SCHOOL OF PUBLIC HEALTH COLLEGE OF HEALTH SCIENCES UNIVERSITY OF GHANA, LEGON OCCUPATIONAL EXPOSURES AND EPIGENETICS ALTERATION AMONG ELECTRONIC WASTE WORKERS AT AGBOGBLOSHIE, GHANA BY IBRAHIM ISSAH (10506768) THIS THESIS IS SUBMITTED TO THE UNIVERSITY OF GHANA, LEGON, IN PARTIAL FULFILMENT OF THE REQUIREMENT FOR THE AWARD OF PHD IN PUBLIC HEALTH DEGREE SEPTEMBER, 2022 University of Ghana http://ugspace.ug.edu.gh i DECLARATION I, Ibrahim Issah, hereby declare that this thesis is the result of my own original research, toward the award of Doctor of Philosophy in Public Health, except for areas where specific references have been made and duly acknowledged. I also affirm that the studies reported in this document were carried out by me under the supervision of my team of academic supervisors. Lastly, I declare that this work has not been submitted, either in part or in whole, to any other institution for an award of a degree. ………………………………………. ………………………………... Ibrahim Issah Date (Student) ………………………………….. Professor Julius Fobil Date (Supervisor) …… ……………………… ………………………………… Dr. John Arko-Mensah Date (Co-supervisor) 24/02/2023 24/02/2023 24/02/2023 University of Ghana http://ugspace.ug.edu.gh ii DEDICATION To my dear wife and our lovely children, Aidan Baiwah, Najmah Hankuri, and Aiman Dadinkai, you are the best support group. University of Ghana http://ugspace.ug.edu.gh iii ACKNOWLEDGEMENTS Many individuals have provided mentorship, support, and encouragement throughout my time at the University of Ghana. This work would not have been possible without this support. First, I would like to acknowledge Dr. John Arko-Mensah for recommending me to the GEOHealth II project, which provided scholarship for my PhD. He did not only recommend me to the project, he mentored me throughout my research, and provided incredible support and encouragement throughout my studies. I am equally grateful to Professor Julius Fobil for accepting me as a PhD student on the GEOHealth II project, and for his immense support during my studies and contribution towards my professional development. I am fortunate enough to have had the opportunity to work with Professor Laura Rozek and Katie Zarins at the University of Michigan School of Public Health. They provided great technical support in the area of epigenetics that enriched my work. I am grateful to Professors Thomas Robins and Stuart Batterman for their mentorship and support during my stay at the University of Michigan and beyond. I would like to recognize all the study participants and the supporting staff of the GEOHealth II project who assisted in field data collection. Many thanks to Prof., Duah Dwomoh for providing me with statistical support. To all the staff of the Department of Biological, Environmental and Occupational Health Sciences, School of Public Health, University of Ghana who contributed in diverse ways towards my studies, I say thank you. I am incredibly blessed to have had the love and support of my family and friends during this challenging and rewarding period of my life. Specifically, I would like to acknowledge my mother, Ramatu Issah, my brothers, Mohammed Seidu and Inusah Issah, and my sister, Mariam Issah. They have been the best support group throughout this time, offering continuous support whenever I needed it. My sincere gratitude to my colleagues on the GEOHealth II project, University of Ghana http://ugspace.ug.edu.gh iv especially, Thomas Peprah Agyekum, for his support throughout my time at the University of Ghana. In addition, I acknowledge Dr Sylvia Takyi who performed the metals analysis during her time at McGill University, Montreal, Canada as a visiting scholar from the GEOHealth II project, with technical assistance by Andrea Santa-Rios, Hélène Lalande, Tianai Zhou, and Jenny Eng. ACKNOWLEDGEMENT OF FUNDING This study was financed by the ½ West Africa-Michigan CHARTER in GEO-Health with funding from the United States National Institutes of Health/Fogarty International Center(US NIH/FIC) (paired grant No 1U2RTW010110-01/5U01TW010101) and Canada’s International Development Research Center (IDRC) (grant No. 108121-001). University of Ghana http://ugspace.ug.edu.gh v TABLE OF CONTENT CONTENTS PAGE DECLARATION ........................................................................................................................ i DEDICATION ........................................................................................................................... ii ACKNOWLEDGEMENTS ......................................................................................................iii ACKNOWLEDGEMENT OF FUNDING ............................................................................... iv TABLE OF CONTENT ............................................................................................................. v LIST OF TABLES ..................................................................................................................... x LIST OF FIGURES .................................................................................................................. xi LIST OF ABBREVIATIONS .................................................................................................xiii OPERATIONAL DEFINITION OF TERMS .......................................................................... xv ABSTRACT ........................................................................................................................... xvii PREFACE ............................................................................................................................... xxi CHAPTER ONE ........................................................................................................................ 1 1.0 INTRODUCTION ............................................................................................................... 1 1.1 Background ...................................................................................................................... 1 1.2 Problem statement ............................................................................................................ 4 1.3 Conceptual framework ..................................................................................................... 6 1.4 Justification ...................................................................................................................... 9 1.5 Objectives ....................................................................................................................... 11 1.5.1 General objective ..................................................................................................... 11 1.5.2 Specific objectives ................................................................................................... 11 1.6 Research questions ......................................................................................................... 11 CHAPTER TWO ..................................................................................................................... 12 2.0 LITERATURE REVIEW .................................................................................................. 12 2.1 Introduction .................................................................................................................... 12 2.2 Electronic waste exposure and DNA damage: A systematic review and meta-analysis University of Ghana http://ugspace.ug.edu.gh vi ..............................................................................................................................................12 2.2.1 Introduction ............................................................................................................. 12 2.2.2 Protocol development and registration .................................................................... 12 2.2.3 Eligibility criteria ..................................................................................................... 13 2.2.4 Information sources and search strategy.................................................................. 13 2.2.5 Study selection ......................................................................................................... 13 2.2.6 Data extraction ......................................................................................................... 15 2.2.7 Risk of bias (quality) assessment ............................................................................. 16 2.2.8 Statistical analysis .................................................................................................... 16 2.2.9 Findings of the systematic review and meta-analysis .............................................. 18 2.2.10 Discussion .............................................................................................................. 33 2.2.11 Conclusion ............................................................................................................. 38 2.3 Electrical and electronic waste (e-waste) as a public health concern ......................... 39 2.4 Overview of DNA methylation .................................................................................. 50 2.4.3 Global DNA methylation and disease ..................................................................... 59 2.4.4 Methods for measuring global DNA methylation ................................................... 61 2.4.5 Occupational metals exposure and global DNA methylation .................................. 64 2.4.6 Occupational particulate matter exposure and global DNA methylation ................ 65 2.5 Conclusion .................................................................................................................. 68 CHAPTER THREE ................................................................................................................. 69 METHODS .............................................................................................................................. 69 3.1 Introduction .................................................................................................................... 69 3.2 Study sites ................................................................................................................... 69 3.3 Study design ................................................................................................................ 71 3.4 Study participants ....................................................................................................... 71 3.5 Sample size ................................................................................................................. 72 3.6 Sampling strategy ....................................................................................................... 72 University of Ghana http://ugspace.ug.edu.gh vii 3.7 Data collection methods ............................................................................................. 74 3.8 Laboratory analysis of metals in blood ....................................................................... 76 3.9 Extraction of DNA from whole blood ........................................................................ 77 3.10 Bisulphite conversion of DNA ................................................................................. 77 3.11 PCR amplification of Bisulphite converted DNA and pyrosequencing ................... 79 3.12 Statistical analysis ..................................................................................................... 81 3.13 Data storage and sharing ........................................................................................... 83 3.14 Ethical consideration ................................................................................................ 