Background Genetic variants have been associated with the risk of coronary heart disease (CHD). We tested whether a composite of these variants could identify the risk of both incident as well as recurrent CHD events and distinguish individuals who derived greater clinical benefit from statin therapy. Methods A community-based cohort and four randomized controlled trials of both primary (JUPITER and ASCOT) and secondary (CARE and PROVE IT-TIMI 22) prevention with statin therapy totaling 48,421 individuals and 3,477 events were included in these analyses. We examined the association of a genetic risk score based on 27 genetic variants with incident or recurrent CHD, adjusting for established clinical predictors. We then investigated the relative and absolute risk reductions in CHD events with statin therapy stratified by genetic risk. Data from studies were combined using meta-analysis. Findings When individuals were divided into low (quintile 1), intermediate (quintiles 2-4), and high (quintile 5) genetic risk categories, a significant gradient of risk for incident or recurrent CHD was demonstrated with the multivariable-adjusted HRs (95% CI) for CHD for the intermediate and high genetic risk categories vs. low genetic risk category being 1.32 (1.20-1.46, P<0.0001) and 1.71 (1.54-1.91, P<0.0001), respectively. In terms of the benefit of statin therapy in the four randomized trials, there was a significant gradient of increasing relative risk reduction across the low, intermediate, and high genetic risk categories (13%, 29%, and 48%, P=0.0277). Similarly, greater absolute risk reductions were seen in those individuals in higher genetic risk categories (P=0.0101), resulting in an approximate three-fold gradient in the number needed to treat (NNT) in the primary prevention trials. Specifically, in the primary prevention trials, the NNT to prevent one MACE over 10 years for the low, intermediate, and high GRS individuals was 66, 42, and 25 in JUPITER and 57, 47, and 20 in ASCOT. Interpretation A genetic risk score identified individuals at increased risk for both incident and recurrent CHD events. Individuals with the highest burden of genetic risk derived the largest relative and absolute clinical benefit with statin therapy.

Background The use of satellite-based aerosol optical depth (AOD) to estimate fine particulate matter (PM2.5) for epidemiology studies has increased substantially over the past few years. These recent studies often report moderate predictive power, which can generate downward bias in effect estimates. In addition, AOD measurements have only moderate spatial resolution, and have substantial missing data. Methods We make use of recent advances in MODIS satellite data processing algorithms (Multi-Angle Implementation of Atmospheric Correction (MAIAC), which allow us to use 1 km (versus currently available 10 km) resolution AOD data. We developed and cross validated models to predict daily PM2.5 at a 1×1km resolution across the northeastern USA (New England, New York and New Jersey) for the years 2003–2011, allowing us to better differentiate daily and long term exposure between urban, suburban, and rural areas. Additionally, we developed an approach that allows us to generate daily high-resolution 200 m localized predictions representing deviations from the area 1×1 km grid predictions. We used mixed models regressing PM2.5 measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We then use generalized additive mixed models with spatial smoothing to generate grid cell predictions when AOD was missing. Finally, to get 200 m localized predictions, we regressed the residuals from the final model for each monitor against the local spatial and temporal variables at each monitoring site. Results Our model performance was excellent (mean out-of-sample R2=0.88). The spatial and temporal components of the out-of-sample results also presented very good fits to the withheld data (R2=0.87, R2=0.87). In addition, our results revealed very little bias in the predicted concentrations (Slope of predictions versus withheld observations = 0.99). Conclusion Our daily model results show high predictive accuracy at high spatial resolutions and will be useful in reconstructing exposure histories for epidemiological studies across this region.

BackgroundAltered patterns of gene expression mediate the effects of particulate matter (PM) on human health, but mechanisms through which PM modifies gene expression are largely undetermined. MicroRNAs (miRNAs) are highly conserved, noncoding small RNAs that regulate the expression of broad gene networks at the posttranscriptional level.ObjectivesWe evaluated the effects of exposure to PM and PM metal components on candidate miRNAs (miR-222, miR-21, and miR-146a) related with oxidative stress and inflammatory processes in 63 workers at an electric-furnace steel plant.MethodsWe measured miR-222, miR-21, and miR-146a expression in blood leukocyte RNA on the first day of a workweek (baseline) and after 3 days of work (postexposure). Relative expression of miRNAs was measured by real-time polymerase chain reaction. We measured blood oxidative stress (8-hydroxyguanine) and estimated individual exposures to PM1 (< 1 μm in aerodynamic diameter), PM10 (< 10 μm in aerodynamic diameter), coarse PM (PM10 minus PM1), and PM metal components (chromium, lead, cadmium, arsenic, nickel, manganese) between the baseline and postexposure measurements.ResultsExpression of miR-222 and miR-21 (using the 2−ΔΔCT method) was significantly increased in postexposure samples (miR-222: baseline = 0.68 ± 3.41, postexposure = 2.16 ± 2.25, p = 0.002; miR-21: baseline = 4.10 ± 3.04, postexposure = 4.66 ± 2.63, p = 0.05). In postexposure samples, miR-222 expression was positively correlated with lead exposure (β = 0.41, p = 0.02), whereas miR-21 expression was associated with blood 8-hydroxyguanine (β = 0.11, p = 0.03) but not with individual PM size fractions or metal components. Postexposure expression of miR-146a was not significantly different from baseline (baseline = 0.61 ± 2.42, postexposure = 1.90 ± 3.94, p = 0.19) but was negatively correlated with exposure to lead (β = −0.51, p = 0.011) and cadmium (β = −0.42, p = 0.04).ConclusionsChanges in miRNA expression may represent a novel mechanism mediating responses to PM and its metal components.

