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Recent studies have linked air pollution to increased risk for behavioral problems during development, albeit with inconsistent findings. Additional longitudinal studies are needed that consider how emotional behaviors may be affected when exposure coincides with the transition to adolescence – a vulnerable time for developing mental health difficulties. This study examines how annual average PM2.5and NO2exposure at ages 9-10 years relates to internalizing and externalizing behaviors over a 2-year follow-up period in a large, nationwide U.S. sample of participants from the Adolescent Brain Cognitive Development (ABCD) Study®. Air pollution exposure was estimated based on the residential address of each participant using an ensemble-based modeling approach. Caregivers answered questions from the Child Behavior Checklist (CBCL) at baseline and annually for two follow-up sessions for a total of 3 waves of data; from the CBCL we obtained scores on internalizing and externalizing problems plus 5 syndrome scales (anxious/depressed, withdrawn/depressed, rule-breaking behavior, aggressive behavior, and attention problems). Zero-inflated negative binomial models were used to examine both the main effect of age as well as the interaction of age with each pollutant on behavior while adjusting for various socioeconomic and demographic characteristics. Overall, the pollution effects moderated the main effects of age with higher levels of PM2.5and NO2leading to an even greater likelihood of having no behavioral problems (i.e., score of zero) with age over time, as well as fewer problems when problems are present as the child ages. Albeit this was on the order equal to or less than a 1-point change. Thus, one year of annual exposure at 9-10 years is linked with very small change in emotional behaviors in early adolescence, which may be of little clinical relevance.

Little epidemiologic research has focused on pollution-related risks in medically vulnerable or marginalized groups. Using a nationwide 50% random sample of 2008-2016 Medicare Part D-eligible Fee-for-Service participants in the US, we identified a cohort with high-risk conditions for cardiovascular and thromboembolic events (CTE) and linked individuals with seasonal average zip code level concentrations of fine particulate matter (PM2.5). We assessed the relationship between seasonal PM2.5 exposure and hospitalization for each of seven CTE-related causes using history-adjusted marginal structural models adjusted for individual demographic and neighborhood socio-economic variables as well as baseline comorbidities, health behaviors, and health service measures. We examined effect modification across geographically- and demographically-defined subgroups. The cohort included 1,934,453 individuals with high-risk conditions (mean age 77, 60% female, 87% white). A 1 μg/m3 increase in PM2.5 exposure was significantly associated with increased risk of six out of seven CTE hospitalization types. Strong increases were observed for transient ischemic attack (HR: 1.039 (1.034,1.044)), venous thromboembolism (HR: 1.031 (1.027,1.035)), and heart failure (HR: 1.019 (1.017,1.020)). Asian Americans were found to be particularly susceptible to thromboembolic effects of PM2.5 (venous thromboembolism HR: 1.063 (1.021,1.106)), while Native Americans were most vulnerable to cerebrovascular effects (transient ischemic attack HR: 1.093 (1.030,1.161)).

Average ambient concentrations of nitrogen dioxide (NO 2 ), an important air pollutant, have declined in the United States since the enactment of the Clean Air Act. Despite evidence that NO 2 disproportionately affects racial/ethnic minority groups, it remains unclear what drives the exposure disparities and how they have changed over time. Here, we provide evidence by integrating high-resolution (1 km × 1 km) ground-level NO 2 estimates, sociodemographic information, and source-specific emission intensity and location for 217,740 block groups across the contiguous United States from 2000 to 2016. We show that racial/ethnic minorities are disproportionately exposed to higher levels of NO 2 pollution compared with Whites across the United States and within major metropolitan areas. These inequities persisted over time and have worsened in many cases, despite a significant decrease in the national average NO 2 concentration over the 17-y study period. Overall, traffic contributes the largest fraction of NO 2 disparity. Contributions of other emission sources to exposure disparities vary by location. Our analyses offer insights into policies aimed at reducing air pollution exposure disparities among races/ethnicities and locations.

