Systemic inflammatory markers associated with cardiovascular disease and acute and chronic exposure to fine particulate matter air pollution (PM2.5) among US NHANES adults with metabolic syndrome

Dabass, Arvind; Talbott, Evelyn O.; Rager, Judith R.; Marsh, Gary M.; Venkat, Arvind; Holguín, Fernando; Sharma, Ravi

doi:10.1016/j.envres.2017.11.042

Cited by 83 publications

(40 citation statements)

References 27 publications

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“…Past medical histories, such as heart failure, hypertension, diabetes mellitus, stroke, myocardial infarction (MI), peripheral vascular disease, or atrial fibrillation diagnosed before undergoing a health examination, were assessed based on the ICD-10 codes ( Supplementary Table 1). Each diagnosis was defined as the first occurrence during at least two different days of outpatient hospital visits or on the first hospital admission ( Supplementary Table 1) [13,14]. The following exclusion criteria were applied ( Fig.…”

Section: Study Cohortmentioning

confidence: 99%

Long-term fine particulate matter exposure and cardiovascular mortality in the general population: a nationwide cohort study

Kim

Yang

Lee

et al. 2020

Journal of Cardiology

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A B S T R A C TBackground: Although eastern Asian countries are exposed to high levels of air pollution, the impact of long-term exposures to fine particulate matter (PM 2.5 ) air pollution on all-cause and cardiovascular mortality is not well identified. We assessed the relationship between long-term PM 2.5 exposure and allcause/cardiovascular mortalities. Methods: We included 436,933 subjects who received national health examinations from the Korean National Health Insurance Service-based National Sample Cohort. We matched subjects' residentialaddress areas with hourly-measurements of PM 2.5 concentration data. We estimated the risk of mortality with average PM 2.5 exposure during the study period using a Cox proportional-hazards model. Results: During 1,683,271 personÁyears, all-cause and cardiovascular mortalities were observed in 6432 and 1603 subjects (382 and 95 per 100,000 personÁyears, respectively). An increase in 10 mg/m 3 in PM 2.5 was associated with increases in all-cause and cardiovascular mortalities by 3.4 % [2.7-4.1] and 4.7 % [3.6-5.8], respectively (each p < 0.001). PM 2.5 was linearly and significantly correlated with these all-cause and cardiovascular mortalities above 18 mg/m 3 of PM 2.5 (p < 0.001), but it was not significant below 18 mg/m 3 of PM 2.5 . To investigate the specific PM 2.5 concentration for raising cardiovascular mortality more, we analyzed the sensitivities/specificities for different PM 2.5 levels, and 18 mg/m 3 showed the highest Youden's index (sensitivity + specificity-1) with c-index of 0.85 (0.84-0.86). PM 2.5 effect on all-cause mortality was more profound in subjects with previous myocardial infarction compared to the opposite population. Conclusions: In the Korean general population exposed to high-air pollution, long-term PM 2.5 exposure was linearly associated with increased risk for all-cause and cardiovascular mortality, especially above 18 mg/m 3 of PM 2.5 .

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Section: Study Cohortmentioning

confidence: 99%

Long-term fine particulate matter exposure and cardiovascular mortality in the general population: a nationwide cohort study

Kim

Yang

Lee

et al. 2020

Journal of Cardiology

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“…Fine particulate matter with a diameter < 2.5 µm (PM 2.5 ) has been associated with adverse health effects, ranging from acute, short-term to chronic health outcomes [1][2][3], including increased respiratory symptoms [4][5][6], worsened asthma [7,8], increased cardiovascular diseases [9,10], decreased lung function [11], and increased premature death from heart or lung diseases [12,13]. With rapidly increasing fleet of vehicles and more stringent emission regulations for industry than previously [14], concentrations of PM 2.5 have been increasingly more affected by local traffic emissions than coal or It has multiple emission sources, and adverse meteorology and terrain-particle matters come from industrial emissions, coal combustion, and motor vehicle exhaust; in winter, heavy coal emission in heating, low wind speed, and high atmospheric stability aggravate concentrating of particle matters; the Mount Taihang-Yanshan surrounding this region may inhibit dispersion of air pollutants from it.…”

Section: Introductionmentioning

confidence: 99%

A Robust Deep Learning Approach for Spatiotemporal Estimation of Satellite AOD and PM2.5

