Beneficial associations between outdoor visible greenness at the workplace and metabolic syndrome in Chinese adults.

Association GVI Green space Greenery Greenness Metabolic syndrome Risk Street view Workplace

Journal

Environment international
ISSN: 1873-6750
Titre abrégé: Environ Int
Pays: Netherlands
ID NLM: 7807270

Informations de publication

Date de publication:
21 Nov 2023
Historique:
received: 13 08 2023
revised: 13 10 2023
accepted: 12 11 2023
medline: 2 1 2024
pubmed: 2 1 2024
entrez: 29 12 2023
Statut: aheadofprint

Résumé

Greenness surrounding residential places has been found to significantly reduce the risk of diseases such as hypertension, obesity, and metabolic syndrome (MetS). However, it is unclear whether visible greenness exposure at the workplace has any impact on the risk of MetS. Visible greenness exposure was assessed using a Green View Index (GVI) based on street view images through a convolutional neural network model. We utilized logistic regression to examine the cross-sectional association between GVI and MetS as well as its components among 51,552 adults aged 18-60 in the city of Hangzhou, China, from January 2018 to December 2021. Stratified analyses were conducted by age and sex groups. Furthermore, a scenario analysis was conducted to investigate the risks of having MetS among adults in different GVI scenarios. The mean age of the participants was 40.1, and 38.5% were women. We found a statistically significant association between GVI and having MetS. Compared to the lowest quartile of GVI, participants in the highest quartile of GVI had a 17% (95% CI: 11-23%) lower odds of having MetS. The protective association was stronger in the males, but we did not observe such differences in different age groups. Furthermore, we found inverse associations between GVI and the odds of hypertension, low high-density lipoprotein cholesterol, obesity, and high levels of FPG. Higher exposure to outdoor visible greenness in the workplace environment might have a protective effect against MetS.

Sections du résumé

BACKGROUND BACKGROUND
Greenness surrounding residential places has been found to significantly reduce the risk of diseases such as hypertension, obesity, and metabolic syndrome (MetS). However, it is unclear whether visible greenness exposure at the workplace has any impact on the risk of MetS.
METHODS METHODS
Visible greenness exposure was assessed using a Green View Index (GVI) based on street view images through a convolutional neural network model. We utilized logistic regression to examine the cross-sectional association between GVI and MetS as well as its components among 51,552 adults aged 18-60 in the city of Hangzhou, China, from January 2018 to December 2021. Stratified analyses were conducted by age and sex groups. Furthermore, a scenario analysis was conducted to investigate the risks of having MetS among adults in different GVI scenarios.
RESULTS RESULTS
The mean age of the participants was 40.1, and 38.5% were women. We found a statistically significant association between GVI and having MetS. Compared to the lowest quartile of GVI, participants in the highest quartile of GVI had a 17% (95% CI: 11-23%) lower odds of having MetS. The protective association was stronger in the males, but we did not observe such differences in different age groups. Furthermore, we found inverse associations between GVI and the odds of hypertension, low high-density lipoprotein cholesterol, obesity, and high levels of FPG.
CONCLUSIONS CONCLUSIONS
Higher exposure to outdoor visible greenness in the workplace environment might have a protective effect against MetS.

Identifiants

pubmed: 38157607
pii: S0160-4120(23)00600-1
doi: 10.1016/j.envint.2023.108327
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

108327

Informations de copyright

Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Jiahao Pan (J)

Center for Clinical Big Data and Analytics School of Public Health and The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310000, Zhejiang, China.

Kejia Hu (K)

Center for Clinical Big Data and Analytics School of Public Health and The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310000, Zhejiang, China.

Xinyan Yu (X)

Department of Health Management Center and Department of General Medicine, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, 310009 Zhejiang, China.

Wenyuan Li (W)

Center for Clinical Big Data and Analytics School of Public Health and The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310000, Zhejiang, China.

Yujie Shen (Y)

Center for Clinical Big Data and Analytics School of Public Health and The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310000, Zhejiang, China.

Zhenya Song (Z)

Department of Health Management Center and Department of General Medicine, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, 310009 Zhejiang, China.

Yi Guo (Y)

Department of Health Management Center and Department of General Medicine, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, 310009 Zhejiang, China.

Min Yang (M)

Center for Clinical Big Data and Analytics School of Public Health and The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310000, Zhejiang, China.

Fang Hu (F)

Department of Health Management Center and Department of General Medicine, The Second Affiliated Hospital Zhejiang University School of Medicine, Hangzhou, 310009 Zhejiang, China.

Qunke Xia (Q)

School of Earth Sciences, Zhejiang University, Hangzhou 310027, China.

Zhenhong Du (Z)

School of Earth Sciences, Zhejiang University, Hangzhou 310027, China; Zhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou 310028, China. Electronic address: duzhenhong@zju.edu.cn.

Xifeng Wu (X)

Center for Clinical Big Data and Analytics School of Public Health and The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310000, Zhejiang, China; The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang 310058, China; Cancer Center, Zhejiang University, Hangzhou, Zhejiang 310058 China. Electronic address: xifengw@zju.edu.cn.

Classifications MeSH