Hormesis for fine particulate matter (PM 2.5)

Wildfire Seasons, Prenatal PM2.5 Exposure, and Respiratory Infections by Age 1 Year: A Population-Based Case-Control Analysis of Critical Developmental Windows

The 2017 and 2018 wildfire seasons in British Columbia (BC), Canada were unprecedented. Among all the pollutants in wildfire smoke, fine particulate matter (PM2.5) poses the most significant risk to human health. There is limited research on prenatal wildfire smoke exposure and its impacts on infant health. We used a population-based nested case-control design to assess the association between daily PM2.5 exposures during specific developmental windows and the occurrence of otitis media or lower respiratory infections by age 1 year, including infections associated with dispensations of the antibiotic amoxicillin. We observed the strongest association between per 10 μg/m3 increase in PM2.5 exposure and otitis media during the fourth window of eustachian tube development (weeks 19-28) with an OR [95% confidence interval] of 1.31 [1.22, 1.41]. Similarly, the canalicular stage of lower respiratory tract development (weeks 18-27) was associated with the highest odds of lower respiratory infections, with an OR of 1.21 [1.15, 1.28]. Measures to reduce wildfire smoke exposure during pregnancy are warranted.

Air pollution and survival in patients with malignant mesothelioma and asbestos-related lung cancer: a follow-up study of 1591 patients in South Korea

BACKGROUND

Despite significant advancements in treatments such as surgery, radiotherapy, and chemotherapy, the survival rate for patients with asbestos-related cancers remains low. Numerous studies have provided evidence suggesting that air pollution induces oxidative stress and inflammation, affecting acute respiratory diseases, lung cancer, and overall mortality. However, because of the high case fatality rate, there is limited knowledge regarding the effects of air pollution exposures on survival following a diagnosis of asbestos-related cancers. This study aimed to determine the effect of air pollution on the survival of patients with malignant mesothelioma and asbestos-related lung cancer.

METHODS

We followed up with 593 patients with malignant mesothelioma and 998 patients with lung cancer identified as asbestos victims between 2009 and 2022. Data on five air pollutants-sulfur dioxide, carbon monoxide, nitrogen dioxide, fine particulate matter with a diameter < 10 μm, and fine particulate matter with a diameter < 2.5 μm-were obtained from nationwide atmospheric monitoring stations. Cox proportional hazard models were used to estimate the association of cumulative air pollutant exposure with patient mortality, while adjusting for potential confounders. Quantile-based g-computation was used to assess the combined effect of the air pollutant mixture on mortality.

RESULTS

The 1-, 3-, and 5-year survival rates for both cancer types decreased with increasing exposure to all air pollutants. The estimated hazard ratios rose significantly with a 1-standard deviation increase in each pollutant exposure level. A quartile increase in the pollutant mixture was associated with a 1.99-fold increase in the risk of malignant mesothelioma-related mortality (95% confidence interval: 1.62, 2.44). For lung cancer, a quartile increase in the pollutant mixture triggered a 1.87-fold increase in the mortality risk (95% confidence interval: 1.53, 2.30).

CONCLUSION

These findings support the hypothesis that air pollution exposure after an asbestos-related cancer diagnosis can negatively affect patient survival.

Air pollution and lung cancer survival in Pennsylvania

OBJECTIVE

Lung cancer is a leading cause of cancer death in the United States. Exposure to outdoor air pollution (OAP) is associated with increased lung cancer incidence, however little is known about the association of OAP and survival after diagnosis.

METHODS

We investigated the effects of OAP and lung cancer survival in Pennsylvania using data from Pennsylvania Cancer Registry. The study population consisted of 252,123 patients diagnosed between 1990 and 2017. The Environmental Protection Agency's ambient air monitoring network provided information on OAP exposure of NO₂, O₃, PM_2.5, and PM₁₀. Mean OAP exposures were calculated by interpolating exposure concentrations from the five nearest monitors within a 50-kilometer radius of each patient's residential address from date of diagnosis to date of death or last contact. Cox proportional-hazards models were used to estimate the hazard ratios (HR) for OAP exposures for overall and lung cancer-specific survival. Statistical analyses were stratified by SEER cancer stage groupings (localized, regional, and distant) and adjusted for individual-level and area-level covariates.

