Increased levels of genotoxic damage in a Bolivian agricultural population exposed to mixtures of pesticides

Science of the Total Environment 695 (2019) 133942

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Increased levels of genotoxic damage in a Bolivian agricultural population exposed to mixtures of pesticides Jessika Barrón Cuenca a,b, Noemí Tirado b,⁎, Josue Barral b, Imran Ali a, Michael Levi c, Ulla Stenius a, Marika Berglund a, Kristian Dreij a,⁎ a b c

Institute of Environmental Medicine, Unit of Biochemical Toxicology, Karolinska Institutet, Box 210, SE-171 77 Stockholm, Sweden Genetic Institute, Medicine Faculty, Universidad Mayor de San Andrés, Saavedra Av. 2246 Miraflores, La Paz, Bolivia Institute of Environmental Medicine, Unit of Metals and Health, Karolinska Institutet, Box 210, SE-171 77 Stockholm, Sweden

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Correlations between pesticide exposure and DNA damage in farmers were investigated. • High exposure to 2,4-D or cyfluthrin was associated with increased genotoxicity. • High exposure to certain pesticide mixtures further strengthened this association. • Age, GST genotype, alcohol and water source influenced levels of genotoxicity.

a r t i c l e

i n f o

Article history: Received 22 May 2019 Received in revised form 13 August 2019 Accepted 14 August 2019 Available online 15 August 2019 Editor: Yolanda Picó Keywords: Mixtures Pesticides Genotoxicity Farmers Bolivia

a b s t r a c t During the past decades, farmers in low to middle-income countries have increased their use of pesticides, and thereby the risk of being exposed to potentially genotoxic chemicals that can cause adverse health effects. Here, the aim was to investigate the correlation between exposure to pesticides and genotoxic damage in a Bolivian agricultural population. Genotoxic effects were assessed in peripheral blood samples by comet and micronucleus (MN) assays, and exposure levels by measurements of 10 urinary pesticide metabolites. Genetic susceptibility was assessed by determination of null frequency of GSTM1 and GSTT1 genotypes. The results showed higher MN frequency in women and farmers active ≥8 years compared to their counterpart (P b 0.05). In addition, age, GST genotype, alcohol consumption, and type of water source influenced levels of genotoxic damage. Individuals with high exposure to tebuconazole, 2,4-D, or cyfluthrin displayed increased levels of genotoxic damage (P b 0.05–0.001). Logistic regression was conducted to evaluate associations between pesticide exposure and risk of genotoxic damage. After adjustment for confounders, a significant increased risk of DNA strand breaks was found for high exposure to 2,4-D, odds ratio (OR) = 1.99 (P b 0.05). In contrast, high exposure to pyrethroids was associated with a reduced risk of DNA strand breaks, OR = 0.49 (P b 0.05). It was also found that high exposure to certain mixtures of pesticides (containing mainly 2,4-D or cyfluthrin) was significantly associated with increased level and risk of genotoxic damage (P b 0.05). In conclusion, our data show that high exposure levels

⁎ Corresponding authors. E-mail addresses: [email protected] (N. Tirado), [email protected] (K. Dreij).

https://doi.org/10.1016/j.scitotenv.2019.133942 0048-9697/© 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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to some pesticides is associated with an increased risk of genotoxic damage among Bolivian farmers, suggesting that their use should be better controlled or limited. © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/4.0/).

1. Introduction Pesticides constitute a wide group of chemicals used for controlling pest and/or weeds, but also for controlling vectors of disease. The fact that pesticides improve crop productivity and thereby increase sales and profits, promotes the use of large amounts. Pesticides are toxic by nature and harmful to living organisms, as such they may also constitute a threat to human health. The study of the effects of exposure to pesticides has attracted increasing attention in recent years (Mostafalou and Abdollahi, 2017). Of concern are short and long term exposures in agricultural workers and also people living adjacent to where pesticides are applied. A large number of studies have shown a relationship between long-term exposure to low levels of pesticides and increased risk of development of several chronic diseases such as asthma, diabetes mellitus, cancer, Parkinson's, Alzheimer's and reproductive disorders (Kim et al., 2017; Ye et al., 2017). The International Agency for Cancer Research (IARC) has classified certain organochlorine and arsenic containing pesticides as human carcinogens (www.iarc.fr), which has resulted in discontinued use. Recently, IARC classified other pesticides that are actively used in many countries worldwide, such as glyphosate, 2,4-dichlorophenoxyacetic acid (2,4-D) and parathion, as probable or possible human carcinogens (www.iarc.fr). For example, exposure to the herbicide 2,4-D has in some studies been associated with increased risk of developing non-Hodgkinlymphoma (NHL). There are however conflicting reports regarding evidence of carcinogenicity. Based on meta-analyses of population based studies IARC concluded that there was inadequate evidence for carcinogenicity in humans (IARC, 2018). In contrast, a meta-analysis which also accounted for exposure levels, found a significant increased risk for NHL in groups highly exposed to 2,4-D (Smith et al., 2017). There is still a limited understanding of how exposure to mixtures of pesticides influence health risks for chronic diseases such as cancer compared to what is known about single pesticides (Hernandez et al., 2017; Bopp et al., 2018). This motivates genotoxic biomonitoring of populations which are exposed to mixtures of pesticides and the identification of risk factors that can be used to implement proper control measures (Legator and Au, 1994; Bull et al., 2006). For assessment of genotoxic damage in population studies, the comet and micronucleus assays are the most commonly used methods, of which the latter also can be considered to be a mutagenicity test (Faust et al., 2004; Bolognesi and Holland, 2016). These assays are used to measure levels of single and double strand breaks or formation of micronuclei due to chromosomal damage. Typically, in sampled blood lymphocytes or buccal mucosa cells. Previous studies have shown significantly increased levels of DNA strand breaks and micronuclei upon exposure to individual pesticides (e.g. 2,4-D, malathion and glyphosate) (Bolognesi et al., 2011; Ghisi et al., 2016) as well as mixtures of pesticides (CarbajalLopez et al., 2016; Benedetti et al., 2018) in farmers and agricultural workers. Moreover, use of personal protection equipment (PPE), crop times and duration of exposure are important determinants (Da Silva et al., 2014; Alleva et al., 2018; Benedetti et al., 2018). The levels of genotoxic damage can also be influenced by genotype differences and different co-exposures associated with life-style. Lack of functional glutathione transferases (GSTs), which are involved in the detoxification of carcinogens (i.e. reactive intermediates), due to polymorphism, has been associated with increased risk of DNA damage and cancer development alongside occupational exposure to pesticides (Da Silva et al., 2012; Matic et al., 2014; Ahluwalia and Kaur, 2018). The matter is complex since GSTs can also contribute to the activation of