BackgroundElderly patients with atrial fibrillation are at higher risk of both ischemic and bleeding events compared to younger patients. In a prespecified analysis from the ENGAGE AF‐TIMI 48 trial, we evaluate clinical outcomes with edoxaban versus warfarin according to age.Methods and ResultsTwenty‐one thousand one‐hundred and five patients enrolled in the ENGAGE AF‐TIMI 48 trial were stratified into 3 prespecified age groups: <65 (n=5497), 65 to 74 (n=7134), and ≥75 (n=8474) years. Older patients were more likely to be female, with lower body weight and reduced creatinine clearance, leading to higher rates of edoxaban dose reduction (10%, 18%, and 41% for the 3 age groups, P<0.001). Stroke or systemic embolic event (1.1%, 1.8%, and 2.3%) and major bleeding (1.8%, 3.3%, and 4.8%) rates with warfarin increased across age groups (P trend<0.001 for both). There were no interactions between age group and randomized treatment in the primary efficacy and safety outcomes. In the elderly (≥75 years), the rates of stroke/systemic embolic event were similar with edoxaban versus warfarin (hazard ratio 0.83 [0.66–1.04]), while major bleeding was significantly reduced with edoxaban (hazard ratio 0.83 [0.70–0.99]). The absolute risk difference in major bleeding (−82 events/10 000 pt‐yrs) and in intracranial hemorrhage (−73 events/10 000 pt‐yrs) both favored edoxaban over warfarin in older patients.ConclusionsAge has a greater influence on major bleeding than thromboembolic risk in patients with atrial fibrillation. Given the higher rates of bleeding and death with increasing age, treatment of elderly patients with edoxaban provides an even greater absolute reduction in safety events over warfarin, compared to treatment with edoxaban versus warfarin in younger patients.Clinical Trial Registration URL: https://www.clinicaltrials.gov/. Unique identifier: NCT00781391.

The moral sense is among the most complex aspects of the human mind. Despite substantial evidence confirming gender-related neurobiological and behavioral differences, and psychological research suggesting gender specificities in moral development, whether these differences arise from cultural effects or are innate remains unclear. In this study, we investigated the role of gender, education (general education and health education) and religious belief (Catholic and non-Catholic) on moral choices by testing 50 men and 50 women with a moral judgment task. Whereas we found no differences between the two genders in utilitarian responses to non-moral dilemmas and to impersonal moral dilemmas, men gave significantly more utilitarian answers to personal moral (PM) dilemmas (i.e., those courses of action whose endorsement involves highly emotional decisions). Cultural factors such as education and religion had no effect on performance in the moral judgment task. These findings suggest that the cognitive-emotional processes involved in evaluating PM dilemmas differ in men and in women, possibly reflecting differences in the underlying neural mechanisms. Gender-related determinants of moral behavior may partly explain gender differences in real-life involving power management, economic decision-making, leadership and possibly also aggressive and criminal behaviors.

