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Background: Hypertensive disorders of pregnancy (HDP) such as preeclampsia and gestational hypertension are major contributors to maternal and child morbidity and mortality. Previous studies have reported associations with selected metals and vitamins but are limited in sample size and non-prospective study designs. We evaluated prospective associations of metal mixtures with HDP and tested interactions by vitamins. Study Design: We measured first trimester (median = 10.1 weeks) concentrations of essential (copper, magnesium, manganese, selenium, zinc) and nonessential (arsenic, barium, cadmium, cesium, mercury, lead) metals in red blood cells (n = 1,386) and vitamins (B12 and folate) in plasma (n = 924) in Project Viva, a pre-birth US cohort. We collected diagnosis of HDP by reviewing medical records. We used multinomial logistic regression and Bayesian Kernel Machine Regression to estimate individual and joint associations of metals with HDP and interactions by vitamins, after adjusting for key covariates. Results: The majority of participants were non-Hispanic white (72.5 %), never smokers (68.5 %) with a mean (SD) age of 32.3 (4.6) years. Fifty-two (3.8 %) developed preeclampsia and 94 (6.8 %) gestational hypertension. A doubling in first trimester erythrocyte copper was associated with 78 % lower odds of preeclampsia (OR=0.22, 95 % confidence interval: 0.08, 0.60). We also observed significant associations between higher erythrocyte total arsenic and lower odds of preeclampsia (OR=0.80, 95 % CI: 0.66, 0.97) and higher vitamin B12 and increased odds of gestational hypertension (OR=1.79, 95 % CI: 1.09, 2.96), but associations were attenuated after adjustment for dietary factors. Lower levels of the overall metal mixture and essential metal mixture were associated with higher odds of preeclampsia. We found no evidence of interactions by prenatal vitamins or between metals. Conclusion: Lower levels of a first-trimester essential metal mixture were associated with an increased risk of preeclampsia, primarily driven by copper. No associations were observed between other metals and HDP after adjustment for confounders and diet.

Background: Studies show that changes in solar and geomagnetic activity (SGA) influence melatonin secretion and the autonomic nervous system. We evaluated associations between solar and geomagnetic activity and cognitive function in the Normative Aging Study from 1992 to 2013. Methods: We used logistic and linear generalized estimating equations and regressions to evaluate the associations between moving averages of sunspot number (SSN) and Kp index (a measure of geomagnetic activity) and a binary measure for Mini-Mental State Examination (MMSE) scores (≤25 or > 25) and six other cognitive tests as continuous measures, combined into one global composite score and considered separately. Results: A one-IQR increase in same-day SSN and Kp index were associated with 17% (95% CI: 3%, 34%) and 19% (95% CI: 4%, 36%) increases in the odds of low MMSE score. We observed small increases in the global cognitive score with increasing SSN, although we observed decreases specifically in relation to the backwards digit span test. Conclusions: Periods of high SGA were associated with cognitive function. SGA may not equally impact all aspects of cognitive function, as evidenced by differences in associations observed for the MMSE, global cognitive score, and individual cognitive tests. Given that much of the pathology of cognitive decline in the elderly remains unexplained, studies specifically targeting decline and with longer follow-up periods are warranted.

Background: Air pollution is a recognized risk factor for cardiovascular disease (CVD). Temperature is also linked to CVD, with a primary focus on acute effects. Despite the close relationship between air pollution and temperature, their health effects are often examined separately, potentially overlooking their synergistic effects. Moreover, fewer studies have performed mixture analysis for multiple co-exposures, essential for adjusting confounding effects among them and assessing both cumulative and individual effects. Methods: We obtained hospitalization records for residents of 14 U.S. states, spanning 2000–2016, from the Health Cost and Utilization Project State Inpatient Databases. We used a grouped weighted quantile sum regression, a novel approach for mixture analysis, to simultaneously evaluate cumulative and individual associations of annual exposures to four grouped mixtures: air pollutants (elemental carbon, ammonium, nitrate, organic carbon, sulfate, nitrogen dioxide, ozone), differences between summer and winter temperature means and their long-term averages during the entire study period (i.e., summer and winter temperature mean anomalies), differences between summer and winter temperature standard deviations (SD) and their long-term averages during the entire study period (i.e., summer and winter temperature SD anomalies), and interaction terms between air pollutants and summer and winter temperature mean anomalies. The outcomes are hospitalization rates for four prevalent CVD subtypes: ischemic heart disease, cerebrovascular disease, heart failure, and arrhythmia. Results: Chronic exposure to air pollutant mixtures was associated with increased hospitalization rates for all CVD subtypes, with heart failure being the most susceptible subtype. Sulfate, nitrate, nitrogen dioxide, and organic carbon posed the highest risks. Mixtures of the interaction terms between air pollutants and temperature mean anomalies were associated with increased hospitalization rates for all CVD subtypes. Conclusions: Our findings identified critical pollutants for targeted emission controls and suggested that abnormal temperature changes chronically affected cardiovascular health by interacting with air pollution, not directly.

