05/05/2023] [Accepted: 06/28/2023] [Indexed: 07/05/2023]
Abstract
BACKGROUND Little evidence exists regarding the combined effect between ambient temperature and air pollution exposure on maternal blood pressure (BP) and hypertensive disorders of pregnancy (HDP). OBJECTIVES To assess effect modification by temperature exposure on the PM1-BP/HDP associations among Chinese pregnant women based on a nationwide study. METHODS We conducted a cross-sectional country-based population study in China, enrolling 86,005 participants from November 2017 to December 2021. BP was measured with standardized sphygmomanometers. HDP was defined according to the American College of Obstetricians and Gynecologists' recommendations. Daily temperature data were obtained from the European Centre for Medium-Range Weather Forecasts. PM1 concentrations were evaluated using generalized additive model. Generalized linear mixed models were used to examine the health effects, controlling for multiple covariates. We also performed a series of stratified and sensitivity analyses. RESULTS The pro-hypertensive effect of PM1 was observed in the first trimester. Cold exposure amplifies the first-trimester PM1-BP/HDP associations, with adjusted estimate (aβ) for systolic blood pressure (SBP) of 3.038 (95 % CI: 2.320-3.755), aβ for diastolic blood pressure (DBP) of 2.189 (95 % CI: 1.503-2.875), and aOR for HDP of 1.392 (95 % CI: 1.160-1.670). Pregnant women who were educated longer than 17 years or living in urban areas appeared to be more vulnerable to the modification in the first trimester. These findings remained robust after sensitivity analyses. CONCLUSIONS First trimester maybe the critical exposure window for the PM1-BP/HDP associations among Chinese pregnant women. Cold exposure amplifies the associations, and those with higher education level or living in urban areas appeared to be more vulnerable.
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Affiliation(s)
- Yongqing Sun
- Prenatal Diagnosis Center, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing 100026, China
| | - Man Zhang
- Central Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing 100026, China
| | - Wenjing Wu
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong 510080, China
| | - Ruixia Liu
- Central Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing 100026, China
| | - Yue Zhang
- Central Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing 100026, China
| | - Shaofei Su
- Central Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing 100026, China
| | - Enjie Zhang
- Central Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing 100026, China
| | - Lijuan Sun
- Department of Ultrasound, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing 100026, China
| | - Wentao Yue
- Central Laboratory, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing 100026, China
| | - Qingqing Wu
- Department of Ultrasound, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing 100026, China.
| | - Gongbo Chen
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne VIC3004, Australia.
| | - Wangjian Zhang
- Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong 510080, China.
| | - Chenghong Yin
- Prenatal Diagnosis Center, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing Maternal and Child Health Care Hospital, Beijing 100026, China.
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Zhao Y, Shen G, Lin X, Zhang L, Fan F, Zhang Y, Li J. Identifying the Relationship between PM 2.5 and Hyperlipidemia Using Mendelian Randomization, RNA-seq Data and Model Mice Subjected to Air Pollution. TOXICS 2023; 11:823. [PMID: 37888673 PMCID: PMC10611378 DOI: 10.3390/toxics11100823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 09/19/2023] [Accepted: 09/25/2023] [Indexed: 10/28/2023]
Abstract
Air pollution is an important public health problem that endangers human health. However, the casual association and pathogenesis between particles < 2.5 μm (PM2.5) and hyperlipidemia remains incompletely unknown. Mendelian randomization (MR) and transcriptomic data analysis were performed, and an air pollution model using mice was constructed to investigate the association between PM2.5 and hyperlipidemia. MR analysis demonstrated that PM2.5 is associated with hyperlipidemia and the triglyceride (TG) level in the European population (IVW method of hyperlipidemia: OR: 1.0063, 95%CI: 1.0010-1.0118, p = 0.0210; IVW method of TG level: OR: 1.1004, 95%CI: 1.0067-1.2028, p = 0.0350). Mest, Adipoq, Ccl2, and Pcsk9 emerged in the differentially expressed genes of the liver and plasma of PM2.5 model mice, which might mediate atherosclerosis accelerated by PM2.5. The studied animal model shows that the Paigen Diet (PD)-fed male LDLR-/- mice had higher total cholesterol (TC), TG, and CM/VLDL cholesterol levels than the control group did after 10 times 5 mg/kg PM2.5 intranasal instillation once every three days. Our study revealed that PM2.5 had causality with hyperlipidemia, and PM2.5 might affect liver secretion, which could further regulate atherosclerosis. The lipid profile of PD-fed Familial Hypercholesterolemia (FH) model mice is more likely to be jeopardized by PM2.5 exposure.
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Affiliation(s)
- Yixue Zhao
- Division of Cardiology, Peking University First Hospital, Beijing 100034, China; (Y.Z.); (G.S.); (X.L.); (L.Z.); (F.F.); (Y.Z.)
| | - Geng Shen
- Division of Cardiology, Peking University First Hospital, Beijing 100034, China; (Y.Z.); (G.S.); (X.L.); (L.Z.); (F.F.); (Y.Z.)
| | - Xipeng Lin
- Division of Cardiology, Peking University First Hospital, Beijing 100034, China; (Y.Z.); (G.S.); (X.L.); (L.Z.); (F.F.); (Y.Z.)
| | - Long Zhang
- Division of Cardiology, Peking University First Hospital, Beijing 100034, China; (Y.Z.); (G.S.); (X.L.); (L.Z.); (F.F.); (Y.Z.)
| | - Fangfang Fan
- Division of Cardiology, Peking University First Hospital, Beijing 100034, China; (Y.Z.); (G.S.); (X.L.); (L.Z.); (F.F.); (Y.Z.)
| | - Yan Zhang
- Division of Cardiology, Peking University First Hospital, Beijing 100034, China; (Y.Z.); (G.S.); (X.L.); (L.Z.); (F.F.); (Y.Z.)
| | - Jianping Li
- Division of Cardiology, Peking University First Hospital, Beijing 100034, China; (Y.Z.); (G.S.); (X.L.); (L.Z.); (F.F.); (Y.Z.)
- Institute of Cardiovascular Disease, Peking University First Hospital, Beijing 100034, China
- State Key Laboratory of Vascular Homeostasis and Remodeling, Beijing 100191, China
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Huang Y, Wu S, Luo H, Yang Y, Xu J, Zhang Y, Wang Q, Shen H, Zhang Y, Yan D, Jiang L, Zhang H, Chen R, Kan H, Cai J, He Y, Ma X. Association of Fine Particulate Matter and Its Components with Macrosomia: A Nationwide Birth Cohort Study of 336 Chinese Cities. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:11465-11475. [PMID: 37493575 DOI: 10.1021/acs.est.3c03280] [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] [Indexed: 07/27/2023]
Abstract
To examine the associations between macrosomia risk and exposure to fine particulate matter (PM2.5) and its chemical components during pregnancy, we collected birth records between 2010 and 2015 in mainland China from the National Free Preconception Health Examination Project and used satellite-based models to estimate concentrations of PM2.5 mass and five main components, namely, black carbon (BC), organic carbon (OC), nitrate (NO3-), sulfate (SO42-), and ammonium (NH4+). Associations between macrosomia risk and prenatal exposure to PM2.5 were examined by logistic regression analysis, and the sensitive subgroups were explored by stratified analyses. Of the 3,248,263 singleton newborns from 336 cities, 165,119 (5.1%) had macrosomia. Each interquartile range increase in concentration of PM2.5 during the entire pregnancy was associated with increased risk of macrosomia (odds ratio (OR) = 1.18; 95% confidence interval (CI), 1.17-1.20). Among specific components, the largest effect estimates were found on NO3- (OR = 1.36; 95% CI, 1.35-1.38) followed by OC (OR = 1.23; 95% CI, 1.22-1.24), NH4+ (OR = 1.22; 95% CI, 1.21-1.23), and BC (OR = 1.21; 95% CI, 1.20-1.22). We also that found boys, women with a normal or lower prepregnancy body mass index, and women with irregular or no folic acid supplementation experienced higher risk of macrosomia associated with PM2.5 exposure.
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Affiliation(s)
- Yuxin Huang
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai 200032, China
| | - Shenpeng Wu
- National Human Genetic Resources Center, National Research Institute for Family Planning, Beijing 100081, China
- Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Huihuan Luo
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai 200032, China
| | - Ying Yang
- National Human Genetic Resources Center, National Research Institute for Family Planning, Beijing 100081, China
| | - Jihong Xu
- National Human Genetic Resources Center, National Research Institute for Family Planning, Beijing 100081, China
| | - Ya Zhang
- National Human Genetic Resources Center, National Research Institute for Family Planning, Beijing 100081, China
| | - Qiaomei Wang
- Department of Maternal and Child Health, National Health Commission of the People's Republic of China, Beijing 100088, China
| | - Haiping Shen
- Department of Maternal and Child Health, National Health Commission of the People's Republic of China, Beijing 100088, China
| | - Yiping Zhang
- Department of Maternal and Child Health, National Health Commission of the People's Republic of China, Beijing 100088, China
| | - Donghai Yan
- Department of Maternal and Child Health, National Health Commission of the People's Republic of China, Beijing 100088, China
| | - Lifang Jiang
- National Health Commission Key Laboratory of Birth Defects Prevention, Henan Key Laboratory of Population Defects Prevention, Henan 450002, China
| | - Hongping Zhang
- Wenzhou People's Hospital/Wenzhou Maternal and Child Health Care Hospital/The Third Clinical Institute Affiliated to Wenzhou Medical University/The Third Affiliated Hospital of Shanghai University, Wenzhou, Zhejiang 325000, China
| | - Renjie Chen
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai 200032, China
| | - Haidong Kan
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai 200032, China
| | - Jing Cai
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai 200032, China
| | - Yuan He
- National Human Genetic Resources Center, National Research Institute for Family Planning, Beijing 100081, China
- Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
| | - Xu Ma
- National Human Genetic Resources Center, National Research Institute for Family Planning, Beijing 100081, China
- Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China
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Yuan C, Liu F, Huang K, Shen C, Li J, Liang F, Yang X, Cao J, Chen S, Hu D, Huang J, Liu Y, Lu X, Gu D. Association of Long-Term Exposure to Ambient Fine Particulate Matter with Atherosclerotic Cardiovascular Disease Incidence Varies across Populations with Different Predicted Risks: The China-PAR Project. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023. [PMID: 37368969 DOI: 10.1021/acs.est.3c01460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/29/2023]
Abstract
Previous studies have established a significant link between ambient fine particulate matter (PM2.5) exposure and atherosclerotic cardiovascular disease (ASCVD) incidence, but whether this association varies across populations with different predicted ASCVD risks was uncertain previously. We included 109,374 Chinese adults without ASCVD at baseline from the Prediction for Atherosclerotic Cardiovascular Disease Risk in China (China-PAR) project. We obtained PM2.5 data of participants' residential address from 2000 to 2015 using a satellite-based spatiotemporal model. Participants were classified into low-to-medium and high-risk groups according to the ASCVD 10-year and lifetime risk prediction scores. Hazard ratios (HRs) and 95% confidence intervals (CIs) for PM2.5 exposure-related incident ASCVD, as well as the multiplication and additive interaction, were calculated using stratified Cox proportional hazard models. The additive interaction between risk stratification and PM2.5 exposure was estimated by the synergy index (SI), the attributable proportion due to the interaction (API), and the relative excess risk due to interaction (RERI). Over the follow-up of 833,067 person-years, a total of 4230 incident ASCVD cases were identified. Each 10 μg/m3 increment of PM2.5 concentration was associated with 18% (HR: 1.18; 95% CI: 1.14-1.23) increased risk of ASCVD in the total population, and the association was more pronounced among individuals having a high predicted ASCVD risk than those having a low-to-medium risk, with the HR (95% CI) of 1.24 (1.19-1.30) and 1.11 (1.02-1.20) per 10 μg/m3 increment in PM2.5 concentration, respectively. The RERI, API, and SI were 1.22 (95% CI: 0.62-1.81), 0.22 (95% CI: 0.12-0.32), and 1.37 (95% CI: 1.16-1.63), respectively. Our findings demonstrate a significant synergistic effect on ASCVD between ASCVD risk stratification and PM2.5 exposure and highlight the potential health benefits of reducing PM2.5 exposure in Chinese, especially among those with high ASCVD risk.
