Air pollution exposure may impact the composition of human milk oligosaccharides

Human milk oligosaccharides (HMOs) impact neonate immunity and health outcomes. However, the environmental factors influencing HMO composition remain understudied. This study examined the associations between ambient air pollutant (AAP) exposure and HMOs at 1-month postpartum. Human milk samples were collected at 1-month postpartum (n = 185). AAP (PM2.5, PM10, NO2) exposure included the 9-month pregnancy period through 1-month postpartum. Associations between AAP with (1) HMO diversity, (2) the sum of sialylated and fucosylated HMOs, (3) 6 a priori HMOs linked with infant health, and (4) all HMOs were examined using multivariable linear regression and principal component analysis (PCA). Exposure to AAP was associated with lower HMO diversity. PM2.5 and PM10 exposure was positively associated with the HMO 3-fucosyllactose (3FL); PM2.5 exposure was positively associated with the sum of total HMOs, sum of fucosylated HMOs, and the HMO 2'-fucosyllactose (2'FL). PCA indicated the PM2.5, PM10, and NO2 exposures were associated with HMO profiles. Individual models indicated that AAP exposure was associated with five additional HMOs (LNFP I, LNFP II, DFLNT, LNH). This is the first study to demonstrate associations between AAP and breast milk HMOs. Future longitudinal studies will help determine the long-term impact of AAP on human milk composition.

Short-term exposure to air pollution and hospital admission after COVID-19 in Catalonia: the COVAIR-CAT study

BACKGROUND

A growing body of evidence has reported positive associations between long-term exposure to air pollution and poor COVID-19 outcomes. Inconsistent findings have been reported for short-term air pollution, mostly from ecological study designs. Using individual-level data, we studied the association between short-term variation in air pollutants [nitrogen dioxide (NO2), particulate matter with a diameter of <2.5 µm (PM2.5) and a diameter of <10 µm (PM10) and ozone (O3)] and hospital admission among individuals diagnosed with COVID-19.

METHODS

The COVAIR-CAT (Air pollution in relation to COVID-19 morbidity and mortality: a large population-based cohort study in Catalonia, Spain) cohort is a large population-based cohort in Catalonia, Spain including 240 902 individuals diagnosed with COVID-19 in the primary care system from 1 March until 31 December 2020. Our outcome was hospitalization within 30 days of COVID-19 diagnosis. We used individual residential address to assign daily air-pollution exposure, estimated using machine-learning methods for spatiotemporal prediction. For each pandemic wave, we fitted Cox proportional-hazards models accounting for non-linear-distributed lagged exposure over the previous 7 days.

RESULTS

Results differed considerably by pandemic wave. During the second wave, an interquartile-range increase in cumulative weekly exposure to air pollution (lag0_7) was associated with a 12% increase (95% CI: 4% to 20%) in COVID-19 hospitalizations for NO2, 8% (95% CI: 1% to 16%) for PM2.5 and 9% (95% CI: 3% to 15%) for PM10. We observed consistent positive associations for same-day (lag0) exposure, whereas lag-specific associations beyond lag0 were generally not statistically significant.

CONCLUSIONS

Our study suggests positive associations between NO2, PM2.5 and PM10 and hospitalization risk among individuals diagnosed with COVID-19 during the second wave. Cumulative hazard ratios were largely driven by exposure on the same day as hospitalization.

Concomitant exposure to air pollution, green space and noise, and risk of myocardial infarction: a cohort study from Denmark

AIMS

The three correlated environmental exposures (air pollution, road traffic noise, and green space) have all been associated with the risk of myocardial infarction (MI). The present study aimed to analyse their independent and cumulative association with MI.

METHODS AND RESULTS

In a cohort of all Danes aged 50 or older in the period 2005-17, 5-year time-weighted average exposure to fine particles (PM2.5), ultrafine particles, elemental carbon, nitrogen dioxide (NO2), and road traffic noise at the most and least exposed façades of residence was estimated. Green space around residences was estimated from land use maps. Cox proportional hazard models were used to estimate hazard ratios (HRs) and 95% confidence interval (CI), and cumulative risk indices (CRIs) were calculated. All expressed per interquartile range. Models were adjusted for both individual and neighbourhood-level socio-demographic covariates. The cohort included 1 964 702 persons. During follow-up, 71 285 developed MI. In single-exposure models, all exposures were associated with an increased risk of MI. In multi-pollutant analyses, an independent association with risk of MI was observed for PM2.5 (HR: 1.026; 95% CI: 1.002-1.050), noise at most exposed façade (HR: 1.024; 95% CI: 1.012-1.035), and lack of green space within 150 m of residence (HR: 1.018; 95% CI: 1.010-1.027). All three factors contributed significantly to the CRI (1.089; 95% CI: 1.076-1.101).

CONCLUSION

In a nationwide cohort study, air pollution, noise, and lack of green space were all independently associated with an increased risk of MI. The air pollutant PM2.5 was closest associated with MI risk.

