Air pollution and heart disease

Relationship between short-term ozone exposure, cause-specific mortality, and high-risk populations: A nationwide, time-stratified, case-crossover study

BACKGROUND

Previous studies reported that short-term exposure to ground-level ozone is associated with mortality risk. However, due to the limited monitored areas, existing studies were limited in assessing the nationwide risk and suggesting specific vulnerable populations to the ozone-mortality risk.

METHODS

We performed a nationwide time-stratified case-crossover study to evaluate the association between short-term ozone and cause-specific mortality in South Korea (2015-2019). A machine learning-ensemble prediction model (a test R2 > 0.96) was used to assess the short-term ozone exposure. Stratification analysis was conducted to examine the high-risk populations, and the excess mortality due to non-compliance with the WHO guideline was also assessed.

RESULTS

For all-cause mortality (1,343,077 cases), the risk associated with ozone (lag0- 1) was weakly identified (odd ratio: 1.005 with 95% CI: 0.997-1.014), and the risk was prominent in mortality with circulatory system diseases. In addition, based on the point estimates, the ozone-mortality risk was higher in people aged less than 65y, and this pattern was also observed in circulatory system disease deaths and urban areas.

CONCLUSIONS

This study provides national estimates of mortality risks associated with short-term ozone. Results showed that the benefits of stricter air quality standards could be greater in vulnerable populations.

Polystyrene nanoplastics induce cardiotoxicity by upregulating HIPK2 and activating the P53 and TGF-β1/Smad3 pathways

Nanoplastics (NPs) pollution has become a global environmental problem, raising numerous health concerns. However, the cardiotoxicity of NPs exposure and the underlying mechanisms have been understudied to date. To address this issue, we comprehensively evaluated the cardiotoxicity of polystyrene nanoplastics (PS-NPs) in both healthy and pathological states. Briefly, mice were orally exposed to four different concentrations (0 mg/day, 0.1 mg/day, 0.5 mg/day, and 2.5 mg/day) of 100-nm PS-NPs for 6 weeks to assess their cardiotoxicity in a healthy state. Considering that individuals with underlying health conditions are more vulnerable to the adverse effects of pollution, we further investigated the cardiotoxic effects of PS-NPs on pathological states induced by isoprenaline. Results showed that PS-NPs induced cardiomyocyte apoptosis, cardiac fibrosis, and myocardial dysfunction in healthy mice and exacerbated cardiac remodeling in pathological states. RNA sequencing revealed that PS-NPs significantly upregulated homeodomain interacting protein kinase 2 (HIPK2) in the heart and activated the P53 and TGF-beta signaling pathways. Pharmacological inhibition of HIPK2 reduced P53 phosphorylation and inhibited the activation of the TGF-β1/Smad3 pathway, which in turn decreased PS-NPs-induced cardiotoxicity. This study elucidated the potential mechanisms underlying PS-NPs-induced cardiotoxicity and underscored the importance of evaluating nanoplastics safety, particularly for individuals with pre-existing heart conditions.

Smartphone Case-Based Gas Sensing Platform for On-site Acetone Tracking

Gas sensor-embedded smartphones would offer the opportunity of on-site tracking of gas molecules for various applications, for example, harmful air pollutant alarms or noninvasive assessment of health status. Nevertheless, high power consumption and difficulty in replacing malfunctioned sensors as well as limited space in the smartphone to host the sensor restrain the relevant advancements. In this article, we create a smartphone case-based sensing platform by integrating the functional units into a smartphone case, which performs a low detection limit of 117 ppb to acetone and high specificity. Particularly, dimming glass-regulated light fidelity (Li-Fi) communication is successfully developed, allowing the sensing platform to operate with relatively low power consumption (around 217 mW). Experimental proof on harmful gas sensing and potential clinic application is implemented with the sensing platform, demonstrating satisfactory sensing performance and acceptable health risk pre-warning accuracy (87%). Thus, the developed smartphone case-based sensing platform would be a good candidate for realizing harmful gas alarms and noninvasive assessment of health status.

