Medicine, Faculty of
Medicine, Department of
DSpace
UBCV
Orach, Juma
Doctor of Philosophy - PhD
University of British Columbia
Air pollution, to which diesel exhaust (DE) is a salient contributor in urban and occupational environments, is a leading contributor to premature mortality and morbidity worldwide. Concentration-responses (C-Rs) are a useful way to assess the magnitude and impact of air pollution exposure using external exposure surrogates and internal response markers. However, concentration-dependent health effects, which are potential markers of the response to DE exposure, are relatively unexplored in controlled human exposures where environmental confounders are limited. To investigate the C-R for DE across a broad range of exposure levels, we conducted a double-blinded crossover controlled human exposure study. Healthy non-smokers aged 19 – 49 were exposed to filtered air (FA) and DE standardized to 20, 50, and 150 µg/m³ PM₂.₅ in randomized order for 4h at the Air Pollution Exposure Laboratory. Before and up to 24h post-exposure, we measured lung function and airway responsiveness to methacholine using spirometry, airway inflammation using fractional exhaled nitric oxide (FeNO) and nasal epithelial lining fluid, symptoms using visual analog score questionnaires, and circulating proteins in blood using proteomics and immunoassays. Linear mixed effects models, repeated measures correlations and sparse partial least squares discriminant analyses were applied for statistical analysis. We identified concentration-dependent alterations in the proteome, which were enriched in complement activation and regulation pathways. Complement factor I was confirmed as significantly decreased by DE, with a potential threshold between 50-150 µg/m³ PM₂.₅. Complement factor I was correlated with changes in airway responsiveness, lung function and nasal cytokines. Although DE had no significant effect on lung function and airway responsiveness, it induced a concentration-dependent increase in FeNO, with a potential threshold between 50-150 µg/m³ PM₂.₅. DE induced a significant increase in symptoms, driven by eye and constitutional symptoms, with a potential threshold below 150 µg/m³ PM₂.₅. Symptoms were correlated with airway inflammation. Taken together, these findings are consistent with concentration-dependent inflammation in the airways and blood that is associated with clinically relevant endpoints, with potential effects thresholds as low as 50 µg/m³ PM₂.₅. My research corroborates known mechanisms of air pollution effects, while advancing novel concentration-dependent endpoints that may be useful markers for biomonitoring.
CONCENTRATION-DEPENDENT EFFECTS OF DIESEL EXHAUST EXPOSURE ON THE HUMAN PROTEOME AND AIRWAYS: A DOUBLE-BLINDED, CROSSOVER STUDY by Juma Orach B.Sc., University of British Columbia, 2018 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Experimental Medicine) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) December 2023 © Juma Orach, 2023 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Concentration-dependent Effects of Diesel Exhaust Exposure on the Human Proteome and Airways: A Double-blinded, Crossover Study Submitted by __Juma Orach__ in partial fulfilment of the requirements for the degree of __Doctor of Philosophy__ in __Experimental Medicine__ Examining Committee: Dr. Christopher Carlsten, Professor, Department of Medicine, UBC Supervisor Dr. Scott Tebbutt, Professor, Department of Medicine, UBC Supervisory Committee Member Dr. Sarah Henderson, Associate Professor, School of Population and Public Health, UBC Supervisory Committee Member Dr. Stephanus F Van Eeden, Professor Emeritus, Department of Medicine, UBC University Examiner Dr. Karen Bartlett, Professor, School of Population and Public Health, UBC University Examiner Dr. James G. Martin, Professor, Department of Medicine, McGill University External Examiner iii Abstract Air pollution, to which diesel exhaust (DE) is a salient contributor in urban and occupational environments, is a leading contributor to premature mortality and morbidity worldwide. Concentration-responses (C-Rs) are a useful way to assess the magnitude and impact of air pollution exposure using external exposure surrogates and internal response markers. However, concentration-dependent health effects, which are potential markers of the response to DE exposure, are relatively unexplored in controlled human exposures where environmental confounders are limited. To investigate the C-R for DE across a broad range of exposure levels, we conducted a double-blinded crossover controlled human exposure study. Healthy non-smokers aged 19 – 49 were exposed to filtered air (FA) and DE standardized to 20, 50, and 150 µg/m3 PM2.5 in randomized order for 4h at the Air Pollution Exposure Laboratory. Before and up to 24h post-exposure, we measured lung function and airway responsiveness to methacholine using spirometry, airway inflammation using fractional exhaled nitric oxide (FeNO) and nasal epithelial lining fluid, symptoms using visual analog score questionnaires, and circulating proteins in blood using proteomics and immunoassays. Linear mixed effects models, repeated measures correlations and sparse partial least squares discriminant analyses were applied for statistical analysis. We identified concentration-dependent alterations in the proteome, which were enriched in complement activation and regulation pathways. Complement factor I was confirmed as significantly decreased by DE, with a potential threshold between 50-150 µg/m3 PM2.5. Complement factor I was correlated with changes in airway responsiveness, lung function and nasal cytokines. Although DE had no significant effect on lung function and airway responsiveness, it induced a concentration-dependent increase in FeNO, with a potential iv threshold between 50-150 µg/m3 PM2.5. DE induced a significant increase in symptoms, driven by eye and constitutional symptoms, with a potential threshold below 150 µg/m3 PM2.5. Symptoms were correlated with airway inflammation. Taken together, these findings are consistent with concentration-dependent inflammation in the airways and blood that is associated with clinically relevant endpoints, with potential effects thresholds as low as 50 µg/m3 PM2.5. My research corroborates known mechanisms of air pollution effects, while advancing novel concentration-dependent endpoints that may be useful markers for biomonitoring. v Lay Summary Air pollution contributes to millions of diseases and premature deaths worldwide each year and diesel exhaust is a major contributor in urban areas and workplaces. Knowledge about the relationship between air pollution levels and health effects could improve our ability to assess the impact of air pollution on human health. However, this relationship is not well-studied in controlled environments where air pollution is closely monitored. Using minimally invasive methods, we studied the self-reported symptoms, airways, and blood of healthy nonsmokers before and after exposure to filtered air and a range of diesel exhaust levels in a controlled environment. My thesis research demonstrates changes in symptoms, airway inflammation and blood proteins that are not only linked to each other, but also related to diesel exhaust exposure levels. These measures could be adapted to monitor the health of individuals routinely exposed to air pollution, draft occupational health guidelines, and evaluate the effectiveness of these interventions. vi Preface This doctoral dissertation was conceived, conducted, and written by Juma Orach, under the supervision of Dr. Christopher Carlsten (PhD supervisor and DICE study principal investigator) at the Air Pollution Exposure Laboratory (APEL). The DICE study was approved by the University of British Columbia (UBC) Clinical Research Ethics Board (H16-03053). I led research participant recruitment and screening between 2018 – 2021, taking over from Agnes Yuen (AY), Dr. Carlsten’s research manager. AY also assisted with scheduling and provided logistical support throughout the study. I was also responsible for the laboratory sample processing for the study, which was largely completed by the APEL staff and Dr. Hang Li, a visiting PhD student. I conducted laboratory assays and microscopy. Additionally, I managed and analyzed data, interpreted findings, and wrote this dissertation and manuscripts mentioned herein. Co-authors assisted with sample collection, mass spectrometry, data interpretation and manuscript revision as detailed below. Chapter 1 was written by me and reviewed by my PhD supervisory committee. A portion of this chapter was published as a peer-reviewed literature review publication below and is included here with permission from Elsevier: Orach, J., Rider, C. F., & Carlsten, C. (2021). Concentration-dependent health effects of air pollution in controlled human exposures. Environment International, 150, 106424. doi.org/10.1016/j.envint.2021.106424 vii CC and I conceived the literature review. I drafted the original manuscript and was assisted by CFR in validating the underlying research. All listed authors reviewed, edited, and approved the final publication. I wrote chapter 2, which was reviewed by my PhD supervisory committee. A version of Chapter 2 has been published as a peer-reviewed research article below: Orach J, Hemshekhar M, Rider CF, et al. Concentration-dependent alterations in the human plasma proteome following controlled exposure to diesel exhaust. Environmental Pollution. 2024;342(November 2023):123087. doi.org/10.1016/j.envpol.2023.123087 CC, CFR, and I conceptualized and designed the work. CC and NM acquired funding for the research. I collected participant samples and data with the logistical support of ACY. I conducted immunoassays, statistical analyses and interpretation, and generated figures and tables. HM, NM, VS and AHL completed mass spectrometry and proteomics data processing. All listed authors reviewed and edited my original draft and approved the final publication. Additionally, I had minor support in general from Ms. Carley Schwartz, Mr. Parteek Johal and Mr. Kevin Lau. I wrote chapter 3, which was reviewed by my PhD supervisory committee. A version of Chapter 3 has been published as a peer-reviewed research article below and is included here with permission from the American Thoracic Society: Orach J, Rider CF, Yuen ACY, Schwartz C, Mookherjee N, Carlsten C (2023). Controlled Diesel Exhaust Exposure Induces a Concentration-Dependent Increase in Airway Inflammation: A Clinical Trial. Annals of the American Thoracic Society A1776-A1776. doi.org/10.1513/AnnalsATS.202209-762OC viii CC, CFR, and I conceptualized and designed the work. CC and NM acquired funding for the research. I conducted the majority of spirometry, FeNO and questionnaires with the administrative support of ACY. I conducted statistical analysis and interpretation and generated figures and tables. Ms. Carley Schwartz managed the APEL facility and collected spirometry data during exposures. All listed authors reviewed and edited my original draft and approved the final publication. Additionally, I had minor support in completing the DICE project in general from Dr. Neeloffer Mookherjee, Ms. Carley Schwartz, Mr. Parteek Johal, Mr. Kevin Lau, Ms. Bora Jin, Ms. Ayman Azhar and Ms. Shuyu Fan as acknowledged in the manuscript. Chapter 4 was written by me and reviewed by my PhD supervisory committee members. A version of this chapter was published as a peer-reviewed original research article below and is included here with permission from Springer Nature: Orach, J., Rider, C. F., Yuen, A. C. Y., & Carlsten, C. (2022). Concentration-dependent increase in symptoms due to diesel exhaust in a controlled human exposure study. Particle and Fibre Toxicology, 19(1), 66. doi.org/10.1186/s12989-022-00506-6 CC, CFR, and I conceptualized and designed the work. I collected most of the spirometry, FeNO and questionnaire data with the administrative support of ACY. I completed statistical analysis and interpretation, and generated figures and tables. After I drafted the original manuscript, all listed authors reviewed, edited, and approved the final publication. I also had minor support in completing the DICE project in general from Dr. Neeloffer Mookherjee, Ms. Carley Schwartz, Mr. Parteek Johal, Mr. Goldman Lam, Mr. Kevin Lau, Ms. Bora Jin, Ms. Ayman Azhar and Ms. Shuyu Fan as acknowledged in the manuscript. ix Table of Contents Abstract ..................................................................................................................................... iii Lay Summary ............................................................................................................................ v Preface ....................................................................................................................................... vi Table of Contents ..................................................................................................................... ix List of Tables............................................................................................................................ xv List of Figures ......................................................................................................................... xvi List of Abbreviations ............................................................................................................. xvii Acknowledgements .................................................................................................................. xx Dedication .............................................................................................................................. xxii Chapter 1 – Background and study design ................................................................................ 1 1.1 Study overview ...................................................................................................................... 1 1.2 Air pollution-associated mortality and morbidity ................................................................. 1 1.3 Traffic-related air pollution and diesel exhaust health effects .............................................. 2 1.3.1 Effects on the airways ..................................................................................................... 3 1.3.2 Systemic effects .............................................................................................................. 4 1.3.3 Effects on symptoms ...................................................................................................... 6 1.4 Internal markers of diesel exhaust exposure ......................................................................... 7 1.5 Concentration-responses for air pollution exposures ............................................................ 8 1.6 Concentration-responses in controlled human air pollution exposure studies .................... 10 x 1.7 Study design ........................................................................................................................ 11 1.7.1 Participant recruitment ................................................................................................. 11 1.7.2 Controlled diesel exhaust exposure .............................................................................. 12 1.7.3 Thesis hypothesis and objectives .................................................................................. 13 1.8 Tables .................................................................................................................................. 15 1.9 Figures ................................................................................................................................. 16 Chapter 2 – Concentration-dependent effects of DE on blood proteins ................................ 18 2.1 Synopsis .............................................................................................................................. 18 2.2 Introduction ......................................................................................................................... 19 2.3 Methods ............................................................................................................................... 21 2.3.1 Controlled Diesel Exhaust Exposures .......................................................................... 21 2.3.2 Blood sample collection and processing ...................................................................... 22 2.3.3 Mass spectrometry for plasma proteomics ................................................................... 23 2.3.4 Electrochemiluminescence assays ................................................................................ 24 2.3.5 Airway function and inflammation measures ............................................................... 24 2.3.6 Statistical analyses ........................................................................................................ 24 2.3.7 Confirmatory enzyme-linked immunosorbent assays (ELISAs) .................................. 26 2.4 Results ................................................................................................................................. 26 2.4.1 Study population and exposures ................................................................................... 26 xi 2.4.2 Diesel exhaust exposure is associated with concentration-dependent alterations in the proteome ................................................................................................................................ 27 2.4.3 Diesel Exhaust exposure is associated with a concentration-dependent decrease in complement factor I ............................................................................................................... 28 2.4.4 Complement factor I is correlated with airway health effects and inflammatory cytokines. ............................................................................................................................... 28 2.5 Discussion ........................................................................................................................... 29 2.6 Tables .................................................................................................................................. 34 2.7 Figures ................................................................................................................................. 39 Chapter 3 – Concentration-dependent effects of DE on the airways ..................................... 44 3.1 Synopsis .............................................................................................................................. 44 3.2 Introduction ......................................................................................................................... 45 3.3 Methods ............................................................................................................................... 47 3.3.1 Controlled Diesel Exhaust Exposures .......................................................................... 47 3.3.2 Spirometry .................................................................................................................... 47 3.3.3 FeNO ............................................................................................................................ 49 3.3.4 Nasal lavage and differential cell counts ...................................................................... 49 3.3.5 Nasal epithelial lining fluid .......................................................................................... 50 3.3.6 Electrochemiluminescence assays ................................................................................ 50 3.3.7 Statistical analysis......................................................................................................... 51 xii 3.4 Results ................................................................................................................................. 52 3.4.1 Study population ........................................................................................................... 52 3.4.2 Exposure characteristics ............................................................................................... 52 3.4.3 DE induces a concentration-dependent increase in FeNO ........................................... 52 3.4.4 DE induces a trend towards a concentration-dependent increase in IL-6 .................... 52 3.4.5 DE had no significant effect on lung function .............................................................. 53 3.4.6 DE had no significant effect on airway hyper-responsiveness ..................................... 53 3.4.7 DE had no significant effect on nasal leukocyte counts ............................................... 53 3.5 Discussion ........................................................................................................................... 53 3.6 Tables .................................................................................................................................. 58 3.7 Figures ................................................................................................................................. 63 Chapter 4 - Concentration-dependent effects of DE on symptoms ........................................ 67 4.1 Synopsis .............................................................................................................................. 67 4.2 Introduction ......................................................................................................................... 68 4.3 Methods ............................................................................................................................... 69 4.3.1 Controlled Diesel Exhaust Exposures .......................................................................... 69 4.3.2 Questionnaires .............................................................................................................. 70 4.3.3 Spirometry .................................................................................................................... 70 4.3.4 FeNO ............................................................................................................................ 71 4.3.5 Statistical analyses ........................................................................................................ 71 xiii 4.4 Results ................................................................................................................................. 72 4.4.1 Study population ........................................................................................................... 72 4.4.2 Exposure characteristics ............................................................................................... 72 4.4.3 DE induced a concentration-dependent increase in total, eye, and constitutional symptoms ............................................................................................................................... 72 4.4.4 The symptom concentration-response was modified by environmental perception .... 73 4.4.5 Symptoms were correlated with airway inflammation ................................................. 74 4.5 Discussion ........................................................................................................................... 74 4.6 Tables .................................................................................................................................. 78 4.7 Figures ................................................................................................................................. 84 Chapter 5 - Conclusion ............................................................................................................... 90 5.1 Summary of salient findings ............................................................................................... 90 5.2 Implications of the findings ................................................................................................ 93 5.2.1 Pathophysiological insights .......................................................................................... 93 5.2.2 Exposure guidelines and policy .................................................................................... 94 5.2.3 Health monitoring ......................................................................................................... 97 5.3 Strengths and limitations ..................................................................................................... 97 5.4 Future directions .................................................................................................................. 99 References ............................................................................................................................... 101 Appendices ................................................................................................................................. 124 xiv Appendix A ............................................................................................................................. 124 Appendix B ............................................................................................................................. 125 xv List of Tables Table 1.1. Controlled diesel exhaust exposure measurements summarized by exposure group and diesel engine.................................................................................................................................. 15 Table 2.1. Participant demographics. ............................................................................................ 34 Table 2.2. Statistically significant (q ≤ 0.05) concentration-responses for proteomics data. ....... 35 Table 2.3. Summary of top proteins selected by LMEMs and sPLSDA. ..................................... 37 Table 2.4. Repeated measures correlations between complement factor I and airway health measures and acute inflammatory cytokines at 24h. .................................................................... 38 Table 3.1. Participant demographics for airway analysis. ............................................................ 58 Table 3.2. Concentration-responses for cytokines in nasal epithelial lining fluid at 24h. ............ 59 Table 3.3. Repeated measures correlations between percentage change in fractional exhaled nitric oxide (FeNO) and type 2 inflammatory cytokines in nasal epithelial lining fluid. ............. 60 Table 3.4. Concentration-responses for differential cell counts from nasal lavage at 24h. .......... 61 Table 3.5. Repeated measures correlations between percentage change in fractional exhaled nitric oxide (FeNO) and cell proportions in nasal epithelial lining fluid. ..................................... 62 Table 4.1. Symptom questions and clusters. ................................................................................. 78 Table 4.2. Participant demographics for symptoms analysis. ....................................................... 79 Table 4.3. Symptom response modification by diesel engine type............................................... 80 Table 4.4. Concentration-response effect modification by environmental perception. ................ 81 Table 4.5. Repeated measures correlations between change in symptoms and airway function measures. ....................................................................................................................................... 83 xvi List of Figures Figure 1.1. Study design. .............................................................................................................. 16 Figure 1.2. Study recruitment flow diagram.. ............................................................................... 17 Figure 2.1. Pathway enrichment of proteins that exhibit a concentration-response. .................... 39 Figure 2.2. Multilevel Sparse Partial Least Discriminant Analysis (sPLSDA) by exposure condition. ...................................................................................................................................... 40 Figure 2.3. Diesel exhaust (DE) concentration-responses for top protein candidates. ................. 41 Figure 2.4. Diesel exhaust effect on complement factor I summarized by nominal exposure condition. ...................................................................................................................................... 42 Figure 2.5. Proposed pathway of diesel exhaust (DE) effects on complement factor I and adverse health effects. ................................................................................................................................ 43 Figure 3.1. Outline of study design and outcomes (Chapter 3). ................................................... 63 Figure 3.2. Diesel exhaust (DE) effect on fractional exhaled nitric oxide (FeNO) summarized by concentration-response (C-R) and nominal exposure category. ................................................... 64 Figure 3.3. Concentration-response of diesel exhaust (DE) and forced expiratory volume in 1 second (FEV1). ............................................................................................................................. 65 Figure 3.4. Concentration-response for methacholine provocation concentration causing a 20% drop in FEV1 (PC20) and dose-response slope (DRS). ................................................................ 66 Figure 4.1. Symptoms visual analog scale (VAS) questionnaires. ............................................... 85 Figure 4.2. Diesel exhaust (DE) concentration-response for symptom categories. ...................... 87 Figure 4.3. Effects of diesel exhaust (DE) on symptom categories by exposure group. .............. 88 Figure 4.4. Effect of perception on the concentration-response between diesel exhaust (DE) and symptoms. ..................................................................................................................................... 