In randomized experiments, Fisher-exact values are available and should be used to help evaluate results rather than the more commonly reported asymptotic values. One reason is that using the latter can effectively alter the question being addressed by including irrelevant distributional assumptions. The Fisherian statistical framework, proposed in 1925, calculates a value in a randomized experiment by using the actual randomization procedure that led to the observed data. Here, we illustrate this Fisherian framework in a crossover randomized experiment. First, we consider the first period of the experiment and analyze its data as a completely randomized experiment, ignoring the second period; then, we consider both periods. For each analysis, we focus on 10 outcomes that illustrate important differences between the asymptotic and Fisher tests for the null hypothesis of no ozone effect. For some outcomes, the traditional value based on the approximating asymptotic Student's distribution substantially subceeded the minimum attainable Fisher-exact value. For the other outcomes, the Fisher-exact null randomization distribution substantially differed from the bell-shaped one assumed by the asymptotic test. Our conclusions: When researchers choose to report values in randomized experiments, 1) Fisher-exact values should be used, especially in studies with small sample sizes, and 2) the shape of the actual null randomization distribution should be examined for the recondite scientific insights it may reveal.
BACKGROUND: The mechanisms by which exposure to particulate matter might increase risk of cardiovascular morbidity and mortality are not fully known. However, few existing studies have investigated the potential role of particle radioactivity. Naturally occurring radionuclides attach to particulate matter and continue to release ionizing radiation after inhalation and deposition in the lungs. We hypothesize that exposure to particle radioactivity increases biomarkers of inflammation.
METHODS: Our repeated-measures study included 752 men in the greater Boston area. We estimated regional particle radioactivity as a daily spatial average of gross beta concentrations from five monitors in the study area. We used linear mixed-effects regression models to estimate short- and medium-term associations between particle radioactivity and biomarkers of inflammation and endothelial dysfunction, with and without adjustment for additional particulate air pollutants.
RESULTS: We observed associations between particle radioactivity on C-reactive protein (CRP), intercellular adhesion molecule-1 (ICAM-1), and vascular cell adhesion molecule-1 (VCAM-1), but no associations with fibrinogen. An interquartile range width increase in mean 7-day particle radioactivity (1.2 × 10 Bq/m) was associated with a 4.9% increase in CRP (95% CI = 0.077, 9.9), a 2.8% increase in ICAM-1 (95% CI = 1.4, 4.2), and a 4.3% increase in VCAM-1 (95% CI = 2.5, 6.1). The main effects of particle radioactivity remained similar after adjustment in most cases. We also obtained similar effect estimates in a sensitivity analysis applying a robust causal model.
CONCLUSION: Regional particle radioactivity is positively associated with inflammatory biomarkers, indicating a potential pathway for radiation-induced cardiovascular effects.
The presence of mycotoxins in food has created concern. Mycotoxin prevalence in our environment has changed in the last few years maybe due to climatic and other environmental changes. Evidence has emerged from in vitro and in vivo models: some mycotoxins have been found to be potentially carcinogenic, embryogenically harmful, teratogenic, and to generate nephrotoxicity. The risk assessment of exposures to mycotoxins at early life stages became mandatory. In this regard, the effects of toxic compounds on zebrafish have been widely studied, and more recently, mycotoxins have been tested with respect to their effects on developmental and teratogenic effects in this model system, which offers several advantages as it is an inexpensive and an accessible vertebrate model to study developmental toxicity. External post-fertilization and quick maturation make it sensitive to environmental effects and facilitate the detection of endpoints such as morphological deformities, time of hatching, and behavioral responses. Therefore, there is a potential for larval zebrafish to provide new insights into the toxicological effects of mycotoxins. We provide an overview of recent mycotoxin toxicological research in zebrafish embryos and larvae, highlighting its usefulness to toxicology and discuss the strengths and limitations of this model system.