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Affiliation(s)
- Chenxi Yuan
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
- Key Laboratory of Cardiovascular Epidemiology, Chinese Academy of Medical Sciences, Beijing 100037, China
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Fangchao Liu
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
- Key Laboratory of Cardiovascular Epidemiology, Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Keyong Huang
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
- Key Laboratory of Cardiovascular Epidemiology, Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Chong Shen
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
- Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Jianxin Li
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
- Key Laboratory of Cardiovascular Epidemiology, Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Fengchao Liang
- School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen 518055, China
| | - Xueli Yang
- Tianjin Key Laboratory of Environment, Nutrition and Public Health; Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, Tianjin 300070, China
| | - Jie Cao
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
- Key Laboratory of Cardiovascular Epidemiology, Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Shufeng Chen
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
- Key Laboratory of Cardiovascular Epidemiology, Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Dongsheng Hu
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China
- Department of Epidemiology and Health Statistics, School of Public Health, Shenzhen University Health Science Center, Shenzhen 518071, China
| | - Jianfeng Huang
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
- Key Laboratory of Cardiovascular Epidemiology, Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Yang Liu
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322 United States
| | - Xiangfeng Lu
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
- Key Laboratory of Cardiovascular Epidemiology, Chinese Academy of Medical Sciences, Beijing 100037, China
| | - Dongfeng Gu
- Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China
- Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China
- Key Laboratory of Cardiovascular Epidemiology, Chinese Academy of Medical Sciences, Beijing 100037, China
- School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen 518055, China
- School of Medicine, Southern University of Science and Technology, Shenzhen 518055, China
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Liao J, Goodrich J, Walker DI, Lin Y, Lurmann F, Qiu C, Jones DP, Gilliland F, Chazi L, Chen Z. Metabolic pathways altered by air pollutant exposure in association with lipid profiles in young adults. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 327:121522. [PMID: 37019258 PMCID: PMC10243191 DOI: 10.1016/j.envpol.2023.121522] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 02/14/2023] [Accepted: 03/26/2023] [Indexed: 06/08/2023]
Abstract
Mounting evidence suggests that air pollution influences lipid metabolism and dyslipidemia. However, the metabolic mechanisms linking air pollutant exposure and altered lipid metabolism is not established. In year 2014-2018, we conducted a cross-sectional study on 136 young adults in southern California, and assessed lipid profiles (triglycerides, total cholesterol, high-density lipoprotein (HDL)-cholesterol, low-density lipoprotein (LDL)-cholesterol, very-low-density lipoprotein (VLDL)-cholesterol), and untargeted serum metabolomics using liquid chromatography-high-resolution mass spectrometry, and one-month and one-year averaged exposures to NO2, O3, PM2.5 and PM10 air pollutants at residential addresses. A metabolome-wide association analysis was conducted to identify metabolomic features associated with each air pollutant. Mummichog pathway enrichment analysis was used to assess altered metabolic pathways. Principal component analysis (PCA) was further conducted to summarize 35 metabolites with confirmed chemical identity. Lastly, linear regression models were used to analyze the associations of metabolomic PC scores with each air pollutant exposure and lipid profile outcome. In total, 9309 metabolomic features were extracted, with 3275 features significantly associated with exposure to one-month or one-year averaged NO2, O3, PM2.5 and PM10 (p < 0.05). Metabolic pathways associated with air pollutants included fatty acid, steroid hormone biosynthesis, tryptophan, and tyrosine metabolism. PCA of 35 metabolites identified three main PCs which together explained 44.4% of the variance, representing free fatty acids and oxidative byproducts, amino acids and organic acids. Linear regression indicated that the free fatty acids and oxidative byproducts-related PC score was associated with air pollutant exposure and outcomes of total cholesterol and LDL-cholesterol (p < 0.05). This study suggests that exposure to NO2, O3, PM2.5 and PM10 contributes to increased level of circulating free fatty acids, likely through increased adipose lipolysis, stress hormone and response to oxidative stress pathways. These alterations were associated with dysregulation of lipid profiles and potentially could contribute to dyslipidemia and other cardiometabolic disorders.
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Affiliation(s)
- Jiawen Liao
- Department of Population and Public Health Sciences, Keck School of Medicine of the University of Southern California, Los Angeles, CA, United States
| | - Jesse Goodrich
- Department of Population and Public Health Sciences, Keck School of Medicine of the University of Southern California, Los Angeles, CA, United States
| | - Douglas I Walker
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, United States
| | - Yan Lin
- Duke Global Health Institute, Duke University, Durham, NC, United States
| | - Fred Lurmann
- Sonoma Technology Inc., Petaluma, CA, United States
| | - Chenyu Qiu
- Department of Population and Public Health Sciences, Keck School of Medicine of the University of Southern California, Los Angeles, CA, United States
| | - Dean P Jones
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, United States
| | - Frank Gilliland
- Department of Population and Public Health Sciences, Keck School of Medicine of the University of Southern California, Los Angeles, CA, United States
| | - Lida Chazi
- Department of Population and Public Health Sciences, Keck School of Medicine of the University of Southern California, Los Angeles, CA, United States
| | - Zhanghua Chen
- Department of Population and Public Health Sciences, Keck School of Medicine of the University of Southern California, Los Angeles, CA, United States.
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22
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Yan Z, Li S, Chen R, Xie H, Wu M, Nan N, Xing Q, Yun Y, Qin G, Sang N. Effects of differential regional PM 2.5 induced hepatic steatosis and underlying mechanism. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 323:121220. [PMID: 36746292 DOI: 10.1016/j.envpol.2023.121220] [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: 11/03/2022] [Revised: 01/28/2023] [Accepted: 02/04/2023] [Indexed: 06/18/2023]
Abstract
Emerging evidence suggests that exposure to PM2.5 is associated with a high risk of nonalcoholic fatty liver disease (NAFLD). NAFLD is typically characterised by hepatic steatosis. However, the underlying mechanisms and critical components of PM2.5-induced hepatic steatosis remain to be elucidated. In this study, ten-month-old C57BL/6 female mice were exposed to PM2.5 from four cities in China (Taiyuan, Beijing, Hangzhou, and Guangzhou) via oropharyngeal aspiration every other day for four weeks. After the exposure period, hepatic lipid accumulation was evaluated by biochemical and histopathological analyses. The expression levels of genes related to lipid metabolism and metabolomic profiles were assessed in the mouse liver. The association between biomarkers of hepatic steatosis (hepatic Oil Red O staining area and serum and liver triglyceride contents) and typical components of PM2.5 was identified using Pearson correlation analysis. Oil Red O staining and biochemical results indicated that PM2.5 from four cities significantly induced hepatic lipid accumulation. The most severe hepatic steatosis was observed after Guangzhou PM2.5 exposure. Moreover, Guangzhou PM2.5-induced the most significant changes in gene expression associated with lipid metabolism, including increased hepatic fatty acid uptake and lipid droplet formation and decreased fatty acid synthesis and lipoprotein secretion. Contemporaneously, exposure to Guangzhou PM2.5 significantly perturbed hepatic lipid metabolism. According to metabolomic analysis, disturbed hepatic lipid metabolism was primarily concentrated in linoleic acid, α-linoleic acid, and arachidonic acid metabolism. Finally, correlation analysis revealed that copper (Cu) and other inorganic components, as well as the majority of polycyclic aromatic hydrocarbons (PAHs), were related to changes in biomarkers of hepatic steatosis. These findings showed that PM2.5 exposure caused hepatic steatosis in aged mice, which could be related to the critical chemical components of PM2.5. This study provides critical information regarding the components of PM2.5, which cause hepatic steatosis.
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Affiliation(s)
- Zhipeng Yan
- College of Environment and Resource, Research Center of Environment and Health, Shanxi University, Shanxi, 030006, PR China
| | - Shuyue Li
- College of Environment and Resource, Research Center of Environment and Health, Shanxi University, Shanxi, 030006, PR China
| | - Rui Chen
- Beijing Key Laboratory of Occupational Safety and Health, Institute of Urban Safety and Environmental Science, Beijing Academy of Science and Technology, Beijing, 100054, PR China; Beijing City University, Beijing, 11418, PR China
| | - Haohan Xie
- Beijing Key Laboratory of Occupational Safety and Health, Institute of Urban Safety and Environmental Science, Beijing Academy of Science and Technology, Beijing, 100054, PR China
| | - Meiqiong Wu
- College of Environment and Resource, Research Center of Environment and Health, Shanxi University, Shanxi, 030006, PR China; School of Public Health, Shanxi Medical University, Shanxi, 030001, PR China
| | - Nan Nan
- College of Environment and Resource, Research Center of Environment and Health, Shanxi University, Shanxi, 030006, PR China
| | - Qisong Xing
- College of Environment and Resource, Research Center of Environment and Health, Shanxi University, Shanxi, 030006, PR China
| | - Yang Yun
- College of Environment and Resource, Research Center of Environment and Health, Shanxi University, Shanxi, 030006, PR China
| | - Guohua Qin
- College of Environment and Resource, Research Center of Environment and Health, Shanxi University, Shanxi, 030006, PR China.
| | - Nan Sang
- College of Environment and Resource, Research Center of Environment and Health, Shanxi University, Shanxi, 030006, PR China
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23
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Hu X, Nie Z, Ou Y, Lin L, Qian Z, Vaughn MG, McMillin SE, Zhou Y, Wu Y, Dong G, Dong H. Long-term exposure to ambient air pollution, circadian syndrome and cardiovascular disease: A nationwide study in China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 868:161696. [PMID: 36682545 DOI: 10.1016/j.scitotenv.2023.161696] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 01/07/2023] [Accepted: 01/15/2023] [Indexed: 06/17/2023]
Abstract
OBJECTIVES Epidemiological evidence suggests associations between ambient air pollution and cardiovascular disease (CVD), while circadian rhythm dysregulation, presented by circadian syndrome (CircS), is emerging as a new proxy to cardiovascular disorder that could provide a bridge between them. The present study aims to clarify the effect of high levels ambient air pollution exposure on CircS and CVD in China. METHODS From the China Health and Retirement Longitudinal Study, we recruited 9116 Chinese participants in 2011 and followed them to 2015. A spatiotemporal model was applied to estimate exposure to particles with diameters ≤2.5 μm (PM2.5). The variable CircS was defined based on 7 components, including the 5 components used to define metabolic syndrome as well as other two components, lack of sleep and depression. The associations between PM2.5 exposure and prevalent CircS as well as incident CVD were modeled via logistic regression analysis displaying odds ratios (ORs) and 95 % CIs (confidence intervals). A mediation analysis was undertaken to identify the potential mediating role of CircS between PM2.5 exposure and CVD. RESULTS The mean age (standard deviation) was 59 (9) and 48.22 % were male. The OR (95 % CI) between the highest (Q4) and the lowest (Q1) quartile of PM2.5 exposure for CircS was 1.13 (1.01-1.28) in 2011 and 1.44 (1.22-1.72) in 2015. The cumulative effect of the components of CircS became more obvious with the increase of the PM2.5 quartile exposure. For the Q4 versus Q1 of PM2.5 increment, the multivariate-adjusted OR (95 % CI) was 1.66 (1.20-2.29) for CVD incidence. CircS partially mediated the association between PM2.5 exposure and CVD. CONCLUSIONS Exposure to PM2.5 is a risk factor for CircS and CVD, and the effect of PM2.5 on CVD may be explained by CircS. Improving air quality would have high value in preventing CircS as well as CVD in public health.
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Affiliation(s)
- Xiangming Hu
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100000, China
| | - Zhiqiang Nie
- Department of Cardiology, Hypertension Research Laboratory, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
| | - Yanqiu Ou
- Department of Cardiology, Hypertension Research Laboratory, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
| | - Lizi Lin
- Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China
| | - Zhengmin Qian
- Department of Epidemiology and Biostatistics, College for Public Health & Social Justice, Saint Louis University, Saint Louis, MO 63104, USA
| | - Michael G Vaughn
- School of Social Work, Saint Louis University, Saint Louis, MO 63103, USA
| | | | - Yingling Zhou
- Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
| | - Yongjian Wu
- Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100000, China.
| | - Guanghui Dong
- Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
| | - Haojian Dong
- Department of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China.
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24
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Montes JOA, Villarreal AB, Piña BGB, Martínez KC, Lugo MC, Romieu I, Cadena LH. Short-Term Ambient Air Ozone Exposure and Components of Metabolic Syndrome in a Cohort of Mexican Obese Adolescents. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4495. [PMID: 36901504 PMCID: PMC10001840 DOI: 10.3390/ijerph20054495] [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: 01/18/2023] [Revised: 02/27/2023] [Accepted: 02/28/2023] [Indexed: 06/18/2023]
Abstract
Ambient air pollution is a major global public health concern; little evidence exists about the effects of short-term exposure to ozone on components of metabolic syndrome in young obese adolescents. The inhalation of air pollutants, such as ozone, can participate in the development of oxidative stress, systemic inflammation, insulin resistance, endothelium dysfunction, and epigenetic modification. Metabolic alterations in blood in components of metabolic syndrome (MS) and short-term ambient air ozone exposure were determined and evaluated longitudinally in a cohort of 372 adolescents aged between 9 to 19 years old. We used longitudinal mixed-effects models to evaluate the association between ozone exposure and the risk of components of metabolic syndrome and its parameters separately, adjusted using important variables. We observed statistically significant associations between exposure to ozone in tertiles in different lag days and the parameters associated with MS, especially for triglycerides (20.20 mg/dL, 95% CI: 9.5, 30.9), HDL cholesterol (-2.56 mg/dL (95% CI: -5.06, -0.05), and systolic blood pressure (1.10 mmHg, 95% CI: 0.08, 2.2). This study supports the hypothesis that short-term ambient air exposure to ozone may increase the risk of some components of MS such as triglycerides, cholesterol, and blood pressure in the obese adolescent population.