The potential role of early life feeding patterns in shaping the infant fecal metabolome: implications for neurodevelopmental outcomes

Infant fecal metabolomics can provide valuable insights into the associations of nutrition, dietary patterns, and health outcomes in early life. Breastmilk is typically classified as the best source of nutrition for nearly all infants. However, exclusive breastfeeding may not always be possible for all infants. This study aimed to characterize associations between levels of mixed breastfeeding and formula feeding, along with solid food consumption and the infant fecal metabolome at 1- and 6-months of age. As a secondary aim, we examined how feeding-associated metabolites may be associated with early life neurodevelopmental outcomes. Fecal samples were collected at 1- and 6-months, and metabolic features were assessed via untargeted liquid chromatography/high-resolution mass spectrometry. Feeding groups were defined at 1-month as 1) exclusively breastfed, 2) breastfed >50% of feedings, or 3) formula fed ≥50% of feedings. Six-month groups were defined as majority breastmilk (>50%) or majority formula fed (≥50%) complemented by solid foods. Neurodevelopmental outcomes were assessed using the Bayley Scales of Infant Development at 2 years. Changes in the infant fecal metabolome were associated with feeding patterns at 1- and 6-months. Feeding patterns were associated with the intensities of a total of 57 fecal metabolites at 1-month and 25 metabolites at 6-months, which were either associated with increased breastmilk or increased formula feeding. Most breastmilk-associated metabolites, which are involved in lipid metabolism and cellular processes like cell signaling, were associated with higher neurodevelopmental scores, while formula-associated metabolites were associated with lower neurodevelopmental scores. These findings offer preliminary evidence that feeding patterns are associated with altered infant fecal metabolomes, which may be associated with cognitive development later in life.

The Creation of a Multidomain Neighborhood Environmental Vulnerability Index Across New York City

Compared to previous studies commonly using a single summary score, we aimed to construct a multidomain neighborhood environmental vulnerability index (NEVI) to characterize the magnitude and variability of area-level factors with the potential to modify the association between environmental pollutants and health effects. Using the Toxicological Prioritization Index framework and data from the 2015-2019 U.S. Census American Community Survey and the 2020 CDC PLACES Project, we quantified census tract-level vulnerability overall and in 4 primary domains (demographic, economic, residential, and health status), 24 subdomains, and 54 distinct area-level features for New York City (NYC). Overall and domain-specific indices were calculated by summing standardized feature values within the subdomains and then aggregating and weighting based on the number of features within each subdomain within equally-weighted primary domains. In citywide comparisons, NEVI was correlated with multiple existing indices, including the Neighborhood Deprivation Index (r = 0.91) and Social Vulnerability Index (r = 0.87) but provided additional information on features contributing to vulnerability. Vulnerability varied spatially across NYC, and hierarchical cluster analysis using subdomain scores revealed six patterns of vulnerability across domains: 1) low in all, 2) primarily low except residential, 3) medium in all, 4) high demographic, economic, and residential 5) high economic, residential, and health status, and 6) high demographic, economic and health status. Created using methods that offer flexibility for theory-based construction, NEVI provided detailed vulnerability metrics across domains that can inform targeted research and public health interventions aimed at reducing the health impacts from environmental exposures across urban centers.

Combining aggregate and individual-level data to estimate individual-level associations between air pollution and COVID-19 mortality in the United States

Imposing stricter regulations for PM2.5 has the potential to mitigate damaging health and climate change effects. Recent evidence establishing a link between exposure to air pollution and COVID-19 outcomes is one of many arguments for the need to reduce the National Ambient Air Quality Standards (NAAQS) for PM2.5. However, many studies reporting a relationship between COVID-19 outcomes and PM2.5 have been criticized because they are based on ecological regression analyses, where area-level counts of COVID-19 outcomes are regressed on area-level exposure to air pollution and other covariates. It is well known that regression models solely based on area-level data are subject to ecological bias, i.e., they may provide a biased estimate of the association at the individual-level, due to within-area variability of the data. In this paper, we augment county-level COVID-19 mortality data with a nationally representative sample of individual-level covariate information from the American Community Survey along with high-resolution estimates of PM2.5 concentrations obtained from a validated model and aggregated to the census tract for the contiguous United States. We apply a Bayesian hierarchical modeling approach to combine county-, census tract-, and individual-level data to ultimately draw inference about individual-level associations between long-term exposure to PM2.5 and mortality for COVID-19. By analyzing data prior to the Emergency Use Authorization for the COVID-19 vaccines we found that an increase of 1 μg/m3 in long-term PM2.5 exposure, averaged over the 17-year period 2000-2016, is associated with a 3.3% (95% credible interval, 2.8 to 3.8%) increase in an individual's odds of COVID-19 mortality. Code to reproduce our study is publicly available at https://github.com/NSAPH/PM_COVID_ecoinference. The results confirm previous evidence of an association between long-term exposure to PM2.5 and COVID-19 mortality and strengthen the case for tighter regulations on harmful air pollution and greenhouse gas emissions.

Long-term exposure to air pollution and risk of SARS-CoV-2 infection and COVID-19 hospitalisation or death: Danish nationwide cohort study

BACKGROUND

Early ecological studies have suggested links between air pollution and risk of coronavirus disease 2019 (COVID-19), but evidence from individual-level cohort studies is still sparse. We examined whether long-term exposure to air pollution is associated with risk of COVID-19 and who is most susceptible.

METHODS

We followed 3 721 810 Danish residents aged ≥30 years on 1 March 2020 in the National COVID-19 Surveillance System until the date of first positive test (incidence), COVID-19 hospitalisation or death until 26 April 2021. We estimated residential annual mean particulate matter with diameter ≤2.5 μm (PM2.5), nitrogen dioxide (NO2), black carbon (BC) and ozone (O3) in 2019 by the Danish DEHM/UBM model, and used Cox proportional hazards regression models to estimate the associations of air pollutants with COVID-19 outcomes, adjusting for age, sex, individual- and area-level socioeconomic status, and population density.

