Long-term PM2.5 exposure and the clinical application of machine learning for predicting incident atrial fibrillation
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ABSTRACT:
SUBMITTER: Kim I
PROVIDER: S-EPMC7530980 | biostudies-literature | 2020 Jan
REPOSITORIES: biostudies-literature
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Project description:Introduction Pollution of air in urban cities across the world has been steadily increasing in recent years. An increasing trend in particulate matter, PM 2.5, is a threat because it can lead to uncontrollable consequences like worsening of asthma and cardiovascular disease. The metric used to measure air quality is the air pollutant index (API). In Malaysia, machine learning (ML) techniques for PM 2.5 have received less attention as the concentration is on predicting other air pollutants. To fill the research gap, this study focuses on correctly predicting PM 2.5 concentrations in the smart cities of Malaysia by comparing supervised ML techniques, which helps to mitigate its adverse effects. Methods In this paper, ML models for forecasting PM 2.5 concentrations were investigated on Malaysian air quality data sets from 2017 to 2018. The dataset was preprocessed by data cleaning and a normalization process. Next, it was reduced into an informative dataset with location and time factors in the feature extraction process. The dataset was fed into three supervised ML classifiers, which include random forest (RF), artificial neural network (ANN) and long short-term memory (LSTM). Finally, their output was evaluated using the confusion matrix and compared to identify the best model for the accurate prediction of PM 2.5. Results Overall, the experimental result shows an accuracy of 97.7% was obtained by the RF model in comparison with the accuracy of ANN (61.14%) and LSTM (61.77%) in predicting PM 2.5. Discussion RF performed well when compared with ANN and LSTM for the given data with minimum features. RF was able to reach good accuracy as the model learns from the random samples by using decision tree with the maximum vote on the predictions.
| S-EPMC9723408 | 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:It is well recognized that exposure to fine particulate matter (PM2.5) affects health adversely, yet few studies from South America have documented such associations due to the sparsity of PM2.5 measurements. Lima's topography and aging vehicular fleet results in severe air pollution with limited amounts of monitors to effectively quantify PM2.5 levels for epidemiologic studies. We developed an advanced machine learning model to estimate daily PM2.5 concentrations at a 1 km2 spatial resolution in Lima, Peru from 2010 to 2016. We combined aerosol optical depth (AOD), meteorological fields from the European Centre for Medium-Range Weather Forecasts (ECMWF), parameters from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), and land use variables to fit a random forest model against ground measurements from 16 monitoring stations. Overall cross-validation R2 (and root mean square prediction error, RMSE) for the random forest model was 0.70 (5.97 μg/m3). Mean PM2.5 for ground measurements was 24.7 μg/m3 while mean estimated PM2.5 was 24.9 μg/m3 in the cross-validation dataset. The mean difference between ground and predicted measurements was -0.09 μg/m3 (Std.Dev. = 5.97 μg/m3), with 94.5% of observations falling within 2 standard deviations of the difference indicating good agreement between ground measurements and predicted estimates. Surface downwards solar radiation, temperature, relative humidity, and AOD were the most important predictors, while percent urbanization, albedo, and cloud fraction were the least important predictors. Comparison of monthly mean measurements between ground and predicted PM2.5 shows good precision and accuracy from our model. Furthermore, mean annual maps of PM2.5 show consistent lower concentrations in the coast and higher concentrations in the mountains, resulting from prevailing coastal winds blown from the Pacific Ocean in the west. Our model allows for construction of long-term historical daily PM2.5 measurements at 1 km2 spatial resolution to support future epidemiological studies.
