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Background: Fine particulate matter (PM2.5) exposure has been associated with liver cancer incidence and mortality in a limited number of studies. We sought to evaluate this relationship for the first time in a U.S. cohort with historical exposure assessment. Methods: We used spatiotemporal prediction models to estimate annual average historical PM2.5 concentrations (1980–2015) at residential addresses of 499,729 participants in the NIH-AARP Diet and Health Study, a cohort in 6 states (California, Florida, Louisiana, New Jersey, North Carolina, and Pennsylvania) and 2 metropolitan areas (Atlanta, Georgia, and Detroit, Michigan) enrolled in 1995–1996 and followed up through 2017. We used a time-varying Cox model to estimate the association for liver cancer and the predominant histologic type, hepatocellular carcinoma (HCC), per 5 µg/m3 increase in estimated outdoor PM2.5 levels, incorporating a 5-year average, lagged 10 years prior to cancer diagnosis and adjusting for age, sex, race/ethnicity, education level and catchment state. We also evaluated PM2.5 interactions with hypothesized effect modifiers. Results: We observed a non-significantly increased risk of liver cancer associated with estimated PM2.5 exposure (Hazard ratio [HR] = 1.05 [0.96–1.14], N = 1,625); associations were slightly stronger for HCC, (84 % of cases; HR = 1.08 [0.98–1.18]). Participants aged 70 or older at enrollment had an increased risk of liver cancer versus other age groups (HR = 1.50 [1.01–2.23]); p-interaction = 0.01) and risk was elevated among participants who did not exercise (HR = 1.81 [1.22–2.70]; p-interaction = 0.01). We found no evidence of effect modification by sex, smoking status, body mass index, diabetes status, or alcohol consumption (p-interaction > 0.05). Conclusions: Our findings in this large cohort suggest that residential ambient PM2.5 levels may be associated with liver cancer risk. Further exploration of the variation in associations by age and physical activity are important areas for future research.

Background: While epidemiologic evidence links higher levels of exposure to fine particulate matter (PM2.5) to decreased cognitive function, fewer studies have investigated links with traffic-related air pollution (TRAP), and none have examined ultrafine particles (UFP, ≤100 nm) and late-life dementia incidence. Objective: To evaluate associations between TRAP exposures (UFP, black carbon [BC], and nitrogen dioxide [NO2]) and late-life dementia incidence. Methods: We ascertained dementia incidence in the Seattle-based Adult Changes in Thought (ACT) prospective cohort study (beginning in 1994) and assessed ten-year average TRAP exposures for each participant based on prediction models derived from an extensive mobile monitoring campaign. We applied Cox proportional hazards models to investigate TRAP exposure and dementia incidence using age as the time axis and further adjusting for sex, self-reported race, calendar year, education, socioeconomic status, PM2.5, and APOE genotype. We ran sensitivity analyses where we did not adjust for PM2.5 and other sensitivity and secondary analyses where we adjusted for multiple pollutants, applied alternative exposure models (including total and size-specific UFP), modified the adjustment covariates, used calendar year as the time axis, assessed different exposure periods, dementia subtypes, and others. Results: We identified 1,041 incident all-cause dementia cases in 4,283 participants over 37,102 person-years of follow-up. We did not find evidence of a greater hazard of late-life dementia incidence with elevated levels of long-term TRAP exposures. The estimated hazard ratio of all-cause dementia was 0.98 (95 % CI: 0.92–1.05) for every 2000 pt/cm3 increment in UFP, 0.95 (0.89–1.01) for every 100 ng/m3 increment in BC, and 0.96 (0.91–1.02) for every 2 ppb increment in NO2. These findings were consistent across sensitivity and secondary analyses. Discussion: We did not find evidence of a greater hazard of late-life dementia risk with elevated long-term TRAP exposures in this population-based prospective cohort study.