RESULTS

138 742 individuals were infected, 11 270 were hospitalised and 2557 died from COVID-19 during 14 months. We detected associations of PM2.5 (per 0.53 μg·m-3) and NO2 (per 3.59 μg·m-3) with COVID-19 incidence (hazard ratio (HR) 1.10 (95% CI 1.05-1.14) and HR 1.18 (95% CI 1.14-1.23), respectively), hospitalisations (HR 1.09 (95% CI 1.01-1.17) and HR 1.19 (95% CI 1.12-1.27), respectively) and death (HR 1.23 (95% CI 1.04-1.44) and HR 1.18 (95% CI 1.03-1.34), respectively), which were strongest in the lowest socioeconomic groups and among patients with chronic respiratory, cardiometabolic and neurodegenerative diseases. We found positive associations with BC and negative associations with O3.

CONCLUSION

Long-term exposure to air pollution may contribute to increased risk of contracting severe acute respiratory syndrome coronavirus 2 infection as well as developing severe COVID-19 disease requiring hospitalisation or resulting in death.

Association between exposure to multiple air pollutants, transportation noise and cause-specific mortality in adults in Switzerland

BACKGROUND

Long-term exposure to air pollution and noise is detrimental to health; but studies that evaluated both remain limited. This study explores associations with natural and cause-specific mortality for a range of air pollutants and transportation noise.

METHODS

Over 4 million adults in Switzerland were followed from 2000 to 2014. Exposure to PM_2.5, PM_2.5 components (Cu, Fe, S and Zn), NO₂, black carbon (BC) and ozone (O₃) from European models, and transportation noise from source-specific Swiss models, were assigned at baseline home addresses. Cox proportional hazards models, adjusted for individual and area-level covariates, were used to evaluate associations with each exposure and death from natural, cardiovascular (CVD) or non-malignant respiratory disease. Analyses included single and two exposure models, and subset analysis to study lower exposure ranges.

RESULTS

During follow-up, 661,534 individuals died of natural causes (36.6% CVD, 6.6% respiratory). All exposures including the PM_2.5 components were associated with natural mortality, with hazard ratios (95% confidence intervals) of 1.026 (1.015, 1.038) per 5 µg/m³ PM_2.5, 1.050 (1.041, 1.059) per 10 µg/m³ NO₂, 1.057 (1.048, 1.067) per 0.5 × 10^-5/m BC and 1.045 (1.040, 1.049) per 10 dB Lden total transportation noise. NO₂, BC, Cu, Fe and noise were consistently associated with CVD and respiratory mortality, whereas PM_2.5 was only associated with CVD mortality. Natural mortality associations persisted < 20 µg/m³ for PM_2.5 and NO₂, < 1.5 10^-5/m BC and < 53 dB Lden total transportation noise. The O₃ association was inverse for all outcomes. Including noise attenuated all outcome associations, though many remained significant. Across outcomes, noise was robust to adjustment to air pollutants (e.g. natural mortality 1.037 (1.033, 1.042) per 10 dB Lden total transportation noise, after including BC).

CONCLUSION

Long-term exposure to air pollution and transportation noise in Switzerland contribute to premature mortality. Considering co-exposures revealed the importance of local traffic-related pollutants such as NO₂, BC and transportation noise.

Prenatal exposure to ambient air pollution is associated with neurodevelopmental outcomes at 2 years of age

BACKGROUND

Higher prenatal ambient air pollution exposure has been associated with impaired neurodevelopment in preschoolers and school-aged children. The purpose of this study was to explore the relationships between prenatal ambient air pollution exposure and neurodevelopment during infancy.

METHODS

This study examined 161 Latino mother-infant pairs from the Southern California Mother's Milk Study. Exposure assessments included prenatal nitrogen dioxide (NO₂) and particulate matter smaller than 2.5 and 10 microns in diameter (PM_2.5 and PM₁₀, respectively). The pregnancy period was also examined as three windows, early, mid, and late, which describe the first, middle, and last three months of pregnancy. Infant neurodevelopmental outcomes at 2 years of age were measured using the Bayley-III Scales of Infant and Toddler Development. Multivariable linear models and distributed lag linear models (DLM) were used to examine relationships between prenatal exposures and neurodevelopmental scores, adjusting for socioeconomic status, breastfeeding frequency, time of delivery, pre-pregnancy body mass index, and infant birthweight and sex.

RESULTS

Higher prenatal exposure to PM₁₀ and PM_2.5 was negatively associated with composite cognitive score (β = -2.01 [-3.89, -0.13] and β = -1.97 [-3.83, -0.10], respectively). In addition, higher average prenatal exposure to PM₁₀ was negatively associated with composite motor (β = -2.35 [-3.95, -0.74]), scaled motor (β = -0.77 [-1.30, -0.24]), gross motor (β = -0.37 [-0.70, -0.04]), fine motor (β = -0.40 [-0.71, -0.09]), composite language (β = -1.87 [-3.52, -0.22]), scaled language (β = -0.61 [-1.18, -0.05]) and expressive communication scaled scores (β = -0.36 [-0.66, -0.05]). DLMs showed that higher prenatal air pollution exposure during mid and late pregnancy was inversely associated with motor, cognitive, and communication language scores.

