Short-term effects of ambient gaseous air pollution on blood platelet mitochondrial DNA methylation and myocardial ischemia.

BACKGROUND: The potential effects of short-term exposure to major ambient gaseous pollutants (ozone: O3, carbon monoxide: CO, and sulfur dioxide: SO2) on platelet mitochondrial DNA (mtDNA) methylation have been uncertain and no studies have examined whether platelet mtDNA methylation levels could modify the associations between ambient gaseous pollutants and the risks of ST-segment depression (STDE) and T-wave inversion events (TIE), two indicators of myocardial ischemia. METHODS: This study used data from a randomized, double-blind, placebo-controlled intervention study with a standardized 24-hour exposure protocol among 110 participants in Beijing. Absolute changes in platelet mtDNA methylation (ACmtDNAm) levels were determined by two repeated measurements on platelet mtDNA methylation levels in blood samples collected before and after the 24-hour exposure period. A multivariable linear regression model and a generalized linear model with a Poisson link function were used to investigate the associations of ambient gaseous pollutants with platelet mtDNA methylation levels, STDE, and TIE, respectively. RESULTS: Short-term O3 exposure was significantly associated with decreased ACmtDNAm at ATP6_P1 but increased ACmtDNAm at mt12sRNA, MT-COX1, and MT-COX1_P2; short-term CO and SO2 exposures were significantly associated with decreased ACmtDNAm at D-loop, MT-COX3- and ATP-related genes. Moreover, short-term O3 exposure was significantly associated with increased risks of STDE and TIE, and ACmtDNAm at MT-COX1 and MT-COX1_P2 modified the association between short-term O3 exposure and STDE events. L-Arg supplementation attenuated the effects of ambient gaseous pollutants, particularly O3, on ACmtDNAm and STDE. CONCLUSIONS: Platelet mtDNA methylation levels are promising biomarkers of short-term exposure to ambient gaseous air pollution, and are likely implicated in the mechanism behind the association of ambient O3 pollution with adverse cardiovascular effects. L-Arg supplementation showed the potential to mitigate the adverse effects of ambient O3 pollution.

Ozone pollution and hospital admissions for cardiovascular events.

AIMS: The available literature on morbidity risk of cardiovascular diseases associated with ambient ozone pollution is still limited. This study examined the potential acute effects of exposure to ambient ozone pollution on hospital admissions of cardiovascular events in China. METHODS AND RESULTS: A two-stage multi-city time-series study approach was used to explore the associations of exposure to ambient ozone with daily hospital admissions (n = 6 444 441) for cardiovascular events in 70 Chinese cities of prefecture-level or above during 2015-17. A 10 µg/m3 increment in 2-day average daily 8 h maximum ozone concentrations was associated with admission risk increases of 0.46% [95% confidence interval (CI): 0.28%, 0.64%] in coronary heart disease, 0.45% (95% CI: 0.13%, 0.77%) in angina pectoris, 0.75% (95% CI: 0.38%, 1.13%) in acute myocardial infarction (AMI), 0.70% (95% CI: 0.41%, 1.00%) in acute coronary syndrome, 0.50% (95% CI: 0.24%, 0.77%) in heart failure, 0.40% (95% CI: 0.23%, 0.58%) in stroke and 0.41% (95% CI: 0.22%, 0.60%) in ischemic stroke, respectively. The excess admission risks for these cardiovascular events associated with high ozone pollution days (with 2-day average 8-h maximum concentrations ≥100 µg/m3 vs. < 70 µg/m3) ranged from 3.38% (95% CI: 1.73%, 5.06%) for stroke to 6.52% (95% CI: 2.92%, 10.24%) for AMI. CONCLUSION: Ambient ozone was associated with increased hospital admission risk for cardiovascular events. Greater admission risks for cardiovascular events were observed under high ozone pollution days. These results provide evidence for the harmful cardiovascular effects of ambient ozone and call for special attention on the control of high ozone pollution.

Short-term exposure to ambient nitrogen dioxide and increased hospitalization burden for depression in China: a multicity analysis.

