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

Associations of green and blue spaces with visual acuity in youths from Shandong Province, China: A large population-based study

While growing evidence highlights the benefits of green and blue spaces for physical and mental health, their combined effects on youth visual acuity remain unclear. This study aimed to evaluate the associations of green and blue spaces with visual acuity in youths. We analyzed data from the 2023 Common Disease and Health Risk Factors Surveillance and Intervention Program among students in Shandong Province, China. Generalized linear mixed-effects models were employed to investigate the independent associations of green and blue spaces with visual acuity in youths. To explore potential interactions, an interaction term for green and blue spaces was incorporated. Quantile g-computation (Qgcomp) models were applied to evaluate their combined effects. Compared to the lowest quartile, higher quartiles (Q2, Q3, and Q4) of the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized difference water index (NDWI), and modified normalized difference water index (MNDWI) were significantly associated with improved visual acuity in both eyes. Additionally, each 0.1 unit increase in NDVI500, EVI500, NDWI500, and MNDWI500 was associated with 0.008 (95 % confidence interval [CI]: 0.007, 0.010), 0.003 (0.002, 0.004), 0.054 (0.028, 0.080), and 0.010 (0.002, 0.018) improvements in right-eye visual acuity, with similar findings for the left eye. A significant interaction was observed between NDVI and NDWI (all P for interaction < 0.001), and combined exposure to green and blue spaces was positively associated with visual acuity (all P < 0.001). Furthermore, demographic and lifestyle factors modified the associations of blue and green spaces with visual acuity levels in youths. Greater exposure to green and blue spaces may benefit visual health in youths, with potential interactive and combined effects. Implementing policies to enhance the availability of green and blue spaces around schools may offer opportunities to alleviate visual impairment in youths.

Towards personalized cardiometabolic risk prediction: A fusion of exposome and AI

The influence of the exposome on major health conditions like cardiovascular disease (CVD) is widely recognized. However, integrating diverse exposome factors into predictive models for personalized health assessments remains a challenge due to the complexity and variability of environmental exposures and lifestyle factors. A machine learning (ML) model designed for predicting CVD risk is introduced in this study, relying on easily accessible exposome factors. This approach is particularly novel as it prioritizes non-clinical, modifiable exposures, making it applicable for broad public health screening and personalized risk assessments. Assessments were conducted using both internal and external validation groups from a multi-center cohort, comprising 3,237 individuals diagnosed with CVD in South Korea within twelve years of their baseline visit, along with an equal number of participants without these conditions as a control group. Examination of 109 exposome variables from participants' baseline visits spanned physical measures, environmental factors, lifestyle choices, mental health events, and early-life factors. For risk prediction, the Random Forest classifier was employed, with performance compared to an integrative ML model using clinical and physical variables. Furthermore, data preprocessing involved normalization and handling of missing values to enhance model accuracy. The model's decision-making process were using an advanced explainability method. Results indicated comparable performance between the exposome-based ML model and the integrative model, achieving AUC of 0.82(+/-)0.01, 0.70(+/-)0.01, and 0.73(+/-)0.01. The study underscores the potential of leveraging exposome data for early intervention strategies. Additionally, exposome factors significant in identifying CVD risk were pinpointed, including daytime naps, completed full-time education, past tobacco smoking, frequency of tiredness/unenthusiasm, and current work status.

PM2.5-mediated cardiovascular disease in aging: Cardiometabolic risks, molecular mechanisms and potential interventions

Air pollution, particularly fine particulate matter (PM2.5) with <2.5 μm in diameter, is a major public health concern. Studies have consistently linked PM2.5 exposure to a heightened risk of cardiovascular diseases (CVDs) such as ischemic heart disease (IHD), heart failure (HF), and cardiac arrhythmias. Notably, individuals with pre-existing age-related cardiometabolic conditions appear more susceptible. However, the specific impact of PM2.5 on CVDs susceptibility in older adults remains unclear. Therefore, this review addresses this gap by discussing the factors that make the elderly more vulnerable to PM2.5-induced CVDs. Accordingly, we focused on physiological aging, increased susceptibility, cardiometabolic risk factors, CVDs, and biological mechanisms. This review concludes by examining potential interventions to reduce exposure and the adverse health effects of PM2.5 in the elderly population. The latter includes dietary modifications, medications, and exploration of the potential benefits of supplements. By comprehensively analyzing these factors, this review aims to provide a deeper understanding of the detrimental effects of PM2.5 on cardiovascular health in older adults. This knowledge can inform future research and guide strategies to protect vulnerable populations from the adverse effects of air pollution.

