"Medicine, Faculty of"@en . "Population and Public Health (SPPH), School of"@en . "DSpace"@en . "UBCV"@en . "Cliff, Rachel"@en . "2016-04-27T02:02:38"@en . "2016"@en . "Master of Science - MSc"@en . "University of British Columbia"@en . "Epidemiological and animal studies suggest that exposure to airborne pollutants may negatively impact the central nervous system (CNS). It is thought that traffic related air pollution (TRAP), and other forms of combustion-derived pollutants, may induce a maladaptive activation of the CNS immune system, however, the exact pathway is not understood. Animal models and epidemiological studies have inherent limitations including potential interspecies differences and residual confounding. Given this, the aim of this research is to examine effects of TRAP on the CNS using a controlled human exposure. 27 healthy adults were exposed to two conditions: filtered air (FA) and diesel exhaust (DE) (300\u00C2\u00B5g PM\u00E2\u0082\u0082.\u00E2\u0082\u0085/m\u00C2\u00B3) for 120 minutes, in a double-blinded crossover study with exposures separated by four-weeks. Prior to and at 0, 3, and 24 hours following exposure, serum and plasma were collected and analyzed for inflammatory cytokines IL-6 and TNF-\u00CE\u00B1, the astrocytic protein S100b, the neuronal cytoplasmic enzyme neuron specific enolase (NSE), and brain derived neurotrophic factor (BDNF). The hypothesis was that IL-6, TNF-\u00CE\u00B1, S100b and NSE would increase and BDNF would decrease following DE exposure. Changes in levels of biomarkers were assessed using a paired t-test to compare the change from baseline at each post-exposure timepoint following DE or FA exposure. A linear mixed effects model was build including exposure and timepoint as covariates, and subject ID as a random effect. Age and gender were examined as potential effect-modifying variables. At no time-point following exposure to DE was a significant increase from baseline seen for IL-6, TNF-\u00CE\u00B1, S100b or NSE, or decrease for BDNF, relative to FA exposure. The linear mixed effects model revealed indication of diurnal behavior for S100B, NSE and BDNF; however, no significant exposure-time-point interaction, suggesting the biomarkers were not affected by DE exposure. These results indicate that short-term exposure to DE amongst young, healthy adults does not acutely affect levels of the measured biomarkers. This study does not disprove a relationship between air pollution and adverse CNS effects and suggests a need to examine the effects of TRAP on the brain using in chronic exposure models or more sensitive CNS endpoints."@en . "https://circle.library.ubc.ca/rest/handle/2429/57879?expand=metadata"@en . "PERIPHERAL BLOOD MARKERS OF CENTRAL NERVOUS SYSTEM EFFECTS FOLLOWING CONTROLLED HUMAN EXPOSURE TO DIESEL EXHAUST Rachel Cliff B.Sc., University of Guelph, 2011 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Occupational and Environmental Hygiene) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2016 \u00C2\u00A9 Rachel Cliff, 2016 ii Abstract Epidemiological and animal studies suggest that exposure to airborne pollutants may negatively impact the central nervous system (CNS). It is thought that traffic related air pollution (TRAP), and other forms of combustion-derived pollutants, may induce a maladaptive activation of the CNS immune system, however, the exact pathway is not understood. Animal models and epidemiological studies have inherent limitations including potential interspecies differences and residual confounding. Given this, the aim of this research is to examine effects of TRAP on the CNS using a controlled human exposure. 27 healthy adults were exposed to two conditions: filtered air (FA) and diesel exhaust (DE) (300\u00C2\u00B5g PM2.5/m3) for 120 minutes, in a double-blinded crossover study with exposures separated by four-weeks. Prior to and at 0, 3, and 24 hours following exposure, serum and plasma were collected and analyzed for inflammatory cytokines IL-6 and TNF-\u00CE\u00B1, the astrocytic protein S100b, the neuronal cytoplasmic enzyme neuron specific enolase (NSE), and brain derived neurotrophic factor (BDNF). The hypothesis was that IL-6, TNF-\u00CE\u00B1, S100b and NSE would increase and BDNF would decrease following DE exposure. Changes in levels of biomarkers were assessed using a paired t-test to compare the change from baseline at each post-exposure timepoint following DE or FA exposure. A linear mixed effects model was build including exposure and timepoint as covariates, and subject ID as a random effect. Age and gender were examined as potential effect-modifying variables. At no time-point following exposure to DE was a significant increase from baseline seen for IL-6, TNF-\u00CE\u00B1, S100b or NSE, or decrease for BDNF, relative to FA exposure. The linear mixed effects model revealed indication of diurnal behavior for S100B, NSE and BDNF; however, no significant exposure-time-point interaction, suggesting the biomarkers were not affected by DE exposure. These results indicate that short-term exposure to DE amongst young, healthy adults does not acutely affect levels of the measured biomarkers. This study does not disprove a relationship between air pollution and adverse CNS effects and suggests a need to examine the effects of TRAP on the brain using in chronic exposure models or more sensitive CNS endpoints. iii Preface The overarching study, of which this thesis was a part, was designed by Dr. Christopher Carlsten and PhD candidate Jason Curran and funded by Health Canada. All human exposures were conducted at Vancouver General Hospital in the Air Pollution Exposure Laboratory. This study was approved by the ethical review board of the University of British Columbia (#H12-03025), Vancouver Costal Health Ethics Board (# V12-03025) and Health Canada\u00E2\u0080\u0099s Research Ethics Board. The thesis content is the original work of the author, Rachel Cliff, and was conducted with the supervision of M.Sc. supervisor Dr. Christopher Carlsten, and thesis committee members Dr. Michael Brauer and Dr. Howard Feldman, all from the University of British Columbia, Faculty of Medicine. A version of Chapter 4 has been published in the journal of Inhalation Toxicology (Cliff, R., Curran, J., Hirota, J.A., Brauer, M., Feldman, H., and Carlsten, C. Effect of diesel exhaust inhalation on blood makers of inflammation and neurotoxicity: a controlled, blinded crossover study. Inhal Toxicol. DOI: 10.3109/08958378/2016.1145770). iv Table of Contents Abstract ii Preface iii Table of Contents iv List of Tables vi List of Figures vii List of Abbreviations viii Acknowledgements x Dedication xi 1 INTRODUCTION 1 1.1 Overview and Historical Context 1 1.2 Objectives and Methodology 4 1.3 Contribution to Literature 5 2 LITERATURE REVIEW 7 2.1 An Overview of Diesel Engine Exhaust 7 2.1.1 Diesel Exhaust Components 7 2.1.2 Diesel Engine Technologies 9 2.1.3 Workplace and Environmental Exposure Levels 10 2.2 Known Health Effects of Traffic Related Air Pollution Exposure 12 2.3 Effects of Air Pollution on the Brain 12 2.3.1 Epidemiological and Human Environmental Studies 14 2.3.2 Proposed Mechanism 17 2.3.3 Research in in vitro Animal Models 21 2.4 Inflammatory Cytokines 23 2.4.1 Inflammatory Cytokines and Air Pollution 23 2.4.2 Inflammatory Cytokines in the Brain 24 2.5 S100B as a Central Nervous System Biomarker 26 2.6 NSE as a Central Nervous System Biomarker 28 2.7 BDNF as a Neuro-Protective Factor 29 2.8 Study Rationale 30 3 METHODS 33 3.1 Hypotheses 33 3.2 The Overarching Study: Effects of Air Pollution on cognition 33 3.3 Experiment Procedures 35 3.3.1 Subject Recruitment 35 3.3.2 Exposures 36 3.3.3 Blood Collection 37 3.3.4 Other Aspects of the Study 38 3.4 Laboratory Methods 38 3.4.1 Blood Processing 38 3.4.2 ELISA Procedures 39 3.5 Statistical Methods 40 3.5.1 Subject Characteristics 40 3.5.2 Descriptive Statistics of the Blood Markers 40 3.5.3 Unpaired and Paired T-tests of Baseline Values and Delta Values from Baseline 40 3.5.4 Developing a Linear Mixed Effect Model 41 4 RESULTS 43 v 4.1 Exposure Characteristics 43 4.2 Study Participants and Number of Blood Draws 44 4.3 Descriptive Statistics of Blood Markers 45 4.4 Baseline Values, Distributions at Each Time Point and Delta Values from Baseline 47 4.5 Linear Mixed Effect Models 54 4.5.1 Inclusion of Demographic Variables and Interaction Terms 54 4.5.2 Final Mixed Effect Models Considering Effect of Exposures, Time the Interaction Between Exposure and Time \u00E2\u0080\u0093 Inflammatory Cytokines 56 4.5.3 Final Mixed Effect Models Considering Effect of Exposures, Time the Interaction Between Exposure and Time \u00E2\u0080\u0093 S100B and NSE 58 4.5.4 Final Mixed Effect Models Considering Effect of Exposures, Time the Interaction Between Exposure and Time \u00E2\u0080\u0093 BDNF 61 4.5.5 Order Effect: TNF-\u00CE\u00B1 63 4.6 Conclusions to Results 65 5 DISCUSSION 67 5.1 Overview 67 5.2 Levels of Biomarkers in Subjects 67 5.2.1 Levels of Inflammatory Cytokines IL-6 and TNF-\u00CE\u00B1 67 5.2.2 Levels of Neurologically Specific Biomarkers, S100B and NSE 68 5.2.3 Levels of BDNF 71 5.3 Study Hypothesis 73 5.3.1 Inflammatory Markers 73 5.3.2 Effect of DE Exposure on the Blood Brain Barrier 77 5.3.3 Effect of DE on the Central Nervous System 79 5.3.4 Effect of DE on BDNF 82 5.4 Risk Assessment and Strengths and Limitations 86 5.5 Recommendations for Future Studies 89 REFERENCES 92 APPENDIX I Summary of the Cognitive Function Parameters Considered in the EAPOC Study 105 APPENDIX II Typical Plate Design of Samples 107 APPENDIX III Histograms and Information to Determine Skewedness of Data 108 APPENDIX IV R Data and Analysis Codes for Mixed Effects Model 114 vi List of Tables Table 3.1: Timeline of typical exposure day 37 Table 4.1: Diesel Exhaust and Filtered Air Exposure Characteristics from the Average Results of the DE and FA runs in this Study 43 Table 4.2: Time of Blood Draws 44 Table 4.3: Subject Characteristics 44 Table 4.4: Characteristics of Subjects in Each Order-Grouping and Unpaired t-test Results to Assess Differences Between Groups 45 Table 4.5: Distribution of the Values of Each Measured Biomarker 47 Table 4.6: Baseline Values by Gender, Exposure Day and Exposure condition and unpaired t-test results to determine if the respective values differed 48 Table 4.7: Concentration (mean, \u00C2\u00B1SD) of Blood Makers at each Timepoint Before and After Exposure 49 Table 4.8: Average Change (\u00C2\u00B1SD) in Concentration of Blood Markers at each Timepoint, Relative to Baseline, Following Exposure and the Number and Percentage of Subjects who Showed a Trend in the Direction of the Study Hypothesis Following DE Exposure Relative to FA 49 Table 4.9: Mixed-effects Model Testing for the Interaction of Gender, Age and Timepoint With Exposure Condition with the Various Biomarkers as Dependent Variables 54 Table 4.10: Mixed-effects Model Testing for Significance of Gender, Age, Timepoints, Exposure Condition, and Exposure Day in Predicting each Inflammatory Marker as Dependent Variables 55 Table 4.11: Final Mixed-effects Model Testing for the Effect of Exposure Condition, Timepoint, and the Interaction Between these Two Terms with the Two Inflammatory Markers as Dependent Variables, or, if Run, the Estimates for the Second Mixed Effects Model Including only Timepoint and Exposure Condition 56 Table 4.12: Least-squares Means Pairwise Comparisons for Exposure and Timepoint and, in the Case of TNF-\u00CE\u00B1, Exposure day for the Two Inflammatory Cytokines 57 Table 4.13: Final Mixed-effects Model Testing for the Effect of Exposure Condition, Timepoint, and the Interaction Between these Two Terms with S100B and NSE as Dependent Variables, or, if run, the Estimates for the Second Mixed Effects Model 58 Table 4.14: Least-squares Means Pairwise Comparisons for Exposure and Timepoint for S100B and NSE 59 Table 4.15: Final Mixed-effects Model Testing for the Effect of Exposure Condition, Timepoint, and the Interaction Between these Two Terms with BDNF as the Dependent Variable, and the Estimates for the Second Mixed Effects Model 61 Table 4.16: Least-squares Means Pairwise Comparisons for Exposure and Timepoint for BDNF 62 Table 4.17: Mixed-effects Model Testing for Significance of Timepoint, Exposure Condition and Timepoint \u00E2\u0080\u0093 Exposure Condition Interaction for TNF-\u00CE\u00B1 examining those who Received FA First and those who Received DE First, Separately 63 vii List of Figures Figure 3.1: Schematic of Overall Study Design 34 Figure 4.1: Delta Values for the Inflammatory Cytokines IL-6 (pg/mL) in EDTA Plasma Samples and TNF-\u00CE\u00B1 (pg/mL) in Serum Samples 51 Figure 4.2: Delta Values for the Inflammatory Cytokines S100B (pg/mL) and NSE (ng/mL) in Serum Samples 52 Figure 4.3: Delta Values for the Inflammatory Cytokines BDNF (pg/mL) in EDTA Plasma Samples 53 Figure 4.4: Levels of EDTA Plasma IL-6 and Serum TNF-\u00CE\u00B1 from Baseline and