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Chandia-Poblete D, Cole-Hunter T, Haswell M, Heesch KC. The influence of air pollution exposure on the short- and long-term health benefits associated with active mobility: A systematic review. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 850:157978. [PMID: 35964755 DOI: 10.1016/j.scitotenv.2022.157978] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 08/08/2022] [Accepted: 08/08/2022] [Indexed: 06/15/2023]
Abstract
Active mobility (AM), defined as walking and cycling for transportation, can improve health through increasing regular physical activity. However, these health improvements could be outweighed by harm from inhaling traffic-related air pollutants during AM participation. The interaction of AM and air pollutants on health is complex physiologically, manifesting as acute changes in health indicators that may lead to poor long-term health consequences. The aim of this study was to systematically review the current evidence of effect modification by air pollution (AP) on associations between AM and health indicators. Studies were included if they examined associations between AM and health indicators being modified by AP or, conversely, associations between AP and health indicators being modified by AM. Thirty-three studies met eligibility criteria. The main AP indicators studied were particulate matter, ultrafine particles, and nitrogen oxides. Most health indicators studied were grouped into cardiovascular and respiratory indicators. There is evidence of a reduction by AP, mainly ultrafine particles and PM2.5, in the short-term health benefits of AM. Multiple studies suggest that long-term health benefits of AM are not negatively associated with levels of the single traffic-related pollutant NO2. However, other studies reveal reduced long-term health benefits of AM in areas affected by high levels of pollutant mixtures. We recommend that future studies adopt consistent and rigorous study designs and include reporting of interaction testing, to advance understanding of the complex relationships between AM, AP, and health indicators.
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Affiliation(s)
- Damian Chandia-Poblete
- School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Victoria Park Road, Kelvin Grove, Queensland 4059, Australia.
| | - Thomas Cole-Hunter
- Section of Environmental Health, Department of Public Health, Faculty of Health and Medical Science, University of Copenhagen, Øster Farimagsgade 5, 1353 Copenhagen, Denmark.
| | - Melissa Haswell
- School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Victoria Park Road, Kelvin Grove, Queensland 4059, Australia; Office of the Deputy Vice Chancellor (Indigenous Strategy and Services) and School of Geosciences, Faculty of Science, University of Sydney, Australia.
| | - Kristiann C Heesch
- School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Victoria Park Road, Kelvin Grove, Queensland 4059, Australia.
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Lorenzo E, Szeszulski J, Shin CN, Todd M, Lee RE. Relationship between walking for active transportation and cardiometabolic health among adults: A systematic review. JOURNAL OF TRANSPORT & HEALTH 2020; 19:100927. [PMID: 34676154 PMCID: PMC8525782 DOI: 10.1016/j.jth.2020.100927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Affiliation(s)
- Elizabeth Lorenzo
- Center for Health Promotion and Disease Prevention, Edson College of Nursing and Health Innovation, Arizona State University, 550 N. 3 St., Phoenix, Arizona, 85004, United States
| | - Jacob Szeszulski
- Center for Health Promotion and Prevention Research, Michael Susan Dell Center for Healthy Living, University of Texas Health Science Center at 7000 Fannin St. #2528 Houston, Texas, United States
| | - Cha-Nam Shin
- Edson College of Nursing and Health Innovation, Arizona State University, 550 N. 3 St., Phoenix, Arizona, 85004, United States
| | - Michael Todd
- Edson College of Nursing and Health Innovation, Arizona State University, 550 N. 3 St., Phoenix, Arizona, 85004, United States
| | - Rebecca E. Lee
- Center for Health Promotion and Disease Prevention, Edson College of Nursing and Health Innovation, Arizona State University, 550 N. 3 St., Phoenix, Arizona, 85004, United States