83 CHAPTER FOUR .................................................................................................................... 84 RESULTS ................................................................................................................................ 84 4.1 Introduction .................................................................................................................... 84 4.2 Sociodemographic characteristics of e-waste workers and reference population .......... 84 4.3 Results for objective 1 .................................................................................................... 86 4.3.1 LINE-1 DNA methylation in e-waste workers and reference population ................... 86 4.3.2 LINE-1 methylation levels of e-waste workers by primary job tasks performed ...88 4.4 Relationship between LINE-1 methylation and anthropometric and lifestyle factors ...89 4.5 Results for objective 2 .................................................................................................... 90 4.5.1 Particulate matter (PM) exposure in personal air among e-waste workers and reference population ......................................................................................................... 90 4.5.2 Personal particulate matter exposure across primary job-tasks ............................... 91 4.5.3 Metal concentrations in blood of e-waste workers and reference population ......... 92 4.6 Results for objective 3 .................................................................................................... 95 4.6.1 Associations between global DNA methylation and PM exposure ......................... 95 4.6.2 Global DNA methylation across job tasks .............................................................. 97 4.7 Results for objective 4 .................................................................................................... 97 4.7.1 Effect of single and multiple metals exposure on DNA methylation ...................... 97 University of Ghana http://ugspace.ug.edu.gh viii 4.7.2 Potential interaction effects of toxic and essential metals on global DNA methylation ..........................................................................................................................................98 CHAPTER FIVE ................................................................................................................... 103 5.0 DISCUSSION .................................................................................................................. 103 5.1 Introduction .................................................................................................................. 103 5.2 Discussion of results for specific objective 1 ............................................................... 103 5.2.1 Global (LINE-1) DNA methylation among e-waste workers and reference population ............................................................................................................................................103 5.2.2 LINE-1 methylation levels of e-waste workers by primary job tasks performed .104 5.3 Discussion of results for specific objective 2 ............................................................... 105 5.3.1 Exposure of e-waste workers to PM at recycling site ............................................ 105 5.3.2 Measurement of metals in blood of e-waste workers and reference population ...106 5.4 Discussion of results for specific objective 3 ............................................................... 108 5.4.1 Associations between global DNA methylation and PM exposure ....................... 108 5.5 Discussion of results for specific objective 4 ............................................................... 111 5.5.1 Association between a mixture of toxic and essential metals and global DNA methylation ..................................................................................................................... 111 CHAPTER SIX ...................................................................................................................... 114 CONCLUSIONS AND RECOMMENDATIONS ................................................................ 114 6.1 Introduction .................................................................................................................. 114 6.2 Conclusions .................................................................................................................. 114 6.3 Recommendations ........................................................................................................ 115 6.3.1 Recommendations e-waste workers ...................................................................... 115 6.3.2 Recommendations to policy makers ...................................................................... 116 6.4 Future research direction .............................................................................................. 116 6.5 Strengths and Limitations of the study ......................................................................... 117 6.5 Contribution to knowledge ........................................................................................... 118 References .............................................................................................................................. 120 University of Ghana http://ugspace.ug.edu.gh ix APPENDICES ....................................................................................................................... 168 Appendix 1: Ethical clearance............................................................................................ 168 Appendix 2: Consent Form ................................................................................................ 170 Appendix 3: Questionnaire................................................................................................. 176 APPENDIX 4: Summary of elemental biomarker quality control measures ..................... 197 Appendix 5: Supplementary Table of Results .................................................................... 198 Appendix 5.1: Search strategies and results from different electronic databases 198 Appendix 5.2: Excluded articles and the reasons for their exclusion ............................ 199 Appendix 5.3: Quality assessment of included studies based on the modified Newcastle– Ottawa Scale for cross-sectional studies ........................................................................ 200 Appendix 5.4: Summary of DNA damage biomarkers and samples used. Blue cells indicate biomarkers assessed in the study, and gray cells indicate the type of sample used. ........................................................................................................................................201 Appendix 5.5: Comparison of the levels of PM2.5 and PM10 exposure with WHO and NAAQS reference values ............................................................................................... 202 Appendix 5.6: Associations between total and specific CpG sites DNA methylation of LINE-1 and levels of PM exposure in e-waste exposure workers (n=100) and controls (n=51) ............................................................................................................................. 203 Appendix 5.7: Associations between total and specific CpG sites DNA methylation of LINE-1 and levels of metals exposure in e-waste exposure workers (n=100) and controls (n=51) ............................................................................................................................. 205 Appendix 6: Abstract of manuscript published related to this work .................................. 206 Appendix 7: Abstract of manuscript published related to this work .................................. 207 Appendix 8: Conference abstract publication and poster presentation related to this work (ISEE 2020). https://ehp.niehs.nih.gov/doi/abs/10.1289/isee.2020.virtual.P-0459 ........... 208 University of Ghana http://ugspace.ug.edu.gh x LIST OF TABLES Table 1: Summary results of previous occupational studies including exposure groups, study location, samples used, and key findings ................................................................................. 19 Table 2: Summary of previous ecological studies, including exposure groups, study location, samples used, and key findings ................................................................................................ 21 Table 3: Previous epidemiological studies of exposure to heavy metals associated with e-waste work ......................................................................................................................................... 45 Table 4: Characteristics of methods used to measure global DNA methylation ..................... 63 Table 5: Previous epidemiological studies on the association between exposure to occupational metals and PM and global DNA methylation .......................................................................... 67 Table 6: Bisulfite conversion thermal cycler conditions ......................................................... 78 Table 7: Characteristics of e-waste workers (n=100) and controls (n=51) enrolled for the study, March 2017-May 2017 at Agbogbloshie and Madina Zongo, Accra, Ghana .......................... 85 Table 8: Relationship between LINE-1 methylation and anthropometric and lifestyle factors ..................................................................................................................................................90 Table 9: Metal concentrations in blood of e-waste workers and reference population ........... 94 Table 10: Metals concentrations in blood across job tasks ...................................................... 94 Table 11: Associations between PM exposure and total vs CpG site specific DNA methylation of LINE-1 in total sample (n=151) .......................................................................................... 96 Table 12: Electronic waste recycling activities and LINE-1 DNA methylation using non-e- waste workers as reference ...................................................................................................... 97 Table 13: Single metal linear regression models of Global Repetitive (LINE-1) Methylation. Mean methylation is modelled for average CpG sites (all) and specific CpG sites (n=151) 100 Table 14: Multiple metals linear regression models of Global Repetitive (LINE-1) Methylation. Mean methylation is modelled for average CpG sites (all) and specific CpG sites (n=151) 101 University of Ghana http://ugspace.ug.edu.gh xi LIST OF FIGURES Figure 1: Conceptual framework showing linkages between PM and metals exposure and alteration of global (LINE-1) DNA methylation ....................................................................... 8 Figure 2: PRISMA flow chart illustrating the process of selecting the studies included in the systematic review ..................................................................................................................... 15 Figure 3: Standardized mean difference of micronuclei frequency for e-waste exposed population vs control................................................................................................................ 31 Figure 4: Sub-group analysis by study setting ......................................................................... 32 Figure 5: Sub-group analysis by study quality......................................................................... 33 Figure 6: Methylation at the 5’ position of the cytosine moiety is catalyzed by DNMT in the presence of S-adenosyl-methionine (SAM) ............................................................................. 