Table S1: Detailed information on all CpGs of interest Table S2: Cancer-associated CpGs in TRGs by pathway at FDR < 0.05, with unspecified skin malignancies excluded Table S3: Methylation trajectory over time by cancer status Table S4: Logistic regression results for DNA methylation measured over 8 years before diagnosis/censoring Table S5: Regulatory elements enrichment analysis results Figure S1: Plots of Beta-coefficients by genomic location for all CpGs on six genes of interest Figure S2: DNA methylation by years to cancer diagnosis/censoring and cancers status with 95% confidence intervals Figure S3: DNA methylation by years to cancer diagnosis/censoring and cancer status for CpG additional sites Figure S4: Regulatory enrichment analysis results

Previous studies have examined the association between prenatal nitrogen dioxide (NO2)—a traffic emissions tracer—and fetal growth based on ultrasound measures. Yet, most have used exposure assessment methods with low temporal resolution, which limits the identification of critical exposure windows given that pregnancy is relatively short. Here, we used NO2 data from an ensemble model linked to residential addresses at birth to fit distributed lag models that estimated the association between NO2 exposure (resolved weekly) and ultrasound biometric parameters in a Massachusetts-based cohort of 9,446 singleton births from 2011–2016. Ultrasound biometric parameters examined included biparietal diameter (BPD), head circumference, femur length, and abdominal circumference. All models adjusted for sociodemographic characteristics, time trends, and temperature. We found that higher NO2 was negatively associated with all ultrasound parameters. The critical window differed depending on the parameter and when it was assessed. For example, for BPD measured after week 31, the critical exposure window appeared to be weeks 15–25; 10-parts-per-billion higher NO2 sustained from conception to the time of measurement was associated with a lower mean z score of −0.11 (95% CI: −0.17, −0.05). Our findings indicate that reducing traffic emissions is one potential avenue to improving fetal and offspring health.

Fine particulate matter (PM2.5) is the largest environmental risk factor impacting human health. While PM2.5 has been measured widely across the world, there has been no high-resolution and gapless global PM2.5 data on a daily scale. We generate a global daily PM2.5 concentration at 1 km resolution using satellite gap-filled aerosol products and machine learning. Daily PM2.5 retrievals agreed well with ground measurements, with sample-, space-, and time-based cross-validated correlations of 0.93, 0.89, and 0.88, respectively. This enables us to unprecedentedly monitor the day-to-day variations of PM2.5, exposure risk, and mortality burden around the globe. More than 96% of the days exceeded the World Health Organization (WHO) recommended daily air quality guidelines (AQG) level (15 μg m-3) in 2020, and 99% of populated areas were exposed to PM2.5 risk at least one day; in particular, the proportions are 91% and 64% similarly in 7 and 30 days, respectively. The annual population-weighted mean PM2.5 concentration was 27.6 μg m-3 (~5.5 times higher than the WHO annual AQG level of 5 μg m-3), resulting in estimated premature deaths of ~4.2 million people and accounting for ~6.6% of total global deaths. Substantial differences are noted in many parts of the world between 2019 and 2020 associated with widespread episodes of wildfires or the COVID-19 shutdowns. The overall air quality in 2020 was significantly better than in 2019 in more than 70% of major cities. The global population-weighted mean PM2.5 decreased by ~5.1%, and the associated number of premature deaths dropped by 56,700.

IntroductionImmune checkpoint inhibitors (ICIs) have revolutionized the treatment of cancer, but preclinical testing of hypotheses such as combination therapies has been complicated, in part due to species incompatibility issues. For example, one of few known permissive animal models for oncolytic adenoviruses is the Syrian hamster, for which an ICI, mainly an anti-PD-L1 monoclonal antibody (mAb) was not previously available. In this study, we developed an anti-Syrian hamster PD-L1 mAb to enable the evaluation of safety and efficacy, when combining anti-PD-L1 with an oncolytic adenovirus encoding tumour necrosis factor alpha (TNFα) and interleukin-2 (IL-2) (Ad5/3-E2F-D24-hTNFα-IRES-hIL-2 or TILT-123).MethodsRecombinant Syrian hamster PD-L1 was expressed and mice immunized for mAb formation using hybridoma technology. Clonal selection through binding and functional studies in vitro, in silico and in vivo identified anti-PD-L1 clone 11B12-1 as the primary mAb candidate for immunotherapy modelling. The oncolytic virus (OV) and ICI combination approach was then evaluated using 11B12-1 and TILT-123 in a Syrian hamster model of pancreatic ductal adenocarcinoma (PDAC).ResultsSupernatants from hybridoma parent subclone 11B12B4 provided the highest positive PD-L1 signal, on Syrian hamster PBMCs and three cancer cell lines (HT100, HapT1 and HCPC1). In vitro co-cultures revealed superior immune modulated profiles of cell line matched HT100 tumour infiltrating lymphocytes when using subclones of 7G2, 11B12 and 12F1. Epitope binning and epitope prediction using AlphaFold2 and ColabFold revealed two distinct functional epitopes for clone 11B12-1 and 12F1-1. Treatment of Syrian hamsters bearing HapT1 tumours, with 11B12-1 induced significantly better (pConclusionsNovel Syrian hamster anti-PD-L1 clone 11B12-1 induces tumour growth control in a hamster model of PDAC. Combining 11B12-1 with oncolytic adenovirus TILT-123 improves tumour growth control further and demonstrates good safety and toxicity profiles.