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Affiliation(s)
- Jorge Octavio Acosta Montes
- Facultad de Enfermería y Nutriología, Universidad Autónoma de Chihuahua, C. Escorza No. 900 Centro, Chihuahua 31000, Chihuahua, Mexico
| | - Albino Barraza Villarreal
- Instituto Nacional de Salud Pública, Av. Universidad No. 655, Col. Santa Maria Ahuacatitlán, Cuernavaca 62100, Morelos, Mexico
| | - Blanca Gladiana Beltrán Piña
- Facultad de Enfermería y Nutriología, Universidad Autónoma de Chihuahua, C. Escorza No. 900 Centro, Chihuahua 31000, Chihuahua, Mexico
| | - Karla Cervantes Martínez
- Instituto Nacional de Salud Pública, Av. Universidad No. 655, Col. Santa Maria Ahuacatitlán, Cuernavaca 62100, Morelos, Mexico
| | - Marlene Cortez Lugo
- Instituto Nacional de Salud Pública, Av. Universidad No. 655, Col. Santa Maria Ahuacatitlán, Cuernavaca 62100, Morelos, Mexico
| | - Isabelle Romieu
- Instituto Nacional de Salud Pública, Av. Universidad No. 655, Col. Santa Maria Ahuacatitlán, Cuernavaca 62100, Morelos, Mexico
| | - Leticia Hernández Cadena
- Instituto Nacional de Salud Pública, Av. Universidad No. 655, Col. Santa Maria Ahuacatitlán, Cuernavaca 62100, Morelos, Mexico
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25
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Li J, Song Y, Shi L, Jiang J, Wan X, Wang Y, Ma Y, Dong Y, Zou Z, Ma J. Long-term effects of ambient PM 2.5 constituents on metabolic syndrome in Chinese children and adolescents. ENVIRONMENTAL RESEARCH 2023; 220:115238. [PMID: 36621550 DOI: 10.1016/j.envres.2023.115238] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 01/04/2023] [Accepted: 01/05/2023] [Indexed: 06/17/2023]
Abstract
Metabolic syndrome (MetS) is considered a main public health issue as it remarkably adds the risk of cardiovascular disease, leading to a heavy burden of disease. There is growing evidence linking fine particulate matter (PM2.5) exposure to MetS. However, the influences of PM2.5 constituents, especially in children and adolescents, remain unclear. Our study was according to a national analysis among Chinese children and adolescents to examine the associations between long-term exposure to PM2.5 main constituents and MetS. A total of 10,066 children and adolescents aged 10-18 years were recruited in 7 provinces in China, with blood tests, health exams, and questionnaire surveys. We estimated long-term exposures to PM2.5 mass and its five constituents, containing black carbon (BC), organic matter (OM), inorganic nitrate (NO3-), sulfate (SO42-), and soil particles (SOIL) from multi-source data fusion models. Mixed-effects logistic regression models were used with the adjustment of a variety of covariates. In the surveyed populations, 2.9% were classified as MetS. From the single-pollutant models, we discovered that long-term exposures to PM2.5 mass, BC, OM, NO3-, as well as SO42-, were significantly associated with the prevalence of MetS, with odds ratios (ORs) per 1 μg/m3 that were 1.02 (95% confidence interval (CI): 1.01, 1.03) for PM2.5 mass, 1.24 (95% CI: 1.14, 1.35) for BC, 1.07 (95% CI: 1.04, 1.11) for OM, 1.09 (95% CI: 1.04, 1.13) for NO3-, and 1.14 (95% CI:1.04, 1.24) for SO42-. The influence of BC on the prevalence of MetS was robust in both the multi-pollutant model and the PM2.5-constituent joint model. The paper indicates long-term exposure to PM2.5 mass and specific PM2.5 constituents, particularly for BC, was significantly associated with a higher MetS prevalence among children and adolescents in China. Our results highlight the significance of establishing further regulations on PM2.5 constituents.
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Affiliation(s)
- Jing Li
- Institute of Child and Adolescent Health, School of Public Health, Peking University, Beijing, 100191, China
| | - Yi Song
- Institute of Child and Adolescent Health, School of Public Health, Peking University, Beijing, 100191, China
| | - Liuhua Shi
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, 30322, USA
| | - Jun Jiang
- Institute of Child and Adolescent Health, School of Public Health, Peking University, Beijing, 100191, China
| | - Xiaoyu Wan
- Institute of Child and Adolescent Health, School of Public Health, Peking University, Beijing, 100191, China
| | - Yaqi Wang
- Institute of Child and Adolescent Health, School of Public Health, Peking University, Beijing, 100191, China
| | - Yinghua Ma
- Institute of Child and Adolescent Health, School of Public Health, Peking University, Beijing, 100191, China
| | - Yanhui Dong
- Institute of Child and Adolescent Health, School of Public Health, Peking University, Beijing, 100191, China.
| | - Zhiyong Zou
- Institute of Child and Adolescent Health, School of Public Health, Peking University, Beijing, 100191, China.
| | - Jun Ma
- Institute of Child and Adolescent Health, School of Public Health, Peking University, Beijing, 100191, China
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26
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Ke P, Xu M, Xu J, Yuan X, Ni W, Sun Y, Zhang H, Zhang Y, Tian Q, Dowling R, Jiang H, Zhao Z, Lu Z. Association of residential greenness with the risk of metabolic syndrome in Chinese older adults: a longitudinal cohort study. J Endocrinol Invest 2023; 46:327-335. [PMID: 36006585 DOI: 10.1007/s40618-022-01904-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 08/12/2022] [Indexed: 01/27/2023]
Abstract
AIMS We aimed to investigate the association between residential greenness and MetS in older Chinese adults. METHODS Longitudinal data on sociodemographic characteristics and lifestyle were collected from the Shenzhen Healthy Ageing Research (SHARE) cohort. Greenness exposure was assessed through satellite-derived Normalized Difference Vegetation Index (NDVI) values in the 250-m, 500-m, and 1250-m radius around the residential address for each participant. MetS was defined by standard guidelines for the Chinese population. RESULTS A total of 49,893 older Chinese adults with a mean age of 70.96 (SD = 5.26) years were included in the study. In the fully adjusted models, participants who lived in the highest quartile of NDVI250-m, NDVI500-m, and NDVI1250-m had a 15% (odds ratio, OR = 0.85, 95% confidence interval, CI: 0.80-0.90), 12% (OR = 0.88, 95% CI: 0.83-0.93), and 11% (OR = 0.89, 95% CI: 0.85-0.95) lower incidence of MetS, respectively, than those living in the lowest quartile (all p-trend < 0.01). Interactions and subgroup analyses showed that age, sex, smoking status, and drinking status were significant effect modifiers (p-interaction for all NDVI < 0.05). CONCLUSIONS Residential greenness is associated with a lower risk of MetS in Chinese older adults, especially for young older adults, females, non-smokers, and non-drinkers.
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Affiliation(s)
- P Ke
- Department of Social Medicine and Health Management, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No. 13 Hangkong Road, Wuhan, 430030, Hubei, People's Republic of China
| | - M Xu
- Department of Social Medicine and Health Management, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No. 13 Hangkong Road, Wuhan, 430030, Hubei, People's Republic of China
| | - J Xu
- Shenzhen Center for Chronic Disease Control, No. 2021 Buxin Road, Shenzhen, 518020, Guangdong, People's Republic of China
| | - X Yuan
- Shenzhen Center for Chronic Disease Control, No. 2021 Buxin Road, Shenzhen, 518020, Guangdong, People's Republic of China
| | - W Ni
- Shenzhen Center for Chronic Disease Control, No. 2021 Buxin Road, Shenzhen, 518020, Guangdong, People's Republic of China
| | - Y Sun
- Shenzhen Center for Chronic Disease Control, No. 2021 Buxin Road, Shenzhen, 518020, Guangdong, People's Republic of China
| | - H Zhang
- Shenzhen Center for Chronic Disease Control, No. 2021 Buxin Road, Shenzhen, 518020, Guangdong, People's Republic of China
| | - Y Zhang
- Shenzhen Center for Chronic Disease Control, No. 2021 Buxin Road, Shenzhen, 518020, Guangdong, People's Republic of China
| | - Q Tian
- School of Public Health, Zhengzhou University, Zhengzhou, Henan, People's Republic of China
| | - R Dowling
- Centre for Alcohol Policy Research, School of Psychology and Public Health, La Trobe University, Bundoora, Melbourne, VIC, 3086, Australia
| | - H Jiang
- Centre for Alcohol Policy Research, School of Psychology and Public Health, La Trobe University, Bundoora, Melbourne, VIC, 3086, Australia.
- Centre for Health Equity, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia.
| | - Z Zhao
- Shenzhen Center for Chronic Disease Control, No. 2021 Buxin Road, Shenzhen, 518020, Guangdong, People's Republic of China.
| | - Z Lu
- Department of Social Medicine and Health Management, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No. 13 Hangkong Road, Wuhan, 430030, Hubei, People's Republic of China.
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27
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Chen YC, Chin WS, Pan SC, Wu CD, Guo YLL. Long-Term Exposure to Air Pollution and the Occurrence of Metabolic Syndrome and Its Components in Taiwan. ENVIRONMENTAL HEALTH PERSPECTIVES 2023; 131:17001. [PMID: 36598238 PMCID: PMC9811992 DOI: 10.1289/ehp10611] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 11/19/2022] [Accepted: 12/05/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Metabolic syndrome (MetS), a major contributor to cardiovascular and metabolic diseases, has been linked with exposure to air pollution. However, the relationship between air pollutants and the five components of MetS [abdominal obesity, elevated triglyceride, decreased high-density lipoprotein cholesterol (HDL-C), elevated blood pressure, and elevated fasting blood glucose levels], has not been clearly described. OBJECTIVE We examined the association between long-term exposure to air pollutants and the occurrence of MetS and its components by using a longitudinal cohort in Taiwan. METHODS The MJ Health Research Foundation is a medical institute that conducts regular physical examinations. The development of MetS, based on a health examination and the medical history of an MJ cohort of 93,771 participants who were enrolled between 2006 and 2016 and had two or more examinations, was compared with estimated exposure to air pollutants in the year prior to health examination. The exposure levels to fine particulate matter [PM with an aerodynamic diameter of ≤2.5μm (PM2.5)] and nitrogen dioxide (NO2) in the participants' residential areas were estimated using a hybrid Kriging/land-use regression (LUR) model executed using the XGBoost algorithm and a hybrid Kriging/LUR model, respectively. Cox regression with time-dependent covariates was conducted to estimate the effects of annual air pollutant exposure on the risk of MetS and its components. RESULTS During the average follow-up period of 3.4 y, the incidence of MetS was 38.1/1,000 person-years. After mutual adjustment and adjustments for potential covariates, the results indicated that every 10-μg/m3 increase in annual PM2.5 concentration was associated with an increased risk of abdominal obesity [adjusted hazard ratio (aHR)=1.07; 95% confidence interval (CI): 1.01, 1.14], hypertriglyceridemia (aHR=1.17; 95% CI: 1.11, 1.23), low HDL-C (aHR=1.09; 95% CI: 1.02, 1.17), hypertension (aHR=1.15; 95% CI: 1.09, 1.21), and elevated fasting blood glucose (aHR=1.15; 95% CI: 1.10, 1.20). Furthermore, PM2.5 and NO2 may increase the risk of developing MetS among people who already "have" some components of MetS. DISCUSSION Our findings suggest that in apparently healthy adults undergoing physical examination, exposure to PM2.5 and NO2 might be associated with the occurrence of MetS and its components. https://doi.org/10.1289/EHP10611.
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Affiliation(s)
- Yi-Chuan Chen
- National Institute of Environmental Health Sciences, National Health Research Institute, Miaoli, Taiwan
| | - Wei-Shan Chin
- School of Nursing, College of Medicine, National Taiwan University (NTU), Taipei, Taiwan
- Department of Nursing, NTU Hospital, Taipei, Taiwan
| | - Shih-Chun Pan
- National Institute of Environmental Health Sciences, National Health Research Institute, Miaoli, Taiwan
| | - Chih-Da Wu
- National Institute of Environmental Health Sciences, National Health Research Institute, Miaoli, Taiwan
- Department of Geomatics, National Cheng Kung University, Tainan, Taiwan
| | - Yue-Liang Leon Guo
- National Institute of Environmental Health Sciences, National Health Research Institute, Miaoli, Taiwan
- Environmental and Occupational Medicine, College of Medicine, NTU and NTU Hospital, Taipei, Taiwan
- Graduate Institute of Environmental and Occupational Health Science, College of Public Health, NTU, Taipei, Taiwan
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28
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Luo H, Zhang Q, Niu Y, Kan H, Chen R. Fine particulate matter and cardiorespiratory health in China: A systematic review and meta-analysis of epidemiological studies. J Environ Sci (China) 2023; 123:306-316. [PMID: 36521994 DOI: 10.1016/j.jes.2022.04.026] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2021] [Revised: 04/21/2022] [Accepted: 04/22/2022] [Indexed: 06/17/2023]
Abstract
This review aimed to systematically summarize the epidemiological literature on the cardiorespiratory effects of PM2.5 published during the 13th Five-Year Plan period (2016-2020) in China. Original articles published between January 1, 2016 and June 30, 2021 were searched in PubMed, Web of Science, the China National Knowledge Internet Database and Wanfang Database. Random- or fixed-effects models were used to pool effect estimates where appropriate. Of 8558 records identified, 145 met the full eligibility criteria. A 10 µg/m³ increase in short-term PM2.5 exposure was significantly associated with increases of 0.70%, 0.86%, 0.38% and 0.96% in cardiovascular mortality, respiratory mortality, cardiovascular morbidity, and respiratory morbidity, respectively. The specific diseases with significant associations included stroke, ischemic heart disease, heart failure, arrhythmia, chronic obstructive pulmonary disease, pneumonia and allergic rhinitis. The pooled estimates per 10 µg/m³ increase in long-term PM2.5 exposure were 15.1%, 11.9% and 21.0% increases in cardiovascular, stroke and lung cancer mortality, and 17.4%, 11.0% and 4.88% increases in cardiovascular, hypertension and lung cancer incidence respectively. Adverse changes in blood pressure, heart rate variability, systemic inflammation, blood lipids, lung function and airway inflammation were observed for either short-term or long-term PM2.5 exposure, or both. Collectively, we summarized representative exposure-response relationships between short- and long-term PM2.5 exposure and a wide range of cardiorespiratory outcomes applicable to China. The magnitudes of estimates were generally smaller in short-term associations and comparable in long-term associations compared with those in developed countries. Our findings are helpful for future standard revisions and policy formulation. There are still some notable gaps that merit further investigation in China.
