International Archives of Occupational and Environmental Health (2021) 94:1931–1944 https://doi.org/10.1007/s00420-021-01733-8 ORIGINAL ARTICLE Global DNA (LINE‑1) methylation is associated with lead exposure and certain job tasks performed by electronic waste workers Ibrahim Issah1  · John Arko‑Mensah1 · Laura S. Rozek2 · Katie R. Zarins2 · Thomas P. Agyekum1 · Duah Dwomoh3 · Niladri Basu4 · Stuart Batterman2 · Thomas G. Robins2 · Julius N. Fobil1 Received: 5 November 2020 / Accepted: 28 March 2021 / Published online: 20 June 2021 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021 Abstract Objective This study assessed the associations between blood and urine levels of toxic metals; cadmium (Cd) and lead (Pb), and methylation levels of the LINE-1 gene among e-waste and control populations in Ghana. Methods The study enrolled 100 male e-waste workers and 51 all-male non-e-waste workers or controls. The concentrations of Cd and Pb were measured in blood and urine using inductively coupled plasma mass spectrometry, while LINE1 methyla- tion levels were assessed by pyrosequencing of bisulfite-converted DNA extracted from whole blood. Single and multiple metals linear regression models were used to determine the associations between metals and LINE1 DNA methylation. Results Blood lead (BPb) and urine lead (UPb) showed higher median concentrations among the e-waste workers than the controls (76.82 µg/L vs 40.25 µg/L, p ≤ 0.001; and 6.89 µg/L vs 3.43 µg/L, p ≤ 0.001, respectively), whereas blood cadmium (BCd) concentration was lower in the e-waste workers compared to the controls (0.59 µg/L vs 0.81 µg/L, respectively, p = 0.003). There was no significant difference in LINE1 methylation between the e-waste and controls (85.16 ± 1.32% vs 85.17 ± 1.11%, p = 0.950). In our single metal linear regression models, BPb was significantly inversely associated with LINE1 methylation in the control group (βBPb = − 0.027, 95% CI − 0.045, − 0.010, p = 0.003). In addition, a weak associa- tion between BPb and LINE1 was observed in the multiple metals analysis in the e-waste worker group (βBPb = − 0.005, 95% CI − 0.011, 0.000, p = 0.058). Conclusion Continuous Pb exposure may interfere with LINE1 methylation, leading to epigenetic alterations, thus serving as an early epigenetic marker for future adverse health outcomes. Keywords Electronic waste · Toxic metals · DNA methylation · LINE-1 * Ibrahim Issah Thomas G. Robins ibrahimissah111@gmail.com trobins@umich.edu John Arko-Mensah Julius N. Fobil papaarko@yahoo.com jfobil@gmail.com Laura S. Rozek 1 rozekl@umich.edu Department of Biological, Environmental and Occupational Health Sciences, School of Public Health, University Katie R. Zarins of Ghana, Legon, P.O. Box LG13, Accra, Ghana kmrents@umich.edu 2 Department of Environmental Health Sciences, University Thomas P. Agyekum of Michigan, 1415 Washington Heights, Ann Arbor, thomaspagyekum@gmail.com MI 48109, USA Duah Dwomoh 3 Department of Biostatistics, School of Public Health, duahdwomoh@gmail.com University of Ghana, P.O. Box LG13, Accra, Ghana Niladri Basu 4 Faculty of Agricultural and Environmental Sciences, McGill niladri.basu@mcgill.ca University, Montreal, Canada Stuart Batterman stuartb@umich.edu Vol.:(012 3456789) 1932 International Archives of Occupational and Environmental Health (2021) 94:1931–1944 Abbreviations site involves open-air burning of electrical cables of all Cd C admium sizes in pits to retrieve oxidized copper wires with flamma- CpG C ytosine-guanine dinucleotide ble materials such as plastics and foam recovered from old DNMTs DNA methyltransferases discarded fridges. This particular activity results in releas- E-waste Electronic waste ing a mixture of toxic chemicals such as PAHs, PCBs and GeoHealth G lobal environmental and occupational toxic metals into the ambient environment. Several studies health have documented high concentrations of PAHs, chlorin- HEI Health Effect Institute ated and brominated dioxin-related compounds (DRCs) LINE1 Long interspersed nucleotide element-1 and dioxin-like polychlorinated biphenyls (DLPCBs), NHANES N ational health and nutrition examination polybrominated diphenyl ethers (PBDEs) and toxic met- survey als in surface soil samples from the Agbogbloshie e-waste PAHs Polycyclic aromatic hydrocarbons recycling site in Ghana (Akortia et al. 2017; Daso et al. Pb L ead 2016; Tue et al. 2016, 2017). Other studies have found POPs P ersistent organic pollutants high levels of PAH-derived metabolites (Feldt, 2014) and SAM S-adenosyl methionine toxic metals (Srigboh et al. 2016; Wittsiepe et al. 2017) in worker blood and urine. Toxic metals are implicated in numerous adverse health Introduction outcomes, including cancers, cardiovascular diseases, neu- rological diseases, reproductive toxicity, renal dysfunction Public health concerns due to the high production volumes and autoimmune diseases (Hu 2002; Rzymski et al. 2015; of electrical and electronic waste (e-waste), especially in Shi et al. 2019). Bal and colleagues extensively studied the low- and middle-income countries, are well documented genotoxicity aspect of metals. They reported that toxic met- (Alabi et al. 2012; Baldé et al. 2017; Orlins and Guan 2016; als largely influence their toxicity either through direct inter- Robinson 2009; Song and Li 2014). The composition of action with nuclear DNA or indirectly through generated electrical and electronic equipment presents a challenge in reactive intermediates reacting with other cellular pathways the management of e-waste because it is simultaneously a such as inhibition of DNA repair mechanisms or both (Bal source of recoverable precious materials (especially metals) et al. 2011). Recent developments in the field of metal tox- as well as a myriad of toxic chemicals (Alabi and Bakare icity and carcinogenicity suggest that genetics alone cannot 2017; Amankwaa et al. 2017; Bakhiyi et al. 2018; Dias et al. fully explain metal-induced chronic diseases, especially can- 