The Impact of Multipollutant Clusters on the Association Between Fine Particulate Air Pollution and Microvascular Function

A review of common statistical methods for dealing with multiple pollutant mixtures and multiple exposures

Traditional environmental epidemiology has consistently focused on studying the impact of single exposures on specific health outcomes, considering concurrent exposures as variables to be controlled. However, with the continuous changes in environment, humans are increasingly facing more complex exposures to multi-pollutant mixtures. In this context, accurately assessing the impact of multi-pollutant mixtures on health has become a central concern in current environmental research. Simultaneously, the continuous development and optimization of statistical methods offer robust support for handling large datasets, strengthening the capability to conduct in-depth research on the effects of multiple exposures on health. In order to examine complicated exposure mixtures, we introduce commonly used statistical methods and their developments, such as weighted quantile sum, bayesian kernel machine regression, toxic equivalency analysis, and others. Delineating their applications, advantages, weaknesses, and interpretability of results. It also provides guidance for researchers involved in studying multi-pollutant mixtures, aiding them in selecting appropriate statistical methods and utilizing R software for more accurate and comprehensive assessments of the impact of multi-pollutant mixtures on human health.

Noise, Air, and Heavy Metal Pollution as Risk Factors for Endothelial Dysfunction

During the last two decades, large epidemiological studies have shown that the physical environment, including noise, air pollution or heavy metals, have a considerable impact on human health. It is known that the most common cardiovascular risk factors are all associated with endothelial dysfunction. Vascular tone, circulation of blood cells, inflammation, and platelet activity are some of the most essential functions regulated by the endothelium that suffer negative effects as a consequence of environmental pollution, causing endothelial dysfunction. In this review, we delineate the impact of environmental risk factors in connection to endothelial function. On a mechanistic level, a significant number of studies suggest the involvement of endothelial dysfunction to fundamentally drive the adverse endothelium health effects of the different pollutants. We focus on well-established studies that demonstrate the negative effects on the endothelium, with a focus on air, noise, and heavy metal pollution. This in-depth review on endothelial dysfunction as a consequence of the physical environment aims to contribute to the associated research needs by evaluating current findings from human and animal studies. From a public health perspective, these findings may also help to reinforce efforts promoting the research for adequate promising biomarkers for cardiovascular diseases since endothelial function is considered a hallmark of environmental stressor health effects.

[Combined effects of different environmental factors on health: air pollution, temperature, green spaces, pollen, and noise]

Environmental factors affect the health and wellbeing of urban residents. However, they do not act individually on humans, but instead show potential synergistic or antagonistic effects. Questions that arise from this are: How does a combination of air pollutants with other environmental factors impact health? How well are these associations evidenced? What methods can we use to look at them? In this article, methodical approaches regarding the effects of a combination of various environmental factors are first described. Environmental factors are then examined, which together with different air pollutants, have an impact on human health such as ambient temperature, noise, and pollen as well as the effect of green spaces. Physical activity and nutrition are addressed regarding the attenuation of health effects from air pollution.While there is often clear evidence of health effects of single environmental stressors, there are still open questions in terms of their interaction. The research methods required for this still need to be further developed. The interrelationship between the different environmental factors make it clear that (intervention) measures for reducing single indicators are also interlinked. Regarding traffic, switching from passive to active transport (e.g., due to safe cycle paths and other measures) leads to less air pollutants, smaller increases in temperature in the long term, and at the same time improved health of the individual. As a result, sensible planning of the built environment has great potential to reduce environmental stressors and improve people's health and wellbeing.

Probabilistic predictive principal component analysis for spatially misaligned and high-dimensional air pollution data with missing observations

Accurate predictions of pollutant concentrations at new locations are often of interest in air pollution studies on fine particulate matters (PM2.5), in which data is usually not measured at all study locations. PM2.5 is also a mixture of many different chemical components. Principal component analysis (PCA) can be incorporated to obtain lower-dimensional representative scores of such multi-pollutant data. Spatial prediction can then be used to estimate these scores at new locations. Recently developed predictive PCA modifies the traditional PCA algorithm to obtain scores with spatial structures that can be well predicted at unmeasured locations. However, these approaches require complete data, whereas multi-pollutant data tends to have complex missing patterns in practice. We propose probabilistic versions of predictive PCA which allow for flexible model-based imputation that can account for spatial information and subsequently improve the overall predictive performance.

Reactive hyperemia and baseline pulse amplitude among smelter workers exposed to fine and ultrafine particles

Objective

Ambient exposure to fine particles is associated with increased cardiovascular morbidity and mortality. Associations between occupational particulate matter (PM) exposure and cardiovascular disease have been studied less. The objective of this study was to examine associations between PM exposure and endothelial function among workers in Norwegian smelters.