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Affiliation(s)
- Huihuan Luo
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China
| | - Qingli Zhang
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China
| | - Yue Niu
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China
| | - Haidong Kan
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China
| | - Renjie Chen
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China.
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Feng S, Meng Q, Guo B, Guo Y, Chen G, Pan Y, Zhou J, Xu J, Zeng Q, Wei J, Xu H, Chen L, Zeng C, Zhao X. Joint exposure to air pollution, ambient temperature and residential greenness and their association with metabolic syndrome (MetS): A large population-based study among Chinese adults. ENVIRONMENTAL RESEARCH 2022; 214:113699. [PMID: 35714687 DOI: 10.1016/j.envres.2022.113699] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Revised: 06/08/2022] [Accepted: 06/12/2022] [Indexed: 06/15/2023]
Abstract
Previous studies assessing adverse health have traditionally focused on a single environmental exposure, failing to reflect the reality of various exposures present simultaneously. Air pollution, ambient temperature and greenness have been proposed as critical environmental factors associated with metabolic syndrome (MetS). However, evidence exploring their joint relationships with MetS is needed for identifying interactive factors and developing more targeted public health interventions. The baseline data was obtained from China Multi-Ethnic Cohort (CMEC). Environmental data of air pollutants (PM2.5, O3) and NDVI for greenness was calculated from satellites data. Ambient temperature data were obtained from European Center for Medium-Range Weather Forecasts (ECMWF). MetS was classified based on National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) using anthropometric measures and biomarkers. Logistic regression models were utilized to examine the combined relationship of MetS with three-year exposure to air pollutants, temperature and NDVI. Relative excess risk due to interaction (RERI) was calculated to evaluate interaction on an additive scale. We found associations between prevalent MetS and interquartile range (IQR) increases in PM2.5 (OR: 1.38; 95% confidence interval [95% CI]: 1.23, 1.55) and O3 (OR: 1.15; 95% CI: 1.09, 1.22). Additive and multiplicative interactions were observed between air pollutants and temperature exposure. Compared to low-temperature level, the relationship between PM2.5 and MetS attenuated (RERI: 0.22, 95% CI: 0.44, -0.04) at high-temperature level, while the relationship between O3 and MetS enhanced (RERI: 0.05, 95% CI: 0.02, 0.11). At low NDVI 250 m, the association between PM2.5 and MetS was stronger (RERI: 0.13, 95% CI: 0.05, 0.19) with high NDVI 250 m as the reference group. Our findings showed that ambient temperature and residential greenness could affect the relationship between air pollutants and MetS.
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Affiliation(s)
- Shiyu Feng
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Qiong Meng
- Department of Epidemiology and Health Statistics, School of Public Health, Kunming Medical University, China
| | - Bing Guo
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yuming Guo
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia
| | - Gongbo Chen
- Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment, Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | | | - Jing Zhou
- Chenghua District Center for Disease Control and Prevention, China
| | - Jingru Xu
- Chongqing Municipal Center for Disease Control and Prevention, China
| | - Qibing Zeng
- School of Public Health, The Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, China
| | - Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
| | - Huan Xu
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Lin Chen
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Chunmei Zeng
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xing Zhao
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China.
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Yang S, Liang X, Dou Q, La Y, Cai J, Yang J, Laba C, Liu Q, Guo B, Yu W, Wang Q, Chen G, Hong F, Jia P, Zhao X. Ethnic disparities in the association between ambient air pollution and risk for cardiometabolic abnormalities in China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 838:155940. [PMID: 35580681 DOI: 10.1016/j.scitotenv.2022.155940] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 05/02/2022] [Accepted: 05/10/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Long-term exposure to ambient air pollution has been associated with cardiometabolic abnormalities (CAs), which, however, may be stronger in vulnerable populations, such as minorities. The variation of the association between ambient air pollution and CAs between the majority (Han) and minority populations in China have been poorly studied. OBJECTIVES We aimed to estimate and compare the Hans' and minorities' risks for CAs associated with long-term exposure to ambient air pollution in Southwest China. METHODS A cross-sectional study was conducted on the basis of the China Multi-Ethnic Cohort. CAs were defined by the presence of at least three pre-defined metabolic dysfunctions (central obesity, elevated triglycerides, reduced high-density lipoprotein cholesterol, elevated blood pressure, and elevated fasting glucose). The concentrations of ambient air pollutants, including particulate matters (PM1, PM2.5, and PM10) and nitrogen dioxide (NO2), were generated from random forest models on the basis of multi-source data. One- and two-pollutant regression models were fit to assess associations between air pollutant exposure and CA risks. Sensitivity analyses were performed to examine the robustness of the associations. RESULTS The final sample included 51,037 Hans and 28,702 minority participants. The prevalence of CAs was 25.0%, slightly higher in the minorities (25.5%) than the Hans (24.4%). The higher risks for CAs in the overall population were associated with each 10 μg/m3 increase in the exposure to PM1 (OR = 1.07 [1.05-1.09]), PM2.5 (OR = 1.11 [1.06-1.17]), PM10 (OR = 1.04 [1.03-1.06]), and NO2 (OR = 1.04 [1.03-1.07]). Compared to the Hans, the higher risks for CAs were observed in the minorities for PM1 (OR = 1.35 [1.18-1.53]), PM2.5 (OR = 1.61 [1.34-1.93]), and PM10 (OR = 1.15 [1.07-1.23]). The associations of metabolic dysfunctions (CA components) with ambient air pollution also varied between the Han and minority populations. CONCLUSIONS The associations between exposure to ambient air pollution and CA risks were stronger in the minorities than Hans. Our findings provide a better understanding of ethnic disparities in CA risks when being exposed to ambient air pollution in China, which also have important implications for other low- and middle-income countries where less health resources (e.g., cohort populations) are available to conduct such studies.
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Affiliation(s)
- Shujuan Yang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China; International Institute of Spatial Lifecourse Health (ISLE), Wuhan University, Wuhan, China
| | - Xian Liang
- Chengdu Center for Disease Control and Prevention, Chengdu, China
| | - Qingyu Dou
- National Clinical Research Center of Geriatrics, Geriatric Medicine Center, West China Hospital, Sichuan University, Chengdu, China; International Institute of Spatial Lifecourse Health (ISLE), Wuhan University, Wuhan, China
| | - Yang La
- Tibet University, Lhasa, China
| | - Jiaojiao Cai
- Chongqing Municipal Center for Disease Control and Prevention, Chongqing, China
| | - Jun Yang
- Yunnan Center for Disease Control and Prevention, Kunming, China
| | - Ciren Laba
- Tibet Center for Disease Control and Prevention, Lhasa, China
| | - Qiaolan Liu
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Bing Guo
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Wanqi Yu
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Qinjian Wang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Gongbo Chen
- 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, China
| | - Feng Hong
- School of Public Health, The Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, China.
| | - Peng Jia
- International Institute of Spatial Lifecourse Health (ISLE), Wuhan University, Wuhan, China; School of Resource and Environmental Sciences, Wuhan University, Wuhan, China.
| | - Xing Zhao
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China.
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Zheng XY, Tang SL, Liu T, Wang Y, Xu XJ, Xiao N, Li C, Xu YJ, He ZX, Ma SL, Chen YL, Meng RL, Lin LF. Effects of long-term PM 2.5 exposure on metabolic syndrome among adults and elderly in Guangdong, China. Environ Health 2022; 21:84. [PMID: 36088422 PMCID: PMC9464395 DOI: 10.1186/s12940-022-00888-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 07/29/2022] [Indexed: 05/20/2023]
Abstract
BACKGROUND We aimed to explore the association between long-term exposure to particulate matter ≤ 2.5 µm (PM2.5) and metabolic syndrome (MetS) and its components including fasting blood glucose (FBG), blood pressure, triglyceride (TG), high-density lipoprotein cholesterol (HDL-c) and waist circumference among adults and elderly in south China. METHODS We surveyed 6628 participants in the chronic disease and risk factors surveillance conducted in 14 districts of Guangdong province in 2015. MetS was defined based on the recommendation by the Joint Interim Societies' criteria. We used the spatiotemporal land-use regression (LUR) model to estimate the two-year average exposure of ambient air pollutants (PM2.5, PM10, SO2, NO2, and O3) at individual levels. We recorded other covariates by using a structured questionnaire. Generalized linear mixed model was used for analysis. RESULTS A 10-μg/m3 increase in the two-year mean PM2.5 exposure was associated with a higher risk of developing MetS [odd ratio (OR): 1.17, 95% confidence interval (CI): 1.01, 1.35], increased risk of fasting blood glucose level. (OR: 1.18, 95% CI: 1.02, 1.36), and hypertriglyceridemia (OR: 1.36, 95% CI: 1.18, 1.58) in the adjusted/unadjusted models (all P < 0.05). We found significant interaction between PM2.5 and the region, exercise on the high TG levels, and an interaction with the region, age, exercise and grain consumption on FBG (P interaction < 0.05). CONCLUSIONS Long-term exposure to PM2.5 was associated with MetS, dyslipidemia and FBG impairment. Efforts should be made for environment improvement to reduce the burden of MetS-associated non-communicable disease.
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Affiliation(s)
- Xue-yan Zheng
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong, China
| | - Si-li Tang
- School of Public Health, Southern Medical University, Guangzhou, China
| | - Tao Liu
- Disease Control and Prevention Institute of Jinan University, Jinan University, Guangzhou, China
| | - Ye Wang
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong, China
| | - Xiao-jun Xu
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong, China
| | - Ni Xiao
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong, China
| | - Chuan Li
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong, China
| | - Yan-jun Xu
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong, China
| | - Zhao-xuan He
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong, China
| | - Shu-li Ma
- Department of Epidemiology and Biostatistics, School of Public Health, Guangdong Pharmaceutical University, Guangzhou, China
| | - Yu-liang Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Guangdong Pharmaceutical University, Guangzhou, China
| | - Rui-lin Meng
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong, China
| | - Li-feng Lin
- Guangdong Provincial Center for Disease Control and Prevention, Guangdong, China
- School of Public Health, Southern Medical University, Guangzhou, China
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Barnett A, Martino E, Knibbs LD, Shaw JE, Dunstan DW, Magliano DJ, Donaire-Gonzalez D, Cerin E. The neighbourhood environment and profiles of the metabolic syndrome. Environ Health 2022; 21:80. [PMID: 36057588 PMCID: PMC9440568 DOI: 10.1186/s12940-022-00894-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Accepted: 08/29/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND There is a dearth of studies on how neighbourhood environmental attributes relate to the metabolic syndrome (MetS) and profiles of MetS components. We examined the associations of interrelated aspects of the neighbourhood environment, including air pollution, with MetS status and profiles of MetS components. METHODS We used socio-demographic and MetS-related data from 3681 urban adults who participated in the 3rd wave of the Australian Diabetes, Obesity and Lifestyle Study. Neighbourhood environmental attributes included area socio-economic status (SES), population density, street intersection density, non-commercial land use mix, percentages of commercial land, parkland and blue space. Annual average concentrations of NO2 and PM2.5 were estimated using satellite-based land-use regression models. Latent class analysis (LCA) identified homogenous groups (latent classes) of participants based on MetS components data. Participants were then classified into five metabolic profiles according to their MetS-components latent class and MetS status. Generalised additive mixed models were used to estimate relationships of environmental attributes with MetS status and metabolic profiles. RESULTS LCA yielded three latent classes, one including only participants without MetS ("Lower probability of MetS components" profile). The other two classes/profiles, consisting of participants with and without MetS, were "Medium-to-high probability of high fasting blood glucose, waist circumference and blood pressure" and "Higher probability of MetS components". Area SES was the only significant predictor of MetS status: participants from high SES areas were less likely to have MetS. Area SES, percentage of commercial land and NO2 were associated with the odds of membership to healthier metabolic profiles without MetS, while annual average concentration of PM2.5 was associated with unhealthier metabolic profiles with MetS. CONCLUSIONS This study supports the utility of operationalising MetS as a combination of latent classes of MetS components and MetS status in studies of environmental correlates. Higher socio-economic advantage, good access to commercial services and low air pollution levels appear to independently contribute to different facets of metabolic health. Future research needs to consider conducting longitudinal studies using fine-grained environmental measures that more accurately characterise the neighbourhood environment in relation to behaviours or other mechanisms related to MetS and its components.
