Biomonitoring exposure assessment to contemporary pesticides in a school children population of Spain

Environmental Research 131 (2014) 77–85

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Biomonitoring exposure assessment to contemporary pesticides in a school children population of Spain Marta Roca a,b, Ana Miralles-Marco a,b, Joan Ferré d, Rosa Pérez a, Vicent Yusà a,b,c,n a

Centre of Public Health Research (CSISP-FISABIO), 21 Avenida Catalunya, 46020 Valencia, Spain Laboratory of Public Health of Valencia, 21 Avenida Catalunya, 46020 Valencia, Spain c Department of Analytical Chemistry, Chemistry University of Valencia, 50 Doctor Moliner, 46100 Burjassot, Spain d Department of Analytical and Organic Chemistry, Universitat Rovira i Virgili, C. Marcel
lí Domingo s/n, 43007 Tarragona, Spain b

art ic l e i nf o

a b s t r a c t

Article history: Received 30 September 2013 Received in revised form 16 January 2014 Accepted 19 February 2014 Available online 21 March 2014

The exposure to pesticides amongst school-aged children (6–11 years old) was assessed in this study. One hundred twenty-five volunteer children were selected from two public schools located in an agricultural and in an urban area of Valencia Region, Spain. Twenty pesticide metabolites were analyzed in children's urine as biomarkers of exposure to organophosphate (OP) insecticides, synthetic pyrethroid insecticides, and herbicides. These data were combined with a survey to evaluate the main predictors of pesticide exposure in the children's population. A total of 15 metabolites were present in the urine samples with detection frequencies (DF) ranging from 5% to 86%. The most frequently detected metabolites with DF 453%, were 3,5,6-trichloro-2-pyridinol (TCPy, metabolite of chlorpyrifos), diethyl phosphate (DEP, generic metabolite of OP insecticides), 2-isopropyl-4-methyl-6-hydroxypyrimidine (IMPY, metabolite of diazinon) and para-nitrophenol (PNP, metabolite of parathion and methyl parathion). The calculated geometric means ranged from 0.47 to 3.36 mg/g creatinine, with TCPy and IMPY showing the higher mean concentrations. Statistical significant differences were found between exposure subgroups (Mann– Whitney test, po0.05) for TCPy, DEP, and IMPY. Children living in the agricultural area had significantly higher concentrations of DEP than those living in the urban area. In contrast, children aged 6–8 years from the urban area, showed statistically higher IMPY levels than those from agricultural area. Higher levels of TCPy were also found in children with high consumption of vegetables and higher levels of DEP in children whose parents did not have university degree studies. The multivariable regression analysis showed that age, vegetable consumption, and residential use of pesticides were predictors of exposure for TCPy, and IMPY; whereas location and vegetable consumption were factors associated with DEP concentrations. Creatinine concentrations were the most important predictors of urinary TCPy and PNP metabolites. & 2014 Elsevier Inc. All rights reserved.

Keywords: Exposure Pesticides Metabolites Urine Children

1. Introduction There is an increasing concern for the health effects associated with the application of pesticides in agricultural and residential areas. In recent years, several studies have reported the occurrence of pesticides in different matrices such as food (Banerjee et al., 2012; Lozowicka et al., 2012; Saito et al., 2012), water (Ensminger et al., 2013; Masia et al., 2013), outdoor and indoor air (Hart et al., 2012; Armstrong et al., 2013), and house dust (Mercier et al., 2011). The ingestion and inhalation of these contaminated media are the main routes of human exposure to pesticides. However, more efforts are required to assess the combined exposure of pesticides

n Corresponding author at: Centre of Public Health Research (CSISP-FISABIO), 21 Avenida Catalunya, 46020 Valencia, Spain. E-mail address: [email protected] (V. Yusà).

http://dx.doi.org/10.1016/j.envres.2014.02.009 0013-9351 & 2014 Elsevier Inc. All rights reserved.

across multiple pathways (cumulative exposure). This can be done through the evaluation of biomarkers of exposure in suitable biological specimens such as urine or plasma (Angerer et al., 2007; Esteban and Castaño, 2009; Kapka-Skrzypczak et al., 2011), also known as human biomonitoring (HBM). Data obtained from HBM studies can contribute to assessing the potential health effects associated with the exposure to pesticides, examining time trends and also the efficacy of regulatory actions (National Research Council, 2006). When these data are related to surveys that collect demographic, occupational, and dietary information, potential predictors of exposure in a specific population can be obtained (Becker et al., 2006). Organophosphate (OP) insecticides and a variety of herbicides including phenoxyacetic acids, chloroacetanilides, and triazines, are some of the most frequently used pesticides in agriculture. Moreover, synthetic pyrethroids have replaced other more toxic insecticides such as organochlorine and OP for domestic and