Urban ecological land and natural-anthropogenic environment interactively drive surface urban heat island: An urban agglomeration-level study in China

The surface urban heat island effect (SUHI) that occurs during rapid urbanization increases the health risks associated with high temperatures. Urban ecological land (UEL) has been shown to play an important role in improving urban heat stress, however, the impact of UEL interactions with the natural-anthropogenic environment on SUHI at the urban agglomeration-scale is less explored. In this study, the Google Earth Engine and GeoDetector were applied to characterize the spatiotemporal patterns of UEL and SUHI in the Guangdong-Hong Kong-Macao Greater Bay Area from 2000 to 2020 by extracting major built-up urban areas and quantifying the impacts of UEL and its interactions with the natural-anthropogenic factors on SUHI. The results show that the evolution of the UEL landscape structure exhibits clear spatiotemporal coupling with SUHI. Specifically, the UEL underwent a dispersion and degradation process in 2000-2015 and a convergence and restoration process in 2015-2020, the SUHI correspondingly transitioned from intensification and continuity to mitigation and contraction. The UEL landscape structure showed a notable impact on the SUHI reduction, and the dominance and richness of the patches explained an average of 19.95% and 16.03% of the SUHI, respectively. Moreover, the interaction between UEL and land urbanization rate and anthropogenic heat release had a dominant effect on SUHI, but this effect significantly declined from 2015 to 2020. With the implementation of ecological restoration projects, the interaction of UEL with topography rapidly increased and the SUHI gradually dominated by the joint interaction of UEL and natural-anthropogenic factors. A synthesis of the varying effects of several factors showed that the dynamic relationship between the development stages of the urban agglomeration's regional system and SUHI may conform to the Environmental Kuznets Curve. SUHI reduction strategies should therefore comprehensively optimize the rational allocation of UEL landscape structures and natural-human elements to promote the well-being of residents.

Fine-Scale Air Pollution Models for Epidemiologic Research: Insights From Approaches Developed in the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air)

PURPOSE OF REVIEW

Epidemiological studies of short- and long-term health impacts of ambient air pollutants require accurate exposure estimates. We describe the evolution in exposure assessment and assignment in air pollution epidemiology, with a focus on spatiotemporal techniques first developed to meet the needs of the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Initially designed to capture the substantial variation in pollutant levels and potential health impacts that can occur over small spatial and temporal scales in metropolitan areas, these methods have now matured to permit fine-scale exposure characterization across the contiguous USA and can be used for understanding long- and short-term health effects of exposure across the lifespan. For context, we highlight how the MESA Air models compare to other available exposure models.

RECENT FINDINGS

Newer model-based exposure assessment techniques provide predictions of pollutant concentrations with fine spatial and temporal resolution. These validated models can predict concentrations of several pollutants, including particulate matter less than 2.5 μm in diameter (PM2.5), oxides of nitrogen, and ozone, at specific locations (such as at residential addresses) over short time intervals (such as 2 weeks) across the contiguous USA between 1980 and the present. Advances in statistical methods, incorporation of supplemental pollutant monitoring campaigns, improved geographic information systems, and integration of more complete satellite and chemical transport model outputs have contributed to the increasing validity and refined spatiotemporal spans of available models. Modern models for predicting levels of outdoor concentrations of air pollutants can explain a substantial amount of the spatiotemporal variation in observations and are being used to provide critical insights into effects of air pollutants on the prevalence, incidence, progression, and prognosis of diseases across the lifespan. Additional enhancements in model inputs and model design, such as incorporation of better traffic data, novel monitoring platforms, and deployment of machine learning techniques, will allow even further improvements in the performance of pollutant prediction models.

An interpretable self-adaptive deep neural network for estimating daily spatially-continuous PM2.5 concentrations across China