Rationale: Early detection of respiratory diseases is critical to facilitate delivery of disease-modifying interventions. Extracellular vesicle-enriched microRNAs (EV-miRNAs) may represent reliable markers of early lung injury. Objectives: Evaluate associations of plasma EV-miRNAs with lung function. Methods: The prospective NAS (Normative Aging Study) collected plasma EV-miRNA measurements from 1996-2015 and spirometry every 3-5 years through 2019. Associations of EV-miRNAs with baseline lung function were modeled using linear regression. To complement the individual miRNA approach, unsupervised machine learning was used to identify clusters of participants with distinct EV-miRNA profiles. Associations of EV-miRNA profiles with multivariate latent longitudinal lung function trajectories were modeled using log binomial regression. Biological functions of significant EV-miRNAs were explored using pathway analyses. Results were replicated in an independent sample of NAS participants and in the HEALS (Health Effects of Arsenic Longitudinal Study). Measurements and Main Results: In the main cohort of 656 participants, 51 plasma EV-miRNAs were associated with baseline lung function (false discovery rate-adjusted P value

Objective To examine the association between ambient temperature and antral follicle count (AFC), a standard measure of ovarian reserve. Design Prospective cohort study. Setting Fertility center at an academic hospital in the northeastern United States. Patient(s) 631 women attending the Massachusetts General Hospital Fertility Center (2005–2015) who participated in the Environment and Reproductive Health Study. Intervention(s) Daily temperature at the women’s residential address was estimated for the 90 days before their antral follicle scan using a spatially refined gridded climate data set. We evaluated the associations between temperature and AFC using Poisson regression with robust standard errors, adjusting for relative humidity, fine particulate matter exposure, age, education, smoking status, year and month of AFC, and diagnosis of diminished ovarian reserve and ovulation disorders. Main Outcome Measure(s) Antral follicle count as measured with transvaginal ultrasonography. Result(s) A 1°C increase in average maximum temperature during the 90 days before ovarian reserve testing was associated with a −1.6% (95% confidence interval [CI], −2.8, −0.4) lower AFC. Associations remained negative, but were attenuated, for average maximum temperature exposure in the 30 days (−0.9%, 95% CI, −1.8, 0.1) and 14 days (−0.8%, 95% CI, −1.6, 0.0) before AFC. The negative association between average maximum temperature and AFC was stronger in November through June than during the summer months, suggesting that timing of heat exposure and acclimatization to heat may be important factors to consider in future research. Conclusion(s) Exposure to higher temperatures was associated with lower ovarian reserve. These results raise concern that rising ambient temperatures worldwide may result in accelerated reproductive aging among women.

Summary: Background: Long-term exposure to air pollution has been linked with an increase in risk of mortality. Whether existing US Environmental Protection Agency standards are sufficient to protect health is unclear. Our study aimed to examine the relationship between exposure to lower concentrations of air pollution and the risk of mortality. Methods: Our nationwide cohort study investigated the effect of annual average exposure to air pollutants on all-cause mortality among Medicare enrolees from the beginning of 2000 to the end of 2016. Patients entered the cohort in the month of January following enrolment and were followed up until the end of the study period in 2016 or death. We restricted our analyses to participants who had only been exposed to lower concentrations of pollutants over the study period, specifically particulate matter less than 2·5 μg/m3 in diameter (PM2·5) at a concentration of up to 12 μg/m3, nitrogen dioxide (NO2) at a concentration of up to 53 parts per billion (ppb), and summer ozone (O3) at concentrations of up to 50 ppb. We adjusted for two types of covariates, which were individual level and postal code-level variables. We used a doubly-robust additive model to estimate the change in risk. We further looked at effect-measure modification by stratification on the basis of demographic and socioeconomic characteristics. Findings: We found an increased risk of mortality with all three pollutants. Each 1 μg/m3 increase in annual PM2·5 concentrations increased the absolute annual risk of death by 0·073% (95% CI 0·071–0·076). Each 1 ppb increase in annual NO2 concentrations increased the annual risk of death by 0·003% (0·003–0·004), and each 1 ppb increase in summer O3 concentrations increased the annual risk of death by 0·081% (0·080–0·083). This increase translated to approximately 11 540 attributable deaths (95% CI 11 087–11 992) for PM2·5, 1176 attributable deaths (998–1353) for NO2, and 15 115 attributable deaths (14 896–15 333) for O3 per year for each unit increase in pollution concentrations. The effects were higher in certain subgroups, including individuals living in areas of low socioeconomic status. Long-term exposure to permissible concentrations of air pollutants increases the risk of mortality. Funding: The US Environmental Protection Agency, National Institute of Environmental Health Services, and Health Effects Institute.