carcinogens, hence some studies have shown lower levels of DNA damage comparing null and wild type in pesticide exposed populations (Kirsch-Volders et al., 2006; Franco et al., 2016). Regarding life-style, consumption of alcohol and tobacco is associated with elevated levels of genotoxic damage through cytotoxic and genotoxic effects (LoConte et al., 2018; O'Keeffe et al., 2018). Elevated levels of exposure to some metals, such as arsenic, lead, cadmium, through drinking water can induce reactive oxygen species causing genotoxic damage and increased risk of cancer, especially gastric cancer (Rahman et al., 2015; Yuan et al., 2016). In the present study we have considered both genetic susceptibility and life-style factors. Exposure to pesticides is of special health concern for farmers who did not receive proper training in handling and use of personal protection equipment (PPE) while spraying (FOA UN, 1990; Clausen et al., 2017). We previously showed that farmers from three agricultural communities in Bolivia are highly exposed to chlorpyrifos, pyrethroids and 2,4-D, as determined by measurements of urinary pesticide metabolites (Barron Cuenca et al., 2019). Notably, a third of the farmers did not follow instructions for amount to apply, and only 17% of the farmers used recommended PPE. We could also confirm that farmers who adhered to recommendations were less exposed to pyrethroids. In the current study, we have determined the correlation between exposure to pesticides and genotoxic damage in the same Bolivian agricultural population. Moreover, we studied whether exposure to multiple pesticides strengthen the potential associations. 2. Materials and methods 2.1. Study population and data collection This cross-sectional study was conducted in three agricultural Bolivian communities; Sapahaqui (Com1), Villa Bolivar (Com2) and Villa 14 de Septiembre (Com3). The study population included 297 men and women at the age of 17 to 70 years. All participants were informed about the project, and signed an informed consent before participation. Data was collected applying a questionnaire with closed and open-ended questions related to lifestyle factors, behavior in relation to pesticide use and handling, and the use of personal protection equipment (PPE). More details about the communities, the recruitment and selection process, and questionnaire can be found in our recent paper (Barron Cuenca et al., 2019). All research procedures included in the study were conducted according to the principles of the Helsinki Declaration, and were reviewed and approved by the Ethical Committee for Research at Universidad Mayor de San Andrés in La Paz, Bolivia and the Regional Ethical Review Board in Stockholm, Sweden. All personal data were pseudonymized and only the corresponding authors have access to the key of the identifiers. 2.2. Blood and urine samples At the day of the interview, two whole blood samples (approximately 3 ml each) were collected in heparin tubes and in EDTA vacutainer tubes for assessment of genotoxicity and genotyping, respectively. All the samples were labeled and stored at 4 °C until transported to the Genetic Institute in La Paz, Bolivia where they were prepared as described below within 20 h of collection. First morning urine samples were collected the same day and immediately aliquoted into a 10 ml polypropylene tube that was labeled and stored at −18 °C in a portable

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freezer (ARB, Alice Springs, Australia). Urine samples were analyzed for pesticide metabolites (UPMs) using a liquid chromatography-triple quadrupole linear ion trap mass spectrometer as described in our previous study (Barron Cuenca et al., 2019), and for metals, as described below. 2.3. Genotoxic damage 2.3.1. Alkaline comet assay The comet assay was performed following established protocols (Tice et al., 2000; Liao et al., 2009). In short, slides were codified and covered with a thin layer of 1.5% agarose. For each sample, an aliquot of 30 μl of whole blood was mixed with 120 μl 0.5% low melting point agarose, loaded onto the slide, covered with a coverslip and stored at 4 °C for 10 min. Next, the coverslip was removed and the cells were lysed in an ice-cold lysis buffer (2.5 M NaCl, 100 mM EDTA, 10 mM Tris-HCl, pH 10, 1% Triton X-100 and 10% DMSO) for 1 h at 4 °C. The slides were then placed in alkaline buffer (1 mM EDTA, 300 mM NaOH, pH 13) for 20 min to allow unwinding of the DNA. Electrophoresis was performed at 4 °C for 20 min (25 V, 0.66 V/cm), then slides were washed 3 × 5 min in a cold neutralization solution (0.4 M Tris-HCl, pH 7.5). Immediately, slides were immersed in absolute ethanol for 1 min and then dried at room temperature. The whole procedure was carried out in dimmed light to minimize artifactual DNA damage. After being stained with ethidium bromide (20 μg/ml, Sigma, St Louis, MO), 100 comets were scored per sample using a fluorescence microscope (Leica DMLB) and Comet assay IV software (Perceptive Instruments). Due to loss of some samples during preparation, 295 samples were analyzed. The results were expressed as % DNA in tail and tail moment (tail length × % DNA in the tail). 2.3.2. Cytokinesis-block micronucleus (CBMN) assay The CBMN assay was performed following established protocols (Fenech, 2000; Bolognesi and Fenech, 2013). In brief, 0.5 ml whole blood was added to 4.5 ml RPMI 1640 medium supplemented with 10% fetal bovine serum, 1% of an antifungal antibiotic solution (SIGMA), and phytohemagglutinin M (1% v/v, Gibco). Samples were incubated for 72 h at 37 °C with addition of cytochalasin-B (final concentration 6 μg/ml, Sigma, St Louis, MO) after 44 h. The lymphocytes were collected by centrifugation at 800 rpm and suspended in a preheated hypotonic solution (5 ml of 0.075 M KCl) for 15 min at 37 °C and washed 3 times in fixative methanol:acetic acid solution (5:1 v/v). The cells were subsequently loaded onto slides and stained with Giemsa solution (6% v/v, Sigma, St Louis, MO) for 5 min. Due to loss of some samples during preparation, 232 samples were analyzed. A total of 1500 binucleated lymphocytes were evaluated per sample by microscopy. The frequency of micronuclei (MN), loss of nuclear material by nucleoplasmic bridges (NPB) and nuclear buds (NBUD), nuclear division index (NDI) and cytotoxicity (apoptosis and necrosis) were scored for following published recommendations (Fenech et al., 2003). 2.4. GST genotype analysis Genomic DNA was obtained from whole blood using Wizard Genomic DNA Purification Kit (Promega, Madison, WI). Isolated DNA was suspended in Tris–EDTA buffer (pH 8.0) and stored at −20 °C. The null frequencies of GSTM1 and GSTT1 were analyzed simultaneously in a single assay using the multiplex PCR approach as described by Abdel-Rahman et al. (Abdel-Rahman et al., 1996) with modifications by Tirado et al. (Tirado et al., 2012). The PCR primers used were the following: GSTM1, 5′-GAACTCCCTGAAAAGCTAAAGC and 5′-GTTGGGCTC AAATATACGGTGG; GSTT1, 5′-TTCCTTACTGGTCCTCACATCTC and 5′TCACCGGATCATGGCCAGCA. As an internal control, exon 7 of the CYP1A1 gene was co-amplified using primers 5′-GAACTGCCACTTCAGC TGTCT and 5′-CAGCTGCATTTGGAAGTGCTC. Multiplex PCR was performed in 25 μl with approx. 100 ng of genomic DNA, 0.2 mM of each

