The extent and robustness of the interaction between exposures to heat and ambient PM2.5 is unclear and little is known of the interaction between exposures to cold and ambient PM2.5. Clarifying these interactions, if any, is crucial due to the omnipresence of PM2.5 in the atmosphere and increasing scope and frequency of extreme temperature events. To investigate both of these interactions, we merged 6 073 575 individual-level mortality records from thirteen states spanning seventeen years with 1 km daily PM2.5 predictions from sophisticated prediction model and 1 km meteorology from Daymet V4. A time-stratified, bidirectional case-crossover design was used to control for confounding by individual-level, long-term and cyclic weekly characteristics. We fitted conditional logistic regressions with an interaction term between PM2.5 and extreme temperature events to investigate the potential interactive effects on mortality. Ambient PM2.5 exposure has the greatest effect on mortality by all internal causes in the 2 d moving average exposure window. Additionally, we found consistently synergistic interactions between a 10 mu g m-3 increase in the 2 d moving average of PM2.5 and extreme heat with interaction odds ratios of 1.013 (95% CI: 1.000, 1.026), 1.024 (95% CI: 1.002, 1.046), and 1.033 (95% CI: 0.991, 1.077) for deaths by all internal causes, circulatory causes, and respiratory causes, respectively, which represent 75%, 156%, and 214% increases in the coefficient estimates for PM2.5 on those days. We also found evidence of interactions on the additive scale with corresponding relative excess risks due to interaction (RERIs) of 0.013 (95% CI: 0.003, 0.021), 0.020 (95% CI: 0.008, 0.031), and 0.017 (95% CI: -0.015, 0.036). Interactions with other PM2.5 exposure windows were more pronounced. For extreme cold, our results were suggestive of an antagonistic relationship. These results suggest that ambient PM2.5 interacts synergistically with exposure to extreme heat, yielding greater risks for mortality than only either exposure alone.

Aging biomarkers are essential tools for quantifying biological aging, but systematic validation has been hindered by methodological inconsistencies and fragmented datasets. Here we show that the ability of traditional aging clocks to predict chronological age does not correlate with mortality prediction capacity (R = 0.12, P = 0.67), suggesting that these metrics capture distinct biological processes. We developed Biolearn, an open-source framework enabling standardized evaluation of 39 biomarkers across over 20,000 individuals from diverse cohorts. The Horvath skin and blood clock achieved the highest chronological age accuracy (R2 = 0.88), while GrimAge2 demonstrated the strongest mortality association (hazard ratio = 2.57) and healthspan prediction (hazard ratio = 2.00). Our systematic evaluation reveals considerable heterogeneity in biomarker performance across different clinical outcomes, with optimal biomarkers varying according to specific application. Biolearn provides unified data processing pipelines with quality control and cell-type deconvolution capabilities, establishing a foundation for reproducible aging research and facilitating development of robust aging biomarkers. Ying, Paulson and collagues have developed an open-source framework, Biolearn, to harmonize and systematically evaluate 39 aging biomarkers across diverse populations, enabling standardized validation and facilitating development of robust aging biomarkers.

Background Investigations into long-term fine particulate matter (PM2.5 ) exposure's impact on nonaccidental and cardiovascular (CVD) deaths primarily involve nonrepresentative adult populations at concentrations above the new Environmental Protection Agency annual PM2.5 standard. Methods Using generalized linear models, we studied PM2.5 exposure on rates of five mortality outcomes (all nonaccidental, CVD, myocardial infarction, stroke, and congestive heart failure) in 12 US states from 2000 to 2016. We aggregated predicted annual PM2.5 exposures from a validated ensemble exposure model, ambient temperature from Daymet predictions, and mortality rates to all census tract-years within the states. We obtained covariates from the decennial Census and the American Community Surveys and assessed effect measure modification by race and education with stratification. Results For each 1-mu g/m(3) increase in annual PM2.5 , we found positive associations with all five mortality outcomes: all nonaccidental (1.08%; 95% confidence interval [CI]: 0.96%, 1.20%), all CVD (1.27%; 95% CI: 1.14%, 1.41%), myocardial infarction (1.89%; 95% CI: 1.67%, 2.11%), stroke (1.08%; 95% CI: 0.87%, 1.30%), and congestive heart failure (2.20%; 95% CI: 1.97%, 2.44%). Positive associations persisted at <8 g/m(3) PM2.5 levels and among populations with only under 65. In our study, race, but not education, modifies associations. High-educated Black had a 2.90% larger increased risk of CVD mortality (95% CI: 2.42%, 3.39%) compared with low-educated non-Black. Conclusion Long-term PM2.5 exposure is associated with nonaccidental and CVD mortality in 12 states, below the new Environmental Protection Agency standard, for both low PM2.5 regions and the general population. Vulnerability to CVD mortality persists among Black individuals regardless of education level.