Immediately Post, 3, and 24 hours After 57 Figure 4.5: Levels of Serum S100B and NSE from Baseline and Immediately post, 3, and 24 hours After 60 Figure 4.6: Levels of Serum BDNF from Baseline and Immediately post, 3, and 24 hours After 62 Figure 4.7: Levels of TNF-\u00CE\u00B1 at Baseline and Immediately Post, 3, and 24 hours after exposure Considering Order 63 viii List of Abbreviations AM Arithmetic mean APEL Air Pollution Exposure Laboratory BBB Blood brain barrier BC Black carbon BDNF Brain-derived neurotrophic factor CANTAB Cambridge Neuropsychologial Test Automated Battery CNS Central nervous system CSF Cerebral spinal fluid CO Carbon monoxide CO2 Carbon dioxide COX-2 Cyclooxygenase-2 DALYs Disability-adjusted life-years DE Diesel exhaust DEPM Diesel exhaust particulate matter EAPOC Effects of Air Pollution on Cognition Study EC Elemental carbon ELISA Enzyme-linked immunosorbent assay FA Filtered air fMRI Functional neuroimaging GE Gasoline engine exhaust GFAP Glial fibrillary acidic protein GM Geometric mean HEPA High-efficiency particulate air HC Hydrocarbons HO Heme oxygenase IL Interleukin kW Kilo-watt LOD Limit of detection LTA Lipoteichoic acid mg/m3 milligrams per cubic meter MMSE Mini-Mental State Examination Mn manganese ng/mL nanograms per milliliter NIOSH National Institute for Occupational Safety and Health NOx Nitrogen oxides NO2 Nitrogen dioxide NSE Neuron specific enolase OC Organic carbon OD optical density OR Odds ratio O2 Oxygen PAH Polyaromatic hydrocarbons ix pg/mL picograms per millilitre PPB Parts per billion PPM Parts per million PM Particulate matter PM2.5 Fine particulate matter; particulate matter with a mass median aerodynamic diameter less than 2.5 micrometers PM10 Inhalable particulate matter; particulate matter with a mass median aerodynamic diameter less than 10 micrometers PNC Particle number concentration ROS Reactive oxygen species SD Standard deviation SMPS Scanning Mobility Particle Sizer SOx Sulfur oxides TBI Traumatic brain injury TEOM Tapered Element Oscillating Microbalance TNF-\u00CE\u00B1 Tumour necrosis factor-alpha TRAP Traffic related air pollution TVOC Total volatile organic compounds TWA 8-hour time weighted average UFPM Ultrafine particulate matter \u00C2\u00B5g micrograms \u00C2\u00B5g/m3 micrograms per cubic meter USEPA US Environmental Protection Agency WHO World Health Organizations 13C Radioactively labeled carbon x Acknowledgements Completing this thesis has been one of the most rewarding experiences of my academic career. Looking back through the steps required to complete the project I know it would not have been possible without the invaluable support of many incredible individuals. I would firstly like to thank my supervisor, Dr. Christopher Carlsten, for his mentorship and assistance throughout every aspect of this project. Dr. Carlsten was positive and encouraging, while still guiding me to perfect the end product. Thank you to my committee members Dr. Michael Brauer and Dr. Howard Feldman, who both provided instrumental feedback throughout the development and completion of my thesis, and enabled me to see the project from a number of different angles. Thank you to all the faculty members of the Occupational and Environmental Hygiene program who helped to make my time a truly enjoyable learning experience, and a special thank you to Dr. Karen Bartlett, Dr. Hugh Davies and Dr. George Astrakianakis for their support. To my fellow OEH students, lab members in the Carlsten group, and Jason Curran \u00E2\u0080\u0093 thank you for being available to answer my questions and discuss my research; it has been a pleasure to meet and work with all of you. To my parents, Ron and Nancy, siblings Andrew and William and my wonderful husband Chris. I never could have completed this chapter of my academic journey without your love and support. And last, but by no means least, thank you to NSERC CREATE-AAP and WorkSafeBC for providing me with funding and academic training. xi Dedication This thesis is dedicated to my family; who celebrate my successes, offer support through my challenges, and continually inspire me to achieve my best. 1 Chapter 1: Introduction 1.1 Overview and Historical Context Throughout the mid 1900\u00E2\u0080\u0099s several extreme air pollution events occurred in western countries, which drew widespread attention, for the first time, to the potential risks of such exposures. The first of these occurred in December of 1930 in Liege, Belgium when stable atmospheric conditions allowed for industrial air pollution from steel mills, coke ovens, smelters and other sources to accumulate in the city. Over the course of two days, 60 people died; more than 10 times the typical mortality rate in the area at that time (1). Later, in 1948, the small town of Donora, Pennsylvania, encountered a similar air pollution event with comparable health consequences. Unusual atmospheric conditions caused emissions from local coke ovens, coal-fired furnaces, and metal works industries to settle in the valley town and 20 people died within a single week: a rate six times the normal mortality rate (1). Finally, what is considered the most well-known air pollution event of the 20th century occurred in London, England from December 5th to December 9th 1952 - an event that became known as the \u00E2\u0080\u009CLondon Fog\u00E2\u0080\u009D. A thick smog, resulting from various industrial processes and coal-burning in homes, power plants, and factories settled in the city as a result of stagnant air conditions. Air pollution levels were approximately five to nineteen times above current regulatory environmental standards (1). It is estimated that the London Fog episode was responsible for causing 4,000 excess deaths in the short-term, and 12,000 additional mortalities in the subsequent months (1,2). These international episodes, particularly the London Fog, sparked an area of epidemiological research focusing on the health impacts of environmental air pollution (1-3). Today, the negative cardiovascular and respiratory health effects of exposure to combustion related air pollution are well documented and generally accepted (3). That said, the scientific community has yet to determine a level of particulate matter (PM) exposure at which no adverse health effects can be detected on a population level (4). In modern times, common 2 sources of combustion related air pollution are from personal and industrial vehicles, and the product of this is often described as traffic related air pollution (TRAP). TRAP itself is a complex mixture of airborne compounds including carbon dioxide (CO2), carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NOx), aldehydes, and PM (5). Although regulations have been developed to control industrial and personal vehicle emissions, worldwide exposure to high levels of anthropogenic and natural air pollution continues to be a public health problem. For example, it is estimated that 89% of the world\u00E2\u0080\u0099s population resides in an area where the World Health Organization\u00E2\u0080\u0099s (WHO) Air Quality Guideline for fine PM (particulate matter with an aerodynamic diameter less than 2.5 \u00C2\u00B5m) is exceeded (6). The 2013 Global Burden of Disease study estimated that environmental PM contributed to 3 million premature deaths and 70 million disability-adjusted life-years, worldwide, in 2013 and in 2016 this estimate was increased to 2.9 million excess deaths (7,8). In addition to the known cardiovascular and respiratory health effects of TRAP exposure, recent epidemiological and animal evidence suggests that air pollution may also negatively impact the central nervous system (CNS) (5). Indeed, various epidemiological studies have shown that exposure to air pollutants is correlated with delayed cognitive development in children and impaired cognitive function in the elderly. Adults residing in areas with higher TRAP levels have been noted to have poorer cognitive function and faster rates of cognitive decline. Such a relationship has been demonstrated in several studies examining populations of both men and women across a spectrum of specific age ranges, all above 55 years old (9-13). In children, a correlation between environmental black carbon and lower cognitive function has been observed (14) and an association between nitrogen dioxide (NO2) exposure in schools and lower neurobehavioral scores has also been documented (15). This evidence suggests that air pollution has potential to impact the CNS at various stages of life, with individuals being particularly susceptible during neurodevelopment and aging. 3 Although the exact mechanisms by which air pollution may impact the CNS are not understood, the pathway is generally hypothesized to involve a maladaptive activation of the innate immune system, spanning four general mechanisms as follows: 1) It is relatively well established that inhalation of PM activates pro-inflammatory cytokines in macrophages, causing a release of various inflammatory responses and oxidative stress (16-18). It is then thought that this inflammatory effect may be transferred to the CNS, leading to the activation of further inflammatory pathways and oxidative stress within the brain; 2) There may be direct entry of PM into the CNS. Ultra-fine PM (<0.1 \u00C2\u00B5m) (UFPM) may transverse alveolar epithelial cell membranes and then be carried by erythrocytes to extra-pulmonary organs, including the boundary with the brain, where they may impair the integrity of the blood brain barrier (BBB) and enter the CNS (18,19); 3) UFPM may bypass the BBB and enter the CNS through direct translocation across the olfactory epithelium (19,20). Alternatively, it has been suggested that air pollutants, including PM, may stimulate vaso-vagal reflexes or respiratory tract irritant receptors in the airways (21-23). The particles may therefore impact afferent autonomic nervous system (ANS) fibers and induces ANS dysfunction (21-24). The impact of air pollutants on the brain may therefore occur through the feedback of the altered ANS control, or be related to a pathway downstream of the pollution-induced cardiovascular effects (5,22). Regardless of the exact pathogenic mechanism, one of the popular overarching hypotheses is that PM exposure is associated with CNS inflammation, which may become clinically relevant with repeated exposures or during susceptible periods of brain development (18,25). In conclusion, it is possible that the peripheral immune response to air pollution is transferred to the CNS, activating inflammatory cascades and microglia \u00E2\u0080\u0093 the innate immune cells of the brain and, additionally, that microglial cells respond to UFPM that reaches the CNS (18). Neurocognitive diseases place significant burden on patients, their families, and public health systems, but the role of environmental risk factors is poorly understood. A recent report estimated that dementia costs US $818 billion, annually, due to societal and economic factors, and the psychological burden on family members of patients is also significant (26). Research to identify and address modifiable risk factors is an important public health priority (27) and 4 determining if exposure to air pollution increases risk of cognitive decline is an important, and relatively unexplored, area of research. Presently, findings from both epidemiological and animal research suggest a relationship between combustion-derived air pollutant exposure and adverse CNS outcomes. However, there is insufficient plausibility to strongly prove this relationship. Given this, further research, including controlled human exposure studies, are needed. 1.2 Objectives and Methodology This study was designed to determine the acute effects of a TRAP exposure on blood biomarkers of systemic inflammation and CNS effects (5). 