Background: Several epigenome-wide association studies (EWAS) of ambient particulate matter with aerodynamic diameter ≤ 2.5 µm (PM2.5) have been reported. However, EWAS of PM2.5 elements (PEs), reflecting different emission sources, are very limited. Objectives: We performed EWAS of short- and intermediate-term exposure to PM2.5 and 13 PEs. We hypothesized that significant changes in DNAm may vary by PM2.5 mass and its elements. Methods: We repeatedly collected blood samples in the Normative Aging Study and measured leukocyte DNA methylation (DNAm) with the Illumina HumanMethylation450K BeadChip. We collected daily PM2.5 and 13 PEs at a fixed central site. To estimate the associations between each PE and DNAm at individual cytosine-phosphate-guanine (CpG) sites, we incorporated a distributed-lag (0–27 d) term in the setting of median regression with subject-specific intercept and examined cumulative lag associations. We also accounted for selection bias due to loss to follow-up and mortality prior to enrollment. Significantly differentially methylated probes (DMPs) were identified using Bonferroni correction for multiple testing. We further conducted regional and pathway analyses to identify significantly differentially methylated regions (DMRs) and pathways. Results: We included 695 men with 1,266 visits between 1999 and 2013. The subjects had a mean age of 75 years. The significant DMPs, DMRs, and pathways varied by to PM2.5 total mass and PEs. For example, PM2.5 total mass was associated with 2,717 DMPs and 10,470 DMRs whereas Pb was associated with 3,173 DMPs and 637 DMRs. The identified pathways by PM2.5 mass were mostly involved in mood disorders, neuroplasticity, immunity, and inflammation, whereas the pathways associated with motor vehicles (BC, Cu, Pb, and Zn) were related with cardiovascular disease and cancer (e.g., “PPARs signaling”). Conclusions: PM2.5 and PE were associated with methylation changes at multiple probes and along multiple pathways, in ways that varied by particle components.

Background/Aims: Early life exposures to marine contaminants can adversely impact child health but modes of action are unclear. Human milk contains extracellular vesicles (EVs) that can transport biologically relevant cargo from mother to infant, including microRNAs (miRNAs), and may partly mediate the effects of pollutants on child health. However, the role of marine pollutants on miRNA expression in milk EVs is unexplored. Methods: We isolated EV RNA from 333 milk samples collected between 2 and 74 days postpartum from a Faroese birth cohort born 1997–2000 and sequenced 2083 miRNAs using a targeted library preparation method. We quantified five perfluoroalkyl substances (PFAS), pesticide metabolite p,p’-dichlorodiphenyldichloroethylene (DDE), and the sum of three major polychlorinated biphenyls (ΣPCBs) in maternal serum at 34 weeks of gestation and maternal hair total mercury (Hg) at birth. We used negative binomial regressions to estimate associations between individual pollutants and 418 reliably expressed EV-miRNAs adjusted for potential confounders. We performed sparse principal components (PCs) analysis to derive the first four components of the EV-miRNA data and examined associations between pollutants and PCs using Bayesian kernel machine regression (BKMR). Results: We observed no associations between pollutants and individual EV-miRNA expression after controlling the false discovery rate at 0.1. However, BKMR suggested that Hg was positively associated with PC1 and negatively associated with PC3, while ΣPCBs was negatively associated with PC3, and two PFAS were associated with PC4. Exploration of PC loadings followed by pathway analyses suggested that miRNAs in PC1 (miR-200b-3p, miR-664a-3p, miR-6738-5p, miR-429, miR-1236-5p, miR-4464, and miR-30b-5p) may be related to Hg neurotoxicity, while remaining PCs require further research. Conclusions: Our findings suggest that groups of milk EV-miRNAs may better serve as environmental biomarkers than individual miRNAs. Future studies are needed to elucidate the role of milk EV-miRNAs in child health following prenatal exposures.