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Affiliation(s)
- Anthony Barnett
- Mary MacKillop Institute for Health Research, Australian Catholic University, 215 Spring St, Melbourne, VIC, Australia.
| | - Erika Martino
- Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Luke D Knibbs
- School of Public Health, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Jonathan E Shaw
- Department of Diabetes and Population Health, Baker Heart and Diabetes Institute, Melbourne, Australia
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - David W Dunstan
- Baker-Deakin Department of Lifestyle and Diabetes, Deakin University, Melbourne, Australia
| | - Dianna J Magliano
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - David Donaire-Gonzalez
- Mary MacKillop Institute for Health Research, Australian Catholic University, 215 Spring St, Melbourne, VIC, Australia
| | - Ester Cerin
- Mary MacKillop Institute for Health Research, Australian Catholic University, 215 Spring St, Melbourne, VIC, Australia
- Department of Community Medicine, UiT The Artic University of Norway, Tromsø, Norway
- School of Public Health, The University of Hong Kong, 7 Sassoon Rd., Sandy Bay, Hong Kong, Hong Kong, SAR, China
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Paz-Aparicio VM, Tapia V, Vasquez-Apestegui BV, Steenland K, Gonzales GF. Intrauterine and Extrauterine Environmental PM 2.5 Exposure Is Associated with Overweight/Obesity (O/O) in Children Aged 6 to 59 Months from Lima, Peru: A Case-Control Study. TOXICS 2022; 10:487. [PMID: 36006166 PMCID: PMC9416618 DOI: 10.3390/toxics10080487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 08/15/2022] [Accepted: 08/18/2022] [Indexed: 06/15/2023]
Abstract
There is evidence that PM2.5 could be obesogenic. Lima is one of the most polluted cities in South America, with an increasing prevalence of childhood obesity. This study aimed to determine the association between PM2.5 exposure of children aged 6 to 59 months and being overweight or obese (O/O) in a significant dataset survey. Cases were defined when weight for height Z-score (WHZ) was >2 standard deviations (SD) from the mean, for each sex. A control was defined when WHZ was between ±2 SD. We used a conditional logistic regression model to calculate the odds ratio (OR) between extrauterine and intrauterine PM2.5 exposure and O/O. Extrauterine PM2.5 exposure was evaluated as a 6-month PM2.5 mean prior to the survey. We found a significant association between O/O and extrauterine (OR: 1.57, 1.51−1.63) and intrauterine (OR: 1.99, 1.88−2.12) PM2.5 exposure for an increment of 10 μg/m3. The ORs increased as the quartile increased in both exposures. We observed a higher association in children aged 6−11 months (OR: 3.07, 2.84−3.31). In conclusion, higher levels of PM2.5 in Lima and Callao were associated with cases of O/O in children from 6 to 59 months, with the association higher for prenatal exposure.
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Affiliation(s)
- Valeria M. Paz-Aparicio
- Laboratorio de Endocrinología y Reproducción, Laboratorios de Investigación y Desarrollo (LID), Departamento de Ciencias Biológicas y Fisiológicas, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima 15102, Peru
| | - Vilma Tapia
- Laboratorio de Endocrinología y Reproducción, Laboratorios de Investigación y Desarrollo (LID), Departamento de Ciencias Biológicas y Fisiológicas, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima 15102, Peru
| | - Bertha Vanessa Vasquez-Apestegui
- Laboratorio de Endocrinología y Reproducción, Laboratorios de Investigación y Desarrollo (LID), Departamento de Ciencias Biológicas y Fisiológicas, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima 15102, Peru
| | - Kyle Steenland
- Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
| | - Gustavo F. Gonzales
- Laboratorio de Endocrinología y Reproducción, Laboratorios de Investigación y Desarrollo (LID), Departamento de Ciencias Biológicas y Fisiológicas, Facultad de Ciencias y Filosofía, Universidad Peruana Cayetano Heredia, Lima 15102, Peru
- Instituto de Investigaciones de la Altura, Universidad Peruana Cayetano Heredia, Lima 15102, Peru
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Tian Y, Fang J, Wang F, Luo Z, Zhao F, Zhang Y, Du P, Wang J, Li Y, Shi W, Liu Y, Ding E, Sun Q, Li C, Tang S, Yue X, Shi G, Wang B, Li T, Shen G, Shi X. Linking the Fasting Blood Glucose Level to Short-Term-Exposed Particulate Constituents and Pollution Sources: Results from a Multicenter Cross-Sectional Study in China. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:10172-10182. [PMID: 35770491 DOI: 10.1021/acs.est.1c08860] [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
Ambient PM2.5 (fine particulate matter with aerodynamic diameters ≤ 2.5 μm) is thought to be associated with the development of diabetes, but few studies traced the effects of PM2.5 components and pollution sources on the change in the fasting blood glucose (FBG). In the present study, we assessed the associations of PM2.5 constituents and their sources with the FBG in a general Chinese population aged over 40 years. Exposure to PM2.5 was positively associated with the FBG level, and each interquartile range (IQR) increase in a lag period of 30 days (18.4 μg/m3) showed the strongest association with an elevated FBG of 0.16 mmol/L (95% confidence interval: 0.04, 0.28). Among various constituents, increases in exposed elemental carbon, organic matter, arsenic, and heavy metals such as silver, cadmium, lead, and zinc were associated with higher FBG, whereas barium and chromium were associated with lower FBG levels. The elevated FBG level was closely associated with the PM2.5 from coal combustion, industrial sources, and vehicle emissions, while the association with secondary sources was statistically insignificant. Improving air quality by tracing back to the pollution sources would help to develop well-directed policies to protect human health.
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Affiliation(s)
- Yanlin Tian
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, China
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control (AEMPC), Nanjing University of Information Science & Technology, Nanjing 210044, 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
| | - Feng Wang
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Zhihan Luo
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, China
| | - Feng Zhao
- 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
| | - Peng 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
| | - Jiaonan 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
| | - Yawei Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - 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
| | - Enmin Ding
- 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
| | - Chengcheng Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Song Tang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Xu Yue
- Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control (AEMPC), Nanjing University of Information Science & Technology, Nanjing 210044, China
| | - Guoliang Shi
- State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Bin Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - Tiantian Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
| | - Guofeng Shen
- College of Urban and Environmental Sciences, Laboratory for Earth Surface Processes, Sino-French Institute for Earth System Science, Peking University, Beijing 100871, China
| | - Xiaoming Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
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Yi W, Zhao F, Pan R, Zhang Y, Xu Z, Song J, Sun Q, Du P, Fang J, Cheng J, Liu Y, Chen C, Lu Y, Li T, Su H, Shi X. Associations of Fine Particulate Matter Constituents with Metabolic Syndrome and the Mediating Role of Apolipoprotein B: A Multicenter Study in Middle-Aged and Elderly Chinese Adults. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:10161-10171. [PMID: 35802126 DOI: 10.1021/acs.est.1c08448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Fine particulate matter (PM2.5) was reported to be associated with metabolic syndrome (MetS), but how PM2.5 constituents affect MetS and the underlying mediators remains unclear. We aimed to investigate the associations of long-term exposure to 24 kinds of PM2.5 constituents with MetS (defined by five indicators) in middle-aged and elderly adults and to further explore the potential mediating role of apolipoprotein B (ApoB). A multicenter study was conducted by recruiting subjects (n = 2045) in the Beijing-Tianjin-Hebei region from the cohort of Sub-Clinical Outcomes of Polluted Air in China (SCOPA-China Cohort). Relationships among PM2.5 constituents, serum ApoB levels, and MetS were estimated by multiple logistic/linear regression models. Mediation analysis quantified the role of ApoB in "PM2.5 constituents-MetS" associations. Results indicated PM2.5 was significantly related to elevated MetS prevalence. The MetS odds increased after exposure to sulfate (SO42-), calcium ion (Ca2+), magnesium ion (Mg2+), Si, Zn, Ca, Mn, Ba, Cu, As, Cr, Ni, or Se (odds ratios ranged from 1.103 to 3.025 per interquartile range increase in each constituent). PM2.5 and some constituents (SO42-, Ca2+, Mg2+, Ca, and As) were positively related to serum ApoB levels. ApoB mediated 22.10% of the association between PM2.5 and MetS. Besides, ApoB mediated 24.59%, 50.17%, 12.70%, and 9.63% of the associations of SO42-, Ca2+, Ca, and As with MetS, respectively. Our findings suggest that ApoB partially mediates relationships between PM2.5 constituents and MetS risk in China.
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Affiliation(s)
- Weizhuo Yi
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, No. 81 Meishan Road, Shushan District, Hefei, Anhui 230031, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, No. 81 Meishan Road, Shushan District, Hefei, Anhui 230031, China
| | - Feng Zhao
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No. 7 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Rubing Pan
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, No. 81 Meishan Road, Shushan District, Hefei, Anhui 230031, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, No. 81 Meishan Road, Shushan District, Hefei, Anhui 230031, China
| | - Yi Zhang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No. 7 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Zhiwei Xu
- School of Public Health, Faculty of Medicine, University of Queensland, 288 Herston Road, Herston, Brisbane, 4006 Queensland, Australia
| | - Jian Song
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, No. 81 Meishan Road, Shushan District, Hefei, Anhui 230031, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, No. 81 Meishan Road, Shushan District, Hefei, Anhui 230031, China
| | - Qinghua Sun
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No. 7 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Peng Du
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No. 7 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Jianlong Fang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No. 7 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Jian Cheng
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, No. 81 Meishan Road, Shushan District, Hefei, Anhui 230031, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, No. 81 Meishan Road, Shushan District, Hefei, Anhui 230031, China
| | - Yingchun Liu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No. 7 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Chen Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No. 7 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Yifu Lu
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No. 7 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Tiantian Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No. 7 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Hong Su
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, No. 81 Meishan Road, Shushan District, Hefei, Anhui 230031, China
- Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, No. 81 Meishan Road, Shushan District, Hefei, Anhui 230031, China
| | - Xiaoming Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No. 7 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
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Poursafa P, Kamali Z, Fraszczyk E, Boezen HM, Vaez A, Snieder H. DNA methylation: a potential mediator between air pollution and metabolic syndrome. Clin Epigenetics 2022; 14:82. [PMID: 35773726 PMCID: PMC9245491 DOI: 10.1186/s13148-022-01301-y] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 06/01/2022] [Indexed: 01/19/2023] Open
Abstract
Given the global increase in air pollution and its crucial role in human health, as well as the steep rise in prevalence of metabolic syndrome (MetS), a better understanding of the underlying mechanisms by which environmental pollution may influence MetS is imperative. Exposure to air pollution is known to impact DNA methylation, which in turn may affect human health. This paper comprehensively reviews the evidence for the hypothesis that the effect of air pollution on the MetS is mediated by DNA methylation in blood. First, we present a summary of the impact of air pollution on metabolic dysregulation, including the components of MetS, i.e., disorders in blood glucose, lipid profile, blood pressure, and obesity. Then, we provide evidence on the relation between air pollution and endothelial dysfunction as one possible mechanism underlying the relation between air pollution and MetS. Subsequently, we review the evidence that air pollution (PM, ozone, NO2 and PAHs) influences DNA methylation. Finally, we summarize association studies between DNA methylation and MetS. Integration of current evidence supports our hypothesis that methylation may partly mediate the effect of air pollution on MetS.
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Affiliation(s)
- Parinaz Poursafa
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Zoha Kamali
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
- Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Eliza Fraszczyk
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - H Marike Boezen
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Ahmad Vaez
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
- Department of Bioinformatics, Isfahan University of Medical Sciences, Isfahan, Iran.
| | - Harold Snieder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
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Zhai L, Yu W. The Co-Occurrence of Anthracosis with Interstitial Lung Disease. SARCOIDOSIS, VASCULITIS, AND DIFFUSE LUNG DISEASES : OFFICIAL JOURNAL OF WASOG 2022; 39:e2022012. [PMID: 36118547 PMCID: PMC9437760 DOI: 10.36141/svdld.v39i2.11792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Accepted: 02/08/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVE Anthracosis is defined as deposition of black pigments in the bronchial mucosa or lung parenchyma. The aim of this study was to investigate the clinical features of patients with coexisting anthracosis and interstitial lung diseases (ILDs). METHODS A total of 335 ILDs patients who underwent bronchoscopy at the affiliated hospital of Qingdao University were included in our study. We enrolled 71 patients who diagnosed with anthracosis by bronchoscopy. The clinical presentations, radiographic features, and bronchoscopic findings of the patients were reviewed. RESULTS Compared with the non-anthracosis group, biomass exposure (48, 67.6% vs. 153, 53.9%, p=0.041), the median pressure of carbon dioxide before six-minute test (42.00 mmHg vs. 40.00 mmHg, P=0.001), the mean peak expiratory flow (115.21 ±23.55 %predicted vs. 104.20±26.17%pre-dicted, P=0.048), the mean level of triglyceride (1.79±1.27 mmol/L vs. 1.51 ±0.74 mmol/L, P=0.034) were significantly increased and the mean oxygen saturation after six-minute test (95.49 ±2.72% vs. 96.56 ±1.27%, P=0.028), the mean cardiac ejection fraction (61.22±2.07% vs.62.08±2.89%, P=0.019) were significantly decreased in the anthracosis group. However, we didn't find significant difference between the two groups in lymph node calcification (p=0.620) and lymphadenectasis (p=0.440). CONCLUSIONS Biomass smoke is a risk factor for anthracosis. Anthracosis produce a bad effect on the oxygenation, cardiac function and lipid metabolism in ILDs patients. The ILDs patients should decrease the exposure of biomass.
