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Abstract Objectives To examine whether long-term air pollution exposure is associated with central hemodynamic and brachial artery stiffness parameters. Methods We assessed central hemodynamic parameters including central blood pressure, cardiac parameters, systemic vascular compliance and resistance, and brachial artery stiffness measures [including brachial artery distensibility (BAD), compliance (BAC), and resistance (BAR)] using waveform analysis of the arterial pressure signals obtained from a standard cuff sphygmomanometer (DynaPulse2000A, San Diego, CA). The long-term exposures to particles with an aerodynamic diameter

Abstract Background Green space exposures may promote child mental health and well-being across multiple domains and stages of development. The aim of this study was to investigate associations between residential green space exposures and child mental and behavioral health at age 4–6 years. Methods Children’s internalizing and externalizing behaviors in the Conditions Affecting Neurocognitive Development and Learning in Early Childhood (CANDLE) cohort in Shelby County, Tennessee, were parent-reported on the Child Behavior Checklist (CBCL). We examined three exposures—residential surrounding greenness calculated as the Normalized Difference Vegetation Index (NDVI), tree cover, and park proximity—averaged across the residential history for the year prior to outcome assessment. Linear regression models were adjusted for individual, household, and neighborhood-level confounders across multiple domains. Effect modification by neighborhood socioeconomic conditions was explored using multiplicative interaction terms. Results Children were on average 4.2 years (range 3.8-6.0) at outcome assessment. Among CANDLE mothers, 65% self-identified as Black, 29% as White, and 6% as another or multiple races; 41% had at least a college degree. Higher residential surrounding greenness was associated with lower internalizing behavior scores (-0.66 per 0.1 unit higher NDVI; 95% CI: -1.26, -0.07) in fully-adjusted models. The association between tree cover and internalizing behavior was in the hypothesized direction but confidence intervals included the null (-0.29 per 10% higher tree cover; 95% CI: -0.62, 0.04). No associations were observed between park proximity and internalizing behavior. We did not find any associations with externalizing behaviors or the attention problems subscale. Estimates were larger in neighborhoods with lower socioeconomic opportunity, but interaction terms were not statistically significant. Conclusions Our findings add to the accumulating evidence of the importance of residential green space for the prevention of internalizing problems among young children. This research suggests the prioritization of urban green spaces as a resource for child mental health.

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.

Abstract Background Airway macrophages (AM), crucial for the immune response in chronic obstructive pulmonary disease (COPD), are exposed to environmental particulate matter (PM), which they retain in their cytoplasm as black carbon (BC). However, whether AM BC accurately reflects environmental PM2.5 exposure, and can serve as a biomarker of COPD outcomes, is unknown. Methods We analyzed induced sputum from participants at 7 of 12 sites SPIROMICS sites for AM BC content, which we related to exposures and to lung function and respiratory outcomes. Models were adjusted for batch (first vs. second), age, race (white vs. non-white), income (

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

Highlights Residential greenspace exposure was found to be associated with moderate cognitive health benefit for older U.S. adults. Dementia subtypes may be differentially impacted by exposure to residential greenspace. Combining multiple greenspace measures may better capture a holistic picture of exposure to greenspace compared to one measure alone.

Abstract Background Neighborhood greenspaces provide opportunities for increased physical activity and social interaction, and thus may reduce the risk of Type 2 diabetes. However, there is little robust research on greenspace and diabetes. In this study, we examine the longitudinal association between neighborhood greenspace and incident diabetes in the Multi-Ethnic Study of Atherosclerosis. Methods A prospective cohort study (N = 6814; 2000-2018) was conducted to examine the association between greenspace, measured as annual and high vegetation season median greenness determined by satellite (Normalized Difference Vegetation Index) within 1000 m of participant homes, and incident diabetes assessed at clinician visits, defined as a fasting glucose level of at least 126 mg/dL, use of insulin or use of hypoglycemic medication, controlling for covariates in stages. Five thousand five hundred seventy-four participants free of prevalent diabetes at baseline were included in our analysis. Results Over the study period, 886 (15.9%) participants developed diabetes. Adjusting for individual characteristics, individual and neighborhood-scale SES, additional neighborhood factors, and diabetes risk factors, we found a 21% decrease in the risk of developing diabetes per IQR increase in greenspace (HR: 0.79; 95% CI: 0.63, 0.99). Conclusions Overall, neighborhood greenspace provides a protective influence in the development of diabetes, suggesting that neighborhood-level urban planning that supports access to greenspace--along with healthy behaviors--may aid in diabetes prevention. Additional research is needed to better understand how an area’s greenness influences diabetes risk, how to better characterize greenspace exposure and usage, and future studies should focus on robust adjustment for neighborhood-level confounders.

