Residential greenness and cardiac conduction abnormalities: epidemiological evidence and an explainable machine learning modeling study

Zhongao Ding; Gongbo Chen; Liying Zhang; Bota Baheti; Ruiyu Wu; Wei Liao; Xiaotian Liu; Jian Hou; Zhenxing Mao; Yuming Guo; Chongjian Wang

doi:10.1016/j.chemosphere.2023.139671

Residential greenness and cardiac conduction abnormalities: epidemiological evidence and an explainable machine learning modeling study

Chemosphere. 2023 Oct:339:139671. doi: 10.1016/j.chemosphere.2023.139671. Epub 2023 Jul 28.

Authors

Zhongao Ding¹, Gongbo Chen², Liying Zhang³, Bota Baheti¹, Ruiyu Wu¹, Wei Liao¹, Xiaotian Liu¹, Jian Hou¹, Zhenxing Mao¹, Yuming Guo⁴, Chongjian Wang⁵

Affiliations

¹ Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China.
² Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
³ Department of Software Engineering, School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, Henan, PR China.
⁴ Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China; Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
⁵ Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou, Henan, PR China; NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, Zhengzhou, Henan, PR China. Electronic address: tjwcj2008@zzu.edu.cn.

PMID: 37517666
DOI: 10.1016/j.chemosphere.2023.139671

Abstract

Background: Previous studies indicated the beneficial influence of residential greenness on cardiovascular disease (CVD), however, the association of residential greenness with cardiac conduction performance remains unclear. This study aims to examine the epidemiological associations between residential greenness and cardiac conduction abnormalities in rural residents, simultaneously exploring the role of residential greenness for cardiac health in an explainable machine learning modeling study.

Methods: A total of 27,294 participants were derived from the Henan Rural Cohort. Two satellite-based indices, the normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI), were used to estimate residential greenness. Independent and combined associations of residential greenness indices and physical activities with electrocardiogram (ECG) parameter abnormalities were evaluated using the logistic regression model and generalized linear model. The Gradient Boosting Machine (GBM) and the SHapely Additive exPlanations (SHAP) were employed in the modeling study.

Results: The odds ratios (OR) and 95% confidence interval (CI) for QRS interval, heart rate (HR), QTc interval, and PR interval abnormalities with per interquartile range in NDVI were 0.896 (0.873-0.920), 0.955 (0.926-0.986), 1.015 (0.984-1.047), and 0.986 (0.929-1.045), respectively. Furthermore, the participants with higher physical activities plus residential greenness (assessed by EVI) were related to a 1.049-fold (1.017-1.081) and 1.298-fold (1.245-1.354) decreased risk for abnormal QRS interval and HR. Similar results were also observed in the sensitivity analysis. The NDVI ranked fifth (SHAP mean value 0.116) in the analysis for QRS interval abnormality risk in the modeling study.

Conclusion: A higher level of residential greenness was significantly associated with cardiac conduction abnormalities. This effect might be strengthened in residents with more physical activities. This study indicated the cruciality of environmental greenness to cardiac functions and also contributed to refining preventive medicine and greenness design strategies.

Keywords: Cardiac conduction system; Electrocardiogram; Explainable machine learning; Residential greenness; Rural residents.

MeSH terms

Cardiovascular Diseases*
China / epidemiology
Heart
Humans