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Ye L, Zhou J, Tian Y, Cui J, Chen C, Wang J, Wang Y, Wei Y, Ye J, Li C, Chai X, Sun C, Li F, Wang J, Guo Y, Jaakkola JJK, Lv Y, Zhang J, Shi X. Associations of residential greenness and ambient air pollution with overweight and obesity in older adults. Obesity (Silver Spring) 2023; 31:2627-2637. [PMID: 37649157 DOI: 10.1002/oby.23856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 05/28/2023] [Accepted: 05/31/2023] [Indexed: 09/01/2023]
Abstract
OBJECTIVE This study aimed to examine the impact of greenness and fine particulate matter <2.5 μm (PM2.5 ) on overweight/obesity among older adults in China. METHODS A total of 21,355 participants aged ≥65 years were included from the Chinese Longitudinal Healthy Longevity Survey between 2000 and 2018. Normalized difference vegetation index (NDVI) with a radius of 250 m and PM2.5 in a 1 × 1-km grid resolution were calculated around each participant's residence. Cox proportional hazards models were used to estimate the effects of NDVI and PM2.5 on overweight/obesity. Interaction and mediation analyses were conducted to explore combined effects. RESULTS The study observed 1895 incident cases of overweight/obesity over 109,566 person-years. For every 0.1-unit increase in NDVI the hazard ratio of overweight/obesity was 0.91 (95% CI: 0.88-0.95), and for every 10-μg/m3 increase in PM2.5 the hazard ratio was 1.11 (95% CI: 1.07-1.14). The effect of NDVI on overweight/obesity was partially mediated by PM2.5 , with a relative mediation proportion of 20.10% (95% CI: 1.63%-38.57%). CONCLUSIONS Greenness exposure appears to lower the risk of overweight/obesity in older adults in China, whereas PM2.5 , acting as a mediator, partly mediated this protective effect.
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Affiliation(s)
- Lihong Ye
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Jinhui Zhou
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yanlin Tian
- Chinese Research Academy of Environmental Sciences, Beijing, China
| | - Jia Cui
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Chen Chen
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jun Wang
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yueqing Wang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Yuan Wei
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Jiaming Ye
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Chenfeng Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- School of Public Health, Anhui Medical University, Hefei, China
| | - Xin Chai
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Chris Sun
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Fangyu Li
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- School of Public Health, Anhui Medical University, Hefei, 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, China
- Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yanbo Guo
- China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, China
| | - Jouni J K Jaakkola
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
- Finnish Meteorological Institute, Helsinki, Finland
| | - 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
| | - Juan Zhang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - 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
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Xu J, Jing Y, Xu X, Zhang X, Liu Y, He H, Chen F, Liu Y. Spatial scale analysis for the relationships between the built environment and cardiovascular disease based on multi-source data. Health Place 2023; 83:103048. [PMID: 37348293 DOI: 10.1016/j.healthplace.2023.103048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 05/12/2023] [Accepted: 05/19/2023] [Indexed: 06/24/2023]
Abstract
To examine what built environment characteristics improve the health outcomes of human beings is always a hot issue. While a growing literature has analyzed the link between the built environment and health, few studies have investigated this relationship across different spatial scales. In this study, eighteen variables were selected from multi-source data and reduced to eight built environment attributes using principal component analysis. These attributes included socioeconomic deprivation, urban density, street walkability, land-use diversity, blue-green space, transportation convenience, ageing, and street insecurity. Multiscale geographically weighted regression was then employed to clarify how these attributes relate to cardiovascular disease at different scales. The results indicated that: (1) multiscale geographically weighted regression showed a better fit of the association between the built environment and cardiovascular diseases than other models (e.g., ordinary least squares and geographically weighted regression), and is thus an effective approach for multiscale analysis of the built environment and health associations; (2) built environment variables related to cardiovascular diseases can be divided into global variables with large scales (e.g., socioeconomic deprivation, street walkability, land-use diversity, blue-green space, transportation convenience, and ageing) and local variables with small scales (e.g., urban density and street insecurity); and (3) at specific spatial scales, global variables had trivial spatial variation across the area, while local variables showed significant gradients. These findings provide greater insight into the association between the built environment and lifestyle-related diseases in densely populated cities, emphasizing the significance of hierarchical and place-specific policy formation in health interventions.
