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Dai C, Sun X, Wu L, Chen J, Hu X, Ding F, Chen W, Lei H, Li X. Associations between exposure to various air pollutants and risk of metabolic syndrome: a systematic review and meta-analysis. Int Arch Occup Environ Health 2024:10.1007/s00420-024-02072-0. [PMID: 38733545 DOI: 10.1007/s00420-024-02072-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 04/30/2024] [Indexed: 05/13/2024]
Abstract
BACKGROUND Metabolic Syndrome (MetS) is a widely observed metabolic disorder that is increasingly prevalent worldwide, leading to substantial societal consequences. Previous studies have conducted two separate meta-analyses to investigate the relationship between MetS and air pollutants. However, these studies yielded conflicting results, necessitating a thorough systematic review and meta-analysis to reassess the link between different air pollutants and the risk of developing MetS. METHODS We conducted a comprehensive search of relevant literature in databases including PubMed, Embase, Cochrane Library, and Web of Science up to October 9, 2023. The search was specifically restricted to publications in the English language. Following the screening of studies investigating the correlation between air pollution and MetS, we utilized random-effects models to calculate pooled effect sizes along with their respective 95% confidence intervals (CIs). We would like to highlight that this study has been registered with PROSPERO, and it can be identified by the registration number CRD42023484421. RESULTS The study included twenty-four eligible studies. The results revealed that an increase of 10 μg/m3 in annual concentrations of PM1, PM2.5, PM10, NO2, SO2, and O3 was associated with a 29% increase in metabolic syndrome (MetS) risk for PM1 (OR = 1.29 [CI 1.07-1.54]), an 8% increase for PM2.5 (OR = 1.08 [CI 1.06-1.10]), a 17% increase for PM10 (OR = 1.17 [CI 1.08-1.27]), a 24% increase for NO2 (OR = 1.24 [CI 1.01-1.51]), a 19% increase for SO2 (OR = 1.19 [CI 1.04-1.36]), and a 10% increase for O3 (OR = 1.10 [CI 1.07-1.13]). CONCLUSION The findings of this study demonstrate a significant association between exposure to fine particulate matter (PM1, PM2.5, PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and the incidence of metabolic syndrome (MetS). Moreover, the results suggest that air pollution exposure could potentially contribute to the development of MetS in humans.
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Affiliation(s)
- Changmao Dai
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 61000, Sichuan Province, China
| | - Xiaolan Sun
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 61000, Sichuan Province, China
| | - Liangqing Wu
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 61000, Sichuan Province, China
| | - Jiao Chen
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 61000, Sichuan Province, China
| | - Xiaohong Hu
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 61000, Sichuan Province, China
| | - Fang Ding
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 61000, Sichuan Province, China
| | - Wei Chen
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 61000, Sichuan Province, China
| | - Haiyan Lei
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 61000, Sichuan Province, China
| | - Xueping Li
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 61000, Sichuan Province, China.
