Association of PM2.5 exposure with hospitalization for cardiovascular disease in elderly individuals in Japan.
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ABSTRACT:
SUBMITTER: Kaihara T
PROVIDER: S-EPMC8110517 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Evidence of associations between exposure to ambient air pollution and health outcomes are sparse in the South Asian region due to limited air pollution exposure and quality health data. This study investigated the potential impacts of ambient particulate matter (PM) on respiratory disease hospitalization in Kandy, Sri Lanka for the year 2019. The Generalized Additive Model (GAM) was applied to estimate the short-term effect of ambient PM on respiratory disease hospitalization. As the second analysis, respiratory disease hospitalizations during two distinct air pollution periods were analyzed. Each 10 μg/m3 increase in same-day exposure to PM2.5 and PM10 was associated with an increased risk of respiratory disease hospitalization by 1.95% (0.25, 3.67) and 1.63% (0.16, 3.12), respectively. The effect of PM2.5 or PM10 on asthma hospitalizations were 4.67% (1.23, 8.23) and 4.04% (1.06, 7.11), respectively (p < 0.05). The 65+ years age group had a higher risk associated with PM2.5 and PM10 exposure and hospital admissions for all respiratory diseases on the same day (2.74% and 2.28%, respectively). Compared to the lower ambient air pollution period, higher increased hospital admissions were observed among those aged above 65 years, males, and COPD and pneumonia hospital admissions during the high ambient air pollution period. Active efforts are crucial to improve ambient air quality in this region to reduce the health effects.
| S-EPMC8466407 | biostudies-literature
Project description:PM2.5, a type of particulate matter with an aerodynamic diameter of less than 2.5 μm, is associated with the occurrence of cardiovascular diseases (CVDs), while greenness seems to be associated with better cardiovascular health. We identified 499,336 CVD cases in Wuhan's 74 municipal hospitals between 2017 and 2019. A high-resolution PM2.5 model and a normalized difference vegetation index (NDVI) map were established to estimate individual exposures. The time-stratified case-crossover design and conditional logistic regression models were applied to explore the associations between PM2.5 and CVDs under different levels of environmental factors. Greenness could alleviate PM2.5-induced hospitalization risks of cardiovascular diseases. Compared with patients in the low-greenness group (ER = 0.99%; 95% CI: 0.71%, 1.28%), patients in the high-greenness group (ER = 0.45%; 95% CI: 0.13%, 0.77%) showed a lower increase in total CVD hospitalizations. After dividing the greenness into quartiles and adding long-term PM2.5 exposure as a control factor, no significant PM2.5-associated hospitalization risks of CVD were identified in the greenest areas (quartile 4), whether the long-term PM2.5 exposure level was high or low. Intriguingly, in the least green areas (quartile 1), the PM2.5-induced excess risk of CVD hospitalization was 0.58% (95% CI: 0.04%, 1.11%) in the long-term high-level PM2.5 exposure group, and increased to 1.61% (95% CI: 0.95%, 2.27%) in the long-term low-level PM2.5 exposure group. In the subgroup analysis, males and participants aged 55-64 years showed more significant increases in the PM2.5-induced risk of contracting CVDs with a reduction in greenness and fine particle exposure conditions. High residential greenness can greatly alleviate the PM2.5-induced risk of cardiovascular admission. Living in the areas with long-term low-level PM2.5 may make people more sensitive to short-term increases in PM2.5, leading to CVD hospitalization.