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primer, 2 mM of dNTPs, 5 μl of 1× PCR buffer, 1.5 mM MgCl2 and 0.05 U/μl of Taq DNA polymerase (GoTaq Flexi, Promega, Madison, WI). The reaction conditions consisted of an initial denaturation at 95 °C for 5 min followed by 30 cycles of denaturation at 94 °C for 1 min, annealing at 61.1 °C for 1 min and extension at 72 °C for 1 min. This was followed by a final extension step of 72 °C for 7 min. The PCR products from two separate PCR reactions were analyzed by electrophoresis on an ethidium bromide stained (5 μg/ml) 2% 3:1 Low EEO agarose gel (Sigma, St Louis, MO). The null frequencies were successfully determined for 279 of the samples by the presence or absence of bands at 480 (GSTT1) and 215 (GSTM1) bp, respectively. A band at 312 bp (CYP1A1) demonstrated successful amplification. 2.5. Assessment of metal exposure In brief, the urine samples were thawed at room temperature on a shaker. The samples were diluted 1:10 with 20% (v/v) of concentrated nitric acid (65% w/w). Concentrations of arsenic (As), cadmium (Cd), and lead (Pb) were determined using Agilent 7700x ICP-MS (Agilent Technologies, Tokyo, Japan) equipped with an ORS collision/reaction cell to minimize spectral interferences, at the Institute of Environmental Medicine, Karolinska Institutet. Polypropylene tubes used for the preparation of the samples and standards were soaked in 10% (v/v) of concentrated nitric acid (65% w/w) for a minimum of 5 h, rinsed four times with deionized water and then dried. The continuous sample introduction system consisted of an auto sampler, a quartz torch with a 2.5-mm diameter injector with a Shield Torch system, a Scott double-pass spray chamber and nickel cones (Agilent Technologies, Tokyo, Japan). A glass concentric Micro Mist nebulizer (Glass Expansion, West Melbourne, Australia) was used for the analysis. More detailed ICP-MS information is given in Table S1. For quality control, reference materials and blanks were treated and analyzed along with the urine samples. Preparation of calibration standards is described in Table S2. Element concentrations were quantified using MassHunter Work Station Software for ICP-MS (Agilent Technologies, Tokyo, Japan). LOD was calculated as three times the standard deviation (SD) of the element concentration in the calibration blanks (n = 9). Recoveries (%) were calculated based on analyzed reference materials. Urinary density (g/ml) was also measured and used for normalization of concentrations. 2.6. Literature analyses using text mining approach To analyze the literature of the studied pesticides, we employed the CRAB3 tool (http://crab3.lionproject.net). Briefly, CRAB3 is an automated text mining tool that efficiently analyzes literature for any given chemical and identifies information on the carcinogenic mode (s) of action (MOA). The analysis is based on the carcinogen MOA taxonomy that covers both genotoxic and non-genotoxic MOAs, as described previously (Korhonen et al., 2012), and can be used for grouping chemicals for risk assessment (Ali et al., 2016). The results are presented as proportion of abstracts in each MOA category, either for the individual pesticides or as an average for all analyzed pesticides. We further processed the CRAB3 data to estimate an assessment score for the three communities. The assessment score was obtained as the product of the proportion (%) of MOA classifications (as a crude hazard estimate) and the frequency (%) of detection of the pesticide metabolites (as an exposure estimate). 2.7. Statistics SPSS version 25 was used for statistical analysis of genotoxicity data using Student's t-test or one-way ANOVA with Bonferroni or Dunnett's testing. The use of parametric tests is based on the general accepted assumption that if the sample sizes are large enough, the sample means can be considered to be basically normally distributed, also known as

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the Central limit theorem. We considered P b 0.05 as statistically significant in all tests. Type of PPE and handling of pesticides were assessed by our previously defined “protection and handling index” (PHI) score (Barron Cuenca et al., 2019). In addition, linear (Pearson) and logistic regression was performed to analyze if exposure to pesticides were associated with increased risk of genotoxic damage. For logistic regression analysis, individuals with urinary pesticide metabolite (UPM) concentrations above 75th percentile were regarded as being highly exposed. Data on pesticide exposure was obtained from our previous paper where 10 UPMs were measured in the study population (Barron Cuenca et al., 2019). Individuals with levels of genotoxic damage (% DNA in tail, tail moment and MN) above 75th percentile were regarded as having high levels. Since age, gender, smoking and alcohol consumption could be confounders these were adjusted for. Confounding factors were selected based on expert knowledge. Since the farmers were exposed to mixtures of pesticides, as assessed by measuring 10 UPMs, we wanted to determine whether co-exposure to high levels of certain pesticides was associated with an increased level or risk of genotoxic damage. To identify the main profiles of pesticide mixture exposures in the population, Ward's hierarchical clustering method was applied to the data of all UPMs, and using the same classification of exposure level as above (≥75th-percentile). The resulting clusters were subsequently compared using one-way ANOVA with Dunnett's testing and logistic regression, with the cluster of individuals that were not highly exposed to any of the pesticides (cluster 0) as reference category. 3. Results and discussion 3.1. Genotoxic and carcinogenic properties of the pesticides in the study characterized using text mining The population included in this study was using, and was exposed to, a large number of pesticides (Barron Cuenca et al., 2019). Many of these pesticides have been classified as being possibly carcinogenic to humans by IARC (www.iarc.fr) and the US EPA (US EPA, 2018), as well as highly or moderately hazardous by WHO (Table 1) (WHO, 2010). In order to better understand the carcinogenic potential and MOA of the measured pesticides, we analyzed the literature (Dec 2018) by using the text mining tool CRAB3. In total, 14,214 PubMed abstracts were found, of which 30% (4285 abstracts) were identified as relevant for classification into genotoxic and non-genotoxic MOAs (Table S3). The MOA profiles for the individual pesticides are shown in Fig. 1A. Mutations, oxidative stress and cell proliferation are the most common MOAs associated with these pesticides. The analysis also showed that thiabendazole had a markedly higher proportion of literature classified under chromosomal changes and immunosuppression. In average, the largest proportion of literature was attributed to oxidative stress for the analyzed pesticides, followed by mutations and cell proliferation (Fig. 1B). Comparing the average assessment score for the three communities indicated that the pesticide use (type and frequency) in Com3 was associated with a slightly higher risk for both genotoxic and non-genotoxic MOAs, and especially cell proliferation and oxidative stress, compared to the other two communities (Fig. 1C). 3.2. Influence of population characteristics and farming activities on levels of genotoxic damage The population characteristics have been described in detail in our previous publication (Barron Cuenca et al., 2019). In short, it consisted of 297 participants from three agricultural communities of which 94% were active farmers with relatively low education level and low consumption of tobacco and alcohol. As can be seen in Fig. 2 and Table 2, and detailed below, certain population characteristics were associated with increased levels of genotoxic damage.