2019; Fowler 2017). Therefore, effective and adequate recy- cer, since most of the metals are weak mutagens. Epigenetic cling processes are required to recover valuable materials mechanisms such as DNA methylation; the most widely while protecting human and environmental health (Ikhlayel studied epigenetic marker, may in part mediate the health 2017). This presents serious challenges for informal sector effects of occupational/environmental exposure to metals e-waste recycling facilities, especially in developing coun- (Arita and Costa 2009). tries without appropriate recycling infrastructure (Ikhlayel Long interspersed nucleotide elements (LINE1) are 2018). repetitive elements or transposons that constitute approxi- The high influx of second-hand electrical and elec- mately 18% of the human genome and are usually heavily tronic products into Ghana in recent years has resulted methylated to ensure genomic stability and integrity (Kahl in a significant increase in the recycling and dumping of et al. 2019). The alteration of LINE1 is used as a proxy for e-waste, which has offered employment opportunities to global methylation changes (Ghosh et al. 2017; Sharma et al. hundreds of young men in the capital Accra (Amankwaa 2019). Decreased levels of LINE1 methylation may result in et al. 2016). The Agbogbloshie e-waste processing and increased mitotic recombination and overall genome insta- recycling site (a.k.a., scrapyard) is the main centre in bility (Kahl et al. 2019; Li et al. 2018), and this has been Ghana for the recovery of reusable materials from e-waste. regarded as a biomarker of effect to different classes of xeno- Agbogbloshie e-waste recyclers are a particularly vulner- biotics in occupational settings (Kahl et al. 2019). A study able group because they are among the largest and busiest among urban pesticide sprayers in Mexico showed decreased informal recyclers worldwide (Srigboh et al. 2016). The levels of LINE1 methylation among the pesticide-exposed workers, often young men, are involved in multiple tasks group (Benitez-Trinidad et al. 2018). In another study in and work in the open using rudimentary tools with little or China where workers in a battery plant were occupationally no personal protective equipment. The recycling process exposed to Pb, LINE1 was inversely associated with Pb lev- itself involves the manual dismantling of old or end-of-use els (Li et al. 2013). Duan and coworkers also reported hypo- electronic and electrical equipment to retrieve reusable methylation of LINE1 among coke-oven workers exposed components. A significant activity at the e-waste recycling to polycyclic aromatic hydrocarbons (PAHs) (Duan 2013). 1 3 International Archives of Occupational and Environmental Health (2021) 94:1931–1944 1933 Significan decrese in LINE1 methylation is observed in on the western side of the Odaw River in central Accra. To several types of cancers (Ehrlich 2002; Hsiung, 2007; Woo the east are various businesses, including banks, pharma- and Kim 2012; Zhu et al. 2011), and is regarded as a hall- ceutical companies, breweries, shops, and various manu- mark of cancer development (Buj et al. 2016; Das and Sin- facturing companies. To the south is a densely populated, gal 2004). In a meta-analysis by Barchitta et al. (2014), the ‘resource-poor’ community with most residents lacking LINE-1 methylation level was significantly lower in cancer access to essential services such as clean water and sani- patients than in control samples (p < 0.001). The difference, tation (Amankwaa et al. 2016). Agbogbloshie scrapyard is however, was confined to tissue samples (p < 0.001) and not the main centre for the recovery of valuable materials from blood (p = 0.23). e-waste, with an estimated population of 80,000 (United Global DNA methylation changes associated with toxic Nations Population Fund 2018). The residents at the site are metal exposure have been less clear and consistent, as dem- predominantly migrants from the northern parts of Ghana onstrated by studies showing both hypermethylation and (Wittsiepe et al. 2017). The recycling process involves man- hypomethylation. These studies were limited by differences ual dismantling, collection, and sorting of electrical equip- in tissues examined, populations studied, methods for meas- ment and burning electrical and electronic items, includ- uring methylation status, and singular analysis of the effect ing plastic materials such as wire insulation, which emit of one metal at a time (Bandyopadhyay et al. 2016; Goodrich considerable smoke and pollute the local environment. The et al. 2013; Hanna et al. 2012; Hossain et al. 2017, 2012; recyclers are mostly young men who use rudimentary tools Lambrou et al. 2012; Li et al. 2013; Majumdar et al. 2010; such as a chisel, hammer, pliers, etc. or sometimes their bare Phetliap et al. 2018; Pilsner et al. 2009; Tajuddin et al. 2013; hands with little or no protective equipment (Acquah et al. Tellez-Plaza et al. 2014; Wright et al. 2010). In addition, the 2019). informal e-waste recycling industry is largely understudied The controls are residents at Madina Zongo in Accra, despite evidence that activities result in release and exposure which is approximately 10 km north of Agbogbloshie. There to several toxic metals (Awasthi et al. 2016). There is lit- are no e-waste recycling activities in the area, and individu- tle published data on the association between human metal als recruited are not involved in any e-waste work. Madina exposure and DNA damage in the e-waste recycling industry Zongo residents are known to be quite similar to e-waste (Alabi et al. 2019; Wang et al. 2011, 2018). To the best of workers with respect to the length of time residing in Accra our knowledge, no previous study has investigated the asso- and the region of the country from where they moved, socio- ciation between metal exposure to the e-waste workers them- economic background, religion, and culture. selves and DNA methylation changes; rather, these