27 human volunteers were each exposed for 120 minutes, on two different occasions, to DE at a concentration of 300\u00C2\u00B5g/m3 PM2.5 or filtered air (FA) in a double-blinded, crossover design. A four-week washout period was allowed between exposures and exposure conditions were randomized and counter-balanced. Various cognitive parameters were assessed by another student on the project and this thesis work is specific to the measurement of biomarkers before and after each exposure. Serum and plasma were collected at four time points before and after each exposure (immediately before and after, and three and twenty-four hours after) and the levels of five specific biomarkers were determined in these samples. The biomarkers were specifically selected with the targeted aim of enhancing our understanding of the effects of TRAP on the CNS. Plasma was analyzed for interleukin-6 (IL-6) and serum for tumour necrosis factor-alpha (TNF-\u00CE\u00B1), as markers of systemic inflammation. Potential insults to the CNS were determined by analyzing serum levels of S100B, an astrocytic protein that has been recorded to increase in the serum following CNS insult (28,29) and a proposed marker of BBB permeability (28,30), and neuron specific enolase (NSE), a marker thought to increased as a result of neuronal death (29,31) and potential marker of neuroinflammation (32). For these four biomarkers, the hypothesis was that levels would increase following DE exposure relative to FA. Finally, brain-derived neurotrophic factor (BDNF), a molecule that is thought to protect neurons and encourage their growth and differentiation, was measured in serum (33,34). We used these biomarkers as a non-invasive approach to assess 5 the effects of a controlled air pollution exposure on the human brain, with the overarching aim of determining if, and to what extent, an acute TRAP exposure can impact the CNS. The objectives of this study were to assess: (1) The presence of an inflammatory effect of diesel exhaust (DE) exposure on the human subjects, characterized by an increase in the inflammatory cytokines IL-6 and TNF-\u00CE\u00B1 immediately and three hours following diesel exhaust exposure. (2) An increase in serum levels of the protein S100B (a marker for CNS effects and a proposed marker for BBB permeability), three and twenty-four hours after exposure. (3) An increase in serum levels of the CNS-specific protein NSE, three and twenty-four hours after exposure, and/or by showing a decrease in serum BDNF levels immediately following exposure. 1.3 Contribution to Literature While the negative cardiovascular and respiratory health effects of TRAP are well documented, new evidence suggests that these exposures may impact the CNS. The potential of TRAP to impact the CNS at any capacity is of obvious concern to the general public, who are environmentally exposed to air pollution, as well as to the substantial number of workers who are experience high levels of TRAP exposures at their place of work. 6 Due to the emerging nature of the concept that combustion related air pollution may impact the brain, there remain many unanswered questions. Both animal models and epidemiological studies have inherent limitations including potential interspecies difference and the issue of residual confounding. Previous in vivo animal experiments and post-mortem analyses studies have had the ability to analyze brain tissue; an option clearly not available for a model involving living human volunteers. Here, blood collection is a feasible and relatively non-invasive alternative. There is potential for circulating proteins to provide indication of the biological mechanism behind the effect of DE on the CNS. The work in this thesis represents a sub-section of a larger study termed the \u00E2\u0080\u009CEffects of Air Pollution On Cognition\u00E2\u0080\u009D (EAPOC) study, in which other cognitive tests are also examined with the similar goal of showing if, and to what extent, DE exposures (at a level representative of high-ambient environmental and occupational conditions) can impact cognitive function. The use of blood markers in this study provide a unique opportunity to complement the cognitive tests and give indication of a systemic and/or CNS-specific response, offering mechanistic information to inform current and future observations. 7 Chapter 2: Literature Review 2.1 An Overview of Diesel Engine Exhaust In 1892 the diesel engine was first patented by Rudolf Diesel. It quickly rose to popularity owing to its improved fuel efficiency compared to other forms of engines available at the time; a major advantage of diesel-powered vehicles still to this day (35). In modern times, diesel engines are used to power machinery such as personal vehicles, trucks, buses, agricultural equipment, locomotives, and ships (35). While it remains true that these engines offer greater fuel economy and durability than gasoline-powered engines, they emit more PM for the same workload (35) and, it is additionally concerning that diesel exhaust particulate matter (DEPM) have mutagenic and carcinogenic properties. Indeed, long-term inhalation of DE has been linked to increased lung cancer mortality in over 35 epidemiological studies (36). In addition, chronic, and acute cardiovascular and respiratory health outcomes have been associated with DE and other TRAP exposure, and DE is known to cause irritation of the eyes and upper respiratory tract. Diesel-powered vehicles are important in industries worldwide and, as a result, exposures to their exhaust is also common; in BC alone, it is estimated that approximately 108,000 people were exposed to diesel fumes in their workplace on an annual basis (37). 2.1.1 Diesel Exhaust Components Complete combustion of diesel fuel would produce emissions of water and CO2 (38), but in real world operation, diesel engine exhaust is in fact a complex mixture of hundreds of pollutants (35). Potential constituents of diesel exhaust (DE) include gases \u00E2\u0080\u0093 such as oxygen (O2), CO2, CO, NOx, sulfur oxides (SOx), low molecular weight hydrocarbons (i.e. benzene, formaldehyde, acetaldehyde, 1,3-butadiene, polyaromatic hydrocarbons (PAHs) and nitro-PAHs), water vapour, and a range of PM (35,39,40). Primary DEPM is formed as a result of incomplete combustion of diesel fuel and is commonly referred to as \u00E2\u0080\u009Cdiesel soot\u00E2\u0080\u009D and consists primarily of an agglomerated elemental carbon (EC) core and ash, surrounded by adsorbed ash, organic 8 compounds and small amounts of sulfate, nitrate, metals and other trace elements and metals (35,39,41,42). In addition, secondary pollutants, such as ozone, can also be generated in the atmosphere as DE ages (35). Of the components within DE, PM and NOx are generally considered the most relevant compounds to human health (43), however, many of the other constituents also have environmental and health consequences (35). For example, CO2 is a major contributor to global warming; NOx and SOx can initiate acid rain; NOx, hydrocarbons, and aldehydes are all ozone precursors. CO is highly toxic to humans as it binds to hemoglobin 250 times more strongly than oxygen \u00E2\u0080\u0093 potentially leading to oxygen deprivation \u00E2\u0080\u0093 that said, levels of CO in DE emissions are generally too diluted in environmental settings to cause an acute hazard (44). Hydrocarbons, NOx and SOx are irritants of the eyes, upper respiratory tract and skin, and additionally, some aldehydes and hydrocarbons released in DE are considered carcinogenic (35,44). As will be discussed in more detail below, the toxicological potential for DEPM is based partially on the size of the PM and its structure. As a pollutant, DE contains less CO, and more NOx, aldehydes and PM, than does gasoline engine exhaust (GE) (38). Ambient PM is usually classified into fractions on the basis of its size: respirable PM is defined as PM with an aerodynamic diameter <10 \u00C2\u00B5m (PM10); coarse particles are those with a diameter between 2.5 and 10 \u00C2\u00B5m (PM2.5-10); fine PM, another subset of PM10, consists of all particles with an aerodynamic diameter <2.5 \u00C2\u00B5m; and finally, UFPM are the smallest size fraction of PM and consist of those with a diameter < 0.1 \u00C2\u00B5m (PM0.1) (40). The elemental core of DEPM has a high capacity for adsorbing various and potentially hazardous compounds such as organic material from unburned fuel, engine, lubricating oil, and other molecules found in the environment (35). By mass, approximately \u00E2\u0089\u00A590% of DEPM is PM2.5, and 1-20% is UFPM (35,40). Although the UFPM fraction contributes little to the overall mass of DEPM, by number, the majority of DEPM is ultrafine (35). The small size of DEPM enables a high percentage of the particles to pass through the upper respiratory tract and readily deposit deep into lungs; while PM10 typically lands in locations within the upper respiratory tract, PM2.5 penetrates deeper and 9 approximately 83% of fine PM deposits in the lower respiratory tract, including the alveoli (38,45). Another factor contributing to its toxicity is that DEPM has a large surface area per mass, allowing for high contact with the adsorbed compounds on its surface (35). This enhanced surface area-to-mass ratio and associated elevated toxicity is of greatest concern in particular for the ultra-fine (PM0.1) fraction of DE (44). Finally, it has been proposed that UFPM may translocated across the alveolar wall, pass into systemic circulation, and reach extra-pulmonary tissue where other toxicological health impacts may additionally occur (44). 2.1.2 Diesel Engine technologies As diesel engines operates through the following general mechanism: fuel is injected into air which has been compressed to a high pressure and temperature, thereby igniting it and releasing the stored chemical energy. The resulting combustion gases power the engine\u00E2\u0080\u0099s piston and, afterwards, are released into the atmosphere as waste exhaust (35). Emissions of diesel engines will vary significantly in terms of chemical composition and PM size depending on operating conditions and engine factors such as: (1) whether the engine is light-duty or heavy duty (i.e. powering an automotive truck, car or small or large industrial equipment), (2) \u00E2\u0080\u009Con-road\u00E2\u0080\u009D or \u00E2\u0080\u009Coff-road\u00E2\u0080\u009D (3) the engine age, (4) whether it is two- or four-stroke and (5) the fuel formulation being used (35). Diesel engines are popular in Europe due to their better fuel economy and, in part, as a result of strong diesel-favouring tax incentives. Additionally, worldwide sales of diesel engines have risen significantly over the past decade also due to their higher fuel economy and durability (40). Since 2005, progressively stricter diesel truck and car emission standards \u00E2\u0080\u0093 mostly concerning CO, hydrocarbons, NOx PM and sulfur \u00E2\u0080\u0093 have been implemented across North America, Europe and Japan (40,46). Significant advances in ultra-low-sulfur diesel fuel and engine technology \u00E2\u0080\u0093 involving electronic controls, oxidation catalysts and diesel particulate filters \u00E2\u0080\u0093 have been developed to meet the newest emission standards (40,47). Although the exhaust of the newest DE engines emit less NOx and PM for the same workload, the technologies have caused 10 the average size of DEPM to be smaller and, therefore, the exhaust typically contains more PM by particle number (48). 2.1.3 Workplace and Environmental Exposure Levels As diesel engines are more efficient and durable than their gasoline counterparts they are frequently used to power machinery in transportation, mining, construction, agriculture and various manufacturing industries (49). In Canada it is estimated that 879,000 Canadians are exposed to DE in their workplace, annually, with the most significant exposures occurring with truck drivers and heavy equipment operators. Occupations of greatest risk for on-road and off-road DE exposure include bus, truck, subway and professional drivers, bus garage workers, tool-booth and parking garage attendants, forklift operators, firefighters, lumberjacks, traffic controllers and car mechanics and workers in railroad, marine, mining and forestry industries (50). Assessing exposure to DE is challenging due to its various components and complex nature; most regulation agencies have exposure limits for the specific gaseous constituents and little, if any, guidelines surrounding DEPM. For example, although WorkSafeBC has exposure limits for gases such as CO, NO2 and SO2, there are no regulations for DEPM, specifically. Respirable dust (PM with aerodynamic diameter \u00E2\u0089\u00A410\u00C2\u00B5m) is limited to 3mg/m3 for an 8-hour time weighted average (TWA), but this does not address DE-specific PM (51). The US Mining Safety and Health Administration is one of the few agencies to set a DEPM limit, which is 160\u00C2\u00B5g/m3 for an 8-hour TWA in underground mines (52). Further complicating this issue, there are a variety of methods that may be used in studies to assess exposure to DEPM. National Institute for Occupational Safety and Health (NIOSH) has published the NIOSH 5040 method which is used to determine the specific mass of organic carbon (OC) and EC in a given volume of air by a thermal optical analysis technique (53). Some studies quantify EC to assess DEPM, whereas others simply examine PM10 or PM2.5, which may be derived from diesel engines, other combustion sources, or 11 non-combustion-derived dusts. Regardless, these exposure methods have been used to estimate exposures to DEPM in various environments and workplaces. In 2009, Pronk et al performed a literature review examining estimated DEPM exposures across several occupational settings. The geometric mean (GM) of EC exposures ranged from 0.9 \u00E2\u0080\u0093 19 \u00C2\u00B5g/m3 across track and bus drivers working in America, Estonia and Sweden. Respirable PM exposures were higher than the EC exposures, with GMs ranging from 20 \u00C2\u00B5g/m3 to 580 \u00C2\u00B5g/m3. For vehicle mechanics, GMs for EC exposure ranged from below the limit of detection ( 27. World Health Organization and Alzheimer\u00E2\u0080\u0099s Disease International. (2012) \u00E2\u0080\u009CDementia: a public health priority\u00E2\u0080\u009D World Health Organization Publications 28. Marchi, N., Cavaglia, M., Fazio, V., Bhudia, S., Hallene, K. and Janigro, D. (2004) \u00E2\u0080\u009CPeripheral markers of blood-brain barrier damage\u00E2\u0080\u009D Clinical Chimica Acta 342: pp. 1 \u00E2\u0080\u0093 12 94 29. Streitburger, D.P., Arelin, K., Kratzsch, J., Thiery, J., Steiner, J., Villringer, A., Mueller, K., and Schroeter, M.L. (2012) \u00E2\u0080\u009CValidating Serum S100B and Neuron-Specific Enolase as Biomarkers for the Human Brain \u00E2\u0080\u0093 A Combined Serum, Gene Expression and MRI Study.