Comparing the three communities showed that individuals from Com1 and Com3 had higher levels of DNA strand breaks and MN frequencies in blood lymphocytes compared to Com2 (P b 0.05–0.001, Fig. 2). We observed higher levels of DNA strand breaks among older participants (N42 years, P b 0.05) and higher MN frequency among women (P b 0.05). Previous studies have shown the same correlations between age and gender and level of DNA damage, and suggested to be due to differences in DNA repair capability and lifestyle factors (Wojda et al., 2007; Moller, 2019). Indeed, the higher levels of genotoxic damage in Com1 and Com3 than in Com2 could partly be explained by a higher average age of the study population in these two communities (46.6 and 42.1 compared to 38.5 years). The gender balance was however the opposite, with a larger proportion of women participating in Com1 and Com3 than in Com2 (roughly 60:40 vs 50:50). In agreement with the low consumption of tobacco among smokers, no differences were observed between non-smokers and smokers. However, male smokers displayed higher levels of DNA strand breaks compared to female smokers (P b 0.05) probably due to the observed higher consumption among men. Notably, consumers of alcohol displayed lower levels of DNA strand breaks and MN frequency (P b 0.05–0.01). A number of studies have shown the association between alcohol consumption and higher MN frequency in peripheral lymphocytes (reviewed in (Fenech and Bonassi, 2011)). The reverse relationship observed here could possibly be explained by dietary habits associated with alcohol consumption or by the presence of some protective factor in the beverage (Greenrod et al., 2005). The habit of chewing coca leaves while spraying did not influence levels of genotoxic damage. This result agrees with a study performed in the Peruvian Andes which did not observe any increased MN frequencies in buccal cells in coca chewers compared to controls (Nersesyan et al., 2013). Following years of extreme drought, there is an ongoing water crisis in Bolivia, and lack of access to clean water is of concern for both rural and urban populations (Rocha-Melogno et al., 2019). Indeed, comparing levels of genotoxic damage in participants with municipal or bought water as main source with other sources (i.e. local well, river, or spring water) demonstrated higher levels of DNA strand breaks among the latter group (P b 0.05). Previous studies have shown that some areas of Bolivia have elevated levels of metals in ground and surface waters (Guedron et al., 2017; De Loma et al., 2019). To assess if differences in water source was associated with different levels of exposure to metals that could impact the levels of measured DNA damage, levels of urinary As, Cd and Pb were measured in a subset of samples. Several heavy metals can induce DNA damage either directly or indirectly by inducing oxidative stress or inhibition of DNA repair (Hartwig, 1994). The results showed that there were no differences in median concentrations of the analyzed metals between participants having access to municipal water and other sources (well, river, spring water), and that they were not elevated (Table S4). We conclude that different metal exposure levels cannot explain differences in genotoxicity. Alternatively, the levels of pesticides in water could explain the increased genotoxicity observed in those that use local water sources. The levels of pesticides in the water were not analyzed unfortunately. However our previous study revealed that about a third of the farmers disposed of pesticides in nearby waters and rivers, which also was their main water source (Barron Cuenca et al., 2019). This practice could thus explain the results. People in these communities not actively working as farmers displayed similar levels of genotoxic damage as the farmers (Table 3). This finding agrees with our previous study showing similar levels of pesticide exposure in the two groups (Barron Cuenca et al., 2019). Other studies have shown that living in an agricultural area without being active as a farmer was associated with pesticide exposure and genotoxic damage (e.g. children and women in Mexico and Colombia (Alvarado-Hernandez et al., 2013; Ruiz-Guzman et al., 2017)). Moreover, women active as farmers displayed higher MN frequency compared to men (P b 0.05). Years active as a farmer, or the time spent spraying per month could be important determinants for induction of

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Fig. 1. Literature analyses of pesticides in the Bolivian population. A) CRAB3 generated MOA profiles over selected MOA categories, for ten pesticide metabolites detected in the Bolivian population. B) An average MOA profile of all the pesticides over selected MOA categories. C) Comparison of assessment scores of the pesticides for each community, based on evidence from literature and metabolite frequency in each community i.e. assessment score = (literature evidence on MOA for pesticide A) × (% frequency of pesticide A metabolite in population).

genotoxic damage. We previously showed that less experienced farmers were more exposed to chlorpyrifos (Barron Cuenca et al., 2019). Here, we observed a clear trend with increasing frequency of MN with increasing number of years active as farmers, and farmers active ≥8 years displayed higher levels compared to farmers active b8 years (P b 0.05). This is in agreement with the positive correlation between duration of pesticide exposure and tail moment observed among

agricultural workers in Egypt (Saad-Hussein et al., 2017). This is most likely due to the fact that the more experienced farmers were also older (cf. Table 2). No clear differences could be observed when comparing the frequency of days spraying per month. Similarly, being better at following instructions for handling pesticides and the use of PPE, assessed by the PHI score, was not associated with decreased levels of genotoxic damage. In agreement, a meta-analysis of population studies

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Table 1 List of pesticides measured as urinary pesticide metabolites (UPMs) in the Bolivian study population. Function

Pesticide

Chemical type

UPM

IARCa

US EPAb

WHOc

Fungicide

Pyrimethanil Tebuconazole Thiabendazole Bifenthrin Chlorpyrifos Cyfluthrin Cypermethrin Permethrin 2,4-Dichlorophenoxy acetic acid 2-Methyl-4-chlorophenoxy acetic acid

Pyrimidine Azoles Benzimidazole Pyrethroid Organophosphate Pyrethroid Pyrethroid Pyrethroid Chlorophenoxy Chlorophenoxy

OH-PYR TEB-OH 5-OH-TBZ CFCA TCP 4F3PBA 3-PBA, DCCA 3-PBA, DCCA 2,4-D MCPA

NL NL NL NL NL NL NL 3 2B 2B

Not likely C Likely C E Not likely C – D Not likely

III II III II II Ib II II II II

Insecticides

Herbicide a b c

IARC classification: 2B = Possibly carcinogenic to humans; 3 = Not classifiable; NL = Not listed. US EPA classification: C = Possible Human Carcinogen; D = Not classifiable; E = Evidence of non-carcinogenicity to humans. WHO hazard classification: Ib = Highly hazardous; II = Moderately hazardous; III = Slightly hazardous.

concluded that no strong conclusions could be drawn about the protective role of PPE, due to limited information, but that findings were more likely to be positive in the absence of PPE usage (Bull et al., 2006). In summary, many parameters associated with life-style and farming activities seem to affect the levels of genotoxic damage in our study population. 3.3. Associations between pesticide exposure and levels of genotoxic damage Assessing the association between pesticide exposure level and genotoxic damage by linear regression did not show any significant correlations (not shown). However, increased DNA damage levels could be observed in highly exposed individuals (UPM concentrations above 75th percentile) for some pesticides. As shown in Table 4, high levels of exposure to tebuconazole (TEB-OH), 2,4-D or cyfluthrin (4F3PBA) were associated with high levels of DNA strand breaks (above the 75th percentile, P b 0.05–0.01). High levels of exposure to cyfluthrin were also associated with high frequency of NBUDs (P b 0.001). In agreement, cyfluthrin induced genotoxic damage (e.g. chromosomal aberrations and MN) in human peripheral lymphocytes in vitro and rat bone marrow cells in vivo (Ila et al., 2008). Similarly, increased MN frequency was found in bovine peripheral lymphocytes exposed to tebuconazole in vitro (Sivikova et al., 2013). No associations were found between high exposure levels of any of the other pesticides and level of genotoxic damage. To determine the impact of exposure to pesticides on the risk of having increased levels of genotoxic damage, a logistic regression analysis