studies were carried out among non-worker populations residing Study design and participant recruitment near informal sector e-waste recycling (Li et al. 2020). The objectives of this study were to (1) quantify the con- This study utilized existing data and specimens from the par- centration of toxic metals in blood and urine (Cd and Pb), ent study (GeoHealth-II), a longitudinal repeated measures (2) evaluate LINE1 DNA methylation, and (3) assess the study with four rounds of data collection from Agbogbloshie association between the concentration of blood and urine workers and the comparison group in Madina Zongo. After toxic metals and LINE1 DNA methylation among e-waste completing a community entry process that included dur- workers and a control group in Ghana. These metals were bar that brought together researchers, leaders of the study considered because of their widespread use in electrical and sites, and e-waste workers, participants were recruited into electronic products, their toxicity and public health signifi- the study. Each potential participant was given a detailed cance (Tchounwou et al. 2012), and co-exposure risk (Fan explanation of the study procedures and objectives, the ben- et al. 2014). efits and possible risks of participating in the study, and was asked to provide written consent if willing to be enrolled. The inclusion criteria were adult males aged 18 years and Methods above who have worked at the e-waste site for at least six months. The study was conducted after receiving ethical Study area and population clearance from the College of Health Sciences Ethical and Protocol Review Committee, University of Ghana (proto- The study was conducted in two locations: the e-waste col identification number CHS-ET/M.4-P 3.9/2015–2016). recycling site in Agbogbloshie, Accra, Ghana, and Madina Briefly, 151 participants were recruited from the first round Zongo, a part of greater Accra located approximately 10 km of data collection from Agbogbloshie (100) and Madina from the e-waste site. The Agbogbloshie e-waste site is the Zongo (51). Due to higher than expected loss to follow-up largest and busiest e-waste dump in Africa (Srigboh et al. rates in the second round of data collection, 56 new par- 2016). The site is situated on the banks of Korle Lagoon ticipants (Agbogbloshie = 42 and Madina Zongo = 14) were 1 3 1934 International Archives of Occupational and Environmental Health (2021) 94:1931–1944 recruited to address this challenge. Some participants pro- all laboratory glassware and plastic were acid-washed vided multiple blood and urine samples during the four sam- (cleaned, soaked for 24 h in 20% nitric acid and rinsed 3 pling periods from March 2017 to August 2018, bringing the times in Milli-Q water) before use. Accuracy and preci- total number of samples to 598. This study utilized data from sion were measured by use of certified reference materials the first sampling event (Agbogbloshie = 100 and Madina [INSPQ; QM-U-Q1109 (urine); and then QM-B-Q1506 Zongo = 51) to investigate associations between heavy metal and QM-B-Q1314 (blood)] obtained from the Institut (Cd, Pb and As) and global (LINE1) methylation levels at National de Sante Publique du Quebec. Additionally, each baseline. batch run contained procedural blanks and replicate runs. For each element analyzed, the theoretical detection limit Data collection was calculated as three times the standard deviation of the mean blank value (Supplemental Table 1). Questionnaire survey All participants answered a questionnaire that was admin- Extraction of DNA from whole blood for LINE‑1 istered by trained staff. Interviews were conducted in Eng- methylation lish, Dagbani, Twi and Hausa. The interview included demographics (age, gender, religion/ethnicity, education, DNA was extracted in the laboratory at Michigan Public measures of socioeconomic position, location of birth and Health using the Qiagen DNA Blood Mini Kit, follow- childhood and location of all residences), information to ing the manufacturer’s recommendations. The purity and assess past and current potential exposures to air pollutants quantity of DNA samples were assessed with the Qubit (use of tobacco/exposure to environmental tobacco smoke, Broad Range Double-stranded DNA assay and Nanodrop exposure to indoor cooking using biomass fuels, type of Spectrophotometer through the University of Michigan housing, detailed job history), personal and family medi- DNA Sequencing Core. The extracted DNA was then cal history (diagnosed illnesses, reported symptoms), and stored at – 20 °C until LINE-1 methylation analysis was other health-related measurements such as weight, height conducted. and blood pressure. Blood and urine sample collection LINE‑1 methylation analysis A temporary structure (clinic) was erected and arranged to Sodium bisulfite conversion was performed on 300 ng allow for the various data collection for each sampling round of extracted genomic DNA using the Qiagen EpiTect in both Agbogbloshie and Madina Zongo. Each participant Bisulfite Kit per the manufacturer’s protocols. PCR at enrollment and in subsequent rounds provided 20 ml of amplification was performed for the promoter region of venous whole blood. Blood was collected by an experienced LINE-1 using a previously published assay (Yang et al. phlebotomist following sterile procedures via venepuncture 2004). In summary, HotStarTaq Master Mix (Qiagen, of the antecubital fossa into EDTA tubes. In addition, each Valencia, CA, USA), water, and desalted FWD/RVS prim- participant was given a 100 ml, sterile plastic container to ers were combined to create a PCR master mix. Finally, provide a urine sample. The urine was then aliquoted into 3 µL of bisulfite-converted DNA was added to each well three 10 ml sterile tubes for storage. The blood and urine to bring the final primer concentration to 0.2 mM and samples were placed in a cooler with ice blocks and then the total reaction volume to 30 µL. PCR product