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gardening use in USA (Barr et al., 2010) and Europe (Corcellas et al., 2012). Literature shows different associations between pesticide exposure and human health effects. Chronic exposures at low doses to OP insecticides have been reported to cause neurotoxic problems, immunotoxicity, carcinogenesis, and endocrine and reproductive health alterations (Hancock et al., 2008; Koureas et al., 2012; Rohlman et al., 2011). Although the long-term health effects of synthetic pyrethroids still remain unknown (Koureas et al., 2012), these compounds have been reported to cause different alterations and allergies as well as neurobehavioral and endocrine disrupting effects (Bolognesi, 2003; McKinlay et al., 2008). In addition, epidemiologic studies suggest that exposure to some herbicides, such as 2,4-D, may be associated with increased risk non-Hodgkins lymphoma, Hodgkin's disease, leukemia, and soft-tissue sarcoma (von Stackelberg, 2013). The potential health consequences resulting from pesticide exposure are more severe in children than in adults. This can be explained by differences in dietary patterns, dose of pesticides in relation to weight, and for certain compounds, their different pharmacokinetics (Weiss et al., 2004; Neri et al., 2006). Consequently, there is currently a great interest in understanding the extent of children's exposure to pesticides to protect them from high exposures. Organophosphate insecticides tend not to bio-accumulate and are typically metabolized to the more reactive oxon form. This form may bind to cholinesterase or be hydrolyzed to dialkyl phosphate metabolites (DAPs) and/or to specific metabolites (Barr et al., 2008). These metabolites are excreted in urine within 24–48 h from exposure (Egeghy et al., 2011). On the other hand, pyrethroids are metabolized by esterases to conjugated metabolites which can be eliminated fast by kidneys within 4–13 h from exposure (Leng et al., 2005; Le Grand et al., 2012; Lin et al., 2011). Half-life time for urinary excretion of herbicides in humans varies from 12 to 72 h (Alexander et al., 2007). Both unmodified molecules and their specific metabolites such as mercapturate compounds are analyzed in urine for exposure assessment (Chevrier et al., 2011). Therefore, measuring these metabolites in urine can provide useful information on recent pesticide exposure (1–3 days) (Kavvalakis and Tsatsakis, 2012). Recently, several studies have focused on assessing children's exposure to different OP insecticides and pyrethroids using urinary metabolites as biomarkers of exposure (Bouchard et al., 2011; Panuwet et al., 2009; Quiros-Alcala et al., 2011; Muñoz-Quezada et al., 2012). However, only a few studies have used pesticide-specific metabolites to evaluate the exposure to the parent compounds (Arcury et al., 2010; Becker et al., 2006; Panuwet et al., 2009). The European Union (EU) is currently promoting HBM across Europe (EU, 2004). Their aim is to integrate biomonitoring studies and environmental and health monitoring programs, to assess human exposure to chemicals in different population groups, mainly children. However, the number of studies in pesticide exposure is limited and most of them do not include a wide range of biomarkers. Valencia Region is the second largest agricultural area in Spain and one of the largest pesticide-consumers in this country. This region was responsible for more than 12% of the total national pesticide consumption in 2009 (AEPLA, 2010). The main irrigated crops are citrus fruit, other fruit trees and garden produce. The main dry crops are vineyards, olive trees, almonds and cereals (Agriculture Department, 2010). In this work, a pilot biomonitoring study was conducted to obtain preliminary data on contemporary pesticide exposure in a Spanish school-aged children population. Urinary levels of OP, pyrethroid and herbicide metabolites were measured and combined with a survey to evaluate the main predictors of pesticide

exposure. To our knowledge, there is no data available on the urinary levels of non-persistent pesticides in a Spanish sample population whereby the results of this pilot study will provide new data on the occurrence of urinary concentrations of these chemicals in a children population from a European area with intensive use of pesticides. The results of this study will be also used to evaluate the feasibility of the methodology employed in terms of participant recruitment, questionnaires, sample analysis and interpretation of the results to apply it in a future larger-scale biomonitoring study.