between UPM concentrations and levels of DNA strand breaks (%DNA in tail and tail moment) and MN frequency (MN) was performed (Table 4). Due to low detection frequency (b15%) or low number of highly exposed individuals (b15%), metabolites CFCA, MCPA, 4F3PBA, 5-OH-TBZ and OH-PYR were left out from the analysis. The analysis indicated increased risk (OR N 1 but P N 0.05) of DNA strand breaks for tebuconazole (TEB-OH), chlorpyrifos (TCP), and 2,4-D and of MN formation for 2,4-D. A significant increased risk was however only observed for 2,4-D with OR of 1.99 (95% CI: 1.10–3.60) for tail moment (P b 0.05), and borderline significant for %DNA in tail [OR = 1.74 (0.96–3.17), P = 0.068]. IARC has recently classified 2,4-D as a possible human carcinogen (IARC, 2018). Based on population, in vivo and in vitro studies, the IARC review found only weak evidence for 2,4-D being genotoxic but strong evidence that it induces oxidative stress. A possible explanation for the strong correlation between exposure and genotoxicity observed here could be due to an induction of strand breaks as a secondary effect of high levels of reactive oxidative species (ROS) (Jena, 2012). In accordance, a recent study demonstrated a significant association between urinary levels of 2,4-D and 8-oxoguanine, a marker of oxidative DNA damage, in corn farmers (Lerro et al., 2017). This supports that ROS levels associated with exposure to 2,4-D are high enough to induce genotoxicity. However, in the current study a firm causative effect of 2,4-D alone cannot be concluded since the population was exposed to a mixture of pesticides. Notably, high exposure levels to pyrethroids (3-PBA) were associated with lower levels of DNA strand breaks (P b 0.05–0.01). Similarly, a reduced OR of high levels of DNA damage was observed for pyrethroids and especially so for the metabolite DCCA (formed

Fig. 2. Levels of genotoxic damage in the Bolivian population. A shows DNA strand breaks (%DNA in tail and Tail moment) and B shows MN frequencies (Micronuclei (MN), Nucleoplasmic bridges (NPB), and Nuclear buds (BNUD)). Data is shown for the total population and for each of the three communities (Com1–3) and represents mean ± SE, n = 64–105 with **P b 0.01; ***P b 0.001 by one-way ANOVA with Bonferroni adjustment (for multiple testing including the 3 communities). ND = not detected.

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Table 2 Population characteristics and levels of genotoxic damage. Variables

MN frequenciesa

DNA strand breaks

Age (years) b42 ≥42 Gender Women Men Smoking habits Non-smokers Smokers Women Men Alcohol consumption None Consumers Women Men Drinking water source Municipal/bottled Other resources Chewing coca while spraying No Yes

n

%DNA in tail

Tail moment

n

MN

NPB

NBUD

148 147

11.41 ± 0.53 13.16 ± 0.66⁎

3.54 ± 0.21 4.44 ± 0.31⁎

120 112

3.92 ± 0.31 4.26 ± 0.32

0.26 ± 0.08 0.31 ± 0.07

1.31 ± 0.19 2.13 ± 0.29⁎

130 165

12.14 ± 0.62 12.38 ± 0.58

3.90 ± 0.26 4.05 ± 0.27

105 127

4.64 ± 0.35⁎ 3.62 ± 0.28

0.29 ± 0.08 0.28 ± 0.07

1.69 ± 0.28 1.72 ± 0.22

208 87 17 70

12.52 ± 0.50 11.69 ± 0.80 7.87 ± 1.28 12.62 ± 0.91⁎

4.05 ± 0.22 3.83 ± 0.36 2.25 ± 0.49 4.21 ± 0.41⁎

165 67 14 53

4.34 ± 0.28 3.45 ± 0.33 2.86 ± 0.52 3.60 ± 0.39

0.33 ± 0.07 0.16 ± 0.08 0.14 ± 0.10 0.17 ± 0.10

1.67 ± 0.20 1.79 ± 0.34 1.21 ± 0.73 1.94 ± 0.39

176 119 39 80

13.30 ± 0.59⁎⁎ 10.77 ± 0.56 11.24 ± 0.90 10.55 ± 0.71

4.50 ± 0.27⁎⁎⁎ 3.22 ± 0.21 3.41 ± 0.35 3.14 ± 0.27

133 99 30 69

4.64 ± 0.31⁎⁎ 3.33 ± 0.31 4.17 ± 0.66 2.97 ± 0.34

0.25 ± 0.06 0.33 ± 0.10 0.33 ± 0.19 0.33 ± 0.12

1.77 ± 0.23 1.63 ± 0.26 1.93 ± 0.52 1.49 ± 0.30

114 181

11.02 ± 0.63 13.08 ± 0.56⁎

3.48 ± 0.27 4.31 ± 0.28⁎

87 145

3.76 ± 0.35 4.28 ± 0.29

0.32 ± 0.08 0.26 ± 0.07

1.60 ± 0.24 1.77 ± 0.24

78 195

12.13 ± 0.81 12.17 ± 0.53

3.90 ± 0.37 3.95 ± 0.23

60 153

3.67 ± 0.41 4.25 ± 0.28

0.28 ± 0.09 0.29 ± 0.07

1.38 ± 0.30 1.81 ± 0.23

Data is presented as mean ± SE. ND = Not detected. a MN = micronuclei, NPB = nucleoplasmic bridges, NBUD = nuclear buds. ⁎ P b 0.05 by Student's t-test (for pairwise testing). ⁎⁎ P b 0.01 by Student's t-test (for pairwise testing). ⁎⁎⁎ P b 0.001 by Student's t-test (for pairwise testing).

from cypermethrin and permethrin) with ORs of 0.49 (95% CI: 0.26–0.89) and 0.53 (95% CI: 0.29–0.96) for %DNA in tail and tail moment, respectively (P b 0.05). This is in contrast to the reported genotoxicity in human peripheral blood lymphocytes exposed to cypermethrin or permethrin in vitro (Barrueco et al., 1992; Kocaman and Topaktas, 2009). In this context, US EPA has classified cypermethrin as possible human carcinogen (US EPA, 2006) whereas a recent systematic review of studies on cancer risk in humans exposed to permethrin concluded that it does not entail a risk of cancer in humans (Boffetta and Desai, 2018).

3.4. Impact of exposure to pesticide mixtures on levels of genotoxic damage Simultaneous exposure to several potentially hazardous compounds such as pesticides is of special concern due to the lack of understanding of mixture effects. To analyze if a joint exposure to high levels of several pesticides was associated with increased levels or risk of genotoxic damage, 8 clusters which contained individuals with similar pesticide mixture exposures were identified, and levels of genotoxic damage were analyzed. One of these clusters contained farmers which were not highly exposed to any pesticide (cluster 0), and was used as

Table 3 Farming activities and levels of genotoxic damage. Variables

Type of work Non-farmer Farmer Women Men Years active b1 1–3 4–7 ≥8 b8 Days/month spraying ≤1 2–10 11–20 ≥20 b20 PHI score Low High