89 xvii List of Abbreviations ATS American Thoracic Society BMI Body Mass Index CAP Concentrated ambient particles CASAC Clean Air Scientific Advisory Committee CFI Complement factor I CHE Controlled human exposure CI Confidence interval COPD Chronic obstructive pulmonary disease C-R Concentration-response DE Diesel exhaust ELF Epithelial lining fluid ELISA Enzyme linked immunosorbent assay EPA Environmental Protection Agency FA Filtered air FeNO Fractional exhaled nitric oxide FEV1 Forced expiratory volume in 1 second FVC Forced vital capacity xviii IFN Interferon IL Interleukin LLOD Lower limit of detection LME Linear mixed effects LMEM Linear mixed effects model MSD Mesoscale Discovery NAAQS National Ambient Air Quality Standards NLF Nasal lavage fluid NOx Nitrogen oxides OEL Occupation Exposure Limit PAH Polycyclic aromatic hydrocarbon PC20 Methacholine provocation concentration to drop FEV1 by 20% PM Particulate matter PM2.5 Particulate matter with aerodynamic diameter less than 2µm SD Standard deviation SE Standard error sPLSDA Sparse partial least square discriminant analysis TNF Tumor necrosis factor xix TRAP Traffic related air pollution VAS Visual analog scale VIP Variable importance in the projection WHO World Health Organization xx Acknowledgements I am deeply grateful to Dr. Christopher Carlsten for giving me the life-changing opportunity to pursue a doctoral degree. Dr. Carlsten is an incredibly hard working and effective researcher from whom I have gained invaluable knowledge and training in the field of air pollution research. I appreciate my supervisory committee members Dr Scott Tebbutt and Dr. Sarah Henderson, who have been a reliable source of sound academic advice for my thesis project. I was also fortunate to be supervised by Dr. Tebbutt in my extra-curricular role as a research assistant at the Centre for Heart Lung Innovation, where he was a role model of compassionate mentorship that values student development and wellbeing. I am grateful to my colleagues and staff in the lung center at the Vancouver General Hospital and Jack Bell Research Center who supported my work on the DICE study. I thank Ms. Agnes Yuen, Dr. Christopher Rider, Mr. Kevin Lau, Mr. Parteek Johal, Ms. Carley Schwartz, Mr. Ryan Huff, Ms. Tina Afshar, Dr. Hang Li and Dr. Min Hyung Ryu. Ms. Carley Schwartz graciously managed all the controlled exposures, and Dr. Hang Li helped refine laboratory processing procedures. Additionally, my scholarly work has benefitted tremendously from Drs. Ryu and Rider’s input. I also thank (then) undergraduate students Ms. Bora Jin, Ms. Isabella Correa, Ms. Christine Chen, Ms. Chantane Yeung, Mr. Wayne Tse, Ms. Ayman Azhar, Ms. Alisa Hashimoto, Ms. Mimi Nguyen, Mr. Goldman Lam for their assistance with data entry and laboratory work. I especially thank Agnes Yuen for her mentorship, guidance, and support. Agnes always approached me without prejudice and went above and beyond her role as a research manager to assist me through critical challenges in my degree. She was always a keen listener, a constructive critic, and a dependable ray of sunshine in the office. xxi I am grateful to my friends and family that have counseled, consoled, and supported me through both joyful and challenging times in my degree. I especially appreciate my partner Jenny for her consistent support in my personal life and graduate studies. She has been a reliable source of companionship, moral support, advice, and knowledge. Jenny has had a role in many moments of tranquility and personal growth throughout my degree. Throughout my life, as in my graduate journey, I am eternally grateful to my brothers, and parents, Juma and Molly. I am especially grateful to my mother, Molly, who has been an anchor for me since the beginning of my journey through life. Her selfless dedication to help those in need in our community, and her unwavering belief in my abilities have given me invaluable inspiration and meaning. I owe most of my achievements to the solid foundation that she laid for me and the enduring support that she continues to give. I thank God for blessing me with exceptional people and opportunities and I look forward to His guidance through the next chapters in my journey. xxii Dedication To my mother; an anchor, a pillar, and an enduring source of inspiration throughout my life. 1 Chapter 1 – Background and study design 1.1 Study overview My thesis project is based on the “Human Study to Develop a Signature of Occupational Diesel Exhaust Exposure” (DICE study). This is the first attempt to elucidate the concentration-response (C-R) to controlled diesel exhaust (DE) exposure with the aim of identifying robust markers across a range of concentrations. With 4-hour crossover exposures to filtered air (FA) and DE standardized to 20, 50 and 150 µg/m3 particulate matter (PM) less than 2.5 µm in diameter, the DICE study covers the broadest range of DE levels and exceeds exposure durations of other controlled human exposures to DE. Moreover, this study is the first C-R study to leverage proteomics for DE effects marker discovery using a controlled experimental setting. Thus, it was uniquely positioned to identify novel markers with a robust relationship to DE exposure level at durations that better approximate real world daily exposure. We report a concentration-dependent alteration in the proteome that is enriched in complement pathways and confirm complement factor I as a concentration-dependent blood-based marker of systemic DE effects. Moreover, we report concentration-dependent increases in airway inflammation and self-reported symptoms. Our work provides evidence of DE effects that are associated with clinically relevant measures of health. The concentration-dependent markers of DE exposure identified here could be validated to better understand the health effects of traffic-related air pollution, foster biomonitoring in high-risk populations, and draft exposure guidelines. 1.2 Air pollution-associated mortality and morbidity Air pollution exposure is one of the leading risk factors for global mortality and morbidity1,2. All-cause air pollution is implicated in approximately 6.5 million deaths annually, largely 2 attributed to ambient air pollution, but also including household and ozone pollution3. Considering additional causes of death beyond ischemic heart disease, stroke, chronic obstructive pulmonary disease (COPD), lung cancer and lower respiratory tract infections brings this estimate closer to 9 million4. Air pollution is generated from diverse natural and anthropogenic sources including volcanic eruptions, wildland fire, residential and industrial energy generation, and transportation5. In urban areas, most pollution is generated by traffic through combustion, brake and road surface friction, and crustal materials resuspension6–8. Toxic particles and gases in air pollution, individually and in combination, are associated with negative impacts on human health6,7,9. Increases in ambient air pollution are associated with increases in cardiopulmonary disease incidence and exacerbation, hospitalizations, medication use, and symptoms10–18. Notably, particulate matter less than 2.5 µm in diameter (PM2.5), which can reach the lower airways upon inhalation, is the leading environmental contributor to the global disease burden and a predictor of pollution-related mortality19,20. Despite decreases in global PM2.5 levels, PM-associated mortality remains a significant public health challenge that is increasing in some parts of the world7,19. Moreover, a majority of the global population still lives in areas with pollution levels exceeding the World Health Organization (WHO) recommendations7,19. Interventions aimed at lowering emissions, bolstered by robust environmental and health monitoring, are crucial in decreasing the impact of air pollution19. 1.3 Traffic-related air pollution and diesel exhaust health effects Traffic contributes up to 90% of the PM in urban areas, a significant portion of which is derived from diesel vehicles6–8. Diesel engines are commonly used worldwide due to their durability and 3 relatively low cost9,21,22. DE comprises a mixture of PM, gases, and vapors, including polycyclic aromatic hydrocarbons (PAHs)23,24. Compared to gasoline and biofuels exhausts, DE is characterized by relatively high PM and nitrogen oxides (NOx)21,22,24,25. DE PM, which is only second (in proportion) to NOx in the pollutant fraction of DE, is dominated by PM2.5 that can reach the lower airways21. This partly explains the toxicity of DE, the utility of PM2.5 as a surrogate for DE in health effects research, and the importance of DE as a contributor to ambient and occupational PM2.56,21. Due to its association with lung cancer risk, DE has been classified by the International Agency for Research on Cancer as a Group 1 carcinogen (carcinogenic to humans)6,26. DE is a useful model to study the health effects of occupational and traffic-related air pollution (TRAP)27,28, partly owed due to its prominent contribution to tailpipe8 and workplace emissions29. Chronic exposure to DE PM2.5 is associated with increased incidence and exacerbation risk for cardiovascular diseases, as well as lung diseases like asthma, cancer, and COPD30–32. Following acute exposures, deleterious changes in adverse symptoms, cognition, cardiopulmonary function, inflammation and oxidative stress mediators have been reported in occupational cohorts33–35 and controlled exposure studies28,36–41. Moreover, DE exacerbates the effects of other pollutants like allergen and ozone28,40,42–47. While the acute and chronic effects of DE exposure differ, acute effects indicate mechanisms and internal marker changes that are related to chronic effects and morbidity6. 1.3.1 Effects on the airways PM and gases in TRAP interact with epithelial and immune cells in the respiratory tract via reactive oxygen species, calcium channels, cell receptors to instigate inflammation and oxidative stress48,49. DE exposure is associated with immune recruitment and activation involving innate 4 responders like neutrophils, macrophages, as well as adaptive responders like T cells49. Additionally, TRAP components interact directly with surfactants, innate immune proteins, and endogenic nitric oxides50,51. These interactions disrupt airway homeostasis by affecting epithelial cells, macrophages and sensory neurons49,52, as well as non-cellular agents like defense peptides and mucins43,53. Secondary effects of TRAP include mucus hypersecretion and smooth muscle contraction, which are associated with increased airway obstruction and hyper-responsiveness44,54,55. In clinical studies and practice, DE-mediated increases in inflammation are indicated by increased fractional exhaled nitric oxide (FeNO)54, as well as increased soluble and cellular inflammatory mediators like IL-6, IL-8, and neutrophil counts49. Declines in lung function associated with DE exposure are indicated by decreases in forced expiratory volume in 1 second (FEV1) and forced vital capacity56, while increased airway hyper-responsiveness is indicated by a decrease in methacholine provocation concentration causing a 20% drop in FEV1 (PC20)42. DE exposure increases susceptibility to airway infections57 and is implicated in the incidence and exacerbation of asthma, chronic obstructive pulmonary disease, and lung cancer31,58. Moreover, local exposure effects spill out beyond the airways via leukopoiesis, contributing to the systemic effects of DE59. 1.3.2 Systemic effects In addition to the spill-over from the airways, ultrafine particles may traverse the airways into the blood to contribute to systemic effects48,60,61. In the blood, PM interacts with immune cells to promote leukopoiesis and the production of pro-inflammatory cytokines and chemokines59. PM exposure is associated with increased IL-6, TNF-a, and MCP-1, among other cytokines, that create a pro-inflammatory state characterized by increased leukocyte numbers, chemotaxis and 5 atherogenesis62. PM also interacts directly with soluble mediators such as complement proteins, nitric oxides, and antioxidants to dysregulate vascular and blood homeostasis63–65. Notably, complement proteins, which foster coagulation and uptake of foreign particles by phagocytes, are altered66,67 and activated by PM exposure68. For example, the prominent pro-inflammatory complement C3, whose activation is regulated by complement factor I (CFI), is increased by increasing PM2.5 concentration67. This indicates a crucial role of complement C3 and its regulators, such as CFI, in the interactions between air pollution and the innate immune system69,70. Beyond C3, the relationship between complement proteins and air pollution is relatively unexplored and may be elucidated by future studies investigating the mechanistic and concentration-response relationship between air pollution and vital complement proteins like CFI. Pollution-mediated effects on complement proteins disrupt immune homeostasis and promote excessive inflammation48. PM-mediated oxidative stress and inflammation are implicated in a myriad of downstream health effects, such as atherosclerosis, thrombosis, autonomic and endothelial dysfunction71,72. Vascular and thrombotic effects contribute significantly to cardiovascular diseases73,74, which are a leading cause of premature death due to air pollution exposure4. Proteomic and metabolomic technologies have been instrumental in examining the far-reaching systemic effects of air pollution by facilitating the study of thousands of proteins and metabolites in the blood75–78. Alterations in metabolites and proteins associated with air pollution exposures have expanded current knowledge on mechanistic pathways and putative internal markers associated with air pollution. For example, Cruz et al., (2021)79 and Vlaanderen et al., (2017)80 identified air pollution-mediated alterations in blood glucose and tyrosine metabolites respectively using untargeted metabolomics, revealing the negative impact of pollution on 6 essential metabolic functions. Mookherjee et al., (2022)81 and Prunicki et al., (2020)82 identified alterations in blood markers linked to atherosclerosis and blood pressure respectively, thereby buttressing the known relationship between air pollution and cardiovascular disease. Notably, proteomic markers associated with specific pollutants in controlled human exposures, and concentration-responses between the blood proteome and DE PM remain relatively unexplored. 1.3.3 Effects on symptoms Symptoms reflect changes in subjective wellbeing and may indicate cardiopulmonary diseases associated with the airway and systemic impacts of TRAP exposure13,83–85. Self-reported symptoms are a non-invasive method of detecting and reporting the effects of TRAP on quality of life85 and are associated with clinically-relevant measures of health in the airways such as FeNO86 and FEV187,88. PM interacts with the skin and exposed mucosa in the airways and eyes to activate sensory nerves, and cause inflammation and oxidative stress89–91. Ultrafine PM, which translocate to the circulatory system, may directly or indirectly affect leukocytes, endothelia and the central nervous system48,92. Epithelial damage, mucosal disruption, pro-inflammatory cytokines and sensory activation may cause irritation, fatigue, headaches and fever93. Additionally, psychological factors associated with perceived exposure, such as anxiety, temperature and noise may modify the self-reported symptoms93. Increased eye irritation, neurological and constitutional symptoms have been reported following controlled DE exposure in humans93–97. Moreover Carlsten et al., (2013)98 reported an increase in symptoms due to perceived exposure, indicating that perceived exposure may in part explain symptoms associated with air pollution. Whether environmental and exposure perception modifies the symptom response is unexplored. Moreover, while increased symptoms have been associated with high PM concentrations in epidemiological and controlled exposure studies, the C-R relationship 7 between symptoms and DE, including low DE concentrations, is unexplored in controlled human exposures. Symptoms, airway health, and blood-based markers remain crucial ways to study the impact of TRAP99. While significant progress has been made to mitigate the impacts of DE pollution through engineering solutions21,100 and exposure regulations25,101, a lot remains to be done in biomonitoring at-risk populations where harm persists. 1.4 Internal markers of diesel exhaust exposure The use of biomonitoring, which reflects internal changes caused by exposure, complements external exposure surrogates that are prone to measurement errors and confounding factors99,102–104. Effective biomonitoring relies on markers that are: 1) highly sensitive and specific even at low exposure levels 2) robustly correlated with external exposure at all relevant concentrations and 3) quantified through highly accurate methods105–108. DE is a complex mixture with multisystem effects6,22. Thus, the search for DE internal markers has been complicated by heterogeneity between diesel engines, atmospheric modification, and shared exposure and effect characteristics with other combustion-generated pollutants6,105,107,109. PAH metabolites, nanoparticulates, and protein adducts have been advanced as potential specific internal markers for DE exposure but significant methodological and reproducibility challenges persist105,110–115. For example, urinary PAH metabolites previously emerged as the most promising internal markers of DE exposure from epidemiological studies116, but are heavily confounded by secondary PAH sources and were not consistently corroborated in controlled exposure studies111. Moreover, a paucity of C-R data from controlled exposure studies, including 8 lower exposure concentrations and clinically-relevant endpoints, limits the translation of internal marker research into practice117. 1.5 Concentration-responses for air pollution exposures C-R relationships are crucial in describing the magnitude and nature of the relationship between exposures and responses118–120. The C-R is closely related to dose-response, a comprehensive exposure-response metric that tracks exposure deposition121. These exposure-response measures estimate the biological gradient, one of several Bradford hill criteria for determining causality122, and a foundation for impact assessment, effect threshold determination, and exposure-response marker discovery107,108,123. Global Burden of Diseases study panels have utilized C-R curves for PM and mortality to estimate the impact of air pollution primarily on ischemic heart disease, stroke, COPD, lung cancer and lower respiratory infections1,3,4,124. PM concentration-dependent effects have been reported for other health endpoints such as cognition125, lung function126, inflammation127–130, and lipid dysfunction131–133. Beyond PM2.5, similar relationships have been reported for gases such as NO2, carbon monoxide, and ozone126,134–136. While most of these C-R estimations have been linear, there is growing evidence of a nonlinear C-R for some endpoints16,19,119,137,138. Specifically, a steep increase in adverse outcomes that flattens out at higher concentrations has been estimated, with greater uncertainty at low concentrations due to sparse data139. By 2019, the global urban PM2.5 average was 35 µg/m3, which is higher than the WHO guideline of 10 µg/m3 PM2.5. At least 86% of urban inhabitants lived in areas that exceed 10 µg/m3 PM2.5 resulting in an excess of 1.8 million deaths19, and exposure levels as high as 503 µg/m3 PM2.5 have been reported140. Moreover, adverse health effects persist in areas below the WHO annual 9 limit141,142, thus lowering emissions remains a crucial public health goal143. In occupational environments with diesel engines, exposures exceeding 397 µg/m3 PM2.5 have been reported, which is above the occupational limit of 50 µg/m3 of DE particles recommended in a critical review by Landwehr et al., (2021)25. Moreover, chronic occupational DE exposure alters response markers linked to cancer and increases cancer risk, even at PM2.5 concentrations below 100 µg/m3 25. For example, GE et al., estimated the DE exposure-response at 0.04%, 0.99% and 3% increases in excess lifetime risk for lung cancer at 1.3, 27, and 67 µg/m3 PM2.5 (1, 20, 50 µg/m3 elemental carbon) respectively for male workers (2020)30. Better C-R estimation will guide mitigation strategies like recommending exposure limits and assessing the impact of emission reduction strategies120,142. Indeed, decreases in air pollution or relocation to areas with lower pollutant concentrations have reflected decreases in response outcomes125,143. Based on the shape of the C-R, for example, reductions in PM2.5 at lower concentrations where the C-R is steep are estimated to confer greater public health benefit19,138,142. However, exposure-response estimates at low PM2.5 exposures are relatively less well-studied and have been recommended for future cohort studies3. Epidemiological studies are instrumental in elucidating the C-R for air pollution144, but are limited by measurement error, methodological variation, confounding exposures, geographical and population differences120,123,145. Studies with more precise monitoring and control over confounders, such as controlled human exposures, complement C-Rs estimated from epidemiology. 10 1.6 Concentration-responses in controlled human air pollution exposure studies Controlled exposure studies are an experimental design where individuals are exposed to a closely monitored and controlled amount of air pollution, typically in a specialized exposure chamber28,146. Compared to other air pollution study designs, such as epidemiology, in vitro and animal models, controlled human exposure (CHE) studies limit confounding while maintaining direct relevance to human physiology144,146. Despite acute timeframes, and limitations in sample sizes and feasible endpoints, CHEs are a valuable tool for investigating specific pollutants and mechanistic pathways144,146,147. While the acute effects of air pollution exposure in CHE may differ from chronic exposure effects in epidemiological studies148, acute exposure studies are crucial in identifying markers of adverse health effects that precede and/or indicate morbidity associated with chronic exposure139. Pollutants studied in CHEs to date include DE, concentrated ambient particles (CAP), woodsmoke, engineered carbon particles, biofuels, gasoline, ozone and NO2117. CHEs have been used to identify C-Rs for acute exposure to specific pollutants. We have previously reviewed the concentration-dependent health effects in CHE117. We identified potential C-R markers, effects thresholds and modifiers for CAP, DE, woodsmoke and engineered nanoparticles. Of the 31 studies identified, the majority (15) focused on CAP while only 4 focused on DE. These 4 DE studies investigated 2h exposures ranging 100 – 300 µg/m3 PM2.5 and focused on inflammation, coagulation, and cardiovascular endpoints in healthy participants and metabolic syndrome patients149–152. Collectively, their findings were suggestive of a threshold below 200 µg/m3 PM2.5, with metabolic syndrome and glutathione s-transferase mu 1 genotype as potential effect modifiers. However, in DE as in other CHE models, the lack of 11 replicative studies, varying methodology and inconsistent estimates limited our conclusions. Moreover, these DE studies did not include exposure concentrations below 100 µg/m3 PM2.5 , investigate non-linear C-R, or report C-R estimates like unit changes in response per µg/m3 PM2.5 to ease interpretation and facilitate comparisons between studies117. Current knowledge of robust markers associated with DE exposure that exhibit a C-R will be advanced by CHEs that better approximate the duration and range of real-world exposures. Moreover, the use of non-invasive markers could facilitate the translation of this knowledge into robust biomonitoring and exposure guidelines for at-risk populations153,154. 1.7 Study design The DICE study (NCT03234790) was approved by the University of British Columbia Research Ethics Board (H16-03053) and conducted between December 2017 and June 2021. Research participants were recruited through referrals, online notices, and advertisements. All participants provided written informed consent before enrollment. Figure 1.1 summarizes the study design. 1.7.1 Participant recruitment Participants were initially screened through a telephone interview to determine suitability for inclusion or exclusion. Inclusion criteria were (1) age 19 – 49 (2) non-smoking (any ex-smokers had to have consistently quit for 6 months or more) (3) ability to communicate proficiently in English (4) good health (at study physician’s discretion). Participants were excluded if they were (1) pregnant/breastfeeding (2) using inhaled corticosteroids (3) having co-existing medical conditions or conflicting time commitments. Eligible participants were invited for in-person screening during which they were informed about the study in detail and encouraged to ask questions. Before commencing screening procedures, 12 participants gave written informed consent. Health and demographic information were thereafter obtained through questionnaires. Baseline lung function was assessed using standard spirometry according to American Thoracic Society (ATS) guidelines155. Underlying cardiac anomalies were screened using electrocardiograms and a brief physical examination by the study physician, who also reviewed all screening assessments. Prior to each subsequent visit, upper respiratory infections were assessed using validated common cold questionnaires156,157 and participation was delayed by at least 4 weeks if a positive result (Common Cold questionnaire score greater than 3) was obtained. Participants were also requested to withhold short- or long-acting bronchodilator use for 8h and 48h respectively, as well as caffeinated beverages 4h before the beginning of a study date. Figure 1.2 summarizes the recruitment process. 1.7.2 Controlled diesel exhaust exposure Double-blinded crossover 4h exposures to FA and 20, 50 and 150 µg/m3 PM2.5 of DE were conducted at the Air Pollution Exposure Laboratory at the University of British Columbia (Vancouver, Canada). Exposures were order-randomized and at least 4 weeks apart. PM2.5 levels in this study are common in urban158 and occupational settings25, and controlled human exposures to DE28,117. Detailed operation and schematics for this setup have been previously published159. Briefly, DE was generated by an EPA Tier 3-compliant, 6.0 kW Coliseum GY6000 generator, with 406 cc Yanmar L 100 EE 4-stroke diesel engine cycled from idle to 2.5 kW load and fueled by ultra-low sulfur diesel (Petro Canada type 2, sulfur < 15 ppm, cetane number 41–44). Raw exhaust was diluted with compressed air and high efficiency particulate air-filtered air before channeling into a 4 ft (width) × 6 ft (depth) × 7 ft (height) exposure booth maintained in negative pressure. Towards the end of the study in February 2021, a new diesel engine (Euro-Stage 5 compliant, 4.5kW 1B30E Hatz engine) was installed to replace the old engine and reflect 13 technological advances, but all other aspects of the setup were retained. Concentrations of PM2.5 (mass concentration), ultrafine particles, gases, total volatile organic compounds, and particle size distribution were monitored in real-time (data recorded every 2 seconds) and maintained at the target nominal levels by the research engineer. Exposure details for the diesel engines are presented in Table 1.1. Exposures were conducted at the same time of day (afternoon) for all participants and all samples were collected at the same timepoints for each visit. During the exposure, participants used a bicycle ergometer (Ergometer DX1, Kettler Corporation, Worcestershire UK) intermittently (15 minutes/hour) at the wattage (0.75 power-to-weight ratio) and revolutions per minute previously determined to achieve ventilation of 20 L/min/m2 using a Wright respirometer (model 295, Anaesthesia Associates, San Marcos, CA, USA) to simulate daily physical activity. Participants were also asked to fill in a questionnaire about the environment in the exposure booth and whether they could determine the exposure condition (DE or FA). While some participants could detect DE odour, this setup has been shown to be effective in blinding both research technicians and participants to true exposures98. 1.7.3 Thesis hypothesis and objectives The overarching aim of my thesis was to identify, using convenient accessible samples, concentration-dependent markers of DE effects that are associated with clinically relevant endpoints. Specific aims are summarized below. Chapter 2 was aimed identifying and evaluating concentration-dependent effects of DE on blood proteins. The hypotheses tested were: 1a) DE exposure induces concentration-dependent changes in the proteome. 14 1b) Blood proteins are correlated with airway health effects measured by FeNO, FEV1, methacholine concentration PC20, and inflammatory cytokines. Chapter 3 was aimed at evaluating the DE C-R in the airways. The tested hypotheses were: 2. DE induces a concentration-dependent decrease in lung function measured by spirometry FEV1. 3. DE induces a concentration-dependent increase in airway responsiveness measured by methacholine PC20. 4. DE induces a concentration-dependent increase in airway inflammation measured by FeNO, differential cell counts, and inflammatory cytokines. Chapter 4 was aimed at evaluating the DE C-R for self-reported symptoms and the role of exposure perception on this response. The tested hypotheses were: 5a) An increase in DE concentration increases symptom severity determined by visual analog score (VAS) symptom questionnaire scores. 5b) Changes in symptoms are unmodified by perceived exposure condition. 