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Affiliation(s)
- Jiwei Xu
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079, PR China
| | - Ying Jing
- Business School, Ningbo Institute of Technology, Zhejiang University, Ningbo, 315100, PR China
| | - Xinkun Xu
- Fujian Provincial Expressway Information Technology Company Limited, Fuzhou, 350000, PR China
| | - Xinyi Zhang
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079, PR China
| | - Yanfang Liu
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079, PR China; Key Laboratory of Geographic Information System of Ministry of Education, Wuhan University, Wuhan, 430079, PR China; Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan, 430079, PR China
| | - Huagui He
- Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou, 510060, PR China
| | - Fei Chen
- Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou, 510060, PR China
| | - Yaolin Liu
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, 430079, PR China; Key Laboratory of Geographic Information System of Ministry of Education, Wuhan University, Wuhan, 430079, PR China; Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan, 430079, PR China.
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Song Y, Li H, Yu H. Effects of green space on physical activity and body weight status among Chinese adults: a systematic review. Front Public Health 2023; 11:1198439. [PMID: 37546310 PMCID: PMC10399589 DOI: 10.3389/fpubh.2023.1198439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 06/29/2023] [Indexed: 08/08/2023] Open
Abstract
Background Green space may provide many benefits to residents' health behaviors and body weight status, but the evidence is still relatively scattered among Chinese adults. The purpose of this study was to review the scientific evidence on the effects of green space on physical activity (PA) and body weight status among Chinese adults. Methods A keyword and reference search was conducted in Pubmed, Web of Science, MEDLINE, and PsycINFO. Studies examining the associations between green space and PA, body mass index (BMI) among Chinese adults were included. The quality of the included literature was evaluated using the National Institutes of Health's Observational Cohort and Cross-Sectional Study Quality Assessment Tool. Results A total of 31 studies were included that met the inclusion criteria, including 25 studies with a cross-sectional design, 3 studies with a longitudinal design, and 3 studies with an experimental design. Street-level green view index and green space accessibility were found to be positively associated with PA, but negatively associated with BMI. In most studies, there was a correlation between green space ratio in local areas and BMI. In addition, green space interventions were effective in increasing PA and decreasing BMI among Chinese adults. In contrast, further evidence is needed to support the association between the design characteristics of green space and PA and BMI. Conclusion Preliminary evidence suggests that green space has a positive effect on PA and BMI among Chinese adults. However, there are contradictory findings, and future studies adopting longitudinal and quasi-experimental studies are needed to further explore the causal relationship between green space and PA and BMI to provide a relevant theoretical basis for policymakers.