Background Redlining has been associated with worse health outcomes and various environmental disparities, separately, but little is known of the interaction between these two factors, if any. We aimed to estimate whether living in a historically-redlined area modifies the effects of exposures to ambient PM 2.5 and extreme heat on mortality by non-external causes. Methods We merged 8,884,733 adult mortality records from thirteen state departments of public health with scanned and georeferenced Home Owners Loan Corporation (HOLC) maps from the University of Richmond, daily average PM 2.5 from a sophisticated prediction model on a 1-km grid, and daily temperature and vapor pressure from the Daymet V4 1-km grid. A case-crossover approach was used to assess modification of the effects of ambient PM 2.5 and extreme heat exposures by redlining and control for all fixed and slow-varying factors by design. Multiple moving averages of PM 2.5 and duration-aware analyses of extreme heat were used to assess the most vulnerable time windows. Results We found significant statistical interactions between living in a redlined area and exposures to both ambient PM 2.5 and extreme heat. Individuals who lived in redlined areas had an interaction odds ratio for mortality of 1.0093 (95% confidence interval [CI]: 1.0084, 1.0101) for each 10 µg m −3 increase in same-day ambient PM 2.5 compared to individuals who did not live in redlined areas. For extreme heat, the interaction odds ratio was 1.0218 (95% CI 1.0031, 1.0408). Conclusions Living in areas that were historically-redlined in the 1930’s increases the effects of exposures to both PM 2.5 and extreme heat on mortality by non-external causes, suggesting that interventions to reduce environmental health disparities can be more effective by also considering the social context of an area and how to reduce disparities there. Further study is required to ascertain the specific pathways through which this effect modification operates and to develop interventions that can contribute to health equity for individuals living in these areas.

Background Mechanistic studies of the effects of environmental risk factors have been exploring the potential role of microRNA(miRNAs) as a possible pathway to clinical disease. In this study we examine whether levels of toenail metals are associated with changes in extracellular miRNA(ex-miRNA) expression. Methods We used data derived from the Normative Aging Study from 1996 to 2014 to conduct our analyses. We looked at associations between measured toenail metals: arsenic, cadmium, lead, manganese, and mercury and 282 ex-miRNAs in this population using canonical correlation analyses (CCAs) and longitudinal median regression. We adjusted for covariates such as age, education, body mass index, drinking and smoking behaviors, diabetes, and where available, seafood consumption. The p-values obtained from regression analyses were corrected for multiple comparisons. Ex-miRNAs identified to be associated with toenail metal levels were further examined using pathway analyses. Results Our dataset included 937 observations from 589 men with an average age of 72.9 years at baseline. Both our correlation and regression analyses identified lead and cadmium as exposures most strongly associated with ex-miRNA expression. Numerous ex-miRNAs were identified as being associated with toenail metal levels. miR-27b-3p, in particular, was found to have high correlation with the first canonical dimension in the CCA and was significantly associated with cadmium in the regression analysis. Pathway analyses revealed messenger RNA (mRNA) targets for the ex-miRNAs that were associated with a number of clinical disorders including cancer, cardiovascular disease, and neurological disorders, etc. Conclusion Toenail metals were associated with changes in ex-miRNA levels in both correlational and regression analyses. The ex-miRNAs identified can be linked to a variety of clinical disorders. Further studies are required to validate these findings.