CONCLUSIONS

Higher exposure to air pollutants during pregnancy, particularly in the mid and late prenatal periods, was inversely associated with scaled and composite motor, cognitive, and language scores at 2 years. These results indicate that prenatal ambient air pollution may negatively impact neurodevelopment in early life.

Superoxide Release by Macrophages through NADPH Oxidase Activation Dominating Chemistry by Isoprene Secondary Organic Aerosols and Quinones to Cause Oxidative Damage on Membranes

Oxidative stress mediated by reactive oxygen species (ROS) is a key process for adverse aerosol health effects. Secondary organic aerosols (SOA) account for a major fraction of fine particulate matter, and their inhalation and deposition into the respiratory tract causes the formation of ROS by chemical and cellular processes, but their relative contributions are hardly quantified and their link to oxidative stress remains uncertain. Here, we quantified cellular and chemical superoxide generation by 9,10-phenanthrenequinone (PQN) and isoprene SOA using a chemiluminescence assay combined with electron paramagnetic resonance spectroscopy as well as kinetic modeling. We also applied cellular imaging techniques to study the cellular mechanism of superoxide release and oxidative damage on cell membranes. We show that PQN and isoprene SOA activate NADPH oxidase in macrophages to release massive amounts of superoxide, overwhelming the superoxide formation by aqueous chemical reactions in the epithelial lining fluid. The activation dose for PQN is 2 orders of magnitude lower than that of isoprene SOA, suggesting that quinones are more toxic. While higher exposures trigger cellular antioxidant response elements, the released ROS induce oxidative damage to the cell membrane through lipid peroxidation. Such mechanistic and quantitative understandings provide a basis for further elucidation of adverse health effects and oxidative stress by fine particulate matter.

A population-based cohort study of traffic congestion and infant growth using connected vehicle data

More than 11 million Americans reside within 150 meters of a highway, an area of high air pollution exposure. Traffic congestion further contributes to environmental pollution (e.g., air and noise), but its unique importance for population health is unclear. We hypothesized that degraded environmental quality specifically from traffic congestion has harmful impacts on fetal growth. Using a population-based cohort of births in Texas (2015-2016), we leveraged connected vehicle data to calculate traffic congestion metrics around each maternal address at delivery. Among 579,122 births, we found consistent adverse associations between traffic congestion and reduced term birth weight (8.9 grams), even after accounting for sociodemographic characteristics, typical traffic volume, and diverse environmental coexposures. We estimated that up to 1.2 million pregnancies annually may be exposed to traffic congestion (27% of births in the United States), with ~256,000 in the highest congestion zones. Therefore, improvements to traffic congestion may yield positive cobenefits for infant health.

How low can you go? Air pollution affects mortality at very low levels

The World Health Organization (WHO) recently released new guidelines for outdoor fine particulate air pollution (PM_2.5) recommending an annual average concentration of 5 μg/m³. Yet, our understanding of the concentration-response relationship between outdoor PM_2.5 and mortality in this range of near-background concentrations remains incomplete. To address this uncertainty, we conducted a population-based cohort study of 7.1 million adults in one of the world's lowest exposure environments. Our findings reveal a supralinear concentration-response relationship between outdoor PM_2.5 and mortality at very low (<5 μg/m³) concentrations. Our updated global concentration-response function incorporating this new information suggests an additional 1.5 million deaths globally attributable to outdoor PM_2.5 annually compared to previous estimates. The global health benefits of meeting the new WHO guideline for outdoor PM_2.5 are greater than previously assumed and indicate a need for continued reductions in outdoor air pollution around the world.

Effects of Acidity on Reactive Oxygen Species Formation from Secondary Organic Aerosols

Reactive oxygen species (ROS) play a critical role in the chemical transformation of atmospheric secondary organic aerosols (SOA) and aerosol health effects by causing oxidative stress in vivo. Acidity is an important physicochemical property of atmospheric aerosols, but its effects on the ROS formation from SOA have been poorly characterized. By applying the electron paramagnetic resonance spin-trapping technique and the Diogenes chemiluminescence assay, we find highly distinct radical yields and composition at different pH values in the range of 1-7.4 from SOA generated by oxidation of isoprene, α-terpineol, α-pinene, β-pinene, toluene, and naphthalene. We observe that isoprene SOA has substantial hydroxyl radical (^•OH) and organic radical yields at neutral pH, which are 1.5-2 times higher compared to acidic conditions in total radical yields. Superoxide (O₂ ^•-) is found to be the dominant species generated by all types of SOAs at lower pH. At neutral pH, α-terpineol SOA exhibits a substantial yield of carbon-centered organic radicals, while no radical formation is observed by aromatic SOA. Further experiments with model compounds show that the decomposition of organic peroxide leading to radical formation may be suppressed at lower pH due to acid-catalyzed rearrangement of peroxides. We also observe 1.5-3 times higher molar yields of hydrogen peroxide (H₂O₂) in acidic conditions compared to neutral pH by biogenic and aromatic SOA, likely due to enhanced decomposition of α-hydroxyhydroperoxides and quinone redox cycling, respectively. These findings are critical to bridge the gap in understanding ROS formation mechanisms and kinetics in atmospheric and physiological environments.