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Affiliation(s)
- LiYing Zhai
- Department of Pulmonary and Critical Care Medicine, the affiliated hospital of Qingdao University
| | - WenCheng Yu
- Department of Pulmonary and Critical Care Medicine, the affiliated hospital of Qingdao University
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Dai S, Wang Z, Yang Y, Du P, Li X. PM 2.5 induced weight loss of mice through altering the intestinal microenvironment: Mucus barrier, gut microbiota, and metabolic profiling. JOURNAL OF HAZARDOUS MATERIALS 2022; 431:128653. [PMID: 35359108 DOI: 10.1016/j.jhazmat.2022.128653] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 03/03/2022] [Accepted: 03/07/2022] [Indexed: 06/14/2023]
Abstract
The experimental evidences linking PM2.5 exposure to weight status disorder and the associated mechanisms were lacked. Here, we demonstrated exposure of 198.52 μg/m3 PM2.5 (Baoji city, China) for 40 days induced body weight loss of male Balb/C mice, and then increased after 14-day recovery. Correspondingly, gut microbiota dysbiosis, ileum metabolism alterations, and histopathological changes of liver and ileum elucidated the underlying mechanism. The richness and function modules of flora in feces significantly reduced after exposure, and the ratios of Bacteroidetes/Firmicutes reduced from 1.58 to 0.79. At genus level, Lactobacillus and Clostridium increased markedly, while Bacteroides and Parabacteroides decreased at day 40. After recovery, Oscillospira became the dominant genus. Additionally, the key metabolites in the ileum mediated by PM2.5 identified by metabolomics included arachidonic acid, prostaglandin H2, prostaglandin F2α, 5(S)-HPETE, AMP, and deoxyadenosine. Accordingly, conjoint analysis between the gut micorbiota and metabolic profiling revealed suppression of Arachidonic acid metabolism, linoleic acid metabolism, and PPAR signaling pathway and stimulation of ABC transporters might contribute to the liver injury, ileum inflammation, and then weight loss of mice. Our findings suggested PM2.5 affected weight status of mice by meditating intestinal microenvironment, and provided new insight for further diagnosis of the air pollution dependent disease.
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Affiliation(s)
- Shuiping Dai
- National Center for Geriatrics Clinical Medicine Research, Department of Geriatrics and Gerontology, West China Hospital, Sichuan University, Chengdu 610041, PR China
| | - Zhenglu Wang
- College of Oceanography, Hohai University, Nanjing, Jiangsu 210098, PR China.
| | - Ying Yang
- Center of Precision Medicine, Precision Medicine Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, PR China
| | - Peng Du
- Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University, Beijing 100875, PR China
| | - Xiqing Li
- Laboratory of Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, PR China
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Lin L, Li T, Sun M, Liang Q, Ma Y, Wang F, Duan J, Sun Z. Global association between atmospheric particulate matter and obesity: A systematic review and meta-analysis. ENVIRONMENTAL RESEARCH 2022; 209:112785. [PMID: 35077718 DOI: 10.1016/j.envres.2022.112785] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 01/17/2022] [Accepted: 01/19/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Among various air pollutants, particulate matter (PM) is the most harmful and representative pollutant. Although several studies have shown a link between particulate pollution and obesity, the conclusions are still inconsistent. METHODS We conducted a systematic review and meta-analysis to pool the effect of PM exposure on obesity. Five databases (including PubMed, Web of Science, Scopus, Embase, and Cochrane) were searched for relevant studies up to Jan 2022. Adjusted risk ratio (RR) with corresponding 95% confidence interval (CI) were retrieved from individual studies and pooled with random effect models by STATA software. Besides, we tested the stability of results by Egger's test, Begg's test, funnel plot, and using the trim-and-fill method to modify the possible asymmetric funnel graph. The NTP-OHAT guidelines were followed to assess the risk of bias. Then the GRADE was used to evaluate the certainty of evidence. RESULTS 26 studies were included in this meta-analysis. 19 studies have shown that PM2.5 can increase the risk of obesity per 10 μg/m3 increment (RR: 1.159, 95% CI: 1.111-1.209), while 15 studies have indicated that PM10 increase the risk of obesity per 10 μg/m3 increment (RR: 1.092, 95% CI: 1.070-1.116). Besides, 5 other articles with maternal exposure showed that PM2.5 increases the risk of obesity in children (RR: 1.06, 95% CI: 1.02-1.11). And we explored the source of heterogeneity by subgroup analysis, which suggested associations between PM and obesity tended to vary by region, age group, participants number, etc. The analysis results showed publication bias and other biases are well controlled, but most certainties of the evidence were low, and more research is required to reduce these uncertainties. CONCLUSION Exposure to PM2.5 and PM10 with per 10 μg/m3 increment could increase the risk of obesity in the global population.
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Affiliation(s)
- Lisen Lin
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China
| | - Tianyu Li
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China
| | - Mengqi Sun
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China
| | - Qingqing Liang
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China
| | - Yuexiao Ma
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China
| | - Fenghong Wang
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China
| | - Junchao Duan
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China.
| | - Zhiwei Sun
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing, 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing, 100069, PR China.
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Prenatal exposure to multiple organochlorine compounds and childhood body mass index. Environ Epidemiol 2022; 6:e201. [PMID: 35702503 PMCID: PMC9187184 DOI: 10.1097/ee9.0000000000000201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 02/17/2022] [Indexed: 11/29/2022] Open
Abstract
Background: Prenatal exposure to organochlorine compounds (OCs) has been associated with increased childhood body mass index (BMI); however, only a few studies have focused on longitudinal BMI trajectories, and none of them used multiple exposure mixture approaches. Aim: To determine the association between in-utero exposure to eight OCs and childhood BMI measures (BMI and BMI z-score) at 4 years and their yearly change across 4–12 years of age in 279 Rhea child-mother dyads. Methods: We applied three approaches: (1) linear mixed-effect regressions (LMR) to associate individual compounds with BMI measures; (2) Bayesian weighted quantile sum regressions (BWQSR) to provide an overall OC mixture association with BMI measures; and (3)Bayesian varying coefficient kernel machine regressions (BVCKMR) to model nonlinear and nonadditive associations. Results: In the LMR, yearly change of BMI measures was consistently associated with a quartile increase in hexachlorobenzene (HCB) (estimate [95% Confidence or Credible interval] BMI: 0.10 [0.06, 0.14]; BMI z-score: 0.02 [0.01, 0.04]). BWQSR results showed that a quartile increase in mixture concentrations was associated with yearly increase of BMI measures (BMI: 0.10 [0.01, 0.18]; BMI z-score: 0.03 [0.003, 0.06]). In the BVCKMR, a quartile increase in dichlorodiphenyldichloroethylene concentrations was associated with higher BMI measures at 4 years (BMI: 0.33 [0.24, 0.43]; BMI z-score: 0.19 [0.15, 0.24]); whereas a quartile increase in HCB and polychlorinated biphenyls (PCB)-118 levels was positively associated with BMI measures yearly change (BMI: HCB:0.10 [0.07, 0.13], PCB-118:0.08 [0.04, 012]; BMI z-score: HCB:0.03 [0.02, 0.05], PCB-118:0.02 [0.002,04]). BVCKMR suggested that PCBs had nonlinear relationships with BMI measures, and HCB interacted with other compounds. Conclusions: All analyses consistently demonstrated detrimental associations between prenatal OC exposures and childhood BMI measures.
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Liu L, Yan LL, Lv Y, Zhang Y, Li T, Huang C, Kan H, Zhang J, Zeng Y, Shi X, Ji JS. Air pollution, residential greenness, and metabolic dysfunction biomarkers: analyses in the Chinese Longitudinal Healthy Longevity Survey. BMC Public Health 2022; 22:885. [PMID: 35509051 PMCID: PMC9066955 DOI: 10.1186/s12889-022-13126-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 03/31/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND We hypothesize higher air pollution and fewer greenness exposures jointly contribute to metabolic syndrome (MetS), as mechanisms on cardiometabolic mortality. METHODS We studied the samples in the Chinese Longitudinal Healthy Longevity Survey. We included 1755 participants in 2012, among which 1073 were followed up in 2014 and 561 in 2017. We used cross-sectional analysis for baseline data and the generalized estimating equations (GEE) model in a longitudinal analysis. We examined the independent and interactive effects of fine particulate matter (PM2.5) and Normalized Difference Vegetation Index (NDVI) on MetS. Adjustment covariates included biomarker measurement year, baseline age, sex, ethnicity, education, marriage, residence, exercise, smoking, alcohol drinking, and GDP per capita. RESULTS At baseline, the average age of participants was 85.6 (SD: 12.2; range: 65-112). Greenness was slightly higher in rural areas than urban areas (NDVI mean: 0.496 vs. 0.444; range: 0.151-0.698 vs. 0.133-0.644). Ambient air pollution was similar between rural and urban areas (PM2.5 mean: 49.0 vs. 49.1; range: 16.2-65.3 vs. 18.3-64.2). Both the cross-sectional and longitudinal analysis showed positive associations of PM2.5 with prevalent abdominal obesity (AO) and MetS, and a negative association of NDVI with prevalent AO. In the longitudinal data, the odds ratio (OR, 95% confidence interval-CI) of PM2.5 (per 10 μg/m3 increase) were 1.19 (1.12, 1.27), 1.16 (1.08, 1.24), and 1.14 (1.07, 1.21) for AO, MetS and reduced high-density lipoprotein cholesterol (HDL-C), respectively. NDVI (per 0.1 unit increase) was associated with lower AO prevalence [OR (95% CI): 0.79 (0.71, 0.88)], but not significantly associated with MetS [OR (95% CI): 0.93 (0.84, 1.04)]. PM2.5 and NDVI had a statistically significant interaction on AO prevalence (pinteraction: 0.025). The association between PM2.5 and MetS, AO, elevated fasting glucose and reduced HDL-C were only significant in rural areas, not in urban areas. The association between NDVI and AO was only significant in areas with low PM2.5, not under high PM2.5. CONCLUSIONS We found air pollution and greenness had independent and interactive effect on MetS components, which may ultimately manifest in pre-mature mortality. These study findings call for green space planning in urban areas and air pollution mitigation in rural areas.
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Affiliation(s)
- Linxin Liu
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Lijing L Yan
- Global Heath Research Center, Duke Kunshan University, Kunshan, China.,School of Public Health, Wuhan University, Wuhan, China.,Institute for Global Health and Development, Peking University, Beijing, China
| | - Yuebin Lv
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 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, China
| | - Tiantian Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Cunrui Huang
- Vanke School of Public Health, Tsinghua University, Beijing, China
| | - Haidong Kan
- School of Public Health, Fudan University, Shanghai, China
| | - Junfeng Zhang
- Nicholas School of the Environment and Duke Global Health Institute, Duke University, Durham, NC, USA
| | - Yi Zeng
- Center for Healthy Aging and Development Studies, National School of Development, Peking University, Beijing, China.,Center for the Study of Aging and Human Development, Duke Medical School, Durham, NC, USA
| | - Xiaoming Shi
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China.,Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - John S Ji
- Vanke School of Public Health, Tsinghua University, Beijing, China.
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Wang Y, Liu F, Yao Y, Chen M, Wu C, Yan Y, Xiang H. Associations of long-term exposure to ambient air pollutants with metabolic syndrome: The Wuhan Chronic Disease Cohort Study (WCDCS). ENVIRONMENTAL RESEARCH 2022; 206:112549. [PMID: 34919954 DOI: 10.1016/j.envres.2021.112549] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 11/19/2021] [Accepted: 12/07/2021] [Indexed: 06/14/2023]
Abstract
BACKGROUND Evidence on the associations between long-term exposure to ambient air pollutants (including particle with aerodynamic diameter ≤10 μm (PM10), particle with aerodynamic diameter ≤2.5 μm (PM2.5), ozone (O3), nitrogen dioxide (NO2), and sulfur dioxide (SO2)) and prevalence of metabolic syndrome (MetS) remains inconclusive. This study aimed to determine the associations based on a case-control study nested in the Wuhan Chronic Disease Cohort study (WCDCS), a population-based study with baseline survey in 2019. METHODS A total of 10,253 residents living in Wuhan were recruited. The 3-year average concentrations of main pollutants (PM10, PM2.5, O3, NO2, and SO2) at residences prior to the survey date were estimated to evaluate the long-term exposures. The generalized linear mixed models were used to investigate the changes in MetS prevalence by an IQR increases in each air pollutant exposure concentrations. Interaction effects between air pollutants and demographic, lifestyle, and dietary factors on MetS were evaluated by including an interactive item in the main model. RESULTS The prevalence of MetS in Wuhan was 9.8%, and the 3-year exposure concentrations of PM10, PM2.5, O3, NO2, and SO2 were 84.1 μg/m3, 50.5 μg/m3, 55.7 μg/m3, 46.0 μg/m3, and 9.4 μg/m3, respectively. Higher PM10, PM2.5 and O3 exposure concentrations were associated with an elevated MetS prevalence (e.g. an IQR increase in PM2.5, OR = 1.193, 95% confidence intervals (95%CIs): 1.028, 1.385; for O3, OR = 1.074, 95%CIs: 1.025, 1.124), whereas NO2, and SO2 were negatively or insignificant correlated with odds of Mets (e.g. an IQR increase in NO2, OR = 0.865, 95%CIs: 0.795, 0.941). Males, smokers, alcohol drinkers and individuals who intake fruits occasionally exposure to PM10 and PM2.5 were found had a higher risk of developing MetS. CONCLUSIONS Long-term exposure to higher concentrations of ambient air pollutants may elevate the prevalence of MetS in populations in Central China. Susceptible individuals especially those with unhealthy lifestyles had a higher risk for MetS.