Rationale: Ambient ultrafine particles (UFPs; with an aerodynamic diameter

Particulate matter with aerodynamic diameter less than 2.5 μm (PM2.5) increases mortality and morbidity.1,2 PM2.5 is composed of a mixture of chemical components that vary across space and time.3 Due to limited hyperlocal data availability, less is known about health risks of PM2.5 components, their US-wide exposure disparities, or which species are driving the biggest intra-urban changes in PM2.5 mass. Here, we developed the first national super-learned models across the US for hyperlocal estimation of annual mean elemental carbon (EC), ammonium (NH4+), nitrate (NO3-), organic carbon (OC), and sulfate (SO42-) concentrations across 3,535 urban areas at a 50-m spatial resolution, and at a 1-km resolution for non-urban areas from 2000 to 2019. Using ensembles of machine learning models and ~82 billion predictions across 20 years, hyperlocal super-learned PM2.5 components are now available for further research. We found remarkable spatiotemporal intra-urban and inter-urban variabilities in PM2.5 components. We anticipate our work to be a critical milestone for conducting new studies on individual and combined health risks of PM2.5 components, environmental justice analysis, or understanding fine-scale spatiotemporal variabilities of PM2.5 composition. Urban planners and regulators may also use these predictions for selecting locations of new daycares, schools, nursing homes, or air-quality monitors.

Growing evidence suggests that fine particulate matter (PM 2.5 ) likely increases the risks of dementia, yet little is known about the relative contributions of different constituents. Here, we conducted a nationwide population-based cohort study (2000 to 2017) by integrating the Medicare Chronic Conditions Warehouse database and two independently sourced datasets of high-resolution PM 2.5 major chemical composition, including black carbon (BC), organic matter (OM), nitrate (NO 3 − ), sulfate (SO 4 2− ), ammonium (NH 4 + ), and soil dust (DUST). To investigate the impact of long-term exposure to PM 2.5 constituents on incident all-cause dementia and Alzheimer’s disease (AD), hazard ratios for dementia and AD were estimated using Cox proportional hazards models, and penalized splines were used to evaluate potential nonlinear concentration–response (C-R) relationships. Results using two exposure datasets consistently indicated higher rates of incident dementia and AD for an increased exposure to PM 2.5 and its major constituents. An interquartile range increase in PM 2.5 mass was associated with a 6 to 7% increase in dementia incidence and a 9% increase in AD incidence. For different PM 2.5 constituents, associations remained significant for BC, OM, SO 4 2− , and NH 4 + for both end points (even after adjustments of other constituents), among which BC and SO 4 2− showed the strongest associations. All constituents had largely linear C-R relationships in the low exposure range, but most tailed off at higher exposure concentrations. Our findings suggest that long-term exposure to PM 2.5 is significantly associated with higher rates of incident dementia and AD and that SO 4 2− , BC, and OM related to traffic and fossil fuel combustion might drive the observed associations.

Empirical models, previously called land-use regression (LUR), are used to understand and predict spatial variability in levels of outdoor air pollution at unmeasured locations, for example, to conduct health risk assessment, environmental epidemiology, or environmental justice analysis. Many methods are used to generate empirical models, yet almost no research compares models generated by separate research groups. We intercompare six national-scale empirical models for year-2010 concentrations of PM2.5 in the US, each generated by a different research group. Despite substantial differences in the statistical methods and input data used to build the models, our main finding is a relatively high degree of agreement among model predictions. For example, in pairwise intercomparisons, the average Pearson correlation coefficient is 0.87 (range: 0.84 to 0.92); the RMSD (root-mean-square-difference; units: μg/m3) is 1.1 on average (range: 0.8 to 1.4), or ~12% of the average concentration; and many best-fit lines are near the 1:1 line. The underlying reason for this agreement is likely that, while the methods and the independent variables differ among the models, in all cases the models are built using, and are calibrated to, the same information: publicly available measurement at US EPA regulatory monitoring stations. Findings here suggest that future improvements to national empirical models will come not from further refinements to the methods (e.g., more-advanced models) but from employing a fundamentally different set of observations, in addition to regulatory monitoring data.