Long-Term Exposure to Source-Specific Fine Particles and Mortality─A Pooled Analysis of 14 European Cohorts within the ELAPSE Project

We assessed mortality risks associated with source-specific fine particles (PM_2.5) in a pooled European cohort of 323,782 participants. Cox proportional hazard models were applied to estimate mortality hazard ratios (HRs) for source-specific PM_2.5 identified through a source apportionment analysis. Exposure to 2010 annual average concentrations of source-specific PM_2.5 components was assessed at baseline residential addresses. The source apportionment resulted in the identification of five sources: traffic, residual oil combustion, soil, biomass and agriculture, and industry. In single-source analysis, all identified sources were significantly positively associated with increased natural mortality risks. In multisource analysis, associations with all sources attenuated but remained statistically significant with traffic, oil, and biomass and agriculture. The highest association per interquartile increase was observed for the traffic component (HR: 1.06; 95% CI: 1.04 and 1.08 per 2.86 μg/m³ increase) across five identified sources. On a 1 μg/m³ basis, the residual oil-related PM_2.5 had the strongest association (HR: 1.13; 95% CI: 1.05 and 1.22), which was substantially higher than that for generic PM_2.5 mass, suggesting that past estimates using the generic PM_2.5 exposure response function have underestimated the potential clean air health benefits of reducing fossil-fuel combustion. Source-specific associations with cause-specific mortality were in general consistent with findings of natural mortality.

A Review of Road Traffic-Derived Non-Exhaust Particles: Emissions, Physicochemical Characteristics, Health Risks, and Mitigation Measures

Implementation of regulatory standards has reduced exhaust emissions of particulate matter from road traffic substantially in the developed world. However, nonexhaust particle emissions arising from the wear of brakes, tires, and the road surface, together with the resuspension of road dust, are unregulated and exceed exhaust emissions in many jurisdictions. While knowledge of the sources of nonexhaust particles is fairly good, source-specific measurements of airborne concentrations are few, and studies of the toxicology and epidemiology do not give a clear picture of the health risk posed. This paper reviews the current state of knowledge, with a strong focus on health-related research, highlighting areas where further research is an essential prerequisite for developing focused policy responses to nonexhaust particles.

The Potential of Sewage Sludge to Predict and Evaluate the Human Chemical Exposome

Chemicals are part of our daily lives, and we are exposed to numerous chemicals through multiple pathways. Relevant scientific evidence contributing to the regulation of hazardous chemicals require a holistic approach to assess simultaneous exposure to multiple compounds. Biomonitoring provides an accurate estimation of exposure to chemicals through very complex and costly sampling campaigns. Finding efficient proxies to predict the risk of chemical exposure in humans is an urgent need to cover large areas and populations at a reasonable cost. We conducted an exploratory study to characterize the human chemical exposome in maternal blood and placenta samples of a population-based birth cohort in Barcelona (2018-2021). Ultimate HRMS-based approaches were applied including wide-scope target, suspect, and nontarget screening. Forty-two chemicals were identified including pesticides, personal care products, or industrial compounds, among others, in the range of ng/mL and ng/g. In parallel, sewage sludge from the wastewater treatment plants serving the residence areas of the studied population were also screened, showing correlations with the type and concentrations of chemicals found in humans. Our findings were suggestive for the potential use of sewage sludge as a proxy of the human exposure and its application in early warning systems to prevent bioaccumulation of hazardous chemicals.

Hydroxyl Radical Production by Air Pollutants in Epithelial Lining Fluid Governed by Interconversion and Scavenging of Reactive Oxygen Species

Air pollution is a major risk factor for human health. Chemical reactions in the epithelial lining fluid (ELF) of the human respiratory tract result in the formation of reactive oxygen species (ROS), which can lead to oxidative stress and adverse health effects. We use kinetic modeling to quantify the effects of fine particulate matter (PM2.5), ozone (O₃), and nitrogen dioxide (NO₂) on ROS formation, interconversion, and reactivity, and discuss different chemical metrics for oxidative stress, such as cumulative production of ROS and hydrogen peroxide (H₂O₂) to hydroxyl radical (OH) conversion. All three air pollutants produce ROS that accumulate in the ELF as H₂O₂, which serves as reservoir for radical species. At low PM2.5 concentrations (<10 μg m^-3), we find that less than 4% of all produced H₂O₂ is converted into highly reactive OH, while the rest is intercepted by antioxidants and enzymes that serve as ROS buffering agents. At elevated PM2.5 concentrations (>10 μg m^-3), however, Fenton chemistry overwhelms the ROS buffering effect and leads to a tipping point in H₂O₂ fate, causing a strong nonlinear increase in OH production. This shift in ROS chemistry and the enhanced OH production provide a tentative mechanistic explanation for how the inhalation of PM2.5 induces oxidative stress and adverse health effects.

Prenatal exposure to ambient air pollutants and early infant growth and adiposity in the Southern California Mother's Milk Study

BACKGROUND

Prior epidemiological and animal work has linked in utero exposure to ambient air pollutants (AAP) with accelerated postnatal weight gain, which is predictive of increased cardiometabolic risk factors in childhood and adolescence. However, few studies have assessed changes in infant body composition or multiple pollutant exposures. Therefore, the objective of this study was to examine relationships between prenatal residential AAP exposure with infant growth and adiposity.