BackgroundMitochondria have small mitochondrial DNA (mtDNA) molecules independent from the nuclear DNA, a separate epigenetic machinery that generates mtDNA methylation, and are primary sources of oxidative-stress generation in response to exogenous environments. However, no study has yet investigated whether mitochondrial DNA methylation is sensitive to pro-oxidant environmental exposures.MethodsWe sampled 40 male participants (20 high-, 20 low-exposure) from each of three studies on airborne pollutants, including investigations of steel workers exposed to metal-rich particulate matter (measured as PM1) in Brescia, Italy (Study 1); gas-station attendants exposed to air benzene in Milan, Italy (Study 2); and truck drivers exposed to traffic-derived Elemental Carbon (EC) in Beijing, China (Study 3). We have measured DNA methylation from buffy coats of the participants. We measured methylation by bisulfite-Pyrosequencing in three mtDNA regions, i.e., the transfer RNA phenylalanine (MT-TF), 12S ribosomal RNA (MT-RNR1) gene and “D-loop” control region. All analyses were adjusted for age and smoking.ResultsIn Study 1, participants with high metal-rich PM1 exposure showed higher MT-TF and MT-RNR1 methylation than low-exposed controls (difference = 1.41, P = 0.002); MT-TF and MT-RNR1 methylation was significantly associated with PM1 exposure (beta = 1.35, P = 0.025); and MT-RNR1 methylation was positively correlated with mtDNA copy number (r = 0.36; P = 0.02). D-loop methylation was not associated with PM1 exposure. We found no effects on mtDNA methylation from air benzene (Study 2) and traffic-derived EC exposure (Study 3).ConclusionsMitochondrial MT-TF and MT-RNR1 DNA methylation was associated with metal-rich PM1 exposure and mtDNA copy number. Our results suggest that locus-specific mtDNA methylation is correlated to selected exposures and mtDNA damage. Larger studies are needed to validate our observations.

BackgroundOxidative stress generation is a primary mechanism mediating the effects of Particulate Matter (PM) on human health. Although mitochondria are both the major intracellular source and target of oxidative stress, the effect of PM on mitochondria has never been evaluated in exposed individuals.MethodsIn 63 male healthy steel workers from Brescia, Italy, studied between April and May 2006, we evaluated whether exposure to PM was associated with increased mitochondrial DNA copy number (MtDNAcn), an established marker of mitochondria damage and malfunctioning. Relative MtDNAcn (RMtDNAcn) was determined by real-time PCR in blood DNA obtained on the 1st (time 1) and 4th day (time 2) of the same work week. Individual exposures to PM10, PM1, coarse particles (PM10-PM1) and airborne metal components of PM10 (chromium, lead, arsenic, nickel, manganese) were estimated based on measurements in the 11 work areas and time spent by the study subjects in each area.ResultsRMtDNAcn was higher on the 4th day (mean = 1.31; 95%CI = 1.22 to 1.40) than on the 1st day of the work week (mean = 1.09; 95%CI = 1.00 to 1.17). PM exposure was positively associated with RMtDNAcn on either the 4th (PM10: β = 0.06, 95%CI = -0.06 to 0.17; PM1: β = 0.08, 95%CI = -0.08 to 0.23; coarse: β = 0.06, 95%CI = -0.06 to 0.17) or the 1st day (PM10: β = 0.18, 95%CI = 0.09 to 0.26; PM1: β = 0.23, 95%CI = 0.11 to 0.35; coarse: β = 0.17, 95%CI = 0.09 to 0.26). Metal concentrations were not associated with RMtDNAcn.ConclusionsPM exposure is associated with damaged mitochondria, as reflected in increased MtDNAcn. Damaged mitochondria may intensify oxidative-stress production and effects.

Satellite-derived Aerosol Optical Depth (AOD) measurements have the potential to provide spatio-temporally resolved predictions of both long and short term exposures, but previous studies have generally shown moderate predictive power and lacked detailed high spatio-temporal resolution predictions across large domains. We aimed at extending our previous work by validating our model in another region with different geographical and metrological characteristics, and incorporating fine scale land use regression and nonrandom missingness to better predict PM2.5 concentrations for days with or without satellite AOD measures. We start by calibrating AOD data for 2000–2008 across the Mid-Atlantic. We used mixed models regressing PM2.5 measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We used inverse probability weighting to account for nonrandom missingness of AOD, nested regions within days to capture spatial variation in the daily calibration, and introduced a penalization method that reduces the dimensionality of the large number of spatial and temporal predictors without selecting different predictors in different locations. We then take advantage of the association between grid-cell specific AOD values and PM2.5 monitoring data, together with associations between AOD values in neighboring grid cells to develop grid cell predictions when AOD is missing. Finally to get local predictions (at the resolution of 50m), we regressed the residuals from the predictions for each monitor from these previous steps against the local land use variables specific for each monitor. “Out-of-sample” ten-fold-cross validation was used to quantify the accuracy of our predictions at each step. For all days without AOD values, model performance was excellent (mean “out-of-sample” R2=0.81, year-to-year variation 0.79–0.84). Upon removal of outliers in the PM2.5 monitoring data, the results of the cross validation procedure was even better (overall mean ”out of sample” R2 of 0.85). Further, cross validation results revealed no bias in the predicted concentrations (Slope of observed vs. predicted = 0.97–1.01). Our model allows one to reliably assess short-term and long-term human exposures in order to investigate both the acute and effects of ambient particles, respectively.