RESULTS

Median survival time was 0.76 [CIs: 0.75, 0.77] years for the study population and for localized, regional, and distant site diagnosis were 2.2 [CIs: 2.17, 2.23], 1.13 [CIs: 1.12, 1.15], and 0.42 [CIs: 0.41, 0.43] years, respectively. NO₂ indicated the greatest HR which increased with increasing magnitude of exposure across all cancer staging groups for deaths before 2-years post-diagnosis. HRs varied by stage and magnitude of OAP exposure with greatest overall effects shown in NO₂ followed by PM_2.5, O_3, and PM₁₀. A subgroup analysis of patients with treatment status information (2010-2017) showed similar associations of increasing HRs with increasing exposure.

CONCLUSION

These findings supported the hypotheses that OAP can influence the carcinogenic process, impairing chemotherapy treatment, and provide important public health implications since environmental factors are not often considered in prognosis of survival after diagnosis.

Isoliquiritigenin Pretreatment Induces Endoplasmic Reticulum Stress-Mediated Hormesis and Attenuates Cisplatin-Induced Oxidative Stress and Damage in LLC-PK1 Cells

Isoliquiritigenin (IsoLQ) is a flavonoid with antioxidant properties and inducer of endoplasmic reticulum (ER) stress. In vitro and in vivo studies show that ER stress-mediated hormesis is cytoprotective; therefore, natural antioxidants and ER stress inducers have been used to prevent renal injury. Oxidative stress and ER stress are some of the mechanisms of damage involved in cisplatin (CP)-induced nephrotoxicity. This study aims to explore whether IsoLQ pretreatment induces ER stress and produces hormesis to protect against CP-induced nephrotoxicity in Lilly Laboratories Cell-Porcine Kidney 1 (LLC-PK1) cells. During the first stage of this study, both IsoLQ protective concentration and pretreatment time against CP-induced toxicity were determined by cell viability. At the second stage, the effect of IsoLQ pretreatment on cell viability, ER stress, and oxidative stress were evaluated. IsoLQ pretreatment in CP-treated cells induces expression of glucose-related proteins 78 and 94 kDa (GRP78 and GRP94, respectively), attenuates CP-induced cell death, decreases reactive oxygen species (ROS) production, and prevents the decrease in glutathione/glutathione disulfide (GSH/GSSG) ratio, free thiols levels, and glutathione reductase (GR) activity. These data suggest that IsoLQ pretreatment has a moderately protective effect on CP-induced toxicity in LLC-PK1 cells, through ER stress-mediated hormesis, as well as by the antioxidant properties of IsoLQ.

Application of cell-based biological bioassays for health risk assessment of PM2.5 exposure in three megacities, China

The determination of PM_2.5-induced biological response is essential for understanding the adverse health risk associated with PM_2.5 exposure. In this study, we conducted cell-based bioassays to measure the toxic effects of PM_2.5 exposure, including cytotoxicity, oxidative stress, genotoxicity and inflammatory response. The concentration-response relationship was analyzed by benchmark dose (BMD) modeling and the BMDL₁₀ was used to estimate the biological potency of PM_2.5 exposure. PM_2.5 samples were collected from three typical megacities of China (Beijing, BJ; Wuhan, WH; Guangzhou, GZ) in typical seasons (winter and summer). The total PM, water-soluble fractions (WSF), and organic extracts (OE) were prepared and subjected to examination of toxic effects. The biological potencies for cytotoxicity, oxidative stress and genotoxicity were generally higher in winter samples, while the inflammatory potency of PM_2.5 was higher in summer samples. The relative health risk (RHR) was determined by integration of the biological potencies and the cumulative exposure level, and the ranks of RHR were BJ-W > WH-W > BJ-S > WH-S > GZ-W > GZ-S. Notably, we note that different PM_2.5 compositions were associated with distinct biological effects, and the health effects distribution of PM_2.5 varied in regions and seasons. These findings demonstrate that the approach of integrated cell-based bioassays could be used for the evaluation of health effects of PM_2.5 exposure.