This study aimed to evaluate the effectiveness of an educational intervention at improving Oral Health Professionals (OHP's) knowledge of HPV and comfortability to discuss vaccination with their American Indian and Alaskan Native patients. OHP's attended an educational lecture covering HPV vaccination. Participants completed four validated questionnaires that encompassed a sociodemographic survey, a pre-lecture questionnaire (pre-Q), a post-lecture questionnaire (post-Q), and a follow-up questionnaire (follow-Q). The McNemar test was used to assess the significance of marginal probabilities in the responses between the pre-Q and post-Q and the Chi-square test to assess responses between the post-Q and follow-Q. A total of 122 OHP's completed the sociodemographic survey, pre-Q, and post-Q. Among these, 29 OHP's completed the eight-week follow-Q. The majority of all the participants were White/Caucasian (41%), 31 to 60 years of age (72%), females (64%), and held a graduate/professional degree (52%). Analysis of the pre-Q responses showed that only 6.8% of OHP's discuss the connection between HPV and oropharyngeal cancer with patients and a lack of information on the topic was the major barrier reported. After the educational intervention (post-Q), 86.5% of OHP's reported they were more likely to recommend the HPV vaccine and 69.8% felt more comfortable administering it. Comparison between the pre-Q and the post-Q showed a significant improvement in overall HPV knowledge. Similarly, a comparison between the post-Q and the follow-Q showed retained knowledge overtime. Our study suggests that the educational intervention was effective at improving OHP's knowledge of HPV and enhancing their comfortability and preparedness to discuss the vaccination with their patients.
The aim of this study was to evaluate the effectiveness of an educational intervention on HPV infection, HPV-related cancers and prevention modalities to improve Oral Health Care Providers (OHPs) knowledge and awareness about these topics, considering the rise of HPV-related malignancies in the USA. Educational sessions on HPV were offered to OHPs between 2016 and 2018 in the New England area. Participants were asked to fill out a questionnaire both before and after each session. Responses from the pre-questionnaire were compared to those from the post-questionnaire to evaluate the effectiveness of the lectures in increasing HPV-related knowledge of the OHPs. Among 277 participants, 263 completed both the pre- and post-questionnaire. A significant improvement was observed for the following categories: epidemiology of HPV infections, HPV-related diseases, and HPV vaccination and prevention. After the educational intervention, OHPs also indicated an increased comfort level in regard to educating their patients about the importance of HPV vaccination. Educational lectures can be effective in increasing OHPs knowledge and awareness about HPV, HPV-related cancers, and vaccination. More educational sessions on HPV are needed to reach a larger number of OHPs. OHPs may be the first to identify signs and symptoms of HPV-related oropharyngeal cancers. In addition, they may encourage their patients to take advantage of the HPV vaccine.
The analysis of the gut microbiome with respect to health care prevention and diagnostic purposes is increasingly the focus of current research. We analyzed around 2000 stool samples from the KORA (Cooperative Health Research in the Region of Augsburg) cohort using high-throughput 16S rRNA gene amplicon sequencing representing a total microbial diversity of 2089 operational taxonomic units (OTUs). We evaluated the combination of three different components to assess the reflection of obesity related to microbiota profiles: (i) four prediction methods (i.e., partial least squares (PLS), support vector machine regression (SVMReg), random forest (RF), and M5Rules); (ii) five OTU data transformation approaches (i.e., no transformation, relative abundance without and with log-transformation, as well as centered and isometric log-ratio transformations); and (iii) predictions from nine measurements of obesity (i.e., body mass index, three measures of body shape, and five measures of body composition). Our results showed a substantial impact of all three components. The applications of SVMReg and PLS in combination with logarithmic data transformations resulted in considerably predictive models for waist circumference-related endpoints. These combinations were at best able to explain almost 40% of the variance in obesity measurements based on stool microbiota data (i.e., OTUs) only. A reduced loss in predictive performance was seen after sex-stratification in waist-height ratio compared to other waist-related measurements. Moreover, our analysis showed that the contribution of OTUs less prevalent and abundant is minor concerning the predictive power of our models.
The study of racial/ethnic inequalities in health is important to reduce the uneven burden of disease. In the case of colorectal cancer (CRC), disparities in survival among non-Hispanic Whites and Blacks are well documented, and mechanisms leading to these disparities need to be studied formally. It has also been established that body mass index (BMI) is a risk factor for developing CRC, and recent literature shows BMI at diagnosis of CRC is associated with survival. Since BMI varies by racial/ethnic group, a question that arises is whether differences in BMI are partially responsible for observed racial/ethnic disparities in survival for CRC patients. This article presents new methodology to quantify the impact of the hypothetical intervention that matches the BMI distribution in the Black population to a potentially complex distributional form observed in the White population on racial/ethnic disparities in survival. Our density mediation approach can be utilized to estimate natural direct and indirect effects in the general causal mediation setting under stronger assumptions. We perform a simulation study that shows our proposed Bayesian density regression approach performs as well as or better than current methodology allowing for a shift in the mean of the distribution only, and that standard practice of categorizing BMI leads to large biases when BMI is a mediator variable. When applied to motivating data from the Cancer Care Outcomes Research and Surveillance (CanCORS) Consortium, our approach suggests the proposed intervention is potentially beneficial for elderly and low-income Black patients, yet harmful for young or high-income Black populations.
