Jing-Shu Zhang1, Zhao-Huan Gui1, Zhi-Yong Zou2, Bo-Yi Yang3, Jun Ma2, Jin Jing1, Hai-Jun Wang4, Jia-You Luo5, Xin Zhang6, Chun-Yan Luo7, Hong Wang8, Hai-Ping Zhao9, De-Hong Pan10, Wen-Wen Bao1, Yu-Ming Guo11, Ying-Hua Ma12, Guang-Hui Dong13, Ya-Jun Chen14. 1. Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China. 2. Institute of Child and Adolescent Health, Peking University, School of Public Health, National Health Commission Key Laboratory of Reproductive Health, Beijing 100191, China. 3. Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China. 4. Department of Maternal and Child Health, School of Public Health, Peking University, Beijing 100191, China. 5. Department of Maternal and Child Health, School of Public Health, Central South University, Changsha 410078, China. 6. School of Public Health, Tianjin Medical University, Tianjin 300070, China. 7. Shanghai Municipal Center for Disease Control and Prevention, Shanghai Institutes of Preventive Medicine, Shanghai 200336, China. 8. Department of Maternal and Child Health, School of Public Health and Management, Chongqing Medical University, Chongqing 400016, China. 9. School of Public Health and Management, Ningxia Medical University, Ningxia, 750004, China. 10. Liaoning Health Supervision Bureau, Shenyang 110005, China. 11. Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3004, Australia. 12. Institute of Child and Adolescent Health, Peking University, School of Public Health, National Health Commission Key Laboratory of Reproductive Health, Beijing 100191, China. Electronic address: yinghuama@bjmu.edu.cn. 13. Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China. Electronic address: donggh5@mail.sysu.edu.cn. 14. Department of Maternal and Child Health, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China. Electronic address: chenyj68@mail.sysu.edu.cn.
Abstract
BACKGROUND: The prevalence of metabolic syndrome (MetS) rapidly increased over the past decades. However, little evidence exists about the effects of long-term exposure to ambient air pollution on MetS in children and adolescents. OBJECTIVE: This study aims to assess the association between long-term ambient air pollution and the prevalence of MetS in a large population of Chinese children and adolescents. METHODS: In 2013, a total of 9,897 children and adolescents aged 10 to 18 years were recruited from seven provinces/municipalities in China. MetS was defined based on the recommendation by the International Diabetes Federation (IDF). Satellite based spatio-temporal models were used to estimate exposure to ambient air pollution (including particles with diameters ≤1.0 µm (PM1), ≤2.5 µm (PM2.5), and ≤10 µm (PM10), and nitrogen dioxide (NO2)). Individual exposure was calculated according to 94 schools addresses. After adjustment for a range of covariates, generalized linear mixed-effects models were utilized to evaluate the associations between air pollutants and the prevalence of MetS and its components. In addition, several stratified analyses were examined according to sex, weight status, outdoor physical activity time, and sugar-sweetened beverages (SSBs) intake. RESULTS: The prevalence of MetS was 2.8%. The odds ratio of MetS associated with a 10 μg/m3 increase in PM1, PM2.5, PM10 and NO2 was 1.20 (95%CI: 0.99, 1.46), 1.31 (95%CI: 1.05, 1.64), 1.32 (95%CI: 1.08, 1.62), and 1.33 (95%CI: 1.03, 1.72), respectively. Regarding the MetS components, we observed associations between all pollutants and abdominal obesity. In addition, long-term PM1 and NO2 exposures were associated with the prevalence of elevated fasting blood glucose. Stratified analyses detected that the associations between air pollutants and the prevalence of MetS were stronger in boys (Pinteraction < 0.05). CONCLUSIONS: We found that long-term exposure to PM2.5, PM10, and NO2 were positively associated with the prevalence of MetS in children and adolescents. Our findings may have certain public health implications for some comprehensive strategy of environment improvement and lifestyles changes in order to reduce the burden of non-communicable disease.
BACKGROUND: The prevalence of metabolic syndrome (MetS) rapidly increased over the past decades. However, little evidence exists about the effects of long-term exposure to ambient air pollution on MetS in children and adolescents. OBJECTIVE: This study aims to assess the association between long-term ambient air pollution and the prevalence of MetS in a large population of Chinese children and adolescents. METHODS: In 2013, a total of 9,897 children and adolescents aged 10 to 18 years were recruited from seven provinces/municipalities in China. MetS was defined based on the recommendation by the International Diabetes Federation (IDF). Satellite based spatio-temporal models were used to estimate exposure to ambient air pollution (including particles with diameters ≤1.0 µm (PM1), ≤2.5 µm (PM2.5), and ≤10 µm (PM10), and nitrogen dioxide (NO2)). Individual exposure was calculated according to 94 schools addresses. After adjustment for a range of covariates, generalized linear mixed-effects models were utilized to evaluate the associations between air pollutants and the prevalence of MetS and its components. In addition, several stratified analyses were examined according to sex, weight status, outdoor physical activity time, and sugar-sweetened beverages (SSBs) intake. RESULTS: The prevalence of MetS was 2.8%. The odds ratio of MetS associated with a 10 μg/m3 increase in PM1, PM2.5, PM10 and NO2 was 1.20 (95%CI: 0.99, 1.46), 1.31 (95%CI: 1.05, 1.64), 1.32 (95%CI: 1.08, 1.62), and 1.33 (95%CI: 1.03, 1.72), respectively. Regarding the MetS components, we observed associations between all pollutants and abdominal obesity. In addition, long-term PM1 and NO2 exposures were associated with the prevalence of elevated fasting blood glucose. Stratified analyses detected that the associations between air pollutants and the prevalence of MetS were stronger in boys (Pinteraction < 0.05). CONCLUSIONS: We found that long-term exposure to PM2.5, PM10, and NO2 were positively associated with the prevalence of MetS in children and adolescents. Our findings may have certain public health implications for some comprehensive strategy of environment improvement and lifestyles changes in order to reduce the burden of non-communicable disease.
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