| S-EPMC9819994 | biostudies-literature
Project description:BackgroundStudies showed that PM2.5 might be associated with various neurogenic diseases such as Alzheimer's Disease (AD). However, this topic had been little studied in Zhejiang province of China. METHODS: In 2018, we established a cohort of AD high-risk population with 1,742 elderly aged 60 and above. In 2020, the cohort was followed up, a total of 1,545 people participated the 2 surveys. Data collection included questionnaires and basic physical examinations. The average residential exposure to PM2.5 for each participant, that in a 5-years period prior to the first survey, was estimated using a satellite-based spatial statistical model. We determined the association between PM2.5 and AD prevalence by cox proportional hazards regression model. RESULTS: This study showed that an increase in the PM2.5 level was an important associated risk factor that contributed to AD. The average PM2.5 exposure levels among the study population ranged from 32.69 μg/m3 to 39.67 μg/m3 from 2013 to 2017, which were much higher than 5 μg/m3 that specified in the WHO air quality guidelines. There was an association between PM2.5 exposure and AD, and the correlations between PM2.5 and Mini-Mental State Examination, Montreal cognitive assessment scale scores were statistically significant. An increase in the PM2.5 level by 10 μg/m3 elevated the risk of AD among residents by 2%-5% (HR model 2-model 4 = 1.02 to 1.05, CI model 2-model 4 = 1.01-1.10). The subgroups of male, with old age, with low education levels, used to work as farmers or blue-collar workers before retirement, overweight and obese were associated with a higher effect of PM2.5.ConclusionsReducing PM2.5 exposure might be a good way to prevent AD.
| S-EPMC9706836 | biostudies-literature
Project description:BackgroundFine particulate matter (PM2.5) exposure is associated with increased morbidity and mortality, particularly for cardiovascular disease. The association between long-term exposure to PM2.5 and measures of lipoprotein subfractions remains unclear. Therefore, we examined associations between long-term PM2.5 exposure and traditional and novel lipoprotein measures in a cardiac catheterization cohort in North Carolina.MethodsThis cross-sectional study included 6587 patients who had visited Duke University for a cardiac catheterization between 2001 and 2010 and resided in North Carolina. We used estimates of daily PM2.5 concentrations on a 1 km-grid based on satellite measurements. PM2.5 predictions were matched to the address of each patient and averaged for the year prior to catheterization date. Serum lipids included HDL, LDL, and triglyceride-rich particle, and apolipoprotein B concentrations (HDL-P, LDL-P, TRL-P, and apoB, respectively). Linear and quantile regression models were used to estimate change in lipoprotein levels with each μg/m3 increase in annual average PM2.5. Models were adjusted for age, sex, race/ethnicity, history of smoking, area-level education, urban/rural status, body mass index, and diabetes.ResultsFor a 1-μg/m3 increment in PM2.5 exposure, we observed increases in total and small LDL-P, LDL-C, TRL-P, apoB, total cholesterol, and triglycerides. The percent change from the mean outcome level was 2.00% (95% CI: 1.38%, 2.64%) for total LDL-P and 2.25% (95% CI: 1.43%, 3.06%) for small LDL-P.ConclusionAmong this sample of cardiac catheterization patients residing in North Carolina, long-term PM2.5 exposure was associated with increases in several lipoprotein concentrations. This abstract does not necessarily reflect U.S. EPA policy.
| S-EPMC6467069 | biostudies-literature
Project description:Exposure to particulate matter with an aerodynamic diameter smaller than 2.5 microns (PM2.5) can affect birth outcomes through physiological pathways such as inflammation. One potential way PM2.5 affects physiology could be through altering DNA methylation (DNAm). Considering that exposures during specific windows of gestation may have unique effects on DNAm, we hypothesized a timing-specific association between PM2.5 exposure during pregnancy and DNAm in the neonatal epithelial-cell epigenome.MethodsAfter collecting salivary samples from a cohort of 91 neonates, DNAm was assessed at over 850,000 cytosine-guanine dinucleotide (CpG) methylation sites on the epigenome using the MethylationEPIC array. Daily ambient PM2.5 concentrations were estimated based on the mother's address of primary residence during pregnancy. PM2.5 was averaged over the first two trimesters, separately and combined, and tested for association with DNAm through an epigenome-wide association (EWA) analysis. For each EWA, false discovery rate (FDR)-corrected P < 0.05 constituted a significant finding and every CpG site with uncorrected P < 0.0001 was selected to undergo pathway and network analysis to identify molecular functions enriched by them.ResultsOur analysis showed that cg18705808 was associated with the combined average of PM2.5. Pathway and network analysis revealed little similarity between the first two trimesters. Previous studies reported that TMEM184A, the gene regulated by cg18705808, has a putative role in inflammatory pathways.ConclusionsThe differences in pathway and network analyses could potentially indicate trimester-specific effects of PM2.5 on DNAm. Further analysis with greater temporal resolution would be valuable to fully characterize the effect of PM2.5 on DNAm and child development.