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Zhou Q, Li X, Zhang J, Duan Z, Mao S, Wei J, Han S, Niu Z. Long-term exposure to PM 1 is associated with increased prevalence of metabolic diseases: evidence from a nationwide study in 123 Chinese cities. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:549-563. [PMID: 38015390 DOI: 10.1007/s11356-023-31098-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 11/14/2023] [Indexed: 11/29/2023]
Abstract
Exposure to particulate matter (PM) has been linked to metabolic diseases. However, the effects of PM with an aerodynamic diameter ≤ 1.0 µm (PM1) on metabolic diseases remain unclear. This study is aimed at assessing the associations of PM1 with metabolic disease risk and quantifying the concentration-response (C-R) relationship of PM1 with metabolic disease risk. A national cross-sectional study was conducted, including 12,495 middle-aged and older adults in 123 Chinese cities. The two-year average concentration of PM1 was evaluated using satellite-based spatiotemporal models. Metabolic diseases, including abdominal obesity, diabetes, hypertension, dyslipidemia, and metabolic syndrome, were identified based on physical examination, blood standard biochemistry examination, and self-reported disease histories. Generalized linear models and C-R curves were used to evaluate the associations of PM1 with metabolic diseases. A total of 12,495 participants were included in this study, with a prevalence of 45.73% for abdominal obesity, 20.22% for diabetes, 42.46% for hypertension, 41.01% for dyslipidemia, and 33.78% for metabolic syndrome. The mean ± standard deviation age of participants was 58.79 ± 13.14 years. In addition to dyslipidemia, exposure to PM1 was associated with increased risks of abdominal obesity, diabetes, hypertension, and metabolic syndrome. Each 10 μg/m3 increase in PM1 concentrations was associated with 39% (odds ratio (OR) = 1.39, 95% confidence interval (CI) 1.33, 1.46) increase in abdominal obesity, 18% (OR = 1.18, 95%CI 1.12, 1.25) increase in diabetes, 11% (OR = 1.11, 95%CI 1.06, 1.16) increase in hypertension, and 25% (OR = 1.25, 95%CI 1.19, 1.31) in metabolic syndrome, respectively. C-R curves showed that the OR values of abdominal obesity, diabetes, hypertension, and metabolic syndrome were increased gradually with the increase of PM1 concentrations. Subgroup analysis indicated that exposure to PM1 was associated with increased metabolic disease risks among participants with different lifestyles and found that solid fuel users were more susceptible to PM1 than clean fuel users. This national cross-sectional study indicated that exposure to higher PM1 might increase abdominal obesity, diabetes, hypertension, and metabolic syndrome risk, and solid fuel use might accelerate the adverse effects of PM1 on metabolic syndrome risk. Further longitudinal cohort studies are warranted to establish a causal inference between PM1 exposure and metabolic disease risk.
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Affiliation(s)
- Qin Zhou
- Department of Orthodontics, College of Stomatology, Xi'an Jiaotong University, No. 98 XiWu Road, Xi'an, 710004, Shaanxi, China
| | - Xianfeng Li
- Department of Reproductive Service Technology, Urumqi Maternal and Child Health Hospital, No. 344 Jiefang South Road, Tianshan District, Urumqi, 830000, China
| | - Jinglong Zhang
- Department of Cardiovascular Surgery, Xijing Hospital, The Fourth Military Medical University, 127 West Changle Road, Xi'an, 710032, China
| | - Zhizhou Duan
- Preventive Health Service, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, 152 Aiguo Road, Nanchang, Jiangxi, China
| | - Shuyuan Mao
- The Third Affiliated Hospital of Zhengzhou University, 7 Kangfu Road, Zhengzhou, 450000, Henan, China
| | - Jing Wei
- Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, USA
| | - Shichao Han
- Department of Urology, Xijing Hospital, The Fourth Military Medical University, 127 West Changle Road, Xi'an, 710032, China
| | - Zhiping Niu
- Department of Environmental Health, School of Public Health, Fudan University, 196 Xietu Road, Shanghai, 200032, China.
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Lan Y, Helbich M. Short-term exposure sequences and anxiety symptoms: a time series clustering of smartphone-based mobility trajectories. Int J Health Geogr 2023; 22:27. [PMID: 37817189 PMCID: PMC10563352 DOI: 10.1186/s12942-023-00348-1] [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: 07/06/2023] [Accepted: 10/04/2023] [Indexed: 10/12/2023] Open
Abstract
BACKGROUND Short-term environmental exposures, including green space, air pollution, and noise, have been suggested to affect health. However, the evidence is limited to aggregated exposure estimates which do not allow the capture of daily spatiotemporal exposure sequences. We aimed to (1) determine individuals' sequential exposure patterns along their daily mobility paths and (2) examine whether and to what extent these exposure patterns were associated with anxiety symptoms. METHODS We cross-sectionally tracked 141 participants aged 18-65 using their global positioning system (GPS) enabled smartphones for up to 7 days in the Netherlands. We estimated their location-dependent exposures for green space, fine particulate matter, and noise along their moving trajectories at 10-min intervals. The resulting time-resolved exposure sequences were then partitioned using multivariate time series clustering with dynamic time warping as the similarity measure. Respondents' anxiety symptoms were assessed with the Generalized Anxiety Disorders-7 questionnaire. We fitted linear regressions to assess the associations between sequential exposure patterns and anxiety symptoms. RESULTS We found four distinctive daily sequential exposure patterns across the participants. Exposure patterns differed in terms of exposure levels and daily variations. Regression results revealed that participants with a "moderately health-threatening" exposure pattern were significantly associated with fewer anxiety symptoms than participants with a "strongly health-threatening" exposure pattern. CONCLUSIONS Our findings support that environmental exposures' daily sequence and short-term magnitudes may be associated with mental health. We urge more time-resolved mobility-based assessments in future analyses of environmental health effects in daily life.