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Affiliation(s)
| | | | - Hongjun Yu
- Department of Physical Education, Tsinghua University, Beijing, China
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Wirtz Baker JM, Pou SA, Niclis C, Haluszka E, Aballay LR. Non-traditional data sources in obesity research: a systematic review of their use in the study of obesogenic environments. Int J Obes (Lond) 2023:10.1038/s41366-023-01331-3. [PMID: 37393408 DOI: 10.1038/s41366-023-01331-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 06/01/2023] [Accepted: 06/21/2023] [Indexed: 07/03/2023]
Abstract
BACKGROUND The complex nature of obesity increasingly requires a comprehensive approach that includes the role of environmental factors. For understanding contextual determinants, the resources provided by technological advances could become a key factor in obesogenic environment research. This study aims to identify different sources of non-traditional data and their applications, considering the domains of obesogenic environments: physical, sociocultural, political and economic. METHODS We conducted a systematic search in PubMed, Scopus and LILACS databases by two independent groups of reviewers, from September to December 2021. We included those studies oriented to adult obesity research using non-traditional data sources, published in the last 5 years in English, Spanish or Portuguese. The overall reporting followed the PRISMA guidelines. RESULTS The initial search yielded 1583 articles, 94 articles were kept for full-text screening, and 53 studies met the eligibility criteria and were included. We extracted information about countries of origin, study design, observation units, obesity-related outcomes, environment variables, and non-traditional data sources used. Our results revealed that most of the studies originated from high-income countries (86.54%) and used geospatial data within a GIS (76.67%), social networks (16.67%), and digital devices (11.66%) as data sources. Geospatial data were the most utilised data source and mainly contributed to the study of the physical domains of obesogenic environments, followed by social networks providing data to the analysis of the sociocultural domain. A gap in the literature exploring the political domain of environments was also evident. CONCLUSION The disparities between countries are noticeable. Geospatial and social network data sources contributed to studying the physical and sociocultural environments, which could be a valuable complement to those traditionally used in obesity research. We propose the use of information available on the Internet, addressed by artificial intelligence-based tools, to increase the knowledge on political and economic dimensions of the obesogenic environment.
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Affiliation(s)
- Julia Mariel Wirtz Baker
- Health Sciences Research Institute (INICSA), National Council of Scientific and Technical Research (CONICET), Faculty of Medical Sciences, National University of Córdoba, Bv. De La Reforma, Ciudad Universitaria, Zip Code 5000, Córdoba, Argentina
- Human Nutrition Research Centre (CenINH), School of Nutrition, Faculty of Medical Sciences, National University of Córdoba, Bv. De La Reforma, Ciudad Universitaria, Zip Code 5000, Córdoba, Argentina
| | - Sonia Alejandra Pou
- Health Sciences Research Institute (INICSA), National Council of Scientific and Technical Research (CONICET), Faculty of Medical Sciences, National University of Córdoba, Bv. De La Reforma, Ciudad Universitaria, Zip Code 5000, Córdoba, Argentina
- Human Nutrition Research Centre (CenINH), School of Nutrition, Faculty of Medical Sciences, National University of Córdoba, Bv. De La Reforma, Ciudad Universitaria, Zip Code 5000, Córdoba, Argentina
| | - Camila Niclis
- Health Sciences Research Institute (INICSA), National Council of Scientific and Technical Research (CONICET), Faculty of Medical Sciences, National University of Córdoba, Bv. De La Reforma, Ciudad Universitaria, Zip Code 5000, Córdoba, Argentina
- Human Nutrition Research Centre (CenINH), School of Nutrition, Faculty of Medical Sciences, National University of Córdoba, Bv. De La Reforma, Ciudad Universitaria, Zip Code 5000, Córdoba, Argentina
| | - Eugenia Haluszka
- Health Sciences Research Institute (INICSA), National Council of Scientific and Technical Research (CONICET), Faculty of Medical Sciences, National University of Córdoba, Bv. De La Reforma, Ciudad Universitaria, Zip Code 5000, Córdoba, Argentina
- Human Nutrition Research Centre (CenINH), School of Nutrition, Faculty of Medical Sciences, National University of Córdoba, Bv. De La Reforma, Ciudad Universitaria, Zip Code 5000, Córdoba, Argentina
| | - Laura Rosana Aballay
- Human Nutrition Research Centre (CenINH), School of Nutrition, Faculty of Medical Sciences, National University of Córdoba, Bv. De La Reforma, Ciudad Universitaria, Zip Code 5000, Córdoba, Argentina.