51 Figure 7: Bimodal pattern of DNA methylation. Unmethylated promotor CpG Island and methylated intronic, intergenic, and repetitive sequences. ...................................................... 51 Figure 8: Association between DNA methylation and gene transcription ............................... 60 Figure 9: Epigenetic alterations over time ............................................................................... 60 Figure 10: Activity map of the Agbogbloshie e-waste site ...................................................... 70 Figure 11: Google earth view of the study site at Madina Zongo, Accra ................................ 71 Figure 12: A flowchart showing data collection steps for the GEOHealth II study ................ 74 Figure 13: Pictures showing data collection and laboratory analysis; A=biological samples collection at Agbogbloshie, B=DNA extraction at University of Michigan School of Public Health ....................................................................................................................................... 80 Figure 14: Violin plots. The violin plots [A-E] present the distribution of individual methylation of LINE-1 and site-specific CpG methylation of LINE-1 in e-waste workers and University of Ghana http://ugspace.ug.edu.gh xii controls. P-values were calculated by t-test. The green line represents median values, and blue lines represent interquartile ranges. ......................................................................................... 88 Figure 15: LINE-1 methylation across primary job-tasks performed by e-waste workers; collectors had the lowest mean methylation levels than burners and dismantlers. The green line indicates median methylation, and the blue lines represent interquartile ranges. .................... 89 Figure 16: Measurement of personal air particulate matter among e-waste workers and reference group. Data are presented as the median (interquartile range). A: PM2.5, and B: PM10, *= p ≤ 0.05, p-values are obtained by Mann-Whitney-U test .................................... 91 Figure 17: Personal air particulate matter concentrations among e-waste workers by primary job tasks performed shows high exposure among burners, A: PM2.5, B: PM10. Data are presented as the median (interquartile range), *= p < 0.05 and ns = non-significant. P- values obtained by Kruskal-Wallis and Dunn’s post-hoc tests ........................................................... 92 Figure 18: Interaction effects of toxic and essential metals on DNA methylation of LINE-1 CpG sites. A=interaction between Cd and Zn on all CpG sites of LINE-1, B=interaction between Cd and Zn on CpG1 of LINE-1, C= interaction between Pb and Zn on CpG1 of LINE- 1, D= interaction between Pb and Mn on Cp2 of LINE-1 ..................................................... 102 University of Ghana http://ugspace.ug.edu.gh xiii LIST OF ABBREVIATIONS %TDNA Percent Tail DNA 5mC 5-methyl Cytosine 8-OHdG 8-hydroxy-2' -deoxyguanosine ANOVA Analysis of Variance BMI Body Mass Index CA Chromosomal Aberration CDC Center for Disease Control and Prevention CpG Cytosine Phosphate Guanine dinucleotide DNA Deoxyribonucleic acid DNMT DNA methyltransferase ELIZA Enzyme-linked Immunosorbent Assay EPA Environmental Protection Agency E-WASTE Electronic Waste GEOHealth Global Environmental and Occupational Health GM Geometric Mean HEI Health Effect Institute ICPMS Inductively Couple Plasma Mass Spectrometry IQR Interquartile Range LASSO Least Absolute Shrinkage and Selection Operator LINE-1 Long Interspersed Nucleotide Element-1 LMIC Low and Middle Income Countries LUMA LUminometric Methylation Assay MESTI Ministry of Environment Science Technology and Innovation MN Micronucleus University of Ghana http://ugspace.ug.edu.gh xiv MOH/GHS Ministry of Health/Ghana Health Service NHANES National Health and Nutrition Examination Survey NOS Newcastle-Ottawa Scale OLS Ordinary Least Squares OPC Optical Particle Counter OTM Olive Tail Moment PAH Polycyclic Aromatic Hydrocarbon PBL Peripheral Blood Lymphocytes PCR Polymerase Chain Reaction PM Particulate Matter PM2.5 Particulate Matter with aerodynamic diameter ≤ 2.5 µm PM10 Particulate Matter with aerodynamic diameter ≤ 10 µm POP Persistent Organic Pollutant PPE Personal Protective Equipment PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses REML Restricted Maximum Likelihood SAM S-adenosyl methionine SD Standard deviation SMD Standardize Mean Difference TEs Trace Elements TET Ten-Eleven Translocation enzyme TL Tail Length TM Tail Moment UFP Ultra Fine Particle WHO World Health Organization University of Ghana http://ugspace.ug.edu.gh xv OPERATIONAL DEFINITION OF TERMS Apoptosis - The process of programmed cell death Bisulfite conversion of DNA – The Incubation of a target DNA with sodium bisulfite which results in conversion of unmethylated cytosine residues into uracil, leaving the methylated cytosines unchanged. Chromosomal aberration - Chromosomal abnormalities occur when there are either deviations in the total number of chromosomes than an individual has or when there is missing, extra or rearranged genetic material on one particular chromosome Comet assay - Comet assay or single cell gel electrophoresis assay is a relatively cheap, simple, rapid, and sensitive method used to assess DNA strand breaks in human cells associated with occupational and environmental exposure to genotoxic agents CpG Island - Stretches of DNA 500–1500 bp long with a GC content greater than 55% DNA damage - A change in the chemical structure and sequence of DNA DNA methylation – The covalent addition of a methyl group to the 5’ position of cytosine to form 5-methyl cytosine Epigenetics - The study of heritable and potentially reversible gene expression changes that do not involve structural alterations in the DNA sequence, such as mutations E-waste - Any discarded, obsolete, or broken electrical or electronic devices or products nearing the end of their useful life Genotoxic agent - A genotoxic agent is a chemical or another agent that damages cellular DNA, resulting in mutations or cancer Meta-analysis - A statistical technique for combining the results from several similar studies Micronucleus - Micronucleus (MN) is formed when a whole chromosome or a lost chromosomal fragment is not included in the main daughter nuclei during mitosis PM10 - Inhalable particles that can penetrate the lungs University of Ghana http://ugspace.ug.edu.gh xvi PM2.5 - Respirable particles that can enter the alveoli Repetitive elements - Repetitive elements are DNA sequences that occur multiple times in the human genome Telomere - A telomere is the end of a chromosome University of Ghana http://ugspace.ug.edu.gh xvii ABSTRACT Background: The techniques used in the informal recycling of e-waste, particularly in low- and middle-income countries (LMICs), are unsophisticated and rudimentary without safeguards for the health and safety of humans and the environment. Particulate matter (PM), including toxic chemical components in the form of metallic and organic compounds are generated and released into the environment during informal e-waste recycling activities. Available data suggests that PM and metals are among the most important risk factors for developing many chronic diseases such as cardiovascular diseases, neurological diseases, reproductive toxicity, renal dysfunction, autoimmune diseases and cancers. Due to the deleterious effects of PM and metals on human health, as well as elevated levels detected in occupational environments, there is a need to determine the intermediate health outcomes associated with pollutants exposure before the onset of clinical occupational disease. Epigenetic modification such as DNA methylation are highly suspected as an intermediary between environmental and occupational exposures and adverse health outcomes. Although research has been carried out on the adverse health effects of e-waste recycling in Ghana and elsewhere, there is still little published data examining the effects of metals and PM on DNA methylation in occupationally exposed populations especially those in the informal sector such as e-waste recyclers. Objective: The objective of this work was to examine the effects of personal particulate matter exposure and a mixture of metals on global DNA methylation among e-waste recyclers and a reference population. Methods: This study made use of biological samples and exposure data collected during the first round of a parent/larger GEOHealth II study - a longitudinal study. One hundred (100) male e-waste workers and fifty-one (51) male non-e-waste workers serving as a reference University of Ghana http://ugspace.ug.edu.gh xviii population were recruited at baseline. The participants provided survey data and blood samples for measurements of concentrations of metals as well as DNA methylation analysis. The methylation levels of long interspersed nucleotide repetitive elements-1 (LINE-1) was measured by pyrosequencing bisulfite-converted DNA from whole blood as a proxy for global DNA methylation. Personal PM2.5 and PM10 were measured over a 4-hour work-shift using real-time particulate matter monitors incorporated into a backpack and worn by study participants (e-waste workers and reference population). The concentrations of selenium (Se), zinc (Zn), manganese (Mn), cadmium (Cd) and lead (Pb) were measured in blood using inductively coupled plasma mass spectrometry (ICPMS). Descriptive statistics were used to determine differences in participant’s characteristics. Multiple linear regression model with robust standard errors (SE) from ordinary least squares (OLS) was used to evaluate the associations between PM and metals exposure on the one hand and LINE-1 DNA methylation on the other hand. Further, ccorresponding interaction terms were incorporated into the regression model to determine possible modification effect of selected toxic metals (Cd and Pb) on DNA methylation caused by essential metals (Mn, Se and Zn) concentrations. Lastly, a further sensitivity analysis using different variants of the outcome model (robust and cross-fit partialling-out least absolute shrinkage and selection operator (LASSO) linear regression models) were performed to compare with the results of the OLS with robust SEs. Results: Personal median concentrations of PM2.5 and PM10 were significantly higher among the e-waste workers than the reference population (PM2.5: median (interquartile range) 77.32(34.08) µg/m3 vs 34.88 (16.55) µg/m3, p < 0.001 and PM10: median (interquartile range) 210.21 (93.32) µg/m3 vs 121.92 (82.93) µg/m3, p < 0.001, respectively). Overall, metals (Cd, Mn, and Se) were significantly higher in the reference group (geometric mean: Cd = 0.8 µg/L, Mn = 14.7 µg/L, and Se = 190.5 µg/L) than those in the e-waste worker group (geometric mean: Cd = 0.6 µg/L, Mn = 11.4 µg/L, and Se = 147 µg/L). Only Pb was significantly higher University of Ghana http://ugspace.ug.edu.gh xix in the e-waste workers (geometric mean: Pb = 79.6 µg/L) compared to the reference group (geometric mean: Pb = 37.7 µg/L). There was no significant difference in LINE-1 methylation among the e-waste workers and the reference group (85.16 ± 1.32% vs 85.17 ± 1.11%, p=0.950). In the linear regression models controlling for confounders, the associations between PM2.5 and PM10, and LINE-1 DNA methylation were not statistically significant among the e- waste workers (βPM2.5 = 0.004; 95% CI; -0.001, 0.010, p = 0.114), and (βPM10 = 0.002; 95% CI; -0.001, 0.005, p = 0.088), respectively and reference population. For metals exposure, the OLS results of multiple metals showed a significant inverse association between Zn and the LINE- 1 DNA methylation among only the e-waste workers (βZn = -1.180, 95% CI: -2.199, -0.161, p = 0.024) which corresponds to a 0.012 decrease in LINE-1 DNA methylation (95% CI: -0.022, -0.002, p = 0.024) for a 1% increase in Zn concentration. The