The effect of air pollution on aggravation of neurodegenerative diseases: an analysis of long-term exposure to fine particulate matter and its components

Background: Air pollution is one of the leading environmental issues in the world today. In 2015, pollution-related diseases accounted for 16% of all deaths worldwide — that is an estimated 9 million premature deaths were linked to air pollution. In addition to the substantial effects on human health, air pollution-related diseases result in productivity losses that reduce countries’ gross domestic product. Although air pollution disproportionately affects middle- and low-income countries, it is still a major issue in high-income countries, such as the United States, where 25% of Americans breath air with pollutant levels above the national regulatory standards. Fine particle matter (particles with diameter ≤ 2.5 μm, PM₂.₅ ) is the most extensively studied air pollutant and it has been causally linked with a wide range of adverse health outcomes, including cardiovascular and pulmonary disease, myocardial infarction, hypertension, congestive heart failure, arrhythmias, chronic obstructive pulmonary disease, and lung cancer. Moreover, recent scientific evidence suggests that PM₂.₅ affects the nervous system and possibly contributes to the development and exacerbation of neurodegenerative diseases. This is increasingly relevant as populations are aging and the number of adults living with neurodegenerative diseases increases, negatively affecting families, communities, and health-care systems around the world. Although millions of people suffer from neurodegenerative diseases, there is currently no treatment that slows the progression of these conditions and no known cure or cause. Thus, determining whether a link exists between air pollution and neurodegenerative diseases is a goal of increasing importance. Objective: The research presented in this dissertation has two main objectives: (1) to characterize the relationship between long-term exposure to PM₂.₅ and disease aggravation in two of the most prevalent neurodegenerative diseases worldwide: Alzheimer’s (AD) and Parkinson’s disease (PD), as well as in the rare and devastating neurodegenerative motor disorder amyotrophic lateral sclerosis (ALS); (2) to identify the specific PM₂.₅ chemical components that are associated with disease aggravation in PD. Methods: We used data from the New York Department of Health Statewide Planning and Research Cooperative System from 2000–2014 to identify patients’ first hospitalization with a primary or secondary diagnosis of AD, PD, or ALS. With these data, we constructed annual AD, PD, and ALS first hospitalization county counts (total and sex- and age-stratified) for all of New York State (NYS). A patient’s first hospital admission was used as a surrogate for disease aggravation, indicating the crossing point into a more severe stage of the disease. We used prediction estimates from well-validated models that incorporate satellite information and ground-based monitoring data to estimate annual PM₂.₅ and PM₂.₅ chemical component (nitrate, sulfate, organic matter, sea salt, black carbon, and soil) concentrations across NYS at a high spatial resolution. In Chapter 2, we used outcome-specific (AD, PD, or ALS) mixed quasi-Poisson models with county-specific random intercepts to assess the relationship between long-term exposure to PM₂.₅ and disease aggravation. In Chapter 3, we used a multi-pollutant mixed quasi-Poisson model with county-specific random intercepts to identify specific PM₂.₅ components associated with disease aggravation in PD. In all analyses, we evaluated potential nonlinear exposure–outcome relationships using penalized splines and accounted for potential confounders. Results: We observed a total of 264,075 AD, 114,514 PD, and 5,569 ALS first admissions between 2000 and 2014. The hospitalization annual average counts per county were 284, 131, and 6 for AD, PD, and ALS, respectively. In Chapter 2, we found a nonlinear association between total PM₂.₅ exposure and PD hospitalizations, which plateaued at higher concentrations of PM₂.₅ (> 13 μg/m³, RR=1.08, 95% CI: 1.04–1.13 for a PM₂.₅ increase from 8 to 10 μg/m³, Figure 2.3). We also found that patients with a first PD hospitalization at age 70 or younger are at slightly higher risk for disease aggravation at lower PM₂.₅ concentrations relative to those age >70. In the case of AD, we observed evidence of a potential association between annual increases in PM₂.₅ exposure and disease aggravation, but only in a sensitivity analysis aiming to decrease outcome misclassification. We found no association for ALS in the main analysis, but we observed an unexpected negative association in those <70 years in the stratified analysis. We found no evidence of effect modification by sex for any of the outcomes. In Chapter 3, we observed a linear association between disease aggravation in PD and long-term exposure to the PM₂.₅ components nitrate (RR = 1.05, 95%CI: 1.02–1.09 per one standard deviation (SD) increase) and organic matter (RR = 1.05, 95%CI: 1.02– 1.07 per one SD increase), and a nonlinear association for black carbon with a negative association above the 96th percentile of the BC concentration distribution (Figure 3.4). We found no evidence of an association with sulfate, sea salt or soil. Conclusion: Overall, our studies provide an analysis of the potential association between long-term exposure to PM₂.₅ , both as an overall pollution mixture and by chem- ical composition, and disease aggravation in AD, PD, and ALS. Our findings suggest that annual increases in county-level PM₂.₅ concentrations are associated with disease aggravation in PD and possibly AD. We found that the PM₂.₅ components organic matter and nitrate are particularly harmful in the association between PM₂.₅ and dis- ease aggravation in PD. Additionally, our results indicate that current national PM₂.₅ standards may not be strict enough to safeguard the population’s neurological health. Specifically, in Chapter 2, we observed that the PM₂.₅ –PD association has a steeper slope at lower concentrations that are well below the current annual National Ambient Air Quality Standards for PM₂.₅ . Thus, our findings warrant further investigation into the potential link between long-term PM₂.₅ exposure and disease aggravation, particularly in the context of PD. Our results also indicate that the chemical composition of PM2.5 affects its neurotoxicity. Further research into how PM₂.₅ composition influences the overall PM₂.₅ adverse effects is needed to fully understand the mechanisms that underlie the association between exposure to PM₂.₅ and aggravation of neurodegenerative diseases