\u00E2\u0080\u009D PLoS ONE 7(8): e43284 30. Kapural, M., Krizanac-Bengez, L.J., Barnett, G., Perl, J., Masaryk, T., Apollo, D., Rasmussen, P., Mayberg, M.R., and Janigro, D. (2002) \u00E2\u0080\u009CSerum S-100B as a possible marker of blood-brain barrier disruption\u00E2\u0080\u009D Brain Research 940: pp. 102 \u00E2\u0080\u0093 104 31. Casmiro, M., Maitan, S., De Pasquale, F., Cova, V., Scarpa, E., Vignatelli, L., (2005). \u00E2\u0080\u009CCerebrospinal fluid and serum neuron-specific enolase concentrations in a normal population\u00E2\u0080\u009D. Eur. J. Neurol. 12, 369\u00E2\u0080\u0093374. 32. Pleines, U.E., Morganti-Kossmann, M., Rancan, M., Joller, H., Trentz, O., and Kossmann, T. (2001) \u00E2\u0080\u009CS-100\u00CE\u00B2 Reflects the Extent of Injury and Outcome, Whereas Neuronal Specific Enolase Is a Better Indicator of Neuroinflammation in Patients with Severe Traumatic Brain Injury.\u00E2\u0080\u009D Journal of Neurotrauma 18(5): pp. 491 \u00E2\u0080\u0093 498 33. Knaepen, K., Goekint, M., Heyman, E.M., and Meeusen, R. (2010) \u00E2\u0080\u009CNeuroplasticity \u00E2\u0080\u0093 Exercise-Induced Response of Peripheral Brain-Derived Neurotrophic Factor\u00E2\u0080\u009D Sports Med 40(9): pp. 765 \u00E2\u0080\u0093 801 34. Wu, S.Y., Wang, T.F, Yu, L., Jen, C.J., Chuang, J., Wu, F., Wu, C.W., and Kuo, Y.M. (2011) \u00E2\u0080\u009CRunning exercise protects the substantia nigra dopaminergic neurons against inflammation-induced degeneration via the activation of BDNF signaling pathway.\u00E2\u0080\u009D Brain, Behaviour, and Immunity 25: pp. 135 \u00E2\u0080\u0093 146 35. US Environmental Protection Agency (EPA). \u00E2\u0080\u009CHealth assessment document for diesel engine exhaust.\u00E2\u0080\u009D National Center for Environmental Assessment, Washington, DC, for the Office of Transportation and Air Quality; EPA/600/8-90/057F. 2002. Web. 29 June 2015. Available from: National Technical Information Service, Springfield, VA; PB2002-107661, and 36. Davis, M.E., Smith, T.J., Laden, F., Hart, J.E., et al (2007) \u00E2\u0080\u009CDriver exposure to combustion particles in the US Trucking Industry\u00E2\u0080\u009D Journal of Occupational Environment Hygiene 4(11): pp. 848 \u00E2\u0080\u0093 854 37. Demers, P., McCaig, K., Astrakianakis, G., Friesen, M., and Weiwei D. (February 2007) \u00E2\u0080\u009CCarcinogen Surveillance Program: Final Report to the Workers\u00E2\u0080\u0099 Compensation Board of British Columbia\u00E2\u0080\u009D Work Safe BC RS2002-03-014. 38. Sydbom, A., Blomberg, A., Parnia, S., Stenfors, N., Sandstrom, T., and Dahlen, S-E. (2001) \u00E2\u0080\u009CHealth effects of diesel exhaust emissions\u00E2\u0080\u009D Eur Respir J 17: pp. 733 \u00E2\u0080\u0093 746 39. Groves, J. and Cain, J.R. (2000) \u00E2\u0080\u009CA survey of exposure to diesel engine exhaust emissions in the workplace\u00E2\u0080\u009D Ann. Occ. Hyg 44(6): pp. 435 \u00E2\u0080\u0093 447 40. HEI Panel on the Health Effects of Traffic Related Air Pollution. (2010) \u00E2\u0080\u009CTraffic-Related Air Pollution: A Critical Review of the Literature on Emissions, Exposure and Health Effects.\u00E2\u0080\u009D Health Effects Institute Special Report 17 41. Clean Air Task Force. \u00E2\u0080\u009CDiesel Soot Health Impacts\u00E2\u0080\u009D Clean Air Task Force 2016 web. 10 Jan. 2016 42. Sharp, J. (2003) \u00E2\u0080\u009CThe public health impact of diesel particulate matter\u00E2\u0080\u009D Sierra Club of Canada, Toronto, ON. 95 43. Krzyzanowski, M., Kuna-Dibbert, B., and Schneider, J. (2005) \u00E2\u0080\u009CHealth effects of transport-related air pollution\u00E2\u0080\u009D World Health Organization ISBN 92 890 13713 7 44. Brook, R.D., Franklin, B., Cascio, W., Hong, Y. et al (2004) \u00E2\u0080\u009CAir pollution and cardiovascular disease: a statement for healthcare professionals from the expert panel on population and prevention science of the American Heart Association.\u00E2\u0080\u009D Circulation 109: pp. 2655 \u00E2\u0080\u0093 2671 45. World Health Organization (2005) \u00E2\u0080\u009CParticulate matter air pollution: how it harms health\u00E2\u0080\u009D Berlin, Copenhagen, Rome. Fact Sheet EURO/04/05 46. United States Environmental Protection Agency \u00E2\u0080\u009CHeavy trucks, buses, and engines\u00E2\u0080\u009D USA EPA 2016 Web. 10 Jan. 2016 47. Hesterberg, T.W., Long, C.M., Sax, S.N., Lapin, C.A. et al (2011) \u00E2\u0080\u009CParticulate matter in New Technology Diesel Exhaust (NTDE) is Quantitatively and Qualitatively Very Different from that Found in Traditional Diesel Exhaust (TDE)\u00E2\u0080\u009D Journal of the Air & Waste Management Association 61(9): 894 \u00E2\u0080\u0093 913 48. Riedl, M., and Diaz-Sanchez, D. (2005) \u00E2\u0080\u009CBiology of diesel exhaust effects on respiratory function\u00E2\u0080\u009D J Allergy Clin Immunol 115: pp. 221 \u00E2\u0080\u0093 228 49. Occupational Safety and Health Administration \u00E2\u0080\u009Csafety and health topics: diesel exhaust\u00E2\u0080\u009D United States Department of Labor 2015 Web. 12 Jan. 2015 50. CAREX Canada \u00E2\u0080\u009CDiesel Engine Exhaust\u00E2\u0080\u009D CAREX CANADA 2015 Web. 12 Jan. 2015 51. WorkSafeBC \u00E2\u0080\u009CGuidelines Part 5: Chemical Agents and Biological Agents\u00E2\u0080\u009D WorkSafeBC 2016 Web, 10 Jan. 2016 52. Mine Safety and Health Administration \u00E2\u0080\u009CTitle 30 code of federal regulations, 30 CFR \u00E2\u0080\u0093 Parts 1-99: Mineral Resources\u00E2\u0080\u009D United States Department of Labor 2014 Web. 12 Jan. 2015 53. Birch, E.B (2002) \u00E2\u0080\u009COccupational Monitoring of Particulate Diesel exhaust by NIOSH Method 5040\u00E2\u0080\u009D Applied occupational and environmental hygiene 17(6): pp. 400 \u00E2\u0080\u0093 405 54. Pronk A, Coble J, Stewart P. (2009) \u00E2\u0080\u009COccupational exposure to diesel engine exhaust: A literature review.\u00E2\u0080\u009D J Expo Sci Environ Epidemiol. Mar 11;19(5):443\u00E2\u0080\u009357. 55. United States Environmental Protection Agency (2016) \u00E2\u0080\u009CNational Ambient Air Quality Standards (NAAQS)\u00E2\u0080\u009D USA EPA 2016 Web. 10 Jan. 2016 56. United States Environmental Protection Agency \u00E2\u0080\u009CAir quality modeling technical support document for the regulatory impact analysis for the revisions to the national ambient air quality standards for particulate matter\u00E2\u0080\u009D Office of Air Quality Planning and Standards 2012 Web. 05 Dec. 2015 57. Zhou, M., He, G., Fan, M., Wang, Z. et al (2015) \u00E2\u0080\u009CSmog episodes, fine particulate pollution and mortality in China.\u00E2\u0080\u009D Environmental Research 136 pp. 396 \u00E2\u0080\u0093 404 58. Ji, D., Liang, L., Wang, Y., Zhang, J., et al (2014) \u00E2\u0080\u009Cthe heaviest particulate air-pollution episodes occurred in northern China in January 2013: insights gained from observations.\u00E2\u0080\u009D Atmospheric Environment 92. pp. 546 - 556 59. \u00E2\u0080\u009CAir Pollution and Public Health: A Guidance Document Risk Managers\u00E2\u0080\u009D Institute for Risk Research 2007, Waterloo, Canada 96 60. Schwartz, J., (2004) \u00E2\u0080\u009CAir Pollution and Children\u00E2\u0080\u0099s Health\u00E2\u0080\u009D Pediatrics 113(4): pp. 1037 \u00E2\u0080\u0093 1044 61. Morgenstern, V., Zutavern, A., Cyrys, J., Brockow, I., Koletzko, S., Kramer, U., Behrendt, H., Herbarth, O., von Berg, A., Bauer, C.P., Wichmann, H.E., and Heinrich (2008) \u00E2\u0080\u009CAtopic Diseases, Allergic Sensitization, and Exposure to Traffic-related Air Pollution in Children.\u00E2\u0080\u009D American Journal of Respiratory and Critical Care Medicine 177: pp. 1331 \u00E2\u0080\u0093 1337 62. Clark, N.A., Demers, P.A., Karr, C.J., Koehoorn, M., Lencar, C., Tamburic, L., and Brauer, M. (2010) \u00E2\u0080\u009CEffect of Early Life Exposure to Air Pollution on Development of Childhood Asthma\u00E2\u0080\u009D Environmental Health Perspectives 118(2): pp. 284 \u00E2\u0080\u0093 291 63. Loop, M.S., Kent, S.T., Al-Hamdan, M.Z., Crosson, W.L., Estes, S.M., Estes, M.G., et al (2013) \u00E2\u0080\u009CFine particulate matter and incident cognitive impairment in the Reasons for geographic and racial differences in stroke (REGARDS) cohort.\u00E2\u0080\u009D PLoS ONE 8(9): e75001. doi:10.1371/journal.pone.0057001 64. Chen, J.C. and Schwartz, J. (2009) \u00E2\u0080\u009CNeurobehavioral effects of ambient air pollution on cognitive performance in US adults.\u00E2\u0080\u009D NeuroToxicology 30: pp. 231 \u00E2\u0080\u0093 239 65. Ranft, U., Schikowski, T., Sufiri, D., Krutmann, J., and Kramer, U. (2009) \u00E2\u0080\u009CLong-term exposure to traffic-related particulate matter impairs cognitive function in the elderly.\u00E2\u0080\u009D Environmental Research 109: pp. 1004 \u00E2\u0080\u0093 1011 66. Chen, J.C., Wang, X., Wellenius, G.A., Serre, M.L. et al (2015) \u00E2\u0080\u009CAmbient air pollution and neurotoxicity on brain structure: evidence from women\u00E2\u0080\u0099s health initiative memory study\u00E2\u0080\u009D Ann Neurol 78: pp. 466 \u00E2\u0080\u0093 476 67. Wilker, E.H., Preis, S.R., Beiser, A.S., Wold, P.A. (2015) \u00E2\u0080\u009CLong-term exposure to fine particulate matter, residential proximity to major roads and measures of brain structure.\u00E2\u0080\u009D Stroke 46: pp. 1161 \u00E2\u0080\u0093 1166 68. Calderon-Garciduenas, L., Reed, W., Maronpot, R.R., Henriquez-Roldan, C. et al (2004) \u00E2\u0080\u009Cbrain inflammation and Alzheimer\u00E2\u0080\u0099s-like pathology in individuals exposed to severe air pollution\u00E2\u0080\u009D Toxicologic Pathology 32: pp. 650 \u00E2\u0080\u0093 658 69. Calderon-Garciduenas, L., Franco-Lira, M., Henriquez-Roldan, C., Osnaya, N., et al (2010) \u00E2\u0080\u009CUrban air pollution: influences on olfactory function and pathology in exposed children and young adults\u00E2\u0080\u009D. Experimental and Toxicologic Pathology 62: pp. 91 \u00E2\u0080\u0093 102 70. Calderon-Garciduenas, L., Kavanaugh, M., Block, M., D\u00E2\u0080\u0099Angiulli, A. et al (2012) \u00E2\u0080\u009Cneuroinflammation, hyperphosphorylated Tau, diffuse amyloid plaques and down-regulation of the cellular prion protein in air pollution exposed children and young adults\u00E2\u0080\u009D Journal of Alzheimer\u00E2\u0080\u0099s Disease 28: pp. 93 \u00E2\u0080\u0093 107 71. Kioumourtzoglou, M.A., Schwrtz, J.D., Weisskopf, M.G., Melly, S.J., Wang, Y., Dominici, F. and Zanobetti, A. (2015) \u00E2\u0080\u009CLong-term PM2.5 exposure and neurological hospital admissions in the Northeastern United States\u00E2\u0080\u009D Environ Health Perspective DOI: 10.1289/ehp.1408973 72. Volk, H.E., Hertz-Picciotto, I., Delwiche, L., Lurmann, F., and McConnell, R. (2011) \u00E2\u0080\u009CResidential Proximity to Freeways and Autism in the CHARGE Study.\u00E2\u0080\u009D Environmental Health Perspectives 119: pp. 873 \u00E2\u0080\u0093 877 73. Windham, G.C., Zhang, L., Gunier, R., Croen, L.A., and Grether, J.K., (2006) \u00E2\u0080\u009CAutism Spectrum Disorders in Relation to Distribution of Hazardous Air Pollutants in the San Francisco Bay Area\u00E2\u0080\u009D Environmental Health Perspectives 114: pp. 1438 \u00E2\u0080\u0093 1444 97 74. Tzivian, L., Winkler, A., Dlugaj, M., Schikowski, T., et al (2015) \u00E2\u0080\u009Ceffect of long-term outdoor air pollution and noise on cognitive and physiological functions in adults\u00E2\u0080\u009D International Journal of Hygiene and Environmental health 218: pp. 1 \u00E2\u0080\u0093 11 75. Ballabh, P., Braun, A., and Nedergaard, M. (2004) \u00E2\u0080\u009CThe blood-brain barrier: an overview. Structure, regulation, and clinical implications.\u00E2\u0080\u009D Neurobiology of Disease 16: pp. 1 \u00E2\u0080\u0093 13 76. Hartz, A.M.S., Bauer, B., Block, M.L., Hong, J.S., and Miller, D.S. (2008) \u00E2\u0080\u009CDiesel exhaust particles induce oxidative stress, proinflammatory signaling, and P-glycoprotein up-regulation at the blood-brain barrier\u00E2\u0080\u009D FASEB Journal 22(8): pp. 2723 \u00E2\u0080\u0093 2733 77. Campbell, A. (2004) \u00E2\u0080\u009CInflammation, Neurodegenerative Diseases, and Environmental Exposures\u00E2\u0080\u009D Annals New York Academy of Sciences pp. 117 \u00E2\u0080\u0093 132 78. Abbott, N.J., Ronnback, L., and Hansson, E. (2006) \u00E2\u0080\u009CAstrocyte-endothelial interactions at the blood-brain barrier\u00E2\u0080\u009D Nature Reviews in Neuroscience 7: pp. 41 \u00E2\u0080\u0093 53 79. Calderon-Garciduenas, L., Azzarelli, B., Acuna, H., Garcia, R., Gambling, T.M., Osnaya, N., Monroy, S., Tizapantzi, M.D.R., Carson, J.L., Villarreal-Calderon, A., and Rewcastle, B. (2002) \u00E2\u0080\u009CAir Pollution and Brain Damage.