Using an Exposome-Wide Approach to Explore the Impact of Urban Environments on Blood Pressure among Adults in Beijing–Tianjin–Hebei and Surrounding Areas of China

Abstract

Existing studies mostly explored the association between urban environmental exposures and blood pressure (BP) in isolation, ignoring correlations across exposures. This study aimed to systematically evaluate the impact of a wide range of urban exposures on BP using an exposome-wide approach. A multicenter cross-sectional study was conducted in ten cities of China. For each enrolled participant, we estimated their urban exposures, including air pollution, built environment, surrounding natural space, and road traffic indicator. On the whole, this study comprised three statistical analysis steps, that is, single exposure analysis, multiple exposure analysis and a cluster analysis. We also used deletion–substitution–addition algorithm to conduct variable selection. After considering multiple exposures, for hypertension risk, most significant associations in single exposure model disappeared, with only neighborhood walkability remaining negatively statistically significant. Besides, it was observed that SBP (systolic BP) raised gradually with the increase in PM2.5, but such rising pattern slowed down when PM2.5 concentration reached a relatively high level. For surrounding natural spaces, significant protective associations between green and blue spaces with BP were found. This study also found that high population density and public transport accessibility have beneficially significant association with BP. Additionally, with the increase in the distance to the nearest major road, DBP (diastolic BP) decreased rapidly. When the distance was beyond around 200 m, however, there was no obvious change to DBP anymore. By cluster analysis, six clusters of urban exposures were identified. These findings reinforce the importance of improving urban design, which help promote healthy urban environments to optimize human BP health.

Phenotypic aging mediates the association between blood cadmium and depression: a population-based study

“…Weighted multivariate linear regression was used to assess the association between blood cadmium concentrations and biological aging indicators. We also used a restricted cubic spline function with three nodes (5th, 50th, and 95th) to explore the potential nonlinearity in the dose-response relationship between blood cadmium concentration and depression and biochemical indicators and depression (Song et al 2022). We also quartile-transformed the biological aging indicators and performed trend tests after replacing them with ordered variables (0-3) in the model.…”

Separating Daily 1 km PM2.5 Inorganic Chemical Composition in China since 2000 via Deep Learning Integrating Ground, Satellite, and Model Data

“…The data set is of high quality with an out-of-sample cross-validated coefficient of determination (CV-R 2 ) of 0.92, an average root-mean-square error (RMSE) of 10.76 μg/m 3 , and a mean absolute error (MAE) of 6.32 μg/m 3 , compared to surface observations. It has been widely used in studies concerning public health, the environment, and the economy; − thus, it is employed here as the primary constraint of total PM 2.5 mass for separating different chemical components. Other satellite remote-sensing products related to land surface cover, topography, and population density were also used, derived from MODIS vegetation index (1 km), Shuttle Radar Topography Mission (90 m), and LandScan (1 km) products, respectively.…”

Ground-level gaseous pollutants (NO2, SO2, and CO) in China: daily seamless mapping and spatiotemporal variations

“…Wu et al, 2022b;Xu et al, 2022c), metabolic syndrome (Guo et al, 2022;S. Han et al, 2022), influenza-like illness (Lu et al, 2023), incident dyslipidemia (Hu et al, 2023), diabetes (Mei et al, 2023), blood pressure (Song et al, 2022;H. Wu et al, 2022a), renal and/or kidney function (S. , neurodevelopmental delays (Su et al, 2022), serum liver enzymes (Y. , overweight and obesity (Chen et al, 2022b), insomnia (Xu et al, 2021), and sleep quality (L. Wang et al, 2022).…”