METHODS

Residential exposure to AAP (particulate matter < 2.5 and 10 microns in aerodynamic diameter [PM2.5, PM10]; nitrogen dioxide [NO2]; ozone [O3]; oxidative capacity [Oxwt: redox-weighted oxidative potential of O3 and NO2]) was modeled by spatial interpolation of monitoring stations via an inverse distance-squared weighting (IDW2) algorithm for 123 participants from the longitudinal Mother's Milk Study, an ongoing cohort of Hispanic mother-infant dyads from Southern California. Outcomes included changes in infant growth (weight, length), total subcutaneous fat (TSF; calculated via infant skinfold thickness measures) and fat distribution (umbilical circumference, central to total subcutaneous fat [CTSF]) and were calculated by subtracting 1-month measures from 6-month measures. Multivariable linear regression was performed to examine relationships between prenatal AAP exposure and infant outcomes. Models adjusted for maternal age, pre-pregnancy body mass index, socioeconomic status, infant age, sex, and breastfeeding frequency. Sex interactions were tested, and effects are reported for each standard deviation increase in exposure.

RESULTS

NO2 was associated with greater infant weight gain (β = 0.14, p = 0.02) and TSF (β = 1.69, p = 0.02). PM10 and PM2.5 were associated with change in umbilical circumference (β = 0.73, p = 0.003) and TSF (β = 1.53, p = 0.04), respectively. Associations of Oxwt (pinteractions < 0.10) with infant length change, umbilical circumference, and CTSF were modified by infant sex. Oxwt was associated with attenuated infant length change among males (β = -0.60, p = 0.01), but not females (β = 0.16, p = 0.49); umbilical circumference among females (β = 0.92, p = 0.009), but not males (β = -0.00, p = 0.99); and CTSF among males (β = 0.01, p = 0.03), but not females (β = 0.00, p = 0.51).

CONCLUSION

Prenatal AAP exposure was associated with increased weight gain and anthropometric measures from 1-to-6 months of life among Hispanic infants. Sex-specific associations suggest differential consequences of in utero oxidative stress. These results indicate that prenatal AAP exposure may alter infant growth, which has potential to increase childhood obesity risk.

The contribution of cooking appliances and residential traffic proximity to aerosol personal exposure

PURPOSE

Indoor and outdoor factors affect personal exposure to air pollutants. Type of cooking appliance (i.e. gas, electricity), and residential location related to traffic are such factors. This research aims to investigate the effect of cooking with gas and electric appliances, as an indoor source of aerosols, and residential traffic as outdoor sources, on personal exposures to particulate matter with an aerodynamic diameter lower than 2.5 μm (PM_2.5), black carbon (BC), and ultrafine particles (UFP).

METHODS

Forty subjects were sampled for four consecutive days measuring personal exposures to three aerosol pollutants, namely PM_2.5, BC, and UFP, which were measured using personal sensors. Subjects were equally distributed into four categories according to the use of gas or electric stoves for cooking, and to residential traffic (i.e. houses located near or away from busy roads).

RESULTS/CONCLUSION

Cooking was identified as an indoor activity affecting exposure to aerosols, with mean concentrations during cooking ranging 24.7-50.0 μg/m³ (PM_2.5), 1.8-4.9 μg/m³ (BC), and 1.4 × 10⁴-4.1 × 10⁴ particles/cm³ (UFP). This study also suggest that traffic is a dominant source of exposure to BC, since people living near busy roads are exposed to higher BC concentrations than those living further away from traffic. In contrast, the contribution of indoor sources to personal exposure to PM_2.5 and UFP seems to be greater than from outdoor traffic sources. This is probably related to a combination of the type of building construction and a varying range of activities conducted indoors. It is recommended to ensure a good ventilation during cooking to minimize exposure to cooking aerosols.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40201-020-00604-7.

Emulating causal dose-response relations between air pollutants and mortality in the Medicare population

BACKGROUND

Fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) are major air pollutants that pose considerable threats to human health. However, what has been mostly missing in air pollution epidemiology is causal dose-response (D-R) relations between those exposures and mortality. Such causal D-R relations can provide profound implications in predicting health impact at a target level of air pollution concentration.

METHODS

Using national Medicare cohort during 2000-2016, we simultaneously emulated causal D-R relations between chronic exposures to fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) and all-cause mortality. To relax the contentious assumptions of inverse probability weighting for continuous exposures, including distributional form of the exposure and heteroscedasticity, we proposed a decile binning approach which divided each exposure into ten equal-sized groups by deciles, treated the lowest decile group as reference, and estimated the effects for the other groups. Binning continuous exposures also makes the inverse probability weights robust against outliers.

RESULTS

Assuming the causal framework was valid, we found that higher levels of PM2.5, O3, and NO2 were causally associated with greater risk of mortality and that PM2.5 posed the greatest risk. For PM2.5, the relative risk (RR) of mortality monotonically increased from the 2nd (RR, 1.022; 95% confidence interval [CI], 1.018-1.025) to the 10th decile group (RR, 1.207; 95% CI, 1.203-1.210); for O3, the RR increased from the 2nd (RR, 1.050; 95% CI, 1.047-1.053) to the 9th decile group (RR, 1.107; 95% CI, 1.104-1.110); for NO2, the DR curve wiggled at low levels and started rising from the 6th (RR, 1.005; 95% CI, 1.002-1.018) till the highest decile group (RR, 1.024; 95% CI, 1.021-1.027).

