BACKGROUND:Unraveling gene-environment interaction can provide a novel insight into early disease prevention. Nevertheless, current understanding of the interplay between genetic predisposition and air pollution in relation to myocardial infarction (MI) risk remains limited. Furthermore, the potential long-term influence of air pollutants on MI incidence risk warrants more conclusive evidence in a community population. OBJECTIVE:We investigated interactions between genetic predisposition and exposure to air pollutants on MI incidence. METHODS:This study incorporated a sample of 456,354 UK Biobank participants and annual mean air pollution (PM2.5, PM10, NO2, and NOx) from the UK Department for Environment, Food and Rural Affairs (2006-2021). The Cox proportional hazards model was employed to explore MI incidence after chronic air pollutants exposure. By quantifying genetic risk through the calculation of polygenic risk score (PRS), this study further examined the interactions between genetic risk and exposure to air pollutants in the development of MI on both additive and multiplicative scales. RESULTS:Among 456,354 participants, 9,114 incident MI events were observed during a median follow-up of 12.08 y. Chronic exposure to air pollutants was linked with an increased risk of MI occurrence. Specifically, the hazard ratios (per interquartile range) were 1.12 (95% CI: 1.10, 1.13) for PM2.5, 1.20 (95% CI: 1.19, 1.22) for PM10, 1.13 (95% CI: 1.12, 1.15) for NO2, and 1.12 (95% CI: 1.11, 1.13) for NOx. In terms of the joint effects, participants with high PRS and high level of air pollution exposure exhibited the greatest risk of MI among all study participants (∼255% to 324%). Remarkably, both multiplicative and additive interactions were detected in the ambient air pollutants exposure and genetic risk on the incidence of MI. DISCUSSION:There were interactions between exposure to ambient air pollutants and genetic susceptibility on the risk of MI onset. Moreover, the joint effects of these two exposures were greater than the effect of each factor alone. https://doi.org/10.1289/EHP14291.

Background and Aims Air pollutants are important contributors to cardiovascular diseases, but associations between long-term exposure to air pollutants and the risk of abdominal aortic aneurysm (AAA) are still unknown.Methods This study was conducted using a sample of 449 463 participants from the UK Biobank. Hazard ratios and 95% confidence intervals for the risk of AAA incidence associated with long-term exposure to air pollutants were estimated using the Cox proportional hazards model with time-varying exposure measurements. Additionally, the cumulative incidence of AAA was calculated by using the Fine and Grey sub-distribution hazards regression model. Furthermore, this study investigated the combined effects and interactions between air pollutants exposure and genetic predisposition in relation to the risk of AAA onset.Results Long-term exposure to particulate matter with an aerodynamic diameter <2.5 m [PM2.5, 1.21 (1.16, 1.27)], particulate matter with an aerodynamic diameter <10 m [PM10, 1.21 (1.16, 1.27)], nitrogen dioxide [NO2, 1.16 (1.11, 1.22)], and nitrogen oxides [NOx, 1.10 (1.05, 1.15)] was found to be associated with an elevated risk of AAA onset. The detrimental effects of air pollutants persisted even in participants with low-level exposure. For the joint associations, participants with both high levels of air pollutants exposure and high genetic risk had a higher risk of developing AAA compared with those with low concentrations of pollutants exposure and low genetic risk. The respective risk estimates for AAA incidence were 3.18 (2.46, 4.12) for PM2.5, 3.09 (2.39, 4.00) for PM10, 2.41 (1.86, 3.13) for NO2, and 2.01 (1.55, 2.61) for NOx.Conclusions In this study, long-term air pollutants exposure was associated with an increased risk of AAA incidence.