linear regression results from OLS with robust SEs and those of the sensitivity analysis yielded similar estimates of the beta- coefficients. Potential interactions between toxic and essential metals on global DNA methylation were observed. Conclusion: In conclusion, the high concentration of breathing zone PM and the body burden of metals detected in both the e-waste workers and reference population in Ghana shows the elevated levels of air pollutants in urban Ghana, particularly the capital city, Accra. Overall, PM concentration did not show significant association with LINE-1 DNA methylation in both the e-waste workers and the reference population. However, for metals exposure, increased blood zinc levels showed a significant decrease in LINE-1 methylation only among the e-waste workers. The results of this study further revealed that alteration of DNA methylation by toxic metals could be modified due to the concentration of essential metals. The alteration of LINE- 1 methylation by metals could serve as an early epigenetic marker for future adverse health outcomes in e-waste workers and other workers with similar exposure. Therefore, effective University of Ghana http://ugspace.ug.edu.gh xx interventions to improve occupational safety for e-waste recycling workers are urgently needed. University of Ghana http://ugspace.ug.edu.gh xxi Publications Related to This Work PREFACE Issah I, Arko-Mensah J, Rozek LS, Zarins KR., Agyekum TP, Dwomoh D, Basu N, Batterman S, Robins TG , Fobil JN Global DNA (LINE-1) methylation is associated with lead exposure and certain job tasks performed by electronic waste workers. International Archives of Occupational and Environmental Health. Issah I, Arko-Mensah J, Rozek LS, Zarins KR., Agyekum TP, Dwomoh D, Batterman S, Robins TG , Fobil JN Association between global DNA methylation (LINE-1) and occupational particulate matter exposure among informal electronic-waste recyclers in Ghana. International Journal of Environmental Health Research Issah I, Arko-Mensah J, Agyekum TP, Dwomoh D, Fobil JN Electronic waste exposure and DNA damage: A systematic review and meta-analysis. Reviews on Environmental health Issah I, Arko-Mensah J, Rozek LS, Zarins KR., Agyekum TP, Dwomoh D, Basu N, Batterman S, Robins TG , Fobil JN Association between toxic and essential metals in blood and global DNA methylation among electronic waste workers in Agbogbloshie, Ghana. Environmental Science and Pollution Research University of Ghana http://ugspace.ug.edu.gh 1 1.1 Background CHAPTER ONE 1.0 INTRODUCTION There are well documented public health concerns arising from the high production volumes of electrical and electronic waste (e-waste), especially in low-to-middle-income countries (LMICs) (Alabi et al., 2012; Baldé et al., 2017; Orlins & Guan, 2016; Robinson, 2009; Song & Li, 2014a). The composition of electrical and electronic equipment (EEE) in the general waste stream presents a challenge in the management and disposal because they are simultaneously a source of recoverable precious materials (especially metals) as well as a wide spectrum of toxic contaminants (Alabi & Bakare, 2017; Amankwaa, Adovor Tsikudo, & Bowman, 2017; Bakhiyi et al., 2018; Dias, Bernardes, & Huda, 2019; Fowler, 2017). Therefore, environmentally less polluting recycling processes are required to recover valuable materials while protecting humans and the environment from undue chemical exposures (Ikhlayel, 2017). This obviously represents serious challenges, since recycling activities in developing countries including Ghana are mostly informal and rudimentary. Currently, there are no truly and properly engineered e-waste recycling facilities in developing countries that are the primary recipients and processors of these wastes (Ikhlayel, 2018). Given the high unemployment rates in developing countries (Nattrass & Seekings, 2018), many youths, and other groups, engage in the informal collection and recycling of e-waste to earn a living. The informal e-waste recovery work in LMICs where proper regulation and controls are lax or absent, and worker protection is often inadequate involves the manual dismantling of e- waste using basic tools or sometimes with bare hands with no protective equipment to retrieve reusable components. Crude methods such as the burning of e-waste material are widely used as the quickest way to recover valuable metals. Such methods result in the formation and release of multiple toxic chemicals into the environment, including human carcinogens such as University of Ghana http://ugspace.ug.edu.gh 2 polycyclic aromatic hydrocarbons (PAHs) and heavy metals (Imran et al., 2017; Yang et al., 2020a), dioxin-like compounds (DLCs) (Dai et al., 2020), or polychlorinated biphenyls (PCBs) (Wittsiepe et al., 2015), and volatile organic compounds (VOCs) (Lin et al., 2021) . E-waste workers and individuals who live near e-waste recycling sites are directly exposed to these air pollutants mainly through inhalation, dermal contact, and ingestion through food and/or water (Perkins et al., 2014; Song & Li, 2015). Several studies have reported massive contamination of e-waste recycling sites by metals and many organic pollutants in India (Awasthi, Zeng, & Li, 2016; Singh, Thind, & John, 2018), China (Xu et al., 2015), Nigeria (Alabi, Adeoluwa, & Bakare, 2019; Ohajinwa et al., 2018), and Ghana (Feldt et al., 2014; Kwarteng et al., 2020; Laskaris et al., 2019; Lin et al., 2021; Srigboh et al., 2016; Takyi et al., 2021; Tue et al., 2016; Wittsiepe et al., 2017b; Wittsiepe et al., 2015). Exposures resulting from informal sector e-waste recovery have been associated with adverse health outcomes, including adverse effects on reproductive health, thyroid function, lung function, growth, and changes in cell function (Grant et al., 2013). The high importation of second-hand electrical and electronic products into Ghana in recent years has resulted in a significant increase in recycling and dumping of e-waste. E-waste recycling has been a source of employment opportunity for hundreds of young men in Accra (Amankwaa, Bowman, & Tsikudo, 2016), especially young men who migrated from the northern part of Ghana. The Agbogbloshie e-waste recovery site is the main center in Ghana for processing e-waste. These recyclers, who are often young men are a particularly vulnerable group because the Agbogbloshie e-waste site is considered one of the largest, busiest and harshest informal recycling sites worldwide (Srigboh et al., 2016). The workers are usually involved in multiple tasks and work exclusively in the open using rudimentary tools with little or no use of personal protective equipment. The recycling process itself involves the manual University of Ghana http://ugspace.ug.edu.gh 3 dismantling of old or end-of-use electronic and electrical equipment to retrieve reusable components. A significant activity at the e-waste recycling site involves open-air burning of electrical cables of all sizes in pits to retrieve oxidized copper wires with flammable materials such as styrofoam recovered from old discarded fridges as fuel. This burning activity results in the release of a mixture of toxic chemicals such as polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs) and oxides of metals, including toxic metals into the ambient environment. Recent studies revealed elevated ambient particulate matter (PM) levels (Kwarteng et al., 2020; Laskaris et al., 2021) and VOCs (Lin et al., 2021) at the Agbogbloshie e-waste site over background levels. Several other studies have documented high concentrations of PAHs, chlorinated and brominated dioxin-related compounds (DRCs) and dioxin-like polychlorinated biphenyls (DLPCBs), polybrominated diphenyl ethers (PBDEs) and metals in surface soil samples from the Agbogbloshie e-waste recycling site in Ghana (Akortia et al., 2017; Daso, Akortia, & Okonkwo, 2016; Tue et al., 2016; Tue et al., 2017). Studies have also reported high levels of PAH-derived metabolites (Feldt et al., 2014) and heavy metals (Srigboh et al., 2016; Wittsiepe et al., 2017b) in workers’ blood and urine. In Ghana, there are no proper e-waste recycling facilities or industry standards (Asante et al., 2012). The agency responsible for the regulation of the management of hazardous waste, including e-waste, is the Environmental Protection Agency (EPA). This agency functions under the auspices of the Ministry of Environment, Science, Technology and Innovation (MESTI) and has regulatory responsibilities. The regulation regarding the management of e- waste is contained in the hazardous and electrical waste control and management act, 2016 (Act 917) (Government of Ghana, 2016). The main objective of this act is “to provide for the control, management and disposal of hazardous waste, electrical and electronic waste and for related purposes” (Government of Ghana, 2016). Noteworthy in the Act with regards to e- waste management is the introduction of an electrical and electronic waste advanced eco-levy. University of Ghana http://ugspace.ug.edu.gh 4 Importers of used electrical and electronic equipment register with EPA and pay the advanced eco-levy to the government of Ghana through METSI and EPA. The equipment then goes through inspection in line with the Basel convention using an export verification portal (DailyGuide, 2019). This is to prevent waste from arriving into the country under the guise of used items. Furthermore, funds generated from the advance eco-levy will be used to provide support for the construction and maintenance of e-waste recycling or treatment plants and to support research into methods of e-waste preservation, prevention and control (Government of Ghana, 2016). In addition, Ghana ratified the Basel convention on the control of the transboundary movement of hazardous waste and its disposal to regulate the importation of e- waste into the country, but like many other developing countries, e-waste in-flow continues unabated due to the lack of financial commitment and political will to effectively enforce these regulations (Amankwah-Amoah, 2016). 1.2 Problem statement Metals are toxicologically important compounds that are ubiquitous in the environment and are largely used in electrical and electronic equipment (Woo, Lee, & Lim, 2016). End of life electrical and electronic equipment (e-waste) are increasingly recognized as a serious, worldwide public health concern due to advances in technology without adequate infrastructure to recycle the generated e-waste (Krishnamoorthy et al., 2018; Kumar & Singh, 2014). The techniques used in the informal recycling of e-waste, particularly in lower- and middle-income countries (LMICs), are basic and primitive, with little or no regard for the health and safety of humans and the environment (Lau et al., 2014). Recyclers often use basic tools such as a hammer, chisel and occasionally screwdrivers and spanners to dismantle and separate the different components (Wang et al., 2010) and a long metal rod to rotate/flip burning items such as insulated wires and circuit boards of various sizes (Acquah et al., 2019; Gullett et al., 2007). Particulate matter (PM), including toxic chemical components in the form University of Ghana http://ugspace.ug.edu.gh 5 of metallic and organic compounds, are generated and released into the environment during informal e-waste recycling activities (Fowler, 2017). There is ample scientific evidence to show that PM and metals are among the most important risk factors for many chronic diseases such as cardiovascular diseases, neurological diseases, reproductive toxicity, renal dysfunction, autoimmune diseases and cancers (Hu, 2002b; Lim et al., 2019; Rzymski et al., 2015; Shi, Jing, & Xi, 2019). Due to the deleterious effects of PM and metals on human health, in addition to their elevated levels in occupational environments (Baloch et al., 