Effects of exposure estimation errors on estimated exposure-response relations for PM2.5

Associations between fine particulate matter (PM2.5) exposure concentrations and a wide variety of undesirable outcomes, from autism and auto theft to elderly mortality, suicide, and violent crime, have been widely reported. Influential articles have argued that reducing National Ambient Air Quality Standards for PM2.5 is desirable to reduce these outcomes. Yet, other studies have found that reducing black smoke and other particulate matter by as much as 70% and dozens of micrograms per cubic meter has not detectably affected all-cause mortality rates even after decades, despite strong, statistically significant positive exposure concentration-response (C-R) associations between them. This paper examines whether this disconnect between association and causation might be explained in part by ignored estimation errors in estimated exposure concentrations. We use EPA air quality monitor data from the Los Angeles area of California to examine the shapes of estimated C-R functions for PM2.5 when the true C-R functions are assumed to be step functions with well-defined response thresholds. The estimated C-R functions mistakenly show risk as smoothly increasing with concentrations even well below the response thresholds, thus incorrectly predicting substantial risk reductions from reductions in concentrations that do not affect health risks. We conclude that ignored estimation errors obscure the shapes of true C-R functions, including possible thresholds, possibly leading to unrealistic predictions of the changes in risk caused by changing exposures. Instead of estimating improvements in public health per unit reduction (e.g., per 10 µg/m3 decrease) in average PM2.5 concentrations, it may be essential to consider how interventions change the distributions of exposure concentrations.

Long-term associations of morbidity with air pollution: A catalog and synthesis

I searched the National Institutes of Health MEDLINE database through January 2017 for long-term studies of morbidity and air pollution and cataloged them with respect to cardiovascular, respiratory, cancer, diabetes, hospitalization, neurological, and pregnancy-birth endpoints. The catalog is presented as an online appendix. Associations with PM_2.5 (particulate matter with an aerodynamic diameter <2.5 μm), PM₁₀ (PM with an aerodynamic diameter <10 μm), and nitrogen dioxide (NO₂) were evaluated most frequently among the 417 ambient air quality studies identified. Associations with total suspended particles (TSP), carbon, ozone, sulfur, vehicular traffic, radon, and indoor air quality were also reported. I evaluated each study in terms of pollutant significance (yes, no), duration of exposure, and publication date. I found statistically significant pollutant relationships (P < 0.05) in 224 studies; 220 studies indicated adverse effects. Among 795 individual pollutant effect estimates, 396 are statistically significant. Pollutant associations with cardiovascular indicators, lung function, respiratory symptoms, and low birth weight are more likely to be significant than with disease incidence, heart attacks, diabetes, or neurological endpoints. Elemental carbon (EC), traffic, and PM_2.5 are most likely to be significant for cardiovascular outcomes; TSP, EC, and ozone (O₃) for respiratory outcomes; NO₂ for neurological outcomes; and PM₁₀ for birth/pregnancy outcomes. Durations of exposure range from 60 days to 35 yr, but I found no consistent relationships with the likelihood of statistical significance. Respiratory studies began ca. 1975; studies of diabetes, cardiovascular, and neurological effects increased after about 2005. I found 72 studies of occupational air pollution exposures; 40 reported statistically significant adverse health effects, especially for respiratory conditions. I conclude that the aggregate of these studies supports the existence of nonlethal physiological effects of various pollutants, more so for non-life-threatening endpoints and for noncriteria pollutants (TSP, EC, PM_2.5 metals). However, most studies were cross-sectional analyses over limited time spans with no consideration of lag or disease latency. Further longitudinal studies are thus needed to investigate the progress of disease incidence in association with air pollution exposure.