2. Materials and methods 2.1. Study site and population Two primary public schools were used as study sites. These schools were representative of an urban and an agricultural (rural) area in Valencia Region: (1) one located in Alzira, an agricultural area surrounded by citrus groves with intensive use of pesticides, and (2) another located in the urban area of Valencia, a commercial and residential area with gardens and green spaces. These study sites were previously characterized in atmospheric air levels of contemporary pesticides in Valencia Region by our research group (Hart et al., 2012; Coscollá et al., 2013). The main crops and pesticides used in these areas are described in detail in the studies published by these authors, being OP compounds such as chlorpyrifos, folpet or dimethoate some of the most widely used compounds. During June 2010, parents from both schools were asked to participate in the pilot study. A total of 125 volunteer children aged between 6 and 11 years were selected: 62 from the school in Alzira and 63 from the school in Valencia. The sample size (n ¼125) selected is consistent with previous pilot biomonitoring studies by other authors (Castaño et al., 2012; Berman et al., 2011; Volkel et al., 2008; Kubwabo et al., 2004) and the recommendation of the International Federation of Clinical Chemists (IFCC) on the calculation and application of coverage intervals for biological reference values (Poulsen et al., 1997). The age range (6–11 years) was selected in order to compare with data from other studies and HBM programs (CDC, 2009; Health Canada, 2009). Parents were fully informed and gave their written consent for children's participation in the study. A questionnaire with detailed information on socio-demographic characteristics, food habits, and residential use of pesticides in each family, was elaborated to perform a survey. From these set of characteristics, seven variables were selected as potential predictors of exposure for the analyzed metabolites: age, gender, location, parent educational level (university degree studies or not), occupational sector (agriculture or other sector), vegetable consumption, and residential use of pesticides. For age, two age groups (from 6 to 8 and from 10 to 11 years) were selected. The rest of the variables were categorized on a self-scale. Vegetable consumption was classified as high, if the number of servings per week exceeded 10, and mediumlow if it was less than 10 servings. Residential use of pesticides was classified as low if chemical products were used less than twice in the month prior to sampling, or as high, if they were used more frequently. 2.2. Urine sample collection The sampling protocol was reviewed and approved by the Ethic Commission of the Center of Public Health Research of Valencia (Spain). Procedures for sampling, transport, and storage of samples were established according to international guidelines and the expert team to support Biomonitoring in Europe protocol for sample collection (ESBIO, 2004). Parents were instructed and provided with a sterile 50 mL polypropylene urine cup the day prior to urine sample collection. A first spot morning urine sample was collected from each participant, kept cool at 4 1C and transported to the laboratory within two hours of collection. Once in the lab, samples were divided in aliquots and stored at  70 1C until analysis. 2.3. Metabolite analysis Twenty urinary pesticide metabolites were selected taking into account the most frequently used pesticides in the region and some recently banned ones, both in agricultural and residential applications. The main metabolites of these parent compounds described in the literature were selected as biomarkers of exposure. These metabolites are listed in Table 1. For OP insecticides, six DAPs were selected as non-specific metabolites including: three dimethyl (DM) phosphates and three diethyl (DE) phosphates, as well as four specific metabolites of different parent compounds (Table 1). For pyrethroids and herbicides, both specific and non-specific metabolites were selected as biomarkers of exposure of some precursor pesticides in agriculture and residential uses.

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Table 1 Biomarkers of exposure to pesticides, classification and principal applications. Metabolites (biomarkers of exposure)

Abbreviation Possible precursor compounds

Classa

Application

3-Phenoxybenoic acid 4-Fuoro-3-phenoxybenzoic acid cis-(2,2-Dichlorovinyl)-2,2dimethylcyclopropane-1-carboxylic acid trans-(2,2-Dichlorovinyl)-2,2dimethylcyclopropane-1-carboxylic acid cis-(2,2-Dibromovinyl)-2,2dimethylcyclopropane-1-carboxylic acid

PBA FPBA cis-DCCA

Commercial pyrethroids Cyfluthrinb Permethrinc, cypermethrinb, cyfluthrinb

Pyrethroid insecticides

Parks and gardens, forestry plantations, agricultural crops, pets and lice

trans-DCCA

Permethrinc, cypermethrinb, cyfluthrinb

Cis-DBCA

Deltamethrinb

Dimethyl phosphate Dimethyl thiophosphate Dimethyl dithiophosphate

DMP DMTP DMDTP

Azinphos-methylc, dichlorvos c, dicrotophos c, dimethoate b, fenitrothion c, fenthion b, malathion b, methyl parathion c, trichlorfon, chlorpyrifos-methyl b, methidathion c, mevinphos, oxydemeton-methyl b, phosmet b, primiphos-methyl b, temephos c, tetrachlorvinphos c, isazofos-methyl c, naled c…

Organophosphate insecticides

All crops, specially fruits and citrus Stores and agricultural facilities

Diethyl phosphate Diethyl thiophosphate Diethyl dithiophosphate 3,5,6-Trichloro-2-pyridinol 2-Diethylamino-6-methyl-4-pyrimidinol 2-Isopropyl-4-methyl-6-hydroxypyrimidine p-Nitrophenol

DEP DETP DEDTP TCPy DEAMPY IMPY PNP

Chlorethoxyphos, chlorpyrifosb coumaphosc, diazinonc, disulfotonc, ethionc, parathionc, phoratec, phosalone, sulfotepc, terbufosc… Chlorpyrifosb, chlorpyrifos-methylb Pirimiphos-methylb Diazinonc Parathionc, methyl parathionc

2,4-Dichlorophenoxyacetic acid 2,4,5-Trichlorophenoxyacetic acid

2,4-D 2,4,5-T

2,4-Dichlorophenoxyacetic acidb 2,4,5-Trichlorophenoxyacetic acidc

Phenoxy herbicides

Alachlor mercapturate Metolachlor mercapturate Atrazine mercapturate

AlaM MetM AtzM

Alachlorc Metolachlorc Atrazinec

Chloroacetanilide herbicides

Weed control, cereal and grain crops

a b c

Regulation 1185/2009/EC. EU Legal situation: approved. EU Legal situation: not approved.