To assess the associations of ambient specific-size PM with brachial-ankle pulse wave velocity (baPWV) and the progression of arterial stiffness. Participants were included from the Kailuan study, the cross-sectional study involved 36,486 participants, while the longitudinal study enrolled 16,871 participants. PM exposures was assessed through satellite-based random forest approaches at a 1 km resolution. Initial observations indicated a link between baseline baPWV and heightened levels of PM1, PM2.5, and PM10 exposure, and greater effects were observed for PM1 (beta: 22.52, 95% CI: 18.14-26.89), followed by PM2.5 (beta: 9.76, 95% CI: 7.52-12.00), and PM10 (beta: 8.88, 95% CI: 7.32-10.45). Furthermore, the growth rate of baPWV was higher in participants exposed to high levels of PM1 exposure (beta: 2.77, 95% CI: 1.19-4.35), succeeded by PM2.5 and PM10. Throughout a median followup period of 4.04 years, arterial stiffness was diagnosed in 1709 subjects. Long-term exposure to PM was linked with an increased risk of incident arterial stiffness, estimated HR for fixed 10 mu g/m3 increments in annual average PM1 was 2.20 (95% CI: 2.01-2.42), PM2.5 was 1.48 (95% CI: 1.41-1.55), and PM10 1.32 (95% CI: 1.27-1.36). PM had a greater impact on men and older individuals (P for interaction <0.001). Long-term exposures to ambient PM1, PM2.5, and PM10 were positively associated with baPWV and an increased risk of arterial stiffness. Higher estimated effects were observed for PM1 than PM2.5 and PM10.

Background Evidence linking air pollutants and the risk of schizophrenia remains limited and inconsistent, and no studies have investigated the joint effect of air pollutant exposure and genetic factors on schizophrenia risk.Aims To investigate how exposure to air pollution affects schizophrenia risk and the potential effect modification of genetic susceptibility.Method Our study was conducted using data on 485 288 participants from the UK Biobank. Cox proportional hazards models were used to estimate the schizophrenia risk as a function of long-term air pollution exposure presented as a time-varying variable. We also derived the schizophrenia polygenic risk score (PRS) utilising data provided by the UK Biobank, and investigated the modification effect of genetic susceptibility.Results During a median follow-up period of 11.9 years, 417 individuals developed schizophrenia (mean age 55.57 years, s.d. = 8.68; 45.6% female). Significant correlations were observed between long-term exposure to four air pollutants (PM2.5; PM10; nitrogen oxides, NOx; nitrogen dioxide, NO2) and the schizophrenia risk in each genetic risk group. Interactions between genetic factors and the pollutants NO2 and NOx had an effect on schizophrenia events. Compared with those with low PRS and low air pollution, participants with high PRS and high air pollution had the highest risk of incident schizophrenia (PM2.5: hazard ratio = 6.25 (95% CI 5.03-7.76); PM10: hazard ratio = 7.38 (95% CI 5.86-9.29); NO2: hazard ratio = 6.31 (95% CI 5.02-7.93); NOx: hazard ratio = 6.62 (95% CI 5.24-8.37)).Conclusions Long-term exposure to air pollutants was positively related to the schizophrenia risk. Furthermore, high genetic susceptibility could increase the effect of NO2 and NOx on schizophrenia risk.

There is mounting recent evidence showing that air pollution exposure may be related to the risk of mental health, yet the association between long-term exposure to air pollution and the risk of incident bipolar disorder (BD) remains unclear. Thus we aim to identify associations between air pollution and the incidence of BD in a prospective population-based cohort. In total, 482,726 participants who were free of BD from the UK Biobank were included in this prospective study. We applied time-varying Cox proportional hazards models, accounting for relevant confounders, and used annual-year moving averages of air pollution as time-varying exposures. The genetic risk for BD was categorized into three categories (low, intermediate, and high) according to the tertiles of polygenic risk score. During a median of 10.79-year follow-up, 923 incident BD events were recorded. Long-term exposures to PM2.5, PM10, NO2, and NOx were associated with increased BD risk. Estimated HRs (95% CIs) for each interquartile range increase in PM2.5, PM10, NO2, and NOx concentrations were 1.31 (1.18-1.45), 1.19 (1.09-1.31), 1.19 (1.08-1.30), and 1.16 (1.07-1.26), respectively. Associations were still observed and even stronger at pollutant concentrations lower than WHO air quality guideline. In subgroup analysis stratified by genetic risk, we observed consistent associations between all pollutants and BD risk in intermediate and high genetic risk groups, but not in low genetic risk group. For example, the HRs (95% CIs) for PM2.5 were 1.00 (0.94-1.53), 1.30 (1.06-1.59), and 1.34 (1.16-1.54) in low, intermediate, and high genetic groups, respectively. In conclusion, long-term exposure to air pollution was significantly associated with an elevated risk of BD. As-sociations of air pollution with BD occurred only within intermediate and high genetic risk categories and were even stronger at the pollutants levels below WHO air quality guidelines. These findings could help inform policy makers regarding ambient air quality standards and BD management.