Accurate estimation of daily spatially-continuous PM_2.5 (fine particulate matter) concentration is a prerequisite to address environmental public health issues, and satellite-based aerosol optical depth (AOD) products have been widely used to estimate PM_2.5 concentrations using statistical-based or machine learning-based models. However, statistical-based models oversimplify the AOD-PM_2.5 relationships, whereas complex machine learning technologies ignore the spatiotemporal heterogeneity of the predictors and demonstrate shortage in interpretation. Besides, large AOD data gaps resulting in PM_2.5 estimation biases have been seldom imputed in previous studies, especially at national scales. To fill the above research gaps, this study attempts to present a feasible methodology to estimate daily spatially-continuous PM_2.5 concentrations in China. The AOD data gaps across China were first imputed via a random forest (RF) model. Then, an interpretable self-adaptive deep neural network (SADNN) model, incorporating AOD, meteorological and other auxiliary predictors, was developed to estimate daily spatially-continuous PM_2.5 concentrations from 2017 to 2018. Five-fold sample (site)-based cross-validation results showed a high accuracy of the SADNN model, with coefficient of determination and root mean square error values equal to 0.86 (0.84) and 13.07 (14.30) μg/m³, respectively, outperforming the standard DNN and the RF model. Furthermore, the SADNN model identified the spatiotemporal patterns of predictor importance, and demonstrated that the boundary layer height, elevation and AOD were the most important predictors both spatially and temporally. And the predictor importance in the Qinghai-Tibet Plateau was different from that in the rest of China. These results enhance our understanding of AOD-PM_2.5 relationships and elucidate the estimated PM_2.5 datasets with complete coverage are applicable for related air pollution studies and epidemiological cohort studies. Moreover, considering the effective nonlinear model capability and interpretability, the SADNN model is beneficial for not only PM_2.5 estimation but also other earth data and scenarios.

Relation between Accumulated Air Pollution Exposure and Sub-Clinical Cardiovascular Disease in 33,723 Danish 60-74-Year-Old Males from the Background Population (AIR-CARD): A Method Article

Cardiovascular disease is one of the main causes of death and disability in the Western world, and there is increasing evidence that air pollution is a risk factor for developing sub-clinical cardiovascular diseases. Previous studies have shown a correlation between cardiovascular disease and short-term exposure to elevated air pollution levels. However, the literature on the impact of long-term effect of air pollution is limited. We have a unique opportunity to evaluate this correlation. The DEHM/UBM/AirGIS model system calculates air pollution in a high temporal and spatial resolution and traces air pollution retrospectively to year 1979. The model calculates accumulated exposure using annual exposure from PM2.5 in relation to home and work addresses and takes into account working hours and holidays. We link the results from this model system to a population-based cardiovascular screening cohort of 33,723 individuals in the age of 60-74 to assess the contribution of the specific accumulated air pollution to the presence of sub-clinical arteriosclerosis in the coronary vessels, abdominal aortic aneurysms, and peripheral arterial disease. This correlation will be further analyzed in relation to specific air pollutants. This study will introduce more precise data for a longer period of time and incorporate participant's home and work addresses.

Concentrated ambient fine particulate matter (PM2.5) exposure induce brain damage in pre and postnatal exposed mice

Air pollution is a public health concern that has been associated with adverse effects on the development and functions of the central nervous system (CNS). However, studies on the effects of exposure to pollutants on the CNS across the entire developmental period still remain scarce. In this study, we investigated the impacts of prenatal and/or postnatal exposure to fine particulate matter (PM_2.5) from São Paulo city, on the brain structure and behavior of juvenile male mice. BALB/c mice were exposed to PM_2.5 concentrated ambient particles (CAP) at a daily concentration of 600 μg/m³ during the gestational [gestational day (GD) 1.5-18.5] and the postnatal periods [postnatal day (PND) 22-90] to filtered air (FA) in both periods (FA/FA), to CAP only in the postnatal period (FA/CAP), to CAP only in the gestational period (CAP/FA), and to CAP in both periods (CAP/CAP). Behavioral tests were performed when animals were at PND 30 and PND 90. Glial activation, brain volume, cortical neuron number, serotonergic and GABAergic receptors, as well as oxidative stress, were measured. Mice at PND 90 presented greater behavioral changes in the form of greater locomotor activity in the FA-CAP and CAP-CAP groups. In general, these same groups explored objects longer and the CAP-FA group presented anxiolytic behavior. There was no difference in total brain volume among groups, but a lower corpus callosum (CC) volume was observed in the CAP-FA group. Also, the CAP-CAP group presented an increase in microglia in the cortex and an increased in astrocytes in the cortex, CC, and C1A and dentate gyrus of hippocampus regions. Gene expression analysis showed a decrease in BDNF in the hippocampus of CAP-CAP group. Treatment of immortalized glial cells with non-cytotoxic doses of ambient PM_2.5 increased micronuclei frequencies, indicating genomic instability. These findings highlight the potential for negative neurodevelopmental outcomes induced by exposure to moderate levels of PM_2.5 in Sao Paulo city.

