The role of nitrogen dioxide in the prevalence of adverse cardiovascular and cerebrovascular diseases in China: a national multi-pollutant geospatial analysis

INTRODUCTION: Cardiovascular and cerebrovascular diseases (CCVDs) pose a severe global health threat, particularly among middle-aged and elderly populations. Ambient air pollution is a well-recognized environmental risk factor for CCVDs, yet existing studies often focus on single pollutants or outcomes, lacking comprehensive evaluations. This study aimed to systematically explore the associations between multiple air pollutants and three core adverse CCVD outcomes (hypertension, heart disease, stroke) in middle-aged and elderly individuals. METHODS AND MATERIALS: Data were integrated from the China Health and Retirement Longitudinal Study (CHARLS) and the China High-resolution Air Pollutants (CHAP) dataset, including seven major air pollutants (PM1, PM2.5, PM10, NO2, SO₂, CO, O₃) and multi-dimensional covariates (demographic characteristics, lifestyle habits, underlying diseases). Two analytical phases were conducted: AQI-related analysis (2015-2020, 20,990 participants) and machine learning analysis (2013-2020, 22,517 participants). Statistical methods included descriptive analysis, univariate linear regression, multivariate logistic regression, restricted cubic spline (RCS) analysis, and six machine learning algorithms (XGBoost, Random Forest, etc.) combined with Shapley Additive Explanations (SHAP) values. RESULTS: City-level univariate linear regression showed significant positive correlations between annual average AQI and the prevalence rates of hypertension (β = 0.0006, P = 0.0125), heart disease (β = 0.0008, P = 0.0147), and stroke (β = 0.0005, P = 0.0248). At the individual level, multivariate logistic regression revealed that each 1-unit increase in AQI was associated with a 3% higher risk of hypertension (OR = 1.003, P < 0.001), 6% higher risk of heart disease (OR = 1.006, P < 0.001), and 4% higher risk of stroke (OR = 1.004, P = 0.0433) after full covariate adjustment. RCS analysis indicated non-linear dose-response relationships, with accelerated risk increases at moderate-to-high AQI levels (≥ 80 for heart disease, ≥ 100 for stroke). Machine learning models exhibited good predictive performance (AUC range: 0.720-0.771), and SHAP value analysis identified NO2 as the most critical pollutant influencing all three outcomes, with significantly higher impact weights than other pollutants. DISCUSSION: This study confirms that long-term air pollution exposure is an independent risk factor for hypertension, heart disease, and stroke in middle-aged and elderly individuals. NO2 plays a core role through mechanisms such as disrupting vascular endothelial function, inducing oxidative stress, and triggering inflammatory responses. These findings provide a scientific basis for precise environmental health interventions and CCVD prevention, particularly emphasizing NO2 control. CONCLUSION: This study confirms that air pollution is an independent risk factor for hypertension, heart disease, and stroke in middle-aged and elderly individuals. Crucially, machine learning analysis identified NO₂ as the dominant pollutant, with a greater impact than particulate matter.