2020; Kwarteng et al., 2020), there is a growing interest to determine the intermediate health outcomes associated with pollutants exposure before the onset of clinical occupational disease (Leelapongwattana & Bordeerat, 2020; Salemi et al., 2017). These intermediate health outcomes are particularly useful information that may provide guidance for the implementation of preventive strategies in populations occupationally exposed to a mixture of chemicals such as those generated through e-waste recycling. Epigenetic modification such as DNA methylation is highly suspected as an intermediary between environmental and occupational exposures and adverse health outcomes (Chervona, Arita, & Costa, 2012; Stein & Davis, 2012). Several researchers have reported that occupational exposure to toxic metals and PM could induce global DNA methylation changes in occupational settings, although these results are largely limited to a single metal. These include hexavalent chromium in a chromate plating facility (Wang et al., 2012), lead in battery plant workers Li et al. (2013), lead in automotive battery factory workers (Devóz et al., 2017), PM2.5 in welders (Fan et al., 2014), and PM10 in steel workers (Tarantini et al., 2009). However, there are still data gaps addressing methylation among occupationally exposed populations in the informal sector (Braun et al., 2016) or the level, how and the extent to which essential elements or toxic metals or their interactions affect University of Ghana http://ugspace.ug.edu.gh 6 DNA methylation (Vidal et al., 2015). In addition, previous studies examined the effects of one chemical at a time on health outcomes. These studies, while useful, do not reflect the reality of exposure to pollutant mixtures in environmental and occupational fields (Deng et al., 2019). Finally, to the best of my knowledge, no previous study has examined the relationship between pollutants exposure and epigenetic alteration among e-waste workers in Ghana. There is, therefore, a justifiable interest to examine the joint and potential interactive effects of different pollutants on DNA methylation among e-waste workers at Agbogbloshie. In addition, studies of this may provide insights about the latency period between chemical exposures and the onset of clinical disease and thus, inform the development of efficient prevention strategies for workers and people living near e-waste sites with uncontrolled exposures and further inform policymakers to strengthen regulation involving the safe disposal of e-waste in Ghana. 1.3 Conceptual framework Informal e-waste recycling at Agbogbloshie release hazardous pollutants (stressors) such as PM and metals. Other sources of PM and metals at Agbogbloshie may include vehicular emissions, burning of refuse pile, outdoor biomass use for commercial cooking, and suspended road dust. These stressors are emitted into various compartments (air, water, food, etc.) and then transported into the environment, which accumulates waiting to interact with a receptor (e.g. worker). E-waste workers come into contact (exposed) with these stressors (PM and metals) mainly through inhalation, ingestion, and/or dermal contact. Factors that contributes to the level of internal exposure (dose) include the intensity (concentration) of the stressors, duration of exposure, and workers activity. Continuous exposure to toxic chemicals could result in epigenetic modification that alters the way the DNA expresses its information without changing the DNA sequence. The most studied epigenetic modification is DNA methylation, which occurs when a methyl group (CH3-) is added to the 5' position of cytosine (5-methylcytosine, 5-mC) (Portela & Esteller, 2010). The University of Ghana http://ugspace.ug.edu.gh 7 process of methylation is catalyzed by DNA methyltransferases (DNMTs), which transfers methyl group from the universal methyl donor, S-adenosyl methionine (SAM) to cytosine (Brocato & Costa, 2013). Occupational and environmental pollutants may therefore alter DNA methylation through the alteration of methylation pathways by direct action on the function of DNMTs and ten-eleven translocation (TET) enzymes, or alteration in the availability of SAM (Ruiz-Hernandez et al., 2015). These alterations may result in genome-wide/global methylation alterations such as increased or decreased methylation. Reduction of global methylation content has been associated with alteration in gene expression and increased genomic instability whereas increased methylation of CpG islands of specific genes silences the gene and result in loss of function (Sun et al., 2018). Therefore, abnormal methylation patterns induced by toxic chemicals could result in the development some degenerative diseases and/or indicate toxic levels of metals that lead to disease throuth multiple pathways. Other domains of stressors that may contribute to epigenetic modifications and lead to an increased risk of disease include nutrition, and psychosocial stress (Thayer & Kuzawa, 2011). For example, nutritional status can influence epigenetic profiles by inhibiting the enzymes that catalyze DNA methylation or by influencing the dietary availability of substrates necessary for these enzymatic processes (Thayer & Kuzawa, 2011). In addition, demographic and lifestyle factors such as age, cigarette smoking and alcohol consumption may also influence the efficiency of DNA methylation (Rakyan et al., 2010; Teschendorff et al., 2010). The conceptual framework is presented in figure 1. University of Ghana http://ugspace.ug.edu.gh Figure 1: Conceptual framework showing linkages between PM and metals exposure and alteration of global (LINE-1) DNA methylation 8 ↑↓Global (LINE-1) DNA methylation Informal e-waste recycling activities Manual dismantling Collection Burning Exposure Stressors PM2.5 and PM10 Toxic metals (Cd & Pb) Essential metals (Se, Zn, & Mn) Stressors Contact Biological mechanisms ↑↓DNMTs activity ↑↓TET activity ↑↓ SAM availability Dose Other emission sources Vehicular emissions Burning refuse pile Commercial cooking Time-activity and behaviour Duration and activity level Receptor Covariates Age, BMI, Cigarette smoking, Alcohol use University of Ghana http://ugspace.ug.edu.gh 10 1.4 Justification The generation of millions of tons of e-waste, the lack of recycling infrastructure and the relaxed regulation on its recycling in developing countries where the e-waste ends up, highlight the need for new public health risk assessment approaches. These new risk assessment methods need to focus on early detection of human illnesses from exposure to toxicants generated from e-waste recycling as well as guide evidence-based policies on the protection of workers. Investigating into DNA damage and epigenetic modifications due to unregulated environmental pollution with toxic chemicals, especially from e-waste will be informative in such a risk assessment process. Chronic diseases such as cancer may take several years to decades to develop; therefore, there is justifiable interest in determining whether biomarkers of DNA damage and epigenetic alterations that may ultimately lead to the development cancers are associated with e-waste exposure. The global incidence of cancer estimated by the International Agency of Research on Cancer (IARC) for 2018 stands at 18.1 million with 9.6 million estimated deaths, and lung cancer is identified as the commonest type with an estimated incidence of 11.6% and mortality of 18.4% (Bray et al., 2018). In Africa, the proportion of cancer deaths (7.3%) was estimated to be higher than the proportion of incidence cases (5.8%). This higher proportion of deaths relative to the incidence is partly attributable to the limited access to early and quality diagnosis and treatment (Bray et al., 2018). Cancer is a significant public health problem in Ghana, and its burden is increasing. According to the Ghana Cancer Registry, cancer is the fourth leading cause of death in Ghana, accounting for about 10% of all deaths (Yarney et al., 2020). The most common cancers in Ghana include breast cancer, cervical cancer, liver cancer, prostate cancer, and colorectal cancer (Laryea et al., 2014). Studies have linked environmental exposures to the burden of cancer in Africa mainly due to the fact that working conditions in Africa are likely to produce higher levels of exposure with no safety standards for the protection of the workers and the environment (McCormack & Schüz, 2012). University of Ghana http://ugspace.ug.edu.gh 10 Chemicals exposure may damage DNA in the cell and may thus lead to the development of several diseases including cancers (da Silva, 2016; Dreval & Pogribny, 2018; Rodgers et al., 2018). Recently, considerable literature has grown around the theme of e-waste exposure and intermediate health outcomes, including DNA damage and cytogenetic alterations (Alabi, Adeoluwa, & Bakare, 2020; Neitzel et al., 2020; Ngo et al., 2020). Intermediate health outcomes such as markers of direct DNA damage (e.g. chromosomal aberrations (CA), micronuclei (MN) frequency, and comet assay parameters (tail length, tail moment, etc.) and epigenetic modifications (e.g. DNA methylation) are indicators of early biological effects, that may provide valuable insight into the mechanism by which our health is influenced by the environment as well as the appropriate levels of exposure in occupational settings. These biomarkers may provide valuable information in designing effective preventive interventions among e-waste workers and other workers with similar exposures, and findings, if adverse will also help shape policy on e-waste recycling. University of Ghana http://ugspace.ug.edu.gh 11 1.5 Objectives 1.5.1 General objective The overarching aim of this research work was to examine the association between personal particulate matter and metals (chemical) exposures on the one hand and global DNA methylation on the other among e-waste recyclers and a reference population. 1.5.2 Specific objectives 1. Assess the level of global DNA methylation among e-waste workers and a reference population 2. Quantify the breathing zone particulate matter and body burden of metals in e-waste workers and a reference population 3. Determine the association between PM2.5 and PM10, and global DNA methylation among e-waste workers and reference population 4. Assess the joint effect of co-exposure to toxic and essential metals on global DNA methylation 1.6 Research questions 1. Are there differences in global DNA methylation levels among e-waste workers a reference population? 2. Are there differences in metals measured in blood and PM2.5 and PM10 measured in the breathing zone of e-waste workers and a reference population? 3. Is there an association between occupational exposure to PM (2.5 and 10) and global DNA methylation level among e-waste workers? 4. Is there a relationship between co-exposure to toxic and essential metals and global DNA methylation? University of Ghana http://ugspace.ug.edu.gh 12 2.1 Introduction CHAPTER TWO 2.0 LITERATURE REVIEW This chapter presents a synthesis of evidence of genetic alteration that is associated with e- waste recycling, and a summary of epidemiological evidence of any association between occupational exposures to particulate matter (PM) and metals on the one hand and global DNA methylation on the other hand. It begins with a presentation of a systematic review and meta- analysis of e-waste exposure and DNA damage, which is immediately followed by a presentation of e-waste as a public health concern, and then an overview of DNA methylation. Gaps and/or limitations identified are discussed. 