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Affiliation(s)
- Yixuan Wang
- Department of Global Health, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan, 430071, China; Global Health Institute, School of Public Health, 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, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan, 430071, China
| | - Yifan Yao
- Department of Global Health, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan, 430071, China; Global Health Institute, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan, 430071, China
| | - Meijin Chen
- Department of Global Health, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan, 430071, China; Global Health Institute, School of Public Health, 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, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan, 430071, China
| | - Yaqiong Yan
- Wuhan Centers for Disease Control and Prevention, No.288 Machang Road, Wuhan, China.
| | - Hao Xiang
- Department of Global Health, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan, 430071, China; Global Health Institute, School of Public Health, Wuhan University, 115# Donghu Road, Wuhan, 430071, China.
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Cheng X, Ji X, Yang D, Zhang C, Chen L, Liu C, Meng X, Wang W, Li H, Kan H, Huang H. Associations of PM 2.5 exposure with blood glucose impairment in early pregnancy and gestational diabetes mellitus. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2022; 232:113278. [PMID: 35131583 DOI: 10.1016/j.ecoenv.2022.113278] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 01/22/2022] [Accepted: 01/30/2022] [Indexed: 06/14/2023]
Abstract
Exposure to fine particulate matter (PM2.5) during pregnancy has been linked to the risk of gestational diabetes mellitus (GDM), while conclusions are inconsistent. In this study we aimed to estimate the effects of prenatal PM2.5 exposure with blood glucose in early pregnancy and the GDM risk. Participants were recruited from the SH-IPMCH-BTH cohort (n = 41,929), a study of air pollution and birth outcome. All participants provided serum samples for analyses of fasting blood glucose (FBG) and HbA1c during early pregnancy. GDM was diagnosed using an oral glucose tolerance test (OGTT) with the time interval of 1 h. Prenatal exposure to PM2.5 was estimated using gap-filled satellite exposure assessments in Shanghai, China. Both FBG and HbA1c levels were significantly and positively associated with PM2.5 exposure during early pregnancy. A 10 μg/m3 increase of PM2.5 exposure from early to middle pregnancy was associated with the risk of GDM (first trimester OR=1.09, 95% CI: 1.02, 1.16; second trimester OR=1.09, 95% CI: 1.03, 1.16; first two trimester OR=1.15, 95%CI: 1.04, 1.28). The combined effects were greater among elevated FBG and HbA1c women with higher PM2.5 exposure in middle trimester (P for interaction=0.037 and 0.001, respectively). This study found that exposure to PM2.5 exposure in the 1st and 2nd trimesters was related to GDM. FBG and HbA1c played roles in the relationship between PM2.5 exposure in the 2nd trimester and GDM.
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Affiliation(s)
- Xiaoyue Cheng
- International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China; Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China
| | - Xinhua Ji
- International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China; Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China
| | - Dongjian Yang
- International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China; Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China
| | - Chen Zhang
- International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Lei Chen
- International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Cong Liu
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, China
| | - Xia Meng
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, China
| | - Weidong Wang
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, China
| | - Huichu Li
- Department of Environmental Health, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Haidong Kan
- School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Laboratory of Health Technology Assessment, Fudan University, Shanghai, China; Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Fudan University, Shanghai, China
| | - Hefeng Huang
- International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China; Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China.
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Guo B, Guo Y, Nima Q, Feng Y, Wang Z, Lu R, Baimayangji, Ma Y, Zhou J, Xu H, Chen L, Chen G, Li S, Tong H, Ding X, Zhao X. Exposure to air pollution is associated with an increased risk of metabolic dysfunction-associated fatty liver disease. J Hepatol 2022; 76:518-525. [PMID: 34883157 DOI: 10.1016/j.jhep.2021.10.016] [Citation(s) in RCA: 95] [Impact Index Per Article: 47.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 09/24/2021] [Accepted: 10/14/2021] [Indexed: 02/08/2023]
Abstract
BACKGROUND & AIMS Accumulating animal studies have demonstrated the harmful contribution of ambient air pollution (AP) to metabolic dysfunction-associated fatty liver disease (MAFLD), but corresponding epidemiological evidence is limited. We examined the associations between long-term AP exposure and MAFLD prevalence in a Chinese population. METHODS We conducted a cross-sectional study of 90,086 participants recruited in China from 2018 to 2019. MAFLD was assessed based on radiologically diagnosed hepatic steatosis and the presence of overweight/obese status, diabetes mellitus, or metabolic dysregulation. Residence-specific levels of air pollutants, including particulate matter with aerodynamic diameters of ≤1 μm (PM1), ≤2.5 μm (PM2.5), and ≤10 μm (PM10), and nitrogen dioxide (NO2), were estimated by validated spatiotemporal models. We used logistic regression models to examine the AP-MAFLD associations and further evaluated potential effect modifications by demographics, lifestyle, central obesity, and diabetes status. RESULTS Increased exposure levels to all 4 air pollutants were significantly associated with increased odds of MAFLD, with odds ratios (ORs) of 1.13 (95% CI 1.10-1.17), 1.29 (1.25-1.34), 1.11 (1.09-1.14), and 1.15 (1.12-1.17) for each 10 μg/m3 increase in PM1, PM2.5, PM10, and NO2, respectively. Further stratified analyses revealed that individuals who are male, alcohol drinkers, and current and previous smokers, those who consume a high-fat diet, and those with central obesity experience more significant adverse effects from AP exposure than other individuals. CONCLUSIONS This study provides evidence that long-term exposure to ambient PM1, PM2.5, PM10, and NO2 may increase the odds of MAFLD in the real world. These effects may be exacerbated by unhealthy lifestyle habits and central obesity. LAY SUMMARY We conducted an epidemiological study on the potential effect of ambient air pollution on the risk of metabolic dysfunction-associated fatty liver disease (MAFLD) in approximately 90 thousand adults in China. We found that long-term exposure to ambient air pollution may increase the odds of MAFLD, especially in individuals who are male, smokers, and alcohol drinkers, those who consume a high-fat diet, and those with central obesity.
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Affiliation(s)
- Bing Guo
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yuming Guo
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Qucuo Nima
- Tibet Center for Disease Control and Prevention, Lhasa, Tibet, China
| | - Yuemei Feng
- Department of Epidemiology and Health Statistics, School of Public Health, Kunming Medical University, Kunming, Yunnan, China
| | - Ziyun Wang
- School of Public Health, the key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, Guizhou, China
| | - Rong Lu
- Chengdu Center for Disease Control and Prevention, Chengdu, Sichuan, China
| | | | - Yue Ma
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Junmin Zhou
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Huan Xu
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Lin Chen
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Gongbo Chen
- Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment; Department of Occupational and Environmental Health, School of Public Health, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Shanshan Li
- Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Huan Tong
- Department of Gastroenterology; Lab of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, China.
| | - Xianbin Ding
- Chongqing Municipal Center for Disease Control and Prevention, Chongqing, China.
| | - Xing Zhao
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China.
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Mun S, Park K, Lee S. Evaluation of thermal sensitivity is of potential clinical utility for the predictive, preventive, and personalized approach advancing metabolic syndrome management. EPMA J 2022; 13:125-135. [PMID: 35265229 PMCID: PMC8897525 DOI: 10.1007/s13167-022-00273-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 01/23/2022] [Indexed: 01/28/2023]
Abstract
A possible association between metabolic disorders and ambient temperature has been suggested, and cold exposure as a way of increasing energy expenditure has gained considerable interest for preventative/therapeutic measures toward metabolic disorders. Although thermal sensitivity, which has recently been studied in regard to its utility as a risk assessment/patient stratification for various diseases, might influence physiological responses to ambient temperature on an individual basis, more studies are needed. We aimed to investigate the association between self-identified thermal intolerance/sensation and metabolic syndrome (MetS) to verify the working hypothesis that individuals with altered thermal sensitivity may have a predisposition to MetS. We fitted generalized additive models for thermal intolerance/sensation using body mass index (BMI) and waist–hip ratio in women, and identified those with higher/lower thermal intolerance/sensation than those predicted by the models. Higher heat intolerance, higher heat sensation, and lower cold intolerance were associated with a higher prevalence of MetS. The risk of having MetS was increased in those who had two or three associated conditions compared with those with none of these conditions. In an analysis for MetS components, significant associations of thermal sensitivity were present with high glucose, triglyceride, and blood pressure levels. Overall, higher heat intolerance/sensation and lower cold intolerance were associated with increased prevalence of MetS even at a similar level of obesity. Our study indicates that evaluation of thermal sensitivity may help identify individuals at high risk for MetS, and lead to more advanced patient stratification and personalized treatment strategies for MetS, including cold-induced thermogenesis.
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Lin J, Zheng H, Xia P, Cheng X, Wu W, Li Y, Ma C, Zhu G, Xu T, Zheng Y, Qiu L, Chen L. Long-term ambient PM 2.5 exposure associated with cardiovascular risk factors in Chinese less educated population. BMC Public Health 2021; 21:2241. [PMID: 34893063 PMCID: PMC8662859 DOI: 10.1186/s12889-021-12163-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 11/01/2021] [Indexed: 11/10/2022] Open
Abstract
INTRODUCTION Long-term exposure to ambient air pollution is related to major cardiovascular risk factors including diabetes, hypertension, hyperlipidemia and overweight, but with few studies in high-concentration nations like China so far. We aimed to investigate the association between long-term exposure to ambient fine particulate matter (particles with an aerodynamic diameter ≤ 2.5 μm, PM2.5) and major cardiovascular risk factors in China. METHODS Adult participants with selected biochemical tests were recruited from the Chinese Physiological Constant and Health Condition (CPCHC) survey conducted from 2007 to 2011. Gridded PM2.5 data used were derived from satellite-observed data with adjustment of ground-observed data. District-level PM2.5 data were generated to estimate the association using multivariate logistic regression model and generalized additive model. RESULTS A total of 19,236 participants from the CPCHC survey were included with an average age of 42.8 ± 16.1 years, of which nearly half were male (47.0%). The annual average PM2.5 exposure before the CPCHC survey was 33.4 (14.8-53.4) μg/m3, ranging from 8.0 μg/m3 (Xiwuqi) to 94.7 μg/m3 (Chengdu). Elevated PM2.5 was associated with increased prevalence of hypertension (odds ratio (OR) =1.022, 95% confidence interval (95%CI): 1.001, 1.043) and decreased prevalence of overweight (OR = 0.926, 95%CI: 0.910, 0.942). Education significantly interacted with PM2.5 in association with all the interesting risk factors. Each 10 μg/m3 increment of PM2.5 was associated with increased prevalence of diabetes (OR = 1.118, 95%CI: 1.037, 1.206), hypertension (OR = 1.101, 95%CI: 1.056, 1.147), overweight (OR = 1.071, 95%CI: 1.030, 1.114) in participants with poor education, but not in well-educated population. PM2.5 exposure was negatively associated with hyperlipidemia in all participants (OR = 0.939, 95%CI: 0.921, 0.957). The results were robust in all the sensitivity analyses. CONCLUSION Association between long-term PM2.5 exposure and cardiovascular risk factors might be modified by education. PM2.5 was associated with a higher prevalence of diabetes, hypertension, and overweight in a less-educated population with time-expose dependency. Long-term exposure to PM2.5 might be associated with a lower prevalence of hyperlipidemia.