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Affiliation(s)
- Yuliang Lan
- Department of Human Geography and Spatial Planning, Faculty of Geosciences, Utrecht University, Princetonlaan 8a, 3584 BC, Utrecht, The Netherlands.
| | - Marco Helbich
- Department of Human Geography and Spatial Planning, Faculty of Geosciences, Utrecht University, Princetonlaan 8a, 3584 BC, Utrecht, The Netherlands
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Wang X, Yang C, Lu L, Bai J, Wu H, Chen T, Liao W, Duan Z, Chen D, Liu Z, Ju K. Assessing the causal effect of long-term exposure to air pollution on cognitive decline in middle-aged and older adults - Empirical evidence from a nationwide longitudinal cohort. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2023; 255:114811. [PMID: 36963183 DOI: 10.1016/j.ecoenv.2023.114811] [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: 09/21/2022] [Revised: 03/14/2023] [Accepted: 03/17/2023] [Indexed: 06/18/2023]
Abstract
Air pollution remains a risk factor for the global burden of disease. Middle-aged and older people are more susceptible to air pollution because of their declining physical function and are more likely to develop diseases from long-term air pollution exposure. Studies of the effects of air pollution on cognitive function in middle-aged and older adults have been inconsistent. More representative and definitive evidence is needed. This study analysed data from the Chinese Family Panel Study, an ongoing nationwide prospective cohort study, collected in waves 2014, 2016 and 2018. Rigorously tested instrument was selected for analysis and participants' PM2.5 and instrument exposures were assessed using high-precision satellite data. The causal relationship between long-term exposure to air pollution and poor cognitive function in middle-aged and older adults was investigated using the Correlated Random Effects Control Function (CRE-CF) method within a quasi-experimental framework. This study included a total of 7042 participants aged 45 years or older. A comparison of CRE-CF with other models (OLS model, ordered probit model, and ordered probit-CRE model) demonstrated the necessity of using CRE-CF given the endogeneity of air pollution. The credibility and validity of the instrumental variable were verified. In the CRE-CF model, long-term exposure to PM2.5 was found to accelerate cognitive decline in middle-aged and older adults (coefficients of -0.159, -0.336 and -0.244 for the total cognitive, verbal and mathematical scores, respectively). Taken together, these results suggest that chronic exposure to ambient air pollution is associated with cognitive decline in middle-aged and older adults, which highlights the need for appropriate protective policies.
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Affiliation(s)
- Xu Wang
- West China Hospital, Sichuan University, Chengdu 610041, China
| | - Chenyu Yang
- Department of Big Data in Health Science, School of Public Health, Zhejiang University School of Medicine, Hangzhou 310058, China
| | - Liyong Lu
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu 610041, China
| | - Jing Bai
- Department of neurology, Xijing Hospital, Xi'an 710032, China
| | - Hao Wu
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu 610041, China
| | - Ting Chen
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu 610041, China
| | - Weibin Liao
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu 610041, China
| | - Zhongxin Duan
- West China Hospital, Sichuan University, Chengdu 610041, China
| | - Dapeng Chen
- Department of Economics, Lehigh University, Bethlehem, PA 18015, United States
| | - Zhenmi Liu
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu 610041, China.
| | - Ke Ju
- School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia.