The analysis of metabolites was performed at the Public Health Laboratory of Valencia (Spain). Validated methodologies based on the methods published by Olsson et al. (2004) and Odetokun et al. (2010), were used. Briefly, for the analysis of specific metabolites of organophosphate, pyrethroid, and herbicide metabolites (Table 1), 5 mL of urine were spiked with isotopically labeled standards, enzymatically hydrolyzed, and extracted using an automatic solid-phase extraction (SPE) using polymeric Strata-X (500 mg/3 mL) cartridges (Phenomenex, Torrance, CA, USA). Sample analysis was performed on a high performance liquid chromatographic tandem mass spectrometric system (LC–MS/MS, Thermofisher Scientific, Bremen, Germany). For the analysis of dialkyl phosphates, 5 mL of urine were spiked with isotopically labeled standards and extracted using Strata-X-AW (500 mg/3 mL) polymeric weak anion cartridges (Phenomenex, Torrance, CA, USA) specific for SPE extraction of polar compounds. The limit of quantification for each compound (LoQ) was calculated experimentally as the lowest point of the calibration curve which reached the desired accuracy (relative standard deviation, RSD o 20%), recovery ( 480%), and the identification criteria for the analyte confirmation established in the 657/2002/CEE Decision (European Commission, 2002). The limits of quantification (LoQ) ranged from 0.2 to 1.6 ng/mL for all metabolites. Internal quality control was achieved through analysis of control samples at different validated levels. Inter-day precision (%RSD) calculated with data from three different days, ranged from 4.2% to 20.6%. Recoveries ranged from 86.6% to 116.6%. Creatinine measurements were performed to normalize metabolite levels, given the various hydration states of each participant at the time of sampling, due to different dilutions of spot urine samples. Creatinine measurements were done according to the kinetic methodology based on the Jaffé alkaline picrate reaction (Larsen, 1972) using an Architect c16000 automatic analyzer (Abbot Diagnostics, Illinois, USA). Analyses were conducted at the Hospital Doctor Peset in Valencia (Spain).

2.4. Statistical analysis Univariate descriptive statistics were calculated using SPSS version 15.0 for Windows (SPSS Inc., Chicago, IL, USA). Firstly, detection frequencies (DF %) of each metabolite were calculated. These calculations were performed considering only the levels exceeding LoQ that met the identification and confirmation criteria

established in the validation method. Secondly, the arithmetic (AM) and geometric means (GM) were calculated in addition to the median, maximum, and 25th, 75th, and 95th percentiles and their corresponding 95% confidence intervals. Data below the LoQ were replaced by LoQ/2, following the procedure describe by Becker et al. (2006) for the German Human Biomonitoring Commission. Urinary concentrations of metabolites were adjusted by creatinine in micrograms per gram of creatinine (mg/g Cre). In addition to individual metabolite concentration, aggregate exposure terms were also calculated. The sum of dimethyl phosphate compounds (∑DMP) was calculated by adding the levels of DMP, DMTP, and DMDTP. The sum of diethyl phosphates (∑DEP) was calculated by adding the levels of DEP, DETP, and DEDTP. The total concentration of dialkyl phosphates (∑DAPs) was calculated as the sum of the six DAP compounds. Total concentrations of specific organophosphate metabolites (∑s-OPs), pyrethroids (∑PYR) and herbicides (∑HERB) were also calculated by adding individual levels corresponding to each pesticide family, and total metabolites (∑TOTAL) as the sum of all compounds. Metabolites with a DFZ 40% were analyzed in more depth using a nonparametric Mann–Whitney test (Massart et al., 1997) to examine differences in urinary metabolite concentrations between subgroups of the variables of exposure at a significance level of po 0.05. The Spearman correlation (Massart et al., 1997) was also calculated to examine pairwise relationships and assess possible collinearity between compounds. A multiple linear regression model, forward selection procedure, was applied after the logarithmic transformation of non-adjusted levels to analyze the relationship between metabolite concentrations in urine and exposure variables using the SPSS software. The p-value used as the entry criterion was 0.05 and p-stay criterion was 0.10. Regression assumptions and influence of potential outliers were verified by residual plots. Confidence intervals were calculated for linear regression coefficients at the 95% level in order to indicate the precision and uncertainty of the sample statistical estimates (Campbell and Garner, 1988). Creatinine concentration was taken into account as predictor in the multivariable analysis. Additionally, an exploratory principal component analysis (PCA) (Massart et al., 1997) was used to identify the factors explaining most of the variance of the metabolite levels using MATLAB v7.0 computing software from The Mathworks (Natick, MA, USA). A data matrix was drawn with 125 rows and 5 columns for the most frequently detected metabolites (DF440%). Before applying the PCA analysis, data below the LoQ were replaced by half their limit of quantification and an autoscaling was applied as a pre-treatment for the different variables.