Background: Prenatal exposure to metals has been individually associated with birth outcomes. However, little is known about the effect of metal mixture, particularly at low exposure levels. Objectives: To estimate individual and joint effects of metal mixture components on birth outcomes. Methods: We used data from 1,391 mother-infant pairs in Project Viva (1999–2002). We measured 11 metals in maternal 1st trimester erythrocyte; abstracted birth weight from medical records; calculated gestational age from last menstrual period or ultrasound; and obtained birth length (n = 729) and head circumference (n = 791) from research measurements. We estimated individual and joint effects of metals using multivariable linear and Bayesian kernel machine regressions. Results: In both single metal and metal mixture analyses, exposure to higher concentrations of arsenic was associated with lower birth weight in males, zinc with higher head circumference in females, and manganese with higher birth length in sex-combined analysis. We also observed sex-specific metal interactions with birth outcomes. Arsenic and manganese showed a synergistic association with birth weight in males, in whom an interquartile range (IQR) increase in arsenic was associated with 25.3 g (95% CI: −79.9, 29.3), 47.9 g (95% CI: −98.0, 2.1), and 72.2 g (95% CI: −129.8, −14.7) lower birth weight when manganese concentrations were at 25th, 50th, and 75th percentiles, respectively. Lead and zinc showed an antagonistic association with head circumference in males, where an IQR increase in lead was associated with 0.18 cm (95% CI: −0.35, −0.02), 0.10 cm (95% CI: −0.25, 0.04), 0.03 cm (95% CI: −0.2, 0.14) smaller head circumference when zinc concentrations were at 25th, 50th, and 75th percentiles, respectively. Exposure to higher concentrations of arsenic was also associated with lower gestational age in males when concentrations of manganese and lead were higher. Discussion: Maternal erythrocyte concentrations of arsenic, manganese, lead, and zinc were individually and interactively associated with birth outcomes. The associations varied by infant sex and exposure level of other mixture components.

Little is known about chemical contaminant exposures of office workers in buildings globally. Complex mixtures of harmful chemicals accumulate indoors from building materials, building maintenance, personal products, and outdoor pollution. We evaluated exposures to 99 chemicals in urban office buildings in the USA, UK, China, and India using silicone wristbands worn by 251 participants while they were at work. Here, we report concentrations of polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs) and other brominated flame retardants (BFRs), organophosphate esters (OPEs), phthalates and phthalate alternatives, pesticides, and polycyclic aromatic hydrocarbons (PAHs). First, we found major differences in office worker chemical exposures by country, some of which can be explained by regulations and use patterns. For example, exposures to several pesticides were substantially higher in India where there were fewer restrictions and unique malaria challenges, and exposures to flame retardants tended to be higher in the USA and UK where there were historic, stringent furniture flammability standards. Higher exposures to PAHs in China and India could be due to high levels of outdoor air pollution that penetrates indoors. Second, some office workers were still exposed to legacy PCBs, PBDEs, and pesticides, even decades after bans or phase-outs. Third, we identified exposure to a contemporary PCB that is not covered under legacy PCB bans due to its presence as an unintentional byproduct in materials. Fourth, exposures to novel BFRs, OPEs, and other chemicals commonly used as substitutes to previously phased-out chemicals were ubiquitous. Fifth, some exposures were influenced by individual factors, not just countries and buildings. Phthalate exposures, for example, were related to personal care product use, country restrictions, and building materials. Overall, we found substantial country differences in chemical exposures and continued exposures to legacy phased-out chemicals and their substitutes in buildings. These findings warrant further research on the role of chemicals in office buildings on worker health.