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Ma Z, Xu C, Kang X, Zhang S, Li H, Tao L, Zheng D, Guo X, Yang X. Changing trajectories of serum uric acid and risk of non-alcoholic fatty liver disease: a prospective cohort study. J Transl Med 2020; 18:133. [PMID: 32192511 PMCID: PMC7081554 DOI: 10.1186/s12967-020-02296-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Accepted: 03/11/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND It is unclear the role of longitudinal trajectory of serum uric acid (SUA) on the development of non-alcoholic fatty liver disease (NAFLD). We aimed to determine whether longitudinal SUA trajectories are associated with the risk of new-onset NAFLD. METHODS We explored the relationship between SUA trajectories and NAFLD in a cohort including 3822 participants. Individual's SUA trajectories from 2012 to 2014 were defined using group-based trajectory modeling analysis in four distinct patterns: trajectory 1 (n = 991, 25.93%), trajectory 2 (n = 1421, 37.18%), trajectory 3 (n = 1156, 30.22%), and trajectory 4 (n = 254, 6.67%). The logistic regression model was used to evaluate the association between SUA changing trajectories and subsequent NAFLD until 2016. Dose-response relationship between SUA changing trajectories and NAFLD risk was evaluated through the testing of trajectory groups as a continuous variable. RESULTS The 2-year incidence of NAFLD was 13.27%. Compared with trajectory 1, the adjusted odds risk for NAFLD development was in a dose-response relationship as follows: 1.27 (95% CI 0.91-1.78) for trajectory 2, 1.89 (95% CI 1.29-2.75) for trajectory 3, and 2.34 (95% CI 1.43-3.83) for trajectory 4. And this dose-response relationship was not affected by age, sex, and abdominal obesity. CONCLUSIONS Higher SUA changing trajectory is a risk factor for NAFLD. This finding highlights the importance of paying attention to SUA changing trajectory on the detection and prevention of NAFLD.
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Affiliation(s)
- Zhimin Ma
- School of Public Health, Capital Medical University, NO. 10 Xitoutiao, Youanmen, Fengtai District, Beijing, 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, NO. 10 Xitoutiao, Youanmen, Fengtai District, Beijing, 100069, China
| | - Chaonan Xu
- School of Public Health, Capital Medical University, NO. 10 Xitoutiao, Youanmen, Fengtai District, Beijing, 100069, China
- Medical Engineering Department, Peking University Third Hospital, NO. 49 HuaYuan BeiLu, Haidian District, Beijing, 100191, China
| | - Xiaoping Kang
- Beijing Xiaotangshan Hospital, NO. 390 Wenquan Street, Xiaotangshan Town, Changping District, Beijing, 102211, China
| | - Shan Zhang
- School of Public Health, Capital Medical University, NO. 10 Xitoutiao, Youanmen, Fengtai District, Beijing, 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, NO. 10 Xitoutiao, Youanmen, Fengtai District, Beijing, 100069, China
| | - Haibin Li
- School of Public Health, Capital Medical University, NO. 10 Xitoutiao, Youanmen, Fengtai District, Beijing, 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, NO. 10 Xitoutiao, Youanmen, Fengtai District, Beijing, 100069, China
| | - Lixin Tao
- School of Public Health, Capital Medical University, NO. 10 Xitoutiao, Youanmen, Fengtai District, Beijing, 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, NO. 10 Xitoutiao, Youanmen, Fengtai District, Beijing, 100069, China
| | - Deqiang Zheng
- School of Public Health, Capital Medical University, NO. 10 Xitoutiao, Youanmen, Fengtai District, Beijing, 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, NO. 10 Xitoutiao, Youanmen, Fengtai District, Beijing, 100069, China
| | - Xiuhua Guo
- School of Public Health, Capital Medical University, NO. 10 Xitoutiao, Youanmen, Fengtai District, Beijing, 100069, China
- Beijing Municipal Key Laboratory of Clinical Epidemiology, NO. 10 Xitoutiao, Youanmen, Fengtai District, Beijing, 100069, China
| | - Xinghua Yang
- School of Public Health, Capital Medical University, NO. 10 Xitoutiao, Youanmen, Fengtai District, Beijing, 100069, China.
- Beijing Municipal Key Laboratory of Clinical Epidemiology, NO. 10 Xitoutiao, Youanmen, Fengtai District, Beijing, 100069, China.
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