Earlier studies have revealed microRNAs (miRNAs) as potential biomarkers for neurological conditions, however, such evidence on psychiatric outcomes is limited. We utilized the Normative Aging Study (NAS) cohort to investigate the associations between extracellular miRNAs (ex-miRNA) and psychiatric symptoms among a group of older male adults, along with the targeted genes and biological pathways. We studied 569 participants with miRNA profile primarily measured in extracellular vesicles isolated from plasma, and psychiatric symptoms reported over 1996-2014 with repeated measures. Global and dimension scales of psychiatric symptoms were measured via the administration of Brief Symptom Inventory (BSI) per visit covering nine aspects of psychiatric health, such as anxiety, depression, hostility, psychoticism, etc. Ex-miRNAs were profiled using small RNA sequencing. Associations of expression of 395 ex-miRNAs (present in >70% samples) with current mental status were assessed using single-miRNA as well as Least Absolute Shrinkage and Selection Operator (LASSO)-based multi-miRNAs linear mixed effects models adjusting for key demographic and behavioral factors. Biological functions were explored using pathway analyses. We identified ex-miRNAs associated with each BSI scale. In particular, hsa-miR-320d was consistently identified for two global scales. Similar overlapping miRNAs across global and dimension scores included hsa-miR-379-3p, hsa-miR-1976, hsa-miR-151a-5p, hsa-miR-151b, hsa-miR-144-3p, etc. Top KEGG pathways for identified miRNAs included p53 signaling, Hippo signaling, FoxO signaling, protein processing in endoplasmic reticulum and several pathways related with cancer and neurological diseases. This study provided early evidence supporting the associations between extracellular miRNAs and psychiatric conditions. MiRNAs may serve as biomarkers of subclinical psychiatric illness in older adults.

Epigenome-wide association studies of ambient fine particulate matter (PM2.5) have been reported. However, few have examined PM2.5 components (PMCs) and sources or included repeated measures. The lack of high-resolution exposure measurements is the key limitation. We hypothesized that significant changes in DNA methylation might vary by PMCs and the sources. We predicted the annual average of 14 PMCs using novel high-resolution exposure models across the contiguous U.S., between 2000–2018. The resolution was 50 m × 50 m in the Greater Boston Area. We also identified PM2.5 sources using positive matrix factorization. We repeatedly collected blood samples and measured leukocyte DNAm with the Illumina HumanMethylation450K BeadChip in the Normative Aging Study. We then used median regression with subject-specific intercepts to estimate the associations between long-term (one-year) exposure to PMCs / PM2.5 sources and DNA methylation at individual cytosine-phosphate-guanine CpG sites. Significant probes were identified by the number of independent degrees of freedom approach, using the number of principal components explaining > 95