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3. Results 3.1. Metabolite levels and detection frequencies Descriptive statistics for the measured urinary concentrations of the 20 analyzed metabolites are summarized in Table 2. The DF of each compound varied from non-detection to 86%, being TCPy (DF ¼86%) and DEP (DF ¼79%) the most frequently detected compounds. The following most frequently detected compounds were s-OPs: IMPY (DF ¼ 57%), PNP (DF ¼ 53%), and DEAMPY (DF ¼48%). Dialkyl phosphates DMTP, DETP, DMP, and pyrethroid metabolites trans-DCCA, cis-DBCA and PBA were also detected, but showing lower DFs than the former ones, with values ranging between 18% and 39%. The rest of the compounds were detected at very low frequencies (DF r18%) or were not detected at all in any sample. This was the case of the pyrethroid metabolite FPBA and most of the analyzed herbicides. All samples contained at least one of the studied metabolites. More specifically, in 98% of samples, at least one s-OP metabolite was detected. At least one DAP or pyrethroid metabolite was detected in 86% and 55% of the samples, respectively. However, herbicides were found in a lower extent and were only detected in 14% of the analyzed samples. The highest GM were obtained for TCPy (3.36 mg/g Cre) and IMPY (3.31 mg/g Cre). Median concentrations and 75th percentile also showed high values both for TCPy and IMPY metabolites. Nevertheless, when the 95th percentile was considered, higher levels were also obtained for DMTP (23.83 mg/g Cre) and DMP (25.12 mg/g Cre). Additionally, maximum levels were obtained for DMP compound (416.86 mg/g Cre).

The results obtained after using the non-parametric Mann–Whitney U-test for two independent samples are shown in Table 3. Analyzing the p-values and the overlapping of confidence intervals we observe that IMPY had a significant (po0.001) higher concentration in younger children (6–8 years) living in homes with a low use of pesticides. Less significant difference was also observed between concentrations obtained for IMPY in urban children (median¼7.75 mg/g Cre, 95% CI¼0.60–14.36 mg/g Cre) and rural children (median¼4.25 mg/g Cre, 95% CI¼ 0.60–6.42 mg/g Cre). For DEP, statistically significant higher levels were obtained for children from the rural area (p-valueo0.01, median¼ 2.90 mg/g Cre, 95% CI¼ 2.26– 3.62 mg/g Cre) and in a lesser extent for those children whose parents did not have university degree studies (p-valueo0.05). The TCPy only showed a statistically significant difference (po0.05) for children with a high consumption of vegetables. Conversely, there was no relationship between PNP and DEAMPY and any of the studied variables. In addition, no relationships were found between gender and parent occupation variables in any of the metabolite concentrations. Table 4 presents the Spearman correlation coefficients for the most frequently detected metabolites. Data showed that only TCPy and DEP were significantly correlated (p o0.001, Rho¼0.323). This could indicate a common pesticide origin for these metabolites. Therefore, it is probable that most of the detected DEP came from the chlorpyrifos, the main OP insecticide used in Valencia Region for crop protection. 3.2. Predictors of pesticide exposure A multiple regression analysis was applied to determine predictors contributing to children's exposure to the most frequently

Table 2 Creatinine-adjusted urinary levels (mg/g Cre) of non-persistent metabolites in children of Valencia Region (n¼125). Metabolites LoQ (ng/mL)

TCPyb DEPb IMPYb PNPb DEAMPYb DMTP DETP trans-DCCA cis-DBCA PBA DMP cis-DCCA DMDTP 2,4-D AlaM DEDTP AtzM MetM 2,4,5-T FPBA ∑TOTALb ∑s-OPs

b

0.80 0.40 1.20 0.80 0.20 0.40 0.40 0.40 0.80 0.80 1.60 0.40 0.40 0.40 1.60 0.40 0.20 1.60 1.60 0.20

DF % Z LoQ AM (SD)