quality transported to the University of Ghana after each sampling was confirmed using 2% agarose gels and gel red stain- day, stored at − 80 °C, and later transported on dry ice to ing. Following amplification, 12 µL of PCR product was the University of Michigan, USA and McGill University, combined with each sequencing primer and analyzed for Canada for DNA extraction and metal analysis, respectively. CpG-specific methylation using the PyroMark MD System (Qiagen, Valencia, CA, USA). Four bisulfite conversion Elemental analysis controls (EpigenDX) and four pyrosequencing controls (Qiagen) were prepared at methylation levels of 0%, 30%, An inductively coupled plasma mass spectrometer (ICPMS 60% and 100%. CpG site-specific methylation percentages Varian; 820MS) was used to detect the levels of blood and (0–100%) were generated for each of the four CpG sites urine Cd, Pb and As. The blood and urine samples were included in the assay. All samples on a plate were rerun digested with nitric acid as detailed by Basu et al. (2011). if any of the controls failed. Samples were measured in All the analytical quality control measures previously duplicate. The reproducibility of the assay in our duplicate described by Srigboh et al. (2016) were used. In summary, samples expressed as the variation in coefficients was 2%. 1 3 International Archives of Occupational and Environmental Health (2021) 94:1931–1944 1935 Statistical analysis group (0.59 µg/L and 0.81 µg/L, respectively, p = 0.003), while concentrations of urine Cd (UCd), which is a bio- Statistical analysis was performed with Stata v15.1 (STATA marker of long-term exposure, did not differ between the Corp LLC. Texas, USA), and GraphPad Prism v8.3.1 was two populations. Blood lead (BPb) and urine lead (UPb), used to generate graphs. The Shapiro–Wilk test was used on the other hand, showed higher significant median con- to assess the normality of continuous variables. The data centrations among the e-waste worker group than the are presented as the mean (standard deviation) for nor- control group (76.82 µg/L and 40.25 µg/L, p = < 0.001; mally distributed data or median (interquartile range) for and 6.89 µg/L and 3.43 µg/L, p ≤ 0.001, respectively) data that were not normally distributed. The nonparamet- (Table 2). ric Mann–Whitney U test was used to compare differences The levels of toxic metals in blood and urine were com- across study sites for continuous abnormally distributed pared to background levels in the US population using the data, while the t test compared normally distributed data. P95 values of the National Health and Nutrition Examina- In the case of categorical variables, the Chi-squared test of tion Survey (NHANES) (Centers for Disease Control and association was used for the comparison, while one-way Prevention 2019). For blood and urine lead, 99% and 97% analysis of variance (ANOVA) compared continuous vari- of the e-waste workers’ samples far exceeded the reference ables (e.g., LINE1) and categorical variables (e.g., job cate- values of 29.3 µg/L and 1.26 µg/L, respectively. In the con- gories). Since Cd and Pb were not normally distributed, they trols, 74% of the population had BPb higher than 29.3 µg/L, were log-transformed for the bivariate analysis. Pearson cor- and 94.1% had UPb higher than 1.26 µg/L (Table 2). relation analysis was used to assess the correlation between the toxic metals and the average methylation of the four CpG sites (LINE1) and methylation of each CpG site. Multivari- LINE1 DNA methylation in e‑waste workers able linear regression models were used to explore relation- and controls ships between DNA methylation and toxic metal biomarker levels in e-waste workers and controls while adjusting for Methylation of four CpG sites of LINE1 was quantified from confounders. We set statistical significance at p < 0.05. whole blood samples (N = 149). As expected, all CpG sites were heavily methylated. There was no significant differ- ence in LINE1 methylation among the e-waste workers and Results the non-e-waste workers (85.16 ± 1.32% vs 85.17 ± 1.11%, p = 0.950). CpG1 showed significantly lower mean methyla- Demographic characteristics tion among the non-e-waste workers compared to the e-waste workers (81.70 ± 1.86% vs 82.48 ± 2.20%, p = 0.034), and Overall, participants in the control group were signifi- CpG4 had the highest (91.28%) mean methylation level cantly older than their e-waste counterparts (25.4 ± 6.3 (Fig. 1). vs 32.5 ± 10.4, Table 1). The BMI of the control group (mean = 23.8 ± 3.5) was significantly higher than that of e-waste workers (mean = 21.6 ± 2.7, Table 1). A majority LINE1 methylation levels of e‑waste workers by primary job of e-waste workers live within 1 km of the e-waste site and tasks performed work on an average of 10 h per day for an average of 6 days per week. Regarding education level, 25.3% of e-waste The main e-waste recycling activities at the Agbogbloshie workers had no formal education compared to 13.0% of site include sorting and transporting e-waste materials, the the controls. Only 16.2% of e-waste workers had second- manual dismantling of larger waste types, and the open burn- ary school education or higher, compared to 52.2% of the ing of smaller insulated wires to recover copper and other controls. The majority of the e-waste workers (80%) earned valuables (Kwarteng et al. 2020). Therefore, the e-waste between 20–80 Ghanaian Cedi (GHC); the equivalence of workers at the Agbogbloshie site in Ghana are categorized 5–15 USD. The prevalence of smoking among the e-waste into three main groups: collectors/sorters, dismantlers and workers was significantly higher (27.8%) than among the burners based on their most recent job tasks (Acquah et al. non-e-waste group (12.2%, Table 1). 