BACKGROUND:Previous studies have linked noise exposure with adverse cardiovascular events. However, evidence remains inconsistent, and most previous studies only focused on traffic noise, excluding other anthropogenic sources like constructions, industrial process and commercial activities. Additionally, few studies have been conducted in the U.S. or evaluated the non-linear exposure-response relationships. METHODS:We conducted a relative incidence analysis study using all cardiovascular diseases mortality as cases (n = 936,019) and external causes mortality (n = 232,491) as contrast outcomes. Mortality records geocoded at residential addresses were obtained from five U.S. states (Indiana, 2007; Kansas, 2007-2009, Missouri, 2010-2019, Ohio, 2007-2013, Texas, 2007-2016). Time-invariant long-term noise exposure was obtained from a validated model developed based on acoustical measurements across 2000-2014. Noises from both natural sources (natural activities, including animals, insects, winds, water flows, thunder, etc.) and anthropogenic sources (human activities, including transportation, industrial activities, community facilities & infrastructures, commercial activities, entertainments, etc.) were included. We used daytime and nighttime total anthropogenic noise & day-night average sound pressure level combining natural and anthropogenic sources as exposures. Logistic regression models were fit controlling for Census tract-level & individual-level characteristics. We examined potential modification by sex by interaction terms and potential non-linear associations by thin plate spline terms. RESULTS:We observed positive associations for daytime anthropogenic L50 (sound level exceeded 50% of time) noise (10-dBA OR = 1.047, 95%CI 1.025-1.069), nighttime anthropogenic L50 noise (10-dBA OR = 1.061, 95%CI 1.033-1.091) in a two-exposure-term model, and overall Ldn (day-night average) sound pressure level (10-dBA OR = 1.064, 95%CI 1.040-1.089) in single-exposure-term model. Females were more susceptible to all three exposures. All exposures showed monotonic positive associations with cardiovascular mortality up to certain thresholds around 45-55 dBA, with a generally flattened or decreasing trend beyond those thresholds. CONCLUSIONS:Both daytime anthropogenic and nighttime anthropogenic noises were associated with cardiovascular disease mortality, and associations were stronger in females.

BACKGROUND AND AIM: Previous studies have shown modification of the effect of PM2.5 on all-cause mortality by ambient temperature, but findings have been conflicted and few studies have looked specifically at extreme temperatures (especially extreme cold). We aimed to estimate the interaction between co-exposures to both PM2.5 and temperature extremes. METHOD: We conducted a case-crossover study with bidirectional control sampling. We used 4,644,810 adult, non-accidental mortality records obtained from the departments of public health of 9 U.S. states, PM2.5 data from a 1-km exposure model, and temperature and relative humidity from the 1-km Daymet and 4-km gridMET surface weather models, respectively. We looked at same-day up to the 8-day moving average of PM2.5. Extreme heat was defined as days where the minimum temperature in a given Census tract in a given year was greater than the 99th percentile of minimum temperature in that tract in that year. We used a similar definition for extreme cold, focusing on the 1st percentile of maximum temperature instead. We modeled the modification with an interaction term and controlled for relative humidity. Less stringent definitions of extreme temperature were also explored. RESULTS: We found significant modification of effect of PM2.5 on mortality by extreme temperature. This finding was consistent across both extremes of temperature at various cutoffs and also across different moving averages of PM2.5. We observed the largest interactions for same-day PM2.5, with interaction odds ratios of 1.0018 (95% CI: 1.0015, 1.0021) for both extreme heat and extreme cold for each 5 μg m-3 increase in PM2.5. We observed the smallest interactions with the 3-day moving average of PM2.5, with interaction odds ratios of 1.0014 (95% CI: 10011, 1.0017). CONCLUSIONS: Exposure to extreme temperature significantly worsens the effect of PM2.5 on mortality. This finding was robust across our sensitivity analyses.

BACKGROUND:While the health effects of air pollution and temperature are widely studied, the molecular effects are poorly understood. Extracellular microRNAs (ex-miRNAs) have the potential to serve as diagnostic or prognostic biomarkers and/or to act as intercellular signaling molecules that mediate the effects of environmental exposures on health outcomes.METHODS:We examined the relationship between short-term exposure to air pollution and ambient temperature and the ex-miRNA profiles of participants in the Normative Aging Study (NAS) from 1999 to 2015. Our exposures were defined as same-day, two-day, three-day, one-week, two-week, and three-week moving averages of PM2.5, NO2, O3, and temperature which were derived from high-resolution spatio-temporal models. The ex-miRNA profiles of the subjects were obtained during follow-up visits. We analyzed the data using a longitudinal quantile regression model adjusted for individual covariates, batch effects, and time trends. We adjusted for multiple comparisons using a false discovery rate (FDR) correction. Ex-miRNAs that were significantly associated with exposures were further investigated using pathway analyses.RESULTS:We found that all the examined exposures were associated with changes in ex-miRNA profiles in our study, particularly PM2.5 which was responsible for most of the statistically significant results. We found 110 statistically significant exposure-outcome relationships that revealed associations with the levels of 52 unique ex-miRNAs. Pathway analyses showed these ex-miRNAs have been linked to target mRNAs, genes, and biological mechanisms that could affect virtually every organ system, and as such may be linked to multiple clinical disease presentations such as cardiovascular disease, respiratory disease, and neurological disease.CONCLUSIONS:Air pollution and temperature exposures were significantly associated with alterations in the ex-miRNA profiles of NAS subjects with possible biological consequences.