Background: DNA methylation-based measures of biological aging have been associated with air pollution and may link pollutant exposures to aging-related health outcomes. However, evidence is inconsistent and there is little information for Black women. Objective: We examined associations of ambient particulate matter

Background: Animal and epidemiologic studies indicate that air pollution may adversely affect fertility. However, the level of evidence is limited and specific pollutants driving the association are inconsistent across studies. Methods: We used data from a web-based preconception cohort study of pregnancy planners enrolled during 2013–2019 (Pregnancy Study Online; PRESTO). Eligible participants self-identified as female, were aged 21–45 years, resided in the United States (U.S.) or Canada, and were trying to conceive without fertility treatments. Participants completed a baseline questionnaire and bi-monthly follow-up questionnaires until conception or 12 months. We analyzed data from 8,747 participants (U.S.: 7,304; Canada: 1,443) who had been trying to conceive for

Background: Studies suggest associations between long-term ambient air pollution exposure and outcomes related to Alzheimer’s disease (AD). Whether a link exists between pollutants and brain amyloid accumulation, a biomarker of AD, is unclear. We assessed whether long-term air pollutant exposures are associated with late-life brain amyloid deposition in Atherosclerosis Risk in Communities (ARIC) study participants. Methods: We used a chemical transport model with data fusion to estimate ambient concentrations of PM2.5 and its components, NO2, NOx, O3 (24-hour and 8-hour), CO, and airborne trace metals. We linked concentrations to geocoded participant addresses and calculated 10-year mean exposures (2002 to 2011). Brain amyloid deposition was measured using florbetapir amyloid positron emission tomography (PET) scans in 346 participants without dementia in 2012–2014, and we defined amyloid positivity as a global cortical standardized uptake value ratio ≥ the sample median of 1.2. We used logistic regression models to quantify the association between amyloid positivity and each air pollutant, adjusting for putative confounders. In sensitivity analyses, we considered whether use of alternate air pollution estimation approaches impacted findings for PM2.5, NO2, NOx, and 24-hour O3. Results: At PET imaging, eligible participants (N = 318) had a mean age of 78 years, 56% were female, 43% were Black, and 27% had mild cognitive impairment. We did not find evidence of associations between long-term exposure to any pollutant and brain amyloid positivity in adjusted models. Findings were materially unchanged in sensitivity analyses using alternate air pollution estimation approaches for PM2.5, NO2, NOx, and 24-hour O3. Conclusions: Air pollution may impact cognition and dementia independent of amyloid accumulation, though whether air pollution influences AD pathogenesis later in the disease course or at higher exposure levels deserves further consideration.

Air pollution has been linked to Alzheimer’s disease and related dementias (ADRD), but the mechanisms connecting air pollution to ADRD have not been firmly established. Air pollution may cause oxidative stress and neuroinflammation and contribute to the deposition of amyloid beta (Aβ) in the brain. We examined the association between fine particulate matter

Background: While strong evidence supports adverse maternal and offspring consequences of air pollution, mechanisms that involve the placenta, a key part of the intrauterine environment, are largely unknown. Previous studies of air pollution and placental gene expression were small candidate gene studies that rarely considered prenatal windows of exposure or the potential role of offspring sex. We examined overall and sex-specific associations of prenatal exposure to fine particulate matter (PM2.5) with genome-wide placental gene expression. Methods: Participants with placenta samples, collected at birth, and childhood health outcomes from CANDLE (Memphis, TN) (n = 776) and GAPPS (Seattle, WA) (n = 205) cohorts of the ECHO-PATHWAYS Consortium were included in this study. PM2.5 exposures during trimesters 1, 2, 3, and the first and last months of pregnancy, were estimated using a spatiotemporal model. Cohort-specific linear adjusted models were fit for each exposure window and expression of >11,000 protein coding genes from paired end RNA sequencing data. Models with interaction terms were used to examine PM2.5-offspring sex interactions. False discovery rate (FDR