| S-EPMC9556110 | biostudies-literature
Project description:To understand how chemical exposure can impact health, researchers need tools that capture the complexities of personal chemical exposure. In practice, fine particulate matter (PM2.5) air quality index (AQI) data from outdoor stationary monitors and Hazard Mapping System (HMS) smoke density data from satellites are often used as proxies for personal chemical exposure, but do not capture total chemical exposure. Silicone wristbands can quantify more individualized exposure data than stationary air monitors or smoke satellites. However, it is not understood how these proxy measurements compare to chemical data measured from wristbands. In this study, participants wore daily wristbands, carried a phone that recorded locations, and answered daily questionnaires for a 7-day period in multiple seasons. We gathered publicly available daily PM2.5 AQI data and HMS data. We analyzed wristbands for 94 organic chemicals, including 53 polycyclic aromatic hydrocarbons. Wristband chemical detections and concentrations, behavioral variables (e.g., time spent indoors), and environmental conditions (e.g., PM2.5 AQI) significantly differed between seasons. Machine learning models were fit to predict personal chemical exposure using PM2.5 AQI only, HMS only, and a multivariate feature set including PM2.5 AQI, HMS, and other environmental and behavioral information. On average, the multivariate models increased predictive accuracy by approximately 70% compared to either the AQI model or the HMS model for all chemicals modeled. This study provides evidence that PM2.5 AQI data alone or HMS data alone is insufficient to explain personal chemical exposures. Our results identify additional key predictors of personal chemical exposure.
| S-EPMC10858395 | biostudies-literature
Project description:Atmospheric particle is one of the risk factors for respiratory disease; however, their injury mechanisms are poorly understood, and prevention methods are highly desirable. We constructed artificial PM2.5 (aPM2.5) particles according to the size and composition of actual PM2.5 collected in Beijing. Using these artificial particles, we created an inhalation-injury animal model. These aPM2.5 particles simulate the physical and chemical characteristics of the actual PM2.5, and inhalation of the aPM2.5 in rat results in a time-dependent change in lung suggesting a declined lung function, injury from oxidative stress and inflammation in lung. Thus, this aPM2.5-caused injury animal model may mimic that of the pulmonary injury in human exposed to airborne particles. In addition, polydatin (PD), a resveratrol glucoside that is rich in grapes and red wine, was found to significantly decrease the oxidative potential (OP) of aPM2.5 in vitro. Treating the model rats with PD prevented the lung function decline caused by aPM2.5, and reduced the level of oxidative damage in aPM2.5-exposed rats. Moreover, PD inhibited aPM2.5-induced inflammation response, as evidenced by downregulation of white blood cells in bronchoalveolar lavage fluid (BALF), inflammation-related lipids and proinflammation cytokines in lung. These results provide a practical means for self-protection against particulate air pollution.
| S-EPMC5220290 | biostudies-literature
Project description:BackgroundAir pollution health studies have been increasingly using prediction models for exposure assessment even in areas without monitoring stations. To date, most studies have assumed that a single exposure model is correct, but estimated effects may be sensitive to the choice of exposure model.MethodsWe obtained county-level daily cardiovascular (CVD) admissions from the New York (NY) Statewide Planning and Resources Cooperative System (SPARCS) and four sets of fine particulate matter (PM2.5) spatio-temporal predictions (2002-2012). We employed overdispersed Poisson models to investigate the relationship between daily PM2.5 and CVD, adjusting for potential confounders, separately for each state-wide PM2.5 dataset.ResultsFor all PM2.5 datasets, we observed positive associations between PM2.5 and CVD. Across the modeled exposure estimates, effect estimates ranged from 0.23% (95%CI: -0.06, 0.53%) to 0.88% (95%CI: 0.68, 1.08%) per 10 µg/m3 increase in daily PM2.5. We observed the highest estimates using monitored concentrations 0.96% (95%CI: 0.62, 1.30%) for the subset of counties where these data were available.ConclusionsEffect estimates varied by a factor of almost four across methods to model exposures, likely due to varying degrees of exposure measurement error. Nonetheless, we observed a consistently harmful association between PM2.5 and CVD admissions, regardless of model choice.