Background: Environmental metal exposures have been associated with multiple deleterious health endpoints. DNA methylation (DNAm) may provide insight into the mechanisms underlying these relationships. Toenail metals are non-invasive biomarkers, reflecting a medium-term time exposure window. Objectives: This study examined variation in leukocyte DNAm and toenail arsenic (As), cadmium (Cd), lead (Pb), manganese (Mn), and mercury (Hg) among elderly men in the Normative Aging Study, a longitudinal cohort.Methods: We repeatedly collected samples of blood and toenail clippings. We measured DNAm in leukocytes with the Illumina HumanMethylation450 K BeadChip. We first performed median regression to evaluate the effects of each individual toenail metal on DNAm at three levels: individual cytosine-phosphate-guanine (CpG) sites, re-gions, and pathways. Then, we applied a Bayesian kernel machine regression (BKMR) to assess the joint and individual effects of metal mixtures on DNAm. Significant CpGs were identified using a multiple testing correction based on the independent degrees of freedom approach for correlated outcomes. The approach considers the effective degrees of freedom in the DNAm data using the principal components that explain >95% variation of the data.Results: We included 564 subjects (754 visits) between 1999 and 2013. The numbers of significantly differentially methylated CpG sites, regions, and pathways varied by metals. For example, we found six significant pathways for As, three for Cd, and one for Mn. The As-associated pathways were associated with cancer (e.g., skin cancer) and cardiovascular disease, whereas the Cd-associated pathways were related to lung cancer. Metal mixtures were also associated with 47 significant CpG sites, as well as pathways, mainly related to cancer and cardio-vascular disease.Conclusions: This study provides an approach to understanding the potential epigenetic mechanisms underlying observed relations between toenail metals and adverse health endpoints.

Psychological stress remains an important risk factor for morbidity and mortality throughout the life course. However, there have been counterintuitive findings reported in previous studies of older persons that examine the relationships of perceived psychological stress with DNA methylation-based markers of aging, which also serve as predictors of morbidity and mortality (epigenetic age/clocks). We aimed to replicate and expand findings from existing work by examining relationships of self-reported stress with nine epigenetic clocks: Hannum, Horvath, Intrinsic, Extrinsic, SkinBloodClock, PhenoAge, GrimAge, DNAm Telomere Length, and Pace of Aging. We analyzed data from 607 male participants (mean age 73.2 years) of the VA Normative Aging Study with one to two study visits from 1999 to 2007 (observations = 956). Stress was assessed via the 14-item Perceived Stress Scale (PSS). Epigenetic age was calculated from DNA methylation measured in leukocytes with the HumanMethylation450 BeadChip. In linear mixed effects models adjusted for demographic/lifestyle/health factors, a standard deviation (sd) increase in PSS was associated with Horvath (β = -0.35-years, 95%CI: -0.61, -0.09, P=0.008) and Intrinsic (β = -0.40-years, 95%CI: -0.67, -0.13, P=0.004) epigenetic age deceleration. However, in models limited to participants with the highest levels of stress (≥ 75th-percentile), Horvath (β = 2.29-years, 95%CI: 0.16, 4.41, P=0.04) and Intrinsic (β = 2.06-years, 95%CI: -0.17, 4.28, P=0.07) age acceleration associations were observed. Our results reinforce the complexity of psychological stress and epigenetic aging relationships and lay a foundation for future studies that explore longitudinal relationships with other adult stress metrics and factors that can influence stress such as resilience measures.