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Associations of residential greenness with unhealthy consumption behaviors: Evidence from high-density Hong Kong using street-view and conventional exposure metrics. Int J Hyg Environ Health 2023; 249:114145. [PMID: 36848736 DOI: 10.1016/j.ijheh.2023.114145] [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: 10/26/2022] [Revised: 02/04/2023] [Accepted: 02/22/2023] [Indexed: 02/27/2023]
Abstract
AIM Residential greenness was theoretically associated with health-related consumption behaviors concerning the socio-ecological model and restoration environment theory, but empirical studies were limited, especially in high-density cities. We examined the associations of residential greenness with unhealthy consumption behaviors (infrequent breakfast consumption, infrequent fruit consumption, infrequent vegetable consumption, alcohol drinking, binge drinking, cigarette smoking, moderate-to-heavy smoking, and heavy smoking) using street-view and conventional greenness metrics in high-density Hong Kong. METHODS This cross-sectional study employed survey data from 1,977 adults and residence-based objective environmental data in Hong Kong. Street-view greenness (SVG) was extracted from Google Street View images using an object-based image classification algorithm. Two conventional greenness metrics were used, including normalized difference vegetation index (NDVI) derived from Landsat 8 remote-sensing images and park density derived from a geographic information system database. In the main analyses, logistic regression analyses together with interaction and stratified models were performed with environmental metrics measured within a 1000-m buffer of residence. RESULTS A standard deviation higher SVG and NDVI were significantly associated with fewer odds of infrequent breakfast consumption (OR = 0.81, 95% CI 0.71-0.94 for SVG; OR = 0.83, 95% CI 0.73-0.95 for NDVI), infrequent fruit consumption (OR = 0.85, 95% CI 0.77-0.94 for SVG; OR = 0.85, 95% CI 0.77-0.94 for NDVI), and infrequent vegetable consumption (OR = 0.78, 95% CI 0.66-0.92 for SVG; OR = 0.81, 95% CI 0.69-0.94 for NDVI). The higher SVG was significantly associated with less binge drinking and the higher SVG at a 400-m buffer and a 600-m buffer were significantly associated with less heavy smoking. Park density was not significantly associated with any unhealthy consumption behaviors. Some of the above significant associations were moderated by moderate physical activity, mental and physical health, age, monthly income, and marital status. CONCLUSIONS This study highlights the potential beneficial impact of residential greenness, especially in terms of street greenery, on healthier eating habits, less binge drinking, and less heavy smoking.
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Air pollution, greenness and risk of overweight among middle-aged and older adults: A cohort study in China. ENVIRONMENTAL RESEARCH 2023; 216:114372. [PMID: 36170901 DOI: 10.1016/j.envres.2022.114372] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 08/31/2022] [Accepted: 09/15/2022] [Indexed: 01/31/2023]
Abstract
BACKGROUND Exposure to air pollution may increase the risk of obesity, but living in greener space may reduce this risk. Epidemiological evidence, however, is inconsistent. METHODS Using data from the China Health and Retirement Longitudinal Study (2011-2015), we conducted a nationwide cohort study of 7424 adults. We measured overweight/obesity according to body mass index. We used annual average ground-level air pollutants, including ozone (O3), nitrogen dioxide (NO2), and particulate matter with aerodynamic diameters ≤2.5 μm (PM2.5), to demonstrate air pollution levels. We used the Normalized difference vegetation index (NDVI) to measure greenness exposure. We used time-varying Cox proportional hazard regression models to analyze the connections among air pollution, greenness, and the development of overweight/obesity in middle-aged and older adults in China. We also conducted mediation analyses to examine the mediating effects of air pollution. RESULTS We found that lower risk of overweight/obesity was associated with more greenness exposure and lower levels of air pollution. We identified that an interquartile increment in NDVI was correlated with a lower hazard ratio (HR) of becoming overweight or obese (HR = 0.806, 95% confidence interval [CI]: 0.754-0.862). Although a 10 μg/m3 increase in PM2.5 and NO2 was correlated with higher risks (HR = 1.049, 95% CI = 1.022-1.075, HR = 1.376, 95% CI = 1.264-1.499). Effects of PM2.5 on being overweight or obese were stronger in men than in women. According to the mediation analysis, PM2.5 and NO2 mediated 8.85% and 19.22% of the association between greenness and being overweight or obese. CONCLUSIONS An increased risk of being overweight or obese in middle-aged and older adults in China was associated with long-term exposure to higher levels of PM2.5 and NO2. This risk was reduced through NDVI exposure, and the associations were partially mediated by air pollutants. To verify these findings, fine-scale studies are needed.