| S-EPMC8383435 | biostudies-literature
Project description:IntroductionLong-term exposure to ambient air pollution is related to major cardiovascular risk factors including diabetes, hypertension, hyperlipidemia and overweight, but with few studies in high-concentration nations like China so far. We aimed to investigate the association between long-term exposure to ambient fine particulate matter (particles with an aerodynamic diameter ≤ 2.5 μm, PM2.5) and major cardiovascular risk factors in China.MethodsAdult participants with selected biochemical tests were recruited from the Chinese Physiological Constant and Health Condition (CPCHC) survey conducted from 2007 to 2011. Gridded PM2.5 data used were derived from satellite-observed data with adjustment of ground-observed data. District-level PM2.5 data were generated to estimate the association using multivariate logistic regression model and generalized additive model.ResultsA total of 19,236 participants from the CPCHC survey were included with an average age of 42.8 ± 16.1 years, of which nearly half were male (47.0%). The annual average PM2.5 exposure before the CPCHC survey was 33.4 (14.8-53.4) μg/m3, ranging from 8.0 μg/m3 (Xiwuqi) to 94.7 μg/m3 (Chengdu). Elevated PM2.5 was associated with increased prevalence of hypertension (odds ratio (OR) =1.022, 95% confidence interval (95%CI): 1.001, 1.043) and decreased prevalence of overweight (OR = 0.926, 95%CI: 0.910, 0.942). Education significantly interacted with PM2.5 in association with all the interesting risk factors. Each 10 μg/m3 increment of PM2.5 was associated with increased prevalence of diabetes (OR = 1.118, 95%CI: 1.037, 1.206), hypertension (OR = 1.101, 95%CI: 1.056, 1.147), overweight (OR = 1.071, 95%CI: 1.030, 1.114) in participants with poor education, but not in well-educated population. PM2.5 exposure was negatively associated with hyperlipidemia in all participants (OR = 0.939, 95%CI: 0.921, 0.957). The results were robust in all the sensitivity analyses.ConclusionAssociation between long-term PM2.5 exposure and cardiovascular risk factors might be modified by education. PM2.5 was associated with a higher prevalence of diabetes, hypertension, and overweight in a less-educated population with time-expose dependency. Long-term exposure to PM2.5 might be associated with a lower prevalence of hyperlipidemia.
| S-EPMC8662859 | biostudies-literature
Project description:Long-term air pollution exposure, notably fine particulate matter, is a global contributor to morbidity and mortality and a known risk factor for coronary artery disease (CAD) and myocardial infarctions (MI). Knowledge of impacts related to source-apportioned PM2.5 is limited. New modeling methods allow researchers to estimate source-specific long-term impacts on the prevalence of CAD and MI. The Catheterization Genetics (CATHGEN) cohort consists of patients who underwent a cardiac catheterization at Duke University Medical Center between 2002 and 2010. Severity of coronary blockage was determined by coronary angiography and converted into a binary indicator of clinical CAD. History of MI was extracted from medical records. Annual averages of source specific PM2.5 were estimated using an improved gas-constrained source apportionment model for North Carolina from 2002 to 2010. We tested six sources of PM2.5 mass for associations with CAD and MI using mixed effects multivariable logistic regression with a random intercept for county and multiple adjustments. PM2.5 fractions of ammonium bisulfate and ammonium nitrate were associated with increased prevalence of CAD (odds ratio [OR] 1.20; 95% CI = 1.11, 1.22 and OR 1.18; 95% CI = 1.05, 1.32, respectively). PM2.5 from ammonium bisulfate and ammonium nitrate were also associated with increased prevalence of MI (OR 1.20; 95% CI = 1.10, 1.29 and OR 1.35; 95% CI = 1.20, 1.53, respectively). Greater PM2.5 concentrations of ammonium bisulfate and ammonium nitrate are associated with greater MI and CAD prevalence. The association with bisulfate suggests aerosol acidity may play a role. Our findings suggest analyses of source specific PM2.5 mass can reveal novel associations.
| S-EPMC8196100 | biostudies-literature