Associations between urban exposome and recurrence risk among survivors of acute myocardial infarction in Beijing, China

Few previous studies have investigated the impacts of coexposure to multiple urban environmental factors on the prognosis of acute myocardial infarction (AMI) events. This study aimed to evaluate the associations between the urban exposome and AMI recurrence. We used data from 88,509 AMI patients from a large cohort obtained from the Beijing Cardiovascular Disease Surveillance System between 2013 and 2019. Twenty-six types of urban exposures were assessed within 300-m, 500-m, and 1000-m buffers of patients' home addresses in the baseline and cumulative average levels. We used the Cox proportional hazard model along with the Elastic Net (ENET) algorithm to estimate the hazard ratios (HRs) of recurrent AMI per interquartile range increase in each selected urban exposure. The increased risk of AMI recurrence was significantly associated with lower urban function diversity in the 500-m buffer, longer distance to subway stations and higher PM2.5 for both baseline and cumulative average exposure. The cumulative averages of two urban factors, including the distance to parks, and the density of fruit and vegetable shops in the 1000-m buffer, were also identified as significant factors affecting the risk of AMI recurrence. These findings can help improve the urban design for promoting human cardiovascular health.

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

Fine particulate matter (PM2.5) chemical composition has strong and diverse impacts on the planetary environment, climate, and health. These effects are still not well understood due to limited surface observations and uncertainties in chemical model simulations. We developed a four-dimensional spatiotemporal deep forest (4D-STDF) model to estimate daily PM2.5 chemical composition at a spatial resolution of 1 km in China since 2000 by integrating measurements of PM2.5 species from a high-density observation network, satellite PM2.5 retrievals, atmospheric reanalyses, and model simulations. Cross-validation results illustrate the reliability of sulfate (SO42-), nitrate (NO3-), ammonium (NH4+), and chloride (Cl-) estimates, with high coefficients of determination (CV-R2) with ground-based observations of 0.74, 0.75, 0.71, and 0.66, and average root-mean-square errors (RMSE) of 6.0, 6.6, 4.3, and 2.3 μg/m3, respectively. The three components of secondary inorganic aerosols (SIAs) account for 21% (SO42-), 20% (NO3-), and 14% (NH4+) of the total PM2.5 mass in eastern China; we observed significant reductions in the mass of inorganic components by 40-43% between 2013 and 2020, slowing down since 2018. Comparatively, the ratio of SIA to PM2.5 increased by 7% across eastern China except in Beijing and nearby areas, accelerating in recent years. SO42- has been the dominant SIA component in eastern China, although it was surpassed by NO3- in some areas, e.g., Beijing-Tianjin-Hebei region since 2016. SIA, accounting for nearly half (∼46%) of the PM2.5 mass, drove the explosive formation of winter haze episodes in the North China Plain. A sharp decline in SIA concentrations and an increase in SIA-to-PM2.5 ratios during the COVID-19 lockdown were also revealed, reflecting the enhanced atmospheric oxidation capacity and formation of secondary particles.

The mediation effect of lipids, blood pressure and BMI between air pollutant mixture and atherosclerotic cardiovascular disease: The CHCN-BTH cohort study

BACKGROUND

The combine effect of air pollutant mixture on atherosclerotic cardiovascular disease (ASCVD) remain undefined. This study aims to explore the association between long-term exposure of air pollutants and ASCVD, focusing on the mediating role of lipids, blood pressure and BMI.

METHODS

This study was based on the CHCN-BTH cohort study. The annual concentrations of air pollutants and PM2.5 constituents were sourced from in the Tracking Air Pollution in China (TAP) and ChinaHighAirPollutants (CHAP) datasets from 2014 to 2019. A Cox mixed-effects model was used to investigate the associations between long-term exposure of air pollutants and ASCVD. The combined impact of the air pollutant mixture was assessed using Quantile g-Computation. Stratified, sensitivity, and mediation analyses were conducted.

RESULTS

A total of 27,134 participants aged 18-80 were recruited in the present study. We found that each IQR increase of PM2.5, PM1, NO2, O3, BC, SO42-, and OM were significantly associated with the incidence of ASCVD, the hazard ratios (HRs) and 95 % confidence interval (CI) were 1.55 (1.35, 1.78), 1.46 (1.27, 1.67), 1.30 (1.21, 1.39), 1.66 (1.41,1.95), 2.14 (1.63, 2.83), 1.65 (1.25, 2.17) and 1.92(1.52, 2.45), respectively. The combined effect of air pollutant mixture on ASCVD was 1.79 (1.46, 2.20), PM2.5 contributed 83.3 % to this combined effect. Mediation effect models suggested that air pollutants and ASCVD might be mediated through SBP, DBP, HDL-C, LDL-C, hsCRP and BMI (mediation proportion range from 1.3 % to 26.1 %), Notably, HDL-C played mediation roles of 11.3 % (7.0 %, 18.4), 26.1 % (17.7 %, 38.1 %) and 25.4 % (15.4, 47.7 %) in the effects of long-term exposure to PM2.5, PM1 and OM on ASCVD, respectively.

