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.