\u00E2\u0080\u009D Toxicologic Pathology 30(3): pp. 373 \u00E2\u0080\u0093 389 80. Glass, C.K., Saijo, K., Winner, B., Marchetto, M.C., and Gage, R.H. (2010) \u00E2\u0080\u009CMechanisms Underlying Inflammation in Neurodegeneration\u00E2\u0080\u009D Cell 140: pp. 918 \u00E2\u0080\u0093 934 81. Takenaka, S., Karg, E., Roth, C., Schulz, H. et al (2001) \u00E2\u0080\u009CPulmonary and systemic distribution of inhaled ultrafine silver particles in rats\u00E2\u0080\u009D Environ Health Persepct 109 (suppl 4): pp. 547 \u00E2\u0080\u0093 551 82. Peters, A., Veronesi, B., Calderon-Garciduenas, L., Gehr, P., Chen, L.C., Geiser, M., Reed, W., Rothen-Rutishauser, B., Schurch, S., and Schulz, H. (2006) \u00E2\u0080\u009CTranslocation and potential neurological effects of fine and ultrafine particles a critical update.\u00E2\u0080\u009D Particle and Fibre Toxicity 3(13) 83. Lockman, P.R., Koziara, J.M., Mumper, R.J., and Allen, D.D. (2004) \u00E2\u0080\u009CNanoparticle Surface Charges Alter Blood-Brain Barrier Integrity and Permeability\u00E2\u0080\u009D Journal of Drug Targeting 12: pp. 635 \u00E2\u0080\u0093 641 84. Kreyling, W.G., Semmler, M., Erbe, F., Mayer, P., Takenaka, S., Schulz, H., Oberdorster, G., and Ziesenis, A. (2002) \u00E2\u0080\u009CTranslocation of ultrafine insoluble iridium particles from lung epithelium to extrapulmonary organs is size dependent but very low\u00E2\u0080\u009D. Journal of Toxicology and Environmental Health, par A, 65(20): 1513 \u00E2\u0080\u0093 1530 85. Calderon-Garciduenas, Mora-Tiscareno, A., Ontiveros, E., Gomez-Garza, G., et al. (2008a) \u00E2\u0080\u009CAir pollution, cognitive defects and brain abnormalities: A pilot study with children and dogs.\u00E2\u0080\u009D Brain and Cognition 68: pp. 117 \u00E2\u0080\u0093 127 86. Lucchini, R.G., Dorman, D.C., Elder, A., and Veronesi, B. (2012) \u00E2\u0080\u009CNeurological impacts from inhalation of pollutants and the nose-brain connection\u00E2\u0080\u009D NeuroToxicology 33: pp. 838 \u00E2\u0080\u0093 841 87. Elder, A., Gelein, R., Silva, V., Feikert, T., Opanashuk, L., Carter, J., Potter, R., Maynard, A., Ito, Y., Finkelstein, J., and Oberdorster, G. (2006) \u00E2\u0080\u009CTranslocation of Inhaled Ultrafine Manganese Oxide Particles to the Central Nervous System\u00E2\u0080\u009D Environmental Health Perspectives 114: pp. 1172 \u00E2\u0080\u0093 1178 88. Sama, P., Long, T.C., Hester, S., Tajubam, J., Parker, J., Chen, L.C. and Veronesi, B. (2007) \u00E2\u0080\u009CThe Cellular and Genomic Response of an Immortalized Microglia Cell Line (BV2) to Concentrated Ambient Particulate Matter\u00E2\u0080\u009D Inhalation Toxicology, 19(13): 1079-1087 89. Block, M.L., Wu, X., Pei, Z., Li, G., Wang, T., Qin, L., Wilson, B., Yang, J., Hong, J.S., and Veronesi, B. (2004) \u00E2\u0080\u009CNanometer size diesel exhaust particles are selectively toxic to dopaminergic neurons: 98 the role of microglia, phagocytosis and NADPM oxidase.\u00E2\u0080\u009D He FASEB Journal express article 10.1096/fj.04-1945fje 90. Block, M.L., Zecca, L., and Hong, J.S., (2007) \u00E2\u0080\u009CMicroglia-mediated neurotoxicity: uncovering the molecular mechanisms\u00E2\u0080\u009D Nature reviews in Neuroscience 8: pp. 57 \u00E2\u0080\u0093 69 91. Lull, M.E., and Block, M.L. (2010) \u00E2\u0080\u009CMicroglial activation and chronic neurodegeneration\u00E2\u0080\u009D Neurotherapeutics: The Journal of American Society for Experimental NeuroTherapeuticsi 7: pp. 354 \u00E2\u0080\u0093 365 92. Gerlofs-Nifland, M.E., van Berlo, D., Cassee, F.R., Schins, R.P.F., Wang, K., and Campbell, A. (2010) \u00E2\u0080\u009CEffect of prolonged exposure to diesel engine exhaust on proinflammatory markers in different regions of the rat brain.\u00E2\u0080\u009D Particle and Fibre Toxicology 7:12 93. Levesque, S., Taetzsch, T., Lull, M.E., Kodavanti, U. et al. (2011a) \u00E2\u0080\u009CDiesel Exhaust Activates and Primes Microglia: Air Pollution, Neuroinflammation, and Regulation of Dopaminergic Neurotoxicity.\u00E2\u0080\u009D Environmental Health Perspectives 119 (8): pp. 1149 \u00E2\u0080\u0093 1155 94. Levesque, S., Surace, M.J., McDonald, J., and Block, M.L. (2011b) \u00E2\u0080\u009CAir pollution & the brain: Subchronic diesel exhaust exposure causes neuroinflammation and elevates early markers of neurodegenerative disease.\u00E2\u0080\u009D Journal of Neuroinflammation 8:105 95. Van Berlo, D., Albercht, C., Knaapen, A.M., Cassee, F.R. et al (2010) \u00E2\u0080\u009Ccomparative evaluation of the effects of short-term inhalation exposure to diesel engine exhaust on rat lung and brain\u00E2\u0080\u009D Arch Toxicol 85: pp. 553 \u00E2\u0080\u0093 562 96. Win Shwe, T.T., Fujimaki, H., Fujitani, Y., and Hirano, S. (2012) \u00E2\u0080\u009Cnovel object recognition ability in female mice following exposure to nanoparticle-rich disease exhaust\u00E2\u0080\u009D Toxicology and applied pharmacology 262: pp. 355 \u00E2\u0080\u0093 362 97. Fonken, L.K., Xu, X., Weil, ZM, Chen, G., Sun, Q., Rajagopalan, S., and Nelson, R.J. (2011) \u00E2\u0080\u009CAir pollution impairs cognition, provokes depressive-like behaviors and alters hippocampal cytokine expression and morphology.\u00E2\u0080\u009D Molecular psychiatry 16: pp. 987 \u00E2\u0080\u0093 995 98. Morgan, T.E., Davis, D.A., Iwata, N., Tanner, J.A., Snyder, D. et al (2011) \u00E2\u0080\u009CGlutamatergic neurons in rodent models respond to nanoscale particulate urban air pollutants in vivo and in vitro.\u00E2\u0080\u009D Environ health Perspect 119: pp. 1003 \u00E2\u0080\u0093 1009 99. Van Eeden, S.F., Tan, W.C., Suwa, T., Mukae, H., Terashima, T., Fuji, T., Qui, D., Vincent, R., and Hogg, J.C. (2001) \u00E2\u0080\u009CCytokines involved in the systemic inflammatory response induced by exposure to particulate matter air pollutants (PM10)\u00E2\u0080\u009D American Journal of Respiratory and Critical Care Medicine 164: pp. 826 \u00E2\u0080\u0093 830 100. Fujii, T., Hayashi, S., Hogg, J.C., Mukae, H., Suwa, T., Goto, Y., Vincent, R., and van Eeden, S.F. (2002) \u00E2\u0080\u009Cinteraction of alveolar macrophages and airway epithelial cells following exposure to particulate matter produces mediators that simulate the bone marrow\u00E2\u0080\u009D American Journal of Respiratory Cell Molecular Biology 27: pp. 34 \u00E2\u0080\u0093 41 101. Ruckerl, R., Greven, S., Ljungman, P., Aalto, P., Antoniades, C., Bellander, T., et al. (2007) \u00E2\u0080\u009CAir pollution and Inflammation (Interleukin-6, C-Reactive Protein, Fibrinogen in Myocardial Infarction Survivors.\u00E2\u0080\u009D Environmental Health Perspectives 115: pp. 1072 \u00E2\u0080\u0093 1080 102. Tompson, A., Zanobetti, A., Silverman, F., Schwartz, J. et al (2010) \u00E2\u0080\u009Cbaseline repeated measures from controlled human exposure studies: associations between ambient air pollution 99 exposure and the systemic inflammatory biomarkers IL-6 and fibrinogen\u00E2\u0080\u009D Environ Health Perspect 118: pp. 120 \u00E2\u0080\u0093 124 103. Tsai, D.-H., Amyai, N., Marques-Vidal, P., Wang, J.-L., Riediker, M., Mooser, V., Paccaud, F., Waeber, G., Vollenweider, P., Bochud, M., (2012). Effects of particulate matter on inflammatory markers in the general adult population. Part Fiber Toxicol 9, 24. 104. Swiston, J.R., Davidson, W., Attridge, S., Li, G.T., Brauer, M., and van Eeden, S.F. (2008) \u00E2\u0080\u009CWood smoke exposure induces a pulmonary and systemic inflammatory response in firefighters.\u00E2\u0080\u009D Eur Respir J 32: pp. 129 \u00E2\u0080\u0093 138 105. Hilt, B., Qvenild, T., Holme, J., Svendsen, K., and Ulvestad, B. (2001) \u00E2\u0080\u009CIncrease in interleukin-6 and fibrinogen after exposure to dust in tunnel construction workers\u00E2\u0080\u009D Occupational Environmental Medicine 59: pp. 9 \u00E2\u0080\u0093 12 106. Tornqvist, H., Mills, N.L., Gonzalez, M., Miller, M.R., et al (2007) \u00E2\u0080\u009Cpersistent endothelial dysfunction in humans after diesel exhaust inhalation\u00E2\u0080\u009D Am J Resp Crit Care Med 176: pp. 395 \u00E2\u0080\u0093 400 107. Yirmiya, R., and Goshen, I. (2011) \u00E2\u0080\u009CImmune modulation of learning, memory, neural plasticity and neurogenesis\u00E2\u0080\u009D Brain, Behavior, and Immunity 25: pp. 181 \u00E2\u0080\u0093 213 108. Takeda, S., Sato, N., and Morishita, R. (2014) \u00E2\u0080\u009Csystemic inflammation, blood-brain barrier vulnerability and cognitive/non-cognitive symptoms in Alzheimer disease: Relevance to pathogenesis and therapy.\u00E2\u0080\u009D Frontiers in aging neuroscience DOi: 10.3389/fnagi.2014.00171 109. Michaud, M., Balardy, L., Moulis, G., Gaudin, C., et al (2013) \u00E2\u0080\u009Cproinflammatory cytokines, aging and age-related diseases\u00E2\u0080\u009D JAMDA 14 pp. 877 \u00E2\u0080\u0093 882 110. Schiepers, O.J.G., Wichers, M.C., and Maes, M (2005) \u00E2\u0080\u009CCytokines and major depression.\u00E2\u0080\u009D Neuro-Psychopharmacology and Biological Psychiatry 29: pp. 201 \u00E2\u0080\u0093 217 111. Gomez-Nicola, D., Teeling, J., Guaza, C., Godbout, J.P., and Taub, D.D. (2013) \u00E2\u0080\u009Cthe role of inflammatory mediators in immune-to-brain communication during health and disease\u00E2\u0080\u009D Mediators of Inflammation volume 2013, article ID 429231, 3 pages 112. Grigoleit, J.S., Kullmann, J.S., Wolf, O.T., Hammes, F., Wegner, A., Jablonowski, S., Engler, H., Gizewski, E., Oberbeck, R., and Schedlowski, M. (2011) \u00E2\u0080\u009CDose-dependent effects of endotoxin on neurobehavioral functions in humans\u00E2\u0080\u009D PLoS ONE 6(12): e28330 113. Wright, C.E., Strike, P.C., Brydon, L., and Steptoe, A. (2005) \u00E2\u0080\u009CAcute inflammation and negative mood: mediation by cytokine activation\u00E2\u0080\u009D Brain, Behavior, and Immunity 19: pp. 345 \u00E2\u0080\u0093 350 114. Skelly, D.T., Hennessy, E., Dansereau, M.A., and Cunningham, C. (2013) \u00E2\u0080\u009CA systematic analysis of the peripheral and CNS effects of systemic LPS, IL-1B, TNF-a and IL-6 Challenges in C57BL/6 Mice\u00E2\u0080\u009D PLoS ONE 8(7): e69123 115. Erta, M., Quintana, A., and Hidalgo, J. (2012) \u00E2\u0080\u009Cinterleukin-6, a major cytokine in the central nervous system\u00E2\u0080\u009D International Journal of Biological Sciences 8(9): pp. 1254 \u00E2\u0080\u0093 1266 116. McAfoose, J., and Baune, B.T. (2009) \u00E2\u0080\u009CEvidence for a cytokine model of cognition.\u00E2\u0080\u009D Neuroscience and Biobehavioral Reviews 33: pp. 355 \u00E2\u0080\u0093 366 117. Dassan, P., Keir, G., and Brown, M.M. (2008) \u00E2\u0080\u009CCriteria for a Clinically Informative Serum Biomarker in Acute Ischaemic Stroke: A review of S100B\u00E2\u0080\u009D Cerebrovascular Disease 27: pp. 295 \u00E2\u0080\u0093 302 100 118. Shinozaki, K., Oda, S., Sadahiro, T., Nakamura, M., Abe, R., Nakada, T., Nomura, F., Nakanishi, K., Kitamura, N., and Hirasawa H. (2009) \u00E2\u0080\u009CSerum S-100B is superior to neuron-specific enolase as an early prognostic biomarker for neurological outcome following cardiopulmonary resuscitation.\u00E2\u0080\u009D Resuscitation 80: pp. 870 \u00E2\u0080\u0093 875 119. O\u00E2\u0080\u0099Connell, K., Thakore, J., and Dev., K.K. (2013) \u00E2\u0080\u009CLevels of S100B are raised in female patients with schizophrenia\u00E2\u0080\u009D BMC Psychiatry 13: 146 120. Schroeter, M.L., Abdul-Khaliq, H., Krebs, M., Diefenbacher, A., and Blasig, I.E. (2009) \u00E2\u0080\u009CNeuron-specific enolase is unaltered whereas S100B is elevated in serum of patients with schizophrenia \u00E2\u0080\u0093 Original research and meta-analysis\u00E2\u0080\u009D Psychiatry Research 167: pp. 66 \u00E2\u0080\u0093 72 121. Mortberg, E., Zetterberg, H., Nordmark, J., Blennow, K., Rosengren, L., and Rubertsson, S. (2011) \u00E2\u0080\u009CS-100B is superior to NSE, BDNF and GFAP in predicting outcome of resuscitation from cardiac arrest with hypothermia treatment.\u00E2\u0080\u009D Resuscitation 82: pp. 26 \u00E2\u0080\u0093 31 122. Pham, N., Fazio, V., Cucullo, L., Teng, Q., Biberthaler, P., Bazarian, J.J. and Janigro, D. (2010) \u00E2\u0080\u009CExtracranial sources of S100B do not affect serum levels.\u00E2\u0080\u009D PLoS ONE 5(9): e12691 123. Nyle, K., Ost, M., Csajbok, L.Z., Nilsson, I., Blennow, K., Nellgard, B., and Rosengren, L. (2006) \u00E2\u0080\u009CIncreased serum-GFAP in patients with severe traumatic brain injury is related to outcome\u00E2\u0080\u009D Journal of the Neurological Sciences 240: pp. 85 \u00E2\u0080\u0093 91 124. Lamers, K.J.B., Vos, P., Verbeek, M.M., Rosmalen, F., van Geel, W.J.A., and van Engelen, B.G.M. (2003) \u00E2\u0080\u009CProtein S-100B, neuron-specific enolse (NSE), myelin basic protein (MBP) and glial fibrillary acidic protein (GFAP) in cerebrospinal fluid (CSF) and blood of neurological patients.