Long-term air pollution exposure and the blood metabolome: The rotterdam study.

BACKGROUND: Air pollution is a well-established risk factor for several adverse health outcomes, but the specific molecular mechanisms, particularly those involving metabolic processes, remain incompletely understood. OBJECTIVE: To evaluate associations between long-term air pollutant exposure and circulating plasma metabolites in two sub-cohorts of the population-based Rotterdam Study. METHODS: We analyzed data from 1455 participants of sub-cohort I (mean age 76.9 years, 58% female, 2002-2004) and 1061 participants from sub-cohort III (mean age 62.6 years, 56% female, 2012-2014). Mean annual exposure to fine particulate matter (PM2.5), black carbon, nitrogen dioxide, and ozone (measured both annually and in warm seasons only) were estimated at residential addresses using land use regression models. Plasma metabolites were measured by Metabolon Inc., using ultra-high-performance liquid chromatography coupled with tandem mass spectrometry. Cross-sectional associations between each air pollutant and 940 metabolites were determined using linear regression models. Benjamini-Hochberg false discovery rate (FDR) was utilized to control for multiple testing. Enrichment analysis was performed on statistically significant associated metabolites to identify significant metabolic pathways (p-value <0.05). RESULTS: In sub-cohort I, PM2.5, black carbon, nitrogen dioxide, annual ozone and ozone in warm season were statistically significantly associated with, respectively, 63, 30, 20, 31, and 41 metabolites (FDR <0.05) mostly belonging to lipid and amino acid sub-classes, and unannotated metabolites. Sphinganine, X - 16576 and 2-pyrrolidinone displayed statistically significant associations across all five air pollutants. In sub-cohort III, black carbon, nitrogen dioxide and ozone in warm seasons were associated with a single unannotated metabolite (X - 24970), and annual ozone with two unannotated metabolites (X - 24970 and X - 24306). Enriched pathways identified in sub-cohort I included pyrimidine metabolism and steroid hormone biosynthesis. CONCLUSIONS: Our study revealed associations of long-term air pollutant exposure with several metabolites and enrichment of two pathways, which are known to be involved in the adrenal and reproductive system and cell metabolism.

Long-term air pollution exposure and markers of cardiometabolic health in the National Longitudinal Study of Adolescent to Adult Health (Add Health).

BACKGROUND: Air pollution exposure is associated with cardiovascular morbidity and mortality. Although exposure to air pollution early in life may represent a critical window for development of cardiovascular disease risk factors, few studies have examined associations of long-term air pollution exposure with markers of cardiovascular and metabolic health in young adults. OBJECTIVES: By combining health data from the National Longitudinal Study of Adolescent to Adult Health (Add Health) with air pollution data from the Fused Air Quality Surface using Downscaling (FAQSD) archive, we: (1) calculated multi-year estimates of exposure to ozone (O3) and particulate matter with an aerodynamic diameter ≤ 2.5 µm (PM2.5) for Add Health participants; and (2) estimated associations between air pollution exposures and multiple markers of cardiometabolic health. METHODS: Add Health is a nationally representative longitudinal cohort study of over 20,000 adolescents aged 12-19 in the United States (US) in 1994-95 (Wave I). Participants have been followed through adolescence and into adulthood with five in-home interviews. Estimated daily concentrations of O3 and PM2.5 at census tracts were obtained from the FAQSD archive and used to generate tract-level annual averages of O3 and PM2.5 concentrations. We estimated associations between average O3 and PM2.5 exposures from 2002 to 2007 and markers of cardiometabolic health measured at Wave IV (2008-09), including hypertension, hyperlipidemia, body mass index (BMI), diabetes, C-reactive protein, and metabolic syndrome. RESULTS: The final sample size was 11,259 individual participants. The average age of participants at Wave IV was 28.4 years (range: 24-34 years). In models adjusting for age, race/ethnicity, and sex, long-term O3 exposure (2002-07) was associated with elevated odds of hypertension, with an odds ratio (OR) of 1.015 (95% confidence interval [CI]: 1.011, 1.029); obesity (1.022 [1.004, 1.040]); diabetes (1.032 [1.009,1.054]); and metabolic syndrome (1.028 [1.014, 1.041]); PM2.5 exposure (2002-07) was associated with elevated odds of hypertension (1.022 [1.001, 1.045]). CONCLUSION: Findings suggest that long-term ambient air pollution exposure, particularly O3 exposure, is associated with cardiometabolic health in early adulthood.