BackgroundLeft unabated, rising temperatures pose an escalating threat to human health. The potential effects of hot temperatures on fetal health have been under-explored. Here, we examined the association between prenatal ambient temperature exposure and fetal growth measures in a Massachusetts-based pregnancy cohort.MethodsWe used ultrasound measurements of biparietal diameter (BPD), head circumference (HC), femur length and abdominal circumference (AC), in addition to birthweight (BW), from 9446 births at Beth Israel Deaconess Medical Center from 2011 to 2016. Ultrasound scans were classified into three distinct gestational periods: 16–23 weeks, 24–31 weeks, 32+ weeks; and z-scores were created for each fetal growth measure using the INTERGROWTH-21st standards. We fitted distributed lag models to estimate the time-varying association between weekly temperature and fetal growth, adjusting for sociodemographic characteristics, seasonal and long-term trends, humidity and particulate matter (PM2.5).ResultsHigher ambient temperature was associated with smaller fetal growth measures. The critical window of exposure appeared to be Weeks 1–20 for ultrasound parameters, and high temperatures throughout pregnancy were important for BW. Associations were strongest for head parameters (BPD and HC) in early to mid-pregnancy, AC late in pregnancy and BW. For example, a 5ºC higher cumulative temperature exposure was associated with a lower mean AC z-score of -0.26 (95% CI: -0.48, -0.04) among 24–31-Week scans, and a lower mean BW z-score of -0.32 (95% CI: -0.51, -0.12).ConclusionHigher temperatures were associated with impaired fetal growth. This has major health implications given that extreme temperatures are more common and escalating.

Background Although the effects of fine particulate matter (particulate matter ≤2.5 μm aerodynamic diameter [PM 2.5 ]) on cardiovascular disease (CVD) morbidity and mortality are well established, little is known about the CVD health effects of particle radioactivity. In addition, there are still questions about which of the PM 2.5 physical, chemical, or biological properties are mostly responsible for its toxicity. Methods and Results We investigated the association between particle radioactivity, measured as gross β activity from highly resolved spatiotemporal predictions, and mortality for CVD, myocardial infarction, stroke, and all‐cause nonaccidental mortality in Massachusetts (2001–2015). Within both difference‐in‐differences model and generalized linear mixed model frameworks, we fit both single‐exposure and 2‐exposure models adjusting for PM 2.5 and examined the interaction between PM 2.5 and gross β activity. We found significant associations between gross β activity and PM 2.5 and each mortality cause. Using difference‐in‐differences and adjusting for PM 2.5 , we found the highest associations with myocardial infarction (rate ratio, 1.16 [95% CI, 1.08–1.24]) and stroke (rate ratio, 1.11 [95% CI, 1.04–1.18]) for an interquartile range increase (0.055 millibecquerels per cubic meter) in gross β activity. We found a significant positive interaction between PM 2.5 and gross β activity, with higher associations between PM 2.5 and mortality at a higher level of gross β activity. We also observed that the associations varied across age groups. The results were comparable between the 2 statistical methods also with and without adjusting for PM 2.5 . Conclusions This is the first study that, using highly spatiotemporal predictions of gross β‐activity, provides evidence that particle radioactivity increases CVD mortality and enhances PM 2.5 CVD mortality. Therefore, particle radioactivity can be an important property of PM 2.5 that must be further investigated. Addressing this important question can lead to cost‐effective air‐quality regulations.

Increased solar and geomagnetic activity (SGA) may alter sympathetic nervous system activity, reduce antioxidant activity, and modulate physiochemical processes that contribute to atmospheric aerosols, all which may reduce pulmonary function.Investigate associations between forced expiratory volume at 1 s (FEVWe conducted a repeated measures analysis in 726 Normative Aging Study participants (Boston, Massachusetts, USA) between 2000 and 2017, using interplanetary magnetic field (IMF), planetary K index (Kp), and sunspot number (SSN) as SGA measures. Linear mixed effects models were used to assess exposure moving averages up to 28 days for both SGA and pollution.Increases in IMF, Kp Index and SSN from the day of the pulmonary function test averaged through day 28 of were associated with a significant decrement in FEVIncreased periods of solar and geomagnetic activity may directly contribute to impaired pulmonary function and also enhance effects of PM