2.2 Electronic waste exposure and DNA damage: A systematic review and meta-analysis 2.2.1 Introduction Although studies have reported evidence of an association between crude e-waste disposal and DNA damage, there has not been any systematic synthesis of evidence linking specifically e- waste exposure to DNA damage in human populations as yet. This systematic review was conducted to assess the evidence of genetic alteration associated with e-waste recycling to provide evidence for the development of efficient prevention strategies for workers and people living near e-waste sites with uncontrolled exposures and strengthen regulation involving the safe disposal of e-waste in general. 2.2.2 Protocol development and registration A review protocol was developed and registered with the International prospective register of systematic reviews (PROSPERO) with registration number CRD42020201149, and it is available from https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=201149. University of Ghana http://ugspace.ug.edu.gh http://www.crd.york.ac.uk/prospero/display_record.php?RecordID=201149 http://www.crd.york.ac.uk/prospero/display_record.php?RecordID=201149 13 The systematic review/meta-analysis was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement checklist (Liberati et al., 2009). 2.2.3 Eligibility criteria This review focused on observational studies on human populations exposed to e-waste disposal. Studies were included if they were original peer-reviewed publications, assessed e- waste exposure and biomarkers of DNA damage, and involved human populations, including women and children. Studies that were not original (e.g., reviews, conference proceedings, letters to the editor, and abstracts) and did not report biomarkers of DNA damage in human populations were excluded. 2.2.4 Information sources and search strategy Articles published in English from January 2000, investigating the associations between e- waste exposure and biomarkers of DNA damage were retrieved through the following three major databases: MEDLINE (Academic Search Complete, CINAHL Complete, Education Research Complete, GreenFILE, Health Source: Nursing/Academic Edition, Library, Information Science & Technology Abstracts), ProQuest, and Scopus. The search terms used included the following keywords: ("electronic waste" OR "e-waste" OR "WEEE") AND ("DNA damage" OR "chromosomal aberration" OR "DNA strand breaks" OR "micronucl*" OR "Sister chromatid exchanges" OR "oxidative DNA damage" OR "genotox*" OR "oxidative stress"). Reference lists of selected articles were hand-searched for relevant publications that were not captured by the electronic search. The search strategy and results of the various databases are presented in the appendix (Appendix 5.1). 2.2.5 Study selection The study selection was conducted in 2 phases. In phase 1, two reviewers independently screened titles and abstracts of publications retrieved from the electronic databases and hand University of Ghana http://ugspace.ug.edu.gh 14 searches. Publications that did not meet the inclusion criteria were excluded. After phase 1 screening, 32 publications advanced to phase 2, full-text screening. In phase 2, the same reviewers independently examined the full-text publications for inclusion. Any discrepancies between reviewers were resolved by a third reviewer. Studies that were excluded at this stage are presented in supplementary table of results (Appendix 5.2) with reasons. Finally, a total of 20 publications met the inclusion criteria. 2.2.5.1 Diagrammatic representation of study selection The flowchart representing the process of study selection is presented in Figure 2. In the initial search of 3 electronic databases, a total of 822 articles were retrieved. Duplicates of 106 were identified and removed, with a total of 717 articles making it to the title and abstract screening stage. After the title and abstract screening, a total of 685 articles were excluded, allowing 32 articles to advance to the full-text screening stage. Out of the 32 full-text articles screened, 12 were excluded either because they were not original articles (5), did not meet the inclusion criteria (3), were retracted articles (1) or shared the same population and outcome with another study already included (3). A total of 20 publications were included in this review, of which seven studies were within the occupational setting, and the rest were ecological studies. Seven of the 20 studies included in this systematic review were included in the meta-analysis. University of Ghana http://ugspace.ug.edu.gh 15 Figure 2: PRISMA flow chart illustrating the process of selecting the studies included in the systematic review 2.2.6 Data extraction The type of data extracted from each of the selected studies were both qualitative and quantitative. The qualitative data include author and year of publication, details of study design (exposure setting and population), the country where the study was conducted, and methods details (samples type, targeted chemicals, outcome measures). The quantitative data extracted University of Ghana http://ugspace.ug.edu.gh 16 included the sample size for each study, and the main findings expressed as means and standard deviations if applicable (Table 1). Data extraction was performed by two reviewers. One reviewer extracted the data, and the second reviewer compared the extracted data with the original report. 2.2.7 Risk of bias (quality) assessment The risk of bias (methodological quality) of each included study was assessed using the modified version of the Newcastle-Ottawa Scale (NOS) for cross-sectional studies developed by Elyasi et al. (2015) to ensure that the conclusions and findings of the reviews were based on the best available evidence. The tool was adjusted to include exposure assessment and a comparison group. The score for each cross-sectional study was calculated based on four categories: group selection (three items), comparability (two items), exposure measurement (1 item), and outcome measurement (one item). The items in the first two categories, 'group selection' and 'comparability', were awarded a maximum of 2 stars and 1 star for items in the remaining categories. The NOS score ranged from 0 (lowest grade) to 10 (highest grade). Studies were considered high quality if they were scored above the median (Hermont et al., 2014), that is, > 5 in this review. The risk of bias assessment was performed by two reviewers. Any discrepancies were resolved by discussion between the 2 reviewers or by the intervention of a third independent reviewer. The NOS tool can be found in Appendix 5.3. 2.2.8 Statistical analysis Nine out of the 20 included studies used Micronuclei (MN) frequency assay to measure the risk of DNA damage associated with e-waste exposure. Seven of these studies were included in the meta-analysis. One study (Alabi et al., 2020), which measured MN in exfoliated buccal cells, was excluded from the meta-analysis because it was considered an outlying study that University of Ghana http://ugspace.ug.edu.gh 17 significantly affected the meta-analysis results. The other excluded study was conducted in the Philippines by Berame et al. (2020), which did not report the mean and standard deviation (SD) values. Studies that reported medians and interquartile range (IQR) or range (Chen et al., 2010; Wang et al., 2011; Yuan et al., 2008) were converted to means and SD using the formulas provided for different sample sizes by Wan et al. (2014). The effect size was calculated using standardized mean differences (SMD) since the studies were conducted in different settings (ecological or occupational). Heterogeneity was determined using Cochran's χ² test and quantified using the I² test. The null hypothesis for heterogeneity was that all studies share a common mean difference for MN frequency. The I2 describes the percentage of differences across studies attributed to heterogeneity rather than chance (Higgins et al., 2003). An I2 value of 25% is considered low heterogeneity, a value between 50% and 75% is moderate, and a value above 75% is considered high heterogeneity (Higgins et al., 2003). The random effect meta-analysis model with restricted maximum likelihood (REML) method (Raudenbush, 2009) was used to calculate the overall SMD and its 95% confidence interval (CI). The REML method performs well with small number of studies and produces an unbiased estimate of the between study variability owing to differences in study designs and interlaboratory reproducibility. The REML assumes normal distribution of the random study effect sizes (Kontopantelis & Reeves, 2012). Forest plots were used to present the results of the meta-analysis. To further explore heterogeneity between studies, subgroup analyses were performed based on study setting, i.e., whether studies were conducted among e-waste workers vs non-e-waste workers (occupational) or studies were conducted among residents of an e-waste exposed town/village vs residents in a neighboring town/village without e-waste exposure University of Ghana http://ugspace.ug.edu.gh 18 (ecological), and quality, (high quality vs low quality) as determined by the NOS for cross- sectional studies. All statistical analyses were conducted using Stata version 16.1 (StataCorp LLC, College Station, TX, USA). 2.2.9 Findings of the systematic review and meta-analysis 2.2.9.1 Study characteristics 2.2.9.1.1 Design and site All 20 studies included in this review were cross-sectional studies. The majority of the studies (15 of 20) were conducted in China, and the remaining five studies were conducted in Nigeria (Alabi et al., 2020), Palestine (Khlaif & Qumsiyeh, 2017), Thailand (Neitzel et al., 2020), Vietnam (Ngo et al., 2020), and the Philippines (Berame et al., 2020). 2.2.9.1.2 Populations studied A total of seven out of the 20 studies included in the review were conducted in occupational settings; the remaining 13 studies targeted people resident in e-waste exposed towns (ecological studies). Only four of the ecological studies targeted children (Ngo et al., 2020; Xu et al., 2018) and neonates (Li et al., 2008; Ni et al., 2014); the remaining nine studies involved adult populations. A total of 17 of the 20 studies included a comparator group. The comparator groups were mostly residents of non-e-waste recycling towns with no history of e-waste exposure. Two of the occupational-related studies (Wang et al., 2018; Yuan et al., 2008) recruited age- and sex-matched farmers as control groups. The sample sizes of the studies included in this review ranged between 48 (He et al., 2015) and 377 (Wang et al., 2010). The majority of the studies (16 of 20) had both male and female participants, three studies had all- male participants (Sheng et al., 2008; Wang et al., 2018; Yu et al., 2018), and 1 study did not describe gender breakdown (Khlaif & Qumsiyeh, 2017). Tables 1 and 2 provide a summary of the study characteristics of the included studies. University of Ghana http://ugspace.ug.edu.gh 19 Table 1: Summary results of previous occupational studies including exposure groups, study location, samples used, and key findings Author Exposure setting Exposed group Country Samples type Exposure Outcome Main findings Alabi et al. (2020) Occupational Scavengers (95) vs Control group (104) Nigeria Blood, Buccal Cells Pb, Ni, Cd, and Cr Micronuclei, Binucleated cells, Pycnosis, Condensed chromatin, Karyorrhesis, Lobbed nuclei Micronuclei: mean (168.04 vs 3.23, p<0.01), Binucleated cells: (42.20 vs 0.08, p<0.01), Pycnosis: (26.02 vs 0.00, p<0.01), Condensed chromatin: (13.72 vs 0.01, p<0.01), Karyorrhesis: (29.47 vs 0.00, p<0.01), Lobbed nuclei: (35.29 vs 0.00, p<0.01). Berame et al. (2020) Occupational e-waste recyclers (40) vs controls (52) Philippines Buccal cells NR Micronuclei frequency E-waste workers had increased micronuclei compared to the control group. Neitzel et al. (2020) Occupational Informal recycling (120) Thailand Urine and blood Pb, Cd, Mn 8-hydroxy-2'- deoxyguanosine (8- OHdG) Men who reported working >48 hours/week had significantly (p=0.045) higher levels of 8-OHdG compared to men working ≤48 hours/week Sheng et al. (2008) Occupational Informal recycling (64) China Dust, hair, and urine PCDD/Fs, PBDEs, and PCBs 8-OHdG Pre- vs postworkshift 8-OHdG: mean (range): 6.4 (0.64-95.74) vs 24.55 (0.37- 343.17) µmol/mol creatinine, p<0.05. Wang et al. (2011) Occupational Informal recycling (48) Vs Controls (56) China TSP, blood and urine Pb, Cu, and Cd Micronuclei in binucleated cells Micronuclei, median (range): median 4·0% (2·0–7·0) vs 1·0% (0·0–2·0), p<0·01. Positive correlation between blood lead and micronuclei in binucleated cells (r=0·245, p<0·01). University of Ghana http://ugspace.ug.edu.gh 29 Table 1: Continued Author Exposure setting Exposed group Country Samples type Exposure Outcome Main findings Wang et al. (2018) Occupational Informal recycling (146) vs farmers (121) Blood and semen PCBs, Pb, Cu Zn, Ca, Mg, Fe and Se Chromosomal aberration, micronuclei, and DNA damage (DNA TDNA%, T.M. and OTM) Chromosomal aberration (%): (8.01 vs 1.80), micronuclei (%): (26.30 vs 4.52), comet assay (greater DNA damage in exposed than in control group), Pall <0.001. duration of exposure is associated with C.A., CBMN, and DNA damage China Yuan et al. (2008) Occupational Informal recycling (23) vs farmers (26) China Blood and urine PBDEs Micronuclei, 8- OHdG Micronuclei, median (range): 5(0–96) vs 0.00 (0–5.00), p<0.001), 8-OHdG, mean±SD: 69.04 ± 222.2 vs 229.97 ± 210.1) µmol/mol of creatinine, p=0.200). Working with e-waste is associated with increased Micronuclei frequencies OR, 38.85; 95%CI (1–1358.71), p=0.044 Abbreviations: Pb-lead, Cd-cadmium, Ni-nickel, Cr-chromium, Mn-manganese, Cu-copper, Zn-zinc, Ca-calcium, Mg-magnesium, Fe-iron, Se-selenium, Hg-mercury PBDEs- polybrominated diphenyl ethers, PCDD/F- Polychlorinated Dibenzo-p-dioxins and Dibenzofurans, PCBs- Polychlorinated Biphenyls, OH-PAHs- hydroxylated polycyclic aromatic hydrocarbons, TSP-total suspended particles, 8-OHdG- 8-Hydroxy-2′-deoxyguanosine, UCB-umbilical cord blood, DNA- Deoxyribonucleic acid, %TDNA-% tail DNA, TM-tail moment, OTM-olive tail moment, CBMN-cytokinetic block micronuclei CA-chromosomal aberration, MNed BNC- micronucleated binucleated cells, NR-not reported, SD-standard deviation University of Ghana http://ugspace.ug.edu.gh 21 Table 2: Summary of previous ecological studies, including exposure groups, study location, samples used, and key findings Author Exposure setting Exposed group Country Sample type Exposure Outcome Main findings Chen et al. (2010) Ecological: exposed town vs control town Population (n=138) 58 vs 80 China Blood NR micronucleated binucleated cells MNed BNC frequency: (median: 4.0%, IQR: 2.0–7.0%) vs (median: 1.0%, IQR: 0.0–2.0%, P < 0.01 He et al. (2015) Ecological: exposed town vs control town Population (n=48) 23 vs 25 China Blood POPs ROS and micronucleus rate Micronucleus rate: (16.74 ± 4.17‰) vs (7.8 ± 1.13‰), p<0.05 Khlaif and Qumsiyeh (2017) Ecological: exposed town vs control town Population (n=61) 45 vs 16 Palestine Blood N.R. Total chromosome aberrations (CA), tail length relative to tail plus nucleus' length (TL/TL + NL) Total chromosome aberrations (CA): mean±SD (4.84±2.9 vs 0.75±0.931, p<0.001). Comet assay: (TL/TL + NL) mean±SD (0.7088± 0.5595 vs 0.520 ± 0.0498, p<0.001 Li et al. (2014) Ecological: proximity group vs remote group Population (n=58) 30 vs 28 China Blood Ca, Cu, Fe, Pb, Mg, Se, and Zn Micronucleus rate Micronucleus rate: (18.27% vs. 7.32%, p<0.05) Li et al. (2008) Ecological: exposed town vs control town Neonates (n=302) 200 vs 102 China UCB Cr comet assay ( injury rate (tailing rate) and the lengths of tail) Cell injury rate (%): 33.20 vs 10.70, p<0.01, Length of tails (%): 4.49 vs 2.09, p<0.01 Lin (2013) Ecological: exposed town vs two control towns (20 and 40km from the exposed town) Puerperae (n=320) 227 vs 93 China Placenta Cd, Pb placental telomere length Placental telomere length: negative correlation with placental Cd concentration (r = -0.138, p = 0.013). University of Ghana http://ugspace.ug.edu.gh 22 Table 2: Continued Author Exposed setting Exposed group Country Sample type Exposure Outcome Main findings Liu et al. (2009) Ecological: exposed towns vs control towns Population (n= 201) 171 vs 30 China Blood NR chromosomal aberrations, micronucleus, DNA percentage in the comet tail (TDNA%), tail moment (TM), and Olive tail moment (OTM) CA rates (%): (5.50 vs 1.70, p<0.001), micronuclear rates (%): (16.92 vs 3.47, p<0.001), comet assay (mean±SD): TDNA% (4.27±0.32 vs 1.18±0.13), TM (0.53±0.09 vs 0.05±0.01), OTM (0.82±0.09 vs 0.19±0.02), p all <0.001. Lu et al. (2016) Ecological: exposed towns vs control towns Population (n=176) 130 vs 46 China Urine OH-PAHs 8-OHdG 8-OHdG, GM order: e-waste area>rural reference>urban reference (16.2>12.3>11.6). Positive association between 8-OHdG and ∑10OH-PAHs in e-waste participants ((β = 0.349; 95% CI: -0.210, 0.488; p <0.001) Ngo et al. (2020) Ecological: exposed town vs control town Children (8-14 years) (n=80) 40 vs 40 Vietnam Blood Pb, Cd, Cr, Ni, and As Comet (Tail Length (µm), Olive Tail Moment (µm), and %Tail DNA Comet assay: Tail length (2.07 ± 0.41µm vs 1.78 ± 0.59, p<0.001), Olive Tail Moment (0.16 ± 0.04µm vs 0.14 ± 0.03, p<0.001), Tail DNA (2.67 ± 0.42% vs 2.22 ± 0.40, p<0.001). Blood arsenic correlated with Tail length (r=0.244, p < 0.05) and Olive Tail Moment (r=0.231, p < 0.05) Ni et al. (2014) Ecological: exposed town vs control town Neonates (n=201) 126 vs 75 China UCB Pb, Cd, Cr, and Ni 8-OHdG UCB plasma 8-OHdG: (median: 179.77 ng/mL vs 159.00 ng/mL, P = 0.028). 8- OHdG correlated with Cd (r = 0.235, P = 0.001), Cr (r = 0.214, P = 0.002), and Ni (r = 0.314, P <0.001) University of Ghana http://ugspace.ug.edu.gh 23 Table 2: continued Author Exposed setting Exposed group China Sample type Exposure Outcome Main findings Wang et al. (2010) Ecological: exposed towns vs control town Population (n=377) 286 vs 91 China Blood and urine Cu, Fe 8-OHdG 8-OHdG in males (mean±SD): (7.75±14.39 vs 9.73±7.39, p<0.01) μmol/mol creatinine. Blood ferrous associated negatively with 8-OHdG (β=- 0.215, p=0.037) Xu et al. (2018) Ecological: exposed town Preschool children (n=118) China Blood and urine Pb, Cd, and Hg 8-OHdG 8-OHdG, median (range): 407.79 (152.05–876.26) ng/g creatinine. Higher Pb and Hg exposure are associated with higher 8-OHdG Yu et al. (2018) Ecological: exposed town vs control town Population (n=57) 32 vs 25 China House dust, and semen PBDEs Tail DNA%, OTM, and apoptosis rate Comet assay (mean±SD): tail DNA% (57.88 ± 6.08 vs 33.55 ± 6.99, p<0.001), OTM (12.15 ± 2.52 vs 5.14 ± 4.86, p<0.001) TUNEL assay: apoptosis rate (32 ± 19 vs 20 ± 8, p= 0.037 Abbreviations: Pb-lead, Cd-cadmium, Ni-nickel, Cr-chromium, Mn-manganese, Cu-copper, Zn-zinc, Ca-calcium, Mg-magnesium, Fe-iron, Se-selenium, Hg-mercury PBDEs- polybrominated diphenyl ethers, PCDD/F- Polychlorinated Dibenzo-p-dioxins and Dibenzofurans, PCBs- Polychlorinated Biphenyls, OH-PAHs- hydroxylated polycyclic aromatic hydrocarbons, TSP-total suspended particles, 8-OHdG- 8-Hydroxy-2′-deoxyguanosine, UCB-umbilical cord blood, DNA- Deoxyribonucleic acid, %TDNA-% tail DNA, TM-tail moment, OTM-olive tail moment, CBMN-cytokinetic block micronuclei CA-chromosomal aberration, MNed BNC- micronucleated binucleated cells, NR-not reported, SD-standard deviation University of Ghana http://ugspace.ug.edu.gh 24 2.2.9.2 Presentation of key findings of previous studies Of the 20 studies included, six (6) biomarkers of DNA damage (micronuclei, comets assay, 8- OHdG, telomere length, apoptosis rate and chromosomal aberrations) were measured in seven (7) different biological matrices (buccal cells, blood, umbilical cord blood (UCB), placenta, urine and semen) (Appendix 5.4). Telomere length (n=1) and apoptosis rate (n=1) were the least biomarkers measured, while the micronuclei frequency rate (n=9) was the most commonly measured biomarker. Finally, whereas 6 studies measured more than one biomarker, the remaining 14 studies measured one biomarker each. Most studies used blood (n=9) and urine (n=7) as biological matrices in which DNA damage was measured. 2.2.9.2.1 Micronuclei frequency Micronuclei (MN) frequency has been widely used as a biomarker to investigate DNA damage in human populations exposed to genotoxic agents (Migliore et al., 2011). In this review, nine studies (Alabi et al., 2020; Berame et al., 2020; Chen et al., 2010; He et al., 2015; Li et al., 2014; Liu et al., 2009; Wang et al., 2011; Wang et al., 2018; Yuan et al., 2008) utilized MN frequency rates as a biomarker of DNA damage associated with e-waste exposure. Five out of the nine studies (Alabi et al., 2020; Berame et al., 2020; Wang et al., 2011; Wang et al., 2018; Yuan et al., 2008) targeted occupationally exposed groups, and the remaining 4 were ecological studies. Almost all the studies were conducted in China except for two (Alabi et al., 2020) and (Berame et al., 2020), which were done in Nigeria and the Philippines, respectively. In the Nigerian study, Alabi et al. (2020) evaluated micronuclei frequency among teenage e- waste scavengers in the Alaba International market, a major electronic waste dumpsite in Lagos. The results show that the average MN in exfoliated buccal cells in the scavenging e- waste workers (n=95) was significantly higher than that in the control group (n=104) (168.04 vs 3.23, p<0.001). Further analysis of other cytogenic alterations, such as markers of cell proliferation (cells with condensed chromatin and binucleated chromatin) and parameters of University of Ghana http://ugspace.ug.edu.gh 25 cell death (pyconostic and karyorrhective cells), were all significantly higher in the Alaba group than in the control group (all p<0.01). Similarly, Berame et al. (2020) measured MN frequency in the buccal epithelium of e-waste recyclers and controls in Payatas, the Philippines. The results showed a significant increase in the number of micronuclei in e-waste workers than controls (p = 0.00) (Berame et al., 2020). All the studies conducted in China reported consistently higher MN in e-waste-exposed populations than in control groups. Wang et al. (2011) and Chen et al. (2010) reported similar results between e-waste-exposed populations and control groups. The median MN frequency was 4% in Guiyu e-waste workers (n=48) compared to 1% in the control group (n=56) (p<0.05) (Wang et al., 2011). Similarly, Chen et al. (2010) evaluated MN frequency in residents of an e-waste site and reported a higher median MN frequency (4%) in the e-waste residents than in the control group (1%), p<0.01). In a study of male e-waste recyclers by Wang et al. (2018), a significant increase in the frequency of MN was observed in those with occupational exposures compared with those with no occupational exposures (26.30 vs 4.52%, p<0.001). In addition, e-waste workers were classified into exposure durations (≤3, 3-6, and >6 years) to investigate the relationship between e-waste exposure and MN frequency. The results showed a significant positive association between MN and duration working with e-waste (Wang et al., 2018). The results from earlier studies (He et al., 2015; Li et al., 2014; Liu et al., 2009; Yuan et al., 2008) all demonstrated a strong and consistent association between e-waste exposure and MN frequency rates. Overall, these studies consistently indicated a link between e-waste exposure and MN frequency in exfoliated buccal cells and peripheral blood lymphocytes (PBLs). University of Ghana http://ugspace.ug.edu.gh 26 2.2.9.2.2 Chromosomal aberrations Three studies examined the association between e-waste exposure and DNA damage using chromosomal aberration (CA) as a biomarker. All three studies showed significantly higher CA frequency among the e-waste exposed group compared to the control groups. Wang et al. (2018) examined DNA damage in peripheral blood lymphocytes among e-waste workers (n=146) and a control group (121) in China. The results of this study showed that the total CA in the e-waste workers was approximately 5-fold higher than that in the control group (8.01% vs 1.8%, p<0.001). The duration of e-waste exposure was also positively correlated with CA. However, no significant difference in CA was observed between smoking e-waste workers and nonsmoking workers (p>0.05). Similarly, according to Liu et al. (2009), individuals (n=171) recruited from three e-waste polluted villages in northern China had significantly increased levels of total CA than those recruited (n=30) from a neighbouring village with no e-waste exposure (5.50% vs 1.70%, p<0.001). The third study by Khlaif and Qumsiyeh (2017) also found a significant mean difference in total CA among the e-waste-exposed group compared to a control group in Palestine (4.83% vs 0.75%, p<0.001). 