Rationale: PM2.5-induced adverse effects on respiratory health may be driven by epigenetic modifications in airway cells. The potential impact of exposure duration on epigenetic alterations in the airways is not yet known. Objectives: We aimed to study associations of fine particulate matter PM2.5 exposure with DNA methylation in nasal cells. Methods: We conducted nasal epigenome-wide association analyses within 503 children from Project Viva (mean age 12.9 y), and examined various exposure durations (1-day, 1-week, 1-month, 3-months and 1-year) prior to nasal sampling. We used residential addresses to estimate average daily PM2.5 at 1 km resolution. We collected nasal swabs from the anterior nares and measured DNA methylation (DNAm) using the Illumina MethylationEPIC BeadChip. We tested 719,075 high quality autosomal CpGs using CpG-by-CpG and regional DNAm analyses controlling for multiple comparisons, and adjusted for maternal education, household smokers, child sex, race/ethnicity, BMI z-score, age, season at sample collection and cell-type heterogeneity. We further corrected for bias and genomic inflation. We tested for replication in a cohort from the Netherlands (PIAMA). Results: In adjusted analyses, we found 362 CpGs associated with 1-year PM2.5 (FDR

Background: Exploring the associations of air pollution and weather variables with blood leukocyte distribution is critical to understand the impacts of environmental exposures on the human immune system. Objectives: As previous analyses have been mainly based on data from cell counters, which might not be feasible in epidemiologic studies including large populations of long-stored blood samples, we aimed to expand the understanding of this topic by employing the leukocyte distribution estimated by DNA methylation profiles. Methods: We measured DNA methylation profiles in blood samples using Illumina HumanMethylation450 BeadChip from 1519 visits of 774 Caucasian males participating in the Normative Aging Study. Leukocyte distribution was estimated using Houseman's and Horvath's algorithms. Data on air pollution exposure, temperature, and relative humidity within 28 days before each blood draw was obtained. Results: After fully adjusting for potential covariates, PM2.5, black carbon, particle number, carbon monoxide, nitrogen dioxide, sulfur dioxide, temperature, and relative humidity were associated with the proportions of at least one subtype of leukocytes. Particularly, an interquartile range-higher 28-day average exposure of PM2.5 was associated with 0.147-, 0.054- and 0.101-unit lower proportions (z-scored) of plasma cells, naïve CD8+ T cells, and natural killers, respectively, and 0.059- and 0.161-unit higher proportions (z-scored) of naïve CD4+ T cells and CD8+ T cells, respectively. Conclusions: Our study suggests that short-term air pollution exposure, temperature, and relative humidity are associated with leukocyte distribution. Our study further provides a successful attempt to use epigenetic patterns to assess the influences of environmental exposures on human immune profiles. Keywords: Air pollution, Weather variations, DNA methylation, Leukocyte distribution, Epigenetic epidemiology, Environmental health

Background: Single nucleotide polymorphisms (SNPs) may influence arsenic methylation efficiency, affecting arsenic metabolism. Whether gene-environment interactions affect arsenic metabolism during pregnancy remains unclear, which may have implications for pregnancy outcomes. Objective: We aimed to investigate main effects as well as potential SNP-arsenic interactions on arsenic methylation efficiency in pregnant women. Method: We recruited 1613 pregnant women in Bangladesh, and collected two urine samples from each participant, one at 4–16 weeks, and the second at 21–37 weeks of pregnancy. We determined the proportions of each arsenic metabolite [inorganic As (iAs)%, monomethylarsonic acid (MMA)%, and dimethylarsinic acid (DMA)%] from the total urinary arsenic level of each sample. A panel of 63 candidate SNPs was selected for genotyping based on their reported associations with arsenic metabolism (including in As3MT, N6AMT1, and GSTO2 genes). We used linear regression models to assess the association between each SNP and DMA% with an additive allelic assumption, as well as SNP-arsenic interaction on DMA%. These analyses were performed separately for two urine collection time-points to capture differences in susceptibility to arsenic toxicity. Result: Intron variants for As3MT were associated with DMA%. rs9527 (β = −2.98%, PFDR = 0.008) and rs1046778 (β = 1.64%, PFDR = 0.008) were associated with this measure in the early gestational period; rs3740393 (β = 2.54%, PFDR = 0.002) and rs1046778 (β = 1.97%, PFDR = 0.003) in the mid-to-late gestational period. Further, As3MT, GSTO2, and N6AMT1 polymorphisms showed different effect sizes on DMA% conditional on arsenic exposure levels. However, SNP-arsenic interactions were not statistically significant after adjusting for false discovery rate (FDR). rs1048546 in N6AMT1 had the highest significance level in the SNP-arsenic interaction test during mid-to-late gestation (β = −1.8% vs. 1.4%, PGxE_FDR = 0.075). Finally, As3MT and As3MT/CNNM2 haplotypes were associated with DMA% at both time points. Conclusion: We found that not all genetic associations reported in arsenic methylation efficiency replicate in pregnant women. Arsenic exposure level has a limited effect in modifying the association between genetic variation and arsenic methylation efficiency. Keywords: Arsenic exposure, Arsenic methylation efficiency, Pregnancy, Single nucleotide polymorphism