When addressing environmental health-related questions, most often, only observational data are collected for ethical or practical reasons. However, the lack of randomized exposure often prevents the comparison of similar groups of exposed and unexposed units. This design barrier leads the environmental epidemiology field to mainly estimate associations between environmental exposures and health outcomes. A recently developed causal inference pipeline was developed to guide researchers interested in estimating the effects of plausible hypothetical interventions for policy recommendations. This article illustrates how this multistaged pipeline can help environmental epidemiologists reconstruct and analyze hypothetical randomized experiments by investigating whether an air pollution reduction intervention decreases the risk of multiple sclerosis relapses in Alsace region, France. The epidemiology literature reports conflicted findings on the relationship between air pollution and multiple sclerosis. Some studies found significant associations, whereas others did not. Two case-crossover studies reported significant associations between the risk of multiple sclerosis relapses and the exposure to air pollutants in the Alsace region. We use the same study population as these epidemiological studies to illustrate how appealing this causal inference approach is to estimate the effects of hypothetical, but plausible, environmental interventions.
We used a randomized crossover experiment to estimate the effects of ozone (vs. clean air) exposure on genome-wide DNA methylation of target bronchial epithelial cells, using 17 volunteers, each randomly exposed on two separated occasions to clean air or 0.3-ppm ozone for two hours. Twenty-four hours after exposure, participants underwent bronchoscopy to collect epithelial cells whose DNA methylation was measured using the Illumina 450 K platform. We performed global and regional tests examining the ozone versus clean air effect on the DNA methylome and calculated Fisher-exact p-values for a series of univariate tests. We found little evidence of an overall effect of ozone on the DNA methylome but some suggestive changes in PLSCR1, HCAR1, and LINC00336 DNA methylation after ozone exposure relative to clean air. We observed some participant-to-participant heterogeneity in ozone responses.
Background: Exposure to mercury (Hg) is associated with adverse developmental effects. However, Hg occurs with a multitude of chemicals. We assessed the associations of developmental exposure to multiple pollutants with children's neurodevelopment using a novel approach.
Methods: Hg, polychlorinated biphenyls (PCBs), and perfluoroalkyl substances were measured in maternal and children's blood at 5-years (n=449 and 419). At 7-years, children were administered Boston Naming Test (BNT) and the Strengths and Difficulties Questionnaire (SDQ). We used the G-formula combined with SuperLearner to estimate independent and joint effects of chemicals at both ages. We constructed flexible exposure-response relationships and assessed interactions.
Results: Most chemicals showed negative relationships with BNT scores. An inter-quartile range (IQR) increase in maternal Hg and perfluorooctanoic acid (PFOA) was associated with 0.15 standard deviation [SD] (95% Confidence Interval [CI]: -0.29,-0.03) and 0.14 SD (95%CI: -0.26,-0.05) lower scores in BNT, whereas a joint IQR increase in the mixture of chemicals was associated with 0.48 SD (95%CI: -0.69,-0.25) lower scores in BNT. An IQR increase in PFOA was associated with 0.11 SD (95%CI: 0.02,0.26) higher total SDQ difficulties scores. Maternal ∑PCBs concentrations were associated with lower SDQ scores (β=-0.09 SD; 95%CI: -0.19,0), whereas 5-years ∑PCBs showed a negative association (β=-0.09 SD; 95%CI: -0.21,0). Finally, a joint IQR increase in the mixture was associated with 0.22 SD (95%CI: 0.04,0.4) higher SDQ scores.
Conclusions: Using a novel statistical approach, we confirmed associations between prenatal mercury exposure and lower cognitive function. The potential developmental effects of PFASs need additional attention.