An Environmental Justice Analysis of Air Pollution Emissions in the United States from 1970 to 2010

&lt;h4&gt;1. Introduction&lt;/h4&gt;&lt;p&gt;Over the last decades, air pollution emissions have decreased substantially; however, inequities in air pollution persist. We evaluate county-level racial/ethnic and socioeconomic disparities in emissions changes from six air pollution source sectors (industry [SO2], energy [SO2, NOx], agriculture [NH3], commercial [NOx], residential [particulate organic carbon], and on-road transportation [NOx]) in the contiguous United States during the 40 years following the Clean Air Act (CAA) enactment (1970-2010). We calculate relative emission changes and examine the differential changes given county demographics using hierarchical nested models. The results show racial/ethnic disparities, particularly in the industry and energy generation source sectors. We also find that median family income is a driver of variation in relative emissions changes in all sectors—counties with median family income &gt;$75K vs. less generally experience larger relative declines in industry, energy, transportation, residential, and commercial-related emissions. Emissions from most air pollution source sectors have, on a national level, decreased following the United States CAA. In this work, we show that the relative reductions in emissions varied across racial/ethnic and socioeconomic groups. This repository houses the code and data used in the analysis presented in the peer-reviewed article: An Environmental Justice Analysis of Air Pollution Emissions in the United States from 1970 to 2010.&lt;/p&gt;&lt;p&gt;Notice that this repository is linked with a journal article:&nbsp;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Yanelli Nunez, Jaime Benavides, Jenni A. Shearston, Elena M. Krieger, Misbath Daouda, Lucas R.F. Henneman, Erin E. McDuffie, Jeff Goldsmith, Joan A. Casey, and Marianthi-Anna Kioumourtzoglou: An Environmental Justice Analysis of Air Pollution Emissions in the United States from 1970 to 2010 [Under review]&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&nbsp;&lt;/p&gt;&lt;h4&gt;2. Code &amp; Datasets&lt;/h4&gt;&lt;p&gt;We present the source code and the results here.&nbsp;The code is developed in R programming (R Core Team (2022)). Please, read carefully the &lt;strong&gt;README.md&lt;/strong&gt; document enclosed within the zip file:&lt;/p&gt;&lt;ul&gt;&lt;li&gt;The zip file &lt;a href="https://zenodo.org/api/records/10059811/draft/files/yanellinunez/USA_emissions_code-v1.0.0.zip/content"&gt;yanellinunez/USA_emissions_code-v1.0.0.zip&lt;/a&gt; contains four folders: &lt;i&gt;code&lt;/i&gt;, &lt;i&gt;data&lt;/i&gt;, &lt;i&gt;figures&lt;/i&gt;, and &lt;i&gt;output.&nbsp;&lt;/i&gt; It also includes a README.md file detailing the contents of each folder and subfolder.&lt;/li&gt;&lt;li&gt;All journal article source code, generated data, and results have been openly published in this repository&lt;/li&gt;&lt;/ul&gt

An overview of methods to address distinct research questions on environmental mixtures: an application to persistent organic pollutants and leukocyte telomere length