Association between short- and long-term exposures to air pollutants and internalizing/externalizing behavior in children aged 4 to 7 years.

The effects of air pollutants on psychological health have attracted increasing attention worldwide. However, there is limited evidence on the association between air pollution and children's psychological development. This study explores the association between short- and long-term exposures to air pollutants and children's internalizing and externalizing behaviors. A total of 2303 children of 4-7 years were included in this study. We assessed their behavior using the Child Behavior Checklist (4-16 years). The prevalence of internalizing and externalizing behavior was 4.77% and 4.43%, respectively. For short-term exposure, CO pollution was associated with children's internalizing behaviors, with each 1 mg/m3 increment leading to an odds ratio (OR) of 1.063 (95% CI 1.005, 1.124), 1.065 (95% CI 1.009, 1.124), 1.067 (95% CI 1.007, 1.131), and 1.122 (95% CI 1.018, 1.236) at lag04, lag05, lag06, and lag0120, respectively. O3 (per 1 g[Formula: see text]/m3) was negatively associated with internalizing problems at lag2 [OR = 0.991 (95% CI 0.983, 0.999)]. NO2 (per 1 g[Formula: see text]/m3) was significantly associated with externalizing behaviors, with the ORs of 1.067 (95% CI 1.024, 1.111) at lag060 and 1.060 (95% CI 1.010, 1.113) at lag0120. For long-term exposure, it indicated that 1-year exposure to CO (per 1 mg/m3) and PM2.5 (per 1 g[Formula: see text]/m3) was positively associated with internalizing behavioral risk [OR = 1.724 (95% CI 1.187, 2.504); PM2.5: OR = 1.236 (95% CI 1.114, 1.371)], whereas NO2 (per 1 g[Formula: see text]/m3) exposure was associated with an increased risk of externalizing behavior [OR = 1.123 (95% CI 1.003, 1.256)]. In addition, the interaction analysis showed that boys were at a higher risk of abnormal behaviors associated with long-term exposure to CO, PM2.5, and NO2. Our findings reveal a potential link between air pollution exposure and abnormal behaviors in kindergarten children after short-/long-term exposure, which is an essential supplement to the studies on the association between air pollution and children's behavioral problems.

Longitudinal associations between metabolites and long-term exposure to ambient air pollution: Results from the KORA cohort study.

BACKGROUND: Long-term exposure to air pollution has been associated with cardiopulmonary diseases, while the underlying mechanisms remain unclear. OBJECTIVES: To investigate changes in serum metabolites associated with long-term exposure to air pollution and explore the susceptibility characteristics. METHODS: We used data from the German population-based Cooperative Health Research in the Region of Augsburg (KORA) S4 survey (1999-2001) and two follow-up examinations (F4: 2006-08 and FF4: 2013-14). Mass-spectrometry-based targeted metabolomics was used to quantify metabolites among serum samples. Only participants with repeated metabolites measurements were included in the current analysis. Land-use regression (LUR) models were used to estimate annual average concentrations of ultrafine particles, particulate matter (PM) with an aerodynamic diameter less than 10 µm (PM10), coarse particles (PMcoarse), fine particles, PM2.5 absorbance (a proxy of elemental carbon related to traffic exhaust, PM2.5abs), nitrogen oxides (NO2, NOx), and ozone at individuals' residences. We applied confounder-adjusted mixed-effects regression models to examine the associations between long-term exposure to air pollution and metabolites. RESULTS: Among 9,620 observations from 4,261 KORA participants, we included 5,772 (60.0%) observations from 2,583 (60.6%) participants in this analysis. Out of 108 metabolites that passed stringent quality control across three study points in time, we identified nine significant negative associations between phosphatidylcholines (PCs) and ambient pollutants at a Benjamini-Hochberg false discovery rate (FDR) corrected p-value < 0.05. The strongest association was seen for an increase of 0.27 µg/m3 (interquartile range) in PM2.5abs and decreased phosphatidylcholine acyl-alkyl C36:3 (PC ae C36:3) concentrations [percent change in the geometric mean: -2.5% (95% confidence interval: -3.6%, -1.5%)]. CONCLUSIONS: Our study suggested that long-term exposure to air pollution is associated with metabolic alterations, particularly in PCs with unsaturated long-chain fatty acids. These findings might provide new insights into potential mechanisms for air pollution-related adverse outcomes.