The association between particulate pollution and cardiovascular morbidity and mortality is well established. While the cardiovascular effects of nationally regulated criteria pollutants (e.g., fine particulate matter [PM] and nitrogen dioxide) have been well documented, there are fewer studies on particulate pollutants that are more specific for traffic, such as black carbon (BC) and particle number (PN). In this paper, we synthesized studies conducted in the Greater Boston Area on cardiovascular health effects of traffic exposure, specifically defined by BC or PN exposure or proximity to major roadways. Large cohort studies demonstrate that exposure to traffic-related particles adversely affect cardiac autonomic function, increase systemic cytokine-mediated inflammation and pro-thrombotic activity, and elevate the risk of hypertension and ischemic stroke. Key patterns emerged when directly comparing studies with overlapping exposure metrics and population cohorts. Most notably, cardiovascular risk estimates of PN and BC exposures were larger in magnitude or more often statistically significant compared to those of PM exposures. Across multiple exposure metrics (e.g., short-term vs. long-term; observed vs. modeled) and different population cohorts (e.g., elderly, individuals with co-morbidities, young healthy individuals), there is compelling evidence that BC and PN represent traffic-related particles that are especially harmful to cardiovascular health. Further research is needed to validate these findings in other geographic locations, characterize exposure errors associated with using monitored and modeled traffic pollutant levels, and elucidate pathophysiological mechanisms underlying the cardiovascular effects of traffic-related particulate pollutants. : Traffic emissions are an important source of particles harmful to cardiovascular health. Traffic-related particles, specifically BC and PN, adversely affect cardiac autonomic function, increase systemic inflammation and thrombotic activity, elevate BP, and increase the risk of ischemic stroke. There is evidence that BC and PN are associated with greater cardiovascular risk compared to PM. Further research is needed to elucidate other health effects of traffic-related particles and assess the feasibility of regulating BC and PN or their regional and local sources.
The field of environmental health has been dominated by modeling associations, especially by regressing an observed outcome on a linear or nonlinear function of observed covariates. Readers interested in advances in policies for improving environmental health are, however, expecting to be informed about health effects resulting from, or more explicitly caused by, environmental exposures. The quantification of health impacts resulting from the removal of environmental exposures involves causal statements. Therefore, when possible, causal inference frameworks should be considered for analyzing the effects of environmental exposures on health outcomes.
An integrated exposomic view of the relation between environment and cardiovascular health should consider the effects of both air and non-air related environmental stressors. Cardiovascular impacts of ambient air temperature, indoor and outdoor air pollution were recently reviewed. We aim, in this second part, to address the cardiovascular effects of noise, food pollutants, radiation, and some other emerging environmental factors. Road traffic noise exposure is associated with increased risk of premature arteriosclerosis, coronary artery disease, and stroke. Numerous studies report an increased prevalence of hypertension in people exposed to noise, especially while sleeping. Sleep disturbances generated by nocturnal noise are followed by a neuroendocrine stress response. Some oxidative and inflammatory endothelial reactions are observed during experimental session of noise exposure. Moreover, throughout the alimentation, the cardiovascular system is exposed to persistent organic pollutants (POPs) as dioxins or pesticides, and plastic associated chemicals (PACs), such as bisphenol A. Epidemiological studies show positive associations of exposures to POPs and PACs with diabetes, arteriosclerosis and cardiovascular disease incidence. POPs and PACS share some abilities to interact with nuclear receptors activating different pathways leading to oxidative stress, insulin resistance and angiotensin potentiation. Regarding radiation, survivors of nuclear explosion have an excess risk of cardiovascular disease. Dose-effect relationships remain debated, but an increased cardiovascular risk at low dose of radiation exposure may be of concern. Some emerging environmental factors like electromagnetic fields, greenspace and light exposure may also require further attention. Non-air related environmental stressors also play an important role in the burden of cardiovascular disease. Specific methodologies should be developed to assess the interactions between air and non-air related pollutants.
Aging is associated with progressive and site-specific changes in DNA methylation (DNAm). These global changes are captured by DNAm clocks that accurately predict chronological age in humans but relatively little is known about how clocks perform . Here we culture primary human fibroblasts across the cellular lifespan (~6 months) and use four different DNAm clocks to show that age-related DNAm signatures are conserved and accelerated . The Skin & Blood clock shows the best linear correlation with chronological time (r = 0.90), including during replicative senescence. Although similar in nature, the rate of epigenetic aging is approximately 62x times faster in cultured cells than in the human body. Consistent with data, cells aged under hyperglycemic conditions exhibit an approximately three years elevation in baseline DNAm age. Moreover, candidate gene-based analyses further corroborate the conserved but accelerated biological aging process in cultured fibroblasts. Fibroblasts mirror the established DNAm topology of the age-related gene in human blood and the rapid hypermethylation of its promoter cg16867657, which correlates with a linear decrease in ELOVL2 mRNA levels across the lifespan. Using generalized additive modeling on twelve timepoints across the lifespan, we also show how single CpGs exhibit loci-specific, linear and nonlinear trajectories that reach rates up to -47% (hypomethylation) to +23% (hypermethylation) per month. Together, these high-temporal resolution global, gene-specific, and single CpG data highlight the conserved and accelerated nature of epigenetic aging in cultured fibroblasts, which may constitute a system to evaluate age-modifying interventions across the lifespan.