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

Abstract Background Millions of Americans are living in food deserts in the United States, however the role of the local food environment on COPD has not been studied. The aim of this study is to determine the association between food deserts and COPD-related outcomes. Method In this cross-sectional analysis we linked data collected from SPIROMICS (SubPopulations and InteRmediate Outcome Measures in COPD Study) between 2010 and 2015 and food desert data, defined as an underserved area that lacks access to affordable healthy foods, from the Food Access Research Atlas. COPD outcomes include percentage of predicted forced expiratory volume in one second (FEV1%), St. George’s Respiratory Questionnaire (SGRQ), COPD Assessment Test (CAT), 6-min walk distance test (6MWD), exacerbations, and air trapping. We used generalized linear mixed models to evaluate the association between living in food deserts and respiratory outcomes, adjusting for age, gender, race, education, income, marital status, BMI, smoking status, pack years, and urban status Results Among 2713 participants, 22% lived in food deserts. Participants living in food deserts were less likely to be white and more likely to have a lower income than those who did not live in food deserts. In the adjusted model controlling for demographics and individual income, living in food deserts was associated lower FEV1% (β = – 2.51, P = 0.046), higher air trapping (β = 2.47, P = 0.008), worse SGRQ (β = 3.48, P = 0.001) and CAT (β = 1.20, P = 0.003) scores, and 56% greater odds of severe exacerbations (P = 0.004). Results were consistent when looking at food access alone, regardless of whether participants lived in low income areas. Conclusions Findings suggest an independent association between food desert and food access alone with COPD outcomes. Health program planning may benefit from addressing disparities in access to food.

Air pollution might affect atherosclerosis through DNA methylation changes in cells crucial to atherosclerosis, such as monocytes. We conducted an epigenome-wide study of DNA methylation in CD14+ monocytes and long-term ambient air pollution exposure in adults participating in the Multi-Ethnic Study of Atherosclerosis (MESA). We also assessed the association between differentially methylated signals and cis-gene expression. Using spatiotemporal models, one-year average concentrations of outdoor fine particulate matter (PM2.5) and oxides of nitrogen (NOX) were estimated at participants’ homes. We assessed DNA methylation and gene expression using Illumina 450k and HumanHT-12 v4 Expression BeadChips, respectively (n = 1,207). We used bump hunting and site-specific approaches to identify differentially methylated signals (false discovery rate of 0.05) and used linear models to assess associations between differentially methylated signals and cis-gene expression. Four differentially methylated regions (DMRs) located on chromosomes 5, 6, 7, and 16 (within or near SDHAP3, ZFP57, HOXA5, and PRM1, respectively) were associated with PM2.5. The DMRs on chromosomes 5 and 6 also associated with NOX. The DMR on chromosome 5 had the smallest p-value for both PM2.5 (p = 1.4×10−6) and NOX (p = 7.7×10−6). Three differentially methylated CpGs were identified for PM2.5, and cg05926640 (near TOMM20) had the smallest p-value (p = 5.6×10−8). NOX significantly associated with cg11756214 within ZNF347 (p = 5.6×10−8). Several differentially methylated signals were also associated with cis-gene expression. The DMR located on chromosome 7 was associated with the expression of HOXA5, HOXA9, and HOXA10. The DMRs located on chromosomes 5 and 16 were associated with expression of MRPL36 and DEXI, respectively. The CpG cg05926640 was associated with expression of ARID4B, IRF2BP2, and TOMM20. We identified differential DNA methylation in monocytes associated with long-term air pollution exposure. Methylation signals associated with gene expression might help explain how air pollution contributes to cardiovascular disease.