Long-term exposure to fine particulate matter air pollution: An ecological study of its effect on COVID-19 cases and fatality in Germany.

BACKGROUND: COVID-19 is a lung disease, and there is medical evidence that air pollution is one of the external causes of lung diseases. Fine particulate matter is one of the air pollutants that damages pulmonary tissue. The combination of the coronavirus and fine particulate matter air pollution may exacerbate the coronavirus' effect on human health. RESEARCH QUESTION: This paper considers whether the long-term concentration of fine particulate matter of different sizes changes the number of detected coronavirus infections and the number of COVID-19 fatalities in Germany. STUDY DESIGN: Data from 400 German counties for fine particulate air pollution from 2002 to 2020 are used to measure the long-term impact of air pollution. Kriging interpolation is applied to complement data gaps. With an ecological study, the correlation between average particulate matter air pollution and COVID-19 cases, as well as fatalities, are estimated with OLS regressions. Thereby, socioeconomic and demographic covariates are included. MAIN FINDINGS: An increase in the average long-term air pollution of 1 µg/m3 particulate matter PM2.5 is correlated with 199.46 (SD = 29.66) more COVID-19 cases per 100,000 inhabitants in Germany. For PM10 the respective increase is 52.38 (SD = 12.99) more cases per 100,000 inhabitants. The number of COVID-19 deaths were also positively correlated with PM2.5 and PM10 (6.18, SD = 1.44, respectively 2.11, SD = 0.71, additional COVID-19 deaths per 100,000 inhabitants). CONCLUSION: Long-term fine particulate air pollution is suspected as causing higher numbers of COVID-19 cases. Higher long-term air pollution may even increase COVID-19 death rates. We find that the results of the correlation analysis without controls are retained in a regression analysis with controls for relevant confounding factors. Nevertheless, additional epidemiological investigations are required to test the causality of particulate matter air pollution for COVID-19 cases and the severity.

Acute and chronic exposure to air pollution in relation with incidence, prevalence, severity and mortality of COVID-19: a rapid systematic review.