Background Numerous methods exist to analyze complex environmental mixtures in health studies. As an illustration of the different uses of mixture methods, we employed methods geared toward distinct research questions concerning persistent organic chemicals (POPs) as a mixture and leukocyte telomere length (LTL) as an outcome. Methods With information on 18 POPs and LTL among 1,003 U.S. adults (NHANES, 2001–2002), we used unsupervised methods including clustering to identify profiles of similarly exposed participants, and Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) to identify common exposure patterns. We also employed supervised learning techniques, including penalized, weighted quantile sum (WQS), and Bayesian kernel machine (BKMR) regressions, to identify potentially toxic agents, and characterize nonlinear associations, interactions, and the overall mixture effect. Results Clustering separated participants into high, medium, and low POP exposure groups; longer log-LTL was found among those with high exposure. The first PCA component represented overall POP exposure and was positively associated with log-LTL. Two EFA factors, one representing furans and the other PCBs 126 and 118, were positively associated with log-LTL. Penalized regression methods selected three congeners in common (PCB 126, PCB 118, and furan 2,3,4,7,8-pncdf) as potentially toxic agents. WQS found a positive overall effect of the POP mixture and identified six POPs as potentially toxic agents (furans 1,2,3,4,6,7,8-hxcdf, 2,3,4,7,8-pncdf, and 1,2,3,6,7,8-hxcdf, and PCBs 99, 126, 169). BKMR found a positive linear association with furan 2,3,4,7,8-pncdf, suggestive evidence of linear associations with PCBs 126 and 169, and a positive overall effect of the mixture, but no interactions among congeners. Conclusions Using different methods, we identified patterns of POP exposure, potentially toxic agents, the absence of interaction, and estimated the overall mixture effect. These applications and results may serve as a guide for mixture method selection based on specific research questions

An environmental justice analysis of air pollution emissions in the United States from 1970 to 2010

Over the last decades, air pollution emissions have decreased substantially; however, inequities in air pollution persist. We evaluate county-level racial/ethnic and socioeconomic disparities in emissions changes from six air pollution source sectors (industry [SO2], energy [SO2, NOx], agriculture [NH3], commercial [NOx], residential [particulate organic carbon], and on-road transportation [NOx]) in the contiguous United States during the 40 years following the Clean Air Act (CAA) enactment (1970-2010). We calculate relative emission changes and examine the differential changes given county demographics using hierarchical nested models. The results show racial/ethnic disparities, particularly in the industry and energy generation source sectors. We also find that median family income is a driver of variation in relative emissions changes in all sectors-counties with median family income &gt;$75 K vs. less generally experience larger relative declines in industry, energy, transportation, residential, and commercial-related emissions. Emissions from most air pollution source sectors have, on a national level, decreased following the United States CAA. In this work, we show that the relative reductions in emissions varied across racial/ethnic and socioeconomic groups

Long-term Traffic-related Air Pollutant Exposure and Amyotrophic Lateral Sclerosis Diagnosis in Denmark: A Bayesian Hierarchical Analysis

Background: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. Limited evidence suggests ALS diagnosis may be associated with air pollution exposure and specifically traffic-related pollutants. Methods: In this population-based case-control study, we used 3,937 ALS cases from the Danish National Patient Register diagnosed during 1989-2013 and matched on age, sex, year of birth, and vital status to 19,333 population-based controls free of ALS at index date. We used validated predictions of elemental carbon (EC), nitrogen oxides (NOx), carbon monoxide (CO), and fine particles (PM2.5) to assign 1-, 5-, and 10-year average exposures pre-ALS diagnosis at study participants' present and historical residential addresses. We used an adjusted Bayesian hierarchical conditional logistic model to estimate individual pollutant associations and joint and average associations for traffic-related pollutants (EC, NOx, CO). Results: For a standard deviation (SD) increase in 5-year average concentrations, EC (SD = 0.42 µg/m3) had a high probability of individual association with increased odds of ALS (11.5%; 95% credible interval [CrI] = -1.0%, 25.6%; 96.3% posterior probability of positive association), with negative associations for NOx(SD = 20 µg/m3) (-4.6%; 95% CrI = 18.1%, 8.9%; 27.8% posterior probability of positive association), CO (SD = 106 µg/m3) (-3.2%; 95% CrI = 14.4%, 10.0%; 26.7% posterior probability of positive association), and a null association for nonelemental carbon fine particles (non-EC PM2.5) (SD = 2.37 µg/m3) (0.7%; 95% CrI = 9.2%, 12.4%). We found no association between ALS and joint or average traffic pollution concentrations. Conclusions: This study found high probability of a positive association between ALS diagnosis and EC concentration. Further work is needed to understand the role of traffic-related air pollution in ALS pathogenesis