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

Background: Exposure to household air pollution (HAP) from cooking with biomass fuel affects billions of people. We hypothesized that HAP from woodsmoke, compared to other household fuels, was associated with adverse cardiovascular outcomes, of which there have been few studies. Methods: A cross-sectional study was completed in 299 females aged 40–70 years in Kaski District, Nepal, during 2017–18. All participants underwent a standard 12-lead ECG, ankle and brachial systolic blood pressure measurement, and 2D color and Doppler echocardiography. Current stove type was confirmed by inspection. Blood pressure, height, and weight were measured using a standardized protocol. Hypertension was defined as ≥140/90 mmHg or prior diagnosis. Hemoglobin A1c (HbA1c) was obtained, with diabetes mellitus defined as a prior diagnosis or HbA1C ≥ 6.5%. We used adjusted linear and logistic multivariable regressions to examine the relationship of stove type with cardiac structure and function. Results: The majority of women primarily used liquified petroleum gas (LPG) stoves (65%), while 12% used biogas, and 23% used wood-burning cook-stoves. Prevalence of major cardiovascular risk factors was 35% with hypertension, 19% with diabetes mellitus, and 15% current smokers. After adjustment, compared to LPG, wood stove use was associated with increased indexed left atrial volume (β = 3.15, 95% CI 1.22 to 5.09) and increased indexed left ventricular end diastolic volume (β = 7.97, 95% CI 3.11 to 12.83). There was no association between stove type and systemic hypertension, left ventricular mass, systolic dysfunction, diastolic dysfunction, pulmonary hypertension, abnormal ankle-brachial index, or clinically significant ECG abnormalities. Conclusion: Biomass fuel use was associated with increased indexed left atrial volume and increased indexed left ventricular diastolic volume in Nepali women, suggesting subclinical adverse cardiac remodeling from HAP in this cross-sectional study. We did not find evidence of an association with hypertension or typical cardiac sequelae of hypertension. Future studies to confirm these results are needed.

Background Arsenic‐related cardiovascular effects at exposure levels below the US Environmental Protection Agency's standard of 10 μg/L are unclear. For these populations, food, especially rice, is a major source of exposure. We investigated associations of rice intake, a marker of arsenic exposure, with subclinical cardiovascular disease (CVD) markers in a multiethnic population. Methods and Results Between 2000 and 2002, MESA (Multi‐Ethnic Study of Atherosclerosis) enrolled 6814 adults without clinical CVD. We included 5050 participants with baseline data on rice intake and markers of 3 CVD domains: inflammation (hsCRP [high‐sensitivity C‐reactive protein], interleukin‐6, and fibrinogen), vascular function (aortic distensibility, carotid distensibility, and brachial flow‐mediated dilation), and subclinical atherosclerosis at 3 vascular sites (carotid intima‐media thickness, coronary artery calcification, and ankle‐brachial index). We also evaluated endothelial‐related biomarkers previously associated with arsenic. Rice intake was assessed by food frequency questionnaire. Urinary arsenic was measured in 310 participants. A total of 13% of participants consumed ≥1 serving of rice/day. Compared with individuals consuming

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

Abstract Background Long-term exposure to high ambient air pollution has been associated with coronary artery calcium (CAC), a marker of cardiovascular disease (CVD). Calcifications of left-sided heart valves are also markers of CVD risk. We investigated whether air pollution was associated with valvular calcification and its progression. Methods We studied 6253 MESA participants aged 45–84 years who underwent two cardiac CT scans 2.5 years apart to quantify aortic valve calcium (AVC) and mitral annular calcium (MAC). CAC was included for the same timeframe for comparison with AVC/MAC. Ambient 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.