BACKGROUND: Air pollution is one of the world's leading mortality risk factors contributing to seven million deaths annually. COVID-19 pandemic has claimed about one million deaths in less than a year. However, it is unclear whether exposure to acute and chronic air pollution influences the COVID-19 epidemiologic curve. METHODS: We searched for relevant studies listed in six electronic databases between December 2019 and September 2020. We applied no language or publication status limits. Studies presented as original articles, studies that assessed risk, incidence, prevalence, or lethality of COVID-19 in relation with exposure to either short-term or long-term exposure to ambient air pollution were included. All patients regardless of age, sex and location diagnosed as having COVID-19 of any severity were taken into consideration. We synthesised results using harvest plots based on effect direction. RESULTS: Included studies were cross-sectional (n = 10), retrospective cohorts (n = 9), ecological (n = 6 of which two were time-series) and hypothesis (n = 1). Of these studies, 52 and 48% assessed the effect of short-term and long-term pollutant exposure, respectively and one evaluated both. Pollutants mostly studied were PM2.5 (64%), NO2 (50%), PM10 (43%) and O3 (29%) for acute effects and PM2.5 (85%), NO2 (39%) and O3 (23%) then PM10 (15%) for chronic effects. Most assessed COVID-19 outcomes were incidence and mortality rate. Acutely, pollutants independently associated with COVID-19 incidence and mortality were first PM2.5 then PM10, NO2 and O3 (only for incident cases). Chronically, similar relationships were found for PM2.5 and NO2. High overall risk of bias judgments (86 and 39% in short-term and long-term exposure studies, respectively) was predominantly due to a failure to adjust aggregated data for important confounders, and to a lesser extent because of a lack of comparative analysis. CONCLUSION: The body of evidence indicates that both acute and chronic exposure to air pollution can affect COVID-19 epidemiology. The evidence is unclear for acute exposure due to a higher level of bias in existing studies as compared to moderate evidence with chronic exposure. Public health interventions that help minimize anthropogenic pollutant source and socio-economic injustice/disparities may reduce the planetary threat posed by both COVID-19 and air pollution pandemics.

Impact of a long-term air pollution exposure on the case fatality rate of COVID-19 patients-A multicity study.

Evidence in the literature suggests that air pollution exposure affects outcomes of patients with COVID-19. However, the extent of this effect requires further investigation. This study was designed to investigate the relationship between long-term exposure to air pollution and the case fatality rate (CFR) of patients with COVID-19. The data on air quality index (AQI), PM2.5, PM10, SO2 , NO2 , and O3 from 14 major cities in China in the past 5 years (2015-2020) were collected, and the CRF of COVID-19 patients in these cities was calculated. First, we investigated the correlation between CFR and long-term air quality indicators. Second, we examined the air pollutants affecting CFR and evaluated their predictive values. We found a positive correlation between the CFR and AQI (1, 3, and 5 years), PM2.5 (1, 3, and 5 years), and PM10 (1, 3, and 5 years). Further analysis indicated the more significant correlation for both AQI (3 and 5 years) and PM2.5 (1, 3, and 5 years) with CFR, and moderate predictive values for air pollution indicators such as AQI (1, 3, and 5 years) and PM2.5 (1, 3, and 5 years) for CFR. Our results indicate that long-term exposure to severe air pollution is associated with higher CFR of COVID-19 patients. Air pollutants such as PM2.5 may assist with the prediction of CFR for COVID-19 patients.

Long-term air pollution exposure and self-reported morbidity: A longitudinal analysis from the Thai cohort study (TCS).

BACKGROUND: Several studies have shown the health effects of air pollutants, especially in China, North American and Western European countries. But longitudinal cohort studies focused on health effects of long-term air pollution exposure are still limited in Southeast Asian countries where sources of air pollution, weather conditions, and demographic characteristics are different. The present study examined the association between long-term exposure to air pollution and self-reported morbidities in participants of the Thai cohort study (TCS) in Bangkok metropolitan region (BMR), Thailand. METHODS: This longitudinal cohort study was conducted for 9 years from 2005 to 2013. Self-reported morbidities in this study included high blood pressure, high blood cholesterol, and diabetes. Air pollution data were obtained from the Thai government Pollution Control Department (PCD). Particles with diameters ≤10 µm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO) exposures were estimated with ordinary kriging method using 22 background and 7 traffic monitoring stations in BMR during 2005-2013. Long-term exposure periods to air pollution for each subject was averaged as the same period of person-time. Cox proportional hazards models were used to examine the association between long-term air pollution exposure with self-reported high blood pressure, high blood cholesterol, diabetes. Results of self-reported morbidity were presented as hazard ratios (HRs) per interquartile range (IQR) increase in PM10, O3, NO2, SO2, and CO. RESULTS: After controlling for potential confounders, we found that an IQR increase in PM10 was significantly associated with self-reported high blood pressure (HR = 1.13, 95% CI: 1.04, 1.23) and high blood cholesterol (HR = 1.07, 95%CI: 1.02, 1.12), but not with diabetes (HR = 1.05, 95%CI: 0.91, 1.21). SO2 was also positively associated with self-reported high blood pressure (HR = 1.22, 95%CI: 1.08, 1.38), high blood cholesterol (HR = 1.20, 95%CI: 1.11, 1.30), and diabetes (HR = 1.21, 95%CI: 0.92, 1.60). Moreover, we observed a positive association between CO and self-reported high blood pressure (HR = 1.07, 95%CI: 1.00, 1.15), but not for other diseases. However, self-reported morbidities were not associated with O3 and NO2. CONCLUSIONS: Long-term exposure to air pollution, especially for PM10 and SO2 was associated with self-reported high blood pressure, high blood cholesterol, and diabetes in subjects of TCS. Our study supports that exposure to air pollution increases cardiovascular disease risk factors for younger population.