5c) Changes in symptoms are correlated with airway health effects measured by FeNO, FEV1, methacholine PC20. Additionally, I would explore the effects of environmental perception on symptoms post hoc based on the hypothesis: 5d) DE symptom C-R is unmodified by perceived environmental characteristics measured by VAS questionnaire score (null hypothesis) 15 1.8 Tables Table 1.1. Controlled diesel exhaust exposure measurements summarized by exposure group and diesel engine. FA (SD) 20 (SD) 50 (SD) 150 (SD) Diesel Engine Tier 5 Tier 3 Tier 5 Tier 3 Tier 5 Tier 3 Tier 5 Tier 3 PM2.5 (µg/m3) 4 (2) 4 (4) 20 (3) 22 (3) 56 (3) 50 (4) 142 (16) 144 (30) NO (ppb) 294 (207) 317 (419) 581 (51) 1537 (928) 1030 (200) 2335 (1328) 1910 (606) 3989 (2421) NO2 (ppb) 22 (22) 21 (12) 39 (38) 87 (110) 83 (95) 126 (81) 97 (103) 144 (117) NOx (ppb) 315 (227) 337 (425) 618 (59) 1619 (995) 1109 (187) 2454 (1375) 2004 (580) 4122 (2446) CO (ppm) 3 (0) 2 (1) 2 (1) 4 (2) 3 (1) 6 (2) 3 (1) 11 (3) CO2 (ppm) 706 (22) 948 (118) 745 (61) 1754 (1513) 817 (19) 2930 (4749) 958 (92) 2438 (1013) TVOC (ppb) 176 (34) 220 (91) 254 (130) 282 (128) 577 (120) 469 (153) 1389 (718) 830 (267) UFP concentration (#/cm3) 209478 (47014) 22588 (27830) 180141 (85908) 29061 (19579) 211055 (94475) 42342 (32323) 300124 (175701) 130226 (119220) Total PNC (#/cm3) 317015 (74918) 31872 (42309) 262936 (130493) 41611 (28972) 320803 (148174) 63804 (47326) 458638 (266781) 216645 (220669) Particle size (nm) 75 (1) 64 (25) 68 (6) 75 (14) 71 (3) 74 (11) 72 (4) 85 (8) Temperature (0C) 24 (1) 24 (2) 23 (2) 24 (2) 23 (2) 25 (2) 24 (2) 24 (1) Relative humidity (%) 38 (7) 38 (5) 36 (7) 38 (8) 39 (14) 39 (8) 36 (19) 37 (8) Abbreviations: CO2 = carbon dioxide; CO = carbon monoxide; NO2 = nitrogen dioxide; NO = nitrogen oxide; NOx = nitrogen oxides; PM2.5 = particulate matter with diameter ≤2.5µm; PNC = particle number concentration; ppb = parts per billion; ppm = parts per million; SD = standard deviation; TVOC = total volatile organic compounds; UFP = ultrafine particles. ANOVA comparisons with significant differences between the diesel engines (p < 0.05) are bolded. 16 1.9 Figures Figure 1.1. Study design. Telephone and in-person screenings were completed to assess inclusion criteria. Consenting participants visited the lab over a span of 2 days for each exposure. Data was collected from participants via questionnaires, blood, methacholine challenge, spirometry, fractional exhaled nitric oxide test (FeNO), and nasal samples at different timepoints (T). Participants were subjected to a 4-hour (h) exposure to filtered air, and diesel exhaust at 20, 50, and 150 µg/m3 PM2.5 with a minimum 4-week washout period between conditions. 17 Figure 1.2. Study recruitment flow diagram. Healthy non-smokers aged 19-49 were recruited by referrals and online advertisements into this double-blinded crossover exposure study where they were exposed to filtered air and diesel exhaust at 3 concentrations (20, 50, and 150 µg/m3 PM2.5) for 4 hours, separated by a ≥4-week washout period. 18 Chapter 2 – Concentration-dependent effects of DE on blood proteins 2.1 Synopsis Traffic-related air pollution (TRAP) exposure is associated with systemic health effects, which can be studied using blood-based markers. Although we have previously shown that high TRAP concentrations alter the plasma proteome, the concentration-response relationship between blood proteins and TRAP, including low exposure concentrations, is relatively unexplored in controlled human exposure studies. To identify concentration-dependent markers of diesel exhaust (DE) using proteomics, we enrolled 15 healthy non-smokers for a double-blinded, crossover study where they were exposed to filtered air (FA) and DE at 20, 50 and 150 µg/m3 PM2.5 for 4h, separated by ≥4-week washouts. We collected blood at 24h post-exposure and used label-free mass spectrometry to quantify proteins in plasma. Proteins exhibiting a concentration-response, as determined by linear mixed effects models (LMEMs), were assessed for pathway enrichment using WebGestalt. Top candidates, identified by sparse partial least squares discriminant analysis and LMEMs, were confirmed using enzyme-linked immunoassays. Thereafter, we assessed correlations between proteins that showed a DE concentration-response and acute inflammatory endpoints, forced expiratory volume in 1 second (FEV1) and methacholine provocation concentration causing a 20% drop in FEV1 (PC20). DE exposure was associated with concentration-dependent alterations in 45 proteins, which were enriched in complement pathways. Of 9 proteins selected for confirmatory immunoassays based on complementary bioinformatic approaches to narrow targets and availability of high-quality 19 assays, complement factor I (CFI) exhibited a significant concentration-dependent decrease (-0.02µg/mL per µg/m3 of PM2.5, p = 0.04). Compared to FA at discrete concentrations, CFI trended downward at 50 (-2.14 ±1.18, p = 0.08) and significantly decreased at 150 µg/m3 PM2.5 (-2.93 ±1.18, p = 0.02). CFI levels were significantly correlated with FEV1, PC20 and nasal IL-6 and IL-1β. Summary We observed concentration-dependent alterations in the proteome, which were enriched in complement activation and regulation pathways, following controlled exposure to DE. Complement factor I, which had a statistically significant concentration-response (-0.02µg/mL per µg/m3 of PM2.5) and association with established measures of airway inflammation, is a potential marker of DE effects at concentrations relevant to occupational and community settings. This research provides insight into the systemic health effects of TRAP and may facilitate future biomonitoring policy using blood-based markers. 2.2 Introduction Air pollution is a leading environmental contributor to global mortality and morbidity1,2. In urban and occupational environments, traffic-related air pollution (TRAP) is associated with increased cardiovascular and pulmonary disease incidence and exacerbation30–32. Diesel exhaust (DE), in particular, is an important source of particulate matter (PM) that is implicated in these adverse outcomes21,24,25. Concentration-response (C-R) relationships, which are crucial for robust dose-response assessment over a broad range of exposures, have been modelled for PM and systemic cardiovascular health endpoints126,160–162, blood lipids163, and blood proteins132,133,164, largely in epidemiological studies. Research into concentration-dependent markers of the 20 pathophysiological effects of TRAP exposure is paramount in evaluating and mitigating its harms99,117. PM and gases interact with cellular and non-cellular components of the respiratory lining fluid like complement proteins and surfactants that opsonize particles for phagocytosis and may be oxidized by particle-bound oxidants48,165. For example, the prominent pro-inflammatory complement C3, whose activation is regulated by complement factor I (CFI), is associated with PM with aerodynamic diameter equal to or less than 2.5µm (PM2.5) concentration67. This indicates a crucial role of complement C3 and its regulators, such as CFI, in the interactions between air pollution and the innate immune system69,70. Collectively, these interactions disrupt immune homeostasis by dysregulating innate and adaptive immune responses and promoting excessive inflammation48. Responses instigated in the airways may spill over to the general circulation through inflammatory cytokines and immune recruitment22,48,61. Moreover, “ultrafine” PM with aerodynamic diameter equal to or less than 0.1μm can translocate into the bloodstream and directly interact with cells in systemic circulation and other organs22,60. In mechanistic investigations and response marker discovery of complex pollutant mixtures with multisystem effects, mass spectrometry coupled with other omics technologies have emerged as valuable tools75–77, through facilitating broad evaluation of thousands of proteins in the proteome78. The proteome in the blood and airway fluids has been profiled for in vitro166,167, ambient82 and occupational air pollution exposures33,168. In controlled human exposure studies, where confounding is limited through precise control of specified air pollution exposures, TRAP effects on the proteome can be studied using exposures to DE as an experimental model. We have previously demonstrated alterations in the bronchial lavage secretome following co-exposure to allergen and DE at 300 µg/m3 of PM2.543. Moreover, we have also reported changes 21 in proteins related to atherosclerosis in the plasma proteome of adults with COPD exposed to DE at 300 µg/m3 PM2.581. These previous studies detailed the effects of DE at 300 µg/m3 PM2.5 on the airway and plasma proteome in atopic and COPD populations respectively. However, C-R relationships between TRAP and the proteome remain unexplored in controlled human exposure studies at relatively low concentrations commonly encountered in urban and occupational settings117. Blood proteins that exhibit significant C-R relationships with PM, identified using proteomics, could be promising internal markers of the harmful effects of air pollution105,107. Using controlled exposure to DE at different concentrations, we have previously demonstrated concentration-dependent increases in symptoms169 and airway inflammation170. In this study, we hypothesized that DE exposure would be associated with concentration dependent changes in the plasma proteome. We also explored relationships between the top candidate protein(s) and clinically relevant airway function and inflammation measures relevant to the pathophysiological impacts of air pollution exposure, such as forced expiratory volume in 1 second (FEV1), fractional exhaled nitric oxide (FeNO), and cytokines. We highlight blood proteins that deepen current insight into the mechanisms of DE effects and provide concentration-response estimates that may facilitate biomonitoring. 2.3 Methods 2.3.1 Controlled Diesel Exhaust Exposures This double-blind crossover study (DICE NCT03234790) was approved by the Institutional Research Ethics Board (H16-03053) at the University of British Columbia and all procedures were performed in compliance with relevant laws and institutional guidelines. Following 22 informed consent, healthy non-smokers aged 19 – 49 were screened by the study physician for cardiac and respiratory disorders. Eligible participants were exposed to filtered air (FA) and DE (standardized to 20, 50, and 150 µg/m3 PM2.5) for 4 hours at the Air Pollution Exposure Laboratory159, over 4 separate occasions, with a minimum 4-week washout period between each exposure. These DE exposure levels are common in urban and occupational environments25,140. Participants completed a validated common cold questionnaire157 to assess upper respiratory infections and had a health assessment before each exposure. Exposures were postponed by at least 4 weeks from the end of a likely or confirmed illness157. Participants were asked to withhold any bronchodilating intake such as caffeine within 4 hours from the beginning of the visit. To simulate light physical activity during exposures, participants intermittently exercised using a bicycle ergometer (Ergometer DX1, Kettler Corporation, Worcestershire UK) at the power-to-weight ratio previously determined to achieve ventilation of 20 L/min/m2. Exposures were conducted at the same time of day (afternoon) for all participants and all samples were collected at the same timepoints for each visit. Study set up and exposure information are detailed in our previous publication169. 2.3.2 Blood sample collection and processing Venipuncture was conducted to collect blood at 24h after the start of each exposure. Blood was collected in 10mL serum and 6 mL EDTA Vacutainer® Hemogard tubes (BD Biosciences, San Jose, CA) for serum and plasma respectively. Blood tubes were inverted several times to mix, allowed to settle at room temperature, and centrifuged at 1300g for 10 minutes. The serum and plasma layers were extracted and stored in Propylene Microcentrifuge Tubes (VWR, PA, USA) at -80 0C. 23 2.3.3 Mass spectrometry for plasma proteomics Plasma samples were filtered and enriched using High SelectTM Top14 abundant protein depletion resin (Cat# A36372) and protein concentrations were determined by Bradford assay. The enriched plasma samples (120 µg) were digested by in-solution trypsin, desalted/cleaned using PierceTM peptide desalting spin column (Cat# 89852) and peptide concentration was determined using the Pierce™ quantitative fluorometric peptide assay. Subsequently, the samples (45 µg) were labelled with different TMT tags using the TMT10plex™ Isobaric Label Reagent Set (Cat# 90110). The labelled sample pools were fractionated using HPLC (35 min/sample) into 20 fractions and concatenated into 10 fractions for 2D MS analysis (90 min/fraction). TMT signal intensities was measured to perform relative quantification among the protein set as follows. Raw data MS files converted into MGF for each TMT run were searched for tryptic peptides against the human Uniprot protein database using X!tandem with standard Orbitrap settings. The peptide TMT intensities were integrated over a window of +-3 mDa and corrected for isotopic overlap using the batch-specific matrix supplied with the TMT label kit. Protein level quantitation required at least two unique peptides with expectation values of log(e)<=-1.5 each, yielding highly confident protein assignments of log(e)<=-3. Peptide to protein assignment was handled by X!tandem. The intensity for a protein in each TMT channel was computed as the sum of the intensities of its member peptides. To limit the influence of methodological artefacts, we adjusted for batch effects using ComBat in R (sva V.3.44.0)171, while protecting the exposure group variable and performed normalization using the trimmed mean of M values (edgeR V.3.38.1)172. 24 2.3.4 Electrochemiluminescence assays A commercially available multiplex immunoassay kit (V-PLEX Pro-inflammatory Cytokine Panel 1, Meso Scale Diagnostics) was used to measure the levels of acute response inflammatory cytokines (IL-1β, IL-6, IL-8, and TNF-α) in nasal epithelial lining fluid collected 24h post-exposure and plasma collected pre- and 24h post-exposure173. Where data were available, baseline and post-exposure data were subtracted to obtain delta values. Assay procedures were performed according to the manufacturer’s protocols with minor optimization as detailed in our previous publication170. Data were presented as natural log-transformed values. 2.3.5 Airway function and inflammation measures FEV1 and methacholine provocation concentration causing a 20% drop in FEV1 (PC20) were measured up to 24h using spirometry, according to the American Thoracic Society/European Respiratory Society guidelines155,174 as detailed in our previous publication170 and Chapter 3 (3.3) . PC20 data were natural log-transformed to limit the impact of outliers. Similarly, airway inflammation was quantified using the level of FeNO175. Baseline values were subtracted from post-exposure values to obtain deltas. 2.3.6 Statistical analyses To identify proteins that exhibited a concentration-response, we analyzed proteins that were detected in all exposure conditions and participants (complete subset) using linear mixed effects models (LMEMs) in R (nlme V.3.1-158)176 with a participant-level random intercept and adjusted for multiple comparisons with a 5% false discovery rate using q values (qvalue V.2.28.0)177. Proteins that exhibited a robust relationship between protein abundance and PM2.5 (q < 0.05) were assessed for pathway enrichment relative to the complete subset of proteins using a WEB-based GEne SeT AnaLysis Toolkit with Reactome as a functional database178. P values 25 for the pathway enrichment analysis were adjusted for multiple comparisons using the Benjamini Hochberg method179. Independently, we used a multilevel sparse partial least squares discriminant analysis (sPLSDA) on the complete subset to identify a subset of proteins that are important in distinguishing exposure groups (mixOmics 6.20.0)180. sPLSDA enables tuning and feature selection using the overall error rate and ℓ1 regularisation (LASSO), facilitating the identification of the simplest combination of features that effectively predict an outcome180,181. In our study, this would enable us to select a minimal combination of proteins that drive discrimination by exposure condition. To determine the optimal sPLSDA hyperparameters, we tuned the model using repeated cross-validation (10 × 5 folds with a 1:4 test: train split), which limits overfitting and improves confidence180, and used a multilevel design to account for repeated measures. After running the optimized multilevel sPLSDA, we used repeated cross-validation (50 × 5 folds with a 1:4 test: train split) for evaluation, thereafter summarizing model performance using area under the curve (AUC) and classification error rate (CER). AUCs were classified as follows: 0.9–1.0 = excellent; 0.8–0.9 = good; 0.7–0.8 = fair; 0.6–0.7 = poor; 0.5–0.6 = fail181. Lastly, we used Variable Importance in the Projection scores to summarize the relative contribution of individual proteins in the exposure classification181. To identify a minimal subset of putative protein markers that discriminate by DE exposure level and exhibit a PM concentration-response, we selected proteins that were indicated by both methods. This multi-pronged alternative to traditional feature selection has been used in metabolomics182,183, and would facilitate the discovery of potential concentration-dependent markers in our study. Following confirmatory assays, we assessed the concentration-responses between protein concentrations and PM2.5 using linear mixed effects models with participant ID 26 as a random intercept. Correlations between the outcomes were assessed using repeated measures correlation (rmcorr V0.5.0)184. Estimates with p values < 0.05 were considered statistically significant, while p = 0.051 – 0.1 were considered to be trending towards significance. 2.3.7 Confirmatory enzyme-linked immunosorbent assays (ELISAs) Top candidate proteins selected from mass spectrometry data analysis were further examined for independent confirmation by ELISAs. Serum and plasma obtained at 24h post-exposure were used to measure the abundance of each selected protein candidate by commercially available ELISAs according to the manufacturer’s instructions. Serum was used for examining the abundance of 8 selected proteins by ELISA, while plasma was only used for fibrinogen gamma ELISA due to limited sample availability. Abcam ELISA kits (Abcam, Boston, MA) were used for complement C2 (ab154132), complement factor B (ab137973), complement factor I (ab195460), gelsolin (ab270215), kininogen-1 (ab108875) and L-selectin (ab45917). ElabScience kits (ElabScience, Wuhan, China) were used for fibrinogen gamma chain (E-EL-H0387) and protein AMBP (E-EL-H0315), while a Cusabio (CUSABIO TECHNOLOGY LLC, Houston, TX, USA) kit was used for complement factor H-related protein 2 (CSB-EL005275HU). 2.4 Results 2.4.1 Study population and exposures Fifteen participants were able to successfully schedule all 4 exposures within the study period. Plasma obtained from 10 participants (N = 1-10) was used for the proteomics profiling of plasma 27 proteins (Table 2.1). Samples obtained from all 15 participants were included all other assays and analyses. Exposures were completed safely and successfully at the target nominal concentrations. Detailed exposure characteristics for this study are detailed in our previous publications169 and in Chapter 1 above. 2.4.2 Diesel exhaust exposure is associated with concentration-dependent alterations in the proteome We detected a total of 2345 proteins with high confidence, 743 of which were detected in all participants and exposure conditions and were therefore included in subsequent analyses. In LMEM analysis of the 743 proteins, 45 proteins exhibited a significant concentration-dependent association with PM2.5 (q < 0.05) (Table 2.2). These proteins were enriched in complement activation and regulation (Figure 2.1). Using sPLSDA and LMEMs, we identified 12 proteins as a minimal subset that captures the concentration-dependent effects of DE (Table 2.3). By visual inspection, concentration-dependent discrimination was observed along component 1 (Figure 2.2A). Repeated cross-validation CERs and AUCs for component 1 indicated that these proteins were good at discriminating FA (AUC (SD) = 0.84 (0.03), CER = 4%) and the DE150 (AUC = 0.89 (0.04), CER = 11%) exposure conditions from other conditions, but failed at distinguishing the DE20 (AUC = 0.52 (0.04), CER = 100%) and DE50 conditions (AUC = 0.36 (0.07), CER = 100%) (Figure 2.2B). This was consistent with a pairwise differential expression analysis (DEqMS 1.14.0), where all 12 proteins in the minimal subset were significantly different between FA and DE150 but no other comparisons (Appendix A). 28 2.4.3 Diesel Exhaust exposure is associated with a concentration-dependent decrease in complement factor I Nine of the 12 candidates above were selected for confirmatory ELISAs based on low LMEM q values, availability of validated ELISA kits and research budget. Except for fibrinogen gamma, all of the 9 proteins assayed had similar concentration-response trends in confirmatory assays as in plasma proteomics (Figure 2.3). CFI exhibited a significant concentration-dependent decrease (-0.02µg/mL per µg/m3 of PM2.5, p = 0.04), which when compared to FA, was not significantly different at 20 µg/m3 PM2.5 (β (SE) = -1.94 (1.18), p = 0.11), but trended downward at 50 µg/m3 PM2.5 (-2.14 (1.18), p = 0.08) and was significantly decreased at 150 µg/m3 PM2.5 (-2.93 (1.18), p = 0.02), in a discrete category analysis (Figure 2.4). Kininogen-1 had indicated a trend towards a significant decrease, but this was not significant after excluding one influential outlier in the FA group. 2.4.4 Complement factor I is correlated with airway health effects and inflammatory cytokines CFI was significantly correlated with changes in FEV1 (r = 0.35, p = 0.02), methacholine PC20 (0.32, p = 0.04), as well as nasal IL1β (-0.31, P = 0.04) and IL-6 (-0.32, p = 0.03), but not nasal IL-8 or any cytokines in plasma (Table 3). Additionally, CFI showed a trend towards positive correlation with FeNO and inverse correlation with nasal tumor necrosis factor α (TNFα) (Table 2.4). 29 2.5 Discussion Air pollution exposure induces both localized and systemic health effects that can be detected in the blood99. In this study, we utilized a proteomics approach to broadly identify proteins that show a concentration-dependent response to TRAP, using a range of DE exposures encountered in occupational and urban environments. DE induced a concentration-dependent alteration in the plasma proteome that was enriched in complement-related pathways. We confirmed CFI as a potential marker of the effects of DE exposure that is also associated with clinically relevant airway health measures. Using LMEMs, we identified 45 proteins that exhibited a significant concentration-response to DE, which were enriched in complement activation and regulation pathways. Complement proteins constitute a crucial link between the innate and adaptive immune responses by opsonizing particles, activating immune mediators, and recruiting immune cells185. Urban PM, through its reactive oxygen species, promotes complement activation and subsequent production of inflammatory cytokines, such as IL-1, IL-6 and TNFα68. Particulate air pollution has been demonstrated to increase complement C366,67 and promote complement activation68. Additionally, complement proteins promote airway mucus secretion and smooth muscle contraction especially in asthma and allergen sensitization186. Previous proteomics studies on asthmatics reported increased complement C3 in the airways and plasma in response to allergen187 and exercise188 respectively. The relationship between air pollution and the complement system has broad implications for pollution-related health effects. In addition to its role in airway hyperresponsiveness186, complement activation has been implicated in increased thrombosis and endothelial dysfunction65,189,190. Given the prominence of these pathways in 30 pollution-related pathophysiology, the proteins we identified may elucidate the cardiovascular and pulmonary health effects of air pollution. Twelve of the proteins identified using LMEMs were also selected by sPLSDA analysis as a minimal subset for discriminating FA from DE exposures, and the DE150 exposure group from the other exposure conditions. This included proteins involved in cell adhesion, such as L-selectin and protocadherin191,192, as well as regulation of inflammation, oxidative stress, and tissue repair, such as gelsolin, protein AMBP, alpha-2-HS-glycoprotein and vitamin D binding protein193–197. While the decrease in negative regulators of inflammation and oxidative stress indicates a pro-inflammatory environment, the decreases in intracellular interaction mediators L-selectin and protocadherin indicate a more complex scenario. Prothrombotic protein kininogen-1 decreased while fibrinogen gamma increased, potentially indicative of the consumption of the former in the kallikrein-kinin system that generates fibrin. The decrease in proteins that regulate the complement pathway was particularly noteworthy. Complement C2 and factor B are components of the classical and alternative pathway C3 convertases respectively, which cleave complement C3 to instigate complement activity (Figure 2.5)198. Complement factor I and H-protein 2 are negative regulators of complement activation by binding to C3b and C4b199–201. The pollution-mediated decrease in complement pathway regulators may explain the consumption of C2 and factor B, and in part, the inflammation caused by air pollution. Overall, these findings indicate that air pollution induces inflammation, thrombosis and tissue damage by dysregulating complement and coagulation proteins. Despite this consistency and insight in pathways, most of the alterations in the proteome were not consistent with confirmatory immunoassay analysis. While all but one (fibrinogen gamma) of the proteins had similar trends in the proteomics as in the confirmatory analysis, only CFI 31 exhibited a significant concentration-response relationship. This discrepancy between the proteomics and confirmatory analyses may be due to several factors. The alterations in the proteome, while robust across different statistical methods, were small in magnitude and may not have been detectable at the confirmatory stage. Additionally, confirmatory immunoassays are limited by the quality and sensitivity of antibodies, which might have affected accurate detection of proteins. Thirdly, while adjustments for multiple comparisons were made where applicable, the large number of proteins in the proteomic panel increased the likelihood of false positives. Although DE exposure did not significantly alter most proteins in our confirmatory analyses, it induced a concentration-dependent decrease in CFI that was significant at 150 µg/m3 PM2.5, when compared to FA. This indicates a consistent relationship between air pollution exposure and CFI, with a potential threshold below 150 µg/m3 PM2.5. Exposures approaching and exceeding 150 µg/m3 PM2.5 are routinely encountered in busy cities140 and occupational environments25, where CFI could be a useful internal indicator of adverse impacts. CFI inhibits complement activation by inactivating complement C3b and C4b200, and CFI deficiency is associated with chronic inflammation199. Therefore, it is possible that a pollution-mediated decrease in CFI may contribute to the increase in inflammation attributed to air pollution202. Moreover, CFI was positively correlated with lung function, but inversely correlated with pro-inflammatory cytokines and airway responsiveness. This could indicate a protective function of CFI, wherein a decrease in CFI results in more inflammation, airway hyper-responsiveness and lung function decline (Figure 2.5). This relationship with clinical health measures buttresses CFI as a potential marker of the health effects of DE exposure106,107. However, these correlations ought to be interpreted cautiously, due to their exploratory and novel nature. Moreover, identifying effects markers specific to DE has been challenging because DE is a complex 32 mixture that elicits overlapping responses with other combustion-derived pollutants105. While our study design allows for greater precision by limiting confounding exposures, the specificity of CFI as a potential marker of DE effects remains an important question. Our work indicates that CFI is a sensitive marker of DE effects across a range of exposure concentrations. Whether it distinguishes DE from other pollutants is beyond the scope of this study and will only be clarified by contrasting similar studies on other pollutants. Only a dearth of controlled human exposure studies have investigated the C-R to TRAP117. To the best of our knowledge, this is the first controlled human exposure study to investigate the C-R for DE across a broad range of exposure levels using plasma proteomics and multivariate exposure-response marker discovery analyses 117. Additionally, this study included relatively low exposure levels and a longer exposure duration than most controlled human exposure studies to date117,147, which better reflects the range of real-world exposures. However, this study’s sample size was moderate, and we included only one time point, which might have limited the power to detect changes in the proteome that were small or manifested at different time points. Moreover, changes in intermittent real-world pollution exposures and dietary intake for each participant might have influenced effects, as these factors were not controlled, but any such unusual influence would bias to null results rather than creating spurious statistical significance. This study might also have been limited by the proteomic and confirmatory assay methods used and ought to be replicated using different methods. Additionally, the target population for this study was young and relatively healthy and therefore may not be generalizable to more susceptible populations. The robustness of CFI as a potential concentration-dependent marker of DE exposure and effects ought to be validated in larger, diverse cohorts, including epidemiological studies, to determine its utility for estimating exposure and correlation with adverse health 33 effects. For example, future targeted controlled human exposure studies with similar or different methods could elucidate the robustness of our findings (inter-laboratory validity) while transitional epidemiological studies could bridge the gap between laboratory investigations and field application 107. Traffic-related air pollution poses adverse health risks that could be better assessed and mitigated using robust exposure-response markers. Using controlled diesel exhaust as a model of traffic-related air pollution exposure in this double-blind crossover study, we identified concentration-dependent alterations in the proteome that were enriched in complement pathways. Complement factor I was the most promising concentration-dependent marker that was altered below 150 µg/m3 PM2.5 and correlated with clinically relevant airway health measures. This research corroborates known systemic effects of air pollution, elucidates the concentration-response to air pollution at lower PM2.5 concentrations, and suggests potential blood-based markers for biomonitoring policy. 