2.2.9.2.3 Comet assay parameters Six studies (Khlaif & Qumsiyeh, 2017; Li et al., 2008; Liu et al., 2009; Ngo et al., 2020; Wang et al., 2018; Yu et al., 2018) utilized the comet assay to measure DNA damage associated with e-waste disposal. One study (Li et al., 2008) examined the tail injury rates and tail length (TL) of neonates born in Guiyu compared to those born in Chaonan. Tail injury rates and TL were observed to be significantly higher in the Guiyu group than in the control group (33.20 vs 10.7, p<0.05) and (4.49 vs 2.09, p<0.01), respectively. The measured umbilical cord blood chromium (UCB Cr) level, which was higher in the exposed neonates, correlated positively with DNA damage parameters (tail injury rate and tail length). Similarly, in a recent study in Vietnam, DNA damage in blood cells given as TL, olive tail moment (OTM) and % tail DNA University of Ghana http://ugspace.ug.edu.gh 27 (%TDNA) were measured in children who resided in an e-waste polluted village and children from a control village. The mean ± standard deviation TL, OTM, and %TDNA of 2.07 ± 0.41, 0.16 ± 0.04, and 2.67 ± 0.42 respectively, in the e-waste exposed children were higher than those in children from a control village with 1.78 ± 0.59, 0.14 ± 0.03, and 2.22 ± 0.40 respectively (pall <0.001) (Ngo et al., 2020). Liu et al. (2009) examined DNA damage in residents of e-waste processing sites in China. The results showed that the averages of %TDNA (4.27 vs 1.18), TM (0.53 vs 0.05), and OTM (0.82 vs 0.19) were significantly higher in the e-waste-exposed group than in the control group. Among the exposed group, females had a higher degree of DNA damage than their male counterparts. Another study performed by Khlaif and Qumsiyeh (2017) found significantly increased DNA damage parameters (tail and nucleus lengths) in an e-waste-exposed population compared with controls in Palestine. Yu et al. (2018) also examined TL and %TDNA in blood lymphocytes of men recruited from e-waste dismantling areas in south China to assess semen quality associated with e-waste exposure. The results of this study showed that the average %TDNA and OTM of 57.88 vs 33.55, p<0.001 and 12.3 vs 5.14, p<0.001 respectively, were significantly higher in the exposed group (n=32) than in the control group (n=25). The results indicate a higher risk of infertility in the e-waste-exposed group. Similarly, Wang et al. (2018) found significantly higher levels of DNA damage (represented by %TDNA, TM and OTM) in spermatozoa and lymphocytes of e-waste-exposed men than in those of the controls. Again, comparing 146 adult men directly and actively involved in e-waste processing with 121 adult vegetable farmers who resided approximately 50 km away with no history of e-waste exposure, Wang et al. (2018) reported the duration of exposure to be significantly positively associated with %TDNA and TM of both University of Ghana http://ugspace.ug.edu.gh 28 spermatozoa and lymphocytes and showed that the e-waste workers were at increased risk of DNA damage compared to the vegetable farmers. 2.2.9.2.4 Oxidative DNA damage Oxidative DNA damage threatens genome stability and has been implicated in the pathogenesis of chronic diseases, including cancers (Dai et al., 2019; El Hassani et al., 2019). 8-Hydroxy- 2′-deoxyguanosine (8-OHdG) is the primary product of oxidative DNA damage and is used as a biomarker of genome stability associated with genotoxic exposure (Miglani et al., 2019). In this review, seven studies (Lu et al., 2016; Neitzel et al., 2020; Ni et al., 2014; Sheng et al., 2008; Wang et al., 2010; Xu et al., 2018; Yuan et al., 2008) examined plasma or urine 8-OHdG as a biomarker of DNA damage associated with e-waste exposure. The results of these studies were contradictory. Three studies (Ni et al., 2014; Wang et al., 2010; Yuan et al., 2008) did not find a significant difference between the e-waste-exposed population and the control group. Ni et al. (2014) did not find any significant difference in UCB plasma concentration of 8-OHdG in neonates born in Guiyu and those born in a control town (median: 162.9 ng/mL vs 153.69 ng/mL, p=0.117). However, neonates whose mothers engaged in e-waste recycling activities had higher UCB plasma concentrations of 8-OHdG than neonates to mothers who were non-occupationally exposed (median: 179.77 ng/mL vs 159.00 ng/mL, p=0.028). In addition, blood Cd, Cr, and Ni were significantly positively associated with UCB plasma 8-OHdG concentration. In contrast, Wang et al. (2010) found higher urinary 8-OHdG in the non-occupationally exposed group than in the occupationally exposed group (mean creatinine levels: 3.78 vs 3.55 nmol/mol p<0.01). Yuan et al. (2008) also reported a statistically insignificant difference in urinary 8-OHdG among 23 e-waste workers and 26 farmers (mean creatinine levels: 69.04 vs 229.97 µmol/mol p=0.200) in China. University of Ghana http://ugspace.ug.edu.gh 29 Lu et al. (2016) examined the urinary concentration of 8-OHdG in people living in and around e-waste dismantling facilities (n=130) and in reference populations from rural (24) and urban (22) areas in China. They reported that urinary 8-OHdG concentrations were in the following order: e-waste dismantling area (GM: 16.2 μg/g Cre) > rural reference area (GM: 12.3 μg/g Cre) > urban reference area (GM: 11.6 μg/g Cre). Another study in Thailand found an association between the duration of e-waste exposure and urinary level 8-OHdG. The study found significantly higher urinary 8-OHdG in men who worked ≥ 48 hours/week than in those who worked ≤ 48 hours/week (Neitzel et al., 2020). Similarly, Sheng et al. (2008) found a sharp increase in the urinary level 8-OHdG from the preworkshift (6.40 ± 1.64 µmol/mol) to the postworkshift (24.55 ± 5.96 µmol/mol), p<0.05. The rise in postworkshift urinary 8-OHdG levels was attributed to oxidative stress on workers during their work time processing e-waste. Xu et al. (2018) also found elevated blood Pb and Hg levels in preschool children living in e- waste-exposed towns to be significantly associated with urinary 8-OHdG concentration. 2.2.9.2.5 Telomere length One study (Lin, 2013) examined 227 placentas of healthy puerperae from Guiyu and 93 placentas from a control group to assess placenta telomere length associated with e-waste exposure. The results showed that placental Cd concentration was negatively correlated with placental telomere length (r = -0.138, p = 0.013) and was observed to be correlated in a dose- dependent manner. 2.2.9.2.6 Apoptosis rate Regarding the apoptosis rate as a biomarker of DNA damage associated with e-waste exposure, Yu et al. (2018) found a significant increase in the apoptosis rate in spermatozoa of residents of e-waste-exposed towns compared to a control group (32 ± 19% vs 20 ± 8%, p= 0.037). University of Ghana http://ugspace.ug.edu.gh 30 2.2.9.3 Risk of bias (quality) assessment All studies included in this review were cross-sectional. The risk assessment scores ranged between 3-7 (maximum of 10). Of the 20 studies, 12 studies (Alabi et al., 2020; He et al., 2015; Li et al., 2008; Lin, 2013; Liu et al., 2009; Ngo et al., 2020; Ni et al., 2014; Wang et al., 2010; Wang et al., 2011; Wang et al., 2018; Yu et al., 2018; Yuan et al., 2008) scored above the median score of 5 and were considered high quality. The appraisal details are summarized in (Appendix 5.3). 2.2.9.4 Estimate of variance across studies and pooled outcomes Of the nine studies that utilized MN assay to measure DNA damage, seven were combined for the meta-analysis. Despite high heterogeneity observed between studies (I2 = 95.81 %), the meta-analysis showed a significantly higher MN frequency among the e-waste exposed population than the control population with an overall SMD of 2.30 (95% CI: 1.36, 3.24, p- value < 0.001) (Figure 3). Potential publication bias was not explored due to the limited number of studies (<10 studies) included in the meta-analysis. In a subgroup analysis considering only ecological studies, four studies were included in the analysis. The pooled estimate of the SMD was 1.68 (95% CI: 0.85, 2.51, p < 0.001), with high variability between studies (I2 = 90.94%) (Figure 4). Considering only studies conducted among e-waste workers (occupational), only three studies were included in the analysis. The combined SMD showed that e-waste recyclers had higher MN than controls (SMD: 3.09 (95% CI: 1.53, 4.66, p < 0.001) (Figure 4). In a further sensitivity analysis stratified by study quality, five studies adjudged "high quality" by the NOS confirmed that MN frequency was higher among the e-waste exposed group compared to the controls (MSD: 2.80, 95% CI: 1.79, 3.81, p < 0.001) (Figure 5). Only two studies were considered "low quality" and did not show a significant difference in MN frequency between the two groups (p = 0.35). University of Ghana http://ugspace.ug.edu.gh 31 Figure 3: Standardized mean difference of micronuclei frequency for e-waste exposed population vs control. University of Ghana http://ugspace.ug.edu.gh 32 Figure 4: Sub-group analysis by study setting University of Ghana http://ugspace.ug.edu.gh 33 Figure 5: Sub-group analysis by study quality 2.2.10 Discussion To the best of my knowledge the systematic review with meta-analysis is the first to specifically assess the risk of DNA damage associated with e-waste processing/disposal in human populations. The review identified and evaluated 20 studies that investigated associations between e-waste exposure and various biomarkers of DNA damage. Nine of these studies measured DNA damage by MN assay of which seven were deemed combinable for meta- analysis. The review provides ample evidence of the deleterious effects of e-waste processing on DNA integrity, as evaluated through well-known DNA damage biomarker assays. Despite high heterogeneity between studies, the overall standardized mean difference (SMD) estimate showed higher MN frequency among the e-waste exposed group compared with the University of Ghana http://ugspace.ug.edu.gh 34 control group with an effect size (SMD) of 2.30 (95% CI =1.36, 3.24). Subgroup analysis by study setting revealed that workers who directly recycle e-waste (occupational) had higher MN frequency (SMD: 3.09, 95% CI: 1.53, 4.66, p < 0.001) compared to occupationally unexposed individuals (SMD: 1.68, 95% CI: 0.85, 2.51, p < 0.001). A possible explanation for these results might be that e-waste workers in the informal sector are continuously involved in multiple tasks and work in the open using rudimentary tools with little or no use of personal protective equipment (Tue et al., 2016; Zhang et al., 2019a). This practice exposes the recyclers to higher levels of toxic chemicals compared to the general population (Song & Li, 2015). The frequency of MN is generally used as a biomarker of effect associated with exposure to genotoxic chemicals (Bolognesi & Holland, 2019; Panico et al., 2020). Th