BACKGROUND:The molecular effects of intermediate and long-term exposure to air pollution and temperature, such as those on extracellular microRNA (ex-miRNA) are not well understood but may have clinical consequences. OBJECTIVES:To assess the association between exposure to ambient air pollution and temperature and ex-miRNA profiles. METHODS:Our study population consisted of 734 participants in the Normative Aging Study (NAS) between 1999 and 2015. We used high-resolution models to estimate four-week, eight-week, twelve-week, six-month, and one-year moving averages of PM2.5, O3, NO2, and ambient temperature based on geo-coded residential addresses. The outcome of interest was the extracellular microRNA (ex-miRNA) profile of each participant over time. We used a longitudinal quantile regression approach to estimate the association between the exposures and each ex-miRNA. Results were corrected for multiple comparisons and ex-miRNAs that were still significantly associated with the exposures were further analyzed using KEGG pathway analysis and Ingenuity Pathway Analysis. RESULTS:We found 151 significant associations between levels of PM2.5, O3, NO2, and ambient temperature and 82 unique ex-miRNAs across multiple quantiles. Most of the significant results were associations with intermediate-term exposure to O3, long-term exposure to PM2.5, and both intermediate and long-term exposure to ambient temperature. The exposures were most often associated with the 75th and 90th percentile of the outcomes. Pathway analyses of significant ex-miRNAs revealed their involvement in biological pathways involving cell function and communication as well as clinical diseases such as cardiovascular disease, respiratory disease, and neurological disease. CONCLUSION:Our results show that intermediate and long-term exposure to all our exposures of interest were associated with changes in the ex-miRNA profile of study participants. Further studies on environmental risk factors and ex-miRNAs are warranted.

Identifying and validating biomarkers of aging is pivotal for understanding the aging process and testing longevity interventions. Despite the development of numerous aging biomarkers, their clinical validation remains elusive, largely due to the lack of cross-population validation, which is hampered by disparate biomarker designs and inconsistencies in dataset structures. To bridge this gap, we introduce Biolearn, an innovative open-source library dedicated to the implementation and application of aging biomarkers. Biolearn facilitates (1) harmonization of existing aging biomarkers, while presenting a structured framework for novel biomarkers in standardized formats; (2) unification of public datasets, ensuring coherent structuring and formatting, thus simplifying cross-population validation studies; and (3) provision of computational methodologies to assess any harmonized biomarker against unified datasets. By furnishing a community-driven platform, Biolearn significantly augments the development, assessment, and validation trajectories of aging biomarkers, paving the way toward more rigorous clinical validation and, ultimately, application in clinical trials targeting healthy longevity. The Biolearn package is open-source and freely available at https://Bio-Learn.github.io/

Background: Numerous studies have documented PM2.5's links with adverse health outcomes. Comparatively fewer studies have evaluated specific PM2.5 components. The lack of exposure measurements and high correlation among different PM2.5 components are two limitations. Methods: We applied a novel exposure prediction model to obtain annual Census tract-level concentrations of 15 PM2.5 components (Zn, V, Si, Pb, Ni, K, Fe, Cu, Ca, Br, SO42-, NO3-, NH4+, OC, EC) in Massachusetts from 2000 to 2015, to which we matched geocoded deaths. All non-accidental mortality, cardiovascular mortality, and respiratory mortality were examined for the population aged 18 or over. Weighted quantile sum (WQS) regression models were used to examine the cumulative associations between PM2.5 components mixture and outcomes and each component's contributions to the cumulative associations. We have fit WQS models on 15 PM2.5 components and a priori identified source groups (heavy fuel oil combustion, biomass burning, crustal matter, non-tailpipe traffic source, tailpipe traffic source, secondary particles from power plants, secondary particles from agriculture, unclear source) for the 15 PM2.5 components. Total PM2.5 mass analysis and single component associations were also conducted through quasi-Poisson regression models. Results: Positive cumulative associations between the components mixture and all three outcomes were observed from the WQS models. Components with large contribution to the cumulative associations included K, OC, and Fe. Biomass burning, traffic emissions, and secondary particles from power plants were identified as important source contributing to the cumulative associations. Mortality rate ratios for cardiovascular mortality were of greater magnitude than all non-accidental mortality and respiratory mortality, which is also observed in cumulative associations estimated from WQS, total PM2.5 mass analysis, and single component associations. Conclusion: We have found positive associations between the mixture of 15 PM2.5 components and all non-accidental mortality, cardiovascular mortality, and respiratory mortality. Among these components, Fe, K, and OC have been identified as having important contribution to the cumulative associations. The WQS results also suggests potential source effects from biomass burning, traffic emissions, and secondary particles from power plants.