2019). The LINE1 methylation level was compared among the Heavy metal concentrations in blood and urine different categories of e-waste workers defined by primary in e‑waste workers and controls job tasks. Overall, e-waste collectors had the lowest mean methylation level than burners and dismantlers (Fig. 2). As shown in Table 2, E-waste workers had lower median However, the difference in methylation was not significant blood cadmium (BCd) concentrations than the control (ANOVA, p = 0.104). 1 3 1 936 International Archives of Occupational and Environmental Health (2021) 94:1931–1944 Table 1 Characteristics of Total E-waste workers Non e-waste workers p value e-waste workers (n = 100) and controls (n = 51) enrolled for Variables 151 100 51 the study BMI (kg/m2), mean (± SD) 22.4 (3.2) 21.6 (2.7) 23.8 (3.5) < 0.001a Age (years), mean (± SD) 27.8 (8.6) 25.4 (6.3) 32.5 (10.4) < 0.001a Workdays/week, mean (± SD) 6.0 (1.0) Hours work/day, mean (± SD) 9.7 (4.4) Sleep location, n (%) n = 97  On the site 54 (55.7)  ≤ 1 km off-site 35 (36.1)  > 1 km off-site 8 (8.3) Education, n (%) n = 145 n = 99 n = 46 < 0.001b  No formal education 31 (21.4) 25 (25.3) 6 (13.0)  Primary 30 (20.7) 26 (26.3) 4 (8.7)  Middle/JHS 44 (30.3) 32 (32.3) 12 (26.1)  Secondary/SHS + 40 (27.6) 16 (16.2) 24 (52.2) Marital status, n (%) n = 150 n = 99 n = 51 0.074b  Single 73 (48.7) 43 (43.4) 30 (58.8)  Married 77 (51.3) 56 (56.6) 21 (41.2) Income, n (%) n = 149 n = 99 n = 50 0.072b  GHC 20–80 120 (80.5) 81 (81.8) 39 (78.0)  GHC 81–140 10 (6.7) 9 (9.1) 1 (2.0)  GHC 141–200 8 (5.4) 5 (5.1) 3 (6.0)  > GHC 200 11 (7.4) 4 (4.0) 7 (14.0) Indoor cooking, n (%) n = 147 n = 98 n = 49 0.009b  Yes 30 (20.4) 14 (14.3) 16 (32.7)  No 117 (79.6) 84 (85.7) 33 (67.4) Alcohol use, n (%) n = 148 n = 99 n = 49 0.086b  Never 125 (84.9) 83 (83.8) 42 (87.7)  Former 6 (4.1) 2 (2.0) 4 (8.2)  Occasional/regular 17 (11.5) 14 (14.1) 3 (6.1) Smoking, n (%) n = 146 n = 97 n = 49 0.033b  Yes 33 (22.6) 27 (27.8) 6 (12.2)  No 113 (77.4) 70 (72.2) 43 (87.8) SD standard deviation, N Total number of participants, n (%) frequency(percent frequency) a p values obtained by t test b p values obtained by chi-square test Relationship between LINE1 methylation Relationship between LINE1 methylation and blood and anthropometric and lifestyle factors and urine levels of Cd and Pb The mean methylation of LINE1 was assessed based on Bivariate analyses (Pearson correlation) showed a trend anthropometric and lifestyle factors such as age, BMI, toward negative correlations between the log-transformed smoking, alcohol consumption and indoor status via one- toxic metals and LINE1 methylation, even though the cor- way analysis of variance (ANOVA) (Table 3). LINE1 meth- relations were not significant (Table 4). No correlation was ylation was not related to age, BMI, smoking or alcohol observed between LINE1 and the specific CpG sites. consumption (pall > 0.05). However, the proximity of e-waste worker residence in relation to the e-waste site was associ- LINE1 methylation and exposure to toxic metals: ated with LINE1 methylation (p = 0.019). A Tukey post hoc multiple linear regression test revealed that LINE1 methylation was statistically lower in workers who lived within 1 km of the e-waste site than Associations between LINE1 DNA methylation and body those who lived on-site (p = 0.034). burden of toxic metals were assessed using a single metal 1 3 International Archives of Occupational and Environmental Health (2021) 94:1931–1944 1937 Table 2 Cadmium and lead Toxic metals (µg/L) Total E-waste Controls p concentrations in blood and urine of e-workers and controls BCd n = 150 n = 100 n = 50 Mean ± SD 0.80 ± 0.59 0.73 ± 0.55 0.93 ± 0.64 0.003 Median (IQR) 0.66 (0.44) 0.59 (0.43) 0.81 (0.55) BCd > 1.17, n (%) 21 (14) 11 (11) 10 (20) BPb n = 150 n = 100 n = 50 Mean ± SD 75.12 ± 58.42 92.35 ± 63.69 40.67 ± 19.12 < 0.001 Median (IQR) 518.95 (44.01) 76.82 (49.37) 40.25 (17.45) BPb > 29.3, n (%) 135 (90.67) 99 (99) 37 (74) UCd n = 149 n = 98 n = 51 0.878 Mean ± SD 0.62 ± 0.98 0.68 ± 1.18 0.50 ± 0.35 Median (IQR) 0.40 (0.45) 0.38 (0.56) 0.42 (0.44) UCd > 0.77, n (%) 31 (20.8) 22 (22.4) 9 (17.6) UPb n = 149 n = 98 n = 51 < 0.001 Mean ± SD 6.49 ± 5.03 7.76 ± 4.87 4.06 ± 4.45 Median (IQR) 5.05 (4.98) 6.89 (5.22) 3.43 (2.38) UPb > 1.26, n (%) 145 (97.3) 97 (99) 48 (94.1) p value estimate from the Wilcoxon rank-sum (Mann–Whitney) test p values < 0.05 were considered significant IQR interquartile range Source of reference values USA, NHANES, Survey 2011–2016 (U.S. Department of Health and Human Services—Centers for Disease Control and Prevention 2019) linear regression and mutual linear regression models, are few studies, if any, that have looked at exposure levels adjusting for potential confounding factors that may influ- and DNA methylation among informal e-waste workers. ence methylation status (age, BMI, smoking, alcohol con- Our study showed that lead levels (BPb and UPb) were sumption and indoor use of biomass fuel for cooking). significantly higher in e-waste workers than in controls. This In the e-waste worker group, the single metal lin- finding is consistent with other biomonitoring studies among ear regression analyses found no significant association e-waste workers at Agbogbloshie (Wittsiepe et al. 2017) and between LINE1 and any of the metals, (pall > 0.05). In the teenage e-waste scavengers in Nigeria (Alabi et al. 2019), control group, only BPb showed significant negative asso- where BPb and serum Pb levels were consistently higher in ciation with LINE1 methylation (βBPb = − 0.027, 95% CI the exposed group than in the controls, suggesting e-waste − 0.045, − 0.010, p = 0.003) (Table 5). The mutual regres- recycling activities as a critical contributor to the elevated sion analysis results were similar to that of the single Pb levels. Contrary to our expectation, BCd was significantly metal analysis with a marginal increase in the association higher among the controls. between BPb and LINE1 methylation only in the control In this study, the control population had generally high group (βBPb = − 0.028, 95% CI − 0.045, − 0.011, p = 0.002). Cd and Pb levels in blood and urine compared with the P95 Noteworthy, we found a