Association between nitrogen oxides and COVID-19 risk: A Mendelian randomization study

Several cross-sectional investigations have indicated an association between nitrogen oxides (NOx) and the spread and fatality of coronavirus disease 2019 (COVID-19); however, none have employed Mendelian randomization (MR). To enhance knowledge of causality, this study examined the association of NOx with COVID-19 risk. Integrated data were obtained from the UK Biobank and integrative epidemiology unit open genome-wide association study for 2-sample MR analysis. Inverse-variance weighting and MR using a robust adjusted profile score were employed for MR analysis, incorporating both multiple random effect and fixed-effect substitution to predict the link between NOx and COVID-19. Furthermore, visual scatter plots, funnel plots, and the leave-one-out method were employed to validate the positive outcomes. MR results revealed that in the inverse-variance weighting method, NOx air pollution exhibited a significant association with COVID-19 (hospitalized vs population; odds ratio = 3.7609; 95% confidence interval = 1.2726-11.1144, P = .0166), while nitrogen dioxide (NO2) air pollution demonstrated a similar association with COVID-19 (hospitalized vs population; odds ratio = 2.9492; 95% confidence interval = 1.0936-7.9537, P = .0326). However, no association was observed between NO2 and NOx exposure with COVID-19 (very severe respiratory confirmed). MR analysis demonstrates a potential causality between NO2 and NOx exposure and the risk of COVID-19. Preventing and controlling air pollution may help slow down and prevent the adverse progression of COVID-19.

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Journal
Medicine
Published
2025-11-28
DOI
https://doi.org/10.1097/md.0000000000046258
Primary Topic
Health, Environment, Cognitive Aging
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article
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article

Association between nitrogen oxides and COVID-19 risk: A Mendelian randomization study

Zixiao Chen, Jiaona Wei, Jing Cao, Haibo Xu
Medicine
Health, Environment, Cognitive Aging
article

Association between nitrogen oxides and COVID-19 risk: A Mendelian randomization study

Zixiao Chen, Jiaona Wei, Jing Cao, Haibo Xu
article en

Abstract

Several cross-sectional investigations have indicated an association between nitrogen oxides (NOx) and the spread and fatality of coronavirus disease 2019 (COVID-19); however, none have employed Mendelian randomization (MR). To enhance knowledge of causality, this study examined the association of NOx with COVID-19 risk. Integrated data were obtained from the UK Biobank and integrative epidemiology unit open genome-wide association study for 2-sample MR analysis. Inverse-variance weighting and MR using a robust adjusted profile score were employed for MR analysis, incorporating both multiple random effect and fixed-effect substitution to predict the link between NOx and COVID-19. Furthermore, visual scatter plots, funnel plots, and the leave-one-out method were employed to validate the positive outcomes. MR results revealed that in the inverse-variance weighting method, NOx air pollution exhibited a significant association with COVID-19 (hospitalized vs population; odds ratio = 3.7609; 95% confidence interval = 1.2726-11.1144, P = .0166), while nitrogen dioxide (NO2) air pollution demonstrated a similar association with COVID-19 (hospitalized vs population; odds ratio = 2.9492; 95% confidence interval = 1.0936-7.9537, P = .0326). However, no association was observed between NO2 and NOx exposure with COVID-19 (very severe respiratory confirmed). MR analysis demonstrates a potential causality between NO2 and NOx exposure and the risk of COVID-19. Preventing and controlling air pollution may help slow down and prevent the adverse progression of COVID-19.

MedicineVol. 104(48)
Hebei Medical University (CN), Second Hospital of Hebei Medical University (CN), First Affiliated Hospital of Hebei Medical University (CN)
Openalex Percentile: Top 53%
Health, Environment, Cognitive Aging
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Association between nitrogen oxides and COVID-19 risk: A Mendelian randomization study — Zixiao Chen, Jiaona Wei, et al. · Medicine (2025) | TGRS Research Map | TGRS