\u00E2\u0080\u009D Brain Research Bulletin 61: pp. 261 \u00E2\u0080\u0093 264 125. Herrmann, M., Vos, P., Wunderlich, M.T., de Bruijn, C.H.M.M., and Lamers, K.J.B. (2000) \u00E2\u0080\u009CRelease of Glial Tissue-Specific Proteins After Acute Stroke: a Comparative Analysis of Serum Concentrations of Protein S-100B and Glial Fibrillary Acidic Protein\u00E2\u0080\u009D Stroke 31: pp. 2670 \u00E2\u0080\u0093 2677 126. Hamed, S.A., Hamed, E.A., and Zakary, M.M. (2009) \u00E2\u0080\u009COxidative stress and S-100B protein in children with bacterial meningitis\u00E2\u0080\u009D BMC Neurology 9:51 127. Van Munster, B.C., Korse, C.M., de Rooij, S.E., Bonfrer, J.M., Zwinderman, A.H., and Korevaar, J.C., (2009) \u00E2\u0080\u009CMarkers of cerebral damage during delirium in elderly patients with hip fracture\u00E2\u0080\u009D BMC Neurology 9:21 128. Gonzalez-Garcia, S., Gonzalez-Quevedo, A., Fernandez-Concpcion, O., Pena-Sanchez, M., et al (2012) \u00E2\u0080\u009Cshort-term prognostic value of serum neuron specific enolase and S100B in acute stroke patients.\u00E2\u0080\u009D Clinical biochemistry 45: pp. 1302 \u00E2\u0080\u0093 1307 129. Romner, B., Ingebrigtsen, T., Kongstad, P., B\u00C3\u00B8rgesen, S.E., 2000. Traumatic brain damage: serum S-100 protein measurements related to neuroradiological findings. J. Neurotrauma 17, 641\u00E2\u0080\u0093647. 130. Goncalves, C.A., Leite, M.C., and Nardin, P. (2008) \u00E2\u0080\u009CBiological and methodological features of the measurement of S100B, a putative marker of brain injury\u00E2\u0080\u009D Clinical Biochemistry 41: pp. 755 \u00E2\u0080\u0093 763 131. Bjursten, H. et al. S100B profiles and cognitive function at high altitude. High Alt. Med. Biol. 11, 31\u00E2\u0080\u009338 (2010). 132. Kleindienst, A., and Bullock, M.R. (2006) \u00E2\u0080\u009Ca critical analysis of the role of the brain and neurotrophic protein S100B in acute brain injury\u00E2\u0080\u009D Journal of Neurotrauma 23(8): pp. 1185 \u00E2\u0080\u0093 1200 101 133. Anand, N. & Stead, L. G. (2005) \u00E2\u0080\u009CNeuron-Specific Enolase as a Marker for Acute Ischemic Stroke: A Systematic Review.\u00E2\u0080\u009D Cerebrovasc. Dis. 20, 213\u00E2\u0080\u0093219 134. Honda, M., Tsuruta, R., Kaneko, T., Kasaoka, S., et al (2010) \u00E2\u0080\u009Cserum glial fibrillary acidic protein is a highly specific biomarker for traumatic brain injury in humans compared with S-100B and neuron-specific enolase\u00E2\u0080\u009D J. Trauma 69: pp. 104 \u00E2\u0080\u0093 109 135. Cortese, G.P., Barrientos, R.M., Maier, S.F., and Paterson, S.L. (2011) \u00E2\u0080\u009CAging and a Peripheral Immune Challenge Interact to Reduce Mature Brain-Derived Neurotrophic Factor and Activation of TrkB, PLCy1, and ERK in Hippocampal Synaptoneurosomes\u00E2\u0080\u009D The Journal of Neuroscience 31(11): pp. 4274 \u00E2\u0080\u0093 4279 136. Bos, L., De Boever, P., Int Panis, L., Sarre, S., and Meeusen, R. (2012) \u00E2\u0080\u009CNegative effects of ultrafine particle exposure during forced exercise on the expression of brain-derived neurotrophic factor in the hippocampus of rats\u00E2\u0080\u009D Neuroscience 223: pp. 131 \u00E2\u0080\u0093 139 137. Rasmussen, P., Brassard, P., Adser, H., Pedersen, M.V., Leick, L., Hart, E., Secher, N.H., Pedersen, B.K., and Pilegaard, H. (2009) \u00E2\u0080\u009CEvidence for a release of brain-derived neurotrophic factor from the brain during exercise\u00E2\u0080\u009D Experimental Physiology 94(10): pp. 1062 \u00E2\u0080\u0093 1069 138. Huang, A.M., Jen, C.J., Chen, H.F., Yu, L., Kuo, Y.M., and Chen, H.I. (2006) \u00E2\u0080\u009Ccompulsive exercise acutely upregulates rat hippocampal brain-derived neurotrophic factor\u00E2\u0080\u009D J Neural Transm 113: pp. 803 \u00E2\u0080\u0093 811 139. Soya, H., Nakamura, T., Deocaris, C.C., Kimpara, A., Imura, M., Fujikawa, T., Chang, H., McEwen, B.S., and Nishijima, T. (2007) \u00E2\u0080\u009CBDNF induction with mild exercise in the rat hippocampus\u00E2\u0080\u009D biochemical and biophysical research communications 358 pp. 961 -967 140. Seifert, T. Brassard, P., Wissenberg, M., Rasmussen, P., Nordby, P., Stallknecht, B., Adser, H., Jakobsen, A.H., Pilegaard, H., Nielsen, H.B., and Secher, N.H. (2010) \u00E2\u0080\u009CEndurance training enhances BDNF release from the human brain\u00E2\u0080\u009D Am J Physiol Regul Integr Comp Physiol 198: pp. 372 \u00E2\u0080\u0093 377 141. Ferris, L.T., Williams, J.S., and Shien, C.L. (2007) \u00E2\u0080\u009CThe effect of acute exercise on serum brain-derived neurotrophic factor levels and cognitive function\u00E2\u0080\u009D Psychobiology and Behavioral Strategies DOI: 10.1249/mss.0b-13e31802f04c7 142. Goekint, M., Roelands, B., Heyman, E., Njemini, R., and Meeusen, R. (2011) \u00E2\u0080\u009Cinfluence of citalopram and environmental temperature on exercise-induced changes in BDNF\u00E2\u0080\u009D neuroscience letters 494: pp. 150 - 154 143. Griffin, E.W., Mullally, S., Foley, C., Warmington, S.A., O\u00E2\u0080\u0099Mara, S.M., and Kelly, A.M. (2011) \u00E2\u0080\u009CAerobic exercise improves hippocampal function and increases BDNF in the serum of young adult males\u00E2\u0080\u009D Physiology & Behavior 104 pp. 934 \u00E2\u0080\u0093 941 144. Barrientos, R.M., Sprunger, D.B., Campeau, S., Watkins, L.R., Rudy, J.W., and Maier, S.F. (2004) \u00E2\u0080\u009CBDNF mRNA expression in rat hippocampus following contextual learning is blocked by intrahippocampal IL-1B administration\u00E2\u0080\u009D Journal of Neuroimmunology 155: pp. 119 \u00E2\u0080\u0093 126 145. Bos, I., Jacobs, L., Nawrot, T.S., de Geus, B., Torfs, R., Panis, L. I., Degraeuwe, B., and Meeusen, R. (2011) \u00E2\u0080\u009CNo exercise-induced increase in serum BDNF after cycling near a major traffic road.\u00E2\u0080\u009D Neuroscience Letters 500: pp. 129 \u00E2\u0080\u0093 132 146. Larson, E.B. (2011) \u00E2\u0080\u009CProspects for delaying the rising tide of worldwide, late-life dementias.\u00E2\u0080\u009D Int Psychogeriatr 102 147. Alzheimer Society of Canada. \u00E2\u0080\u009CAbout Dementia\u00E2\u0080\u009D Alzheimer Society of Canada 2011 Web. 30 Jul. 2013. 148. Birger, N., Gould, T., Stewart, J., Miller, M.R., Larson, T., and Carlsten, C. (2011) \u00E2\u0080\u009CThe Air Pollution Exposure Laboratory (APEL) for controlled human exposure to diesel exhaust and other inhalants: characterization and comparison to existing facilities.\u00E2\u0080\u009D Inhalation Toxicology 23(4): pp. 219 \u00E2\u0080\u0093 225 149. Cao, J.-J., Chow, J.C., Watson, J.G., Lee, S.C., et al. \u00E2\u0080\u009CWinter and Summer PM2.5 Chemical Compositions in Fourteen Chinese Cities\u00E2\u0080\u009D. J. Air Waste Manag. Assoc. 62, 1214\u00E2\u0080\u00931226 (2012). 150. Mills, N. L. (2005) \u00E2\u0080\u009CDiesel Exhaust Inhalation Causes Vascular Dysfunction and Impaired Endogenous Fibrinolysis.\u00E2\u0080\u009D Circulation 112, 3930\u00E2\u0080\u00933936 151. Begliuomini, S., Lenzi, E., Ninni, F., Casarosa, E., Merlini, S., Pluchino, N., Valentino, V., Luisi, S., Luisi, M. and Genazzani, A.R. (2008) \u00E2\u0080\u009Cplasma brain-derived neurotrophic factor daily variations in men: correlation with cortisol circadian rhythm\u00E2\u0080\u009D Journal of Endocrinology 197: pp. 429 \u00E2\u0080\u0093 435 152. Bloomfield, S.M., McKinney, J., Smith, L., Brisman, J. (2007) \u00E2\u0080\u009Creliability of S100B in predicting severity of central nervous system injury\u00E2\u0080\u009D Neurocrit Care 6: pp. 121 \u00E2\u0080\u0093 138 153. Biberthaler, P., Linsenmeier, U., Pfeifer, K.J., Kroetz, M., Mussack, T., Kanz, K.G. et al (2005) \u00E2\u0080\u009CSerum S100B concentration provides additional information for the indication of computed tomography in patients after minor head injury\u00E2\u0080\u009D Sock 25(5): pp. 446 \u00E2\u0080\u0093 453 154. Pluchino, N., Cubeddu, A., Begliuomini, S., Merlinin, S. et al (2009) \u00E2\u0080\u009Cdaily variation of brain-derived neurotrophic factor and cortisol in women with normal menstrual cycles, undergoing oral contraception and in post menopause.\u00E2\u0080\u009D Human Reproduction 24(9): pp. 2303 \u00E2\u0080\u0093 2309 155. Giese, M., Beck, J., Brand, S., Muheim, F., Hemmeter, U., Hatzinger, M., Holsboer-Trachsler, E., and Eckert, A. (2014) \u00E2\u0080\u009Cfast BDNF serum level increase and diurnal BDNF oscillations are associated with therapeutic response after partial sleep deprivation.\u00E2\u0080\u009D Journal of Psychiatric Research 59: pp. 1 -7 156. Kurita, M., Nishino, S., Kato, M., Numata, Y., Sato, T. (2012) \u00E2\u0080\u009Cplasma brain-derived neurotrophic factor levels predict the clinical outcome of depression treatment in a naturalistic study\u00E2\u0080\u009D PLoS ONE 7(6): e39212. doi:10.1371/journal.pone.0039212 157. Skogstrand, m K., Ekelund, C.K., Thorsen, P. Vogel, I., Jacobsson, Bo., Norgaard-Pedersen, B., Hougaard, D.M. (2008) \u00E2\u0080\u009Ceffects of blood sample handling procedures on measurable inflammatory markers in plasma, serum and dried blood spot samples\u00E2\u0080\u009D Journal of immunological methods 336 pp. 78 \u00E2\u0080\u0093 84 158. Chio, A.J., Kim, C., and Devlin, R.B. (2000) \u00E2\u0080\u009CConcentrated ambient air particles induce mild pulmonary inflammation in healthy human volunteers.\u00E2\u0080\u009D American Journal of Respiratory and Critical Care Medicine 162(3): pp. 981 \u00E2\u0080\u0093 988 159. Jacobs, L., Nawrot, T.S., de Geus, B., Meeusen, R., Degraeuwe, B., Bernard, A., Sughis, M., Nemery, B., and Int Panis, L. (2010) \u00E2\u0080\u009CSubclinical responses in healthy cyclists briefly exposed to traffic-related air pollution: an intervention study.\u00E2\u0080\u009D Environmental Health 9:64 103 160. Holmes, C., Cunningham, C., Zotova, E., Woolford, J., Dean, C., Kerr, S., Culliford, D., and Perry, V.H. (2009) \u00E2\u0080\u009Csystemic inflammation and disease progression in Alzheimer disease\u00E2\u0080\u009D Neurology 72: pp. 768 \u00E2\u0080\u0093 774 161. Delfino, R.J., Staimer, N., Tjoa, T., Gillen, D.L., Polidori, A., Arhami, M., Kleinman, M.T., et al (2009) \u00E2\u0080\u009CAir pollution exposures and circulating biomarkers of effect in a susceptible population: clues and potential causal component mixtures and mechanisms\u00E2\u0080\u009D Environ Health Perspective 117: pp. 1232 \u00E2\u0080\u0093 1238 162. Delfino, R.J., Staimer, N., Tjoa, T., Gillen, D.L., Polidori, A., Arhami, M., Gillen, D.L., Kleinman, M.T., et al (2008) \u00E2\u0080\u009Ccirculating biomarkers of inflammation, antioxidant activity, and platelet activation are associated with primary combustion aerosols in subjects with coronary artery disease.\u00E2\u0080\u009D Environ Health Perspect 116: pp. 898 \u00E2\u0080\u0093 909 163. Bosson, J., Pourazar, J., Forsberg, B., Adelroth, E., Sandstrom, T., and Blomberg A. (2007) \u00E2\u0080\u009Cozone enhances the airway inflammation initiated by diesel exhaust\u00E2\u0080\u009D Respiratory medicine 101: pp. 1140 - 1146 164. Ye, H., Wang, L., Yang, X.K., Fan, L.P. Wang, Y.G, and Guo, L. (2015) \u00E2\u0080\u009Cserum S100B levels may be associated with cerebral infarction: a meta-analysis\u00E2\u0080\u009D Journal of Neurological Sciences 348: pp. 81 \u00E2\u0080\u0093 88 165. Bailey, D. M., Roukens, R., Knauth, M., Kallenberg, K. et al. (2006) \u00E2\u0080\u009CFree radical-mediated damage to barrier function is not associated with altered brain morphology in high-altitude headache\u00E2\u0080\u009D. J. Cereb. Blood Flow Metab. 26, 99\u00E2\u0080\u0093111 166. Blyth, B.J., Farhavar, A., Gee, C., Hawthron, B., He, H., Nayak, A., Stocklein, V., and Bazarian, J.J. (2009) \u00E2\u0080\u009Cvalidation of serum markers of blood-brain barrier disruption in traumatic brain injury\u00E2\u0080\u009D Journal of Neurotrauma 26: pp. 1497 \u00E2\u0080\u0093 1507 167. Marchi, N., Rasmussen, P., Kapural, M., Fazio, V., Kight, K., Mayberg, M.R., Kanner, A. et al (2003) \u00E2\u0080\u009CPeripheral markers of brain damage and blood brain barrier dysfunction\u00E2\u0080\u009D Restorative Neurology and Neuroscience 21: pp. 109 \u00E2\u0080\u0093 121 168. Tobwala, S., Zhang, X., Zheng, Y., Wang, H.J., Banks, W., and Ercal, N. (2013) \u00E2\u0080\u009Cdisruption of the integrity and function of brain microvascular endothelial cells in culture by exposure to diesel engine exhaust particles\u00E2\u0080\u009D Toxicology Letters 220: pp. 1 \u00E2\u0080\u0093 7 169. Calderon-Garciuenas, L., Franco-Lira, M., Mora-Tiscareno, A., Medina-Cortina, H., Torres-Jardon, R., and Kavanaugh, M. (2013) \u00E2\u0080\u009CEarly Alzheimer\u00E2\u0080\u0099s and Parkinson\u00E2\u0080\u0099s Disease Pathology in Urban Children: Friend versus Foe Responses \u00E2\u0080\u0093 it is time to face the evidence\u00E2\u0080\u009D BioMed Research International 170. Kleine, T.O., Benes, L., and Zofel, P. (2003) \u00E2\u0080\u009Cstudies of the brain specificity of S100B and neuron-specific enolase (NSE) in blood serum of acute care patients\u00E2\u0080\u009D Brain Research Bulletin 61: pp. 265 \u00E2\u0080\u0093 279 171. Yardan, T., Cevik, Y., Donderici, O., Kavalci, C., et al (2009) \u00E2\u0080\u009Celevated serum S100B protein and neuron-specific enolase levels in carbon monoxide poisoning\u00E2\u0080\u009D American Journal of Emergency Medicine 27: pp. 848 \u00E2\u0080\u0093 842 172. Bohmer, A.E., Oses, J.P., Schmidt, A.P., Schweister, C., et al (2011) \u00E2\u0080\u009Cneuron-specific enolase, S100B, and Glial Fibrillary Acidic Protein Levels as Outcome Predictors in Patients with Severe Traumatic Brain Injury.