34 2.6 Tables Table 2.1. Participant demographics. Participant ID* Sex Age BMI FEV1 % predicted Methacholine PC20 (mg/mL) FeNO (ppb) 1 Male 35 24.6 104.2 170.0 14.4 2 Female 21 18.7 89.4 676.5 8.8 3 Female 27 22.8 98.1 16.7 18.3 4 Female 47 28.6 83.9 1.0 60.9 5 Male 44 22.0 99.0 172.9 13.3 6 Male 20 21.9 109.6 298.5 22.8 7 Male 35 29.6 98.2 443.4 21.5 8 Female 20 20.3 84.9 151.9 21.1 9 Male 25 30.1 96.4 205.5 20 10 Female 28 24.0 87.3 174.1 17.8 11 Male 29 23.2 96.6 37.1 29.9 12 Male 22 25.1 104.1 518.7 18.6 13 Female 23 25.9 108.4 159.1 12.9 14 Male 34 32.7 101.8 309.6 23.9 15 Female 21 18.6 103.8 149.3 7.8 Mean (SD) 8 M | 5 F 28.7 (8.6) 24.5 (4.2) 97.7 (8.2) 232.3 (189.5) 20.8 (12.5) Age, BMI, and lung function were measured during screening. Methacholine PC20 and FeNO, which were not measured at screening, were calculated by averaging all pre-exposure measurements. Abbreviations: BMI = body-mass index; FeNO = fractional exhaled nitric oxide; FEV1 = forced expiratory volume in 1 s; PC20 = methacholine provocation concentration causing a 20% drop in FEV1. *Participants 1-10 contributed to initial proteomics screening, while all 1-15 were included in subsequent analyses. 35 Table 2.2. Statistically significant (q ≤ 0.05) concentration-responses for proteomics data. UniProt ID Protein name Estimate (SE) q value P05156 Complement factor I -1.19E-03 (1.76E-04) 1.28E-04 P00751 Complement factor B -1.58E-03 (2.66E-04) 4.52E-04 P14151 L-selectin -1.25E-03 (2.11E-04) 4.52E-04 P06396 Gelsolin -1.08E-03 (2.05E-04) 1.78E-03 P01042 Kininogen-1 -1.34E-03 (2.73E-04) 4.11E-03 P02760 Protein AMBP -8.15E-04 (1.71E-04) 4.76E-03 P06681 Complement C2 -7.21E-04 (1.52E-04) 4.76E-03 P02679 Fibrinogen gamma chain 2.16E-03 (4.72E-04) 6.72E-03 P08603 Complement factor H -8.84E-04 (1.98E-04) 7.31E-03 P36980 Complement factor H-related protein 2 -1.17E-03 (2.60E-04) 7.31E-03 P02774 Vitamin D-binding protein -1.05E-03 (2.48E-04) 1.05E-02 P04217 Alpha-1B-glycoprotein -8.90E-04 (2.08E-04) 1.05E-02 P07225 Vitamin K-dependent protein S -8.69E-04 (2.08E-04) 1.05E-02 P07307 Asialoglycoprotein receptor 2 1.49E-03 (3.56E-04) 1.05E-02 Q92743 Serine protease HTRA1 3.41E-03 (8.16E-04) 1.05E-02 Q9NPG4 Protocadherin-12 9.38E-04 (2.29E-04) 1.22E-02 O94985 Calsyntenin-1 -9.02E-04 (2.24E-04) 1.29E-02 P02765 Alpha-2-HS-glycoprotein -9.64E-04 (2.40E-04) 1.29E-02 P15169 Carboxypeptidase N catalytic chain -7.28E-04 (1.80E-04) 1.29E-02 P05155 Plasma protease C1 inhibitor -1.13E-03 (2.90E-04) 1.65E-02 O00533 Neural cell adhesion molecule L1-like protein -1.19E-03 (3.10E-04) 1.87E-02 P00734 Prothrombin -6.76E-04 (1.78E-04) 1.99E-02 P02749 Beta-2-glycoprotein 1 -8.60E-04 (2.29E-04) 2.02E-02 P03952 Plasma kallikrein -8.73E-04 (2.32E-04) 2.02E-02 Q14956 Transmembrane glycoprotein NMB -1.32E-03 (3.50E-04) 2.02E-02 P05452 Tetranectin -8.54E-04 (2.32E-04) 2.26E-02 P13598 Intercellular adhesion molecule 2 -8.53E-04 (2.33E-04) 2.26E-02 36 Q96IY4 Carboxypeptidase B2 -1.28E-03 (3.48E-04) 2.26E-02 O75636 Ficolin-3 -1.32E-03 (3.67E-04) 2.58E-02 Q8WZ42 Titin -9.44E-04 (2.63E-04) 2.58E-02 Q9H4G4 Golgi-associated plant pathogenesis-related protein 1 -1.26E-03 (3.53E-04) 2.58E-02 P35916 Vascular endothelial growth factor receptor 3 2.04E-03 (5.74E-04) 2.71E-02 P00736 Complement C1r subcomponent -7.54E-04 (2.15E-04) 2.81E-02 P04004 Vitronectin -1.60E-03 (4.55E-04) 2.81E-02 Q14624 Inter-alpha-trypsin inhibitor heavy chain H4 -7.19E-04 (2.06E-04) 2.85E-02 P10909 Clusterin -1.03E-03 (2.99E-04) 3.15E-02 Q9UNW1 Multiple inositol polyphosphate phosphatase 1 -7.13E-04 (2.08E-04) 3.19E-02 Q9UM47 Neurogenic locus notch homolog protein 3 1.67E-03 (4.90E-04) 3.26E-02 Q9Y646 Carboxypeptidase Q 1.53E-03 (4.56E-04) 3.67E-02 P00746 Complement factor D -1.01E-03 (3.05E-04) 3.88E-02 P43652 Afamin -7.99E-04 (2.43E-04) 4.13E-02 P13987 CD59 glycoprotein -1.79E-03 (5.47E-04) 4.25E-02 P54802 Alpha-N-acetylglucosaminidase -8.96E-04 (2.77E-04) 4.62E-02 P54108 Cysteine-rich secretory protein 3 -1.31E-03 (4.08E-04) 4.92E-02 P24387 Corticotropin-releasing factor-binding protein -8.37E-04 (2.63E-04) 4.92E-02 Abbreviation: SE, standard error. Concentration-responses were estimated using linear mixed effects models with participant ID as a random intercept. 37 Table 2.3. Summary of top proteins selected by LMEMs and sPLSDA. UniProt ID Protein name LMEM sPLSDA β (SE) q value VIP Score P05156 Complement factor I -1.19E-03 (1.76E-04) 1.28E-04 16.18 P00751 Complement factor B -1.58E-03 (2.66E-04) 4.52E-04 9.41 P14151 L-selectin -1.25E-03 (2.11E-04) 4.52E-04 12.03 P06396 Gelsolin -1.08E-03 (2.05E-04) 1.78E-03 9.15 P01042 Kininogen-1 -1.34E-03 (2.73E-04) 4.11E-03 0.73 P02760 Protein AMBP -8.15E-04 (1.71E-04) 4.76E-03 5.78 P06681 Complement C2 -7.21E-04 (1.52E-04) 4.76E-03 5.73 P02679 Fibrinogen gamma chain 2.16E-03 (4.72E-04) 6.72E-03 3.03 P08603 Complement factor H-related protein 2 -1.17E-03 (2.60E-04) 7.31E-03 2.96 P02774 Vitamin D-binding protein -1.05E-03 (2.48E-04) 1.05E-02 0.84 Q9NPG4 Protocadherin-12 9.38E-04 (2.29E-04) 1.22E-02 8.87 P02765 Alpha-2-HS-glycoprotein -9.64E-04 (2.40E-04) 1.29E-02 0.21 Abbreviations: LMEM, linear mixed effects model; SE, standard error; sPLSDA, sparse partial least squares discriminant analysis; VIP = Variable importance in the projection (for component 1). 38 Table 2.4. Repeated measures correlations between complement factor I and airway health measures and acute inflammatory cytokines at 24h. Complement factor I correlates r (95% CI) p value Clinical measures FeNO % Δ24h 0.29 (-0.01, 0.54) 0.05 FEV1 % Δ24h 0.35 (0.06, 0.59) 0.02 Log PC20 Δ24h 0.32 (0.01, 0.58) 0.04 Cytokines in nasal ELF Log IL-1β 24h -0.31 (-0.55, -0.01) 0.04 Log IL-6 24h -0.32 (-0.57, -0.03) 0.03 Log IL-8 24h -0.11 (-0.39, -0.19) 0.47 Log TNFα 24h -0.26 (-0.52, 0.04) 0.08 Cytokines in plasma Log IL-1β Δ24h 0.07 (-0.24, 0.36) 0.67 Log IL-6 Δ24h -0.06 (-0.35, 0.25) 0.71 Log IL-8 Δ24h -0.05 (-0.34, 0.25) 0.74 Log TNFα Δ24h -0.04 (-0.33, 0.26) 0.79 Where data were available, post-exposure values were subtracted from pre-exposure baselines to obtain Δ24h. Repeated measures correlation coefficients were estimated while accounting for participant ID; p < 0.05 are bolded. Abbreviations: ELF = epithelial lining fluid; IL = interleukin; FeNO = fractional exhaled nitric oxide; FEV1 = forced expiratory volume in 1 second; PC20 = methacholine provocation concentration to cause a 20% drop in FEV1 = TNF, tumor necrosis factor. 39 2.7 Figures Figure 2.1. Pathway enrichment of proteins that exhibit a concentration-response. Label free mass spectrometry was used to quantify proteins in plasma (at 24h) obtained from 15 healthy non-smokers exposed to filtered air and DE standardized to 20, 50 and 150 µg/m3 PM2.5 for 4h, separated by ≥4-week washout periods. Forty-five proteins that exhibit a significant (q < 0.05) concentration-response were identified using linear mixed effects models with a participant-level intercept to account for repeated measures. Pathway enrichment was determined by over-representation analysis using WebGestalt, with Reactome set as the functional database. 40 Figure 2.2. Multilevel Sparse Partial Least Discriminant Analysis (sPLSDA) by exposure condition. Label free mass spectrometry was used to quantify proteins in plasma (at 24h) obtained from 10 healthy non-smokers exposed to filtered air and DE standardized to 20, 50 and 150 µg/m3 PM2.5 for 4h, separated by ≥4-week washout periods. sPLSDA with a multilevel adjustment for repeated measures was used on the complete proteome to identify a minimal subset of proteins that discriminate by exposure condition. Panel A presents the multilevel sPLSDA; X axis is component 1 while Y axis is component 2. Panel B presents receiver operator characteristic curves for sPLSDA component 1; X axis is 100 – Specificity (%), while Y axis is sensitivity (%). The sPLSDA analysis was tuned and assessed for model performance using repeated cross-validation. Concentration-dependent discrimination was observed along component 1 (panel A), and features were good at discriminating FA and DE150 from other conditions but failed to discriminate DE20 and DE50 from other exposure categories. 41 Figure 2.3. Diesel exhaust (DE) concentration-responses for top protein candidates. Enzyme-linked immunosorbent assays were used to quantify proteins in blood (at 24h) from 15 healthy non-smokers exposed to filtered air and DE standardized to 20, 50 and 150 µg/m3 PM2.5 for 4h, separated by ≥4-week washout periods. Y axes show protein concentration (µg/mL), while X axes show PM2.5 µg/m3. Shaded grey regions are 95% confidence intervals. Linear mixed effects models were used with a participant-level intercept to account for repeated measures: **p < 0.05, *p = 0.051 – 0.1. 42 Figure 2.4. Diesel exhaust effect on complement factor I summarized by nominal exposure condition. Enzyme-linked immunosorbent assays were used to quantify proteins in blood (at 24h) from 15 healthy non-smokers exposed to filtered air and DE standardized to 20, 50 and 150 µg/m3 PM2.5 for 4h, separated by ≥4-week washout periods. Y axis shows protein concentration (µg/mL), while X axis shows nominal exposure condition. Colored lines represent individual participants. Linear mixed effects models were used with a participant-level intercept to account for repeated measures: **p < 0.05, *p = 0.051 – 0.1. 43 Figure 2.5. Proposed pathway of diesel exhaust (DE) effects on complement factor I and adverse health effects. (1) DE induces a decrease in complement factor I. (2) Complement factor I is a negative regulator of complement activation, whose decrease causes increased complement activation. (3) Complement activation contributes to inflammation caused by air pollution exposure. (4) Inflammation promotes immune activation and tissue damage resulting in adverse health effects. In this study, DE exposure was associated with a concentration-dependent decrease in complement factor I. Complement factor I was positively correlated with forced expiratory volume in 1 second (FEV1) and methacholine concentration to cause a 20% drop in FEV1, and inversely correlated with pro-inflammatory cytokines interleukin-1β and interleukin-6. 44 Chapter 3 – Concentration-dependent effects of DE on the airways 3.1 Synopsis Air pollution exposure negatively affects the airways, and chronic exposure studies have documented concentration-dependent worsening of lung function, airway inflammation and responsiveness. However, relatively less is known about the acute concentration-dependent effects of air pollution on the airways in controlled exposure studies, where confounding factors are limited. To investigate the concentration-response (C-R) relationship between airway health measures and traffic-related air pollution (TRAP), we exposed 17 healthy smokers to filtered air (FA) and diesel exhaust (DE) standardized to 20, 50 and 150 µg/m3 of particulate matter with diameter less than 2.5µm (PM2.5), separated by a minimum 4-week washout. Lung function, airway inflammation and responsiveness were measured using spirometry, fractional exhaled nitric oxide (FeNO) and the methacholine challenge, respectively, before and at 4h and 24h from the beginning of the exposure. Additionally, nasal differential cell counts and cytokines in nasal lavage and epithelial lining fluid were used to measure nasal inflammation. We determined C-Rs and relationships between outcomes using linear mixed effects models and repeated measures correlations. DE exposure induced a concentration-dependent increase in %ΔFeNO at 4h (0.16ppb per µg/m3 of PM2.5, p = 0.01). Compared to FA, %ΔFeNO trended towards an increase at concentrations of 20 µg/m3 (β=18.66±8.76, p = 0.08) and 50 µg/m3 (β=19.33±8.92, p = 0.08) and increased significantly at 150 µg/m3 (β=34.43±8.92, p < 0.00). Additionally, DE exposure induced a trend 45 towards increased nasal IL-6 at 24h (β=1.01±1.00, p = 0.08). DE exposure had no significant effects on FeNO at 24h, airway responsiveness, lung function, or nasal cell counts. Summary We observed a concentration-dependent increase in airway inflammation measured by FeNO (0.16 ppb per µg/m3 of PM2.5) immediately following controlled DE exposure. Increasing trends at 20 µg/m3 PM2.5 indicate potential acute adverse effects at concentrations below current Canadian and U.S National Ambient Air Quality 24-hour standards of 28 and 35 µg/m3 PM2.5. Thus, our research contributes concentration-response estimates for acute airway inflammation and indicates that FeNO may be a sensitive non-invasive marker of acute inflammation at exposure levels routinely experienced in the community and occupational environments. 3.2 Introduction Air pollution exposure is a risk factor for the development and exacerbation of lung diseases1,4,203. Pulmonary disease mortality accounts for at least 40% of the estimated 6.5 million ambient and household pollution-attributed deaths globally3,204. PM, which is predominantly generated by traffic in urban environments6–8, has emerged as a prominent cause of adverse cardiopulmonary health effects203,205. C-R relationships, which describe responses across a range of exposure levels, have been modelled for chronic PM exposure and chronic obstructive pulmonary disease, lower respiratory infections, and lung cancer4,19,30,137,206,207. Moreover, C-R relationships have been studied for other pulmonary health endpoints, such as airway inflammation markers, respiratory symptoms, and lung function12,140,208–210. Typically, PM exposure is associated with a steep worsening of pulmonary outcomes at lower exposure levels, which flattens out at higher exposures19,119. While these concentration-dependent effects are 46 well-studied from chronic exposure studies, acute exposure C-Rs are relatively less well understood117. Controlled human exposure (CHE) studies minimize confounding conditions and closely monitor acute responses, thereby facilitating robust investigation into the causal and mechanistic links between air pollution and respiratory health effects. Upon inhalation, PM and gases interact with immune and structural airway cells55,203,211,212. PM2.5 enters the lower airways and the ultrafine fraction may traverse into the blood circulatory system60. In the airways, PM2.5 induce inflammation and oxidative stress49,212,213, as well as smooth muscle contraction and mucus hypersecretion48,55. The subsequent increase in airway obstruction and hyperresponsiveness (AHR) can be measured through changes in forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), specific airway resistance, and methacholine response56,210. Airway inflammation has been measured through cellular and soluble mediators214–216, and endogenous nitric oxide51. FeNO has particularly emerged as a non-invasive exposure-response marker that is also associated with lung function measures like FEV1, and FEV1: FVC86,217. In the context of DE, which is a model of TRAP28, reductions in FEV1, FEV1: FVC have been reported35,42,218–221. To date, the C-R relationship between DE and respiratory outcomes has not been investigated in controlled human exposures. We aimed to elucidate the C-R relationship between DE and airway function and inflammation using minimally invasive methods. We prioritized minimally-invasive techniques due to their feasibility in translation to clinical practice and occupational health monitoring107,153. Primarily, we investigated the C-R relationship between DE and lung function measured by FEV1, as well as AHR measured by methacholine provocation concentration to cause a 20% drop in FEV1 (PC20). We hypothesized that an increase in DE concentration would be associated with a decrease in FEV1 and PC20. Secondly, we investigated the C-R relationship between DE and 47 inflammation measured by FeNO, nasal leukocytes and cytokines. We hypothesized that an increase in DE would be associated with an increase in inflammation. Where appropriate, we explored the correlations between these measures. We reported C-R estimates for airway health measures that could be used to assess the health impact of TRAP. 3.3 Methods 3.3.1 Controlled Diesel Exhaust Exposures The DICE study (NCT03234790) and exposure setup are detailed in Chapter 1 (1.6) above. Briefly, this was a double-blind crossover study at the Air Pollution Exposure Laboratory approved by the University of British Columbia Research Ethics Board (H16-03053). After informed consent, healthy non-smokers aged 19 – 49 were screened for respiratory and cardiac abnormalities by a study physician. Participants were, in randomized order, exposed to filtered air and DE at concentrations routinely experienced in the community (20, 50, 150 µg/m3)158 for 4 hours, separated by a 4-week washout period. Before each exposure, participants confirmed that they did not have upper respiratory infection symptoms based on a standard Common Cold Questionnaire156,157. During the exposure, participants exercised on a stationary bike (Ergometer DX1, Kettler Corporation, Worcestershire UK) intermittently (15 minutes/hour) at a standardized power-to-weight ratio to achieve a ventilation of 15 L/min/m2 body surface area, which simulates light physical activity. Study endpoints are summarized in Figure 3.1. 3.3.2 Spirometry Lung function was assessed before, during and 24h post-exposure by standard spirometric testing according to American Thoracic Society (ATS) guidelines155. Airway responsiveness was assessed pre- and 24h post-exposure by methacholine PC20 using the 2-minute tidal breathing 48 method174. Briefly, participants breathed in 0.9% NaCl saline followed by methacholine (Methapharm, ON, CA) through a VixOneTM nebulizer (CAREstream medical, AZ, USA) attached to a 5L/min airflow. Spirometry was conducted between breaths to observe change in FEV1. This was repeated for doubling doses of methacholine from 0.5mg/mL either to a maximum of 128mg/mL or until a 20% drop in FEV1 (from saline FEV1) was observed. Provocative concentration of methacholine to cause a 20% drop in FEV1 (PC20) was estimated using the equations below222,223: i) Interpolated, if 20% drop in FEV1 is observed below 128mg/mL 𝑃𝐶20 = 𝑎𝑛𝑡𝑖𝑙𝑜𝑔 {𝑙𝑜𝑔𝐶1 + (𝑙𝑜𝑔𝐶2 − 𝑙𝑜𝑔𝐶1)(20 − 𝑅1)(𝑅2 − 𝑅1)} , where C1 = 2nd highest methacholine concentration, C2 = highest methacholine concentration, R1 = FEV1 drop at C1, R2 = FEV1 drop at C2 ii) Extrapolated, if 20% drop in FEV1 is not observed by 128mg/mL 223: 𝑃𝐶20 = (20𝑅1) × 128 Additionally, the methacholine dose-response slope (DRS), which is robust to PC20 extrapolation errors, was determined using the equation below224: DRS = % 𝐹𝑎𝑙𝑙 𝑖𝑛 𝐹𝐸𝑉1𝐶𝑢𝑚𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝑚𝑒𝑡ℎ𝑎𝑐ℎ𝑜𝑙𝑖𝑛𝑒 𝑑𝑜𝑠𝑒 After the methacholine challenge, participants inhaled 2 puffs of Novo-Salbutamol HFA (VCH, BC, CA) to restore lung function before the DE exposure. 49 3.3.3 FeNO FeNO was assessed using a NIOX VERO® machine (NIOX, ON, CA) according to ATS/European Respiratory Society guidelines175. Briefly, participants breathed slowly into the mouthpiece at an exhalation pressure of 10 – 20cm H2O, to maintain a fixed flow rate of 50 ±5ml/s according to the manufacturer’s instructions. Duplicate measurements were taken pre-exposure, 4h and 24h from the beginning of the exposure. 3.3.4 Nasal lavage and differential cell counts Nasal lavage, a minimally-invasive tool for determining effects of inhaled pollutants in the upper airways225,226, was used to assess cellular airway effects. At 24h from the start of the exposure, participants sprayed 8mL of room temperature saline (0.9% NaCl) into their nostrils alternately and drained the nasal lavage fluid (NLF) into a sterile cup. NLF was mixed using a pipet, sieved through a 40 µm cell strainer (VWR, PA, USA), and centrifuged at 350g for 10 minutes. Supernatant was aliquoted and the cell pellet was resuspended in 100 µl room temperature RPMI 1640 w/out L-Glutamine (ThermoFisher Scientific, MA, USA) for counting on a hematocytometer. Approximately 100,000 cells from the sample were pipetted into the hole puncture of a white filter sheet pre-wet using room temperature RPMI 1640 w/out L-Glutamine (VWR, PA, USA) and secured with a cytospin holder and ring. Slides were centrifuged at 140g for 5 minutes and air-dried for at least 20 minutes. Staining was done using a Hemacolor® stain set (Sigma-Aldrich, ON, CA) according to manufacturer’s instructions adapted from Wright’s staining procedure227. Briefly, cells were fixed using methanol, air-dried, and stained with eosin and methylene blue. Two independent technicians that were blinded to exposure information manually counted the slides to a total of 500 cells when possible. When initial counts were not within acceptable 95% confidence intervals adjusted for total count228,229, another count was 50 performed by a third technician. Differential cell counts were expressed as percentage of specific leukocytes per total leukocyte count. 3.3.5 Nasal epithelial lining fluid After draining the nasal lavage fluid, pre-weighed Leukosorb strips were inserted into each nostril and clamped with a nose clip for 2 minutes. The filter strips were then transferred into 1.5mL microcentrifuge tubes and stored at -800C. Nasal epithelial lining fluid (ELF) was eluted according to the procedure described by Rebuli et al.,230. Briefly, the strips were placed into 500µL tubes pierced at the bottom with an 18-gauge needle, which were transferred into 1.5mL microcentrifuge tubes. An elution solution (100µL of 1% BSA+0.05% Triton X-100) was pipetted onto the strips, which were thereafter centrifuged at 13,523g for 2 minutes. The 500µL tubes were transferred to new 1.5mL microcentrifuge tubes and centrifuged again as before. The eluate from the two 1.5mL microcentrifuge tubes was combined and immediately used for multiplex assays. 3.3.6 Electrochemiluminescence assays A commercially available multiplex immunoassay kit (V-PLEX Pro-inflammatory Cytokine Panel 1, Meso Scale Discovery (MSD)) was used to measure the levels (pg/mL) of inflammatory cytokines (interferon-γ, interleukin (IL) 10, IL-12p70, IL-13, IL-1β, IL-2, IL-4, IL-6, IL-8, and TNF-α) in nasal epithelial lining fluid173. Assay procedures were performed according to the manufacturer’s protocols with minor optimization. Briefly, pre-coated assay plates were washed with 3 times with 300µL wash buffer (0.05% TWEEN in PBS). 25 µL of each sample was pipetted singly into 25µL MSD Diluent 2 (2-fold dilution) and incubated overnight at 2 – 8 0C. On the following day, plates were washed and incubated with 25µL of detection antibody in MSD Diluent 3 at room temperature. After a final wash step, 2X MSD read buffer was added 51 and the plates were read immediately and analyzed using an MSD instrument (Meso® Quick Plex SQ 120, Model 1300) and Workbench® software. For samples below the lower limit of detection (LLOD), a value half of the LLOD was imputed based on the assay-specific LLOD. 3.3.7 Statistical analysis All data were manually double-entered into Excel spreadsheets or REDCAP 10.4.0 (© 2021 Vanderbilt University) and checked for accuracy using R Studio version 4.0.3 comparison tool (Arsenal package V.3.6.2) and in-built REDCAP tools, respectively. Where pre-exposure data were recorded, the change (Δ) or percentage change at subsequent timepoints was determined. Statistical diagnoses were conducted to assess model assumptions, identify best fits, and justify transformations. When appropriate to achieve better model suitability (typically by limiting the influence of outliers), log-transformed values were used. Effects of the exposures on outcomes were analyzed using linear mixed effects (LME) models (nlme package V.3.1-153) in R studio (V.4.1.2), with a participant-specific intercept to adjust for repeated measures. To estimate the C-R, model 1) of outcomes and PM2.5 was fitted, while model 2) of outcomes and exposure groups was fitted to identify potential effects thresholds. 1) 𝑌𝑖,𝑗 = 𝛽1(𝑃𝑀2.5) + 𝛽0 + µ𝑖 + 𝜀𝑖,𝑗 2) 𝑌𝑖,𝑗 = 𝛽1(𝑒𝑥𝑝𝑜𝑠𝑢𝑟𝑒 𝑔𝑟𝑜𝑢𝑝) + 𝛽0 + µ𝑖 + 𝜀𝑖,𝑗 where i = ith individual, j = jth repeated measurement, β1 = slope, β0 = overall intercept, µ = participant intercept, ε = error term. Correlations between outcomes were determined using repeated measures correlations (rmcorr package V.0.4.5) in R studio. P values from all statistical tests were adjusted for multiple comparisons using the Bejamini Hochberg method (5% false discovery rate). P values ≤ 0.05 52 were considered statistically significant, while values between 0.05 – 0.1 were “marginally significant trends”. 3.4 Results 3.4.1 Study population Nominal exposure targets were safely and successfully achieved. Of the 20 participants enrolled in the study, 15 participants completed all and 2 completed 75% of the visits (missing one in each of DE50 and DE150); these were included in the spirometry, FeNO and nasal cell count outcomes (Table 3.1). For the inflammatory cytokine assays, only the 15 that completed all visits were included (N1 – 15, Table 3.1) 3.4.2 Exposure characteristics Exposure data are presented by nominal PM2.5 exposure group and diesel engine in Table 1.1. 3.4.3 DE induces a concentration-dependent increase in FeNO PM2.5 concentration was positively associated with percentage change in FeNO at 4h from the start of the exposure (β = 0.16 ± 0.06, p = 0.01) (Figure 3.2). Compared to FA, this increase trended upwards at 20 (18.57 ± 8.76, p = 0.08) and 50 (19.33 ± 8.92, p = 0.08), and was significant at 150 (34.43 ± 8.92, p < 0.00) µg/m3 PM2.5. However, there was no significant effect at 24h. 3.4.4 DE induces a trend towards a concentration-dependent increase in IL-6 All cytokines increased with PM2.5, but only IL-6 trended towards a statistically significant increase (β = 1.01±1.00, p = 0.08) (Table 3.2). Trends towards significant positive associations observed for IL-12 and IL-2 were not robust to multiple comparisons adjustment. 53 Of the type 2 inflammatory cytokines IL-4 and IL-13, which are typically associated with FeNO54, only IL-4 was positively correlated with FeNO increase, but this effect was no longer significant after multiple comparisons adjustment (Table 3.3). 3.4.5 DE had no significant effect on lung function DE did not significantly affect FEV1 at any of the timepoints or FEV1 area under the curve (Figure 3.3), which captures the trend across all time points. This was consistent across other measures of lung function such as FVC, FEV1: FVC, peak expiratory flow and forced expiratory flow between 25% and 75% of effort (data not shown). 3.4.6 DE had no significant effect on airway hyper-responsiveness Data were log transformed for analysis. While DE decreased the methacholine PC20, this effect was not statistically significant. Similarly, there was no significant change in the methacholine dose-response slope (Figure 3.4). 3.4.7 DE had no significant effect on nasal leukocyte counts The average total cell and leukocyte counts were 395±175 and 239±177 respectively. Sixty eight percent of the slides contained >50% leukocytes, and only 65% contained >100 leukocytes per mL. Of all the cell types, only lymphocytes trended towards a C-R increase (Table 3.4), but this effect was not robust to multiple comparisons and lymphocyte counts were very low overall (98% <10 cells/mL). None of the cell populations was correlated with FeNO (Table 3.5). 