86 79 57 53 48 39 36 26 23 23 18 10 9 9 5 0 0 0 0 0 100 98

b

∑DAPs

86

∑DEPb ∑PYRb ∑DMPb ∑HERB

80 55 42 14

5.65 (12.45) 4.00 (7.68) 9.84 (16.82) 1.37 (1.66) 2.84 (10.36) 5.40 (18.11) 1.70 (4.28) 2.16 (10.58) 0.94 (1.28) 4.76 (26.18) 8.60 (43.20) 0.46 (1.46) 0.95 (3.41) 0.23 (0.20) 1.13 (1.67) NE NE NE NE NE

GM (CI)a

3.36 (2.74–4.10) 1.77 (1.40–2.24) 3.31 (2.52–4.35) 0.96 (0.81–1.13) 0.47 (0.35–0.64) NE NE NE NE NE NE NE NE NE NE NE NE NE NE NE

70.50 (104.36) 44.18 (37.93–51.47) 19.70 (22.95) 13.04 (11.14–15.27) 20.85 (49.41) 7.85 (6.33–9.72) 5.89 (9.95) 3.00 (2.46–3.65) 6.03 (19.70) 2.47 (2.10–2.92) 14.96 (47.76) 3.15 (2.44–4.06) 3.06 (1.68) NE

Median (CI)

Percentile 25th (CI)

75th (CI)

95th (CI)

Maximum

1.91 (1.49–2.44) 1.01 (0.20–1.41) o LoQ o LoQ o LoQ o LoQ o LoQ o LoQ o LoQ o LoQ o LoQ o LoQ o LoQ o LoQ o LoQ o LoQ o LoQ o LoQ o LoQ o LoQ

6.16 (5.10–7.32) 4.09 (3.43–4.90) 11.70 (8.69–17.54) 1.61 (1.32–2.26) 2.01 (1.46–3.09) 3.22 (1.59–4.98) 1.39 (0.55–2.12) oLoQ oLoQ oLoQ oLoQ oLoQ oLoQ oLoQ oLoQ oLoQ oLoQ oLoQ oLoQ oLoQ

12.97 (8.93–48.40) 10.62 (7.91–45.75) 37.71 (29.46–43.99) 4.14 (3.47–5.81) 7.59 (5.95–41.73) 23.83 (10.77–95.04) 7.08 (4.85–18.69) 4.44 (1.74–60.97) 3.77 (2.94–5.21) 12.33 (3.77–44.19) 25.12 (13.81–170.86) 1.26 (0.44–7.42) 4.95 (0.28–15.56) 0.43 ( oLoQ–0.55) oLoQ oLoQ oLoQ oLoQ oLoQ oLoQ

123.92 58.02 150.04 13.97 100.30 139.09 36.22 82.74 8.56 64.416 416.86 13.17 28.74 2.33 14.13 oLoQ oLoQ oLoQ oLoQ oLoQ

17.47 9.60 (8.39–10.55) 35.40 161 (14.58–21.36) (34.09–50.16) (135.12–615.04) 9.59 3.86–12.94) 3.00 (2.59–3.54) 21.47 (16.56–30.21) 62.51 (47.80–139.94)

881.40

3.40 (2.76–4.10) 2.28 (1.76–2.82) 5.16 (0.60–6.99) 0.93 (0.40–1.11) o LoQ o LoQ o LoQ o LoQ o LoQ o LoQ o LoQ o LoQ o LoQ o LoQ o LoQ o LoQ o LoQ o LoQ o LoQ o LoQ

3.88 (3.36–4.42) 2.68 (2.16–3.22) o LoQ o LoQ o LoQ

2.61 (1.80–3.01) 1.41 (0.60–1.81) o LoQ o LoQ o LoQ

9.89 (6.77–13.20) 5.68 (4.18–7.22) 1.34 o LoQ  4.06) 4.22 (2.59–5.98) oLoQ

71.79 (37.81–346.09) 17.90 (12.96–64.64) 21.90 (8.99–117.90) 53.89 (24.86–281.45) 4.89 (2.70–11.11)

155.45 417.86 70.85 160.82 417.26 16.03

LoQ¼Limit of quantification; DF¼detection frequency; AM¼ arithmetic mean; SD¼ standard deviation; GM¼geometric mean; CI: confidence interval 95%; NE¼ not estimated. a b

GM calculated only for major metabolites (DF440%). Most frequently detected compounds.