Methods

We examined endothelial function with Endo-PAT equipment after a working day (WD) and on a day off (DO) in 59 furnace workers recruited from three metal smelters in Norway. The difference in baseline pulse amplitude (BPA) and reactive hyperemia index (RHI) between the 2 days was analysed in relation to individual exposure to PM < 250 nm (PM₂₅₀) or the respirable aerosol fraction of particles, and adjusted for relevant covariates.

Results

The exposure to PM₂₅₀ ranged from 0.004 to 5.7 mg/m³. The mean BPA was significantly higher on WD relative to DO (772 vs. 535, p = 0.001). This difference was associated with PM concentrations among participants ≥ 34 years, but not among the younger workers. Reactive hyperemia was significantly lower on workdays relative to days off (1.70 vs. 1.84, p = 0.05). This difference was observed only among participants above the age 34. No associations with PM exposure were observed.

Conclusions

PM exposure was associated with higher BPA among participants older than 34 years. BPA reflects microvessel pulsatility. Our results may indicate an age-dependent cardiovascular susceptibility to PM exposure. Endothelial function measured by RHI was reduced on WD among participants 34 years and older, but we found no associations between PM exposure and RHI.

Time-varying associations between prenatal metal mixtures and rapid visual processing in children

BACKGROUND

Humans are exposed to mixtures of chemicals across their lifetimes, a concept sometimes called the "exposome." Mixtures likely have temporal "critical windows" of susceptibility like single agents and measuring them repeatedly might help to define such windows. Common approaches to evaluate the effects of chemical mixtures have focused on their effects at a single time point. Our goal is to expand upon these previous techniques and examine the time-varying critical windows for metal mixtures on subsequent neurobehavior in children.

METHODS

We propose two methods, joint weighted quantile sum regression (JWQS) and meta-weighted quantile sum regression (MWQS), to estimate the effects of chemical mixtures measured across multiple time points, while providing data on their critical windows of exposure. We compare the performance of both methods using simulations. We also applied both techniques to assess second and third trimester metal mixture effects in predicting performance in the Rapid Visual Processing (RVP) task from the Cambridge Neuropsychological Test Automated Battery (CANTAB) assessed at 6-9 years in children who are part of the PROGRESS (Programming Research in Obesity, GRowth, Environment and Social Stressors) longitudinal cohort study. The metals, arsenic, cadmium (Cd), cesium, chromium, lead (Pb) and antimony (Sb) were selected based on their toxicological profile.

RESULTS

In simulations, JWQS and MWQS had over 80% accuracy in classifying exposures as either strongly or weakly contributing to an association. In real data, both JWQS and MWQS consistently found that Pb and Cd exposure jointly predicted longer latency in the RVP and that second trimester exposure better predicted the results than the third trimester. Additionally, both JWQS and MWQS highlighted the strong association Cd and Sb had with lower accuracy in the RVP and that third trimester exposure was a better predictor than second trimester exposure.

CONCLUSIONS

Our results indicate that metal mixtures effects vary across time, have distinct critical windows and that both JWQS and MWQS can determine longitudinal mixture effects including the cumulative contribution of each exposure and critical windows of effect.

Long- and short-term air pollution exposure and measures of arterial stiffness in the Framingham Heart Study

BACKGROUND

Studies of air pollution exposure and arterial stiffness have reported inconsistent results and large studies employing the reference standard of arterial stiffness, carotid-femoral pulse-wave velocity (CFPWV), have not been conducted.

AIM

To study long-term exposure to ambient fine particles (PM2.5), proximity to roadway, and short-term air pollution exposures in relation to multiple measures of arterial stiffness in the Framingham Heart Study.

METHODS

We assessed central arterial stiffness using CFPWV, forward pressure wave amplitude, mean arterial pressure and augmentation index. We investigated long-and short-term air pollution exposure associations with arterial stiffness with linear regressions using long-term residential PM2.5 (2003 average from a spatiotemporal model using satellite data) and proximity to roadway in addition to short-term averages of PM2.5, black carbon, particle number, sulfate, nitrogen oxides, and ozone from stationary monitors.

RESULTS

We examined 5842 participants (mean age 51 ± 16, 54% women). Living closer to a major roadway was associated with higher arterial stiffness (0.11 m/s higher CFPWV [95% CI: 0.01, 0.22] living <50 m vs 400 ≤ 1000 m). We did not observe association between arterial stiffness measures and long-term PM2.5 or short-term levels of PM2.5, particle number, sulfate or ozone. Higher levels of black carbon and nitrogen oxides in the previous days were unexpectedly associated with lower arterial stiffness.

CONCLUSIONS

Long-term exposure to PM2.5 was not associated with arterial stiffness but positive associations with living close to a major road may suggest that pollutant mixtures very nearby major roads, rather than PM2.5, may affect arterial stiffness. Furthermore, short-term air pollution exposures were not associated with higher arterial stiffness.