CONCLUSIONS

This study provided more robust evidence of the causal relations between air pollution exposures and mortality. The emulated causal D-R relations provided significant implications for reviewing the national air quality standards, as they inferred the number of potential early deaths prevented if air pollutants were reduced to specific levels; for example, lowering each air pollutant concentration from the 70th to 60th percentiles would prevent 65,935 early deaths per year.

Air pollution and COVID-19 mortality in the United States: Strengths and limitations of an ecological regression analysis

Assessing whether long-term exposure to air pollution increases the severity of COVID-19 health outcomes, including death, is an important public health objective. Limitations in COVID-19 data availability and quality remain obstacles to conducting conclusive studies on this topic. At present, publicly available COVID-19 outcome data for representative populations are available only as area-level counts. Therefore, studies of long-term exposure to air pollution and COVID-19 outcomes using these data must use an ecological regression analysis, which precludes controlling for individual-level COVID-19 risk factors. We describe these challenges in the context of one of the first preliminary investigations of this question in the United States, where we found that higher historical PM2.5 exposures are positively associated with higher county-level COVID-19 mortality rates after accounting for many area-level confounders. Motivated by this study, we lay the groundwork for future research on this important topic, describe the challenges, and outline promising directions and opportunities.

Evaluating the impact of long-term exposure to fine particulate matter on mortality among the elderly

Many studies link long-term fine particle (PM2.5) exposure to mortality, even at levels below current U.S. air quality standards (12 micrograms per cubic meter). These findings have been disputed with claims that the use of traditional statistical approaches does not guarantee causality. Leveraging 16 years of data-68.5 million Medicare enrollees-we provide strong evidence of the causal link between long-term PM2.5 exposure and mortality under a set of causal inference assumptions. Using five distinct approaches, we found that a decrease in PM2.5 (by 10 micrograms per cubic meter) leads to a statistically significant 6 to 7% decrease in mortality risk. Based on these models, lowering the air quality standard to 10 micrograms per cubic meter would save 143,257 lives (95% confidence interval, 115,581 to 170,645) in one decade. Our study provides the most comprehensive evidence to date of the link between long-term PM2.5 exposure and mortality, even at levels below current standards.

Ozone effects on blood biomarkers of systemic inflammation, oxidative stress, endothelial function, and thrombosis: The Multicenter Ozone Study in oldEr Subjects (MOSES)

The evidence that exposure to ozone air pollution causes acute cardiovascular effects is mixed. We postulated that exposure to ambient levels of ozone would increase blood markers of systemic inflammation, prothrombotic state, oxidative stress, and vascular dysfunction in healthy older subjects, and that absence of the glutathione S-transferase Mu 1 (GSTM1) gene would confer increased susceptibility. This double-blind, randomized, crossover study of 87 healthy volunteers 55-70 years of age was conducted at three sites using a common protocol. Subjects were exposed for 3 h in random order to 0 parts per billion (ppb) (filtered air), 70 ppb, and 120 ppb ozone, alternating 15 min of moderate exercise and rest. Blood was obtained the day before, approximately 4 h after, and approximately 22 h after each exposure. Linear mixed effect and logistic regression models evaluated the impact of exposure to ozone on pre-specified primary and secondary outcomes. The definition of statistical significance was p<0.01. There were no effects of ozone on the three primary markers of systemic inflammation and a prothrombotic state: C-reactive protein, monocyte-platelet conjugates, and microparticle-associated tissue factor activity. However, among the secondary endpoints, endothelin-1, a potent vasoconstrictor, increased from pre- to post-exposure with ozone concentration (120 vs 0 ppb: 0.07 pg/mL, 95% confidence interval [CI] 0.01, 0.14; 70 vs 0 ppb: -0.03 pg/mL, CI -0.09, 0.04; p = 0.008). Nitrotyrosine, a marker of oxidative and nitrosative stress, decreased with increasing ozone concentrations, with marginal significance (120 vs 0 ppb: -41.5, CI -70.1, -12.8; 70 vs 0 ppb: -14.2, CI -42.7, 14.2; p = 0.017). GSTM1 status did not modify the effect of ozone exposure on any of the outcomes. These findings from healthy older adults fail to identify any mechanistic basis for the epidemiologically described cardiovascular effects of exposure to ozone. The findings, however, may not be applicable to adults with cardiovascular disease.

Statistical software for analyzing the health effects of multiple concurrent exposures via Bayesian kernel machine regression

BACKGROUND

Estimating the health effects of multi-pollutant mixtures is of increasing interest in environmental epidemiology. Recently, a new approach for estimating the health effects of mixtures, Bayesian kernel machine regression (BKMR), has been developed. This method estimates the multivariable exposure-response function in a flexible and parsimonious way, conducts variable selection on the (potentially high-dimensional) vector of exposures, and allows for a grouped variable selection approach that can accommodate highly correlated exposures. However, the application of this novel method has been limited by a lack of available software, the need to derive interpretable output in a computationally efficient manner, and the inability to apply the method to non-continuous outcome variables.

METHODS

This paper addresses these limitations by (i) introducing an open-source software package in the R programming language, the bkmr R package, (ii) demonstrating methods for visualizing high-dimensional exposure-response functions, and for estimating scientifically relevant summaries, (iii) illustrating a probit regression implementation of BKMR for binary outcomes, and (iv) describing a fast version of BKMR that utilizes a Gaussian predictive process approach. All of the methods are illustrated using fully reproducible examples with the provided R code.