CONCLUSIONS

Long-term, high-level air pollutant exposure was significantly associated with an elevated risk of ASCVD, particularly for PM2.5. Blood pressure, lipids and BMI, especially HDL-C, may mediate the effects of air pollutants exposure on ASCVD.

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

Depression is a serious public health problem today, especially in middle-aged and older adults. Although the etiology of the disease has not been fully elucidated, environmental factors are increasingly not negligible. Cadmium is widely used in industrial production. The general population may be chronically exposed to low doses of cadmium. This study aimed to investigate the association between blood cadmium and depression and to explore the mediating role of aging indicators in this process. We conducted a cross-sectional study on blood cadmium (N = 7195, age ≥ 20 years) using data from the 2007-2010 National Health and Nutrition Examination Survey (NHANES). Aging indicators (biological and phenotypic age) are calculated by combining multiple biochemical and/or functional indicators. To determine the relationship between blood cadmium concentrations and depressive symptoms, we used weighted multivariate logistic regression and restricted cubic spline functions and employed mediation analysis to explore the possible mediating effects of aging indicators in the process. We found a significant positive association between blood cadmium and depression with an odds ratio (OR) and 95% confidence interval (CI): 1.22 (1.04,1.43). Restricted cubic spline analysis found a linear positive association between blood cadmium and depression. In the fully covariate-adjusted model, we found a positive association between blood cadmium and biological age and phenotypic age with β and 95% CI: 1.02 (0.65, 1.39) and 2.35 (1.70, 3.01), respectively. In the mediation analysis, we found that phenotypic age mediated 21.32% of the association between blood cadmium and depression. These results suggest that even exposure to low doses of cadmium can increase the risk of depression and that this process may be mediated by phenotypic aging.

Exposure to fine particulate matter constituents and cognitive function performance, potential mediation by sleep quality: A multicenter study among Chinese adults aged 40-89 years

Although exposure to fine particulate matter (PM_2.5) has been associated with cognitive decline, little is known about which PM_2.5 constituents are more harmful. Recent study on the association between PM_2.5 and sleep quality prompted us to propose that sleep quality may mediate the adverse effects of PM_2.5 components on cognitive decline. Understanding the association between PM_2.5 constituents and cognitive function, as well as the mediating role of sleep quality provides a future intervention target for improving cognitive function. Using data involving 1834 participants from a multicenter cross-sectional study in nine cities of the Beijing-Tianjin-Hebei (BTH) region in China, we undertook multivariable linear regression analyses to quantify the association of annual moving-average PM_2.5 and its chemical constituents with cognitive function and to assess the modifying role of exposure characteristic in this association. Besides, we examined the extent to which this association of PM_2.5 constituents with cognitive function was mediated via sleep quality by a mediation analysis. We observed significantly negative associations between an increase of one interquartile range increase in PM_2.5 [-0.876 (95 % CI: -1.205, -0.548)], organic carbon [-0.481 (95 % CI: -0.744, -0.219)], potassium [-0.344 (95 % CI: -0.530, -0.157)], iron [-0.468 (95 % CI: -0.646, -0.291)], and ammonium ion [-0.125 (95 % CI: -0.197, -0.052)] and cognitive decline. However, we didn't find any individual components more harmful than PM_2.5. Poor sleep quality partially mediated the estimated associations, which were explained ranging from 2.28 % to 11.99 %. Stratification analyses showed that people living in areas with lower greenspace were more susceptible to specific PM_2.5 components. Our study suggests that the adverse effect of suffering from PM_2.5 components is more pronounced among individuals with poor sleep quality, amplifying environmental inequalities in health. Besides reducing environmental pollution, improving sleep quality may be another measure worth considering to improve cognition if our research is confirmed in the future.

The underlying mechanism of PM2.5-induced ischemic stroke

Under the background of global industrialization, PM_2.5 has become the fourth-leading risk factor for ischemic stroke worldwide, according to the 2019 GBD estimates. This highlights the hazards of PM_2.5 for ischemic stroke, but unfortunately, PM_2.5 has not received the attention that matches its harmfulness. This article is the first to systematically describe the molecular biological mechanism of PM_2.5-induced ischemic stroke, and also propose potential therapeutic and intervention strategies. We highlight the effect of PM_2.5 on traditional cerebrovascular risk factors (hypertension, hyperglycemia, dyslipidemia, atrial fibrillation), which were easily overlooked in previous studies. Additionally, the effects of PM_2.5 on platelet parameters, megakaryocytes activation, platelet methylation, and PM_2.5-induced oxidative stress, local RAS activation, and miRNA alterations in endothelial cells have also been described. Finally, PM_2.5-induced ischemic brain pathological injury and microglia-dominated neuroinflammation are discussed. Our ultimate goal is to raise the public awareness of the harm of PM_2.5 to ischemic stroke, and to provide a certain level of health guidance for stroke-susceptible populations, as well as point out some interesting ideas and directions for future clinical and basic research.