MN frequenciesa

DNA strand breaks n

%DNA in tail

Tail moment

n

MN

NPB

NBUD

22 273 109 164

13.73 ± 1.60 12.16 ± 0.44 11.76 ± 0.67 12.43 ± 0.58

4.62 ± 0.69 3.93 ± 0.19 3.72 ± 0.28 4.08 ± 0.27

19 213 87 126

4.05 ± 0.93 4.08 ± 0.23 4.71 ± 0.38⁎ 3.65 ± 0.28

0.21 ± 0.21 0.29 ± 0.06 0.30 ± 0.08 0.29 ± 0.07

1.89 ± 0.59 1.69 ± 0.18 1.62 ± 0.31 1.74 ± 0.22

7 14 32 220 53

16.11 ± 1.54 10.32 ± 1.78 11.84 ± 1.35 12.20 ± 0.49 12.00 ± 0.98

4.85 ± 0.63 2.99 ± 0.66 3.83 ± 0.58 3.98 ± 0.22 3.74 ± 0.40

6 10 27 170 43

1.83 ± 1.05 3.20 ± 0.63 3.41 ± 0.59 4.32 ± 0.26⁎ 3.14 ± 0.42

ND 0.10 ± 0.10 0.37 ± 0.21 0.30 ± 0.06 0.26 ± 0.13

1.17 ± 1.17 1.40 ± 0.72 1.56 ± 0.71 1.75 ± 0.19 1.47 ± 0.50

25 105 42 101 172

11.85 ± 1.24 12.50 ± 0.72 10.40 ± 0.90 12.62 ± 0.78 11.90 ± 0.53

3.84 ± 0.49 4.01 ± 0.30 3.01 ± 0.31 4.26 ± 0.38 3.74 ± 0.21

20 82 39 72 141

4.30 ± 0.71 4.10 ± 0.37 3.51 ± 0.54 4.32 ± 0.39 3.96 ± 0.28

0.20 ± 0.12 0.29 ± 0.10 0.44 ± 0.16 0.24 ± 0.08 0.32 ± 0.07

1.85 ± 0.47 2.05 ± 0.34 1.72 ± 0.48 1.22 ± 0.24 1.93 ± 0.25

178 95

11.94 ± 0.54 12.58 ± 0.77

3.85 ± 0.25 4.09 ± 0.32

136 77

3.96 ± 0.28 4.31 ± 0.40

0.28 ± 0.07 0.31 ± 0.09

1.59 ± 0.21 1.87 ± 0.35

Data is presented as mean ± SE. ND = Not detected. a MN = micronuclei, NPB = nucleoplasmic bridges, NBUD = nuclear buds. ⁎ P b 0.05 by Student's t-test (for pairwise testing).

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Table 4 Associations between pesticide exposure and levels and risk of genotoxic damage. Pesticide exposurea

TEB-OH Low High ORd TCP Low High OR 3-PBA Low High OR DCCA Low High OR 2,4-D Low High OR CFCA Low High 4F3PBA Low High MCPA Low High 5-OH-TBZ Low High OH-PYR Low High

MN frequenciesb

DNA strand breaks n

%DNA in tail

Tail moment

n

MN

NPB

NBUD

222 73

11.76 ± 0.45c 13.87 ± 1.00⁎ 1.12 (0.60–2.09)

3.69 ± 0.18 4.90 ± 0.50⁎⁎ 1.46 (0.79–2.70)

178 54

4.12 ± 0.26 3.96 ± 0.44 0.75 (0.34–1.65)

0.34 ± 0.07 0.11 ± 0.06

1.66 ± 0.20 1.85 ± 0.37

221 74

11.86 ± 0.47 13.52 ± 0.95 1.30 (0.71–2.40)

3.81 ± 0.20 4.51 ± 0.45 0.94 (0.50–1.77)

169 63

4.22 ± 0.26 3.71 ± 0.41 0.75 (0.36–1.53)

0.30 ± 0.06 0.25 ± 0.09

1.54 ± 0.18 2.16 ± 0.41

222 73

12.93 ± 0.47⁎⁎ 10.29 ± 0.91 0.63 (0.32–1.26)

4.21 ± 0.21⁎ 3.29 ± 0.42 0.56 (0.28–1.14)

170 62

4.24 ± 0.27 3.65 ± 0.38 0.74 (0.34–1.57)

0.28 ± 0.06 0.31 ± 0.12

1.74 ± 0.21 1.61 ± 0.33

189 106

12.74 ± 0.51 11.46 ± 0.74 0.49 (0.26–0.89)‡

4.13 ± 0.22 3.73 ± 0.34 0.53 (0.29–0.96)‡

146 86

4.20 ± 0.30 3.88 ± 0.33 0.68 (0.35–1.32)

0.33 ± 0.07 0.21 ± 0.07

1.61 ± 0.23 1.87 ± 0.27

221 74

11.57 ± 0.46 14.41 ± 0.93⁎⁎ 1.74 (0.96–3.17)

3.62 ± 0.20 5.09 ± 0.44⁎⁎ 1.99 (1.10–3.60)‡

182 50

3.98 ± 0.25 4.46 ± 0.50 1.15 (0.54–2.41)

0.32 ± 0.06 0.16 ± 0.09

1.66 ± 0.20 1.86 ± 0.38

292 3

12.34 ± 0.43 6.44 ± 1.05

4.01 ± 0.19 1.58 ± 0.29

253 3

4.01 ± 0.21 2.33 ± 1.20

0.27 ± 0.05 ND

1.72 ± 0.16 0.67 ± 0.67

260 35

11.95 ± 0.45 14.72 ± 1.15⁎

3.87 ± 0.20 4.86 ± 0.51

221 35

4.07 ± 0.23 3.49 ± 0.41

0.31 ± 0.06 ND

1.44 ± 0.15 3.37 ± 0.63⁎⁎⁎

290 5

12.28 ± 0.43 12.18 ± 3.66

3.99 ± 0.19 3.74 ± 1.20

251 5

4.00 ± 0.21 3.60 ± 1.50

0.26 ± 0.05 0.60 ± 0.40

1.74 ± 0.16 ND

283 12

12.36 ± 0.43 10.40 ± 1.90

4.02 ± 0.19 3.27 ± 0.72

245 11

4.02 ± 0.21 3.36 ± 1.14

0.28 ± 0.05 ND

1.70 ± 0.16 1.91 ± 0.84

266 29

12.45 ± 0.44 10.75 ± 1.50

4.03 ± 0.19 3.57 ± 0.74

232 15

4.10 ± 0.23 3.00 ± 0.51

0.28 ± 0.06 0.13 ± 0.07

1.74 ± 0.17 1.38 ± 0.32

a For definition of UPM abbreviations, see Table 1. Metabolites CFCA, 4F3PBA. MCPA, 5-OH-TBZ and OH-PYR were left out from the regression analysis due to low detection frequency (b15%) or low number of highly exposed individuals. b MN = micronuclei, NPB = nucleoplasmic bridges, NBUD = nuclear buds. c Data is presented as mean ± SE. ND = Not detected. d OR = Odds ratio. Data is presented as OR (95% confidence interval). The model was adjusted for age, gender, smoking and alcohol consumption. ⁎ P b 0.05 by Student's t-test. ⁎⁎ P b 0.01 by Student's t-test. ⁎⁎⁎ P b 0.001 by Student's t-test. ‡ P b 0.05 by logistic regression and indicates increased or reduced risk of high levels of genotoxic damage compared to farmers with low UPM concentrations.