Background: Decay products of radioactive materials may attach to ambient fine particles and form radioactive aerosol. Internal ionizing radiation source from inhaled radioactive aerosol may contribute to the fine particulate matter (PM2.5)-inflammation pathway. However, few studies in humans have examined the associations. Objectives: To examine the associations between particle radioactivity and biomarkers of oxidative stress and inflammation among participants from the Framingham Offspring and Third Generation cohorts. Methods: We included 3996 participants who were not current smokers and lived within 50 km from our central air pollution monitoring station. We estimated regional mean gross beta radioactivity from monitors in the northeastern U.S. as a surrogate for ambient radioactive particles, and calculated the 1- to 28-day moving averages. We used linear regression models for fibrinogen, tumor necrosis factor α, interleukin-6, and myeloperoxidase which were measured once, and linear mixed effect models for 8-epi-prostaglandin F2α, C-reactive protein, intercellular adhesion molecule-1 (ICAM-1), monocyte chemoattractant protein-1 (MCP-1), P-selectin, and tumor necrosis factor receptor-2 that were measured up to twice, adjusting for demographics, individual- and area-level socioeconomic positions, time, meteorology, and PM2.5. We also examined whether the associations differed by median age, sex, diabetes status, PM2.5 levels, and black carbon levels. Results: The mean age was 54 years and 54% were women. An interquartile range (3 × 10−3 pCi/m3) higher beta radioactivity level at the 7-day moving average was associated with 5.09% (95% CI: 0.92, 9.43), 2.65% (1.10, 4.22), and 4.71% (95% CI: 3.01, 6.44) higher levels of interleukin-6, MCP-1, and P-selectin, but with 7.01% (95% CI: −11.64, −2.15) and 2.70% (95% CI: −3.97, −1.42) lower levels of 8-epi-prostaglandin F2α and ICAM-1, respectively. Conclusions: Regional mean particle radioactivity was positively associated with interleukin-6, MCP-1, and P-selectin, but negatively with ICAM-1 and 8-epi-prostaglandin F2α among our study participants. Keywords: Gross beta radiation, Particle radioactivity, Epidemiology, Environment, Inflammation, Oxidative stress

Per- and polyfluoroalkyl substances (PFAS), polybrominated diphenyl ethers (PBDEs), and organophosphate esters (OPEs) are found in building materials and associated with thyroid disease, infertility, and impaired development. This study’s objectives were to (1) compare levels of PFAS, PBDEs, and OPEs in dust from spaces with conventional versus “healthier” furniture and carpet, and (2) identify other product sources of flame retardants in situ. We measured 15 PFAS, 8 PBDEs, and 19 OPEs in dust from offices, common areas, and classrooms having undergone either no intervention (conventional rooms in older buildings meeting strict fire codes; n = 12), full “healthier” materials interventions (rooms with “healthier” materials in buildings constructed more recently or gut-renovated; n = 7), or partial interventions (other rooms with at least “healthier” foam furniture but more potential building contamination; n = 28). We also scanned all materials for bromine and phosphorus as surrogates of PBDEs and OPEs respectively, using x-ray fluorescence. In multilevel regression models, rooms with full “healthier” materials interventions had 78% lower dust levels of PFAS than rooms with no intervention (p