3.5 Discussion TRAP exposure induces various deleterious effects on the airways that may be elucidated by robust C-R data. In this controlled exposure study, we investigated the C-R between DE and lung function and inflammation using minimally invasive procedures. We reported concentration-54 dependent increases in FeNO and nasal IL-6, but lung function, airway hyperresponsiveness and nasal cells were not significantly altered. These findings of concentration-dependent effects on FeNO, starting at relatively low nominal exposure levels (20 µg/m3 PM2.5), suggest adverse effects at levels below current 24h PM2.5 exposure guidelines and FeNO as a reliable tool to assess these effects. Air pollutants interact with airways cells through oxidative stress, aryl hydrocarbon receptors and toll-like receptors to induce inflammation 48,231. Interestingly, we did not observe a significant effect on cell proportions in the nasal epithelium. This was similar to a 300 µg/m3 PM2.5 exposure study by Rudell et al., (1999) 232, but contrasts with observations by others, who predominantly sampled the conducting and lower airways 35,46,233. Notably, changes in cell proportions have been more commonly reported in the context of co-exposures 46,234. Thus, discrepancies could reflect differences in study designs and sampling. Even in the absence of a cellular response, we observed a trend towards a concentration-dependent increase in nasal IL-6. This is consistent with alterations in soluble mediators of airway inflammation reported by other CHE studies, which predominantly sampled the conducting and lower airways35,41,233. The IL-6 trend in this study, which corroborates the DE-induced acute inflammation reported by Behndig et al.,233 at 100 µg/m3 PM10 (2011) and elucidates the biological gradient of this response in the upper airways, may be better demonstrated with a larger sample size. Like cell proportions, other nasal cytokines that were unaltered in this study appear to be induced following co-exposure with allergen in atopic participants235,236. Airway inflammation activates inducible nitric oxide synthase, thereby promoting endogenous nitric oxide generation detectable in exhaled breath as FeNO51,237–240. The utility of FeNO in 55 monitoring airway inflammation from respiratory diseases is well established238, but relatively less is known about its robustness in monitoring the effects of air pollution. In this study, we identified a trend towards an increase in FeNO at low nominal PM2.5 concentrations (20 & 50 µg/m3) and a significant increase at 150 µg/m3 – DE exposure levels lower than that previously reported by Barath et al.241. Notably, with the exception of particle-depletion studies, our study included the lowest target PM2.5 concentration (20 µg/m3) for controlled exposure to DE to date28, including those investigating C-R117. Interestingly, the association between FeNO and type 2 inflammation, which has been predominantly demonstrated in the context of asthmatics51, was absent in our non-asthmatic population. This could reflect differences in the pathophysiological pathways underlying the TRAP-induced FeNO response by asthma status that warrant further study. We did not observe any significant effects of DE exposure on lung function and airway responsiveness, which contrasts observations by Xu et al., 2013226. However, most CHE studies reporting lung function effects involved asthmatics221,242, co-exposures with ozone220 or allergen42,218, or different CHE exposure models35. Like our study, some CHE exposure studies reported no effects with healthy63,97,241,243,244, asthmatics242, and different CHE exposure models94,245. Changes in airway hyperresponsiveness have been reported in asthmatics242,246 and atopic participants42,47, but not healthy participants243. Consistent with those studies, the single-source pollutant acute TRAP exposure model with short exposure duration did not significantly affect the lung function or airway responsiveness of the healthy population in this study. Although the exposure duration of this study transcends other controlled human exposures to DE, we were still inevitably unable to precisely recapitulate the duration and multi-source nature of real-world exposures, and we reported effects at the predetermined timepoints within 24 56 hours. Interestingly, our observations were consistent with a semi-experimental 5-hour exposure study by Janssen et al., where environmental pollution was associated with an increase in FeNO and nasal IL-6, but not lung function247. As we focussed on minimally-invasive measures, our study did not capture compartmentalized effects in the lower airways best explored by bronchoscopy216. Moreover, although our nasal epithelial lining fluid method is a reliable nasal sampling approach for soluble mediators230, cytokine levels may have been affected by the preceding nasal lavage procedure. Lastly, we recruited a limited sample of healthy participants that may not be generalizable to susceptible populations. Future studies ought to replicate these novel findings at low exposure levels and investigate how susceptibility factors like respiratory and metabolic disease modify the C-R. The adverse effects of air pollution on the airways are a significant public health challenge that is projected to worsen with urbanization and increasing traffic in developing parts of the world. Thus, identifying robust markers of traffic-related air pollution remains a crucial way to understand, assess and mitigate pollution effects. Using minimally invasive methods, we identified and estimated significant concentration-response relationships between acute DE PM2.5 exposure and FeNO across a broad range of exposure concentrations. We reported respiratory effects at exposure levels lower those previously reported in controlled human studies to date, and relevant to the current Canadian and U.S National Ambient Air Quality 24-hour standards of 28 and 35 µg/m3 PM2.5. These findings provide evidence for improved exposure guidelines and the use of FeNO as a tool for health monitoring. 57 Copyright information Reprinted with permission of the American Thoracic Society. Copyright © 2023 American Thoracic Society. All rights reserved. Citation: Orach J, Rider CF, Yuen ACY, Schwartz C, Mookherjee N, Carlsten C (2023). Controlled Diesel Exhaust Exposure Induces a Concentration-Dependent Increase in Airway Inflammation. Annals of the American Thoracic Society A1776-A1776. doi:10.1513/AnnalsATS.202209-762OC The Annals of the American Thoracic Society is an official journal of the American Thoracic Society. 58 3.6 Tables Table 3.1. Participant demographics for airway analysis. ID number Sex Age BMI FEV1 % predicted Methacholine PC20 FeNO 1 Male 35 24.58 104.21 169.95 14.38 2 Female 21 18.71 89.38 676.52 8.75 3 Female 27 22.82 98.11 16.6 18.25 4 Female 47 28.6 83.85 1.06 60.88 5 Male 44 22.03 99.03 172.92 13.25 6 Male 20 21.86 109.62 298.5 22.75 7 Male 35 29.56 98.22 443.42 21.5 8 Female 20 20.25 84.92 151.92 21.12 9 Male 29 23.18 96.59 37.13 29.88 10 Male 25 30.14 96.36 205.54 20 11 Female 28 23.95 87.32 174.12 17.75 12 Male 22 25.11 104.13 518.74 18.62 13 Female 23 25.92 108.44 159.05 12.88 14 Male 34 32.72 101.75 309.58 23.88 15 Female 21 18.56 103.83 149.32 7.75 16 Male 28 30.89 106.99 130.76 58.33 17 Female 20 21.67 108.28 79.63 26 Mean (SD) N/A 28.18 (8.34) 24.74 (4.31) 98.88 (8.32) 217.34 (182.42) 23.29 (14.85) Abbreviations: BMI = Body-mass index; FEV1 = forced expiratory volume in 1 second; PC20 = methacholine provocation concentration causing a 20% drop in FEV1; FeNO = fractional exhaled nitric oxide. Age, BMI and FEV1 predicted were recorded during study screening. Baseline methacholine PC20 and FeNO, which were not recorded at screening, were calculated by averaging pre-exposure values for all the exposures. 59 Table 3.2. Concentration-responses for cytokines in nasal epithelial lining fluid at 24h. Cytokine β±std error Adjusted p value IFN-γ 1.00±1.00 0.50 IL-1ß 1.00±1.00 0.78 IL-2 1.00±1.00 0.44 IL-4 1.00±1.00 0.64 IL-6 1.01±1.00 0.08 IL-8 1.00±1.00 0.78 IL-10 1.00±1.00 0.50 IL-12p70 1.00±1.00 0.64 IL-13 1.00±1.00 0.50 TNF-α 1.00±1.00 0.56 Abbreviations: IFN = interferon; IL = interleukin; TNF = tumor necrosis factor. Concentration-responses were estimated using linear mixed effects models, with participant ID as a random effect. Cytokine concentrations (pg/mL) were log-transformed for analysis but are presented as back log transformed values. 60 Table 3.3. Repeated measures correlations between percentage change in fractional exhaled nitric oxide (FeNO) and type 2 inflammatory cytokines in nasal epithelial lining fluid. Cytokines Correlation coefficient±std error with FeNO at 4h Adjusted p value IL-4 0.33±0.14 0.11 IL-13 0.24±0.14 0.21 with FeNO at 24h IL-4 0.19±0.15 0.27 IL-13 0.08±0.15 0.58 Abbreviation: IL = interleukin. Coefficients were estimated using repeated measures correlation accounting for participant ID. Percentage change in FeNO was correlated with log-transformed cytokine concentrations (pg/mL). 61 Table 3.4. Concentration-responses for differential cell counts from nasal lavage at 24h. Cell type (as percentage of leukocytes) β±std error Adjusted p value All leukocytes a 0.05±0.06 0.89 Eosinophils -0.01±0.01 0.89 Lymphocytes 0.02±0.01 0.35 Macrophages/monocytes 0.01±0.05 0.90 Neutrophils -0.01±0.04 0.90 Concentration-responses were estimated using linear mixed effects models with participant ID as a random effect. Cell counts were expressed as a percentage of total leukocytes and regressed with PM2.5. a Percentage of leukocytes in total cell count, a measure of the leukocyte: epithelial cell ratio. 62 Table 3.5. Repeated measures correlations between percentage change in fractional exhaled nitric oxide (FeNO) and cell proportions in nasal epithelial lining fluid. Cell type (as percentage of leukocytes) Correlation coefficient±Std error with FeNO at 4h Adjusted p value All leukocytes a 0.07±0.15 1.00 Eosinophils -0.15±0.15 1.00 Lymphocytes 0.02±0.15 1.00 Macrophages/monocytes 0.13±0.15 1.00 Neutrophils -0.10±0.15 1.00 with FeNO at 24h All leukocytes a 0.24±0.14 1.00 Eosinophils -0.05±0.15 1.00 Lymphocytes -0.03±0.15 1.00 Macrophages/monocytes 0.08±0.15 1.00 Neutrophils 0.19±0.15 1.00 a Percentage of leukocytes in total cell count, a measure of leukocyte: epithelial cell ratio. 63 3.7 Figures Figure 3.1. Outline of study design and outcomes (Chapter 3). We conducted a double-blind crossover study where participants were exposed to filtered air (FA) and diesel exhaust (DE) diluted to 3 nominal concentrations (20, 50, 150 µg/m3 PM2.5). Exposures lasted 4h and were separated by a washout period of at least 4 weeks. Airway health outcomes were measured over a 24h period. 64 Figure 3.2. Diesel exhaust (DE) effect on fractional exhaled nitric oxide (FeNO) summarized by concentration-response (C-R) and nominal exposure category. FeNO data were collected from participants before, during and up to 24h after exposure to filtered air (FA) and DE standardized to 20, 50 and 150 µg/m3 PM2.5. Panel A shows the C-R for FeNO; the Y-axis shows percentage change in FeNO from baseline at 4h (left) and 24h (right), while the X-axis shows PM2.5. The dashed line represents 0 (no change from baseline) and grey zones indicate 95% confidence intervals. Panel B shows the DE effect on FeNO summarized by nominal exposure category; the Y-axis shows percentage change in FeNO from baseline at 4h (left) and 24h (right), while the X-axis shows nominal exposure category. Statistical analysis was conducted using linear mixed effects models, with participant ID as a random effect: **p < 0.05, *p = 0.05 – 0.1. 65 Figure 3.3. Concentration-response of diesel exhaust (DE) and forced expiratory volume in 1 second (FEV1). Spirometry data were collected from participants before, during and up to 24h after exposure to filtered air (FA) and DE standardized to 20, 50 and 150 µg/m3 PM2.5. The Y-axis shows percentage change in FEV1 from baseline or FEV1 area under the curve (AUC), while the X-axis shows PM2.5 values. The dashed line represents 0 (no change from baseline) and grey zones indicate 95% confidence intervals. Statistical analysis was conducted using linear mixed effects models, with participant ID as a random effect. 66 Figure 3.4. Concentration-response for methacholine provocation concentration causing a 20% drop in FEV1 (PC20) and dose-response slope (DRS). Spirometry data were collected from participants before and 24h after exposure to filtered air (FA) and diesel exhaust (DE) standardized to 20, 50 and 150 µg/m3 PM2.5. The Y-axis shows change (delta) in log PC20 (left) and DRS (right) from baseline, while the X-axis shows PM2.5 values. The dashed line represents 0 (no change from baseline) and grey zones indicate 95% confidence intervals. Statistical analysis was conducted using linear mixed effects models, with participant ID as a random effect. 67 Chapter 4 - Concentration-dependent effects of DE on symptoms 4.1 Synopsis Traffic-related air pollution (TRAP) exposure adversely affects wellbeing and quality of life. These effects can be studied safely and non-invasively using self-reported symptoms. However, little is known about the concentration-response (C-R) relationship between TRAP and symptoms, especially in controlled exposure settings where confounders are limited. Moreover, how environmental perception affects the C-R for symptoms is largely unknown. Using controlled exposures to filtered air (FA) and diesel exhaust (DE) standardized to 20, 50 and 150 µg/m3 particulate matter with diameter less than less than 2.5 µm (PM2.5) separated by a minimum 4-week washout, we investigated symptoms in 17 healthy non-smokers immediately before and at 4h and 24h from the beginning of the exposure. We measured visual analog scale (VAS) score responses grouped into chest, constitutional, eye, neurological and nasal clusters. After subtracting scores from pre-exposure values, we utilized linear mixed effects models to investigate the relationship between change in symptoms and PM2.5 and its interaction with environmental perception. We also measured the correlation between symptoms and airway function and inflammation. While all symptom clusters trended upwards at 4h, DE exposure induced a statistically significant concentration-dependent increase in total symptoms at 4h (β±standard error = 0.05 ± 0.03, p = 0.04), driven mainly by constitutional (0.01 ± 0.01, p = 0.03) and eye (0.02 ± 0.01, p = 0.05) clusters, that was correlated with airway inflammation. Perceived increasing temperature attenuated symptoms in both clusters, while noise and anxiety attenuated and enhanced eye and constitutional symptoms respectively. Compared to FA, symptoms were significantly increased 68 at 150 µg/m3 PM2.5 for the total (8.45 ± 3.92, p = 0.04) and eye (3.18 ± 1.55, p = 0.05) clusters, with increasing trends in the constitutional (1.49 ± 0.86, p = 0.09) and nasal (1.71 ± 0.96, p = 0.08) clusters. Summary Using controlled human exposure to a range of DE concentrations, we observed a concentration-dependent increase in total symptoms (0.05cm per µg/m3 of PM2.5), primarily due to eye and constitutional symptoms, that was modified by perceived anxiety, noise, and temperature, and correlated with airway inflammation. This is consistent with inflammatory and sensory effects of TRAP with a threshold below 150 µg/m3 PM2.5. Our research elucidates the TRAP concentration-response at exposure levels routinely encountered in the community, and highlights the utility of VAS as a tool for monitoring health symptoms. 4.2 Introduction TRAP is a risk factor for morbidity1,248,249 and adversely affects subjective wellbeing and quality of life85. Self-assessed symptoms questionnaires are a useful non-invasive250 way to monitor these health effects13,83,84. Symptoms are also robust indicators of the pollution-associated exacerbation of cardiopulmonary diseases13,128,251,252 and are correlated with other clinical health measures, such as fractional exhaled nitric oxide (FeNO)86 and forced expiratory volume in 1 second (FEV1)87,88. DE comprises gases and PM that induce oxidative stress, inflammation, and sensory nerve activation upon contact with mucosal surfaces248. PM with aerodynamic diameter less than 2.5µm (PM2.5) may traverse alveolar epithelia into the blood stream to instigate systemic responses, in addition to spill over effects from local inflammation248. Additionally, 69 psychological factors like exposure perception have implicated98. Thus, typically reported symptoms include irritation of mucosal surfaces like the eyes, respiratory and constitutional sickness94,140,253–255. Concentration-response (C-R) relationships are crucial in establishing robust links between cause and effect, and are useful in evaluating and mitigating the impact of air pollution117. There is growing evidence for a C-R relationship between self-assessed symptoms and air pollution in epidemiological research128,140,252,256. In controlled human exposure studies, where confounding is limited and air pollution exposure is closely monitored147, increases in symptoms after diesel exhaust (DE) and biofuel have been reported93–97. However, the C-R relationship between DE (as a model of TRAP) and symptoms in a controlled human exposure setting and the role of psychological factors like perceived exposure in this relationship are unexplored117. In this study, we investigated the C-R for self-assessed symptoms after controlled human exposure. We hypothesized that an increase in DE concentration would induce an increase in symptoms. Additionally, we explored the role of exposure perception in this relationship, and the correlation between symptoms and airway function. We report concentration-dependent effects of TRAP on symptoms and relationships with airway function that could inform efforts to non-invasively monitor the health impacts of air pollution. 4.3 Methods 4.3.1 Controlled Diesel Exhaust Exposures The DICE study (NCT03234790) and exposure setup are detailed in Chapter 1 (1.6) above. Briefly, this was a double-blind crossover study at the Air Pollution Exposure Laboratory approved by the University of British Columbia Research Ethics Board (H16-03053). Healthy 70 non-smokers aged 19 – 49 were exposed to filtered air and DE at concentrations routinely experienced in the community (20, 50, 150 µg/m3 PM2.5) for 4 hours, separated by a 4-week washout period158. Participants confirmed that they did not have upper respiratory infection symptoms based on a standard Common Cold Questionnaire before the exposure156,157. 4.3.2 Questionnaires Symptoms typically associated with air pollution exposure in the literature97,98,250 were evaluated by the participants using a VAS questionnaire257,258 pre-exposure, 4h and 24h post-exposure (Figure 4.1). To assess exposure perception, participants responded to a VAS questionnaire about the environment in the exposure booth and were asked if they thought their exposure was to FA or DE. 4.3.3 Spirometry Spirometry procedures are detailed in Chapter 3 (3.3.2). Briefly, lung function was measured before, and at 4h and 24h from the beginning of the exposure according to European Respiratory Society/ American Thoracic Society guidelines155. The methacholine response was used to measure airway responsiveness before and at 24h from the beginning of the exposure using the 2-min tidal breathing technique174, and methacholine concentration to cause a 20% drop in FEV1 (PC20) was estimated using the appropriate equations222,223. Lung function was restored thereafter using Novo-Salbutamol HFA (TEVA, ON, CA). Participants exercised on a stationary bike (Ergometer DX1, Kettler Corporation, Worcestershire UK) intermittently during the exposure (15 minutes/hour) at a standardized power-to-weight ratio to achieve a ventilation of 15 L/min/m2 body surface area, which simulates light physical activity. 71 4.3.4 FeNO FeNO before, and at 4h and 24h from the beginning of the exposure was measured using a NIOX VERO® (NIOX, ON, CA) machine according to European Respiratory Society guidelines/American Thoracic Society175 as detailed in Chapter 3 (3.3.3). 4.3.5 Statistical analyses VAS distances were manually measured by at least 2 independent technicians, entered into REDCAP 10.4.0 (© 2021 Vanderbilt University) and checked for consistency and accuracy using in-built REDCAP tools. Symptom scores at baseline were subtracted from 4h and 24h scores to obtain delta values. To limit the penalty of multiple comparisons, symptoms were analyzed at the cluster level (summarized in Table 4.1) similar to Carlsten et al., (2013)98. Linear mixed effects models with a participant-specific intercept to adjust for repeated measures were used to assess the effects of diesel exhaust exposure on symptoms (package nlme V.3.1-157). To estimate the C-R, model 1) of delta values and PM2.5 was fitted, while model 2) of delta values and exposure groups was fitted to identify potential effects thresholds. To evaluate the effect of perception on symptoms from PM2.5 exposure, a model 3) was fit with participant’s environmental/exposure perception as a modifier of the relationship between symptoms and PM2.5. 1) 𝑌𝑖,𝑗 = 𝛽1(𝑃𝑀2.5) + 𝛽0 + µ𝑖 + 𝜀𝑖,𝑗 2) 𝑌𝑖,𝑗 = 𝛽1(𝑒𝑥𝑝𝑜𝑠𝑢𝑟𝑒 𝑔𝑟𝑜𝑢𝑝) + 𝛽0 + µ𝑖 + 𝜀𝑖,𝑗 3) 𝑌𝑖,𝑗 = 𝛽1(𝑃𝑀2.5) ∗ 𝑒𝑥𝑝𝑜𝑠𝑢𝑟𝑒 𝑝𝑒𝑟𝑐𝑒𝑝𝑡𝑖𝑜𝑛 + 𝛽0 + µ𝑖 + 𝜀𝑖,𝑗 where i = ith individual, j = jth repeated measurement, β1 = slope, β0 = overall intercept, µ = participant intercept, ε = error term. 72 Model assumptions were checked and where appropriate, data were log-transformed to limit the influence of outliers. All statistical analysis was performed using R version 4.2.0. Repeated measures correlations were used to investigate correlations between outcomes. P values ≤ 0.05 were considered statistically significant, while p values 0.05 – 0.1 were considered to be “trending towards significance”. 4.4 Results 4.4.1 Study population Of the 20 participants enrolled in the study, 15 completed all symptom questionnaires and were included in the analysis. Additionally, 2 participants who completed all but one of the exposures (one in each of DE50 and DE150) were included in the analysis to a total of 17 participants (Table 4.2). Three participants withdrew from the study due to scheduling complaints and were excluded. 4.4.2 Exposure characteristics Exposure data are presented by nominal PM2.5 exposure group and diesel engine in Table 1.1. While there were some differences between the engines across some measures, engine type did not significantly modify symptom responses (Table 4.3). 4.4.3 DE induced a concentration-dependent increase in total, eye, and constitutional symptoms While all symptom clusters trended upwards at 4h, DE induced a statistically significant concentration-dependent increase in total symptoms (β ± standard error = 0.05 ± 0.03, p = 0.04), driven mainly by the constitutional (0.01 ± 0.01, p = 0.03) and eye (0.02 ± 0.01, p = 0.05) 73 symptom clusters (Figure 4.2). Of the underlying questions, participants primarily reported itching and stinging in the eyes (p = 0.03) and itchiness or dryness of the skin (p = 0.06). When this analysis was redone with only the 15 participants that completed all four exposures, the results were largely unchanged except that eye symptoms were no longer significantly changed (P>0.1) and hence total symptoms became marginally significant (p = 0.05 – 0.1). However, this analysis was adversely affected by outliers in the smaller subset. Compared to FA, DE at 150 µg/m3 induced an increase in total (8.45 ± 7.69, p = 0.04) and eye (3.18 ± 3.03, p = 0.05) symptoms, and trends in the nose (1.71 ± 1.88, p = 0.08) and constitutional (1.49 ± 1.68, p = 0.09) symptoms (Figure 4.3). These effects were absent at 24 hours. 4.4.4 The symptom concentration-response was modified by environmental perception Increasing noise (-0.07 ± 0.03, p = 0.01) and temperature (-0.06 ± 0.03, p = 0.02) perception attenuated total symptoms, driven by effects in both the eye (noise effect = -0.02 ± 0.01, p = 0.02; temperature effect = -0.03 ± 0.01, p = 0.01) and constitutional categories (noise effect = -0.01 ± 0.01, p = 0.03) (Table 4.4). Constitutional symptoms were enhanced by anxiety (0.01 ± 0.00, p = 0.03). The concentration-response in symptoms above was not modified by whether participants thought they were exposed to DE or FA (Figure 4.4). 74 4.4.5 Symptoms were correlated with airway inflammation Total symptoms were moderately positively correlated with ΔFeNO at 4h (r = 0.29 ± 0.13, p = 0.04) and 24h (r = 0.39 ± 0.12, p < 0.00), driven by effects in the constitutional and eye categories (Table 4.5). Symptoms were not correlated with methacholine PC20 and FEV1. 4.5 Discussion Exposure to air pollution is associated with adverse health effects that can be assessed using symptoms13,83,84. In this study, we investigated and identified concentration-dependent increases primarily in eye and constitutional symptoms that were modified by environmental perception. PM and gases routinely interact with exposed surfaces of the body such as the skin89,90 and eyes91, where they induce inflammation. In the eyes, these pollutants can cause oxidative stress259, disrupt mucin, and induce loss of microvilli, corneal and goblet cells260–262. The resulting destruction of epithelial integrity and tear film precipitates dryness and irritation91,263. In the airways, PM2.5 induces inflammation and oxidative stress that may involve systemic immune mobilization60. This “spill over”, in addition to the penetration of ultrafine particulate matter into the blood stream60 may be responsible for constitutional, neurological and systemic symptoms due to air pollution exposure93. The correlation between symptoms and airway inflammation in our study indicates inflammation as a potential pathophysiological pathway. However, symptoms were unaccompanied by effects on lung function, similar to others94,226 assessing similar endpoints. Indeed, the lung function of healthy populations is relatively resilient to acute DE exposure25,28,146. We observed an upward trend in all symptom clusters at 4h from the beginning of the exposure, contributing to a statistically significant increase in total symptoms, that was driven mainly by 75 eye and constitutional symptoms. Our findings corroborate DE-induced eye and constitutional symptoms reported in other controlled exposure studies93,95–97,232. We did not observe any significant neurological or airway symptoms reported by these studies and others226. In contrast, other studies including rhinitis264 and metabolic syndrome98 cohorts did not report effects on symptoms. Carlsten et al., (2013) also reported a prominent role of perceived exposure condition (DE or FA) in symptoms after DE98, although not as an effect modifier. In this study, perceived higher temperatures and noise levels attenuated the increase in total, eye and constitutional symptoms, while anxiety enhanced constitutional symptoms. Current knowledge on the relationships between air pollution exposure and anxiety265–267, noise268,269 and temperature76,209,270–272 is mixed and indicates variation by endpoints. Air pollution acts synergistically with and directly on temperature and anxiety respectively in some studies209,266,271 but not others265,267. Notably, these studies investigated ambient, not perceived, environmental factors. In controlled human exposure studies, where the environment is relatively constant, the relationship between air pollution and environmental perception is relatively unexplored. While noise and acute air pollution exposure affect some endpoints273,274, they had no significant effect on anxiety symptoms93. In our study, where environmental temperature and noise were consistent, the interactions we report may reflect a delineation between perceived and true environment. This “mismatch” could be explained by individual or localized psychological and physiological factors like annoyance, discomfort and sensitivity, which independently influence environmental perception and subjective symptoms275–278. While the underlying psychological triggers in our experimental setting are unclear, it is possible that a primary feeling, such as discomfort, influenced perception. Discomfort, for example, may underlie anxiety-related enhancement of symptoms. Discomfort due to perceived coldness, which is associated with eye 76 irritation279, may explain the attenuation of symptoms with increasing perceived temperature. The attenuation of symptoms by increased perceived noise is surprising, considering that others have reported enhancement in different experimental settings275,276,280. Interestingly, perceived exposure condition (DE or FA) did not modify symptoms, indicating effective experimental blinding, but this analysis may be limited by the unbalanced comparison groups. Different demographics, methodology, endpoints, and exposure durations limit direct comparison between this study and others. The unclear psychological triggers and small interaction effect sizes highlight the need for further studies into environmental perception. This is the first DE exposure study to use multiple nominal DE concentrations to examine the C-R for symptoms and its interaction with environmental perception. The significant increase in symptoms at 150 µg/m3 PM2.5 compared to FA is consistent with an effect threshold below 150 µg/m3 PM2.5, and similar to a threshold below 140 µg/m3 of total suspended particles suggested by Mølhave et al281. Vilcassim et al., also reported an increase in symptoms when participants travelled from low (<35 µg/m3 PM2.5) to high (>100 µg/m3 PM2.5) pollution cities140, estimating a 40 µg/m3 PM2.5 threshold. The resolution of symptoms at 24 hours after acute exposure in our study is consistent with recovery after the cessation of air pollution exposure observed by both Vilcassim et al. and Mølhave et al140,281. The transient symptom response, relatively small effect sizes and absence of a relationship with lung function indicates that these effects are subclinical in this population. Nevertheless, these findings contribute to biological plausibility, useful estimates, and potential thresholds for assessing the health impacts of air pollution. Although the exposure duration in this study is the longest for a controlled DE study to date, it does not fully replicate complex day-to-week long real-world exposures28. Additionally, our 77 study recruited healthy non-smokers and may not be sufficiently generalizable to more vulnerable populations282. Future studies may explore how known susceptibility factors modify the C-R relationship, as well as underlying mechanisms associated with symptoms. We report novel findings on perceived environmental modifiers that warrant replication in future studies. This controlled DE exposure study detailed a concentration-response relationship between particulate matter and self-reported symptoms and identified perceived temperature, noise, and anxiety as potential modifiers of this relationship. This research not only highlights the utility of visual analog scale questionnaires as non-invasive tools for assessing symptoms from air pollution exposure, but also provides effect estimates over a range of epidemiologically relevant PM2.5 levels. This may be crucial in adopting self-reported questionnaires for health monitoring and developing public health guidelines. Copyright information. A version of this chapter is available with permission from Springer Nature and as an open access article. It is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as appropriate credit is given to the original author(s) and the source. Citation: Orach, J., Rider, C.F., Yuen, A.C.Y. et al. Concentration-dependent increase in symptoms due to diesel exhaust in a controlled human exposure study. Part Fibre Toxicol 19, 66 (2022). https://doi.org/10.1186/s12989-022-00506-6 78 4.6 Tables Table 4.1. Symptom questions and clusters. Symptom category Question Chest Please rate your level of throat irritation Do you feel a need to cough? Do you suffer from shortness of breath right now? Are you currently experiencing a whistling or hissing sound while breathing? Are you experiencing chest pain? Constitutional Is your skin itchy or dry? Are you sweating right now? Do you have a fever right now? How would you rate your general wellbeing? Eye Do your eyes itch or sting? Please rate how dry your eyes are right now Are your eyes running or watering? Neurological Do you feel sleepy or drowsy? Do you have a headache right now? Do you find it difficult to concentrate? Are you currently feeling nauseous? Nose Does your nose feel irritated, itchy, stinging or dry? Do you currently have a runny nose? Is your nose blocked right now? Symptom scores (standardized to a 10cm scale) were summed within each cluster and also as a total including all clusters. 