We designed and built a network of monitors for ambient air pollution equipped with low-cost gas sensors to be used to supplement regulatory agency monitoring for exposure assessment within a large epidemiological study. This paper describes the development of a series of hourly and daily field calibration models for Alphasense sensors for carbon monoxide (CO; CO-B4), nitric oxide (NO; NO-B4), nitrogen dioxide (NO2; NO2-B43F), and oxidizing gases (OX-B431)—which refers to ozone (O3) and NO2. The monitor network was deployed in the Puget Sound region of Washington, USA, from May 2017 to March 2019. Monitors were rotated throughout the region, including at two Puget Sound Clean Air Agency monitoring sites for calibration purposes, and over 100 residences, including the homes of epidemiological study participants, with the goal of improving long-term pollutant exposure predictions at participant locations. Calibration models improved when accounting for individual sensor performance, ambient temperature and humidity, and concentrations of co-pollutants as measured by other low-cost sensors in the monitors. Predictions from the final daily models for CO and NO performed the best considering agreement with regulatory monitors in cross-validated root-mean-square error (RMSE) and R2 measures (CO: RMSE = 18 ppb, R2 = 0.97; NO: RMSE = 2 ppb, R2 = 0.97). Performance measures for NO2 and O3 were somewhat lower (NO2: RMSE = 3 ppb, R2 = 0.79; O3: RMSE = 4 ppb, R2 = 0.81). These high levels of calibration performance add confidence that low-cost sensor measurements collected at the homes of epidemiological study participants can be integrated into spatiotemporal models of pollutant concentrations, improving exposure assessment for epidemiological inference.

The molecular mechanisms mediating the impact of environmental factors in atherosclerosis are unclear. Here, the authors examine CD14+ blood monocyte’s transcriptome and epigenome signatures to find differential methylation and expression of ARID5B to be associated with human atherosclerosis.

Abstract Background We conducted a genome-wide association study (GWAS) of subclinical interstitial lung disease (ILD), defined as high attenuation areas (HAA) on CT, in the population-based Multi-Ethnic Study of Atherosclerosis Study. Methods We measured the percentage of high attenuation areas (HAA) in the lung fields on cardiac CT scan defined as voxels with CT attenuation values between -600 and -250 HU. Genetic analyses were performed in MESA combined across race/ethnic groups: non-Hispanic White (n = 2,434), African American (n = 2,470), Hispanic (n = 2,065) and Chinese (n = 702), as well as stratified by race/ethnicity. Results Among 7,671 participants, regions at genome-wide significance were identified for basilar peel-core ratio of HAA in FLJ35282 downstream of ANRIL (rs7852363, P = 2.1x10−9) and within introns of SNAI3-AS1 (rs140142658, P = 9.6x10−9) and D21S2088E (rs3079677, P = 2.3x10−8). Within race/ethnic groups, 18 additional loci were identified at genome-wide significance, including genes related to development (FOXP4), cell adhesion (ALCAM) and glycosylation (GNPDA2, GYPC, GFPT1 and FUT10). Among these loci, SNP rs6844387 near GNPDA2 demonstrated nominal evidence of replication in analysis of n = 1,959 participants from the Framingham Heart Study (P = 0.029). FOXP4 region SNP rs2894439 demonstrated evidence of validation in analysis of n = 228 White ILD cases from the Columbia ILD Study compared to race/ethnicity-matched controls from MESA (one-sided P = 0.007). In lung tissue from 15 adults with idiopathic pulmonary fibrosis compared to 15 adults without lung disease. ANRIL (P = 0.001), ALCAM (P = 0.03) and FOXP4 (P = 0.046) were differentially expressed. Conclusions Our results suggest novel roles for protein glycosylation and cell cycle disinhibition by long non-coding RNA in the pathogenesis of ILD.

Background We hypothesized that measures of common carotid artery echolucency and grayscale texture features were associated with cardiovascular disease (CVD) risk factors and could predict CVD events. Methods and Results Using a case‐cohort design, we measured common carotid artery ultrasound images from 1788 participants in Exam 1 of the MESA study (Multi‐Ethnic Study of Atherosclerosis) to derive 4 grayscale features: grayscale median, entropy, gray level difference statistic‐contrast, and spatial gray level dependence matrices‐angular second moment. CVD risk factor associations were determined by linear regression. Cox proportional hazard models with inverse selection probability weighting and adjustments for age, sex, race/ethnicity, CVD risk factors, and C‐reactive protein were used to determine if standardized values for grayscale median, entropy, gray level difference statistic‐contrast, and spatial gray level dependence matrices‐angular second moment could predict incident coronary heart disease, stroke, and total CVD events over a median 13 years follow‐up. Participants were mean (SD) 63.1 (10.3) years of age, 52.6% female, 32.1% white, 27.8% black, 23.3% Hispanic, and 16.8% Chinese. There were 283 coronary heart disease, 120 stroke, and 416 CVD events. Several associations of grayscale features with CVD risk factors were identified. In fully adjusted models, higher gray level difference statistic‐contrast was associated with a lower risk of incident coronary heart disease (hazard ratio 0.82, 95% CI 0.71–0.94, padj=0.005) and CVD events (hazard ratio 0.87, 95% CI 0.77–0.98, padj=0.018); higher spatial gray level dependence matrices‐angular second moment was associated with a higher risk of CVD events (hazard ratio 1.09, 95% CI 1.00–1.19, padj=0.044). Conclusions Gray level difference statistic‐contrast and spatial gray level dependence matrices‐angular second moment predicted CVD events independent of risk factors, indicating their potential use as biomarkers to assess future CVD risk.