2020

Remote Sensing

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Accurate estimation of fine particulate matter with diameter ≤2.5 μm (PM2.5) at a high spatiotemporal resolution is crucial for the evaluation of its health effects. Previous studies face multiple challenges including limited ground measurements and availability of spatiotemporal covariates. Although the multiangle implementation of atmospheric correction (MAIAC) retrieves satellite aerosol optical depth (AOD) at a high spatiotemporal resolution, massive non-random missingness considerably limits its application in PM2.5 estimation. Here, a deep learning approach, i.e., bootstrap aggregating (bagging) of autoencoder-based residual deep networks, was developed to make robust imputation of MAIAC AOD and further estimate PM2.5 at a high spatial (1 km) and temporal (daily) resolution. The base model consisted of autoencoder-based residual networks where residual connections were introduced to improve learning performance. Bagging of residual networks was used to generate ensemble predictions for better accuracy and uncertainty estimates. As a case study, the proposed approach was applied to impute daily satellite AOD and subsequently estimate daily PM2.5 in the Jing-Jin-Ji metropolitan region of China in 2015. The presented approach achieved competitive performance in AOD imputation (mean test R2: 0.96; mean test RMSE: 0.06) and PM2.5 estimation (test R2: 0.90; test RMSE: 22.3 μg/m3). In the additional independent tests using ground AERONET AOD and PM2.5 measurements at the monitoring station of the U.S. Embassy in Beijing, this approach achieved high R2 (0.82–0.97). Compared with the state-of-the-art machine learning method, XGBoost, the proposed approach generated more reasonable spatial variation for predicted PM2.5 surfaces. Publically available covariates used included meteorology, MERRA2 PBLH and AOD, coordinates, and elevation. Other covariates such as cloud fractions or land-use were not used due to unavailability. The results of validation and independent testing demonstrate the usefulness of the proposed approach in exposure assessment of PM2.5 using satellite AOD having massive missing values.

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“…Generally, PM is known to cause abnormal inflammatory and coagulation responses in the entire body [5,6].…”

Section: Introductionmentioning

confidence: 99%

Relationship between Particulate Matter (PM₁₀) and Airway Inflammation Measured with Exhaled Nitric Oxide Test in Seoul, Korea

Choi

Sim

et al. 2020

Canadian Respiratory Journal

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Purpose. Particulate matter (PM) is increasing every year in Asia. It is not fully understood how the airway is affected when inhaling PM. We investigated the correlation between particulate matter with a diameter of less than 10 μm (PM10) and fractional exhaled nitric oxide (FeNO) to determine whether PM causes airway inflammation. Material and Methods. We analyzed patients who visited our outpatient clinic and tested FeNO from January 2016 to December 2017 at the Korea University Guro Hospital. PM10 data were provided by the government of the Republic of South Korea, and measuring station of PM10 is located 800 meters from the hospital. We analyzed the correlation between PM10 and FeNO by a Pearson correlation analysis and by a multivariate linear regression analysis. To identify the most correlated times, we analyzed the correlation between the FeNO and PM10 daily average from the day of visit to 4 days before visit. Results. FeNO positively correlated with PM10 at two days before hospital visit in the Pearson correlation (Pearson correlation coefficient = 0.057; P-value = 0.023) and in the multivariate linear regression analysis (B = 0.051, P-value = 0.026). If the PM10 increased by 100 μg/m3, the FeNO result was expected to rise to 8.3 ppb in healthy people without respiratory disease. Conclusion. The positive correlation was found in both healthy people and asthmatic patients. Therefore, PM10 can increase airway inflammation.

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Systemic inflammatory markers associated with cardiovascular disease and acute and chronic exposure to fine particulate matter air pollution (PM2.5) among US NHANES adults with metabolic syndrome

Cited by 83 publications

References 27 publications

Long-term fine particulate matter exposure and cardiovascular mortality in the general population: a nationwide cohort study

Long-term fine particulate matter exposure and cardiovascular mortality in the general population: a nationwide cohort study

A Robust Deep Learning Approach for Spatiotemporal Estimation of Satellite AOD and PM2.5

Relationship between Particulate Matter (PM₁₀) and Airway Inflammation Measured with Exhaled Nitric Oxide Test in Seoul, Korea

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Systemic inflammatory markers associated with cardiovascular disease and acute and chronic exposure to fine particulate matter air pollution (PM2.5) among US NHANES adults with metabolic syndrome

Cited by 83 publications

References 27 publications

Long-term fine particulate matter exposure and cardiovascular mortality in the general population: a nationwide cohort study

Long-term fine particulate matter exposure and cardiovascular mortality in the general population: a nationwide cohort study

A Robust Deep Learning Approach for Spatiotemporal Estimation of Satellite AOD and PM2.5

Relationship between Particulate Matter (PM10) and Airway Inflammation Measured with Exhaled Nitric Oxide Test in Seoul, Korea

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Product

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Relationship between Particulate Matter (PM₁₀) and Airway Inflammation Measured with Exhaled Nitric Oxide Test in Seoul, Korea