Long-term traffic air and noise pollution in relation to mortality and hospital readmission among myocardial infarction survivors.

BACKGROUND: There is relatively little evidence of health effects of long-term exposure to traffic-related pollution in susceptible populations. We investigated whether long-term exposure to traffic air and noise pollution was associated with all-cause mortality or hospital readmission for myocardial infarction (MI) among survivors of hospital admission for MI. METHODS: Patients from the Myocardial Ischaemia National Audit Project database resident in Greater London (n = 1 8,138) were followed for death or readmission for MI. High spatially-resolved annual average air pollution (11 metrics of primary traffic, regional or urban background) derived from a dispersion model (resolution 20 m × 20 m) and road traffic noise for the years 2003-2010 were used to assign exposure at residence. Hazard ratios (HR, 95% confidence interval (CI)) were estimated using Cox proportional hazards models. RESULTS: Most air pollutants were positively associated with all-cause mortality alone and in combination with hospital readmission. The largest associations with mortality per interquartile range (IQR) increase of pollutant were observed for non-exhaust particulate matter (PM(10)) (HR = 1.05 (95% CI 1.00, 1.10), IQR = 1.1 µg/m(3)); oxidant gases (HR = 1.05 (95% CI 1.00, 1.09), IQR = 3.2 µg/m(3)); and the coarse fraction of PM (HR = 1.05 (95% CI 1.00, 1.10), IQR = 0.9 µg/m(3)). Adjustment for traffic noise only slightly attenuated these associations. The association for a 5 dB increase in road-traffic noise with mortality was HR = 1.02 (95% CI 0.99, 1.06) independent of air pollution. CONCLUSIONS: These data support a relationship of primary traffic and regional/urban background air pollution with poor prognosis among MI survivors. Although imprecise, traffic noise appeared to have a modest association with prognosis independent of air pollution.

Modelled air pollution levels versus EC air quality legislation - results from high resolution simulation.

An appropriate method for evaluating the air quality of a certain area is to contrast the actual air pollution levels to the critical ones, prescribed in the legislative standards. The application of numerical simulation models for assessing the real air quality status is allowed by the legislation of the European Community (EC). This approach is preferable, especially when the area of interest is relatively big and/or the network of measurement stations is sparse, and the available observational data are scarce, respectively. Such method is very efficient for similar assessment studies due to continuous spatio-temporal coverage of the obtained results. In the study the values of the concentration of the harmful substances sulphur dioxide, (SO2), nitrogen dioxide (NO2), particulate matter - coarse (PM10) and fine (PM2.5) fraction, ozone (O3), carbon monoxide (CO) and ammonia (NH3) in the surface layer obtained from modelling simulations with resolution 10 km on hourly bases are taken to calculate the necessary statistical quantities which are used for comparison with the corresponding critical levels, prescribed in the EC directives. For part of them (PM2.5, CO and NH3) this is done for first time with such resolution. The computational grid covers Bulgaria entirely and some surrounding territories and the calculations are made for every year in the period 1991-2000. The averaged over the whole time slice results can be treated as representative for the air quality situation of the last decade of the former century.