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Li X, Wang Q, Feng C, Yu B, Lin X, Fu Y, Dong S, Qiu G, Jin Aik DH, Yin Y, Xia P, Huang S, Liu N, Lin X, Zhang Y, Fang X, Zhong W, Jia P, Yang S. Associations and pathways between residential greenness and metabolic syndromes in Fujian Province. Front Public Health 2022; 10:1014380. [PMID: 36620251 PMCID: PMC9815145 DOI: 10.3389/fpubh.2022.1014380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 11/22/2022] [Indexed: 12/24/2022] Open
Abstract
Background Greenness exposure is beneficial to human health, but its potential mechanisms through which the risk for metabolic syndrome (MetS) could be reduced have been poorly studied. We aimed to estimate the greenness-MetS association in southeast China and investigate the independent and joint mediation effects of physical activity (PA), body mass index (BMI), and air pollutants on the association. Methods A cross-sectional study was conducted among the 38,288 adults based on the Fujian Behavior and Disease Surveillance (FBDS), established in 2018. MetS was defined as the presence of three or more of the five components: abdominal obesity, elevated triglyceride, reduced high-density lipoprotein cholesterol (HDL-C), high blood pressure, and elevated fasting glucose. The residential greenness exposure was measured as the 3-year mean values of the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) within the 250, 500, and 1,000 meters (m) buffer zones around the residential address of each participant. Logistic regression models were used to estimate the greenness-MetS association. The causal mediation analysis was used to estimate the independent and joint mediation effects of PA, BMI, particulate matter with an aerodynamic diameter of 2.5 μm (PM2.5), particulate matter with an aerodynamic diameter ≤ 10 μm (PM10), nitrogen dioxide (NO2), and sulfur dioxide (SO2). Results Each interquartile range (IQR) increase in greenness was associated with a decrease of 13% (OR = 0.87 [95%CI: 0.83, 0.92] for NDVI500m and OR = 0.87 [95%CI: 0.82, 0.91] for EVI500m) in MetS risk after adjusting for covariates. This association was stronger in those aged < 60 years (e.g., OR = 0.86 [95%CI: 0.81, 0.92] for NDVI500m), males (e.g., OR = 0.73 [95%CI: 0.67, 0.80] for NDVI500m), having an educational level of primary school or above (OR = 0.81 [95%CI: 0.74, 0.89] for NDVI500m), married/cohabitation (OR = 0.86 [95%CI: 0.81, 0.91] for NDVI500m), businessman (OR = 0.82 [95%CI: 0.68, 0.99] for NDVI500m), other laborers (OR = 0.77 [95%CI: 0.68, 0.88] for NDVI500m), and non-smokers (OR = 0.77 [95%CI: 0.70, 0.85] for NDVI500m). The joint effect of all six mediators mediated about 48.1% and 44.6% of the total effect of NDVI500m and EVI500m on the MetS risk, respectively. Among them, BMI showed the strongest independent mediation effect (25.0% for NDVI500m), followed by NO2 and PM10. Conclusion Exposure to residential greenness was associated with a decreased risk for MetS. PA, BMI, and the four air pollutants jointly interpreted nearly half of the mediation effects on the greenness-MetS association.