Every day humans are exposed to mixtures of chemicals, such as lead (Pb) and manganese (Mn). An underappreciated aspect of studying the health effects of mixtures is the role that the exposure biomarker media (blood, hair, etc.) may play in estimating the effects of the mixture. Different biomarker media represent different aspects of each chemical’s toxicokinetics, thus no single medium can fully capture the toxicokinetic profile for all the chemicals in a mixture. A potential solution to this problem is to combine exposure data across different media to derive integrated estimates of each chemical’s internal concentration. This concept, formalized as a multi-media biomarker (MMB) has proven effective for estimating the health impacts of Pb exposure, but may also be useful to estimate mixture effects, such as the joint effects of metals like Pb and Mn, while factoring in how the association changes based upon the biomarker media. Levels of Pb and Mn were quantified in five media: blood, hair, nails, urine, and saliva in the Public Health Impact of Metals Exposure (PHIME) project, a study of Italian adolescents aged 10–14 years. MMBs were derived for both metals using weighted quantile sum (WQS) regression across the five media. Age-adjusted Wechsler Intelligence Scale for Children (WISC) IQ scores, measured at the same time as the exposure measures, were the primary outcome and models were adjusted for sex and socioeconomic status. The levels Pb and Mn were relatively low, with median blood Pb of 1.27 (IQR: 0.84) μg/dL and median blood Mn of 1.09 (IQR: 0.45) μg/dL. Quartile increases in a Pb-Mn combination predicted decreased Full Scale IQ of 1.9 points (95% CI: 0.3, 3.5) when Pb and Mn exposure levels were estimated using MMBs, while individual regressions for each metal were not associated with Full Scale IQ. Additionally, a quartile increase in the WQS index of Pb and Mn, measured using MMBs, were associated with reductions in Verbal IQ by 2.8 points (1.0, 4.5). Weights that determine the contributions of the metals to the joint effect highlighted that the contribution of the Pb-Mn was 72–28% for Full Scale IQ and 42–58% for Verbal IQ. We found that the joint effects of Pb and Mn are strongly affected by the medium used to measure exposure and that the joint effects of the Pb and Mn MMBs on cognition were the stronger than any individual biomarker. Thus, increase power and accuracy for measuring mixture effects compared to individual biomarkers. As the number of chemicals in mixtures increases, appropriate biomarker selection will become increasingly important and MMBs are a natural way to reduce bias in such analyses.

Background: Whole-body and thoracic ionizing radiation exposure are both associated with the development of renal dysfunction. However, whether low-level environmental radiation from air pollution affects renal function remains unknown. Objectives: We investigated the association of particle radioactivity (PR) with renal function defined by the estimated glomerular filtration rate (eGFR) and chronic kidney disease (CKD) in the Normative Aging Study. Methods: This longitudinal analysis included 2491 medical visits from 809 white males enrolled between 1999 and 2013. The eGFR was calculated using the CKD-EPI and MDRD equations, and CKD cases were identified as those with an eGFR

Weather impacts on the chemical composition of PM2.5 varies significantly over space and time given the diversity of particle components and the complex mechanisms governing particle formation and removal. In this study, we employed generalized additive models (GAMs) to estimate weather-associated changes in PM2.5 composition in the US during 1988–2017. We considered seven components of ambient PM2.5, which included elemental carbon (EC), organic carbon (OC), nitrate, sulfate, sodium, ammonium, and silicon. The impact of long-term weather changes on each PM2.5 component was defined in our study as “weather penalty”. During our study period, temperature increased in four regions, including the Industrial Midwest and Northeast during the warm and cold season; and Upper Midwest and West in the cold season. Wind speed decreased in the both seasons. Relative humidity increased in the warm season and decreased in the cold season. The weather changes between 1988 and 2017 were associated with most PM2.5 components during both warm and cold seasons. The direction and the magnitude of the weather penalty varied considerably over the space and season. In the warm season, our findings suggest a nationwide weather penalty for EC, OC, nitrate, sulfate, sodium, ammonium, and silicon of 0.04, 0.21, 0.04, 0.35, −0.01, 0.05, and 0.01 μg/m3, respectively. In the cold season, the estimated total penalty was 0.04, 0.21, 0.06, 0.04, −0.01, −0.02, and 0.02 μg/m3, respectively. Keywords: Air pollution, PM2.5 speciation, Weather, Trend analysis