M. Roca et al. / Environmental Research 131 (2014) 77–85

81

Table 3 Median urinary concentration differences (mg/g Cre) of principal metabolites detected by characteristics of study population (n¼ 125). Characteristics

N (%)

Age 6–8 years 9-11 years

TCPY (mg/g Cre)

DEP (mg/g Cre)

IMPY (mg/g Cre)

PNP (mg/g Cre)

DEAMPY (mg/g Cre)

Median

95% CI

Median

95% CI

Median

95% CI

Median

95% CI

Median

95% CI

67(53.6%) 58(46.4%)

3.92 2.85

3.29–5.59 2.45–3.91

2.44 2.12

1.63–3.84 1.62–2.79

8.31*** 0.60

5.76–11.87 0.60–4.87

0.99 0.88

0.40–1.31 0.40–1.11

0.10 0.47

0.10–0.72 0.10–1.50

Gender Male Female Location Agricultural (Alzira) Urban (Valencia)

67(53.6%) 58(46.4%)

3.80 2.74

2.99–4.35 2.39–4.01

2.18 2.46

1.62–2.81 1.67–3.31

5.76 4.60

0.60–7.15 0.60–7.74

0.99 0.88

0.40–1.27 0.40–1.15

0.18 0.10

0.10–1.08 0.10–1.17

62(49.6%) 63(50.4%)

3.76 2.99

2.90–4.55 2.50–4.09

2.90** 1.63

2.26–3.62 1.02–2.33

4.25* 7.75

0.60–6.42 0.60–14.36

1.11 0.4

0.83–1.30 0.40–0.98

0.10 0.18

0.10–1.02 0.10–1.17

University degree No Yes

47(37.6%) 78(62.4%)

3.70 3.31

2.50–4.34 2.71–4.23

3.20* 2.20

1.80–3.92 1.42–2.56

4.30 5.64

0.60–7.05 0.60–9.58

0.83 0.97

0.40–1.11 0.40–1.31

0.10 0.14

0.10–1.15 0.10–1.10

13(10.4%) 112(89.6%)

4.22 3.36

1.92–8.40 2.67–4.01

2.23 2.29

0.2–4.82 1.66–2.83

5.76 5.03

0.6–9.24 0.6–7.09

0.81 0.94

0.40–1.43 0.40–1.13

0.10 0.14

0.10–0.49 0.10–107

27(21.6%) 98(78.4%)

3.87* 2.66

2.99–4.34 2.09–3.43

2.29 2.22

1.77–2.81 0.2–3.74

4.49 6.12

0.6–7.12 0.6–9.92

0.94 0.40

0.40–1.15 0.40–1.24

0.10 0.46

0.10–1.03 0.10–1.09

107(85.6%) 18(14.4%)

3.05 4.16

2.60–3.94 3.06–5.86

2.28 2.28

1.63–2.82 1.70–3.53

6.41*** 0.6

4.29–7.31 0.6–0.6

0.94 0.67

0.40–1.20 0.40–1.12

0.10 0.10

0.10–0.79 0.10–1.57

Occupational sector Agriculture Other Vegetable consumption Low-medium ( o 10 times/week) High (Z 10 times/week) Residential use of pesticides Low (o 2 times/last month) High (Z 2 times/last month)

N ¼ number of participants; % ¼ percentage of participants. n

p-value for Mann–Whitney test o 0.05. p-value for Mann–Whitney test o 0.01. nnn p-value for Mann–Whitney test o 0.001. nn

Table 4 Correlation matrix for the most frequently detected metabolites.

TCPY Rho Sig. DEP Rho Sig.

TCPY

DEP

IMPY

PNP

DEAMPY

1.000 –

0.323** 0.000

 0.096 0.289

0.145 0.105

0.131 0.146

1.000 –

 0.050 0.578

 0.037 0.682

0.045 0.614

1.000 –

0.020 0.828

0.028 0.753

1.000 –

 0.007 0.935

IMPY Rho Sig. PNP Rho Sig. DEAMPY Rho Sig.

1.000 –

Rho¼ Spearman correlation coefficient; Sig. ¼ significance level. nn

p o0.01; confidence level¼95%.

detected metabolites. Factors that were shown to increase urinary metabolite concentrations in the descriptive analysis were investigated in the multiple regression analysis. The regression coefficients as well as the statistical p-values and 95% confidence intervals for the factors influencing concentration of metabolites are given in Table 5. According to the models described in Table 5, a high consumption of vegetables was related with an increasing amount of DEP and TCPy in urine samples. Levels of DEP were higher in children living in the rural location than in the urban one. Age-related differences were only found for IMPY, with young children (6–8 years) showing significant higher levels compared with older children (9–11 years). Urinary concentrations of IMPY increased with low use of pesticides. However, high residential use