| S-EPMC6671674 | biostudies-literature
Project description:RationalePM2.5-induced adverse effects on respiratory health may be driven by epigenetic modifications in airway cells. The potential impact of exposure duration on epigenetic alterations in the airways is not yet known.ObjectivesWe aimed to study associations of fine particulate matter PM2.5 exposure with DNA methylation in nasal cells.MethodsWe conducted nasal epigenome-wide association analyses within 503 children from Project Viva (mean age 12.9 y), and examined various exposure durations (1-day, 1-week, 1-month, 3-months and 1-year) prior to nasal sampling. We used residential addresses to estimate average daily PM2.5 at 1 km resolution. We collected nasal swabs from the anterior nares and measured DNA methylation (DNAm) using the Illumina MethylationEPIC BeadChip. We tested 719,075 high quality autosomal CpGs using CpG-by-CpG and regional DNAm analyses controlling for multiple comparisons, and adjusted for maternal education, household smokers, child sex, race/ethnicity, BMI z-score, age, season at sample collection and cell-type heterogeneity. We further corrected for bias and genomic inflation. We tested for replication in a cohort from the Netherlands (PIAMA).ResultsIn adjusted analyses, we found 362 CpGs associated with 1-year PM2.5 (FDR < 0.05), 20 CpGs passing Bonferroni correction (P < 7.0x10-8) and 10 Differentially Methylated Regions (DMRs). In 445 PIAMA participants (mean age 16.3 years) 11 of 203 available CpGs replicated at P < 0.05. We observed differential DNAm at/near genes implicated in cell cycle, immune and inflammatory responses. There were no CpGs or regions associated with PM2.5 levels at 1-day, 1-week, or 1-month prior to sample collection, although 2 CpGs were associated with past 3-month PM2.5.ConclusionWe observed wide-spread DNAm variability associated with average past year PM2.5 exposure but we did not detect associations with shorter-term exposure. Our results suggest that nasal DNAm marks reflect chronic air pollution exposure.
| S-EPMC8823376 | biostudies-literature
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:Fine-particulate pollution is a major public health concern in China. Accurate assessment of the population exposed to PM2.5 requires high-resolution pollution and population information. This paper assesses China's potential population exposure to PM2.5, maps its spatiotemporal variability, and simulates the effects of the recent air pollution control policy. We relate satellite-based Aerosol Optical Depth (AOD) retrievals to ground-based PM2.5 observations. We employ block cokriging (BCK) to improve the spatial interpolation of PM2.5 distribution. We use the subdistrict level population data to estimate and map the potential population exposure to PM2.5 pollution in China at the subdistrict level, the smallest administrative unit with public demographic information. During 8 April 2013 and 7 April 2014, China's population-weighted annual average PM2.5 concentration was nearly 7 times the annual average level suggested by the World Health Organization (WHO). About 1322 million people, or 98.6% of the total population, were exposed to PM2.5 at levels above WHO's daily guideline for longer than half a year. If China can achieve its Action Plan on Prevention and Control of Air Pollution targets by 2017, the population exposed to PM2.5 above China's daily standard for longer than half a year will be reduced by 85%.
| S-EPMC6313548 | biostudies-literature
Project description:Objective: Particulate matter (PM) with a diameter of 2.5 μm or less (PM2.5) can cross the blood-placental barrier causing adverse foetal outcomes. However, the impact of maternal exposure to low-levels of PM2.5 on liver health and the metabolic profile is unclear. This study aimed to investigate hepatic responses to long-term gestational low-dose PM2.5 exposure, and whether the removal of PM after conception can prevent such effects. Method: Female Balb/c mice (8 weeks) were exposed to PM2.5 (5 μg/day) for 6 weeks prior to mating, during gestation and lactation to model living in a polluted environment (PM group). In a sub-group, PM2.5 exposure was stopped post-conception to model mothers moving to areas with clean air (pre-gestation, Pre) group. Livers were studied in 13-week old offspring. Results: Female offspring in both PM and Pre groups had increased liver triglyceride and glycogen levels, glucose intolerance, but reduced serum insulin and insulin resistance. Male offspring from only the Pre group had increased liver and serum triglycerides, increased liver glycogen, glucose intolerance and higher fasting glucose level. Markers of oxidative stress and inflammation were increased in females from PM and Pre groups. There was also a significant sex difference in the hepatic response to PM2.5 with differential changes in several metabolic markers identified by proteomic analysis. Conclusions: Maternal PM exposure exerted sex-dependent effects on liver health with more severe impacts on females. The removal of PM2.5 during gestation provided limited protection in the offspring's metabolism regardless of sex.