reference category for comparisons. Detailed description of the clusters is shown in Fig. 3. The results showed that most clusters displayed higher levels of DNA strand breaks compared to cluster 0 (Table 5). Lower levels were only found in cluster 3, which was the cluster dominated by the pyrethroids cypermethrin and permethrin (34% 3-PBA and 27% DCCA) and in accordance with the results for 3-PBA presented in Table 4. Only individuals in cluster 7 had significantly higher levels of DNA strand breaks (%DNA in tail and Tail moment) compared to cluster 0 (P b 0.05). Cluster 7 mainly consisted of the pyrethroids cyfluthrin (51%, 4F3PBA), permethrin and cypermethrin (27%, DCCA), and chlorpyrifos (18%, TCP) (Fig. 3B). This is also in agreement with the observation of association between 4F3BPA and genotoxic damage presented in Table 4. For all clusters, MN frequencies were lower or similar compared to cluster 0. Logistic regression analysis for the individual pesticides showed that high exposure levels to 2,4-D was associated with a significantly increased risk of genotoxic damage. Indeed, the same correlation was found here. Cluster 2, which was dominated by 2,4-D (80%), was the only cluster that showed an increased risk for genotoxic damage with OR of 2.94 (95% CI: 1.12–7.73, P b 0.05) for tail moment (Table 5). The ORs for MN frequencies were all ≤1. Together, the results presented in Tables 4 and 5 show a clear association between high exposure level to certain pesticides and increased DNA strand breaks, but not with increased chromosomal

aberrations. This could be due to different cellular mechanisms that are induced in response to DNA damage in order to limit more severe mutagenic events, including DNA repair, cell cycle arrest and apoptosis. Previous studies have suggested that induction of DNA strand breaks but not micronuclei formation is due to efficient repair of the DNA damage (Cavallo et al., 2009). To assess the role of cell cycle arrest and apoptosis we compared the nuclear division index (NDI) and frequency of apoptotic cells for the different clusters (Table S5) (Kirsch-Volders and Fenech, 2001). In agreement with the high levels of DNA damage, clusters 2 and 7 displayed the strongest effects. Cluster 7 was the only cluster with a lower NDI compared to cluster 0 (1.46 compared to 1.57) and cluster 2 was the cluster with the highest frequency of apoptotic cells (3.20 compared to 1.98 for Cluster 0). These results suggest that both cell cycle arrest and apoptosis are involved in the DNA damage response to pesticides mixture exposure, which might reduce the formation of more severe chromosomal damages and mutagenesis. Mixture effects of pesticides are not well studied, but several pesticides have been shown to induce ROS, as discussed above, and a possible point of interaction would be the concomitant induction of ROS and repression of antioxidant enzymes, leading to a higher load of oxidative DNA damage and increased genomic instability (Hernandez et al., 2017). The data presented here cannot give

J. Barrón Cuenca et al. / Science of the Total Environment 695 (2019) 133942

9

Fig. 3. Detailed description of the 8 clusters of pesticide mixture exposure. A) Dendogram of Ward's hierarchical clustering illustrating similarity of pesticide mixture exposure among the study participants. All 10 UPMs were included and exposure was classified as binary being either below or above 75th percentile. The 8 clusters are indicated by colored boxes. B) Pesticide composition of the 7 clusters of pesticide mixtures found at high levels (N75th percentile) in the study population. Cluster 0 included only individuals with lower exposure levels to pesticides (b75th percentile). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

information about whether the observed associations were due to interaction effects (synergistic or antagonistic) between pesticides or other chemicals, but motivates further research by a more systematic approach and using suitable in vitro testing systems to identify predictive markers of possible interaction effects. This would also benefit the development of clinical biomarkers and preventive efforts among agricultural populations.

3.5. Influence of GST genotypes on levels of genotoxic damage A number of previous studies have shown an association between GSTM1 and GSTT1 null genotype and increased risk of genotoxic damage among agricultural workers exposed to pesticides (Da Silva et al., 2012; Matic et al., 2014; Gomez-Martin et al., 2015; Ahluwalia and Kaur, 2018; Saad-Hussein et al., 2019). In the current study population, the majority

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Table 5 Associations between pesticide mixture exposures (by clusters) and levels and risk of genotoxic damage. Cluster

Main pesticidesa

DNA strand breaks

MN frequenciesb

n

%DNA in tail

Tail moment

n

MN

NPB

NBUD

Cluster 0 Cluster 1 ORd Cluster 2 OR Cluster 3 OR Cluster 4 OR Cluster 5 OR Cluster 6 OR Cluster 7 OR

NA TEB-OH, 2,4-D

72 34

0.70 ± 0.19 1.24 ± 0.44

0.35 ± 0.22

2.15 ± 0.67

3-PBA, DCCA, TCP

37

0.44 ± 0.21

1.53 ± 0.45

TCP, TEB-OH

23

0.22 ± 0.13

2.17 ± 0.71

DCCA, OH-P, 2,4-D

41

0.19 ± 0.09

1.61 ± 0.39

DCCA, TEB-OH, 3-PBA, TCP, 2,4-D

37

0.10 ± 0.06

2.14 ± 0.54

4F3PBA, DCCA, TCP

23

ND

3.73 ± 0.84⁎⁎⁎

1.66 (0.58–4.77)

1.99 (0.68–5.80)

4.68 ± 0.54 3.48 ± 0.66 0.53 (0.17–1.65) 4.70 ± 0.91 1.14 (0.38–3.37) 3.68 ± 0.59 0.55 (0.20–1.51) 4.83 ± 0.89 1.06 (0.34–3.30) 3.55 ± 0.47 0.41 (0.13–1.24) 4.21 ± 0.53 0.44 (0.14–1.36) 3.36 ± 0.54 0.21 (0.04–1.01)

0.55 ± 0.15 0.08 ± 0.08

28

3.29 ± 0.27 4.92 ± 0.61 2.17 (0.85–5.54) 4.39 ± 0.57 2.94 (1.12–7.73)‡ 2.70 ± 0.40 0.55 (0.17–1.82) 4.95 ± 0.83 1.99 (0.68–5.80) 3.57 ± 0.47 1.66 (0.67–4.16) 4.43 ± 0.70 1.46 (0.56–3.82) 5.45 ± 0.69⁎

63 27

2,4-D, MCPA

10.95 ± 0.69c 14.17 ± 1.28 1.58 (0.62–4.02) 13.10 ± 1.34 2.11 (0.81–5.51) 9.26 ± 1.06 0.59 (0.19–1.78) 14.95 ± 1.75 2.03 (0.72–5.67) 11.26 ± 1.11 1.07 (0.42–2.72) 12.70 ± 1.40 1.22 (0.48–3.13) 15.98 ± 1.51⁎