High-resolution, high-quality exposure modeling is critical for assessing the health effects of ambient PM2.5 in epidemiological studies. Using sparse regulatory PM2.5 measurements as principal model inputs may result in two issues in exposure prediction: (1) they may affect the models’ accuracy in predicting PM2.5 spatial distribution; (2) the internal validation based on these measurements may not reliably reflect the model performance at locations of interest (e.g., a cohort’s residential locations). In this study, we used the PM2.5 measurements from a publicly available commercial low-cost PM2.5 network, PurpleAir, with an external validation dataset at the residential locations of a representative sample of participants from the Adult Changes in Thought - Air Pollution (ACT-AP) study, to improve the accuracy of exposure prediction at the cohort participant locations. We also proposed a metric based on principal component analysis (PCA) - the PCA distance - to assess the similarity between monitor and cohort locations to guide monitor deployment and data selection. The analysis was based on a spatiotemporal modeling framework with 51 “gold-standard” monitors and 58 PurpleAir monitors for model development, as well as 105 home monitors at the cohort locations for model validation, in the Puget Sound region of Washington State from June 2017 to March 2019. After including calibrated PurpleAir measurements as part of the dependent variable, the external spatiotemporal validation R2 and root-mean-square error, RMSE, for two-week concentration averages improved from 0.84 and 2.22 μg/m3 to 0.92 and 1.63 μg/m3, respectively. The external spatial validation R2 and RMSE for long-term averages over the modeling period improved from 0.72 and 1.01 μg/m3 to 0.79 and 0.88 μg/m3, respectively. The exposure predictions incorporating PurpleAir measurements demonstrated sharper urban-suburban concentration gradients. The PurpleAir monitors with shorter PCA distances improved the model’s prediction accuracy more substantially than the monitors with longer PCA distances, supporting the use of this similarity metric.

Background: There is mounting evidence that long-term exposure to air pollution is related to accelerated cognitive decline in aging populations. Factors that influence individual susceptibility remain largely unknown, but may involve the apolipoprotein E genotype E4 (APOE-ε4) allele. Objectives: We assessed whether the association between long-term exposure to ambient air pollution and cognitive decline differed by APOE-ε4 status and cognitive risk factors. Methods: The Washington Heights Inwood Community Aging Project (WHICAP) is a prospective study of aging and dementia. Neuropsychological testing and medical examinations occur every 18–24 months. We used mixed-effects models to evaluate whether the association between markers of ambient air pollution (nitrogen dioxide [NO2]), fine [PM2.5], and coarse [PM10] particulate matter) and the rate of decline in global and domain-specific cognition differed across strata defined by APOE-ε4 genotypes and cognitive risk factors, adjusting for sociodemographic factors and temporal trends. Results: Among 4821 participants with an average of 6 years follow-up, higher concentrations of ambient air pollution were associated with more rapid cognitive decline. This association was more pronounced among APOE-ε4 carriers (p

Low-cost air monitoring sensors are an appealing tool for assessing pollutants in environmental studies. Portable low-cost sensors hold promise to expand temporal and spatial coverage of air quality information. However, researchers have reported challenges in these sensors′ operational quality. We evaluated the performance characteristics of two widely used sensors, the Plantower PMS A003 and Shinyei PPD42NS, for measuring fine particulate matter compared to reference methods, and developed regional calibration models for the Los Angeles, Chicago, New York, Baltimore, Minneapolis-St. Paul, Winston-Salem and Seattle metropolitan areas. Duplicate Plantower PMS A003 sensors demonstrated a high level of precision (averaged Pearson′s r = 0.99), and compared with regulatory instruments, showed good accuracy (cross-validated R2 = 0.96, RMSE = 1.15 µg/m3 for daily averaged PM2.5 estimates in the Seattle region). Shinyei PPD42NS sensor results had lower precision (Pearson′s r = 0.84) and accuracy (cross-validated R2 = 0.40, RMSE = 4.49 µg/m3). Region-specific Plantower PMS A003 models, calibrated with regulatory instruments and adjusted for temperature and relative humidity, demonstrated acceptable performance metrics for daily average measurements in the other six regions (R2 = 0.74–0.95, RMSE = 2.46–0.84 µg/m3). Applying the Seattle model to the other regions resulted in decreased performance (R2 = 0.67–0.84, RMSE = 3.41–1.67 µg/m3), likely due to differences in meteorological conditions and particle sources. We describe an approach to metropolitan region-specific calibration models for low-cost sensors that can be used with caution for exposure measurement in epidemiological studies. Keywords: Low-cost monitors (LCM), Calibration, Air pollution, Fine particulate matter, Multicenter study design, Air quality system network (AQS)