Background: Evidences on the association between exposure to air pollution and liver enzymes was scarce in low pollution area. We aimed to investigate the association between air pollution and liver enzyme levels and further explore whether alcohol intake influence this association.Methods: This population-based cross-sectional study included 425,773 participants aged 37 to 73 years from the UK Biobank. Land Use Regression was applied to assess individual exposures to ambient air pollutants. Levels of liver enzymes including AST, ALT, GGT and ALP were determined using the enzyme rate method. Multiple linear regression models were performed to explore the associations between air pollutants and liver enzymes.Results: Long-term low-level exposure to PM2.5 (per 5-μg/m3 increase) was significantly associated with AST (0.59% increase, 95% CI, 0.41 to 0.77%), ALT (0.31% increase, 0.03 to 0.59%), and GGT (1.55% increase, 1.18 to 1.93%); PM10 (per 10-μg/m3 increase) with AST (0.45% increase, 0.25 to 0.65%), ALT (0.52% increase, 0.21 to 0.83%), and GGT (0.69% increase, 0.28 to 1.11%) levels; NOX (per 20-μg/m3 increase) with AST (0.17% increase, 0.13 to 0.22%) and GGT (0.37% increase, 0.27 to 0.47%) levels; and NO2 (per 10-μg/m3 increase) with AST (0.22% increase, 0.17 to 0.27%) and GGT (0.30% increase, 0.19 to 0.41%) levels. Significant modification effects by alcohol consumption were found (p-interaction<0.05). The effects of pollutants on AST, ALT and GGT levels gradually increased along with the weekly alcohol drinking frequency.Conclusions: Long-term low-level air pollutants exposure was associated with elevated liver enzyme levels. In addition, alcohol intake may exacerbate the effect of air pollution on liver enzymes.

Whether brachial-ankle pulse wave velocity (baPWV) is a better predictive indicator than blood pressure (BP) for atherosclerotic cardiovascular diseases (ASCVD) events and all-cause mortality in the general population has not yet been established. The current study included 47,659 participants from the Kailuan cohort in China, who underwent the baPWV test and were free of ASCVD, atrial fibrillation, and cancer at baseline. The hazard ratios (HRs) of ASCVD and all-cause mortality were evaluated using the Cox proportional hazards model. The predictive ability of baPWV, systolic BP (SBP), and diastolic BP (DBP) for ASCVD and all-cause mortality was evaluated using the area under the curve (AUC) and concordance index (C-index). Within the median follow-up period of 3.27 and 3.32 person-years, 885 ASCVD events and 259 deaths occurred, respectively. The HRs of ASCVD and all-cause mortality increased with the increase of baPWV, SBP, and DBP. When baPWV, SBP, and DBP were analyzed as continuous variables, the adjusted HRs were 1.29 (95% CI, 1.22–1.37), 1.28 (95% CI, 1.20–1.37), and 1.26 (95% CI, 1.17–1.34) for each standard deviation increase, respectively. The AUC and C-index for baPWV in predicting ASCVD and all-cause mortality were 0.744 and 0.750, respectively, while those for SBP were 0.697 and 0.620, those for DBP were 0.666 and 0.585. The AUC and C-index of baPWV were higher than those of SBP and DBP ( P < 0.001). Therefore, baPWV is an independent predictor of ASCVD and all-cause mortality in the general Chinese population, and its predictive ability is superior to that of BP. baPWV is a more ideal screening method for ASCVD in large-scale population.