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Affiliation(s)
- Jianfeng Lin
- Department of Nephrology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Hua Zheng
- Department of Nephrology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Peng Xia
- Department of Nephrology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Xinqi Cheng
- Department of Laboratory Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Wei Wu
- Department of Laboratory Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Yang Li
- Department of Nephrology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Chaochao Ma
- Department of Laboratory Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
| | - Guangjin Zhu
- Department of Pathophysiology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Tao Xu
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yali Zheng
- Department of Nephrology, Affiliated Ningxia People's Hospital of Ningxia Medical University, Yinchuan, China
| | - Ling Qiu
- Department of Laboratory Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
| | - Limeng Chen
- Department of Nephrology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
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Qin P, Luo X, Zeng Y, Zhang Y, Li Y, Wu Y, Han M, Qie R, Wu X, Liu D, Huang S, Zhao Y, Feng Y, Yang X, Hu F, Sun X, Hu D, Zhang M. Long-term association of ambient air pollution and hypertension in adults and in children: A systematic review and meta-analysis. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 796:148620. [PMID: 34274662 DOI: 10.1016/j.scitotenv.2021.148620] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 06/16/2021] [Accepted: 06/19/2021] [Indexed: 06/13/2023]
Abstract
AIMS The association of long-term ambient air pollution and hypertension has been inconsistently reported. We performed an updated systematic review and meta-analysis to assess the association between long-term exposure to ambient air pollution and risk of hypertension in adults and in children. METHODS PubMed, EMBASE, and Web of Science were searched up to August 7, 2020 for published articles examining the association of long-term exposure to ambient air pollution, including particulate matter (PM; ultrafine particles, PM1, PM1-2.5, PM2.5, PM2.5-10 and PM10), nitrogen dioxide (NO2), nitrogen oxides (NOx), sulfur dioxide (SO2), ozone (O3), carbon monoxide (CO) and hypertension. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) for hypertension with each 10-μg/m3 increase in air pollutants were calculated by random-effects models. RESULTS We included 57 studies (53 of adults and 4 of children) in the meta-analysis. Risk of hypertension was significantly increased in adults with each 10-μg/m3 increase in exposure to PM2.5 (OR 1.10, 95% CI 1.07-1.14; I2 = 93.1%; n = 37), PM10 (1.04, 1.02-1.07; I2 = 44.8%; n = 22), and SO2 (1.21, 1.08-1.36; I2 = 96.6%; n = 3). Hypertension was not significantly associated with PM1 (n = 2), PM2.5-10 (n = 16), NO2 (n = 27), or NOx (n = 17). In children, the summary ORs (95% CIs) for each 10-μg/m3 increase in PM2.5, PM10, SO2 and O3 were 2.82 (0.51-15.68; I2 = 83.8%; n = 2), 1.15 (1.01-1.32; I2 = 0; n = 2), 8.57 (0.13-575.58; I2 = 94.2%; n = 2), and 1.26 (0.81-1.09, I2 = 91.6%; n = 2), respectively. CONCLUSIONS Long-term ambient air pollution is a potential risk factor for hypertension in adults. More studies are needed to explore the effects of long-term air pollution on hypertension in children.
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Affiliation(s)
- Pei Qin
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, China; Guangdong Provincial Key Laboratory of Regional Immunity and Diseases, Department of Pathology, Shenzhen University School of Medicine, Shenzhen, Guangdong, China
| | - Xinping Luo
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, China; Guangdong Provincial Key Laboratory of Regional Immunity and Diseases, Department of Pathology, Shenzhen University School of Medicine, Shenzhen, Guangdong, China
| | - Yunhong Zeng
- Department of Health Management, Shenzhen Hospital of University of Chinese Academy of Sciences, Shenzhen, China
| | - Yanyan Zhang
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, China; Guangdong Provincial Key Laboratory of Regional Immunity and Diseases, Department of Pathology, Shenzhen University School of Medicine, Shenzhen, Guangdong, China
| | - Yang Li
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, China; Guangdong Provincial Key Laboratory of Regional Immunity and Diseases, Department of Pathology, Shenzhen University School of Medicine, Shenzhen, Guangdong, China; The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, China
| | - Yuying Wu
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, China; Guangdong Provincial Key Laboratory of Regional Immunity and Diseases, Department of Pathology, Shenzhen University School of Medicine, Shenzhen, Guangdong, China
| | - Minghui Han
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Henan, China
| | - Ranran Qie
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Henan, China
| | - Xiaoyan Wu
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, China; Guangdong Provincial Key Laboratory of Regional Immunity and Diseases, Department of Pathology, Shenzhen University School of Medicine, Shenzhen, Guangdong, China; Department of Health Management, Shenzhen Hospital of University of Chinese Academy of Sciences, Shenzhen, China
| | - Dechen Liu
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Henan, China
| | - Shengbing Huang
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Henan, China
| | - Yang Zhao
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Henan, China
| | - Yifei Feng
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Henan, China
| | - Xingjin Yang
- Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Henan, China
| | - Fulan Hu
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, China; Guangdong Provincial Key Laboratory of Regional Immunity and Diseases, Department of Pathology, Shenzhen University School of Medicine, Shenzhen, Guangdong, China
| | - Xizhuo Sun
- The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, China
| | - Dongsheng Hu
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, China; Guangdong Provincial Key Laboratory of Regional Immunity and Diseases, Department of Pathology, Shenzhen University School of Medicine, Shenzhen, Guangdong, China; The Affiliated Luohu Hospital of Shenzhen University Health Science Center, Shenzhen, Guangdong, China; Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Henan, China
| | - Ming Zhang
- Department of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, Guangdong, China; Guangdong Provincial Key Laboratory of Regional Immunity and Diseases, Department of Pathology, Shenzhen University School of Medicine, Shenzhen, Guangdong, China.
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Kwon S, Lee M, Crowley G, Schwartz T, Zeig-Owens R, Prezant DJ, Liu M, Nolan A. Dynamic Metabolic Risk Profiling of World Trade Center Lung Disease: A Longitudinal Cohort Study. Am J Respir Crit Care Med 2021; 204:1035-1047. [PMID: 34473012 DOI: 10.1164/rccm.202006-2617oc] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Rationale: Metabolic syndrome (MetSyn) increases the risk of World Trade Center (WTC) lung injury (LI). However, the temporal relationship of MetSyn, exposure intensity, and lung dysfunction is not well understood. Objective: To model the association of longitudinal MetSyn characteristics with WTC lung disease to define modifiable risk. Methods: Firefighters, for whom consent was obtained (N = 5,738), were active duty on September 11, 2001 (9/11). WTC-LI (n = 1,475; FEV1% predicted <lower limit of normal [LLN]) and non-WTC-LI (n = 4,263; FEV1% predicted ⩾LLN at all exams) was the primary outcome, and FVC% predicted <LLN and FEV1/FVC <0.70 were secondary outcomes. We assessed 1) the effect of concurrent MetSyn on longitudinal lung function by linear mixed models, 2) the temporal effect of MetSyn and exposure by Weibull proportional hazards, 3) the effects of MetSyn's rate of change by two-stage models, and 4) the nonlinear joint effect of longitudinal MetSyn components by a partially linear single-index model (PLSI). Measurements and Main Results: WTC-LI cases were more often ever-smokers, arrived in the morning (9/11), and had MetSyn. Body mass index ⩾30 kg/m2 and high-density lipoprotein <40 mg/dl were most contributory to concurrent loss of FEV1% predicted and FVC% predicted while conserving FEV1/FVC. Body mass index ⩾30 kg/m2 and dyslipidemia significantly predicted WTC-LI, FVC% predicted <LLN in a Weibull proportional hazards model. Dynamic risk assessment of WTC-LI on the basis of MetSyn and exposure showed how reduction of MetSyn factors further reduces WTC-LI likelihood in susceptible populations. PLSI demonstrates that MetSyn has a nonlinear relationship with WTC lung disease, and increases in cumulative MetSyn risk factors exponentially increase WTC-LI risk. An interactive metabolic-risk modeling application was developed to simplify PLSI interpretation. Conclusions: MetSyn and WTC exposure contribute to the development of lung disease. Dynamic risk assessment may be used to encourage treatment of MetSyn in susceptible populations. Future studies will focus on dietary intervention as a disease modifier.
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Affiliation(s)
- Sophia Kwon
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine
| | - Myeonggyun Lee
- Division of Biostatistics, Department of Population Health, and
| | - George Crowley
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine
| | - Theresa Schwartz
- Bureau of Health Services and Office of Medical Affairs, Fire Department of New York, Brooklyn, New York; and
| | - Rachel Zeig-Owens
- Bureau of Health Services and Office of Medical Affairs, Fire Department of New York, Brooklyn, New York; and.,Department of Epidemiology and Population Health and
| | - David J Prezant
- Bureau of Health Services and Office of Medical Affairs, Fire Department of New York, Brooklyn, New York; and.,Pulmonary Medicine Division, Department of Medicine, Montefiore Medical Center and Albert Einstein College of Medicine, Bronx, New York
| | - Mengling Liu
- Division of Biostatistics, Department of Population Health, and.,Department of Environmental Medicine, New York University School of Medicine, New York, New York
| | - Anna Nolan
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine.,Department of Environmental Medicine, New York University School of Medicine, New York, New York.,Bureau of Health Services and Office of Medical Affairs, Fire Department of New York, Brooklyn, New York; and
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Zang ST, Luan J, Li L, Wu QJ, Chang Q, Dai HX, Zhao YH. Air pollution and metabolic syndrome risk: Evidence from nine observational studies. ENVIRONMENTAL RESEARCH 2021; 202:111546. [PMID: 34265350 DOI: 10.1016/j.envres.2021.111546] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 06/05/2021] [Accepted: 06/15/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND AIMS Globally, the number of metabolic syndrome (MetS) cases has increased substantially over time. However, the association between air pollution (AP) and MetS risk has been contradictory in observational studies. This is the first reported meta-analysis quantitatively exploring the aforementioned association. METHODS We searched PubMed, Embase, and Web of Science database entries up to September 14, 2020, and searches were updated up to December 6, 2020 to identify eligible articles on the AP-MetS risk association. No language restriction was imposed. Random-effects models were applied to estimate summary and subgroup effect sizes with 95% confidence intervals (CIs). PROSPERO registration number: CRD42020210431. RESULTS Eight articles (nine studies) were eligible for the meta-analysis. Increased MetS prevalence was not found to be associated with particulate matter less than 1 μm (PM1), 2.5 μm (PM2.5), and 10 μm (PM10) in diameter or nitrogen dioxide (NO2), and the summary effect sizes were 1.33 (95% CI: 0.95-1.85), 1.34 (95% CI: 0.96-1.89), 1.18 (95% CI: 0.98-1.19), and 1.28 (95% CI: 0.89-1.82), respectively, based on cross-sectional studies. The summary results indicated no association between each 10 μg/m3 increase in PM2.5 and MetS incidence (effect size 2.78 [95% CI: 0.70-11.02]), based on cohort studies. Subgroup analysis demonstrated that MetS incidence in older men increased dramatically by 992% with each 10 μg/m3 increase in PM2.5. CONCLUSIONS The evidence presented here suggests that although exposure to PM1, PM2.5, PM10, or NO2 was not found to have a significant association with the occurrence of MetS, the statistical significance of the relationship between exposure to PM1, PM2.5, or PM10 and MetS prevalence was approximately borderline. More studies on AP-MetS risk association in low-/middle-income countries, as well as on the association between other air pollutants and MetS risk, are warranted. A sufficient number of high-quality studies is required to perform a meaningful meta-analysis of the relationship between air pollutants and MetS.
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Affiliation(s)
- Si-Tian Zang
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, No. 36 Sanhao Street, Heping District, Shenyang, Liaoning, 110004, China; Clinical Research Center, Shengjing Hospital of China Medical University, No. 39 Huaxiang Road, Tiexi District, Shenyang, Liaoning, 110022, China.
| | - Jie Luan
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, No. 36 Sanhao Street, Heping District, Shenyang, Liaoning, 110004, China; Clinical Research Center, Shengjing Hospital of China Medical University, No. 39 Huaxiang Road, Tiexi District, Shenyang, Liaoning, 110022, China.
| | - Ling Li
- Center for Precision Medicine Research and Training, University of Macau, Avenida da Universidade Taipa, Macau, 999078, China.
| | - Qi-Jun Wu
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, No. 36 Sanhao Street, Heping District, Shenyang, Liaoning, 110004, China; Clinical Research Center, Shengjing Hospital of China Medical University, No. 39 Huaxiang Road, Tiexi District, Shenyang, Liaoning, 110022, China.
| | - Qing Chang
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, No. 36 Sanhao Street, Heping District, Shenyang, Liaoning, 110004, China; Clinical Research Center, Shengjing Hospital of China Medical University, No. 39 Huaxiang Road, Tiexi District, Shenyang, Liaoning, 110022, China.
| | - Hui-Xu Dai
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, No. 36 Sanhao Street, Heping District, Shenyang, Liaoning, 110004, China; Clinical Research Center, Shengjing Hospital of China Medical University, No. 39 Huaxiang Road, Tiexi District, Shenyang, Liaoning, 110022, China.
| | - Yu-Hong Zhao
- Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, No. 36 Sanhao Street, Heping District, Shenyang, Liaoning, 110004, China.
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Positive Association between Indoor Gaseous Air Pollution and Obesity: An Observational Study in 60 Households. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182111447. [PMID: 34769965 PMCID: PMC8582717 DOI: 10.3390/ijerph182111447] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Revised: 10/25/2021] [Accepted: 10/28/2021] [Indexed: 01/02/2023]
Abstract
This study aims to analyze whether exposure to indoor air pollution affects obesity. In our research, we recruited 127 participants, with an average age of 43.30 ± 15.38 years old, residing in 60 households. We monitored indoor air quality for 24 h, and conducted both questionnaire surveys and collected serum samples for analysis, to assess the relationship between indoor air pollutant exposure and obesity. After adjusting for demographic characteristics, the results showed that CO2 exposure is positively associated with being overweight and with a higher risk of being abdominally obese. Exposures to CO and formaldehyde were also positively associated with being overweight. IQR increase in TVOC was positively associated with increases in the risk of a high BMI, being abdominally obese and having a high body fat percentage. Two-pollutant models demonstrate that TVOCs presented the strongest risks associated with overweightness. We concluded that persistent exposure to indoor gaseous pollutants increases the risk of overweightness and obesity, as indicated by the positive association with BMI, abdominal obesity, and percentage body fat. TVOCs display the strongest contribution to obesity.
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