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Exposure to ultrafine particles and the incidence of asthma in children: A population-based cohort study in Montreal, Canada. Environ Epidemiol 2022; 7:e236. [PMID: 36777524 PMCID: PMC9916019 DOI: 10.1097/ee9.0000000000000236] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 11/17/2022] [Indexed: 02/14/2023] Open
Abstract
Asthma is the most prevalent chronic respiratory disease in children. The role of ultrafine particles (UFPs) in the development of the disease remains unclear. We used a population-based birth cohort to evaluate the association between prenatal and childhood exposure to low levels of ambient UFPs and childhood-onset asthma. Methods The cohort included all children born and residing in Montreal, Canada, between 2000 and 2015. Children were followed for asthma onset from birth until <13 years of age. Spatially resolved annual mean concentrations of ambient UFPs were estimated from a land use regression model. We assigned prenatal exposure according to the residential postal code at birth. We also considered current exposure during childhood accounting for time-varying residence location. We estimated hazard ratios (HRs) using Cox proportional hazards models adjusted for age, sex, neighborhood material and social deprivation, calendar year, and coexposure to ambient nitrogen dioxide (NO2) and fine particles (PM2.5). Results The cohort included 352,966 children, with 30,825 children developing asthma during follow-up. Mean prenatal and childhood UFP exposure were 24,706 particles/cm3 (interquartile range [IQR] = 3,785 particles/cm3) and 24,525 particles/cm3 (IQR = 3,427 particles/cm3), respectively. Both prenatal and childhood UFP exposure were not associated with childhood asthma onset in single pollutant models (HR per IQR increase of 0.99 [95% CI = 0.98, 1.00]). Estimates of association remained similar when adjusting for coexposure to ambient NO2 and PM2.5. Conclusion In this population-based birth cohort, childhood asthma onset was not associated with prenatal or childhood exposure to low concentrations of UFPs.
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Chen Z, Liu P, Xia X, Wang L, Li X. The underlying mechanism of PM2.5-induced ischemic stroke. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 310:119827. [PMID: 35917837 DOI: 10.1016/j.envpol.2022.119827] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 07/04/2022] [Accepted: 07/19/2022] [Indexed: 06/15/2023]
Abstract
Under the background of global industrialization, PM2.5 has become the fourth-leading risk factor for ischemic stroke worldwide, according to the 2019 GBD estimates. This highlights the hazards of PM2.5 for ischemic stroke, but unfortunately, PM2.5 has not received the attention that matches its harmfulness. This article is the first to systematically describe the molecular biological mechanism of PM2.5-induced ischemic stroke, and also propose potential therapeutic and intervention strategies. We highlight the effect of PM2.5 on traditional cerebrovascular risk factors (hypertension, hyperglycemia, dyslipidemia, atrial fibrillation), which were easily overlooked in previous studies. Additionally, the effects of PM2.5 on platelet parameters, megakaryocytes activation, platelet methylation, and PM2.5-induced oxidative stress, local RAS activation, and miRNA alterations in endothelial cells have also been described. Finally, PM2.5-induced ischemic brain pathological injury and microglia-dominated neuroinflammation are discussed. Our ultimate goal is to raise the public awareness of the harm of PM2.5 to ischemic stroke, and to provide a certain level of health guidance for stroke-susceptible populations, as well as point out some interesting ideas and directions for future clinical and basic research.
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Affiliation(s)
- Zhuangzhuang Chen
- Department of Neurology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Peilin Liu
- Department of Neurology, The Second Hospital of Tianjin Medical University, Tianjin, China
| | - Xiaoshuang Xia
- Department of Neurology, The Second Hospital of Tianjin Medical University, Tianjin, China; Tianjin Interdisciplinary Innovation Centre for Health and Meteorology, Tianjin, China
| | - Lin Wang
- Department of Geriatrics, The Second Hospital of Tianjin Medical University, Tianjin, China; Tianjin Interdisciplinary Innovation Centre for Health and Meteorology, Tianjin, China
| | - Xin Li
- Department of Neurology, The Second Hospital of Tianjin Medical University, Tianjin, China; Tianjin Interdisciplinary Innovation Centre for Health and Meteorology, Tianjin, China.
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