Background: The impact of residential greenness on incident idiopathic pulmonary fibrosis (IPF) is unknown. We aimed to assess the association between residential greenness and incident IPF, identify underlying pathways, and further evaluate the effect among different genetic subgroups. Methods: 469,348 participants in the UK Biobank were included and followed until December 2020. Normalized difference vegetation index (NDVI) within 300-, 500-, 1000-, and 1500-m buffers (NDVI300m, NDVI500m, NDVI1000m, and NDVI1500m) were employed as indicators of greenness. The polygenic risk score (PRS) was constructed based on 13 independent SNPs. Cox models were fitted to assess the association of residential greenness with incident IPF. Casual mediation analyses were applied to evaluate potential mediators. Findings: After a median follow-up of 11.85 years, 1574 IPF cases were identified. We found residential greenness inversely associated with incident IPF. The HRs (95%CIs) for each interquartile increase of NDVI300m, NDVI500m, NDVI1000m, NDVI1500m were 0.93 (0.87, 0.99), 0.92 (0.86, 0.98), 0.89 (0.83, 0.95), and 0.89 (0.83, 0.95), respectively. The association was stronger among individuals with intermediate or high genetic risk. In mediation analyses, the main mediators identified were PM2.5 and NO2, with proportion mediated estimated to be 31.92% and 40.61% respectively for NDVI300m. Interpretation: Residential greenness was associated with reduced risk of incident IPF.

Evidence on the association between systolic blood pressure (SBP) trajectories and arterial stiffness progression is scarce. The current study aimed to identify the association between SBP trajectories and the progression of arterial stiffness over time in Chinese adults. This study included 30,384 adult participants. Latent mixture modeling was used to identify the SBP trajectory patterns from 2006 to 2010. The brachial–ankle pulse wave velocity (baPWV) was used to measure arterial stiffness. The associations between SBP trajectories and the progression of arterial stiffness were explored using multiple linear regression models. We identified five distinct SBP trajectories and took the low-stable group as the reference. In the cross-sectional analysis, the four SBP trajectories were significantly associated with higher baPWV levels (p < 0.001) compared with the reference. In the longitudinal analysis, after adjusting for covariates and the baseline baPWV, the SBP trajectories were significantly associated with the progression of the baPWV, with corresponding β (95% CI) values of 23.3 (17.2–29.5) cm/s per year for the moderate-stable group, 44.8 (36.6–52.9) cm/s per year for the moderate-increasing group, 54.6 (42.2–67.0) cm/s per year for the elevated-decreasing group, and 66.8 (54.7–79.0) cm/s per year for the elevated-stable group. Similar significant results were also observed in the non-hypertensive population. In conclusion, SBP trajectories were related to the baseline baPWV and the progression of the baPWV. Blood pressure control may be considered a therapeutic target to further reduce the risk of arterial stiffness.

Background Large hemispheric infarction (LHI) is a severe form of stroke with high mortality and disability rates. The purpose of this study was to explore predictive indicators of the in-hospital mortality of LHI patients treated conservatively without decompressive hemicraniectomy. Method We performed a retrospective study of 187 consecutive patients with LHI between January 1, 2016 to May 31, 2019. The receiver operating curves were preformed to evaluate predictive performance of demographics factors, biomarkers and radiologic characteristics. Significant prognostic factors were combined to build a nomogram to predict the risk of in-hospital death of individual patients. Result One hundred fifty-eight patients with LHI were finally enrolled, 58 of which died. Through multivariate logistic regression analysis, we identified that independent prognostic factors for in-hospital death were age (adjusted odds ratio [aOR] = 1.066; 95% confidence interval [CI], 1.025–1.108; P = 0.001), midline shift (MLS, aOR = 1.330, 95% CI, 1.177–1.503; P < 0.001), and neutrophil-to-lymphocyte ratio (NLR, aOR = 3.319, 95% CI, 1.542–7.144; P = 0.002). NLR may serve as a better predictor than white blood count (WBC) and neutrophil counts. Lastly, we used all of the clinical characteristics to establish a nomogram for predicting the prognosis, area under the curve (AUC) of this nomogram was 0.858 (95% CI, 0.794–0.908). Conclusion This study shows that age, MLS, and admission NLR value are independent predictors of in-hospital mortality in patients with LHI. Moreover, nomogram, serve as a precise and convenient tool for the prognosis of LHI patients.