Abstract Rationale Studies examining the association of short-term air pollution exposure and daily deaths have typically been limited to cities and used citywide average exposures, with few using causal models. Objectives To estimate the associations between short-term exposures to fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) and all-cause and cause-specific mortality in multiple US states using census tract or address exposure and including rural areas, using a double negative control analysis. Methods We conducted a time-stratified case-crossover study examining the entire population of seven US states from 2000–2015, with over 3 million non-accidental deaths. Daily predictions of PM2.5, O3, and NO2 at 1x1 km grid cells were linked to mortality based on census track or residential address. For each pollutant, we used conditional logistic regression to quantify the association between exposure and the relative risk of mortality conditioning on meteorological variables, other pollutants, and using double negative controls. Results A 10 μg/m3 increase in PM2.5 exposure at the moving average of lag 0–2 day was significantly associated with a 0.67% (95%CI: 0.34–1.01%) increase in all-cause mortality. 10 ppb increases in NO2 or O3 exposure at lag 0–2 day were marginally associated with and 0.19% (95%CI: −0.01-0.38%) and 0.20 (95% CI-0.01, 0.40), respectively. The adverse effects of PM2.5 persisted when pollution levels were restricted to below the current global air pollution standards. Negative control models indicated little likelihood of omitted confounders for PM2.5, and mixed results for the gases. PM2.5 was also significantly associated with respiratory mortality and cardiovascular mortality. Conclusions Short-term exposure to PM2.5 and possibly O3 and NO2 are associated with increased risks for all-cause mortality. Our findings delivered evidence that risks of death persisted at levels below currently permissible.

Background and Aim Metals have previously been associated with adverse effects in multiple organ systems. However, the molecular mechanisms by which they cause these effects are not fully elucidated. MicroRNAs are short non-coding RNA sequences that play an important role in the regulation of gene expression and may be a mechanism by which metals influence health. Methods The study population consisted of496 persons (with 694 samples) enrolled in the Normative Aging Study (NAS). This cohort began recruiting male veteran who lived in the New England area in 1963 and has followed up with them every three to five years ever since. Our study period includes visits from 1999 to 2014. Extracellular microRNAs were sequenced from participants' plasma, and toenail samples were used to measure levels of metals, namely: arsenic (As), cadmium (Cd), lead (Pb), manganese (Mn), and mercury (Hg). We used canonical correlation analysis (CCA) to find whether metal levels correspond with microRNA levels. We further assessed the relationship between metals and microRNA using Bayesian kernel machine regression (BKMR). Results Our preliminary CCA results showed very strong correlations between the first five canonical variables of the metals and those of the microRNA, with correlation values of 0.937, 0.923, 0.903, 0.894, 0.886, respectively. However, only the correlation between the first canonical variables was statistically significant. Pb and Cd showed the strongest association with the first canonical variable of the metals dataset. The strongest associations between the first canonical variable in the microRNA set were with the following microRNAs: miR-182-5p, miR-183-5p, miR-516b-5p, and miR-26a-5p. Conclusions There was a strong correlation between the first canonical variables of the metals and the microRNA, indicating a potential association between them. Future analyses will use BKMR to assess the joint effect of metals on the microRNA profile. Keywords Metals, microRNA, Epigenetics, Mixtures Analysis