IMPLICATIONS

Relationships of air pollution with excess mortality are better known than with long-term antecedent morbidity. I cataloged 489 studies of cardiovascular, respiratory, cancer, and neurological effects, diabetes, and birth outcomes with respect to 12 air pollutants. About half of the studies reported statistically significant relationships, more frequently with noncriteria than with criteria pollutants. Indoor and cumulative exposures, coarse or ultrafine particles, and organic carbon were seldom considered. Significant relationships were more likely with less-severe endpoints such as blood pressure, lung function, or respiratory symptoms than with incidence of cancer, chronic obstructive pulmonary disease (COPD), heart failure, or diabetes. Most long-term studies are based on spatial relationships; longitudinal studies are needed to link the progression of pollution-related morbidity to mortality, especially for the cardiovascular system.

Exposure scenario: Another important factor determining the toxic effects of PM2.5 and possible mechanisms involved

Worsening air pollution is a serious threat to public health in many urban and heavily industrialized areas. Particle size and chemical composition are well known determinants of the pathological response to air pollution. In addition, pathological responses may depend on the exposure profile (or scenario) of air pollution. For instance, we previously demonstrated that repeated exposure to low levels of fine airborne particulate matter (PM2.5) induced distinct epigenetic changes compared to acute high-doses exposure. In the present study, we evaluated the differential pathological responses of BEAS-2B human bronchial epithelial cells to two distinct PM2.5 exposure scenarios: 24-h exposure to high-doses PM2.5 (0, 6, 12, 24, 48, 96 μg/cm²) and 10 days' repeated exposure to low levels of PM2.5 (0, 1.5, 3, 6 μg/cm²). Acute exposure to high concentrations of PM2.5 caused ROS burst, marked DNA damage, dysfunction of the endoplasmic reticulum (ER) stress response, autophagy and necrotic cell death. In contrast, repeated low levels of PM2.5 led to sustained low-grade ROS accumulation, milder DNA damage, ER stress/unfolded protein response (UPR), S-phase arrest, apoptosis, and autophagy. Notably, most cells surviving repeated low-level exposure showed a series of abnormal adaptive responses, such as inhibition of mitochondria biogenesis and epigenetic dysregulation. These results indicate that different PM2.5 exposure scenarios induce distinct forms cytotoxicity and adaptive response. In addition to particle size and chemical composition, exposure scenario may be a critical factor determining the toxic health effects of PM2.5.

Association of long-term PM2.5 exposure with mortality using different air pollution exposure models: impacts in rural and urban California

Most PM2.5-associated mortality studies are not conducted in rural areas where mortality rates may differ when population characteristics, health care access, and PM2.5 composition differ. PM2.5-associated mortality was investigated in the elderly residing in rural-urban zip codes. Exposure (2000-2006) was estimated using different models and Poisson regression was performed using 2006 mortality data. PM2.5 models estimated comparable exposures, although subtle differences were observed in rate ratios (RR) within areas by health outcomes. Cardiovascular disease (CVD), ischemic heart disease (IHD), and cardiopulmonary disease (CPD), mortality was significantly associated with rural, urban, and statewide chronic PM2.5 exposures. We observed larger effect sizes in RRs for CVD, CPD, and all-cause (AC) with similar sizes for IHD mortality in rural areas compared to urban areas. PM2.5 was significantly associated with AC mortality in rural areas and statewide; however, in urban areas, only the most restrictive exposure model showed an association. Given the results seen, future mortality studies should consider adjusting for differences with rural-urban variables.

Dissection of the hormetic curve: analysis of components and mechanisms

The relationship between the dose of an effector and the biological response frequently is not described by a linear function and, moreover, in some cases the dose-response relationship may change from positive/adverse to adverse/positive with increasing dose. This complicated relationship is called "hormesis". This paper provides a short analysis of the concept along with a description of used approaches to characterize hormetic relationships. The whole hormetic curve can be divided into three zones: I - a lag-zone where no changes are observed with increasing dose; II - a zone where beneficial/adverse effects are observed, and III - a zone where the effects are opposite to those seen in zone II. Some approaches are proposed to analyze the molecular components involved in the development of the hormetic character of dose-response relationships with the use of specific genetic lines or inhibitors of regulatory pathways. The discussion is then extended to suggest a new parameter (half-width of the hormetic curve at zone II) for quantitative characterization of the hormetic curve. The problems limiting progress in the development of the hormesis concept such as low reproducibility and predictability may be solved, at least partly, by deciphering the molecular mechanisms underlying the hormetic dose-effect relationship.