79 Table 4.2. Participant demographics for symptoms analysis. ID number Sex Age BMI FEV1 % predicted 1 Male 35 24.58 104.21 2 Female 21 18.71 89.38 3 Female 27 22.82 98.11 4 Female 47 28.6 83.85 5 Male 44 22.03 99.03 6 Male 20 21.86 109.62 7 Male 35 29.56 98.22 8 Female 20 20.25 84.92 9 Male 29 23.18 96.59 10 Male 25 30.14 96.36 11 Female 28 23.95 87.32 12 Male 22 25.11 104.13 13 Female 23 25.92 108.44 14 Male 34 32.72 101.75 15 Female 21 18.56 103.83 16 Male 28 30.89 106.99 17 Female 20 21.67 108.28 Mean (SD) (F/M = 8/9) 28.18 (8.34) 24.74 (4.31) 98.88 (8.32) Abbreviations: BMI = Body-mass index; FEV1 = forced expiratory volume in 1 second. Data were recorded at the date of screening. 80 Table 4.3. Symptom response modification by diesel engine type. Symptom category (timepoint) β (std error) p value Eyes (4h) -0.04 (0.02) 0.14 Eyes (24h) -0.02 (0.01) 0.13 Nose (4h) 0.00 (0.02) 0.91 Nose (24h) 0.00 (0.01) 0.89 Chest (4h) 0.00 (0.01) 0.82 Chest (24h) 0.00 (0.01) 0.75 Neurological (4h) -0.01 (0.02) 0.65 Neurological (24h) -0.01 (0.01) 0.72 Constitutional (4h) -0.01 (0.01) 0.48 Constitutional (24h) 0.00 (0.01) 0.87 Total (4h) -0.06 (0.06) 0.37 Total (24h) -0.02 (0.03) 0.53 PM2.5 was correlated with most of the other components of DE. 81 Table 4.4. Concentration-response effect modification by environmental perception. Symptom category (at 4h) Perception measure β (std error) P value Total Lighting -0.02 (0.03) 0.44 Glare -0.01 (0.01) 0.29 Noise -0.07 (0.03) 0.01 Temperature -0.06 (0.03) 0.02 Humidity -0.03 (0.05) 0.50 Air circulation 0.03 (0.03) 0.30 Air quality -0.03 (0.04) 0.52 Odor 0.03 (0.03) 0.23 Ventilation 0.00 (0.01) 0.72 Anxiety 0.02 (0.02) 0.38 Constitutional Lighting -0.01 (0.01) 0.08 Glare 0.00 (0.00) 0.50 Noise -0.01 (0.01) 0.06 Temperature -0.01 (0.01) 0.03 Humidity 0.00 (0.01) 0.96 Air circulation 0.01 (0.01) 0.32 Air quality 0.01 (0.01) 0.34 Odor 0.01 (0.01) 0.09 Ventilation 0.00 (0.00) 0.55 Anxiety 0.01 (0.00) 0.03 Eyes Lighting 0.01 (0.01) 0.46 Glare -0.01 (0.00) 0.23 Noise -0.02 (0.01) 0.02 Temperature -0.03 (0.01) 0.01 82 Humidity 0.00 (0.02) 0.91 Air circulation 0.01 (0.01) 0.19 Air quality -0.03 (0.02) 0.13 Odor 0.01 (0.01) 0.27 Ventilation 0.00 (0.00) 0.60 Anxiety 0.00 (0.01) 0.94 Symptom scores were summed into categories. Significant effects (p < 0.05) are bolded. 83 Table 4.5. Repeated measures correlations between change in symptoms and airway function measures. Symptom category (at 4h) Correlate Timepoint (h) r (std error) P value Total Percentage ΔFeNO 4 0.29 (0.13) 0.04 24 0.39 (0.12) <0.00 Percentage ΔFEV1 4 0.04 (0.14) 0.78 24 0.07 (0.14) 0.64 Δlog PC20 24 -0.06 (0.15) 0.71 Constitutional Percentage ΔFeNO 4 0.12 (0.14) 0.41 24 0.34 (0.13) 0.02 Percentage ΔFEV1 4 0.04 (0.14) 0.79 24 0.17 (0.14) 0.24 Δlog PC20 24 -0.02 (0.15) 0.91 Eyes Percentage ΔFeNO 4 0.32 (0.13) 0.02 24 0.40 (0.12) <0.00 Percentage ΔFEV1 4 0.04 (0.14) 0.78 24 0.09 (0.14) 0.55 Δlog PC20 24 -0.07 (0.15) 0.65 Abbreviations: FEV1 = forced expiratory volume in 1 second; FeNO = fractional exhaled nitric oxide; PC20 = methacholine provocation concentration to drop FEV1 by 20%. Symptom scores were summed into clusters. Significant effects (p < 0.05) are bolded. 84 4.7 Figures 85 Figure 4.1. Symptoms visual analog scale (VAS) questionnaires. Panel A shows the symptoms VAS questionnaire, while panel B shows the VAS environmental perception questionnaire. Symptom questionnaires were completed by participants before, and at 4h and 24h after the beginning of the exposure. Perception questionnaires were completed in the exposure booth, at the beginning and end of the exposure. 86 87 Figure 4.2. Diesel exhaust (DE) concentration-response for symptom categories. Symptoms were recorded before, and at 4 and 24 hours after the start of exposures to filtered air and DE standardized to 20, 50 and 150 µg/m3 PM2.5. X axes show change in symptom scores from baseline, while Y axes show PM2.5 concentrations (µg/m3). Shaded grey regions represent 95% confidence intervals, and the horizontal dashed lines represent 0 (no change from baseline). Linear mixed effects models were fitted with participant ID as a random effect: *p ≤ 0.05. 88 Figure 4.3. Effects of diesel exhaust (DE) on symptom categories by exposure group. Symptoms were recorded before, and at 4 and 24 hours after the start of exposures to filtered air and DE standardized to 20, 50 and 150 µg/m3 PM2.5. X axes show represent change in symptom scores from baseline; Y axes show nominal exposure conditions. Horizontal dashed lines represent 0 (no change from baseline). Linear mixed effects models were fitted with participant ID as a random effect: **p ≤ 0.05, *p = 0.051 – 0.1. 89 Figure 4.4. Effect of perception on the concentration-response between diesel exhaust (DE) and symptoms. Symptoms data was recorded before, and at 4 and 24 hours after the start of exposures to filtered air and DE standardized to 20, 50 and 150 µg/m3 PM2.5. Y axes show change in symptom scores from baseline; X axes show PM2.5 concentrations (µg/m3). Shaded grey regions represent 95% confidence intervals, and the horizontal dashed lines represent 0 (no change from baseline). Linear mixed effects models were fitted with perceived exposure condition as an interaction term and participant ID as a random effect. 90 Chapter 5 - Conclusion 5.1 Summary of salient findings This DICE study represents a significant expansion on the known C-R relationship between traffic-related air pollution and adverse health effects. We have previously reviewed C-Rs in controlled human air pollution exposures and identified a paucity of DE studies evaluating exposure across a broad range of concentrations, or reporting C-R estimates117. To the best of my knowledge, this is the first double-blind crossover controlled human exposure study to investigate the DE C-R using 3 nominal DE exposure concentrations (in addition to control air). Furthermore, the inclusion of a condition similar to occupational and 24h ambient exposure limits is novel. We identified concentration-dependent changes in soluble mediators in the blood and upper airways, as well as clinically relevant symptoms and airway inflammation. To evaluate the concentration-dependent effects of traffic related air pollution on the proteome, I tested the following hypotheses in Chapter 2: • DE exposure induces concentration-dependent changes in the proteome. • Blood proteins are correlated with airway health effects measured by FeNO, FEV1, methacholine PC20 and nasal cytokines. DE exposure induced a concentration-dependent alteration in the proteome that was enriched in complement activation and regulation proteins. We confirmed a concentration-dependent decrease in CFI (0.02µg/mL per µg/m3 of PM2.5) that trended downwards at 50 µg/m3 PM2.5 and decreased significantly at 150 µg/m3 PM2.5, when compared to FA. Moreover, CFI was associated with increased airway inflammation (nasal IL-6 and IL-1β) and responsiveness, as well as lung function decline. Given the vital role of CFI in regulated complement-mediated 91 inflammation200, these findings indicate that DE adversely impacts innate immunity in a way that may be related to lung function and airway hyper-responsiveness. This work provides C-R estimates for CFI as a putative blood marker of systemic DE effects with a potential threshold between 50 – 150 µg/m3 PM2.5. To evaluate the concentration-dependent effects of traffic-related air pollution on the airways, I tested the following hypotheses in chapter 3: • DE induces a concentration-dependent decrease in lung function measured by spirometry forced expiratory volume in 1 second (FEV1). • DE induces a concentration-dependent increase in airway responsiveness measured by methacholine provocation concentration to cause a 20% FEV1 drop (PC20). • DE induces a concentration-dependent increase in airway inflammation measured by fractional exhaled nitric oxide (FeNO), differential cell counts, and inflammatory cytokines. DE induced a concentration-dependent increase in FeNO (0.16ppb per µg/m3 of PM2.5) that trended upwards at 20 and 50 µg/m3 PM2.5 and was significantly increased at 150 µg/m3 PM2.5 compared to FA170. Additionally, we observed a trend towards increasing nasal IL-6. However, we found no significant effect of DE exposure on lung function, airway hyperresponsiveness and airway differential cell counts. This is likely due to the relatively healthy cohort and low exposure doses of DE, as significant effects have been reported in asthmatics, co-exposures with allergen and chronic exposures. Regardless, our work provides C-R estimates for FeNO as a potentially reliable non-invasive measure of DE effects on the airways, with a potential threshold between 20 – 150 µg/m3 PM2.5. 92 To evaluate the concentration-dependent effects on symptoms and exposure condition perception, I tested the following hypotheses in chapter 4: • An increase in DE concentration increases symptom severity determined by visual analog score (VAS) symptom questionnaire scores. • Changes in symptoms are unmodified by perceived exposure condition. • Changes in symptoms are correlated with airway health effects measured by FeNO, FEV1 and methacholine PC20. Additionally, I explored the effects of environmental perception post hoc based on the hypothesis: • DE symptom concentration-response is unmodified by perceived environmental characteristics (null hypothesis) DE induced a concentration-dependent increase in symptoms, driven by constitutional and eye symptoms169. Compared to FA, this C-R was significant at 150 µg/m3 PM2.5. This concentration-response was modified by perceived noise, temperature, and anxiety. Symptoms were correlated with FeNO but not FEV1 and methacholine PC20, suggesting a relationship with inflammatory phenomena. Perceived exposure condition did not modify symptom responses. We provide C-R estimates for symptom VAS scores as non-invasive measures of symptoms induced by DE, with a potential threshold below 150 µg/m3. Air pollution continues to be a leading cause of morbidity and premature death worldwide, especially where diesel engine use is growing7,8. Understanding the pathways through which it impacts health will facilitate better health recommendations and therapeutics52,283. Identifying and elucidating the concentration-dependent measures and markers of air pollution health effects 93 will improve impact assessment and biomonitoring99,117. My thesis research makes valuable contributions to both understanding and better assessing air pollution effects, buttressing the foundation for future research, health monitoring, exposure limits and guidelines. 5.2 Implications of the findings Health policymakers and practitioners rely on sound scientific evidence to draft effective policies and practices to mitigate the harms of air pollution exposure284,285. For example, in drafting the National Ambient Air Quality Standards (NAAQS) “to protect the public health with an adequate margin of safety”, the United States Environmental Protection Agency (EPA) relies on exposure-response associations from epidemiological research, as well as experimental human and animal studies139. In determining the causality for criteria pollutants like PM2.5, the EPA notes that “the body of health evidence for PM2.5 includes not only the repeated demonstration of associations in epidemiologic studies, but also includes support for biological plausibility established by human clinical and animal toxicology studies.”139. Given the novel concentration-response and pathophysiological insights established using sound methodology in the DICE study, our findings are a solid contribution to this evidence base. 5.2.1 Pathophysiological insights Changes in specific soluble mediators and nitric oxide in exhaled air provide mechanistic insight into the adverse effects of air pollution. A decrease in the complement regulator complement factor I indicates a cardinal role of innate immunity in inflammation due to air pollution exposure. Specifically, the decrease in negative regulators of complement activation like complement factor I may be an important pathway for the inflammatory and prothrombotic effects of TRAP65,189,190. This pro-inflammatory phenomenon is corroborated by increases in FeNO, which indicates the acute activation of inducible nitric oxide synthase in the airways51,237–94 240. Overall, the acute increase in FeNO at 4 hours reflects an immediate inflammatory response in the airways, while a decrease in complement factor I at 24 hours indicates a delayed systemic response lasting at least 24 hours post-exposure. Moreover, the association between complement factor I and lung function and airway responsiveness, as well as FeNO with symptoms indicates that these mechanisms may be interconnected and linked to clinically relevant health effects. While these relationships are known in general, I report novel C-R relationships over a broad range of concentrations that indicate a more robust relationship of specific markers with exposure. These findings expand the current mechanistic understanding of air pollution effects and may be important targets for further research. 5.2.2 Exposure guidelines and policy Because C-R relationships provide insight into how increases in exposure concentration relate to changes in response markers107, they are essential in drafting exposure limits and guidelines286. The EPA relies on C-R associations to establish causality, assess risks of exposure, and estimate the benefits of PM2.5 reduction at different exposure limits139. For example, after observing PM2.5-associated deaths at the previous annual air quality standard of 12 µg/m3 PM2.5, (2012), the EPA utilized linear C-R functions from large multicity U.S. epidemiologic studies to estimate a 21 – 27% reduction in PM2.5-associated health risks by revising the standard to 9 µg/m3 PM2.5139. However, the Clean Air Scientific Advisory Committee (CASAC) noted that the NAAQS estimation process was limited by inadequate adjustment for confounders in the underlying studies287. Moreover, knowledge of the true shape of the PM2.5 C-R curve was limited by a paucity of data, especially at low concentrations, which only supports a linear C-R function currently139. Therefore, both the EPA and CASAC highlighted the need for more controlled human exposure studies, with longer exposure durations and a broader range of PM2.5 95 concentrations, that limit confounding factors and can provide information about dose, timing of effects and sensitive populations139,287. While no ambient exposure guidelines exist for DE specifically, DE is regulated in occupational settings using exposure surrogates and occupational exposure limits (OELs) that vary widely by jurisdiction288. OEL values, which are based on reviews of animal and human toxicological data, are defined by the Canadian Centre for Occupational Health and Safety as “the maximum airborne concentration of a toxic substance to which a worker can be exposed over a period of time without suffering any harmful consequences”288. Most occupational health authorities in Canada base their 8-hour time-weighted OELs for DE on the American Conference of Governmental Industrial Hygienists Threshold Limit Values and Biological Exposure Indices101,288. OELs vary by province and are expressed as either total carbon or respirable combustible dust101. Despite the important role of PM2.5 in the effects of DE exposure6, there are no occupational exposure guidelines based on PM2.5 in Canada288. The only PM-based guidance available (occupational exposure limit of 50 µg/m3 DE PM) is based on a critical review by Landwehr et al., (2021)25. Our CHE study fits into the knowledge gaps for both occupational and ambient short-term exposure recommendations. We include concentrations close to current Canadian and U.S NAAQS 24-hour standards of 28 and 35 µg/m3 PM2.599. The EPA has indicated that PM2.5 is “likely to be causal” for the short-term respiratory effects of air pollution139. Thus, our observation of an increasing trend in FeNO at 20 µg/m3 PM2.5 contributes to the evidence of airway inflammation below the current 24-hour ambient exposure standards. Although ambient air pollution is a mixture with diverse sources289, our findings suggest that PM2.5 from traffic, which is a major ambient air pollution contributor6,8, acutely affects the airways even at low 96 exposure concentrations and durations. Our observation of increasing trends in FeNO at 50 µg/m3 PM2.5, as well as significant changes in FeNO, symptoms and complement factor I at 150 µg/m3 PM2.5 indicate that DE induces effects at these occupationally-relevant PM2.5 concentrations (2 – 75 µg/m3 PM2.5, 8-hour time-weighted average)25,169,170. This research provides evidence for PM2.5 as a useful exposure surrogate for DE exposure that is associated with adverse health effects potentially beginning at 20 µg/m3 and increasing significantly at 150 µg/m3 PM2.5. As with ambient scenarios, PM2.5 in the workplace may be generated from diverse sources25, which might explain the lack of PM2.5-based OELs for DE, and presents a challenge in translating these findings directly into the current OEL framework. Moreover, real-world exposures exceed the 4-hour duration of our study, potentially limiting the translation of our findings in chronic exposure scenarios. However, PM2.5 is still an important pollutant in both occupational and ambient environments8,25, DE is a prominent PM contributor in both scenarios6,8,25, and acute effects markers may signify pathophysiological impacts related to chronic exposure effects that typically have larger exposure-response estimates148. Moreover, most current air pollution C-R research is based on observational chronic exposure scenarios, thus our research fills a relative knowledge gap, highlights the deleterious impacts of air pollution even at relatively low concentrations and short durations, and elucidates the link between acute and chronic effects. Notably, the effects reported here occurred after acute exposure in a relatively healthy young cohort, suggesting that current exposure guidelines may be insufficient in protecting the public, especially susceptible populations, from the harmful effects of air pollution exposure. Thus, my thesis research may contribute to revisions of ambient air quality standards and future PM2.5-based standards for occupational environments. 97 5.2.3 Health monitoring The concentration-response markers identified in this study are potential markers of DE effects on health that may be used for biomonitoring99. We show that FeNO and VAS symptom scores, which require minimally invasive methods to obtain, are concentration-dependent measures that can be easily adapted in future studies and health monitoring situations. While relatively more invasive to obtain, soluble mediators in the blood and nasal epithelia, such as complement factor I and IL-6 may complement FeNO and symptom questionnaires in assessing the internal response to TRAP exposure. While finding specific effects markers of a complex mixture like DE continues to be extremely challenging105, precise non-invasive markers that are associated with exposure level and other health endpoints are a vital step forward153. These can be adapted in at-risk populations to monitor health and compensate workers suffering from the adverse effects of excessive TRAP exposure. 5.3 Strengths and limitations DICE was the first double-blinded crossover controlled DE exposure study to investigate the concentration-response to DE across a broad range of concentrations, including low concentrations close to ambient and occupational exposure limits. Additionally, the exposure duration of 4 hours and reporting of linear C-R estimates in this study, are unprecedented in C-R studies for DE117. The controlled exposure model limits confounding from additional exposure sources27, rare occasions of which would in any case bias towards null results (avoid spuriously significant exposure-related findings), while the double-blinded design limits the impact of experimental perception. The crossover design means that each participant is their own control, thereby limiting the impact of individual variability while increasing statistical power. The inclusion of a broad range of exposure concentrations for each participant is arguably the most 98 unique feature of this study, which allows evaluation of effects at exposure levels below other controlled DE exposure studies but relevant to ambient exposure guidelines. Moreover, the 4-hour exposure duration also exceeds similar studies and better approximates (typically longer) real world scenarios. Lastly, we used robust molecular, cellular, and statistical methods to generate standardized concentration-response (per µg/m3) estimates that are relatively unreported in controlled DE studies. Therefore, the DICE study was poised to investigate momentous research questions using robust methods to generate novel findings. Despite these vital strengths, the DICE study also had important limitations. While it had the merits of a controlled exposure model, it also inherited limitations such as acute exposures, relatively short exposure durations, single-source pollutants and limited timepoints which do not fully capture the complex picture of (typically longer) real-world multi-source air pollution exposures. Additionally, although we subtracted baselines values for each exposure where possible, we were unable to account for the possible influence of intermittent exposures outside the laboratory. Thirdly, our sample size, while similar to other controlled exposure studies, was still relatively modest, thus increasing the likelihood of missing responses with small effect sizes. Moreover, while we controlled for multiple comparisons on most major outcomes, we also reported many unadjusted estimates for secondary endpoints. Because we selected only non-invasive or minimally invasive measures for better feasibility in downstream adaptations, we were unable to deeply investigate effects in the lower airways. Lastly, the DICE study recruited a young healthy non-smoking cohort, and thus may not be generalizable to susceptible populations such as asthmatics. 99 5.4 Future directions My thesis leaves many fascinating avenues for further exploration. The DICE study is a testament that controlled human exposure C-R studies, while challenging to complete with multiple exposures and visits, are feasible and valuable. Findings reported here ought to be corroborated by replicative studies, and more importantly, complemented by similar studies with different populations, analytical approaches, and pollutant exposures. For example, studies in susceptible populations such as those with susceptible genotypes and chronic respiratory illnesses would provide more insight into how these conditions modify the TRAP C-R. Furthermore, effect modification by demographic factors could facilitate the discovery or validation of susceptibility factors. Among the significant C-R effects in our healthy population, for example, sex significantly modified the C-R for complement factor I whereas age and BMI had no impact on CFI, FeNO or total symptoms (Appendix B). Notably, the overall concentration-dependent decrease in CFI was driven by males, indicating a greater sex-dependent sensitivity to DE effects detectible in blood CFI. Future studies with larger sample sizes could more rigorously investigate these effects using balanced stratified analyses. Overall, this research would confer more personalized higher-resolution health recommendations. Although a combination of LME regression and LME ANOVAs enabled us to linear generate C-R estimates and report potential nonlinear trends, future controlled exposure C-R research could benefit from robust non-linear modelling to elucidate the shape of the C-R curves 290. While this may require a larger data set to improve confidence, it would provide more concise threshold estimates and complement current epidemiological research. Controlled exposure C-R studies with other pollutants like woodsmoke and gasoline exhaust would not only expand current knowledge of air pollution C-R, but also confer information about 100 the specificity of the endpoints to different pollutant mixtures and components. Additionally, studies could investigate the impact of pharmacological interventions like antioxidants as well as mechanical interventions like particulate filters on the C-R for particulate pollutants. To validate the real-world utility of the significant endpoints identified here, future studies ought to investigate these measures in at-risk populations such as those routinely exposed to high levels of TRAP occupationally. Epidemiological studies could elucidate how these markers relate to natural exposure levels, and more importantly, if they predict adverse outcomes like pollution-mediated respiratory diseases and workplace injury. This is a crucial step in graduating my thesis work from the laboratory into real-world health policy and practice. 101 References 1. Cohen AJ, Brauer M, Burnett R, et al. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. Lancet. 2017;389(10082):1907-1918. doi:10.1016/S0140-6736(17)30505-6 2. Liu C, Chen R, Sera F, et al. Ambient Particulate Air Pollution and Daily Mortality in 652 Cities. N Engl J Med. 2019;381(8):705-715. doi:10.1056/NEJMoa1817364 3. Forouzanfar MH, Afshin A, Alexander LT, et al. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016;388(10053):1659-1724. doi:10.1016/S0140-6736(16)31679-8 4. Burnett R, Chen H, Szyszkowicz M, et al. Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter. Proc Natl Acad Sci. 2018;115(38):201803222. doi:10.1073/pnas.1803222115 5. Manisalidis I, Stavropoulou E, Stavropoulos A, Bezirtzoglou E. Environmental and Health Impacts of Air Pollution: A Review. Front Public Heal. 2020;8:14. doi:10.3389/fpubh.2020.00014 6. Reis H, Reis C, Sharip A, et al. Diesel exhaust exposure, its multi-system effects, and the effect of new technology diesel exhaust. Environ Int. 2018;114:252-265. doi:10.1016/j.envint.2018.02.042 7. Rajagopalan S, Landrigan PJ. Pollution and the Heart. Longo DL, ed. https://doi.org/101056/NEJMra2030281. 2021;385(20):1881-1892. doi:10.1056/NEJMRA2030281 8. Anenberg SC, Miller J, Henze DK, Minjares R, Achakulwisut P. The global burden of transportation tailpipe emissions on air pollution-related mortality in 2010 and 2015. Environ Res Lett. 2019;14(9). doi:10.1088/1748-9326/ab35fc 9. Health Effects Institute. Traffic-Related Air Pollution: A Critical Review of the Literature on Emissions , Exposure , and Health Effects A Special Report of the HEI Panel on the Health Effects of Traffic-Related Air Pollution - Executive Summary.; 2010. Accessed November 25, 2019. www.healtheffects.org 10. Yang D, Yang X, Deng F, Guo X. Ambient air pollution and biomarkers of health effect. In: Advances in Experimental Medicine and Biology. Vol 1017. Springer New York LLC; 2017:59-102. doi:10.1007/978-981-10-5657-4_4 11. Rahman MM, Nahar K, Begum BA, Hopke PK, Thurston GD. Respiratory Emergency Department Visits Associations with Exposures to PM2.5 Mass, Constituents, and Sources in Dhaka, Bangladesh Air Pollution. Ann Am Thorac Soc. Published online 2021:1-46. doi:10.1513/AnnalsATS.202103-252OC 12. Wang M, Aaron CP, Madrigano J, et al. Association between long-term exposure to 102 ambient air pollution and change in quantitatively assessed emphysema and lung function. JAMA - J Am Med Assoc. 2019;322(6):546-556. doi:10.1001/jama.2019.10255 13. Williams AM, Phaneuf DJ, Barrett MA, Su JG. Short-term impact of PM 2.5 on contemporaneous asthma medication use: Behavior and the value of pollution reductions. Proc Natl Acad Sci U S A. 2019;116(12):5246-5253. doi:10.1073/pnas.1805647115 14. Croft DP, Zhang W, Lin S, et al. The association between respiratory infection and air pollution in the setting of air quality policy and economic change. Ann Am Thorac Soc. 2019;16(3):321-330. doi:10.1513/AnnalsATS.201810-691OC 15. Nirel R, Adar SD, Dayan U, et al. Fine and coarse particulate matter exposures and associations with acute cardiac events among participants in a telemedicine service: A case-crossover study. Environ Health Perspect. 2018;126(9). doi:10.1289/EHP2596 16. Nirel R, Levy I, Adar SD, et al. Concentration-response relationships between hourly particulate matter and ischemic events: A case-crossover analysis of effect modification by season and air-mass origin. Sci Total Environ. 2021;760. doi:10.1016/j.scitotenv.2020.143407 17. Zheng Q, Liu H, Zhang J, Chen D. The effect of ambient particle matters on hospital admissions for cardiac arrhythmia: a multi-city case-crossover study in China. Environ Heal. 2018;17(1):60. doi:10.1186/s12940-018-0404-z 18. Pope CA, Muhlestein JB, Anderson JL, et al. Short-term exposure to fine particulate matter air pollution is preferentially associated with the risk of ST-segment elevation acute coronary events. J Am Heart Assoc. 2015;4(12). doi:10.1161/JAHA.115.002506 19. Southerland VA, Brauer M, Mohegh A, et al. Global urban temporal trends in fine particulate matter (PM2·5) and attributable health burdens: estimates from global datasets. Lancet Planet Heal. 2022;5196(21):1-8. doi:10.1016/S2542-5196(21)00350-8 20. GBD 2019 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet (London, England). 2020;396(10258):1223-1249. doi:10.1016/S0140-6736(20)30752-2 21. Reşitoğlu İA, Altinişik K, Keskin A. The pollutant emissions from diesel-engine vehicles and exhaust aftertreatment systems. Clean Technol Environ Policy. 2015;17(1):15-27. doi:10.1007/s10098-014-0793-9 22. Steiner S, Bisig C, Petri-Fink A, Rothen-Rutishauser B. Diesel exhaust: current knowledge of adverse effects and underlying cellular mechanisms. Arch Toxicol. 2016;90(7):1541-1553. doi:10.1007/s00204-016-1736-5 23. Huang W, Smith TJ, Ngo L, et al. Characterizing and biological monitoring of polycyclic aromatic hydrocarbons in exposures to diesel exhaust. Environ Sci Technol. 2007;41(8):2711-2716. doi:10.1021/es062863j 24. Yu CH, Patton AP, Zhang A, Fan ZH, Weisel CP, Lioy PJ. Evaluation of Diesel Exhaust Continuous Monitors in Controlled Environmental Conditions. J Occup Environ Hyg. 2015;12(9):577-587. doi:10.1080/15459624.2015.1022652 103 25. Landwehr KR, Larcombe AN, Reid A, Mullins BJ. Critical Review of Diesel Exhaust Exposure Health Impact Research Relevant to Occupational Settings: Are We Controlling the Wrong Pollutants? Expo Heal. 2021;13(2):141-171. doi:10.1007/s12403-020-00379-0 26. Silverman DT. Diesel Exhaust and Lung Cancer - Aftermath of Becoming an IARC Group 1 Carcinogen. Am J Epidemiol. 