BackgroundFew studies have evaluated the association between secondhand smoke (SHS) and subclinical cardiovascular disease among ethnically diverse populations. This study assesses the impact of SHS on inflammation and atherosclerosis (carotid intima‐media thickness, coronary artery calcification, and peripheral arterial disease). Methods and ResultsWe examined 5032 nonsmoking adults aged 45 to 84 years without prior cardiovascular disease participating in the Multi‐Ethnic Study of Atherosclerosis (MESA) from 2000 to 2002. SHS exposure was determined by self‐report, and urinary cotinine was measured in a representative subset (n=2893). The multi‐adjusted geometric mean ratios (95% CIs) for high‐sensitivity C‐reactive protein and interleukin‐6 comparing 407 participants with SHS ≥12 h/wk versus 3035 unexposed participants were 1.13 (1.02–1.26) and 1.04 (0.98–1.11), respectively. The multi‐adjusted geometric mean ratio for carotid intima‐media thickness was 1.02 (0.97–1.07). Fibrinogen and coronary artery calcification were not associated with SHS. The prevalence of peripheral arterial disease (ankle‐brachial index ≤0.9 or ≥1.4) was associated with detectable urinary cotinine (odds ratio, 2.10; 95% CI, 1.09–4.04) but not with self‐reported SHS. Urinary cotinine was not associated with inflammation or carotid intima‐media thickness. ConclusionsDespite limited exposure assessment, this study supports the association of SHS exposure with inflammation and peripheral arterial disease.

BackgroundRecent studies have failed to establish a causal relationship between high‐density lipoprotein cholesterol levels (HDL‐C) and cardiovascular disease (CVD), shifting focus to other HDL measures. We previously reported that smaller/denser HDL levels are protective against cerebrovascular disease. This study sought to determine which of small+medium HDL particle concentration (HDL‐P) or large HDL‐P was more strongly associated with carotid intima‐media thickening (cIMT) in an ethnically diverse cohort. Methods and ResultsIn cross‐sectional analyses of participants from the Multi Ethnic Study of Atherosclerosis (MESA), we evaluated the associations of nuclear magnetic resonance spectroscopy–measured small+medium versus large HDL‐P with cIMT measured in the common and internal carotid arteries, through linear regression. After adjustment for CVD confounders, low‐density lipoprotein cholesterol (LDL‐C), HDL‐C, and small+medium HDL‐P remained significantly and inversely associated with common (coefficient=−1.46 μm; P=0.00037; n=6512) and internal cIMT (coefficient=−3.82 μm; P=0.0051; n=6418) after Bonferroni correction for 4 independent tests (threshold for significance=0.0125; α=0.05/4). Large HDL‐P was significantly and inversely associated with both cIMT outcomes before HDL‐C adjustment; however, after adjustment for HDL‐C, the association of large HDL‐P with both common (coefficient=1.55 μm; P=0.30; n=6512) and internal cIMT (coefficient=4.84 μm; P=0.33; n=6418) was attenuated. In a separate sample of 126 men, small/medium HDL‐P was more strongly correlated with paraoxonase 1 activity (rp=0.32; P=0.00023) as compared to both total HDL‐P (rp=0.27; P=0.0024) and large HDL‐P (rp=0.02; P=0.41) measures. ConclusionsSmall+medium HDL‐P is significantly and inversely correlated with cIMT measurements. Correlation of small+medium HDL‐P with cardioprotective paraoxonase 1 activity may reflect a functional aspect of HDL responsible for this finding.