Exploring Relevant Time Windows in the Association Between PM2.5 Exposure and Amyotrophic Lateral Sclerosis: A Case-Control Study in Denmark

Studies suggest a link between particulate matter less than or equal to 2.5 μm in diameter (PM2.5) and amyotrophic lateral sclerosis (ALS), but to our knowledge critical exposure windows have not been examined. We performed a case-control study in the Danish population spanning the years 1989-2013. Cases were selected from the Danish National Patient Registry based on International Classification of Diseases codes. Five controls were randomly selected from the Danish Civil Registry and matched to a case on vital status, age, and sex. PM2.5 concentration at residential addresses was assigned using monthly predictions from a dispersion model. We used conditional logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for confounding. We evaluated exposure to averaged PM2.5 concentrations 12-24 months, 2-6 years, and 2-11 years pre-ALS diagnosis; annual lagged exposures up to 11 years prediagnosis; and cumulative associations for exposure in lags 1-5 years and 1-10 years prediagnosis, allowing for varying association estimates by year. We identified 3,983 cases and 19,915 controls. Cumulative exposure to PM2.5 in the period 2-6 years prediagnosis was associated with ALS (OR = 1.06, 95% CI: 0.99, 1.13). Exposures in the second, third, and fourth years prediagnosis were individually associated with higher odds of ALS (e.g., for lag 1, OR = 1.04, 95% CI: 1.00, 1.08). Exposure to PM2.5 within 6 years before diagnosis may represent a critical exposure window for ALS

Long-term Traffic-related Air Pollutant Exposure and Amyotrophic Lateral Sclerosis Diagnosis in Denmark: A Bayesian Hierarchical Analysis

Background: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. Limited evidence suggests ALS diagnosis may be associated with air pollution exposure and specifically traffic-related pollutants. Methods: In this population-based case-control study, we used 3,937 ALS cases from the Danish National Patient Register diagnosed during 1989-2013 and matched on age, sex, year of birth, and vital status to 19,333 population-based controls free of ALS at index date. We used validated predictions of elemental carbon (EC), nitrogen oxides (NOx), carbon monoxide (CO), and fine particles (PM2.5) to assign 1-, 5-, and 10-year average exposures pre-ALS diagnosis at study participants' present and historical residential addresses. We used an adjusted Bayesian hierarchical conditional logistic model to estimate individual pollutant associations and joint and average associations for traffic-related pollutants (EC, NOx, CO). Results: For a standard deviation (SD) increase in 5-year average concentrations, EC (SD = 0.42 µg/m3) had a high probability of individual association with increased odds of ALS (11.5%; 95% credible interval [CrI] = -1.0%, 25.6%; 96.3% posterior probability of positive association), with negative associations for NOx(SD = 20 µg/m3) (-4.6%; 95% CrI = 18.1%, 8.9%; 27.8% posterior probability of positive association), CO (SD = 106 µg/m3) (-3.2%; 95% CrI = 14.4%, 10.0%; 26.7% posterior probability of positive association), and a null association for nonelemental carbon fine particles (non-EC PM2.5) (SD = 2.37 µg/m3) (0.7%; 95% CrI = 9.2%, 12.4%). We found no association between ALS and joint or average traffic pollution concentrations. Conclusions: This study found high probability of a positive association between ALS diagnosis and EC concentration. Further work is needed to understand the role of traffic-related air pollution in ALS pathogenesis

Altered proliferation and networks in neural cells derived from idiopathic autistic individuals.

Autism spectrum disorders (ASD) are common, complex and heterogeneous neurodevelopmental disorders. Cellular and molecular mechanisms responsible for ASD pathogenesis have been proposed based on genetic studies, brain pathology and imaging, but a major impediment to testing ASD hypotheses is the lack of human cell models. Here, we reprogrammed fibroblasts to generate induced pluripotent stem cells, neural progenitor cells (NPCs) and neurons from ASD individuals with early brain overgrowth and non-ASD controls with normal brain size. ASD-derived NPCs display increased cell proliferation because of dysregulation of a β-catenin/BRN2 transcriptional cascade. ASD-derived neurons display abnormal neurogenesis and reduced synaptogenesis leading to functional defects in neuronal networks. Interestingly, defects in neuronal networks could be rescued by insulin growth factor 1 (IGF-1), a drug that is currently in clinical trials for ASD. This work demonstrates that selection of ASD subjects based on endophenotypes unraveled biologically relevant pathway disruption and revealed a potential cellular mechanism for the therapeutic effect of IGF-1