RESULTS

Applying the methods to a continuous outcome example illustrated the ability of the BKMR implementation to estimate the health effects of multi-pollutant mixtures in the context of a highly nonlinear, biologically-based dose-response function, and to estimate overall, single-exposure, and interactive health effects. The Gaussian predictive process method led to a substantial reduction in the runtime, without a major decrease in accuracy. In the setting of a larger number of exposures and a dichotomous outcome, the probit BKMR implementation was able to correctly identify the variables included in the exposure-response function and yielded interpretable quantities on the scale of a latent continuous outcome or on the scale of the outcome probability.

CONCLUSIONS

This newly developed software, integrated suite of tools, and extended methodology makes BKMR accessible for use across a broad range of epidemiological applications in which multiple risk factors have complex effects on health.

Ozone exposure is associated with acute changes in inflammation, fibrinolysis, and endothelial cell function in coronary artery disease patients

BACKGROUND

Air pollution is a major risk factor for cardiovascular disease, of which ozone is a major contributor. Several studies have found associations between ozone and cardiovascular morbidity, but the results have been inconclusive. We investigated associations between ozone and changes across biological pathways associated with cardiovascular disease.

METHODS

Using a panel study design, 13 participants with coronary artery disease were assessed for markers of systemic inflammation, heart rate variability and repolarization, lipids, blood pressure, and endothelial function. Daily measurements of ozone and particulate matter (PM2.5) were obtained from central monitoring stations. Single (ozone) and two-pollutant (ozone and PM2.5) models were used to assess percent changes in measurements per interquartile ranges of pollutants.

RESULTS

Per interquartile increase in ozone, changes in tissue plasminogen factor (6.6%, 95% confidence intervals (CI) = 0.4, 13.2), plasminogen activator inhibitor-1 (40.5%, 95% CI = 8.7, 81.6), neutrophils (8.7% 95% CI = 1.5, 16.4), monocytes (10.2%, 95% CI = 1.0, 20.1), interleukin-6 (15.9%, 95% CI = 3.6, 29.6), large-artery elasticity index (-19.5%, 95% CI = -34.0, -1.7), and the baseline diameter of the brachial artery (-2.5%, 95% CI = -5.0, 0.1) were observed. These associations were robust in the two-pollutant model.

CONCLUSIONS

We observed alterations across several pathways associated with cardiovascular disease in 13 coronary artery disease patients following ozone exposures, independent of PM2.5. The results support the biological plausibility of ozone-induced cardiovascular effects. The effects were found at concentrations below the EPA National Ambient Air Quality Standards for both ozone and PM2.5.

A genome-wide trans-ethnic interaction study links the PIGR-FCAMR locus to coronary atherosclerosis via interactions between genetic variants and residential exposure to traffic

Air pollution is a worldwide contributor to cardiovascular disease mortality and morbidity. Traffic-related air pollution is a widespread environmental exposure and is associated with multiple cardiovascular outcomes such as coronary atherosclerosis, peripheral arterial disease, and myocardial infarction. Despite the recognition of the importance of both genetic and environmental exposures to the pathogenesis of cardiovascular disease, studies of how these two contributors operate jointly are rare. We performed a genome-wide interaction study (GWIS) to examine gene-traffic exposure interactions associated with coronary atherosclerosis. Using race-stratified cohorts of 538 African-Americans (AA) and 1562 European-Americans (EA) from a cardiac catheterization cohort (CATHGEN), we identify gene-by-traffic exposure interactions associated with the number of significantly diseased coronary vessels as a measure of chronic atherosclerosis. We found five suggestive (P<1x10^-5) interactions in the AA GWIS, of which two (rs1856746 and rs2791713) replicated in the EA cohort (P < 0.05). Both SNPs are in the PIGR-FCAMR locus and are eQTLs in lymphocytes. The protein products of both PIGR and FCAMR are implicated in inflammatory processes. In the EA GWIS, there were three suggestive interactions; none of these replicated in the AA GWIS. All three were intergenic; the most significant interaction was in a regulatory region associated with SAMSN1, a gene previously associated with atherosclerosis and B cell activation. In conclusion, we have uncovered several novel genes associated with coronary atherosclerosis in individuals chronically exposed to increased ambient concentrations of traffic air pollution. These genes point towards inflammatory pathways that may modify the effects of air pollution on cardiovascular disease risk.

Human Alveolar Macrophages May Not Be Susceptible to Direct Infection by a Human Influenza Virus

The current studies were undertaken to determine the susceptibility of human alveolar macrophages (AMs) to influenza A virus (IAV) infection in comparison with autologous peripheral blood-derived monocytes-macrophages (PBMs). AMs and PBMs were exposed to IAV in vitro and examined for their ability to bind and internalize IAV, and synthesize viral proteins and RNA. PBMs but not AMs demonstrated binding and internalization of the virus, synthesizing viral proteins and RNA. Exposure of AMs in the presence of a sialidase inhibitor or anti-IAV antibody resulted in viral protein synthesis by the cells. Exposure of AMs to fluorescein isothiocyanate-labeled IAV in the presence of anti-fluorescein isothiocyanate antibody also resulted in viral protein synthesis. Thus, human AMs are apparently not susceptible to direct infection by a human IAV but are likely to be infected indirectly in the setting of exposure in the presence of antibody that binds the challenging strain of IAV.