Associations between multipollutant day types and select cardiorespiratory outcomes in Columbia, South Carolina, 2002 to 2013

Background

Health studies of air pollution are increasingly aiming to study associations between air pollutant mixtures and health.

Objective

Estimate associations between observed combinations of ambient air pollutants and select cardiorespiratory outcomes in Columbia, SC during 2002 to 2013.

Methods

We estimate associations using a two-stage approach. First, we identified a collection of observed pollutant combinations, which we define as multipollutant day types (MDTs), by applying a self-organizing map (SOM) to daily measures of nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), and particulate matter ≤ 2.5 microns (PM_2.5). Then, overdispersed Poisson time-series models were used to estimate associations between MDTs and each outcome using a 'clean' MDT referent and controlling for long-term, seasonal, and day-of-the-week trends and meteorology. Outcomes included daily emergency department visits for asthma and upper respiratory infection (URI), and hospital admissions for congestive heart failure (CHF) and ischemic heart disease (IHD).

Results

We found that a number of MDTs were significantly and positively associated (point estimates ranged from~2-5%) with cardiorespiratory outcomes in Columbia when compared to days with low pollution. Estimated associations revealed that outcomes for asthma, URIs, and IHD increased 2-4% on warm, dry days experiencing elevated levels of O₃ and PM_2.5. We also found that cooler days with higher NO₂ pollution associated with increased asthma, CHF, and IHD outcomes (2-5%).

Conclusion

Our analysis continues support for using self-organizing maps to develop multipollutant exposure metrics and further illustrates how such metrics can be applied to explore associations between pertinent pollutant combinations and health.

Statistical Approaches to Address Multi-Pollutant Mixtures and Multiple Exposures: the State of the Science

PURPOSE OF REVIEW

The purpose of this review is to describe the most recent statistical approaches to estimate the effect of multi-pollutant mixtures or multiple correlated exposures on human health.

RECENT FINDINGS

The health effects of environmental chemicals or air pollutants have been widely described. Often, there exists a complex mixture of different substances, potentially highly correlated with each other and with other (environmental) stressors. Single-exposure approaches do not allow disentangling effects of individual factors and fail to detect potential interactions between exposures. In the last years, sophisticated methods have been developed to investigate the joint or independent health effects of multi-pollutant mixtures or multiple environmental exposures. A classification of the most recent methods is proposed. A non-technical description of each method is provided, together with epidemiological applications and operational details for implementation with standard software.

Environmental Exposures and Cardiovascular Disease: A Challenge for Health and Development in Low- and Middle-Income Countries

Environmental exposures in low- and middle-income countries lie at the intersection of increased economic development and the rising public health burden of cardiovascular disease. Increasing evidence suggests an association of exposure to ambient air pollution, household air pollution from biomass fuel, lead, arsenic, and cadmium with multiple cardiovascular disease outcomes, including hypertension, coronary heart disease, stroke, and cardiovascular mortality. Although populations in low- and middle-income countries are disproportionately exposed to environmental pollution, evidence linking these exposures to cardiovascular disease is derived from populations in high-income countries. More research is needed to further characterize the extent of environmental exposures.

Residential Proximity to Major Roads, Exposure to Fine Particulate Matter, and Coronary Artery Calcium: The Framingham Heart Study

OBJECTIVE

Long-term exposure to traffic and particulate matter air pollution is associated with a higher risk of cardiovascular disease, potentially via atherosclerosis promotion. Prior research on associations of traffic and particulate matter with coronary artery calcium Agatston score (CAC), an atherosclerosis correlate, has yielded inconsistent findings. Given this background, we assessed whether residential proximity to major roadway or fine particulate matter were associated with CAC in a Northeastern US study.

APPROACH AND RESULTS

We measured CAC ≤2 times from 2002 to 2005 and 2008 to 2011 among Framingham Offspring or Third-Generation Cohort participants. We assessed associations of residential distance to major roadway and residential fine particulate matter (2003 average; spatiotemporal model) with detectable CAC, using generalized estimating equation regression. We used linear mixed effects models to assess associations with loge(CAC). We also assessed associations with CAC progression. Models were adjusted for demographic variables, socioeconomic position markers, and time. Among 3399 participants, 51% had CAC measured twice. CAC was detectable in 47% of observations. At first scan, mean age was 52.2 years (standard deviation 11.7); 51% male. There were no consistent associations with detectable CAC, continuous CAC, or CAC progression. We observed heterogeneous associations of distance to major roadway with odds of detectable CAC by hypertensive status; interpretation of these findings is questionable.

CONCLUSIONS

Our findings add to prior work and support evidence against strong associations of traffic or fine particulate matter with the presence, extent, or progression of CAC in a region with relatively low levels of and little variation in fine particulate matter.