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Affiliation(s)
- Xiaoqing Li
- Department for Chronic and Noncommunicable Disease Control and Prevention, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, China
| | - Qinjian Wang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Chuanteng Feng
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China,Institute for Disaster Management and Reconstruction, 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, Chengdu, China
| | - Xi Lin
- Department for Chronic and Noncommunicable Disease Control and Prevention, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, China
| | - Yao Fu
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Shu Dong
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Ge Qiu
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, China,International Institute of Spatial Lifecourse Health (ISLE), Wuhan University, Wuhan, China
| | - Darren How Jin Aik
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, China,International Institute of Spatial Lifecourse Health (ISLE), Wuhan University, Wuhan, China
| | - Yanrong Yin
- Department for Chronic and Noncommunicable Disease Control and Prevention, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, China
| | - Pincang Xia
- Department for HIV/AIDS and STDs Control and Prevention, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, China
| | - Shaofen Huang
- Department for Chronic and Noncommunicable Disease Control and Prevention, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, China
| | - Nian Liu
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xiuquan Lin
- Department for Chronic and Noncommunicable Disease Control and Prevention, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, China
| | - Yefa Zhang
- Department for Chronic and Noncommunicable Disease Control and Prevention, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, China
| | - Xin Fang
- Department for Chronic and Noncommunicable Disease Control and Prevention, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, China
| | - Wenling Zhong
- Department for Chronic and Noncommunicable Disease Control and Prevention, Fujian Provincial Center for Disease Control and Prevention, Fuzhou, China,*Correspondence: Wenling Zhong
| | - Peng Jia
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, China,International Institute of Spatial Lifecourse Health (ISLE), Wuhan University, Wuhan, China,Peng Jia
| | - 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,Shujuan Yang
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Liang X, Liu F, Liang F, Ren Y, Tang X, Luo S, Huang D, Feng W. Association of decreases in PM2.5 levels due to the implementation of environmental protection policies with the incidence of obesity in adolescents: A prospective cohort study. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2022; 247:114211. [PMID: 36306623 DOI: 10.1016/j.ecoenv.2022.114211] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 10/16/2022] [Accepted: 10/18/2022] [Indexed: 06/16/2023]
Abstract
AIMS To explore the association between decreased levels of particulate matter (≤2.5 µm; PM2.5) due to the implementation of environmental protection policies and the incidence of obesity in adolescents in Chongqing, China through a prospective cohort study. METHODS A total of 2105 children (52.02% male; aged 7.33 ± 0.60 years at baseline) were enrolled from the Chongqing Children's Health Cohort. A mixed linear regression model was used to analyse the relationships of PM2.5 levels with obesity indicators after adjusting for covariates. Additionally, a Poisson regression model was used to determine the relationship between PM2.5 exposure and the incidence of overweight/obesity. RESULTS The average PM2.5 exposure levels from participant conception to 2014, from 2015 to 2017, and from 2018 to 2019 were 66.64 ± 5.33 μg/m3, 55.49 ± 3.78 μg/m3, and 42.50 ± 1.87 μg/m3, respectively; these levels significantly decreased over time (P < 0.001). Throughout the entire follow-up period, the incidence of overweight/obesity after a ≥ 25 μg/m3 decrease in the PM2.5 level was 4.57% among females; this incidence was the lowest among females who experienced remarkable decreases in PM2.5 exposure. A 1-µg/m3 decrease in the PM2.5 level significantly decreased the body mass index (BMI), BMI z score (BMIz), and weight of adolescents (all P < 0.001). Compared with a < 20-μg/m3 decrease in the PM2.5 level, a ≥ 25-μg/m3 decrease protected against increased BMI (net difference= -0.93; 95% confidence interval [CI]: (-1.23,-0.63) kg/m2), BMIz (-0.28 (-0.39, -0.17)), weight (-1.59 (-2.44, -0.74) kg), and incidence of overweight/obesity (0.48 (0.37, 0.62), P < 0.001). Moreover, compared with a < 20-μg/m3 decrease in the PM2.5 level, a ≥ 25-μg/m3 decrease resulted in significant absolute differences in BMI (-1.26 (-1.56, -0.96) kg/m2), BMIz (-0.53 (-0.65, -0.40)) and weight (-3.01 (-3.8, -2.19) kg) (all P < 0.001). CONCLUSIONS This study showed the etiological relevance of declining PM2.5 concentrations for the incidence of obesity in children and adolescents, suggesting that controlling ambient air pollutants may prevent the development of obesity in this age group. Continuous implementation of environmental protection policies in China has led to substantial health benefits.