marginal association between BPb values of the NHANES survey (Centers for Disease Con- and LINE1 methylation in the e-waste exposed group in the trol and Prevention 2019). The high concentration of toxic multiple metals analysis (βBPb = − 0.005, 95% CI − 0.011, metals in the unexposed group could be attributed to the − 0.000, p = 0.058) (Table 5). reliance on solid fuels for cooking, a significant source of ambient or outdoor air pollution. The Health Effects Insti- tute (HEI)—Ghana working group estimated that residential Discussion sources (including household cooking, lighting and heat- ing) contributed approximately 65% of the total national pri- Informal e-waste recycling activities generate a consider- mary P M2.5 in Ghana, followed by transport and road dust able amount of air pollutants, including toxic metals, some (13.9%) (Health Effects Institute 2019). Additionally, this of which are carcinogenic (Alabi & Bakare 2017). Several study’s control group was composed of population-based studies (Goodrich et al. 2013; Li et al. 2011, 2013; Wen subjects, most of whom live and work near a busy high- et al. 2016) have reported associations between occupational way with frequent vehicular traffic and may be exposed to heavy metal exposure and DNA methylation; however, there traffic-related air pollutants. Furthermore, drinking water 1 3 1 938 International Archives of Occupational and Environmental Health (2021) 94:1931–1944 92 p = 0.950 95 p = 0.034 90 p = 0.789 90 90 88 88 8685 86 84 80 84 82 75 82 80 80 70 78 te ol te ol l as ntr as t r ste tro -w Co -w Co n -w a on E E E C p = 0.162 100 p = 0.403 95 90 90 85 80 80 75 70 ste l e l a ntr o sta ntr o E- w Co -w CoE Fig. 1 violin plots. The violin plots present the distribution of the The green line represents the median, and the blue lines represent the individual methylation of LINE1 and specific CpG sites of LINE1 interquartile range. in e-waste workers and controls. P values were calculated by t test. could be a major source of toxic metal exposure in Ghana. (Fe) (GM; 18 μg/l), among others, even though the samples For example, Asante et al. (2012) estimated the levels of were taken from the same water treatment source. The use of trace element (T.E.s) concentrations in tap water in Accra, metal (galvanized iron) pipes for water distribution in Ghana Ghana, and found significant variations in heavy metal con- and corroded household plumbing systems were reported as centrations such as barium (Ba) (GM; 36 μg/l), manganese possible sources of toxic metals found in tap water (Asante (Mn) (GM; 19.5 μg/l), zinc (Zn) (GM; 18.7 μg/l) and iron et al. 2012). Further, the high body burden of Cd in both 1 3 CpG3% LINE1(%) CpG1% CpG4% CpG2% International Archives of Occupational and Environmental Health (2021) 94:1931–1944 1939 92 p = 0.104 Table 3 Relationship between LINE1 methylation and anthropomet- ric and lifestyle factors 90 Variable LINE1 methylation Total E-waste Non e-waste p value 88 (n = 151) workers workers (n = 100) (n = 51) 86 Age (years)  ≤ 20 85.3 (1.1) 85.2 (1.2) 85.4 (0.6) 0.817 84  21–30 85.1 (1.4) 85.1 (1.4) 85.3 (1.3) 0.520  31–40 85.1 (1.2) 85.3 (1.3) 84.8 (1.2) 0.3995  > 40 85.3 (1.2) 85.73 (2.0) 85.2 (1.0) 0.541 82 p = 0.885 p = 0.803 p = 0.697 Smoking 80  Yes 85.2 (1.2) 85.2 (1.3) 85.1 (0.7) 0.805 ers ers ors  No 85.2 (1.3) 85.2 (1.3) 85.2 (1.2) 0.892rn tl ct p = 0.914 p = 0.830 p = 0.832 Bu anism Co lle Alcohol intake D  occasional/ 85.4 (1.4) 85.4 (1.5) 85.6 (1.3) 0.797 regular Fig. 2 LINE1 methylation across primary job-tasks performed by  former 85.2 (1.6) 84.2 (2.5) 85.7 (1.2) 0.339 e-waste workers; collectors had the lowest mean methylation levels  never 85.2 (1.2) 85.2 (1.3) 85.1 (1.1) 0.744 than burners and dismantlers. The green bar indicates median meth- p = 0.675 p = 0.471 p = 0.481 ylation, and the blue bars represent the interquartile range BMI (kg/m2)  Low weight 84.8 (1.1) 84.6 (1.2) 85.2 (0.5) 0.492  Normal weight 85.2 (1.3) 85.2 (1.3) 85.2 (1.1) 0.796 the e-waste workers and controls could be attributed to cad-  Overweight 85.3 (1.2) 84.9 (1.3) 85.5 (1.1) 0.307 mium exposure from contaminated food other than from  Obesity 84.5 (1.3) 85.7 (0.0) 84.2 (1.3) 0.364 occupational exposure. Tubers of yam and potatoes, rice p = 0.363 p = 0.529 p = 0.244 and vegetables are staple foodstuffs for this population and Sleep location are mostly farmed in contaminated soil (Bortey-Sam et al.  On-site – 85.4 (1.4) – – 2015); therefore, exposure to Cd in this population could be  ≤ 1 km off-site – 84.7 (1.1) – – via the ingestion of these contaminated foodstuffs.  ≥ 1 km off-site – 85.8 (1.4) – – Our findings show that LINE1 was heavily methyl- – p = 0.019 – – ated in the whole blood of e-waste workers (mean ± SD: Indoor cooking 85.2 ± 1.3%). Overall, there was no significant difference  Yes 85.1 (1.3) 85.3 (1.5) 84.9 (1.1) 0.370 in LINE1 methylation between the e-waste workers and  No 85.2 (1.2) 85.2 (1.3) 85.3 (1.1) 0.574 controls in this study. Even though CpG1 showed a signifi- 0.733 p = 0.635 p = 0.252 cant difference between the e-waste workers and controls, this was not sufficient to influence the overall difference in Body Mass Index (BMI) according to World Health Organiza- LINE1 methylation between the groups. This is consistent tion (WHO) parameters: low weight (≤ 18.5  kg/m 2); normal weight (> 18.5  kg/m2 and ≤ 24.9  kg/m2); overweight (> 24.9  kg/m2, with Ghosh et al. (2017) findings, where no significant differ- and ≤ 29.9 kg/m2), and obesity (≥ 30 kg/m2). p values were obtained ence in LINE1 methylation was observed between workers by the ANOVA test exposed to multi-wall carbon nanotubes and controls. The non-significant difference in LINE1 methylation between our study populations may be attributable to the choice of (Bollati et al. 2009), and children and adolescents (Burris the control group in this study since the categorization of et al. 2011), where environmental exposures greatly affect hyper- or hypomethylation is