23 37 19 35 31 23

a

Pesticides with N10% contribution to the profile. For detailed description see Fig. 3B and for definition of UPM abbreviations see Table 1. NA = Not applicable. MN = micronuclei, NPB = nucleoplasmic bridges, NBUD = nuclear buds. Data is presented as mean ± SE. ND = Not detected. d OR = Odds ratio. Data is presented as OR (95% confidence interval). ⁎ P b 0.05 by one-way ANOVA with Dunnett's test and compared to cluster 0. ⁎⁎⁎ P b 0.001 by one-way ANOVA with Dunnett's test and compared to cluster 0. ‡ P b 0.05 by logistic regression and indicates increased or reduced risk of high levels of genotoxic damage compared to cluster 0. b c

of the participants were GSTM1 null (54%), while a large proportion were GSTT1 positive (69%). The population in Com3 displayed the lowest frequency of GSTM1 null (34%) and highest of GSTT1 null (35%) (Table S6). The influence of GST genotype on level of genotoxic damage is shown in Table 6 Individuals with GSTM1 null genotype displayed higher levels of DNA strand breaks compared to positive individuals (P b 0.05 for Tail moment and P = 0.067 for %DNA in tail). Singh et al. (2011) also observed an increase in DNA damage in pesticide-exposed workers with GSTM1 null genotype in Delhi, India. For GSTT1, levels of DNA strand breaks were also higher in the null group compared to GSTT1 positive, although not statistically significant. In contrast, frequency of MN was higher among GST positive individuals, and especially for GSTM1 (P b 0.05). This seemingly reverse relationship was first shown in a meta-analysis and for GSTT1 null workers exposed to occupational genotoxins including pesticides (Kirsch-Volders et al., 2006), and has since been confirmed also to occur for GSTM1 in pesticides exposed workers in South America (Tirado et al., 2012; Franco et al., 2016). A possible explanation for this relationship is that GSTs also can activate carcinogens. For NPBs and NBUDs, no differences were observed. In agreement, taking the genotype of both GSTs into account higher levels of DNA strand breaks were observed in subjects which were null for both, and especially so for tail moment (P b 0.05), compared to the double positive genotype. This was also observed in a

study of Punjab agricultural workers (Abhishek et al., 2010). For MN, the GSTM1 null/GSTT1 positive individuals displayed lower frequencies compared the double positive (P b 0.05). None of observed associations could be explained by differences in gender and age distribution between the genotype groups (Table S6). Thus there is a complex and partly contradictory relation between genotypes and different genotoxicity indices observed in this study, that confirms earlier data. The mechanistic basis for the enigmatic complexity is currently unknown. 3.6. Additional considerations There are certain limitations of our study. We did not obtain information about the time interval between last spraying and sampling, as the latest exposure might impact most strongly on the results. Of concern among the pesticides that the farmers used, but that were not analyzed, are glyphosate, chlorothalonil and mancozeb (Barron Cuenca et al., 2019). Glyphosate is classified as probably carcinogenic to humans by IARC, with support of strong evidence of genotoxicity in vitro and in vivo (IARC, 2017). Chlorothalonil is classified as possibly carcinogenic to humans by IARC (IARC, 1999), and the European Food Safety Agency recently concluded that it cannot be excluded that some metabolites of chlorothalonil are genotoxic (Arena et al., 2018). Mancozeb is listed as a

Table 6 GST genotypes and levels of genotoxic damage. Genotypes

GSTM1 Positive Null GSTT1 Positive Null GSTM1/GSTT1 Positive/Positive Positive/Null Null/Positive Null/Null

MN frequenciesa

DNA strand breaks n

%DNA in tail

Tail moment

n

MN

NPB

NBUD

125 152

11.42 ± 0.61 13.03 ± 0.62

3.50 ± 0.25 4.41 ± 0.28⁎

110 109

4.53 ± 0.33⁎ 3.44 ± 0.29

0.30 ± 0.08 0.29 ± 0.08

1.52 ± 0.22 1.84 ± 0.28

192 85

12.04 ± 0.54 12.90 ± 0.76

3.87 ± 0.23 4.29 ± 0.35

160 59

4.16 ± 0.27 3.51 ± 0.38

0.29 ± 0.06 0.32 ± 0.13

1.78 ± 0.22 1.42 ± 0.27

97 28 95 57

11.69 ± 0.71 10.49 ± 1.15 12.40 ± 0.80 14.09 ± 0.95

3.64 ± 0.30 3.03 ± 0.43 4.11 ± 0.36 4.91 ± 0.46⁎

83 27 77 32

4.84 ± 0.40 3.56 ± 0.53 3.43 ± 0.34⁎ 3.47 ± 0.56

0.31 ± 0.10 0.26 ± 0.16 0.26 ± 0.08 0.38 ± 0.19

1.64 ± 0.27 1.15 ± 0.34 1.92 ± 0.36 1.66 ± 0.42

Data is presented as mean ± SE. a MN = micronuclei, NPB = nucleoplasmic bridges, NBUD = nuclear buds. ⁎ P b 0.05 by Student's t-test (for pairwise testing) or one-way ANOVA with Dunnett's test (for multiple testing of combined GST genotype with Positive/Positive as reference category).

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probable human carcinogen by US EPA (US EPA, 2005), and has been shown to induce genotoxicity in vitro and in vivo (Srivastava et al., 2012; Yahia et al., 2019). Clearly these pesticides could also contribute to the results obtained here. There is thus a need to further develop analytical methodology for biomonitoring of human pesticide exposure. The strengths of this study included determination of genotoxic damage by comet and micronucleus assays, using well-established methods (Fenech, 2000; Tice et al., 2000). The assessment of correlation between exposure and genotoxic damage was based on quantitative data for multiple pesticides from our previous publication (Barron Cuenca et al., 2019). Also, a number of confounding factors were addressed in the study, either by questionnaire or measurements. Lastly, the three included communities represented the different climate and altitudes found in Bolivia, thereby representing most agricultural communities in Bolivia. 4. Conclusion This is the first study investigating the impact of exposure to pesticide mixtures on genetic stability in humans in Bolivia combining a quantitative assessment of exposure with a comprehensive assessment of genotoxicity. We showed that, for certain pesticides, there was a strong positive correlation between high exposure levels and genotoxic damage. Especially so for the herbicide 2,4-D, which has been classified as a possible carcinogen to humans by IARC. Notably, a strong negative correlation was found for some pesticides. Moreover, we could confirm that a joint exposure to high levels of a mixture of pesticides could further influence the risk level, with certain pesticides being stronger drivers of genotoxicity. We also confirmed that lifestyle factors such as alcohol consumption, source of drinking water, and GST genotype affected the level of genotoxic damage. This study warrants further investigations to confirm the presented results and highlight the enigmatic nature of underlying mechanism of pesticide mixture toxicity. Finally, the need and importance of education and training of farmers to limit exposure levels and health effects are emphasized. Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2019.133942. CRediT authorship contribution statement Jessika Barrón Cuenca:Conceptualization, Data curation, Formal analysis, Investigation, Visualization, Writing - original draft.Noemí Tirado:Conceptualization, Data curation, Funding acquisition, Investigation, Project administration, Resources, Supervision, Writing - original draft.Josue Barral:Investigation.Imran Ali:Data curation, Formal analysis, Investigation, Visualization, Writing - original draft.Michael Levi: Data curation, Formal analysis, Investigation, Writing - original draft. Ulla Stenius:Conceptualization, Funding acquisition, Resources.Marika Berglund:Conceptualization, Data curation, Funding acquisition, Project administration, Writing - original draft.Kristian Dreij:Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing - original draft, Writing - review & editing. Acknowledgements The authors warmly thank all the people in the communities who participated in this study and the people who contacted them, Rubén Araujo (Pastoral Social Cáritas Corocoro, La Paz) and Angela del Callejo (Universidad Mayor de San Simón, Cochabamba). We are also grateful to Pablo Almaraz for their technical assistance, to the members of Toxicology Genetics unit at the Genetic Institute (Universidad Mayor de San Andrés, La Paz) and all workers at the local health care centers for their assistance during the field activities. We thank Karin Leander (Karolinska Institutet, Stockholm) for assistance with statistical analysis.

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