Bo-Yi Yang1, Iana Markevych2, Joachim Heinrich3, Gayan Bowatte4, Michael S Bloom5, Yuming Guo6, Shyamali C Dharmage7, Bin Jalaludin8, Luke D Knibbs9, Lidia Morawska10, Zhengmin Min Qian11, Duo-Hong Chen12, Huimin Ma13, Da Chen14, Shao Lin5, Mo Yang1, Kang-Kang Liu1, Xiao-Wen Zeng1, Li-Wen Hu1, Guang-Hui Dong15. 1. Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China. 2. Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Ziemssenstraße 1, 80336, Munich, Germany; Institute of Epidemiology, Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany; Division of Metabolic and Nutritional Medicine, Dr. von Hauner Children's Hospital, Munich, Ludwig Maximilian University of Munich, Munich, Germany. 3. Institute and Clinic for Occupational, Social and Environmental Medicine, University Hospital, LMU Munich, Ziemssenstraße 1, 80336, Munich, Germany; Comprehensive Pneumology Center Munich, German Center for Lung Research, Ziemssenstraße 1, 80336, Munich, Germany. 4. Allergy and Lung Health Unit, Centre for Epidemiology and Biostatistics, School of Population & Global Health, The University of Melbourne, Melbourne, VIC, 3010, Australia; National Institute of Fundamental Studies, Kandy, 20000, Sri Lanka. 5. Department of Environmental Health Sciences, University at Albany, State University of New York, Rensselaer, NY, 12144, USA; Department of Epidemiology and Biostatics, University at Albany, State University of New York, Rensselaer, NY, 12144, USA. 6. Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, 3004, Australia. 7. Allergy and Lung Health Unit, Centre for Epidemiology and Biostatistics, School of Population & Global Health, The University of Melbourne, Melbourne, VIC, 3010, Australia; Murdoch Children Research Institute, Melbourne, VIC, 3010, Australia. 8. Centre for Air Quality and Health Research and Evaluation, Glebe, NSW, 2037, Australia; Population Health, South Western Sydney Local Health District, Liverpool, NSW, 2170, Australia; Ingham Institute for Applied Medical Research, Liverpool, NSW, 2170, Australia; School of Public Health and Community Medicine, The University of New South Wales, Kensington, NSW, 2052, Australia. 9. School of Public Health, The University of Queensland, Herston, Queensland, 4006, Australia. 10. International Laboratory for Air Quality and Health, Queensland University of Technology (QUT), GPO Box 2434, Brisbane, Queensland, 4001, Australia. 11. Department of Epidemiology, College for Public Health and Social Justice, Saint Louis University, Saint Louis, 63104, USA. 12. Department of Air Quality Forecasting and Early Warning, Guangdong Environmental Monitoring Center, State Environmental Protection Key Laboratory of Regional Air Quality Monitoring, Guangdong Environmental Protection Key Laboratory of Atmospheric Secondary Pollution, Guangzhou, 510308, China. 13. State Key Laboratory of Organic Geochemistry and Guangdong Key Laboratory of Environmental Protection and Resources Utilization, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, 510640, China. 14. School of Environment, Guangzhou Key Laboratory of Environmental Exposure and Health, Guangdong Key Laboratory of Environmental Pollution and Health, Jinan University, Guangzhou, 510632, China. 15. Guangzhou Key Laboratory of Environmental Pollution and Health Risk Assessment, Guangdong Provincial Engineering Technology Research Center of Environmental and Health Risk Assessment, Department of Preventive Medicine, School of Public Health, Sun Yat-sen University, Guangzhou, 510080, China. Electronic address: donggh5@mail.sysu.edu.cn.