dependent on the methylation the epigenome. No significant relationship was observed levels of the comparator group (Phetliap et al. 2018). For between age and LINE1 methylation in both the e-waste example, the differences in age between the e-waste workers workers and controls. This conforms to other studies that did and controls could affect our comparison of their methyla- not report differences between age and LINE1 methylation tion levels. However, the average age of the e-waste workers (Benitez-Trinidad et al. 2018; Marques-Rocha et al. 2016; (25.4 ± 6.3 years) and controls (32.5 ± 10.4 years) are not Zhu et al. 2012). In addition, indoor and outdoor urban air in the category of vulnerable window including the elderly pollution, especially in the form of particulate matter (PM), 1 3 LINE1(%) 1940 International Archives of Occupational and Environmental Health (2021) 94:1931–1944 Table 4 Pearson correlation Toxic metals LINE1 CpG site1 CpG site2 CpG site3 CpG site4 coefficients between LINE1 and specific CpG site methylation R p R p r P R p R P and log-transformed blood and urine Cd and Pb BCd − 0.06 0.476 − 0.00 0.996 − 0.11 0.176 − 0.11 0.171 0.04 0.668 BPb − 0.14 0.085 0.04 0.599 − 0.13 0.128 − 0.15 0.076 − 0.13 0.104 UCd − 0.14 0.089 − 0.14 0.088 − 0.06 0.498 − 0.06 0.445 − 0.08 0.333 UPb − 0.03 0.729 0.11 0.200 0.02 0.833 − 0.01 0.940 − 0.13 0.117 R correlation co-efficient, p p value Table 5 Single metal linear Predictors, µg/L E-waste workers Controls regression models and multiple metals linear regression models LINE1 for LINE1 in e-waste workers (n = 100) and controls (n = 51) β (95% CI) p value β (95% CI) p value Models1  Blood Cd − 0.112 (− 0.804, 0.581) 0.750 0.008 (− 0.555, 0.572) 0.975  Urine Cd − 0.055 (− 0.295, 0.185) 0.651 − 0.408 (− 1.465, 0.650) 0.440  Blood Pb − 0.003 (− 0.007, 0.002) 0.213 − 0.027 (− 0.043, − 0.010) 0.003  Urine Pb 0.014 (− 0.046, 0.074) 0.644 − 0.037 (− 0.115, 0.042) 0.354 Models2  Blood Cd 0.072 (− 0.663, 0.806) 0.846 − 0.053 (− 0.564, 0.458) 0.835  Urine Cd − 0.166 (− 0.451, 0.118) 0.249 − 0.539 (− 1.558, 0.480) 0.290  Blood Pb − 0.005 (− 0.011, 0.000) 0.058 − 0.028 (− 0.045, − 0.011) 0.002  Urine Pb 0.071 (− 0.013, 0.154) 0.095 − 0.021 (− 0.096, 0.054) 0.580 Models1 = single metal linear regression models Models2 = multiple metals linear regression models All models adjusted for age, BMI, smoking status, alcohol intake, and indoor biomass fuel use for cooking. Bold p values are statistically significant generated from the widespread use of biomass fuelwood occupational exposure assessment among informal sector as an energy source for cooking is a major public health workers. E-waste collectors often travel off-site within com- concern in Ghana (Cobbinah et al. 2017). This and other munities (primarily by foot, bicycle or tricycle) to purchase sources, such as vehicular emissions, exposes a large propor- or scavenge e-waste materials (Acquah et al. 2019; Laskaris tion of the urban population to PM, from which our control et al. 2019) and may, therefore, be exposed to other sources group for this study was recruited. Therefore, our control of pollutants (including vehicular emissions and road dust) group is substantially exposed to a high concentration of outside the e-waste recycling site. The decline in LINE1 ambient air pollution, which could explain the lack of differ- methylation among collectors could, therefore, be attributed ence in LINE1 methylation in our study since traffic-related to the higher exposures due to multiple exposure sources. particles (PM2.5) altered the level of LINE-1 methylation in Our single metal multivariable regressions showed that blood cells (Baccarelli et al. 2009). BPb was associated with decreased LINE1 methylation in Workers at the e-waste recycling site in Ghana are only the controls. This association was marginally stronger involved in multiple tasks since job titles and task proto- when Cd was adjusted for in the multiple metals regres- cols are not present, and previous studies (Feldt et al. 2014; sion analysis. The associations between BPb and LINE1 Wittsiepe et al. 2015), as well as this study, derived job methylation among only the controls could be attributed to categories based on workers’ self-report. Regarding the the significant concentration of BCd in the controls. Cad- job tasks performed by the e-waste workers, collectors had mium could add to the burden of Pb on DNA methylation, lower LINE1 methylation, even though the difference was as observed in our multiple metals regression analysis. Our not statistically significant. This could be attributable to finding is consistent with previous occupational exposure job misclassification, which may conceal a significant dif- studies in China (Li et al. 2013) and Brazil (Devóz et al. ference in health effects (Laskaris et al. 2019). The use of 2017). In the Chinese study, methylation levels of LINE1 wearable cameras proposed by Laskaris et al. (2019) can were assessed among battery workers exposed to Pb (n = 53) significantly minimize task misclassification and improve and a healthy control group (n = 57), and they reported a 1 3 International Archives of Occupational and Environmental Health (2021) 94:1931–1944 1941 significant decrease in LINE1 methylation among the Pb- The present study’s strength was that we examined two exposed group (Li et al. 2013). Among workers in an auto- toxic metals concurrently using two types of biological motive factory in Brazil, there was a negative association media (blood and urine) for the exposure assessment. between Pb exposure and global DNA methylation (Devóz Additionally, the present study benefitted from high- et al. 2017). Lead is reported to alter DNA methylation quality protocols for recruiting participants, conducting by decreasing the activities of DNA methyltransferase interviews, collecting biological samples and laboratory (DNMT), the enzyme that regulates the DNA methylation analyses. reaction by catalyzing methyl groups ( CH3) through SAM In conclusion, the high internal concentration of toxic to DNA, which initiates DNA hypomethylation (Sanchez metals in the control group in this study suggests that toxic et al. 2017). However, a recent epigenome-wide study did metals exposure is a nationwide problem in Ghana. How- not show any significant methylation changes in DNMTs ever, we found that e-waste workers tend to have a higher among workers occupationally exposed to Pb, suggesting concentration of Pb in particular, which was associated with that other genes may mediate Pb-induced DNA methylation global DNA hypomethylation, as shown in LINE1 methyla- changes (Zhang et al. 2019). tion in both e-waste workers and controls. This may serve as In the e-waste worker group, a weak association was an early epigenetic marker that mediates the adverse effects observed between BPb and LINE1 methylation (p = 0.058) of Pb exposure. In addition, e-waste collectors had decreased only in the multiple metals regression model. The weak LINE1 methylation levels compared to the other categories association in the e-waste workers suggests that Cd adds to of workers. To the best of our knowledge, this is the first the burden of Pb on LINE1 methylation in both the e-waste study that examined this population in an epigenetic con- workers and controls. Cadmium is both an occupational and text. Since global methylation provides important prelimi- environmental toxicant and is established as a carcinogen nary information about genome stability, further epigenetic that likely acts via epigenetics mechanism due to its weak epidemiologic studies are needed using candidate genes and mutagenicity (Martinez-Zamudio and Ha 2011; Takigu- other epigenetic markers, such as histone modifications, to chi et al. 2003). Few studies have reported on the associa- provide a comprehensive understanding of specific pathways tions between environmental Cd exposure and global DNA through which toxic metals exert their toxic effects, espe- methylation with acute exposure associated with increased cially among unprotected informal sector workers. methylation, whereas chronic exposure is associated with decreased methylation (Martinez-Zamudio and Ha 2011). Supplementary Information The online version contains supplemen- For example, Hossain et al. (2012), reported an inverse asso- tary material available at https://d oi.o rg/1 0.1 007/s 00420-0 21-0 1733-8. ciation between UCd, which is a marker of chronic exposure Acknowledgements The authors wish to thank all the study partici- and LINE1 methylation in women exposed to low levels of pants and the supporting staff of the GEOHealth II project. The authors environmental Cd. The mechanism by which Cd influence also acknowledge the University of Michigan sequencing core for run- DNA methylation is not fully understood and is reported to ning the LINE1 pyrosequencing. We further acknowledge the dedi- alter DNA methyltransferases and ten–eleven translocation cated help of the phlebotomist, trained interpreters and dietitians who facilitated the data collection process. In addition, technical assistance (TET) enzyme activities (Ruiz-Hernandez et al. 2015). in the lab was provided by Andrea Santa-Rios, Hélène Lalande, Tianai There are some limitations to this study. First, exposure in Zhou, and Jenny Eng. the informal e-waste recycling sector is to a complex mixture of chemicals, including PAHs and other persistent organic Author contributions Conceptualization: II, TGR, JNF, LSR and pollutants (POPs), which may also alter DNA methylation, JA-M; Methodology: II, LSR, KRZ, JNF and JA-M; Formal analysis and investigation: II, DD, TGR, JNF, JA-M, NB, TPA, LSR, and KRZ; data of which were not included in this study. Second, other Writing—original draft preparation: II; Writing—review and editing: domains of environmental exposure, such as nutrition and II, LSR, KRZ, JNF, JA-M, and TPA; Funding acquisition: TGR, SB, psychosocial stress, may result in epigenetic modifications NB, and JNF; Resources: TGR, JNF Supervision: JNF, TGR, JA-M, that may contribute to an increased risk of disease (Thayer and LSR. All authors read and approved the final manuscript. and Kuzawa 2011), data of which were not considered for Funding This study was financed by the ½ West Africa-Michigan this study. Third, attempts to match the age of controls to CHARTER in GEO-Health with funding from the United States e-waste workers were made during recruitment, but consent National Institutes of Health/Fogarty International Center (US NIH/ to participate was higher among the older controls. This FIC) (paired Grant no 1U2RTW010110-01/5U01TW010101) and issue was addressed by adjusting for age in all of our analysis Canada’s International Development Research Center (IDRC) (Grant no. 108121–001). models. Finally, DNA was extracted from circulating blood that does not represent a potential target organ such as blad- Availability of data and materials The datasets generated and/or ana- der, pancreas, or stomach. However, the use of circulating lyzed during the current study are not publicly available due to privacy blood DNA as a proxy for specific organ health status is reasons but are available from the corresponding author on reasonable reported (Barchitta et al. 2014; Ponomaryova 2021). request. 1 3 1942 International Archives of Occupational and Environmental Health (2021) 94:1931–1944 Declarations Bal W, Protas AM, Kasprzak KS (2011) Genotoxicity of metal ions: chemical insights. Met Ions Life Sci 8:319–373 Ethics approval The study was conducted after receiving ethical clear- Baldé C, Forti V, Gray V, Kuehr R, Stegmann P (2017) The global ance from the College of Health Sciences Ethical and Protocol Review E-waste monitor-2017. Bonn/Geneva/Vienna: United Nations Committee, University of Ghana (protocol identification number CHS- University (UNU), international telecommunication union (ITU). ET/M.4-P 3.9/2015–2016). 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