We present a randomization-based inferential framework for experiments characterized by a strongly ignorable assignment mechanism where units have independent probabilities of receiving treatment. Previous works on randomization tests often assume these probabilities are equal within blocks of units. We consider the general case where they differ across units and show how to perform randomization tests and obtain point estimates and confidence intervals. Furthermore, we develop rejection-sampling and importance-sampling approaches for conducting randomization-based inference conditional on any statistic of interest, such as the number of treated units or forms of covariate balance. We establish that our randomization tests are valid tests, and through simulation we demonstrate how the rejection-sampling and importance-sampling approaches can yield powerful randomization tests and thus precise inference. Our work also has implications for observational studies, which commonly assume a strongly ignorable assignment mechanism. Most methodologies for observational studies make additional modeling or asymptotic assumptions, while our framework only assumes the strongly ignorable assignment mechanism, and thus can be considered a minimal-assumption approach.
OBJECTIVES: The prevalence of childhood obesity is significantly higher among racial and/or ethnic minority children in the United States. It is unclear to what extent well-established obesity risk factors in infancy and preschool explain these disparities. Our objective was to decompose racial and/or ethnic disparities in children's weight status according to contributing socioeconomic and behavioral risk factors.
METHODS: We used nationally representative data from ∼10 700 children in the Early Childhood Longitudinal Study Birth Cohort who were followed from age 9 months through kindergarten entry. We assessed the contribution of socioeconomic factors and maternal, infancy, and early childhood obesity risk factors to racial and/or ethnic disparities in children's BMI scores by using Blinder-Oaxaca decomposition analyses.
RESULTS: The prevalence of risk factors varied significantly by race and/or ethnicity. African American children had the highest prevalence of risk factors, whereas Asian children had the lowest prevalence. The major contributor to the BMI score gap was the rate of infant weight gain during the first 9 months of life, which was a strong predictor of BMI score at kindergarten entry. The rate of infant weight gain accounted for between 14.9% and 70.5% of explained disparities between white children and their racial and/or ethnic minority peers. Gaps in socioeconomic status were another important contributor that explained disparities, especially those between white and Hispanic children. Early childhood risk factors, such as fruit and vegetable consumption and television viewing, played less important roles in explaining racial and/or ethnic differences in children's BMI scores.
CONCLUSIONS: Differences in rapid infant weight gain contribute substantially to racial and/or ethnic disparities in obesity during early childhood. Interventions implemented early in life to target this risk factor could help curb widening racial and/or ethnic disparities in early childhood obesity.
Weather characteristics have been suggested by many social scientists to influence criminality. A recent study suggested that climate change may a substantial increase in criminal activities during the twenty-first century. The additional number of crimes to climate have been ethoroughly discussed the first draft of the paper. Allstimated by associational models, which are not optimal to quantify impacts of weather conditions on criminality. Using the Rubin Causal Model and crime data reported daily between 2012 and 2017, this study examines whether changes in heat index, a proxy for apparent temperature, and rainfall occurrence, influence the number of violent crimes in Boston. On average, more crimes are reported on temperate days compared to extremely cold days, and on dry days compared to rainy days. However, no significant differences in the number of crimes between extremely hot days versus less warm days could be observed. The results suggest that weather forecasts could be integrated into crime prevention programs in Boston. The weather-crime relationship should be taken into account when assessing the economic, sociological, or medical impact of climate change. Researchers and policy makers interested in the effects of environmental exposures or policy interventions on crime should consider data analyses conducted with causal inference approaches.
BACKGROUND: Triggers of multiple sclerosis (MS) relapses are essentially unknown. PM exposure has recently been associated with an increased risk of relapses.
OBJECTIVES: We further explore the short-term associations between PM, NO, benzene (CH), O, and CO exposures, and the odds of MS relapses' occurrence.