\u00E2\u0080\u009D Neurosurgery 68: pp. 1624 \u00E2\u0080\u0093 1621 104 173. Bhang, S.Y., Choi, S.W., and Ahn, J.H. (2009) \u00E2\u0080\u009Cchanges in plasma brain-derived neurotrophic factor levels in smokers after smoking cessation\u00E2\u0080\u009D Neuroscience Letters 468: pp. 7 \u00E2\u0080\u0093 11 174. Lommatzsch, M., Zingler, D., Schuhbaeck, K., and Schloetcke, K., Zingler, C., Schuff-Werner, P., and Virchow, J.C. (2005) \u00E2\u0080\u009Cthe impact of age, weight, and gender on BDNF levels in human platelets and plasma\u00E2\u0080\u009D Neurobiology of Aging 26: pp. 115 \u00E2\u0080\u0093 123 175. Karege, F., Bondolfi, G., Gervasoni, N., Schwald, M., Aubry, J.M., and Bertschy, G. (2005) \u00E2\u0080\u009Clow brain-derived neurotrophic factor (BDNF) levels in serum of depressed patients probably results from lowered platelet BDNF release unrelated to platelet reactivity\u00E2\u0080\u009D Biol Psychiatry 57: pp. 1068 \u00E2\u0080\u0093 1072 176. Goncalves, C.A., Leite, M.C., and Nardin, P. (2008) \u00E2\u0080\u009Cbiological and methodological features of the measurement of S100B, a putative marker of brain injury\u00E2\u0080\u009D Clinical Biochemistry 41: pp. 755 \u00E2\u0080\u0093 763 105 Appendix I: Summary of the Cognitive Function Parameters Considered in EAPOC Study *Focus of work by PhD student Jason Curran but provided here for context a) CANTAB Battery: A CANTAB battery was administered to each subject a total of 8 times (4 batteries for each exposure: prior to and immediately following exposure, 3-hours post-exposure, and 24-hours post-exposure). Specifically, each battery includes the following tests: i. RVP: Rapid Visual Processing is a task of continuous performance and visual sustained attention. It is sensitive to changes in the parietal and frontal lobe areas of the brain. The task consists of a 2-minute practice stage and a 7-minute assessed stage during which single numerals appear at a rate of 100 digits per minute. Participants must identify a series of target sequences (2-4-6, 3-5-7, and 4-6-8) and touch a button immediately after to indicate they have detected the sequence. A\u00E2\u0080\u0099 prime is a signal detection measure that reflects target sensitivity, regardless of the participant\u00E2\u0080\u0099s tendency, or bias, to respond. \u00EF\u0082\u00B7 Main outcome for analysis: A\u00E2\u0080\u0099 prime; mean latency \u00EF\u0082\u00B7 Subsidiary outcome: total false alarms ii. DMS: Delayed Matching to Sample is a task which assesses forced choice recognition memory for non-verbalisable patterns. DMS tests both simultaneous and delayed (short-term) visual recognition memory. It is sensitive to changes in the medial temporal lobe area, with some input from the frontal lobes. Participants were shown a complex visual pattern and then, after a delay of 0s, 4s or 12s, will be shown four choice patterns. Participants must identify the pattern which exactly matches the target image. \u00EF\u0082\u00B7 Main outcome for analysis: mean correct latency (all delays) \u00EF\u0082\u00B7 Subsidiary outcome measures: % correct (all delays, 12s, 4s, 0s), median correct latency (12s, 4s, 0s), probability of error iii. RTI: Reaction Time is a task of simple and choice reaction time, movement and vigilance. The participant must hold down a button until a yellow spot appears on the screen, then release the button and touch where the yellow spot appeared. The spot appears in a single location in the simple reaction time task and in one of five locations in the choice reaction time task. \u00EF\u0082\u00B7 Main outcome for analysis: mean five-choice reaction time \u00EF\u0082\u00B7 Subsidiary outcome measures: median of reaction time (simple and five-choice test), median movement time, (simple and five-choice tests), error score (simple and five-choice tests). iv. AST: Attention Switching Task is a sensitive measure of frontal lobe and \u00E2\u0080\u0098executive\u00E2\u0080\u0099 function. The test measures the ability of the participant to switch attention between the direction or location of an arrow on the screen. In the task, an arrow appears on the screen and can be 106 located on the left or right side and pointing either to the left or to the right. Each trial is preceded by a cue, \u00E2\u0080\u0098which side?\u00E2\u0080\u0099 or \u00E2\u0080\u0098which direction?\u00E2\u0080\u0099, indicating how the participant should respond. The task assesses two aspects of cognitive function: it allows for detection of a Stroop-like effect \u00E2\u0080\u0093 by comparing latencies and errors from trials in which arrow direction and location are congruent \u00E2\u0080\u0093 and a task-switching effect \u00E2\u0080\u0093 by comparing response latencies and errors from trials in which participants have to follow the same rule versus a switch rule. \u00EF\u0082\u00B7 Main outcome analysis: median response latency (switched, non-switched), median response latency (congruent, incongruent) \u00EF\u0083\u00A0 these are used to calculate \u00E2\u0080\u0098switch cost\u00E2\u0080\u0099 (switched \u00E2\u0080\u0093 non-switched) and \u00E2\u0080\u0098congruency cost\u00E2\u0080\u0099 (incongruent \u00E2\u0080\u0093 congruent) \u00EF\u0082\u00B7 Subsidiary outcome measures: total correct trials, median reaction latency, switch errors, non-switch errors, congruent errors, incongruent errors v. SWM: Spatial Working Memory is a test sensitive to measure of the frontal lobe and \u00E2\u0080\u0098executive\u00E2\u0080\u0099 function which tests a subject\u00E2\u0080\u0099s ability to retain spatial information and to manipulate remembered items in working memory. The test begins with a number of coloured squares being shown on the screen. The aim of the test is that, by process of elimination, the subject will find one blue \u00E2\u0080\u0098tolken\u00E2\u0080\u0099 in each of a number of boxes until all are found. b) VAS, Mood state assessment: The mood state assessment questionnaire will be administered using the CANTAB touchscreen tablet following each CANTA battery. c) Static Balance Assessment: Balance assessment will be conducted before and immediately after each exposure condition using the Balance Error Scoring System (BESS). In brief, the test is an easily administered challenge which incorporates three different stances (double, single leg and tandem). This protocol is commonly used in sports medicine. d) fMRI: Functional MR and spectroscopy wase performed at the Child and family Research Imaging Facility (Women\u00E2\u0080\u0099s and Children\u00E2\u0080\u0099s Hospital) Each subject underwent a functional MRI of the brain before and after exposure to DE and FA. Functional neuroimaging (fMRI) is a magnetic resonance imaging technique which assesses the organization of the brain and its activity through detection of the change in magnetiation between oxygen-rich and oxygen-poor blood to assess changes in blood flow related to neural activity in the brain. The scanning protocol was be as follows: resting stat fMRI scan, following by fMRI administration of the Sternberg test of working memory. The major goal of the MRI portion of the project iwa to compare changes in the active network from baseline (pre-exposure) to post-exposure in both FA and DE conditions and demonstrate changes in the network of active brain regions following DE exposure. Specifically, the Sternberg test of working memory is employed as the aim of this study, in particular, is to assess changes in the active brain regions associated with the task of working memory. 107 Appendix II: Typical Plate Design of Samples All ELISA analysis was performed on a 96-well (12*8) plate. In order to prevent error from \u00E2\u0080\u009Cbatch effects\u00E2\u0080\u009D care was taken to ensure all samples from each subject were ran on the same plate with its own internal control (standard curve). However, within each plate the samples were not randomized, which may have potentially created errors due to \u00E2\u0080\u009Cedge effect\u00E2\u0080\u009D on the plate. This was not initially concerned a concern as subject exposures were supposedly counter balanced; this would mean that half of the DE exposures would be ran in the upper 4 wells of the ELISA plate, and the other half would be run on the lower 4 wells, and similar for FA. However, after the ELISA kits had been run it was discovered that the subjects were not properly counter balanced as more subjects were exposed to FA first (n=17) than DE first (n=10). This means that more of the FA exposures were ran on the top 4 wells of the plate than the bottom. Reasons why this was not concerning are as follows: (1) a 4-tip micropipette tip was used, meaning that reagents were added to the top 4 wells using the same respective pipette tip as the bottom 4 wells and (2) with the exception of TNF-\u00CE\u00B1, exposure day (which was directly correlated with location on the ELISA plate) was not associated with levels of the other biomarkers in the linear mixed effect models. The following schematic shows the typical design of the plates for subjects who had a complete set of blood draws; note that the subject numbers are interchangeable and that all samples were run in duplicates with the exception of TNF-\u00CE\u00B1. Figure AII.1: typical layout of ELISA plate 1 2 3 4 5 6 7 8 9 10 11 12 A STD Curve [highest] STD Curve [highest] Sub# 101 Day*: 1 Time#: 1 #101 Day: 1 Time: 1 #102 Day: 1 Time: 1 #102 Day: 1 Time: 1 #103 Day: 1 Time: 1 #103 Day: 1 Time: 1 #104 Day: 1 Time: 1 #104 Day: 1 Time: 1 #105 Day: 1 Time: 1 #105 Day: 1 Time: 1 B STD Curve dilution 1 STD Curve dilution 1 #101 Day: 1 Time: 2 #101 Day: 1 Time: 2 #102 Day: 1 Time: 2 #102 Day: 1 Time: 2 #103 Day: 1 Time: 2 #103 Day: 1 Time: 2 #104 Day: 1 Time: 2 #104 Day: 1 Time: 2 #105 Day: 1 Time: 2 #105 Day: 1 Time: 2 C STD Curve dilution 2 STD Curve dilution 2 #101 Day: 1 Time: 3 #101 Day: 1 Time: 3 #102 Day: 1 Time: 3 #102 Day: 1 Time: 3 #103 Day: 1 Time: 3 #103 Day: 1 Time: 3 #104 Day: 1 Time: 3 #104 Day: 1 Time: 3 #105 Day: 1 Time: 3 #105 Day: 1 Time: 3 D STD Curve dilution 3 STD Curve dilution 3 #101 Day: 1 Time: 4 #101 Day: 1 Time: 4 #102 Day: 1 Time: 4 #102 Day: 1 Time: 4 #103 Day: 1 Time: 4 #103 Day: 1 Time: 4 #104 Day: 1 Time: 4 #104 Day: 1 Time: 4 #105 Day: 1 Time: 4 #105 Day: 1 Time: 4 E STD Curve dilution 4 STD Curve dilution 4 #101 Day: 2 Time: 1 #101 Day: 2 Time: 1 #102 Day: 2 Time: 1 #102 Day: 2 Time: 1 #103 Day: 2 Time: 1 #103 Day: 2 Time: 1 #104 Day: 2 Time: 1 #104 Day: 2 Time: 1 #105 Day: 2 Time: 1 #105 Day: 2 Time: 1 F STD Curve dilution 5 STD Curve dilution 5 #101 Day: 2 Time: 2 #101 Day: 2 Time: 2 #102 Day: 2 Time: 2 #102 Day: 2 Time: 2 #103 Day: 2 Time: 2 #103 Day: 2 Time: 2 #104 Day: 2 Time: 2 #104 Day: 2 Time: 2 #105 Day: 2 Time: 2 #105 Day: 2 Time: 2 G STD Curve dilution 6 STD Curve dilution 6 #101 Day: 2 Time: 3 #101 Day: 2 Time: 3 #102 Day: 2 Time: 3 #102 Day: 2 Time: 3 #103 Day: 2 Time: 3 #103 Day: 2 Time: 3 #104 Day: 2 Time: 3 #104 Day: 2 Time: 3 #105 Day: 2 Time: 3 #105 Day: 2 Time: 3 H BLANK 1 BLANK 2 #101 Day: 2 Time: 4 #101 Day: 2 Time: 4 #102 Day: 2 Time: 4 #102 Day: 2 Time: 4 #103 Day: 2 Time: 4 #103 Day: 2 Time: 4 #104 Day: 2 Time: 4 #104 Day: 2 Time: 4 #105 Day: 2 Time: 4 #105 Day: 2 Time: 4 *Day represents exposure day #time represents exposure time point. 108 Appendix III: Histograms and Information to Determine Skewedness of Data Marker: IL-6 Marker Parameter Value Untransformed data Log-transformed data IL-6 Arithmetic mean 1.025 -0.246 Median 0.740 -0.301 Standard deviation (SD) 0.916 0.701 Geometric SD (Sg = e^SD) (GSD) 2.02 Marker Test p-value Untransformed data Log-transformed data IL-6 Skewness 0.0000 0.0048 Kurtosis 0.0000 0.7784 Shapiro-wilk test 0.00000 0.00473 Conclusion: neither the untransformed nor the log-transformed IL-6 data matched a perfect distribution, however, the decision to log-transform the data was made based on the following rationale: the geometric standard deviation was larger than 1.5 and the arithmetic mean was also larger than the median (1.025 vs. 0.740), suggesting skewed data. Although the statistical testing for skewness and the Shapiro-Wilk test suggested that neither the transformed or untransformed data sets followed a normal distribution (p-value > 0.05), however, the un-transformed data had yielded considerably lower p-values and therefore failed the tests \u00E2\u0080\u009Cmore strongly\u00E2\u0080\u009D. Finally, through visualization of the histograms and quantile plots it appeared that the data was more closely normally distributed once log-transformed. 