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Affiliation(s)
- Xiaohua Liang
- Department of Clinical Epidemiology and Biostatistics, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing 400016, 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
| | - Fengchao Liang
- School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen 518055, China
| | - Yanling Ren
- Department of Clinical Epidemiology and Biostatistics, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing 400016, China
| | - Xian Tang
- Department of Clinical Epidemiology and Biostatistics, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing 400016, China
| | - Shunqing Luo
- Department of Clinical Epidemiology and Biostatistics, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing 400016, China; Department of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China; School of Public Health and Emergency Management, Southern University of Science and Technology, Shenzhen 518055, China
| | - Daochao Huang
- Department of Clinical Epidemiology and Biostatistics, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing 400016, China
| | - Wei Feng
- Department of Clinical Epidemiology and Biostatistics, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing 400016, China
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9
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Jiang J, Xiang Z, Liu F, Li N, Mao S, Xie B, Xiang H. Associations of residential greenness with obesity and BMI level among Chinese rural population: findings from the Henan Rural Cohort Study. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:74294-74305. [PMID: 35635662 DOI: 10.1007/s11356-022-20268-0] [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: 12/19/2021] [Accepted: 04/11/2022] [Indexed: 06/15/2023]
Abstract
In recent years, increasing evidence supports the notion that obesity risk is affected by residential greenness. However, limited studies have been established in low- and middle-income countries, especially in China. The study aimed to evaluate the associations of residential greenness with obesity and body mass index (BMI) level in Chinese rural-dwelling adults. A total of 39,259 adults from the Henan Rural Cohort Study (HRCS) were included in the analyses. According to the guideline for prevention and control of overweight and obesity in Chinese adults, obesity was defined as BMI ≥ 28 kg/m2. Residential greenness was measured by satellite-based normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). Generalized linear mixed models were used to study the associations between exposure to residential greenness with obesity and BMI level. Higher residential greenness was significantly correlated with lower odds of obesity and BMI level. For example, in the full-adjusted analyses, an interquartile range (IQR) increase in EVI500-m was linked with reduced odds of obesity (OR = 0.77, 95%CI 0.72-0.82) and BMI level (β = - 0.41 kg/m2, 95%CI - 0.48 to - 0.33 kg/m2). Mediation analyses showed air pollution and physical activity could be potential mediators in these associations. Besides, we found that the association of NDVI500-m with BMI was stronger in females and low-income populations. Higher residential greenness was associated with a lower prevalence of obesity and BMI level, particularly among females and the low-income population. These relationships were partially mediated by reducing air pollution and increasing physical activity.
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Affiliation(s)
- Jie Jiang
- Department of Global Health, School of Public Health, Wuhan University, Wuhan, 430071, Hubei, China
- Global Health Institute, Wuhan University, Wuhan, 430071, Hubei, China
| | - Zixi Xiang
- Department of Global Health, School of Public Health, Wuhan University, Wuhan, 430071, Hubei, China
- Global Health Institute, Wuhan University, Wuhan, 430071, Hubei, China
| | - Feifei Liu
- Department of Global Health, School of Public Health, Wuhan University, Wuhan, 430071, Hubei, China
- Global Health Institute, Wuhan University, Wuhan, 430071, Hubei, China
| | - Na Li
- Department of Global Health, School of Public Health, Peking University, Beijing, 100871, China
| | - Shuyuan Mao
- The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Bo Xie
- School of Urban Design, Wuhan University, Wuhan, 430072, Hubei, China
| | - Hao Xiang
- Department of Global Health, School of Public Health, Wuhan University, Wuhan, 430071, Hubei, China.
- Global Health Institute, Wuhan University, Wuhan, 430071, Hubei, China.
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