| S-EPMC9686974 | biostudies-literature
Project description:BackgroundStudies suggested that PM2.5 exposure could lead to adverse reproductive effects on male animals. However, the underlying mechanism is still not clear. Besides, animals in the majority of previous studies were exposed to PM2.5 through intratracheal instillation which should be improved. In addition, limited amount of research has been conducted in China where the PM2.5 concentration is higher and the PM2.5 components are different. The aim of this work is to explore the effects of concentrated ambient PM2.5 (CAP) on mice sperm quality and testosterone biosynthesis.MethodsA total of 12 male C57BL/6 mice were exposed to filtered air (FA) or CAP for 125 days using the Shanghai Meteorological and Environmental Animal Exposure System. The mice sperm concentration, sperm motility, DNA fragmentation index, high DNA stainability and plasma testosterone were analyzed. Testicular histology and sperm morphology were observed through optical microscope. Testosterone biosynthesis related gene expressions were analyzed using real-time PCR, including cytochrome P450 CHOL side-chain cleavage enzyme (P450scc), steroidogenic acute regulatory protein (StAR), 3β-hydroxysteroid dehydrogenase (3β HSD), 17β-hydroxysteroid dehydrogenase, cytochrome P450 aromatase (P450arom), estrogen receptor (ER), androgen receptor (AR) and follicle stimulating hormone receptor (FSHR).ResultsExposure to CAP resulted in disturbance of various stages of spermatogenesis and significant higher percentage of abnormal sperm (FA vs. CAP: 24.37% vs. 44.83%) in mice testis. CAP exposure significantly decreased sperm concentration (43.00 × 106 vs. 25.33 × 106) and motility (PR: 63.58% vs. 55.15%; PR + NP: 84.00% vs. 77.08%) in epididymis. Plasma testosterone concentration were significantly declined (0.28 ng/ml vs. 0.69 ng/ml) under CAP exposure. Notably, the levels of testosterone biosynthesis related genes, StAR, P450scc, P450arom, ER and FSHR were significantly decreased with CAP exposure.ConclusionConcentrated ambient PM2.5 exposure altered mice sperm concentration, motility and morphology, which might be mediated primarily by the decline in testosterone concentration and testosterone biosynthesis process.
| S-EPMC6885350 | biostudies-literature
Project description:BackgroundChildhood exposure to air pollution has been linked with maladaptive cognitive development; however, less is known about the association between prenatal fine particulate matter (PM2.5) exposure and childhood behavior.ObjectivesOur aim was to assess the association between prenatal PM2.5 exposure and behavioral development in 4-6 year old children residing in Mexico City.MethodsWe used data from 539 mother-child pairs enrolled in a prospective birth cohort in Mexico City. We estimated daily PM2.5 exposure using a 1 km2 satellite-based exposure model and averaged over each trimester of pregnancy. We assessed childhood behavior at 4-6 years of age using the parent-completed Behavioral Assessment Scale for Children (BASC-2) composite scores and subscales. We used linear regression models to estimate change in BASC-2 T-scores with trimester specific 5-μg/m3 increases in PM2.5. All models were mutually adjusted for PM2.5 exposures during the other trimesters, maternal factors including age, education, socioeconomic status, depression, and IQ, child's age at study visit, and season. We additionally assessed sex-specific effects by including an interaction term between PM2.5 and sex.ResultsHigher first trimester PM2.5 exposure was associated with reduced Adaptive Skills scores (β: -1.45, 95% CI: -2.60, -0.30). Lower scores on the Adaptive Skills composite score and subscales indicate poorer functioning. For PM2.5 exposure during the first trimester, decrements were consistent across adaptive subscale scores including Adaptability (β: -1.51, 95% CI: -2.72, -0.30), Social Skills (β: -1.63, 95% CI: -2.90, -0.36), and Functional Communication (β: -1.21, 95% CI: -2.21, -0.21). The association between 1st trimester PM2.5 and depression was stronger in males than females (β for males: 1.52, 95% CI: -0.41, 3.45; β for females: -0.13, 95% CI: -1.99, 1.72; p-int: 0.07).ConclusionsExposure to PM2.5 during early pregnancy may be associated with impaired behavioral development in children, particularly for measures of adaptive skills. These results suggest that air pollution impacts behavioral domains as well as cognition, and that the timing of exposure may be critical.
| S-EPMC7708408 | biostudies-literature