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Influence of PM_2.5 Exposure Level on the Association between Alzheimer's Disease and Allergic Rhinitis: A National Population-Based Cohort Study.

ABSTRACT: Alzheimer's disease (AD) is an irreversible neurodegenerative disease that leads to dementia, health impairment, and high economic cost. Allergic rhinitis (AR) is a chronic inflammatory and allergic disease of the respiratory system that leads to health problems and has major effects on the daily lives of patients and their caregivers. Particulate matter (PM) refers to air pollutants 2.5 μm or less in diameter that are a source of concern because of their role in numerous diseases, including AR and other neurodegenerative diseases. To date, no study has demonstrated how PM_2.5 exacerbates AR and results in AD. We conducted a national population-based cohort study by obtaining patient data from the National Health Insurance Research Database of Taiwan for the 2008-2013 period. PM_2.5 concentration data were obtained from the ambient air quality monitoring network established by the Environmental Protection Administration of Taiwan. Monthly PM_2.5 exposure levels were categorized into quartiles from Q1-Q4. The Cox proportional hazards analysis, after adjusting for age, sex, low income, and urbanization level, revealed that patients with AR had an elevated risk of developing AD (hazard ratio (HR): 2.008). In addition, the cumulative incidence of AD in the AR group was significantly higher than in the comparison group. The PM_2.5 levels at Q2-Q4 (crude HR: 1.663-8.315; adjusted HR: 1.812-8.981) were stratified on the basis of the PM_2.5 exposure group and revealed that AR patients exposed to PM_2.5 are significantly prone to develop AD. In addition, the logistic regression analyses, after adjustment, demonstrated that an increase in the PM_2.5 exposure level at Q2-Q4 (adjusted odds ratio (OR): 2.656-5.604) increased the risk of AR in AD patients. In conclusion, an increased PM_2.5 exposure level could be correlated with AR, which could in turn cause AD. AR increased the risk of AD, in which exposure to PM_2.5 increases the higher probability of AD.

SUBMITTER: Li RL

PROVIDER: S-EPMC6765937 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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.

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.

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Influence of PM_2.5 Exposure Level on the Association between Alzheimer's Disease and Allergic Rhinitis: A National Population-Based Cohort Study.

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Dataset Information

Influence of PM2.5 Exposure Level on the Association between Alzheimer's Disease and Allergic Rhinitis: A National Population-Based Cohort Study.

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OmicsDI is part of the ELIXIR infrastructure

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Influence of PM_2.5 Exposure Level on the Association between Alzheimer's Disease and Allergic Rhinitis: A National Population-Based Cohort Study.