Abstract
BACKGROUND: Residing in greener places may be protective against diabetes mellitus (DM) but evidence is scarce and comes mainly from developed countries. OBJECTIVES: To investigate associations of residential greenness with DM prevalence and glucose-homeostasis markers in Chinese adults and whether these associations were mediated by air pollution, physical activity, and body mass index. METHODS: In 2009, a total of 15,477 adults from the cross-sectional 33 Communities Chinese Health Study provided blood samples and completed a questionnaire. We considered fasting and 2-h glucose and insulin concentrations, as well as the homoeostasis model assessment of insulin resistance and β-cell function, as glucose-homeostasis markers. DM was defined according to the American Diabetes Association's recommendations. Residential greenness was estimated by two satellite-derived vegetation indexes - Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI). Nitrogen dioxide and particulate matter ≤2.5 μm were used as air pollution proxies. Associations were assessed by two-level adjusted logistic and linear regression models. RESULTS: A 0.1-unit increase in NDVI500 m and SAVI500 m was significantly associated with lower odds of DM by factors of 0.88 (95% Confidence Interval 0.82-0.94) and 0.80 (0.72-0.90), respectively. Higher greenness was also significantly associated with lower fasting and 2-h glucose levels, 2-h insulin level, as well as lower insulin resistance and higher β-cell function. Air pollution and body mass index significantly mediated 6.9-51.1% and 8.6-78.7% these associations, respectively, while no mediation role was observed for physical activity. CONCLUSIONS: Higher residential greenness appears to be associated with a lower prevalence of DM. This association might be due to glucose and insulin metabolism and pancreatic β-cell function. Lower levels of air pollution and body mass index can be pathways linking greenspace to diabetes.
BACKGROUND: Residing in greener places may be protective against diabetes mellitus (DM) but evidence is scarce and comes mainly from developed countries. OBJECTIVES: To investigate associations of residential greenness with DM prevalence and glucose-homeostasis markers in Chinese adults and whether these associations were mediated by air pollution, physical activity, and body mass index. METHODS: In 2009, a total of 15,477 adults from the cross-sectional 33 Communities Chinese Health Study provided blood samples and completed a questionnaire. We considered fasting and 2-h glucose and insulin concentrations, as well as the homoeostasis model assessment of insulin resistance and β-cell function, as glucose-homeostasis markers. DM was defined according to the American Diabetes Association's recommendations. Residential greenness was estimated by two satellite-derived vegetation indexes - Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI). Nitrogen dioxide and particulate matter ≤2.5 μm were used as air pollution proxies. Associations were assessed by two-level adjusted logistic and linear regression models. RESULTS: A 0.1-unit increase in NDVI500 m and SAVI500 m was significantly associated with lower odds of DM by factors of 0.88 (95% Confidence Interval 0.82-0.94) and 0.80 (0.72-0.90), respectively. Higher greenness was also significantly associated with lower fasting and 2-h glucose levels, 2-h insulin level, as well as lower insulin resistance and higher β-cell function. Air pollution and body mass index significantly mediated 6.9-51.1% and 8.6-78.7% these associations, respectively, while no mediation role was observed for physical activity. CONCLUSIONS: Higher residential greenness appears to be associated with a lower prevalence of DM. This association might be due to glucose and insulin metabolism and pancreatic β-cell function. Lower levels of air pollution and body mass index can be pathways linking greenspace to diabetes.
Authors: Hari S Iyer; Peter James; Linda Valeri; Francis Bajunirwe; Joan Nankya-Mutyoba; Marina Njelekela; Faraja Chiwanga; Vikash Sewram; IkeOluwapo Ajayi; Clement Adebamowo; Shona Dalal; Todd G Reid; Timothy R Rebbeck; Hans-Olov Adami; Michelle D Holmes Journal: Environ Res Date: 2020-10-31 Impact factor: 6.498
Authors: Bo-Yi Yang; Yuming Guo; Iana Markevych; Zhengmin Min Qian; Michael S Bloom; Joachim Heinrich; Shyamali C Dharmage; Craig A Rolling; Savannah S Jordan; Mika Komppula; Ari Leskinen; Gayan Bowatte; Shanshan Li; Gongbo Chen; Kang-Kang Liu; Xiao-Wen Zeng; Li-Wen Hu; Guang-Hui Dong Journal: JAMA Netw Open Date: 2019-03-01
Authors: Hector A Olvera-Alvarez; Matthew H E M Browning; Andreas M Neophytou; Gregory N Bratman Journal: Int J Environ Res Public Health Date: 2021-01-10 Impact factor: 3.390
Authors: Linxin Liu; Lijing L Yan; Yuebin Lv; Yi Zhang; Tiantian Li; Cunrui Huang; Haidong Kan; Junfeng Zhang; Yi Zeng; Xiaoming Shi; John S Ji Journal: BMC Public Health Date: 2022-05-04 Impact factor: 4.135
Authors: Alessandro Rigolon; Matthew H E M Browning; Olivia McAnirlin; Hyunseo Violet Yoon Journal: Int J Environ Res Public Health Date: 2021-03-04 Impact factor: 3.390