Planetary Epidemiology: Towards First Principles

PURPOSE OF REVIEW

To combine evolutionary principles of competition and co-operation with limits to growth models, generating six principles for a new sub-discipline, called "planetary epidemiology." Suggestions are made for how to quantify four principles.

RECENT FINDINGS

Climate change is one of a suite of threats increasingly being re-discovered by health workers as a major threat to civilization. Although "planetary health" is now in vogue, neither it nor its allied sub-disciplines have, as yet, had significant impact on epidemiology. Few if any theorists have sought to develop principles for Earth system human epidemiology, in its ecological, social, and technological milieu. The principles of planetary epidemiology described here can be used to stimulate applied, quantitative work to explore past, contemporary, and future population health, at scales from local to planetary, in order to promote enduring health. It is also proposed that global well-being will decline this century, without radical reform.

Application of the Public Health Exposome Framework to Estimate Phenotypes of Resilience in a Model Ohio African-American Women's Cohort

We report integration of the United States Environmental Protection Agency's (USEPA) United States Environmental Justice Screen (EJSCREEN) database with our Public Health Exposome dataset to interrogate 9232 census blocks to model the complexity of relationships among environmental and socio-demographic variables toward estimating adverse pregnancy outcomes [low birth weight (LBW) and pre-term birth (PTB)] in all Ohio counties. Using a hill-climbing algorithm in R software, we derived a Bayesian network that mapped all controlled associations among all variables available by applying a mapping algorithm. The results revealed 17 environmental and socio-demographic variables that were represented by nodes containing 69 links accounting for a network with 32.85% density and average degree of 9.2 showing the most connected nodes in the center of the model. The model predicts that the socio-economic variables low income, minority, and under age five populations are correlated and associated with the environmental variables; particulate matter (PM_2.5) level in air, proximity to risk management facilities, and proximity to direct discharges in water are linked to PTB and LBW in 88 Ohio counties. The methodology used to derive significant associations of chemical and non-chemical stressors linked to PTB and LBW from indices of geo-coded environmental neighborhood deprivation serves as a proxy for design of an African-American women's cohort to be recruited in Ohio counties from federally qualified community health centers within the 9232 census blocks. The results have implications for the development of severity scores for endo-phenotypes of resilience based on associations and linkages for different chemical and non-chemical stressors that have been shown to moderate cardio-metabolic disease within a population health context.

Differential Susceptibility in Ambient Particle-Related Risk of First-Ever Stroke: Findings From a National Case-Crossover Study

Different populations may respond differently to exposure to ambient fine particulate matter, defined as particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5); however, less is known about the distribution of susceptible individuals among the entire population. We conducted a time-stratified case-crossover study to assess associations between stroke risk and exposure to PM2.5. During 2013-2015, 1,356 first-ever stroke events were derived from a large representative sample, the China National Stroke Screening Survey (CNSSS) database. Daily PM2.5 average exposures with a spatial resolution of 0.1° were estimated using a data assimilation approach combining satellite measurements, air model simulations, and monitoring values. The distribution of susceptibility was derived according to individual-specific associations with PM2.5 modified by different combinations of individual-level characteristics and their joint frequencies among all of the CNSSS participants (n = 1,292,010). We found that first-ever stroke was statistically significantly associated with PM2.5 (per 10-μg/m3 increment of exposure, odds ratio = 1.049, 95% confidence interval (CI): 1.038, 1.061). This association was modified by demographic (e.g., sex), lifestyle (e.g., overweight/obesity), and medical history (e.g., diabetes) variables. The combined association with PM2.5 varied from 0.966 (95% CI: 0.920, 1.013) to 1.145 (95% CI: 1.080, 1.215) per 10-μg/m3 increment in different subpopulations. We found that most of the CNSSS participants were at increased risk of PM2.5-related stroke, while only a small proportion were highly susceptible.

Are metals and pyrene levels additional factors playing a pivotal role in air pollution-induced inflammation in taxi drivers?

This study aimed to evaluate which xenobiotic (As, Hg, Pb or pyrenes) is primarily responsible for the inflammatory process in taxi drivers. Multiple regression analysis showed that Hg is the main xenobiotic responsible for the increase of cytokine levels. These associations suggest that co-exposure to pollutants could be a risk factor for health effects.