Background: Epigenetic age, as defined by DNA methylation, may be influenced by air pollution exposure. Objective: To evaluate the relationship between NO2, particulate matter (PM), PM components and accelerated epigenetic age. Methods: In a sample of non-Hispanic white women living in the contiguous U.S. (n = 2747), we estimated residential exposure to PM2.5, PM10 and NO2 using a model incorporating land-use regression and kriging. Predictive k-means was used to assign participants to clusters representing different PM2.5 component profiles. We measured DNA methylation (DNAm) in blood using the Illumina's Infinium HumanMethylation450 BeadChip and calculated DNAm age using the Hannum, Horvath and Levine epigenetic clocks. Age acceleration was defined based on residuals after regressing DNAm age on chronological age. We estimated associations between interquartile range (IQR) increases in pollutants and age acceleration using linear regression. For PM2.5, we stratified by cluster membership. We examined epigenome-wide associations using robust linear regression models corrected with false discovery rate q-values. Results: NO2 was inversely associated with age acceleration using the Hannum clock (β = −0.24, 95% CI: −0.47, −0.02). No associations were observed for PM10. For PM2.5, the association with age acceleration varied by PM2.5 component cluster. For example, with the Levine clock, an IQR increase in PM2.5 was associated with an over 6-year age acceleration in a cluster that has relatively high fractions of crustal elements relative to overall PM2.5 (β = 6.57, 95% CI: 2.68, 10.47), and an almost 2-year acceleration in a cluster characterized by relatively low sulfur fractions (β = 1.88, 95% CI: 0.51, 3.25). In a cluster distinguished by lower relative nitrate concentrations, PM2.5 was inversely associated with age acceleration (β = −1.33, 95% CI: −2.43, −0.23). Across the epigenome, NO2 was associated with methylation at 2 CpG sites. Conclusion: Air pollution was associated with epigenetic age, a marker of mortality and disease risk, among certain PM2.5 component profiles. Keywords: Breast cancer, Air pollution, Particulate matter, Clustering, Mixtures

Background: Cortisol, a stress hormone released by the activation of the hypothalamic-pituitary-adrenal (HPA) axis, is critical to the body's adaptive response to physiological and psychological stress. Cortisol has also been implicated in the health effects of air pollution through the activation of the sympathetic nervous system. This study evaluates the cross-sectional and longitudinal association between several air pollutants and salivary cortisol. Methods: We used data from the Multi-Ethnic Study of Atherosclerosis (MESA), a cohort of 45–85 years old participants from six US cities. Salivary cortisol was evaluated at two time points between 2004 and 2006 and then again from 2010 to 2012. Cortisol samples were taken several times per day on two or three consecutive days. Particulate matter

Research on racial/ethnic health disparities and socioeconomic position has not fully considered occupation. However, because occupations are racially patterned, certain occupational characteristics may explain racial/ethnic difference in health. This study examines the role of occupational characteristics in racial/ethnic disparities in all-cause mortality. Data are from a U.S. community-based cohort study (n=6342, median follow-up: 12.2 years), in which 893 deaths (14.1%) occurred. We estimated mortality hazard ratios (HRs) for African Americans, Hispanics, and Chinese Americans compared with whites. We also estimated the proportion of the HR mediated by each of two occupational characteristics, substantive complexity of work (e.g., problem solving, inductive/deductive reasoning on the job) and hazardous conditions (e.g., noise, extreme temperature, chemicals), derived from the Occupational Information Network database (O*NET). Analyses were adjusted for age, sex, nativity, working status at baseline, and study sites. African Americans had a higher rate of all-cause death (HR 1.41; 95% confidence interval [CI]: 1.19–1.66) than whites. Chinese-American ethnicity was protective (HR 0.59, CI: 0.40–0.85); Hispanic ethnicity was not significantly different from whites (HR 0.88; CI: 0.67–1.17). Substantive complexity of work mediated 30% of the higher rate of death for African Americans compared with whites. For other groups, mediation was not significant. Hazardous conditions did not significantly mediate mortality in any racial/ethnic group. Lower levels of substantive complexity of work mediate a substantial part of the health disadvantage in African Americans. This job characteristic may be an important factor in explaining racial health disparities.