Evidence for the increased hospitalization burden, including admissions, expenditures and length of hospital stay (LOS) for depression attributable to ambient nitrogen dioxide (NO2) is lacking. We investigated the associations between short-term exposure to ambient NO2 and attributable admissions, hospitalization expenditures and LOS for depression in 57 Chinese cities during 2013-2017 using a well-established two-stage time-series study approach. Short-term exposure to ambient NO2 was associated with significantly increased admissions, hospitalization expenditures and LOS for depression, and the attributable fractions were 6.87% (95% CI: 2.90%, 10.65%), 7.12% (3.01%, 11.04%) and 6.12% (2.59%, 9.50%) at lag02, respectively. The projected total attributable admissions, hospitalization expenditures and LOS for depression related to ambient NO2 at the national level were 23,335 (9,863, 36,181) admissions, 318.70 (134.43, 492.21) million CNY and 539.55 (227.99, 836.99) thousand days during the study period, respectively. Short-term exposure to ambient NO2 is associated with increased hospitalization burden for depression.

Cardiovascular Disease Prediction by Machine Learning Algorithms Based on Cytokines in Kazakhs of China.

BACKGROUND: Cardiovascular disease (CVD) is the leading cause of mortality worldwide. Accurately identifying subjects at high-risk of CVD may improve CVD outcomes. We sought to systematically examine the feasibility and performance of 7 widely used machine learning (ML) algorithms in predicting CVD risks. METHODS: The final analysis included 1508 Kazakh subjects in China without CVD at baseline who completed follow-up. All subjects were randomly divided into the training set (80%) and the test set (20%). L1-penalized logistic regression (LR), support vector machine with radial basis function (SVM), decision tree (DT), random forest (RF), k-nearest neighbors (KNN), Gaussian naive Bayes (NB), and extreme gradient boosting (XGB) were employed for prediction CVD outcomes. Ten-fold cross-validation was used during model developing and hyperparameters tuning in the training set. Model performance was evaluated in the test set in light of discrimination, calibration, and clinical usefulness. RF was applied to obtain the variable importance of included variables. Twenty-two variables, including sociodemographic characteristics, medical history, cytokines, and synthetic indices, were used for model development. RESULTS: Among 1508 subjects, 203 were diagnosed with CVD over a median follow-up of 5.17 years. All 7 models had moderate to excellent discrimination (AUC ranged from 0.770 to 0.872) and were well calibrated. LR and SVM performed identically with an AUC of 0.872 (95% CI: 0.829-0.907) and 0.868 (95% CI: 0.825-0.904), respectively. LR had the lowest Brier score (0.078) and the highest sensitivity (97.1%). Decision curve analysis indicated that SVM was slightly better than LR. The inflammatory cytokines, such as hs-CRP and IL-6, were identified as strong predictors of CVD. CONCLUSION: SVM and LR can be applied to guide clinical decision-making in the Kazakh Chinese population, and further study is required to ensure their accuracies.

External validation of three atherosclerotic cardiovascular disease risk equations in rural areas of Xinjiang, China.

BACKGROUND: To externally validate the Prediction for ASCVD Risk in China (PAR) risk equation for predicting the 5-year atherosclerotic cardiovascular disease (ASCVD) risk in the Uyghur and Kazakh populations from rural areas in northwestern China and compare its performance with those of the pooled cohort equations (PCE) and Framingham risk score (FRS). METHODS: The final analysis included 3347 subjects aged 40-74 years without CVD at baseline. The 5-year ASCVD risk was calculated using the PAR, PCE, and FRS. Discrimination, calibration, and clinical usefulness of the three equations in predicting the 5-year ASCVD risk were assessed before and after recalibration. RESULTS: Of 3347 included subjects, 1839 were female. We observed 286 ASCVD events in within 5-year follow-up. All three risk equations had moderate discrimination in both men and women. C-indices of PAR, PCE, and FRS were 0.727 (95% CI, 0.725-0.729), 0.727 (95% CI, 0.725-0.729), and 0.740 (95% CI, 0.738-0.742), respectively, in men; the corresponding C-indices were 0.738 (95% CI, 0.737-0.739), 0.731 (95% CI, 0.730-0.732), and 0.761 (95% CI, 0.760-0.762), respectively, in women. PCE, PAR and FRS substantially underestimated the 5-year ASCVD risk in women by 70, 23 and 51%, respectively. However, PAR and FRS fairly predicted the risk in men and PAR was well calibrated. The calibrations of the three risk equations could be changed by recalibration. The decision curve analyses demonstrated that at the threshold risk of 5%, PCE was the most clinically useful in both men and women after recalibration. CONCLUSIONS: All three risk equations underestimated the 5-year ASCVD risk in women, while PAR and FRS fairly predicted that in men. However, the results of predictive performances for three risk equations are inconsistent, more accurate risk equations are required in the primary prevention of ASCVD aiming to this Uyghur and Kazakh populations.