0.511.5Density0.00 2.00 4.00 6.00 8.00il60.2.4.6.8Density-2 -1 0 1 2il6_log0.000.250.500.751.00Normal F[(il6-m)/s]0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)0.000.250.500.751.00Normal F[(il6_log-m)/s]0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)109 Marker: TNF-\u00CE\u00B1 Marker Parameter Value Untransformed data Log-transformed data TNF-\u00CE\u00B1 Arithmetic mean 1.015 -0.102 Median 1.070 0.677 Standard deviation (SD) 0.384 0.582 Geometric SD (Sg = e^SD) (GSD) 1.790 Marker Test p-value Untransformed data Log-transformed data TNF-\u00CE\u00B1 Skewness 0.0166 0.0000 Kurtosis 0.1104 0.0000 Shapiro-wilk test 0.00191 0.0000 Conclusion: neither the untransformed nor the log-transformed TNF-\u00CE\u00B1 data matched a perfect distribution, however, the decision to NOT log-transform the data was made based on the following rationale: although the geometric standard deviation was larger than 1.5 (1.790) suggesting the data was slightly skewed, the arithmetic mean was comparable to the median (1.015 vs. 1.070). Although the statistical testing for skewness and the Shapiro-Wilk test suggested that neither the transformed or untransformed data sets followed a normal distribution (p-value > 0.05), the transformed data yielded considerably lower p-values and therefore failed the tests \u00E2\u0080\u009Cmore strongly\u00E2\u0080\u009D. Finally, through visualization of the histograms and quantile plots it appeared that the data was more closely normally distributed prior to being log-transformed. 0.511.5Density0.00 0.50 1.00 1.50 2.00tnfa0.511.5Density-3 -2 -1 0 1tnfa_log0.000.250.500.751.00Normal F[(tnfa-m)/s]0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)0.000.250.500.751.00Normal F[(tnfa_log-m)/s]0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)110 Marker: S100B Marker Parameter Value Untransformed data Log-transformed data S100B Arithmetic mean 16.960 2.660 Median 16.015 2.774 Standard deviation (SD) 9.155 0.642 Geometric SD (Sg = e^SD) (GSD) 1.90 Marker Test p-value Untransformed data Log-transformed data S100B Skewness 0.000 0.0000 Kurtosis 0.0493 0.0033 Shapiro-wilk test 0.000001 0.0000 Conclusion: neither the untransformed nor the log-transformed S100B data matched a perfect normal distribution, however, the decision to NOT log-transform the data was made based on the following rationale: although the geometric standard deviation was larger than 1.5 (1.90), suggesting the data was slightly skewed, the arithmetic mean was comparable to the median (16.960 vs. 16.015). The statistical testing for skewness and the Shapiro-Wilk test suggested that neither the transformed or untransformed data sets followed a normal distribution (p-value > 0.05) and there was essentially no difference between the p-values yielded. Given this inconclusive evidence, more visualization of the histograms was critical. The histograms and quantile plots it appeared that the data was more closely normally distributed prior to being log-transformed. 0.02.04.06.08Density0.00 10.00 20.00 30.00 40.00 50.00s100b0.2.4.6.81Density0 1 2 3 4s100b_log0.000.250.500.751.00Normal F[(s100b-m)/s]0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)0.000.250.500.751.00Normal F[(s100b_log-m)/s]0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)111 Marker: NSE Marker Parameter Value Untransformed data Log-transformed data NSE Arithmetic mean 2.61 0.89 Median 2.51 0.92 Standard deviation (SD) 0.859 0.504 Geometric SD (Sg = e^SD) (GSD) 1.66 Marker Test p-value Untransformed data Log-transformed data NSE Skewness 0.0000 0.0000 Kurtosis 0.0000 0.0000 Shapiro-wilk test 0.00000 0.0000 Conclusion: neither the untransformed nor the log-transformed NSE data matched a perfect normal distribution, however, the decision to NOT log-transform the data was made based on the following rationale: the geometric standard deviation was slightly larger than 1.5 (1.66), suggesting the data may be skewed, the arithmetic mean was comparable to the median (2.61 vs. 2.51). The statistical testing for skewness and the Shapiro-Wilk test suggested that neither the transformed or untransformed data sets followed a normal distribution (p-value > 0.05) and there was essentially no difference between the p-values yielded. Given this inconclusive evidence, more visualization of the histograms was critical. The histograms and quantile plots it appeared that the data was more closely normally distributed prior to being log-transformed. 0.2.4.6.8Density0.00 2.00 4.00 6.00 8.00nse0.511.52Density-6 -4 -2 0 2nse_log0.000.250.500.751.00Normal F[(nse-m)/s]0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)0.000.250.500.751.00Normal F[(nse_log-m)/s]0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)112 Marker: BDNF Marker Parameter Value Untransformed data Log-transformed data BDNF Arithmetic mean 27779 10.147 Median 26723 10.190 Standard deviation (SD) 11026 0.429 Geometric SD (Sg = e^SD) (GSD) 1.536 Marker Test p-value Untransformed data Log-transformed data BDNF Skewness 0.0368 0.0097 Kurtosis 0.0004 0.6594 Shapiro-wilk test 0.00018 0.00074 Conclusion: neither the untransformed nor the log-transformed BDNF data matched a perfect normal distribution, and it was challenging to determine whether log-transformation should occur, however, the decision to log-transform the data was made based on the following rationale: the geometric standard deviation was only slightly above 1.5 (1.53), suggesting the data may be skewed. The arithmetic mean was significantly larger that the median in the untransformed data set (27779 vs. 26723), however, the values were comparable when the data was log-transformed (10.147 vs. 10.190), suggesting the un-transformed data was skewed, whereas the log-transformed data was not. The statistical testing for skewness and the Shapiro-Wilk tests suggested that neither the transformed or untransformed data sets followed a normal distribution (p-value > 0.05), although the skewness tests did suggest that the untransformed data set was less skewed. The histogram and quantile plots were comparable with both the 01.0e-052.0e-053.0e-054.0e-055.0e-05Density0.00 20000.00 40000.00 60000.00bdnf0.511.5Density8.5 9 9.5 10 10.5 11bdnf_log0.000.250.500.751.00Normal F[(bdnf-m)/s]0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)0.000.250.500.751.00Normal F[(bdnf_log-m)/s]0.00 0.25 0.50 0.75 1.00Empirical P[i] = i/(N+1)113 untransformed and transformed data, however, overall it appeared that the data was more closely normally distributed after being log-transformed. 114 Appendix IV: R Data and Analysis Codes for Mixed Effects Model Data Sets used for statistical analysis Classes \u00E2\u0080\u0098tbl_df\u00E2\u0080\u0099, \u00E2\u0080\u0098tbl\u00E2\u0080\u0099 and 'data.frame': 224 obs. of 41 variables: $ SubjectID :Factor w/ 27 levels \"101\",\"102\",\"103\",..: 1 1 1 1 1 1 1 1 2 2 ... $ Gender :Factor w/ 2 levels \"M\",\"F\": 2 2 2 2 2 2 2 2 1 1 ... $ screeningage :num 22.5 22.5 22.5 22.5 22.5 ... $ exposure1age : num 22.6 22.6 22.6 22.6 22.6 ... $ exposure2age : num 22.7 22.7 22.7 22.7 22.7 ... $ daysbetweenexp : num 27.4 27.4 27.4 27.4 27.4 ... $ exposuretype : Factor w/ 2 levels \"FA\",\"DE\": 1 1 1 1 2 2 2 2 2 2 ... $ exposureday : Factor w/ 2 levels \"D1\",\"D2\": 1 1 1 1 2 2 2 2 1 1 ... $ ordertype : Factor w/ 2 levels \"1st\",\"2nd\": 1 1 1 1 1 1 1 1 2 2 ... $ timepoint : Factor w/ 4 levels \"Pre\",\"Post\",\"3hr\",..: 1 2 3 4 1 2 3 4 1 2 ... $ hemolysis : num NA NA NA NA NA NA NA NA NA NA ... $ il6 : num 0.53 0.8 0.76 0.47 0.37 0.56 0.57 0.87 1.77 1.96 ... $ tnfa : num 0.62 0.39 0.47 0.65 1.12 1.12 1.21 0.58 0.92 0.66 ... $ tnfaorigional : num 0.62 0.39 0.47 0.65 1.12 1.12 1.21 0.58 0.92 0.66 ... $ s100b : num 14.21 16.42 9.36 15.48 10.42 ... $ nse : num 6.86 6.3 0.01 2.75 2.5 3.3 1.94 2.75 2.79 2.4 $ nseorigional : num 6.86 6.3 0.01 2.75 2.5 3.3 1.94 2.75 2.79 2.41 ... $ bdnf : num 39593 31695 29510 41594 35405 ... $ il6basedif : num NA 0.27 0.23 -0.06 NA ... $ tnfabasedif : num NA -0.23 0.03 -0.15 NA ... $ s100bbasedif : num NA 2.21 -4.85 1.27 NA ... $ nsebasedif : num NA -0.56 -6.85 -4.11 NA 0.8 -0.56 0.25 NA -0.38 ... $ bdnfbasedif : num NA -7898 -10083 2001 NA ... $ sleepqual : Factor w/ 5 levels \"1\",\"2\",\"3\",\"4\",..: 3 3 3 4 2 2 2 NA 4 4 ... $ caffeine : num 0 0 0 0 0 0 0 NA 1 1 ... Mixed Effects Model 1. Testing for order interaction >il6ordexp <- lmer(log(il6)~exposuretype*exposureday+(1|SubjectID),data=eapoc) >anova(il6ordexp) Analysis of Variance Table of type III with Satterthwaite approximation for degrees of freedom Sum Sq Mean Sq NumDF DenDF F.value Pr(>F) exposuretype 0.11658 0.11658 1 174.77 0.75447 0.38626 exposureday 0.45190 0.45190 1 174.77 2.92445 0.08902 . exposuretype:exposureday 0.10687 0.10687 1 24.83 0.69161 0.41354 --- Signif. codes: 0 \u00E2\u0080\u0098***\u00E2\u0080\u0099 0.001 \u00E2\u0080\u0098**\u00E2\u0080\u0099 0.01 \u00E2\u0080\u0098*\u00E2\u0080\u0099 0.05 \u00E2\u0080\u0098.\u00E2\u0080\u0099 0.1 \u00E2\u0080\u0098 \u00E2\u0080\u0099 1 2. Testing for interaction between gender and age >il6expgend <- lmer(log(il6)~exposuretype*Gender+(1|SubjectID),data=eapoc) >anova(il6expgend) 115 Analysis of Variance Table of type III with Satterthwaite approximation for degrees of freedom Sum Sq Mean Sq NumDF DenDF F.value Pr(>F) exposuretype 0.311756 0.311756 1 174.857 1.98504 0.1606 Gender 0.245260 0.245260 1 24.864 1.56164 0.2231 exposuretype:Gender 0.002631 0.002631 1 174.857 0.01675 0.8972 3. Determining individual fixed effect variables independently (exposure type, time-point, gender, age and exposure day) >il6fix <- lmer(log(il6)~exposuretype+Gender+timepoint+exposure1age+exposureday+(1|SubjectID),data=eapoc) >anova(il6fix) Analysis of Variance Table of type III with Satterthwaite approximation for degrees of freedom Sum Sq Mean Sq NumDF DenDF F.value Pr(>F) exposuretype 0.11658 0.11658 1 171.535 0.7616 0.38405 Gender 0.27359 0.27359 1 23.539 1.7872 0.19405 timepoint 0.70636 0.23545 3 172.125 1.5381 0.20642 exposure1age 0.63398 0.63398 1 23.482 4.1414 0.05329 . exposureday 0.45190 0.45190 1 171.535 2.9520 0.08758 . --- Signif. codes: 0 \u00E2\u0080\u0098***\u00E2\u0080\u0099 0.001 \u00E2\u0080\u0098**\u00E2\u0080\u0099 0.01 \u00E2\u0080\u0098*\u00E2\u0080\u0099 0.05 \u00E2\u0080\u0098.\u00E2\u0080\u0099 0.1 \u00E2\u0080\u0098 \u00E2\u0080\u0099 1 4. Determining effect of exposure and timepoint alone and as interaction >il6mixed1 <- lmer(log(il6)~exposuretype*timepoint+(1|SubjectID),data=eapoc) >anova(il6mixed1) Analysis of Variance Table of type III with Satterthwaite approximation for degrees of freedom Sum Sq Mean Sq NumDF DenDF F.value Pr(>F) exposuretype 0.32827 0.32827 1 169.72 2.09170 0.1499 timepoint 0.70782 0.23594 3 170.27 1.50339 0.2155 exposuretype:timepoint 0.08871 0.02957 3 169.72 0.18842 0.9042 5. Determining effect of exposure and timepoint alone >il6mixedf <- lmer(log(il6)~exposuretype+timepoint+(1|SubjectID),data=eapoc) >anova(il6mixedf) Analysis of Variance Table of type III with Satterthwaite approximation for degrees of freedom Sum Sq Mean Sq NumDF DenDF F.value Pr(>F) exposuretype 0.31822 0.31822 1 172.73 2.0567 0.1533 timepoint 0.70811 0.23604 3 173.27 1.5255 0.2096 6. Post hoc-testing 116 >pw1 <- summary(lsmeans(il6mixedf,revpairwise~timepoint)) >pw1$contrasts contrast estimate SE df t.ratio p.value Post - Pre 0.13112421 0.07675723 173.30 1.708 0.3225 3hr - Pre 0.02523862 0.07777141 173.43 0.325 0.9882 3hr - Post -0.10588559 0.07887151 173.77 -1.343 0.5372 24hr - Pre -0.02347522 0.07895164 173.71 -0.297 0.9908 24hr - Post -0.15459942 0.07933781 173.45 -1.949 0.2118 24hr - 3hr -0.04871384 0.08038247 173.63 -0.606 0.9301 Results are averaged over the levels of: exposuretype Results are given on the log scale. P value adjustment: tukey method for comparing a family of 4 estimates >pw2 <- summary(lsmeans(il6mixedf,revpairwise~exposuretype)) >pw2$contrasts contrast estimate SE df t.ratio p.value DE - FA -0.04749634 0.03381105 173.01 -1.405 0.1619 Results are averaged over the levels of: timepoint "@en . "Thesis/Dissertation"@en . "2016-05"@en . "10.14288/1.0300275"@en . "eng"@en . "Occupational and Environmental Hygiene"@en . "Vancouver : University of British Columbia Library"@en . "University of British Columbia"@en . "Attribution-NonCommercial-NoDerivatives 4.0 International"@* . "http://creativecommons.org/licenses/by-nc-nd/4.0/"@* . "Graduate"@en . "Peripheral blood markers of central nervous system effects following controlled human exposure to diesel exhaust"@en . "Text"@en . "http://hdl.handle.net/2429/57879"@en .