METHODS: Using a case-crossover design, we studied 424 MS patients living in the Strasbourg area, France between 2000 and 2009 (1783 relapses in total). Control days were chosen to be ± 35 days relative to the case (relapse) day. Exposure was modeled through ADMS-Urban software at the census block scale. We consider single-pollutant and multi-pollutant conditional logistic regression models coupled with a distributed-lag linear structure, stratified by season ("hot" vs. "cold"), and adjusted for meteorological parameters, pollen count, influenza-like epidemics, and holidays.
RESULTS: The single-pollutant analyses indicated: 1) significant associations between MS relapse incidence and exposures to NO, PM, and O, and 2) seasonality in these associations. For instance, an interquartile range increase in NO (lags 0-3) and PM exposure were associated with MS relapse incidence (OR = 1.08; 95%CI: [1.03-1.14] and OR = 1.06; 95%CI: [1.01-1.11], respectively) during the "cold" season (i.e., October-March). We also observed an association with O and MS relapse incidence during "hot" season (OR = 1.16; 95%CI: [1.07-1.25]). CH and CO were not significantly related to MS relapse incidence. However, using multi-pollutant models, only O remained significantly associated with the odds of relapse triggering during "hot" season.
CONCLUSION: We observed significant single-pollution associations between the occurrence of MS relapses and exposures to NO, O and PM, only O remained significantly associated with occurrence of MS relapses in the multi-pollutant model.
Consider a statistical analysis that draws causal inferences from an observational dataset, inferences that are presented as being valid in the standard frequentist senses; i.e. the analysis produces: (1) consistent point estimates, (2) valid -values, valid in the sense of rejecting true null hypotheses at the nominal level or less often, and/or (3) confidence intervals, which are presented as having at least their nominal coverage for their estimands. For the hypothetical validity of these statements, the analysis must embed the observational study in a hypothetical randomized experiment that created the observed data, or a subset of that hypothetical randomized data set. This multistage effort with thought-provoking tasks involves: (1) a purely that precisely formulate the causal question in terms of a hypothetical randomized experiment where the exposure is assigned to units; (2) a that approximates a randomized experiment before any outcome data are observed, (3) a comparing the outcomes of interest in the exposed and non-exposed units of the hypothetical randomized experiment, and (4) a providing conclusions about statistical evidence for the sizes of possible causal effects. Stages 2 and 3 may rely on modern computing to implement the effort, whereas Stage 1 demands careful scientific argumentation to make the embedding plausible to scientific readers of the proffered statistical analysis. Otherwise, the resulting analysis is vulnerable to criticism for being simply a presentation of scientifically meaningless arithmetic calculations. The conceptually most demanding tasks are often the most scientifically interesting to the dedicated researcher and readers of the resulting statistical analyses. This perspective is rarely implemented with any rigor, for example, completely eschewing the first stage. We illustrate our approach using an example examining the effect of parental smoking on children's lung function collected in families living in East Boston in the 1970s.
Air pollution is composed of particulate matter (PM) and gaseous pollutants, such as nitrogen dioxide and ozone. PM is classified according to size into coarse particles (PM), fine particles (PM) and ultrafine particles. We aim to provide an original review of the scientific evidence from epidemiological and experimental studies examining the cardiovascular effects of outdoor air pollution. Pooled epidemiological studies reported that a 10μg/m increase in long-term exposure to PM was associated with an 11% increase in cardiovascular mortality. Increased cardiovascular mortality was also related to long-term and short-term exposure to nitrogen dioxide. Exposure to air pollution and road traffic was associated with an increased risk of arteriosclerosis, as shown by premature aortic and coronary calcification. Short-term increases in air pollution were associated with an increased risk of myocardial infarction, stroke and acute heart failure. The risk was increased even when pollutant concentrations were below European standards. Reinforcing the evidence from epidemiological studies, numerous experimental studies demonstrated that air pollution promotes a systemic vascular oxidative stress reaction. Radical oxygen species induce endothelial dysfunction, monocyte activation and some proatherogenic changes in lipoproteins, which initiate plaque formation. Furthermore, air pollution favours thrombus formation, because of an increase in coagulation factors and platelet activation. Experimental studies also indicate that some pollutants have more harmful cardiovascular effects, such as combustion-derived PM and ultrafine particles. Air pollution is a major contributor to cardiovascular diseases. Promotion of safer air quality appears to be a new challenge in cardiovascular disease prevention.