Evaluation of the ecotoxicity of pollutants with bioluminescent microorganisms

This chapter deals with the use of bioluminescent microorganisms in environmental monitoring, particularly in the assessment of the ecotoxicity of pollutants. Toxicity bioassays based on bioluminescent microorganisms are an interesting complement to classical toxicity assays, providing easiness of use, rapid response, mass production, and cost effectiveness. A description of the characteristics and main environmental applications in ecotoxicity testing of naturally bioluminescent microorganisms, covering bacteria and eukaryotes such as fungi and dinoglagellates, is reported in this chapter. The main features and applications of a wide variety of recombinant bioluminescent microorganisms, both prokaryotic and eukaryotic, are also summarized and critically considered. Quantitative structure-activity relationship models and hormesis are two important concepts in ecotoxicology; bioluminescent microorganisms have played a pivotal role in their development. As pollutants usually occur in complex mixtures in the environment, the use of both natural and recombinant bioluminescent microorganisms to assess mixture toxicity has been discussed. The main information has been summarized in tables, allowing quick consultation of the variety of luminescent organisms, bioluminescence gene systems, commercially available bioluminescent tests, environmental applications, and relevant references.

Improving causal inferences in risk analysis

Recent headlines and scientific articles projecting significant human health benefits from changes in exposures too often depend on unvalidated subjective expert judgments and modeling assumptions, especially about the causal interpretation of statistical associations. Some of these assessments are demonstrably biased toward false positives and inflated effects estimates. More objective, data-driven methods of causal analysis are available to risk analysts. These can help to reduce bias and increase the credibility and realism of health effects risk assessments and causal claims. For example, quasi-experimental designs and analysis allow alternative (noncausal) explanations for associations to be tested, and refuted if appropriate. Panel data studies examine empirical relations between changes in hypothesized causes and effects. Intervention and change-point analyses identify effects (e.g., significant changes in health effects time series) and estimate their sizes. Granger causality tests, conditional independence tests, and counterfactual causality models test whether a hypothesized cause helps to predict its presumed effects, and quantify exposure-specific contributions to response rates in differently exposed groups, even in the presence of confounders. Causal graph models let causal mechanistic hypotheses be tested and refined using biomarker data. These methods can potentially revolutionize the study of exposure-induced health effects, helping to overcome pervasive false-positive biases and move the health risk assessment scientific community toward more accurate assessments of the impacts of exposures and interventions on public health.

Temperature, Not Fine Particulate Matter (PM2.5), is Causally Associated with Short-Term Acute Daily Mortality Rates: Results from One Hundred United States Cities

Exposures to fine particulate matter (PM2.5) in air (C) have been suspected of contributing causally to increased acute (e.g., same-day or next-day) human mortality rates (R). We tested this causal hypothesis in 100 United States cities using the publicly available NMMAPS database. Although a significant, approximately linear, statistical C-R association exists in simple statistical models, closer analysis suggests that it is not causal. Surprisingly, conditioning on other variables that have been extensively considered in previous analyses (usually using splines or other smoothers to approximate their effects), such as month of the year and mean daily temperature, suggests that they create strong, nonlinear confounding that explains the statistical association between PM2.5 and mortality rates in this data set. As this finding disagrees with conventional wisdom, we apply several different techniques to examine it. Conditional independence tests for potential causation, non-parametric classification tree analysis, Bayesian Model Averaging (BMA), and Granger-Sims causality testing, show no evidence that PM2.5 concentrations have any causal impact on increasing mortality rates. This apparent absence of a causal C-R relation, despite their statistical association, has potentially important implications for managing and communicating the uncertain health risks associated with, but not necessarily caused by, PM2.5 exposures.