2018;187(6):1149-1152. doi:10.1093/aje/kwy036 27. Birger N, Gould T, Stewart J, Miller MR, Larson T, Carlsten C. The Air Pollution Exposure Laboratory (APEL) for controlled human exposure to diesel exhaust and other inhalants: Characterization and comparison to existing facilities. Inhal Toxicol. 2011;23(4):219-225. doi:10.3109/08958378.2011.562256 28. Long E, Carlsten C. Controlled human exposure to diesel exhaust: results illuminate health effects of traffic-related air pollution and inform future directions. Part Fibre Toxicol. 2022;19(1):1-35. doi:10.1186/s12989-022-00450-5 29. Li J, Yin P, Wang H, et al. The disease burden attributable to 18 occupational risks in China: An analysis for the global burden of disease study 2017. Environ Heal A Glob Access Sci Source. 2020;19(1). doi:10.1186/s12940-020-00577-y 30. Ge C, Peters S, Olsson A, et al. Diesel Engine Exhaust Exposure, Smoking, and Lung Cancer Subtype Risks. A Pooled Exposure–Response Analysis of 14 Case–Control Studies. Am J Respir Crit Care Med. 2020;202(3):402-411. doi:10.1164/rccm.201911-2101OC 31. Möhner M, Wendt A. A critical review of the relationship between occupational exposure to diesel emissions and lung cancer risk. Crit Rev Toxicol. 2017;47(3):185-224. doi:10.1080/10408444.2016.1266598 32. Neophytou AM, Picciotto S, Costello S, Eisen EA. Occupational Diesel Exposure, Duration of Employment, and Lung Cancer. Epidemiology. 2016;27(1):21-28. doi:10.1097/ede.0000000000000389 33. Drizik E, Corbett S, Zheng Y, et al. Transcriptomic changes in the nasal epithelium associated with diesel engine exhaust exposure. Environ Int. 2020;137(December 2019):105506. doi:10.1016/j.envint.2020.105506 34. Wang H, Duan H, Meng T, et al. Local and systemic inflammation may mediate diesel engine exhaust-induced lung function impairment in a chinese occupational cohort. Toxicol Sci. 2018;162(2):372-382. doi:10.1093/toxsci/kfx259 35. Mehus AA, Reed RJ, Lee VST, et al. Comparison of Acute Health Effects from Exposures to Diesel and Biodiesel Fuel Emissions. J Occup Environ Med. 2015;57(7):705-712. doi:10.1097/JOM.0000000000000473 36. Tousoulis D, Fountoulakis P, Oikonomou E, et al. Acute exposure to diesel affects inflammation and vascular function. Eur J Prev Cardiol. 2020;0(0):1-10. doi:10.1177/2047487319898020 37. Faherty T, Delgado-Saborit JM, Jaume U, et al. Acute Diesel Exhaust Exposure Causes a Delayed Reduction in Cognitive Control. Published online 2021. doi:10.21203/rs.3.rs-1064957/v1 104 38. Stiegel MA, Pleil JD, Sobus JR, Madden MC. Inflammatory cytokines and white blood cell counts response to environmental levels of diesel exhaust and ozone inhalation exposures. PLoS One. 2016;11(4). doi:10.1371/journal.pone.0152458 39. Cosselman KE, Krishnan RM, Oron AP, et al. Blood Pressure Response to Controlled Diesel Exhaust Exposure In Human Subjects. Am J Respir Crit Care Med. 2016;193(9):1000-1007. doi:10.1164/rccm.201506-1247OC 40. Wauters A, Esmaeilzadeh F, Bladt S, et al. Pro-thrombotic effect of exercise in a polluted environment: A P-selectin-and CD63-related platelet activation effect. Thromb Haemost. 2015;113(1):118-124. doi:10.1160/TH14-03-0251 41. Wooding DJ, Ryu MH, Li H, Alexis NE, Pena O, Carlsten C. Acute air pollution exposure alters neutrophils in never-smokers and at-risk humans. Eur Respir J. 2020;55(4):1901495. doi:10.1183/13993003.01495-2019 42. Wooding DJ, Ryu MH, Hüls A, et al. Particle Depletion Does Not Remediate Acute Effects of Traffic-related Air Pollution and Allergen. A Randomized, Double-Blind Crossover Study. Am J Respir Crit Care Med. 2019;200(5):565-574. doi:10.1164/rccm.201809-1657OC 43. Mookherjee N, Piyadasa H, Ryu MH, et al. Inhaled diesel exhaust alters the allergen-induced bronchial secretome in humans. Eur Respir J. 2018;51(1):1701385. doi:10.1183/13993003.01385-2017 44. Kramer MM, Hirota JA, Sood A, Teschke K, Carlsten C. Airway and serum adipokines after allergen and diesel exposure in a controlled human crossover study of atopic adults. Transl Res. 2017;182:49-60. doi:10.1016/j.trsl.2016.11.001 45. Clifford RL, Jones MJ, MacIsaac JL, et al. Inhalation of diesel exhaust and allergen alters human bronchial epithelium DNA methylation. J Allergy Clin Immunol. 2017;139(1):112-121. doi:10.1016/j.jaci.2016.03.046 46. Carlsten C, Blomberg A, Pui M, et al. Diesel exhaust augments allergen-induced lower airway inflammation in allergic individuals: A controlled human exposure study. Thorax. 2016;71(1):35-44. doi:10.1136/thoraxjnl-2015-207399 47. Rider CF, Yamamoto M, Günther OP, et al. Controlled diesel exhaust and allergen coexposure modulates microRNA and gene expression in humans: Effects on inflammatory lung markers. J Allergy Clin Immunol. 2016;138(6):1690-1700. doi:10.1016/j.jaci.2016.02.038 48. Glencross DA, Ho T-R, Camiña N, Hawrylowicz CM, Pfeffer PE. Air pollution and its effects on the immune system. Free Radic Biol Med. 2020;151:56-68. doi:10.1016/j.freeradbiomed.2020.01.179 49. Ghio AJ, Smith CB, Madden MC. Diesel exhaust particles and airway inflammation. Curr Opin Pulm Med. 2012;18(2):144-150. doi:10.1097/MCP.0b013e32834f0e2a 50. Biagioni BJ, Tam S, Chen Y-WWR, Sin DD, Carlsten C. Effect of controlled human exposure to diesel exhaust and allergen on airway surfactant protein D, myeloperoxidase and club (Clara) cell secretory protein 16. Clin Exp Allergy. 2016;46(9):1206-1213. 105 doi:10.1111/cea.12732 51. Alving K, Malinovschi A. Basic aspects of exhaled nitric oxide. In: Exhaled Biomarkers: Basic Aspects of Exhaled Nitric Oxide. European Respiratory Society; 2010:1-31 (49:1e31). doi:10.1183/1025448x.00028509 52. Robinson RK, Birrell MA, Adcock JJ, et al. Mechanistic link between diesel exhaust particles and respiratory reflexes. J Allergy Clin Immunol. 2018;141(3):1074-1084.e9. doi:10.1016/j.jaci.2017.04.038 53. Na HG, Kim YD, Choi YS, Bae CH, Song SY. Diesel exhaust particles elevate MUC5AC and MUC5B expression via the TLR4-mediated activation of ERK1/2, p38 MAPK, and NF-κB signaling pathways in human airway epithelial cells. Biochem Biophys Res Commun. 2019;512(1):53-59. doi:10.1016/j.bbrc.2019.02.146 54. Escamilla-Gil JM, Fernandez-Nieto M, Acevedo N. Understanding the Cellular Sources of the Fractional Exhaled Nitric Oxide (FeNO) and Its Role as a Biomarker of Type 2 Inflammation in Asthma. Carbone F, ed. Biomed Res Int. 2022;2022:1-9. doi:10.1155/2022/5753524 55. Pandya RJ, Solomon G, Kinner A, Balmes JR. Diesel exhaust and asthma: Hypotheses and molecular mechanisms of action. Environ Health Perspect. 2002;110(SUPPL. 1):103-112. doi:10.1289/ehp.02110s1103 56. McCreanor J, Cullinan P, Nieuwenhuijsen J M, et al. Respiratory Effects of Exposure to Diesel Traffic in Persons with Asthma. N Engl J Med. 2007;357(23):pp 2348--2358. Accessed November 29, 2018. www.nejm.org 57. Pawlak EA, Noah TL, Zhou H, et al. Diesel exposure suppresses natural killer cell function and resolution of eosinophil inflammation: A randomized controlled trial of exposure in allergic rhinitics. Part Fibre Toxicol. 2016;13(1). doi:10.1186/s12989-016-0135-7 58. Shin S, Bai L, Burnett RT, et al. Air Pollution as a Risk Factor for Incident Chronic Obstructive Pulmonary Disease and Asthma. A 15-Year Population-based Cohort Study. Am J Respir Crit Care Med. 2021;203(9):1138-1148. doi:10.1164/rccm.201909-1744OC 59. Nemmar A, Inuwa IM. Diesel exhaust particles in blood trigger systemic and pulmonary morphological alterations. Toxicol Lett. 2008;176(1):20-30. doi:10.1016/j.toxlet.2007.09.006 60. Kwon H-S, Ryu MH, Carlsten C. Ultrafine particles: unique physicochemical properties relevant to health and disease. Exp Mol Med. Published online 2020:1-11. doi:10.1038/s12276-020-0405-1 61. Gong J, Zhu T, Kipen H, et al. Comparisons of ultrafine and fine particles in their associations with biomarkers reflecting physiological pathways. Environ Sci Technol. 2014;48(9):5264-5273. doi:10.1021/es5006016 62. Pope CA, Bhatnagar A, McCracken JP, Abplanalp W, Conklin DJ, O’Toole T. Exposure to Fine Particulate Air Pollution Is Associated with Endothelial Injury and Systemic Inflammation. Circ Res. 2016;119(11):1204-1214. 106 doi:10.1161/CIRCRESAHA.116.309279 63. Langrish JP, Unosson J, Bosson JA, et al. Altered nitric oxide bioavailability contributes to diesel exhaust inhalation-induced cardiovascular dysfunction in man. J Am Heart Assoc. 2013;2(1). doi:10.1161/JAHA.112.004309 64. Knuckles TL, Lund AK, Lucas SN, Campen MJ. Diesel exhaust exposure enhances venoconstriction via uncoupling of eNOS. Toxicol Appl Pharmacol. 2008;230(3):346-351. doi:10.1016/j.taap.2008.03.010 65. Carter AM. Complement Activation: An Emerging Player in the Pathogenesis of Cardiovascular Disease. Scientifica (Cairo). 2012;2012:1-14. doi:10.6064/2012/402783 66. van Larebeke N, Husson B, De Coen W, Pluygers E. Immunologic and other biological parameters as a function of smoking status and of residence in areas differing in terms of air pollution. Int J Environ Health Res. 2003;13(1):55-69. doi:10.1080/0960312021000063313 67. Pei L, Zhao M, Xu J, et al. Associations of ambient fine particulate matter and its constituents with serum complement C3 in a panel study of older adults in China. Environ Pollut. 2019;252(Pt B):1019-1025. doi:10.1016/j.envpol.2019.05.096 68. Choi MS, Jeon H, Yoo S-M, Lee M-S. Activation of the Complement System on Human Endothelial Cells by Urban Particulate Matter Triggers Inflammation-Related Protein Production. Int J Mol Sci. 2021;22(7):3336. doi:10.3390/ijms22073336 69. Sui Z, Song X, Wu Y, et al. The cytotoxicity of PM2.5 and its effect on the secretome of normal human bronchial epithelial cells. Environ Sci Pollut Res. 2022;29(50):75966-75977. doi:10.1007/S11356-022-20726-9/FIGURES/7 70. Jheng YT, Putri DU, Chuang HC, et al. Prolonged exposure to traffic-related particulate matter and gaseous pollutants implicate distinct molecular mechanisms of lung injury in rats. Part Fibre Toxicol. 2021;18(1):1-16. doi:10.1186/S12989-021-00417-Y/FIGURES/6 71. Bai N, Kido T, Suzuki H, et al. Changes in atherosclerotic plaques induced by inhalation of diesel exhaust. Atherosclerosis. 2011;216(2):299-306. doi:10.1016/j.atherosclerosis.2011.02.019 72. Lee MS, Eum K Do, Fang SC, Rodrigues EG, Modest GA, Christiani DC. Oxidative stress and systemic inflammation as modifiers of cardiac autonomic responses to particulate air pollution. Int J Cardiol. 2014;176(1):166-170. doi:10.1016/j.ijcard.2014.07.012 73. Lederer AM, Fredriksen PM, Nkeh-Chungag BN, et al. Cardiovascular effects of air pollution: current evidence from animal and human studies. Am J Physiol Circ Physiol. 2021;320(4):H1417-H1439. doi:10.1152/ajpheart.00706.2020 74. Franchini M, Mannucci PM. Thrombogenicity and cardiovascular effects of ambient air pollution. Blood. 2011;118(9):2405-2412. doi:10.1182/BLOOD-2011-04-343111 75. Nassan FL, Wang C, Kelly RS, et al. Ambient PM2.5 species and ultrafine particle exposure and their differential metabolomic signatures. Environ Int. 2021;151:106447. 107 doi:10.1016/j.envint.2021.106447 76. Nassan FL, Kelly RS, Kosheleva A, et al. Metabolomic signatures of the long-term exposure to air pollution and temperature. Environ Heal. 2021;20(1):3. doi:10.1186/s12940-020-00683-x 77. Kim S, Hollinger H, Radke EG. ‘Omics in environmental epidemiological studies of chemical exposures: A systematic evidence map. Environ Int. 2022;164:107243. doi:10.1016/j.envint.2022.107243 78. Mayer P, Mayer B, Mayer G. Systems biology building a useful model from multiple markers and profiles. Nephrol Dial Transplant. 2012;27(11):3995-4002. doi:10.1093/ndt/gfs489 79. Cruz R, Pasqua L, Silveira A, et al. Traffic-related air pollution and endurance exercise: Characterizing non-targeted serum metabolomics profiling. Environ Pollut. 2021;291:118204. doi:10.1016/j.envpol.2021.118204 80. Vlaanderen JJ, Janssen NA, Hoek G, et al. The impact of ambient air pollution on the human blood metabolome. Environ Res. 2017;156:341-348. doi:10.1016/j.envres.2017.03.042 81. Mookherjee N, Ryu MH, Hemshekhar M, Orach J, Spicer V, Carlsten C. Defining the effects of traffic-related air pollution on the human plasma proteome using an aptamer proteomic array: A dose-dependent increase in atherosclerosis-related proteins. Environ Res. 2022;209:112803. doi:10.1016/j.envres.2022.112803 82. Prunicki M, Cauwenberghs N, Ataam JA, et al. Immune biomarkers link air pollution exposure to blood pressure in adolescents. Environ Heal. 2020;19(1):108. doi:10.1186/s12940-020-00662-2 83. Hegseth MN, Oftedal BM, Höper AC, et al. Self-reported traffic-related air pollution and respiratory symptoms among adults in an area with modest levels of traffic. PLoS One. 2019;14(12). doi:10.1371/journal.pone.0226221 84. Chambers L, Finch J, Edwards K, Jeanjean A, Leigh R, Gonem S. Effects of personal air pollution exposure on asthma symptoms, lung function and airway inflammation. Clin Exp Allergy. 2018;48(7):798-805. doi:10.1111/cea.13130 85. Orru K, Orru H, Maasikmets M, Hendrikson R, Ainsaar M. Well-being and environmental quality: Does pollution affect life satisfaction? Qual Life Res. 2016;25(3):699-705. doi:10.1007/s11136-015-1104-6 86. Bjermer L, Alving K, Diamant Z, et al. Current evidence and future research needs for FeNO measurement in respiratory diseases. Respir Med. 2014;108(6):830-841. doi:10.1016/j.rmed.2014.02.005 87. Torén K, Schiöler L, Lindberg A, et al. The ratio FEV 1 /FVC and its association to respiratory symptoms—A Swedish general population study. Clin Physiol Funct Imaging. 2021;41(2):181-191. doi:10.1111/cpf.12684 88. Betchley C, Koenig JQ, Van Belle G, Checkoway H, Reinhardt T. Pulmonary function 108 and respiratory symptoms in forest firefighters. Am J Ind Med. 1997;31(5):503-509. doi:10.1002/(SICI)1097-0274(199705)31:5<503::AID-AJIM3>3.0.CO;2-U 89. Dijkhoff IM, Drasler B, Karakocak BB, et al. Impact of airborne particulate matter on skin: a systematic review from epidemiology to in vitro studies. Part Fibre Toxicol. 2020;17(1):35. doi:10.1186/s12989-020-00366-y 90. Drakaki E, Dessinioti C, Antoniou C V. Air pollution and the skin. Front Environ Sci. 2014;2(3):156-157. doi:10.3389/fenvs.2014.00011 91. Torricelli AAM, Novaes P, Matsuda M, et al. Correlation Between Signs and Symptoms of Ocular Surface Dysfunction and Tear Osmolarity With Ambient Levels of Air Pollution in a Large Metropolitan Area. Cornea. 2013;32(4):e11-e15. doi:10.1097/ICO.0b013e31825e845d 92. Guxens M, Sunyer J. A review of epidemiological studies on neuropsychological effects of air pollution. Swiss Med Wkly. 2012;142(1). doi:10.4414/smw.2011.13322 93. Laumbach RJ, Kipen HM, Kelly-Mcneil K, et al. Sickness response symptoms among healthy volunteers after controlled exposures to diesel exhaust and psychological stress. Environ Health Perspect. 2011;119(7):945-950. doi:10.1289/ehp.1002631 94. Gren L, Dierschke K, Mattsson F, et al. Lung function and self-rated symptoms in healthy volunteers after exposure to hydrotreated vegetable oil (HVO) exhaust with and without particles. Part Fibre Toxicol. 2022;19(1):9. doi:10.1186/s12989-021-00446-7 95. Mudway IS, Stenfors N, Duggan ST, et al. An in vitro and in vivo investigation of the effects of diesel exhaust on human airway lining fluid antioxidants. Arch Biochem Biophys. 2004;423(1):200-212. doi:10.1016/j.abb.2003.12.018 96. Rudell B, Ledin MC, Hammarström U, Stjernberg N, Lundbäck B, Sandström T. Effects on symptoms and lung function in humans experimentally exposed to diesel exhaust. Occup Environ Med. 1996;53(10):658-662. doi:10.1136/oem.53.10.658 97. Muala A, Sehlstedt M, Bion A, et al. Assessment of the capacity of vehicle cabin air inlet filters to reduce diesel exhaust-induced symptoms in human volunteers. Environ Heal A Glob Access Sci Source. 2014;13(1). doi:10.1186/1476-069X-13-16 98. Carlsten C, Oron AP, Curtiss H, Jarvis S, Daniell W, Kaufman JD. Symptoms in response to controlled diesel exhaust more closely reflect exposure perception than true exposure. PLoS One. 2013;8(12). doi:10.1371/journal.pone.0083573 99. Brucker N, Nascimento SN, Bernardini L, Charão MF, Garcia SC. Biomarkers of exposure, effect, and susceptibility in occupational exposure to traffic‐related air pollution: A review. J Appl Toxicol. 2020;40(6):722-736. doi:10.1002/jat.3940 100. Ayodhya AS, Narayanappa KG. An overview of after-treatment systems for diesel engines. Environ Sci Pollut Res. 2018;25(35):35034-35047. doi:10.1007/s11356-018-3487-8 101. Barn P, Keefe AR, Slot N, et al. Canada Should Move Toward Adopting Harmonized Evidence-Based OELs to Consistently and Adequately Protect Workers. Ann Work Expo 109 Heal. Published online December 18, 2020:1-6. doi:10.1093/annweh/wxaa110 102. Albertini R, Bird M, Doerrer N, et al. The use of biomonitoring data in exposure and human health risk assessments. Environ Health Perspect. 2006;114(11):1755-1762. doi:10.1289/ehp.9056 103. Tranfo G. The growing importance of the human biomonitoring of exposure. Int J Environ Res Public Health. 2020;17(11):2-4. doi:10.3390/ijerph17113934 104. Schulte PA, Hauser JE. The use of biomarkers in occupational health research, practice, and policy. Toxicol Lett. 2012;213(1):91-99. doi:10.1016/j.toxlet.2011.03.027 105. Morgott DA. Factors and Trends Affecting the Identification of a Reliable Biomarker for Diesel Exhaust Exposure. Crit Rev Environ Sci Technol. 2014;44(16):1795-1864. doi:10.1080/10643389.2013.790748 106. Gil F, Pla A. Biomarkers as biological indicators of xenobiotic exposure. J Appl Toxicol. 2001;21(4):245-255. doi:10.1002/jat.769 107. Scherer G. Biomonitoring of inhaled complex mixtures - Ambient air, diesel exhaust and cigarette smoke. Exp Toxicol Pathol. 2005;57(SUPPL. 1):75-110. doi:10.1016/j.etp.2005.05.007 108. Lynch HN, Loftus CT, Cohen JM, Kerper LE, Kennedy EM, Goodman JE. Weight-of-evidence evaluation of associations between particulate matter exposure and biomarkers of lung cancer. Regul Toxicol Pharmacol. 2016;82:53-93. doi:10.1016/j.yrtph.2016.10.006 109. Fox JR, Cox DP, Drury BE, et al. Chemical characterization and in vitro toxicity of diesel exhaust particulate matter generated under varying conditions. Air Qual Atmos Heal. 2015;8(5):507-519. doi:10.1007/s11869-014-0301-8 110. Brewer K, Egyed M, Huang L, Ling B, Matz C, Rouleau M. Human Health Risk Assessment for Diesel Exhaust.; 2016. Accessed February 13, 2019. http://publications.gc.ca/collections/collection_2016/sc-hc/H129-60-2016-eng.pdf 111. Lu SS, Sobus JR, Sallsten G, et al. Are urinary PAHs biomarkers of controlled exposure to diesel exhaust? Biomarkers. 2014;19(4):332-339. doi:10.3109/1354750X.2014.910553 112. Du M, Mullins BJ, Franklin P, et al. Measurement of urinary 1-aminopyrene and 1-hydroxypyrene as biomarkers of exposure to diesel particulate matter in gold miners. Sci Total Environ. 2019;685:723-728. doi:10.1016/j.scitotenv.2019.06.242 113. Shen M, Bin P, Li H, et al. Increased levels of etheno-DNA adducts and genotoxicity biomarkers of long-term exposure to pure diesel engine exhaust. Sci Total Environ. 2016;543:267-273. doi:10.1016/j.scitotenv.2015.10.165 114. Gong J, Zhu T, Kipen H, et al. Urinary polycyclic aromatic hydrocarbon metabolites as biomarkers of exposure to traffic-emitted pollutants. Environ Int. 2015;85:104-110. doi:10.1016/j.envint.2015.09.003 115. Miller-Schulze JP, Paulsen M, Kameda T, et al. Evaluation of urinary metabolites of 1-110 nitropyrene as biomarkers for exposure to diesel exhaust in taxi drivers of Shenyang, China. J Expo Sci Environ Epidemiol. 2013;23(2):170-175. doi:10.1038/jes.2012.40 116. Sobus JR, Pleil JD, Madden MC, Funk WE, Hubbard HF, Rappaport SM. Identification of surrogate measures of diesel exhaust exposure in a controlled chamber study. Environ Sci Technol. 2008;42(23):8822-8828. doi:10.1021/es800813v 117. Orach J, Rider CF, Carlsten C. Concentration-dependent health effects of air pollution in controlled human exposures. Environ Int. 2021;150:106424. doi:10.1016/j.envint.2021.106424 118. Aunan K, Pan XC. Exposure-response functions for health effects of ambient air pollution applicable for China - A meta-analysis. Sci Total Environ. 2004;329(1-3):3-16. doi:10.1016/j.scitotenv.2004.03.008 119. Yan M, Wilson A, Bell ML, et al. The Shape of the Concentration – Response Association between Fine Particulate Health Impact Assessment. Environ Health Perspect. 2019;127(June):1-13. doi:10.1289/EHP4464 120. Glasgow G, Ramkrishnan B, Smith AE. A simulation-based assessment of the ability to detect thresholds in chronic risk concentration-response functions in the presence of exposure measurement error. Qureshi A, ed. PLoS One. 2022;17(3):e0264833. doi:10.1371/journal.pone.0264833 121. Urch B, Brook JR, Wasserstein D, et al. Relative contributions of PM2.5 chemical constituents to acute arterial vasoconstriction in humans. In: Inhalation Toxicology. Vol 16. ; 2004:345-352. doi:10.1080/08958370490439489 122. Hill AB. The Environment and Disease: Association or Causation? Proc R Soc Med. 1965;58(5):295-300. doi:10.1177/003591576505800503 123. Cox LAT. Do causal concentration–response functions exist? A critical review of associational and causal relations between fine particulate matter and mortality. Crit Rev Toxicol. 2017;47(7):603-631. doi:10.1080/10408444.2017.1311838 124. Ostro B, Spadaro J V., Gumy S, et al. Assessing the recent estimates of the global burden of disease for ambient air pollution: Methodological changes and implications for low- and middle-income countries. Environ Res. 2018;166:713-725. doi:10.1016/j.envres.2018.03.001 125. Younan D, Wang X, Millstein J, et al. Air quality improvement and cognitive decline in community-dwelling older women in the United States: A longitudinal cohort study. PLOS Med. 2022;19(2):1-24. doi:10.1371/journal.pmed.1003893 126. Frampton MW, Balmes JR, Bromberg PA, et al. Effects of short-term increases in personal and ambient pollutant concentrations on pulmonary and cardiovascular function: A panel study analysis of the Multicenter Ozone Study in oldEr subjects (MOSES 2). Environ Res. 2022;205:112522. doi:10.1016/J.ENVRES.2021.112522 127. Xu H, Wang T, Liu S, et al. Extreme Levels of Air Pollution Associated With Changes in Biomarkers of Atherosclerotic Plaque Vulnerability and Thrombogenicity in Healthy Adults. Circ Res. 2019;124(5):e30-e43. doi:10.1161/CIRCRESAHA.118.313948 111 128. Wu S, Ni Y, Li H, et al. Short-term exposure to high ambient air pollution increases airway inflammation and respiratory symptoms in chronic obstructive pulmonary disease patients in Beijing, China. Environ Int. 2016;94:76-82. doi:10.1016/j.envint.2016.05.004 129. Hajat A, Allison M, Diez-Roux A V., et al. Long-term exposure to air pollution and markers of inflammation, coagulation, and endothelial activation a repeat-measures analysis in the multi-ethnic study of atherosclerosis (MESA). Epidemiology. 2015;26(3):310-320. doi:10.1097/eDe.0000000000000267 130. Gruzieva O, Merid SK, Gref A, et al. Exposure to traffic-related air pollution and serum inflammatory cytokines in children. Environ Health Perspect. 2017;125(6). doi:10.1289/EHP460 131. Li J, Zhou C, Xu H, et al. Ambient air pollution is associated with HDL (High-Density Lipoprotein) dysfunction in healthy adults. Arterioscler Thromb Vasc Biol. 2019;39(3):513-522. doi:10.1161/ATVBAHA.118.311749 132. Wu X (May), Broadwin R, Basu R, et al. Associations between fine particulate matter and changes in lipids/lipoproteins among midlife women. Sci Total Environ. 2019;654:1179-1186. doi:10.1016/j.scitotenv.2018.11.149 133. Bell G, Mora S, Greenland P, Tsai M, Gill E, Kaufman JD. Association of Air Pollution Exposures With High-Density Lipoprotein Cholesterol and Particle Number. Arterioscler Thromb Vasc Biol. 2017;37(5):976-982. doi:10.1161/ATVBAHA.116.308193 134. Chen SY, Chan CC, Su TC. Particulate and gaseous pollutants on inflammation, thrombosis, and autonomic imbalance in subjects at risk for cardiovascular disease. Environ Pollut. 2017;223:403-408. doi:10.1016/j.envpol.2017.01.037 135. Ierodiakonou D, Zanobetti A, Coull BA, et al. Ambient air pollution, lung function, and airway responsiveness in asthmatic children. J Allergy Clin Immunol. 2016;137(2):390-399. doi:10.1016/j.jaci.2015.05.028 136. Sanchez KM, Layton AM, Garofano R, et al. Nitrogen Dioxide Pollutant Exposure and Exercise-induced Bronchoconstriction in Urban Childhood Asthma: A Pilot Study. ANNALSATS. 2022;19. Accessed January 2, 2022. www.atsjournals.org. 137. Shin S, Bai L, Burnett RT, et al. Air Pollution as a Risk Factor for Incident Chronic Obstructive Pulmonary Disease and Asthma. A 15-Year Population-based Cohort Study. Am J Respir Crit Care Med. 2021;203(9):1138-1148. doi:10.1164/rccm.201909-1744OC 138. Papadogeorgou G, Dominici F. A causal exposure response function with local adjustment for confounding: Estimating health effects of exposure to low levels of ambient fine particulate matter. Ann Appl Stat. 2020;14(2):850-871. doi:10.1214/20-AOAS1330 139. U.S. Environmental Protection Agency. Policy Assessment for the Review of the Particulate Matter National Ambient Air Quality Standards. US Environ Prot Agency, Off Air Qual Plan Stand. 2020;EPA-452/R-. 140. Vilcassim MJR, Thurston GD, Chen L-C, et al. Exposure to air pollution is associated with adverse cardiopulmonary health effects in international travellers. J Travel Med. 2019;26(5):taz032. doi:10.1093/jtm/taz032 112 141. Stafoggia M, Oftedal B, Chen J, et al. Long-term exposure to low ambient air pollution concentrations and mortality among 28 million people: results from seven large European cohorts within the ELAPSE project. Lancet Planet Heal. 2022;6(1):e9-e18. doi:10.1016/S2542-5196(21)00277-1 142. Wei Y, Yazdi MD, Di Q, et al. Emulating causal dose-response relations between air pollutants and mortality in the Medicare population. Environ Heal. 2021;20(1):53. doi:10.1186/s12940-021-00742-x 143. Chen H, Kaufman JS, Olaniyan T, et al. Changes in exposure to ambient fine particulate matter after relocating and long term survival in Canada: quasi-experimental study. BMJ. 2021;375:n2368. doi:10.1136/bmj.n2368 144. Rom WN, Boushey H, Caplan A. Experimental human exposure to air pollutants is essential to understand adverse health effects. Am J Respir Cell Mol Biol. 2013;49(5):691-696. doi:10.1165/rcmb.2013-0253PS 145. Coffman E, Burnett RT, Sacks JD. Quantitative characterization of uncertainty in the concentration-response relationship between long-Term PM2.5Exposure and Mortality at Low Concentrations. Environ Sci Technol. 2020;54(16):10191-10200. doi:10.1021/acs.est.0c02770 146. Ghio A, Sobus J, Pleil J, Madden M. Controlled human exposures to diesel exhaust. Swiss Med Wkly. 2012;142(MAY). doi:10.4414/smw.2012.13597 147. Long E, Schwartz C, Carlsten C. Controlled human exposure to diesel exhaust: a method for understanding health effects of traffic-related air pollution. Part Fibre Toxicol. 2022;19(1):1-22. doi:10.1186/S12989-022-00454-1/FIGURES/4 148. Yang Y, Ruan Z, Wang X, et al. Short-term and long-term exposures to fine particulate matter constituents and health: A systematic review and meta-analysis. Environ Pollut. 2019;247:874-882. doi:10.1016/j.envpol.2018.12.060 149. Tong H, Rappold AG, Caughey M, et al. Cardiovascular effects caused by increasing concentrations of diesel exhaust in middle-aged healthy GSTM1 null human volunteers. Inhal Toxicol. 2014;26(6):319-326. doi:10.3109/08958378.2014.889257 150. Peretz A, Sullivan JH, Leotta DF, et al. Diesel Exhaust Inhalation Elicits Acute Vasoconstriction in Vivo. Environ Health Perspect. 2008;116(7):937-942. doi:10.1289/ehp.11027 151. Carlsten C, Kaufman JD, Peretz A, Trenga CA, Sheppard L, Sullivan JH. Coagulation markers in healthy human subjects exposed to diesel exhaust. Thromb Res. 2007;120(6):849-855. doi:10.1016/j.thromres.2007.01.005 152. Peretz A, Kaufman JD, Trenga CA, et al. Effects of diesel exhaust inhalation on heart rate variability in human volunteers. Environ Res. 2008;107(2):178-184. doi:10.1016/j.envres.2008.01.012 153. Scarpa MC, Kulkarni N, Maestrelli P. The role of non-invasive biomarkers in detecting acute respiratory effects of traffic-related air pollution. Clin Exp Allergy. 2014;44(9):1100-1118. doi:10.1111/cea.12373 113 154. Mirowsky J, Gordon T. Noninvasive effects measurements for air pollution human studies: Methods, analysis, and implications. J Expo Sci Environ Epidemiol. 2015;25(4):354-380. doi:10.1038/jes.2014.93 155. Graham BL, Steenbruggen I, Barjaktarevic IZ, et al. Standardization of spirometry 2019 update an official American Thoracic Society and European Respiratory Society technical statement. Am J Respir Crit Care Med. 2019;200(8):E70-E88. doi:10.1164/rccm.201908-1590ST 156. Jackson GG, Dowling HF, Spiesman IG, Boand A V. Transmission of the Common Cold to Volunteers Under Controlled Conditions: I. The Common Cold as a Clinical Entity. AMA Arch Intern Med. 1958;101(2):267-278. doi:10.1001/archinte.1958.00260140099015 157. Powell H, Smart J, Wood LG, et al. Validity of the Common Cold Questionnaire (CCQ) in Asthma Exacerbations. Mant D, ed. PLoS One. 2008;3(3):e1802. doi:10.1371/journal.pone.0001802 158. Buzzard NA, Clark NN, Guffey SE. Investigation into pedestrian exposure to near-vehicle exhaust emissions. Environ Heal. 2009;8(1):13. doi:10.1186/1476-069X-8-13 159. Birger N, Gould T, Stewart J, Miller MR, Larson T, Carlsten C. The Air Pollution Exposure Laboratory (APEL) for controlled human exposure to diesel exhaust and other inhalants: characterization and comparison to existing facilities. Inhal Toxicol. 2011;23(4):219-225. doi:10.3109/08958378.2011.562256 160. Hudda N, Eliasziw M, Hersey SO, et al. Effect of Reducing Ambient Traffic-Related Air Pollution on Blood Pressure. Hypertension. 2021;77(3):823-832. doi:10.1161/HYPERTENSIONAHA.120.15580 161. Kirrane EF, Luben TJ, Benson A, et al. A systematic review of cardiovascular responses associated with ambient black carbon and fine particulate matter. Environ Int. 2019;127:305-316. doi:10.1016/j.envint.2019.02.027 162. Delfino RJ, Sioutas C, Malik S. Potential role of ultrafine particles in associations between airborne particle mass and cardiovascular health. Environ Health Perspect. 2005;113(8):934-946. doi:10.1289/ehp.7938 163. Chen H, Zhu Y, Shi L, et al. Long-term exposure to air pollution and lipometabolic disturbance: A systematic review and meta-analysis. medRxiv. Published online 2020. doi:10.1101/2020.05.19.20106849 164. Frampton MW, Stewart JC, Oberdörster G, et al. Inhalation of Ultrafine Particles Alters Blood Leukocyte Expression of Adhesion Molecules in Humans. Environ Health Perspect. 