Background: Radon is an inert gas formed from the decay of naturally-occurring materials in the earth's crust. It infiltrates into homes from soil, water, and construction materials. Its decay products are radionuclides, which attach to ambient particles. Residential radon is one of the leading risk factors for lung cancer. The scarce evidence for associations with other mortality causes originates mostly from occupational studies. Methods: In a cohort study with 14 years of follow-up (2000−2013), we evaluated the association between chronic radon exposure and all-cause mortality, and explored whether there are subpopulations who are more vulnerable to radon effects. We included 87,296,195 person-years of follow-up from all Medicare beneficiaries in the Mid-Atlantic and Northeastern U.S. states. We examined the association between the logarithm of county-averaged radon (ln(Rn)) and mortality and assessed effect modification by chronic conditions. Results: An interquartile range increase in the ln(Rn) was associated with a 2·62% increase (95% CI 2·52%; 2·73%) in mortality, independent of PM2.5 exposure. Larger mortality risks were observed among individuals with respiratory, cardiovascular and metabolic diseases, with the highest associations observed among those with diabetes (4·98% increase), heart failure (4·58% increase), and chronic obstructive pulmonary disease (4·49% increase). Conclusion: We found an increased risk for all-cause mortality associated with increased radon exposure. The risk was enhanced among susceptible individuals with chronic conditions. We believe this is the first cohort study to identify populations at higher risk for non-malignant health consequences of radon exposure. Due to the limitations in exposure assessment and availability of individual confounders, these findings should be interpreted with caution. Keywords: Radon, Radiation, Mortality, PM2.5

Previous studies have suggested increased risk of respiratory diseases and mortality following short-term exposures to ionizing radiation. However, the short-term respiratory effects of low-level environmental radiation associated with air pollution particles have not been considered. Although ambient particulate matter (PM) has been reproducibly linked to decreased lung function and to increased respiratory related morbidity, the properties of PM promoting its toxicity are uncertain. As such, we evaluated whether lung function was associated with exposures to radioactive components of ambient PM, referred to as particle radioactivity (PR). For this, we performed a repeated-measures analysis of 839 men to examine associations between PR exposure and lung function using mixed-effects regression models, adjusting for potential confounders. We examined whether PR-lung function associations changed after adjusting for PM2.5 (particulate matter≤2.5 μm) or black carbon, and vice versa. PR was measured by the USEPA's radiation monitoring network. We found that higher PR exposure was associated with a lower forced vital capacity (FVC) and forced expiratory volume in 1 second (FEV1). An IQR increase in 28-day PR exposure was associated with a 2.4% lower FVC [95% confidence interval (CI): 1.4, 3.4% p

Background: Heterogeneity in the response to PM2.5 is hypothesized to be related to differences in particle composition across monitoring sites which reflect differences in source types as well as climatic and topographic conditions impacting different geographic locations. Identifying spatial patterns in particle composition is a multivariate problem that requires novel methodologies. Objectives: Use cluster analysis methods to identify spatial patterns in PM2.5 composition. Verify that the resulting clusters are distinct and informative. Methods: 109 monitoring sites with 75% reported speciation data during the period 2003–2008 were selected. These sites were categorized based on their average PM2.5 composition over the study period using k-means cluster analysis. The obtained clusters were validated and characterized based on their physico-chemical characteristics, geographic locations, emissions profiles, population density and proximity to major emission sources. Results: Overall 31 clusters were identified. These include 21 clusters with 2 or more sites which were further grouped into 4 main types using hierarchical clustering. The resulting groupings are chemically meaningful and represent broad differences in emissions. The remaining clusters, encompassing single sites, were characterized based on their particle composition and geographic location. Conclusions: The framework presented here provides a novel tool which can be used to identify and further classify sites based on their PM2.5 composition. The solution presented is fairly robust and yielded groupings that were meaningful in the context of air-pollution research. Keywords: Multi-pollutant mixtures, Cluster analysis, Effect modification, Air pollution profiles, k-Means