2006;114(1):51-58. doi:10.1289/ehp.7962 165. Kumar A, Bicer EM, Morgan AB, et al. Enrichment of immunoregulatory proteins in the biomolecular corona of nanoparticles within human respiratory tract lining fluid. Nanomedicine Nanotechnology, Biol Med. 2016;12(4):1033-1043. doi:10.1016/j.nano.2015.12.369 166. Xiao GG, Wang M, Li N, Loo JA, Nel AE. Use of Proteomics to Demonstrate a Hierarchical Oxidative Stress Response to Diesel Exhaust Particle Chemicals in a 114 Macrophage Cell Line. J Biol Chem. 2003;278(50):50781-50790. doi:10.1074/jbc.M306423200 167. Montgomery MT, Sajuthi SP, Cho S-H, et al. Genome-Wide Analysis Reveals Mucociliary Remodeling of the Nasal Airway Epithelium Induced by Urban PM 2.5. Am J Respir Cell Mol Biol. 2020;63(2):172-184. doi:10.1165/rcmb.2019-0454OC 168. Rahman ML, Bassig BA, Dai Y, et al. Proteomic analysis of serum in workers exposed to diesel engine exhaust. Environ Mol Mutagen. 2022;63(1):18-28. doi:10.1002/em.22469 169. Orach J, Rider CF, Yuen ACY, Carlsten C. Concentration-dependent increase in symptoms due to diesel exhaust in a controlled human exposure study. Part Fibre Toxicol. 2022;19(1):66. doi:10.1186/s12989-022-00506-6 170. Orach J, Rider CF, Yuen ACY, Schwartz C, Mookherjee N, Carlsten C. Controlled Diesel Exhaust Exposure Induces a Concentration-dependent Increase in Airway Inflammation: A Clinical Trial. Ann Am Thorac Soc. 2023;20(6):834-842. doi:10.1513/AnnalsATS.202209-762OC 171. Leek J, Johnson W, Parker H, et al. sva: Surrogate Variable Analysis. 2022;3.44.0. https://bioconductor.org/packages/release/bioc/html/sva.html 172. Robinson MD, McCarthy DJ, Smyth GK. edgeR : a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010;26(1):139-140. doi:10.1093/bioinformatics/btp616 173. Günther A, Becker M, Göpfert J, Joos T, Schneiderhan-Marra N. Comparison of Bead-Based Fluorescence Versus Planar Electrochemiluminescence Multiplex Immunoassays for Measuring Cytokines in Human Plasma. Front Immunol. 2020;11:2486. doi:10.3389/fimmu.2020.572634 174. Coates AL, Wanger J, Cockcroft DW, et al. ERS technical standard on bronchial challenge testing: General considerations and performance of methacholine challenge tests. Eur Respir J. 2017;49(5):1601526. doi:10.1183/13993003.01526-2016 175. Dweik RA, Boggs PB, Erzurum SC, et al. An Official ATS Clinical Practice Guideline: Interpretation of Exhaled Nitric Oxide Levels (F <scp>e</scp> NO ) for Clinical Applications. Am J Respir Crit Care Med. 2011;184(5):602-615. doi:10.1164/rccm.9120-11ST 176. Pinheiro J, Bates D, Team RC. nlme: Linear and Nonlinear Mixed Effects Models. Published online 2022. https://cran.r-project.org/package=nlme 177. Storey J, Bass A, Dabney A, Robinson D. qvalue: Q-value estimation for false discovery rate control. Published online 2022. http://github.com/jdstorey/qvalue 178. Wang J, Duncan D, Shi Z, Zhang B. WEB-based GEne SeT AnaLysis Toolkit (WebGestalt): update 2013. Nucleic Acids Res. 2013;41(W1):W77-W83. doi:10.1093/nar/gkt439 179. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J R Stat Soc Ser B. 1995;57(1):289-300. 115 doi:10.1111/j.2517-6161.1995.tb02031.x 180. Rohart F, Gautier B, Singh A, Lê Cao KA. mixOmics: An R package for ‘omics feature selection and multiple data integration. PLOS Comput Biol. 2017;13(11):e1005752. doi:10.1371/JOURNAL.PCBI.1005752 181. Xia J, Broadhurst DI, Wilson M, Wishart DS. Translational biomarker discovery in clinical metabolomics: an introductory tutorial. Metabolomics. 2013;9(2):280-299. doi:10.1007/s11306-012-0482-9 182. Perttula K, Schiffman C, Edmands WMB, et al. Untargeted lipidomic features associated with colorectal cancer in a prospective cohort. BMC Cancer. 2018;18(1):996. doi:10.1186/s12885-018-4894-4 183. Kalia V, Kulick ER, Vardarajan B, et al. Linking air pollution exposure to blood-based metabolic features in a community-based aging cohort. Published online 2022:1-42. doi:doi.org/10.1101/2022.12.29.22284045 184. Bakdash JZ, Marusich LR. Repeated Measures Correlation. Front Psychol. 2017;8(MAR):456. doi:10.3389/fpsyg.2017.00456 185. Wills-Karp M. Complement activation pathways: A bridge between innate and adaptive immune responses in asthma. In: Proceedings of the American Thoracic Society. Vol 4. ; 2007:247-251. doi:10.1513/pats.200704-046AW 186. Walters DM, Breysse PN, Schofield B, Wills-Karp M. Complement Factor 3 Mediates Particulate Matter–Induced Airway Hyperresponsiveness. Am J Respir Cell Mol Biol. 2002;27(4):413-418. doi:10.1165/rcmb.4844 187. Rhim T, Choi Y-S, Nam B-Y, et al. Plasma protein profiles in early asthmatic responses to inhalation allergen challenge. Allergy. 2009;64(1):47-54. doi:10.1111/j.1398-9995.2008.01817.x 188. Gharib SA, Nguyen E V., Lai Y, Plampin JD, Goodlett DR, Hallstrand TS. Induced sputum proteome in healthy subjects and asthmatic patients. J Allergy Clin Immunol. 2011;128(6):1176-1184.e6. doi:10.1016/j.jaci.2011.07.053 189. Takahashi K, Banda NK, Holers VM, Van Cott EM. Complement component factor B has thrombin-like activity. Biochem Biophys Res Commun. 2021;552:17. doi:10.1016/J.BBRC.2021.02.134 190. Noris M, Galbusera M. The complement alternative pathway and hemostasis. Immunol Rev. 2023;313(1):139-161. doi:10.1111/imr.13150 191. Ivetic A, Green HLH, Hart SJ. L-selectin: A major regulator of leukocyte adhesion, migration and signaling. Front Immunol. 2019;10(MAY):1068. doi:10.3389/fimmu.2019.01068 192. Bouillot S, Tillet E, Carmona G, et al. Protocadherin-12 cleavage is a regulated process mediated by ADAM10 protein: Evidence of shedding up-regulation in pre-eclampsia. J Biol Chem. 2011;286(17):15195-15204. doi:10.1074/jbc.M111.230045 116 193. Piktel E, Levental I, Durnaś B, Janmey PA, Bucki R. Plasma gelsolin: Indicator of inflammation and its potential as a diagnostic tool and therapeutic target. Int J Mol Sci. 2018;19(9):2516. doi:10.3390/ijms19092516 194. Kristiansson A, Davidsson S, Johansson ME, et al. α1-Microglobulin (A1M) Protects Human Proximal Tubule Epithelial Cells from Heme-Induced Damage In Vitro. Int J Mol Sci. 2020;21(16):1-16. doi:10.3390/IJMS21165825 195. Xie MZ, Liu JL, Gao QZ, et al. Proteomics-based evaluation of the mechanism underlying vascular injury via DNA interstrand crosslinks, glutathione perturbation, mitogen-activated protein kinase, and Wnt and ErbB signaling pathways induced by crotonaldehyde. Clin Proteomics. 2022;19(1):1-17. doi:10.1186/S12014-022-09369-7/FIGURES/5 196. Swallow CJ, Partridge EA, Macmillan JC, et al. α2HS-glycoprotein, an Antagonist of Transforming Growth Factor β In vivo , Inhibits Intestinal Tumor Progression. Cancer Res. 2004;64(18):6402-6409. doi:10.1158/0008-5472.CAN-04-1117 197. Bozzetto S, Carraro S, Giordano G, Boner A, Baraldi E. Asthma, allergy and respiratory infections: The vitamin D hypothesis. Allergy Eur J Allergy Clin Immunol. 2012;67(1):10-17. doi:10.1111/j.1398-9995.2011.02711.x 198. Markiewski MM, Lambris JD. The Role of Complement in Inflammatory Diseases From Behind the Scenes into the Spotlight. Am J Pathol. 2007;171(3):715-727. doi:10.2353/ajpath.2007.070166 199. Nilsson SC, Sim RB, Lea SM, Fremeaux-Bacchi V, Blom AM. Complement factor I in health and disease. Mol Immunol. 2011;48(14):1611-1620. doi:10.1016/j.molimm.2011.04.004 200. Lachmann PJ. The story of complement factor I. Immunobiology. 2019;224(4):511-517. doi:10.1016/j.imbio.2019.05.003 201. Takeda K, Thurman JM, Tomlinson S, et al. The Critical Role of Complement Alternative Pathway Regulator Factor H in Allergen-Induced Airway Hyperresponsiveness and Inflammation. J Immunol. 2012;188(2):661-667. doi:10.4049/jimmunol.1101813 202. Fiorito G, Vlaanderen J, Polidoro S, et al. Oxidative stress and inflammation mediate the effect of air pollution on cardio- and cerebrovascular disease: A prospective study in nonsmokers. Environ Mol Mutagen. 2018;59(3):234-246. doi:10.1002/em.22153 203. Kurt OK, Zhang J, Pinkerton KE. Pulmonary health effects of air pollution. Curr Opin Pulm Med. 2016;22(2):138-143. doi:10.1097/MCP.0000000000000248 204. WHO. Burden of Disease from Ambient Air Pollution for 2012.; 2012. doi:10.1289/ehp.1307049 205. Pinkerton KE, Chen CY, Mack SM, Upadhyay P, Wu CW, Yuan W. Cardiopulmonary Health Effects of Airborne Particulate Matter: Correlating Animal Toxicology to Human Epidemiology. Toxicol Pathol. 2019;47(8):954-961. doi:10.1177/0192623319879091 206. Huang Y, Zhu M, Ji M, et al. Air Pollution, Genetic Factors, and the Risk of Lung Cancer: 117 A Prospective Study in the UK Biobank. Am J Respir Crit Care Med. 2021;204(7):817-825. doi:10.1164/rccm.202011-4063OC 207. Vermeulen R, Silverman DT, Garshick E, Vlaanderen J, Portengen L, Steenland K. Exposure-Response Estimates for Diesel Engine Exhaust and Lung Cancer Mortality Based on Data from Three Occupational Cohorts. Environ Health Perspect. 2014;122(2):172-177. doi:10.1289/ehp.1306880 208. Int Panis L, Provost EB, Cox B, et al. Short-term air pollution exposure decreases lung function: a repeated measures study in healthy adults. Environ Health. 2017;16(1):60. doi:10.1186/s12940-017-0271-z 209. Wu S, Deng F, Hao Y, et al. Fine particulate matter, temperature, and lung function in healthy adults: Findings from the HVNR study. Chemosphere. 2014;108:168-174. doi:10.1016/j.chemosphere.2014.01.032 210. Guo C, Lv S, Liu Y, Li Y. Biomarkers for the adverse effects on respiratory system health associated with atmospheric particulate matter exposure. J Hazard Mater. 2022;421:126760. doi:10.1016/j.jhazmat.2021.126760 211. Ghio AJ, Smith CB, Madden MC. Diesel exhaust particles and airway inflammation. Curr Opin Pulm Med. 2012;18(2):144-150. doi:10.1097/MCP.0b013e32834f0e2a 212. Saber AT, Jacobsen NR, Jackson P, et al. Particle-induced pulmonary acute phase response may be the causal link between particle inhalation and cardiovascular disease. Wiley Interdiscip Rev Nanomedicine Nanobiotechnology. 2014;6(6):517-531. doi:10.1002/wnan.1279 213. Laumbach RJ, Kipen HM, Ko S, et al. A controlled trial of acute effects of human exposure to traffic particles on pulmonary oxidative stress and heart rate variability. Part Fibre Toxicol. 2014;11(1):1-12. doi:10.1186/s12989-014-0045-5 214. Berger M, de Boer JD, Lutter R, et al. Pulmonary challenge with carbon nanoparticles induces a dose-dependent increase in circulating leukocytes in healthy males. BMC Pulm Med. 2017;17(1):121. doi:10.1186/s12890-017-0463-x 215. Alexis NE, Huang YCT, Rappold AG, Kehrl H, Devlin RB, Peden DB. Patients with asthma demonstrate airway inflammation after exposure to concentrated ambient particulate matter. Am J Respir Crit Care Med. 2014;190(2):235-237. doi:10.1164/rccm.201401-0126LE 216. Behndig AF, Mudway IS, Brown JL, et al. Airway antioxidant and inflammatory responses to diesel exhaust exposure in healthy humans. Eur Respir J. 2006;27(2):359-365. doi:10.1183/09031936.06.00136904 217. Barraza-Villarreal A, Sunyer J, Hernandez-Cadena L, et al. Air pollution, airway inflammation, and lung function in a cohort Study of Mexico City Schoolchildren. Environ Health Perspect. 2008;116(6):832-838. doi:10.1289/ehp.10926 218. Zhang X, Hirota JA, Yang C, Carlsten C. Effect of GST variants on lung function following diesel exhaust and allergen co-exposure in a controlled human crossover study. Free Radic Biol Med. 2016;96:385-391. doi:10.1016/j.freeradbiomed.2016.04.202 118 219. Giles L V, Carlsten C, Koehle MS. The effect of pre-exercise diesel exhaust exposure on cycling performance and cardio-respiratory variables. Inhal Toxicol. 2012;24(12):783-789. doi:10.3109/08958378.2012.717649 220. Madden MC, Stevens T, Case M, et al. Diesel exhaust modulates ozone-induced lung function decrements in healthy human volunteers. Part Fibre Toxicol. 2014;11(1):37. doi:10.1186/s12989-014-0037-5 221. Hussain S, Laumbach RJ, Coleman J, et al. Controlled exposure to diesel exhaust causes increased nitrite in exhaled breath condensate among subjects with asthma. J Occup Environ Med. 2012;54(10):1186-1191. doi:10.1097/JOM.0b013e31826bb64c 222. Sterk PJ, Fabbri LM, Quanjer PH, et al. Airway responsiveness. Standardized challenge testing with pharmacological, physical and sensitizing stimuli in adults. In: European Respiratory Journal, Supplement. Vol 6. ; 1993:53-83. 223. Jokic R, Davis EE, Cockcroft DW. Methacholine PC20 extrapolation. Chest. 1998;114(6):1796-1797. doi:10.1378/chest.114.6.1796-a 224. de Meer G, Marks GB, de Jongste JC, Brunekreef B. Airway responsiveness to hypertonic saline: Dose-response slope or PD15? Eur Respir J. 2005;25(1):153-158. doi:10.1183/09031936.04.00008004 225. Koren HS, Hatch GE, Graham DE. Nasal lavage as a tool in assessing acute inflammation in response to inhaled pollutants. Toxicology. 1990;60(1-2):15-25. doi:10.1016/0300-483X(90)90159-E 226. Xu Y, Barregard L, Nielsen J, et al. Effects of diesel exposure on lung function and inflammation biomarkers from airway and peripheral blood of healthy volunteers in a chamber study. Part Fibre Toxicol. 2013;10(1):60. doi:10.1186/1743-8977-10-60 227. Dunning K, Safo AO. The ultimate Wright-Giemsa stain: 60 years in the making. Biotech Histochem. 2011;86(2):69-75. doi:10.3109/10520295.2010.515496 228. Laboratory Quality Assurance Program - College of Physicians & Surgeons of Saskatchewan. Laboratory Guidelines.; 2014. 229. Stiene-Martin EA, Lotspeich-Steininger CA, Koepke JA. Clinical Hematology : Principles, Procedures, Correlations. 2nd ed. (Stiene-Martin EA, ed.). Philadelphia : Lippincott-Raven; 1998. Accessed March 3, 2021. https://www-worldcat-org.ezproxy.library.ubc.ca/title/clinical-hematology-principles-procedures-correlations/oclc/37031312 230. Rebuli ME, Speen AM, Clapp PW, Jaspers I. Novel applications for a noninvasive sampling method of the nasal mucosa. Am J Physiol Cell Mol Physiol. 2017;312(2):L288-L296. doi:10.1152/ajplung.00476.2016 231. Huff RD, Carlsten C, Hirota JA. An update on immunologic mechanisms in the respiratory mucosa in response to air pollutants. J Allergy Clin Immunol. 2019;143(6):1989-2001. doi:10.1016/j.jaci.2019.04.012 232. Rudell B, Wass U, Hörstedt P, et al. Efficiency of automotive cabin air filters to reduce 119 acute health effects of diesel exhaust in human subjects. Occup Environ Med. 1999;56(4):222-231. doi:10.1136/oem.56.4.222 233. Behndig AF, Larsson N, Brown JL, et al. Proinflammatory doses of diesel exhaust in healthy subjects fail to elicit equivalent or augmented airway inflammation in subjects with asthma. Thorax. 2011;66(1):12-19. doi:10.1136/thx.2010.140053 234. Alexis NE, Carlsten C. Interplay of air pollution and asthma immunopathogenesis: A focused review of diesel exhaust and ozone. Int Immunopharmacol. 2014;23(1):347-355. doi:10.1016/j.intimp.2014.08.009 235. Bastain TM, Gilliland FD, Li YF, Saxon A, Diaz-Sanchez D. Intraindividual reproducibility of nasal allergic responses to diesel exhaust particles indicates a susceptible phenotype. Clin Immunol. 2003;109(2):130-136. doi:10.1016/S1521-6616(03)00168-2 236. Diaz-Sanchez D, Tsien A, Fleming J SA. Combined diesel exhaust particulate and ragweed allergen challenge markedly enhances human in vivo nasal ragweed-specific IgE and skews cytokine production to a T helper cell 2-type pat- tern. J Immunol. 1997;158(5):2406-2413. Accessed June 3, 2019. http://www.jimmunol.org/ 237. Lane C, Knight D, Burgess S, et al. Epithelial inducible nitric oxide synthase activity is the major determinant of nitric oxide concentration in exhaled breath. Thorax. 2004;59(9):757-760. doi:10.1136/thx.2003.014894 238. Menzies-Gow A, Mansur AH, Brightling CE. Clinical utility of fractional exhaled nitric oxide in severe asthma management. Eur Respir J. 2020;55(3):1901633. doi:10.1183/13993003.01633-2019 239. Guo FH, De Raeve HR, Rice4 TW, Stuehr DJ, Thunnisseni FBJM, Erzurum SC. Continuous Nitric Oxide Synthesis by Inducible Nitric Oxide Synthase in Normal Human Airway Epithelium in Vivo. Vol 92.; 1995. 240. Korhonen R, Lahti A, Kankaanranta H, Moilanen E. Nitric Oxide Production and Signaling in Inflammation. Curr Drug Target -Inflammation Allergy. 2005;4(4):471-479. doi:10.2174/1568010054526359 241. Barath S, Mills NL, Ädelroth E, Olin AC, Blomberg A. Diesel exhaust but not ozone increases fraction of exhaled nitric oxide in a randomized controlled experimental exposure study of healthy human subjects. Environ Heal A Glob Access Sci Source. 2013;12(1):36. doi:10.1186/1476-069X-12-36 242. Carlsten C, MacNutt MJ, Zhang Z, Sava F, Pui M. Anti-oxidant N-acetylcysteine diminishes diesel exhaust-induced increased airway responsiveness in person with airway hyper-reactivity. Toxicol Sci. 2014;139(2):479-487. doi:10.1093/toxsci/kfu040 243. Nightingale JA, Maggs R, Cullinan P, et al. Airway inflammation after controlled exposure to diesel exhaust particulates. Am J Respir Crit Care Med. 2000;162(1):161-166. doi:10.1164/ajrccm.162.1.9908092 244. Sehlstedt M, Behndig AF, Boman C, Blomberg A, Sandström T, Pourazar J. Airway inflammatory response to diesel exhaust generated at urban cycle running conditions. 120 Inhal Toxicol. 2010;22(14):1144-1150. doi:10.3109/08958378.2010.529181 245. Ghio AJ, Kim C, Devlin RB. Concentrated Ambient Air Particles Induce Mild Pulmonary Inflammation in Healthy Human Volunteers. Crit Care Med. 2000;162(3):981-988. Accessed June 6, 2019. www.atsjournals.org 246. Nordenhäll C, Pourazar J, Ledin MC, Levin JO, Sandström T, Ädelroth E. Diesel exhaust enhances airway responsiveness in asthmatic subjects. Eur Respir J. 2001;17(5):909-915. doi:10.1183/09031936.01.17509090 247. Janssen NAH, Strak M, Yang A, et al. Associations between three specific a-cellular measures of the oxidative potential of particulate matter and markers of acute airway and nasal inflammation in healthy volunteers. Occup Environ Med. 2015;72(1):49-56. doi:10.1136/oemed-2014-102303 248. Leikauf GD, Kim S-H, Jang A-S. Mechanisms of ultrafine particle-induced respiratory health effects. Exp Mol Med. Published online 2020. doi:10.1038/s12276-020-0394-0 249. Muñoz X, Barreiro E, Bustamante V, Lopez-Campos JL, González-Barcala FJ, Cruz MJ. Diesel exhausts particles: Their role in increasing the incidence of asthma. Reviewing the evidence of a causal link. Sci Total Environ. 2019;652:1129-1138. doi:10.1016/j.scitotenv.2018.10.188 250. Gong H, Linn WS, Sioutas C, et al. Controlled exposures of healthy and asthmatic volunteers to concentrated ambient fine particles in Los Angeles. Inhal Toxicol. 2003;15(4):305-325. doi:10.1080/08958370304455 251. Pirozzi CS, Mendoza DL, Xu Y, Zhang Y, Scholand MB, Baughman RP. Short-term particulate air pollution exposure is associated with increased severity of respiratory and quality of life symptoms in patients with fibrotic sarcoidosis. Int J Environ Res Public Health. 2018;15(6):1077. doi:10.3390/ijerph15061077 252. Yu O, Sheppard L, Lumley T, Koenig JQ, Shapiro GG. Effects of Ambient Air Pollution on Symptoms of Asthma in Seattle-Area Children Enrolled in the CAMP Study. Environ Health Perspect. 2000;108(12):1209. doi:10.2307/3434835 253. Mandell JT, Idarraga M, Kumar N, Galor A. Impact of Air Pollution and Weather on Dry Eye. J Clin Med. 2020;9(11):3740. doi:10.3390/jcm9113740 254. Xu Y, Kåredal M, Nielsen J, et al. Exposure, respiratory symptoms, lung function and inflammation response of road-paving asphalt workers. Occup Environ Med. 2018;75:494-500. doi:10.1136/oemed-2017-104983 255. Li Z, Commodore A, Hartinger S, et al. Biomonitoring Human Exposure to Household Air Pollution and Association with Self-reported Health Symptoms – A Stove Intervention Study in Peru. Environ Int. 2016;97:195-203. doi:10.1016/j.envint.2016.09.011 256. Barraza-Villarreal A, Sunyer J, Hernandez-Cadena L, et al. Air pollution, airway inflammation, and lung function in a cohort Study of Mexico City Schoolchildren. Environ Health Perspect. 2008;116(6):832-838. doi:10.1289/ehp.10926 257. Aitken RCB. A Growing Edge of Measurement of Feelings [Abridged]: Measurement of 121 Feelings Using Visual Analogue Scales. Proc R Soc Med. 1969;62(10):989-993. doi:10.1177/003591576906201006 258. Bausewein C, Farquhar M, Booth S, Gysels M, Higginson IJ. Measurement of breathlessness in advanced disease: A systematic review. Respir Med. 2007;101(3):399-410. doi:10.1016/j.rmed.2006.07.003 259. Yoon S, Han S, Jeon K-J, Kwon S. Effects of collected road dusts on cell viability, inflammatory response, and oxidative stress in cultured human corneal epithelial cells. Toxicol Lett. 2018;284:152-160. doi:10.1016/j.toxlet.2017.12.012 260. Tan G, Li J, Yang Q, et al. Air pollutant particulate matter 2.5 induces dry eye syndrome in mice. Sci Rep. 2018;8(1):17828. doi:10.1038/s41598-018-36181-x 261. Song SJ, Hyun S-W, Lee TG, Park B, Jo K, Kim C-S. New application for assessment of dry eye syndrome induced by particulate matter exposure. Ecotoxicol Environ Saf. 2020;205:111125. doi:10.1016/j.ecoenv.2020.111125 262. Mu N, Wang H, Chen D, et al. A Novel Rat Model of Dry Eye Induced by Aerosol Exposure of Particulate Matter. Investig Opthalmology Vis Sci. 2022;63(1):39. doi:10.1167/iovs.63.1.39 263. Mo Z, Fu Q, Lyu D, et al. Impacts of air pollution on dry eye disease among residents in Hangzhou, China: A case-crossover study. Environ Pollut. 2019;246:183-189. doi:10.1016/j.envpol.2018.11.109 264. Larsson N, Brown J, Stenfors N, et al. Airway inflammatory responses to diesel exhaust in allergic rhinitics. Inhal Toxicol. 2013;25(3):160-167. doi:10.3109/08958378.2013.765932 265. Petrowski K, Bührer S, Strauß B, Decker O, Brähler E. Examining air pollution (PM10), mental health and well-being in a representative German sample. Sci Rep. 2021;11(1):18436. doi:10.1038/s41598-021-93773-w 266. Power MC, Kioumourtzoglou M-A, Hart JE, Okereke OI, Laden F, Weisskopf MG. The relation between past exposure to fine particulate air pollution and prevalent anxiety: observational cohort study. BMJ. 2015;350:h1111. doi:10.1136/bmj.h1111 267. Vert C, Sánchez-Benavides G, Martínez D, et al. Effect of long-term exposure to air pollution on anxiety and depression in adults: A cross-sectional study. Int J Hyg Environ Health. 2017;220(6):1074-1080. doi:10.1016/j.ijheh.2017.06.009 268. Huang J, Deng F, Wu S, Lu H, Hao Y, Guo X. The impacts of short-term exposure to noise and traffic-related air pollution on heart rate variability in young healthy adults. J Expo Sci Environ Epidemiol. 2013;23(5):559-564. doi:10.1038/jes.2013.21 269. Biel R, Danieli C, Shekarrizfard M, et al. Acute cardiovascular health effects in a panel study of personal exposure to traffic-related air pollutants and noise in Toronto, Canada. Sci RepoRtS |. 123AD;10:16703. doi:10.1038/s41598-020-73412-6 270. Zanobetti A, Luttmann-Gibson H, Horton ES, et al. Brachial artery responses to ambient pollution, temperature, and humidity in people with type 2 diabetes: A repeated-measures study. Environ Health Perspect. 2014;122(3):242-248. doi:10.1289/ehp.1206136 122 271. Li H, Bai H, Yang C, et al. Acute effects of ambient temperature and particulate air pollution on fractional exhaled nitric oxide: A panel study among diabetic patients in Shanghai, China. J Epidemiol. 2017;27(12):584-589. doi:10.1016/j.je.2017.01.002 272. Anenberg SC, Haines S, Wang E, Nassikas N, Kinney PL. Synergistic health effects of air pollution, temperature, and pollen exposure: a systematic review of epidemiological evidence. Environ Heal. 2020;19(1):130. doi:10.1186/s12940-020-00681-z 273. Stockfelt L, Xu Y, Gudmundsson A, et al. A controlled chamber study of effects of exposure to diesel exhaust particles and noise on heart rate variability and endothelial function. https://doi.org/101080/0895837820222065388. Published online April 27, 2022:1-12. doi:10.1080/08958378.2022.2065388 274. Hemmingsen JG, Møller P, Jantzen K, et al. Controlled exposure to diesel exhaust and traffic noise - Effects on oxidative stress and activation in mononuclear blood cells. Mutat Res - Fundam Mol Mech Mutagen. 2015;775:66-71. doi:10.1016/j.mrfmmm.2015.03.009 275. Chiarini B, D’Agostino A, Marzano E, Regoli A. The perception of air pollution and noise in urban environments: A subjective indicator across European countries. J Environ Manage. 2020;263(March):110272. doi:10.1016/j.jenvman.2020.110272 276. Martens AL, Reedijk M, Smid T, et al. Modeled and perceived RF-EMF, noise and air pollution and symptoms in a population cohort. Is perception key in predicting symptoms? Sci Total Environ. 2018;639:75-83. doi:10.1016/j.scitotenv.2018.05.007 277. Peng M, Zhang H, Evans RD, Zhong X, Yang K. Actual Air Pollution, Environmental Transparency, and the Perception of Air Pollution in China. J Environ Dev. 2019;28(1):78-105. doi:10.1177/1070496518821713 278. Pantavou K, Lykoudis S, Psiloglou B. Air quality perception of pedestrians in an urban outdoor Mediterranean environment: A field survey approach. Sci Total Environ. 2017;574:663-670. doi:10.1016/j.scitotenv.2016.09.090 279. Azuma K, Ikeda K, Kagi N, Yanagi U, Osawa H. Prevalence and risk factors associated with nonspecific building-related symptoms in office employees in Japan: relationships between work environment, Indoor Air Quality, and occupational stress. Indoor Air. 2015;25(5):499-511. doi:10.1111/ina.12158 280. Lercher P, Schmitzberger R, Kofler W. Perceived traffic air pollution, associated behavior and health in an alpine area. Sci Total Environ. 1995;169(1-3):71-74. doi:10.1016/0048-9697(95)04634-D 281. Mølhave L, Kjærgaard SK, Attermann J. Sensory and other neurogenic effects of exposures to airborne office dust. Atmos Environ. 2000;34(28):4755-4766. doi:10.1016/S1352-2310(00)00266-1 282. Andersen MHG, Loft S, Bønløkke JH, Saber AT, Vogel U, Møller P. In: Ambient Combustion Ultrafine Particles and Health: CONTROLLED HUMAN EXPOSURE STUDIES. (Brugge D, Fuller CH, eds.). Nova Science Publishers, Inc; 2021. Accessed March 6, 2021. https://www.novapublishers.com/wp-content/uploads/2021/02/Controlled-Human-Exposure-Studies.pdf 123 283. Li J, Li WX, Bai C, Song Y. Particulate matter-induced epigenetic changes and lung cancer. Clin Respir J. 2017;11(5):539-546. doi:10.1111/crj.12389 284. Cromar KR, Lee AG, Harkema JR, Annesi-Maesano I. Science-based Policy Recommendations for Fine Particulate Matter in the United States. Am J Respir Crit Care Med. 2022;206(9):1067-1069. doi:10.1164/rccm.202203-0507ED 285. Sokhi RS, Moussiopoulos N, Baklanov A, et al. Advances in air quality research – current and emerging challenges. Atmos Chem Phys. 2022;22(7):4615-4703. doi:10.5194/acp-22-4615-2022 286. Kim KH, Kabir E, Kabir S. A review on the human health impact of airborne particulate matter. Environ Int. 2015;74:136-143. doi:10.1016/j.envint.2014.10.005 287. Cox LA, Boylan J, Frampton MW, et al. CASAC Review of the EPA’s Policy Assessment for the Review of the National Ambient Air Quality Standards for Particulate Matter (EPA-CASAC-20-001).; 2019. doi:10.2175/193864705783867675 288. Keefe A, Peters C, Telfer J, Slot N, Shergill S, Jardine K. Setting an Occupational Exposure Limit for Diesel Engine Exhaust in Canada: Challenges and Opportunities.; 2019. 289. Adams K, Greenbaum DS, Shaikh R, van Erp AM, Russell AG. Particulate matter components, sources, and health: Systematic approaches to testing effects. J Air Waste Manag Assoc. 2015;65(5):544-558. doi:10.1080/10962247.2014.1001884 290. Rhomberg LR, Chandalia JK, Long CM, Goodman JE. Measurement error in environmental epidemiology and the shape of exposure-response curves. Crit Rev Toxicol. 2011;41(8):651-671. doi:10.3109/10408444.2011.563420 124 Appendices Appendix A Table A1.1. Differential expression for proteomics with adjusted p value ≤ 0.05. UniProt ID Protein name Log fold change for FA vs DE150 Adjusted p value P05156 Complement factor I# -0.18 0.00 P14151 L-selectin# -0.18 0.01 Q9NPG4 Protocadherin-12# 0.17 0.01 P00751 Complement factor B# -0.22 0.01 P06396 Gelsolin# -0.16 0.02 P02679 Fibrinogen gamma chain# 0.33 0.02 P58335 Anthrax toxin receptor 2 0.29 0.02 P36980 Complement factor H-related protein 2# -0.17 0.03 P02760 Protein AMBP# -0.13 0.03 P13598 Intercellular adhesion molecule 2 -0.15 0.03 P35916 Vascular endothelial growth factor receptor 3 0.34 0.03 P01042 Kininogen-1# -0.18 0.03 P06702 Protein S100-A9 -0.35 0.03 P02749 Beta-2-glycoprotein 1 -0.15 0.03 P04217 Alpha-1B-glycoprotein -0.14 0.03 P02774 Vitamin D-binding protein# -0.15 0.05 P02765 Alpha-2-HS-glycoprotein# -0.14 0.05 P06681 Complement C2# -0.11 0.05 Abbreviations: FA = filtered air; DE = diesel exhaust. Pairwise differential expression analysis (DEqMS 1.14.0) was used to assess significant differences in protein abundance between exposure conditions. All significant differences were detected in the FA vs DE150 comparison; results with Benjamini Hochberg adjusted p values ≤ 0.05 are presented. # Proteins that were also selected by sparse partial least squares discriminant analysis and linear mixed effects models. 125 Appendix B Table B1.1. Significant concentration-response effect modification by demographic factors. Endpoint regressed with PM2.5 (Timepoint) Age (SE) BMI (SE) Sex:Female (SE) Complement factor I (24h) 4.24E-05 (9.12E-04) -7.24E-04 (1.87E-03) 3.95E-02 (1.43E-02) FeNO (%Δ4h) -6.08E-03 (7.25E-03) -4.39E-03 (1.49E-02) 7.92E-02 (1.23E-01) Total symptoms (Δ4h) -1.79E-03 (2.99E-03) -5.35E-03 (6.10E-03) 3.87E-02 (5.04E-02) Abbreviations: BMI = body mass index; FeNO = fractional exhaled nitric oxide; SE = standard error. Concentration-responses were estimated using linear mixed effects models with participant ID as a random intercept and demographic factors as interaction terms. Significant effects (p < 0.05) are bolded. 126 Figure B1.1. Concentration-response effect modification of complement factor I by sex. Enzyme-linked immunosorbent assays were used to quantify complement factor I in blood (at 24h) from 15 health non-smokers exposed to filtered air and DE standardized to 20, 50 and 150 µg/m3 PM2.5 for 4h, separated by ≥4-week washout periods. Y axes show protein concentration (µg/mL), while X axes show PM2.5 µg/m3. Shaded grey regions are 95% confidence intervals. Linear mixed effects models were used with an interaction term for sex and a participant ID intercept to account for repeated measures: **p < 0.05, *p = 0.051 – 0.1.
Thesis/Dissertation
eng
Experimental Medicine
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Carlsten, Christopher Russell
Concentration-dependent effects of diesel exhaust exposure on the human proteome and airways : a double-blinded, crossover study
Text