of pesticides was based on a small sample (n ¼27) and the results must therefore be interpreted with caution. Creatinine-related differences were also obtained for the TCPy and PNP. Fig. 1 shows the biplot of scores and loadings obtained in the PCA analysis of the first two principal components (PC1 and PC2) which account for the 44.28% of the total variance of data. The loadings obtained showed that the PC1 was influenced mainly by DEP, IMPY, and PNP metabolites. This PC had moderate positive loadings for both IMPY and PNP metabolites, while DEP metabolite was negatively correlated. Finally, DEAMPY and TCPy showed a minimal contribution and presented a negative correlation with IMPY and PNP. The PC2 showed a different trend for loadings, being DEAMPY, PNP, and TCPy the metabolites that contributed the most to this component. Within these metabolites, only DEAMPY had a positive loading, while PNP and TCPy showed a negative correlation. The DEP and IMPY showed minimal contributions and presented a negative correlation. In the score plot (Fig. 1), one group of samples could be distinguished in the right upper quadrant, tending towards PC1 axis. These samples corresponded to children from Valencia and were related to the presence of IMPY. This compound presented a high positive contribution in PC1 and to a less extent in PC2. In contrast, in the left lower quadrant, levels of DEP and TCPy were influenced by samples from both locations.

4. Discussion In the light of other international biomonitoring programs data shown in Fig. 2 (CDC, 2009; Health Canada, 2009; Becker et al., 2006) and research conducted in Chiang Mai province of Thailand (Panuwet et al., 2009) for s-OP (creatinine-adjusted levels calculated for the 95th percentile of the most frequently detected metabolites), children from Valencia Region had similar urinary

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M. Roca et al. / Environmental Research 131 (2014) 77–85

Table 5 Results of Multiple linear regression models with influencing factors on metabolite concentrations. Metabolites

Factors

Standardised coefficient

95% confidence interval for beta

Beta (beta)

Sig

Lower bound

Upper bound

R2

Adj R2

DEP (n¼ 125)

Locationa Vegetable consumptionb

0.361 (0.412)  0.167 (  0.232)

o 0.001 0.048

0.223  0.461

0.604  0.002

0.150

0.137

TCPY (n¼ 125)

Creatininec Vegetable consumption

0.376 (0.968)  0.188 (  0.225)

o 0.001 0.024

0.542  0.419

1.394  0.030

0.176

0.163

IMPY (n¼ 125)

Aged Residential use of pesticidese

 0.223 (0.493) 0.259 (  0.298)

0.010 0.030

 0.523 0.173

 0.073 0.813

0.131

0.117

PNP (n¼ 125)

Creatinine

o 0.001

0.430

1.133

0.136

0.129

0.369 (0.781)

Beta ¼standardized regression coefficient; beta¼ no standardized regression coefficient; Sig ¼significance; R2 ¼squared multiple correlation of predictor variable; Adj R2 ¼ adjusted squared multiple correlation of predictor variable. a

Geographic location (urban ¼0, rural¼1). Vegetable consumption (high ¼0, low¼ 1). c Continuous measure of creatinine in urine children (log transformed g/L). d Age categorized as 6–8 years old ( ¼0) or 9–11 years old ( ¼1). e Exposed to residential pesticides (yes ¼0, no ¼ 1). b

Fig. 1. Biplot of scores (▼ ¼ Valencia, urban location; * ¼Alzira, rural location) and loadings (▲¼ OP; ● ¼ DAP) onto the first and second principal component for major urinary metabolite pesticides by location.

Fig. 2. Comparison of 95th percentile data (mg/g Cre) obtained in our study and other biomonitoring studies.

concentrations of TCPy and PNP than the US population (CDC, 2009) and Chiang Mai children (Panuwet et al., 2009). For the

other s-OP metabolites, our data showed higher concentrations than those reported by the CDC survey (CDC, 2009), especially for IMPY (37.71 mg/g Cre). These results indicate a high exposure to diazinon in Valencia Region. The DEP concentration in Valencia Region (10.62 mg/g Cre), however, was considerably lower than the data reported in the Canadian study (25.1 mg/g Cre) (Health Canada, 2009). Organophosphate insecticides, especially chlorpyrifos, are the most commonly used pesticides in vegetable crops, grapes, fruits, and citrus in Valencia Region (Agricultural Department, 2012). Therefore, it is not surprising to find high levels of s-OP metabolites and generic DAP metabolites, such as DEP, in human urine, especially in children with high vegetable consumption and living in agricultural areas (Table 3). However, specific metabolites of diazinon and parathion (IMPY and PNP) were also detected at high frequencies (57–79%). These compounds are not approved for vegetable crop protection since 2001 and 2007, respectively (EC, 2001, 2007). Hart et al. (2012) pointed out the possible illegal use of diazinon in a recent study on pesticides in ambient air in Valencia Region. In this study, the diazinon was detected frequently both in Valencia (71% detection, 5.3–39.7 pg/m3) and Alzira (45% detection, 8.1–176.1 pg/m3). There is no evidence that these compounds could be present in imported foods from other countries because these pesticides have not been detected in retail samples (EFSA, 2013). Other authors have reported an inexplicably excess of urinary IMPY both in urban and rural areas; suggesting this biomarker may not be an appropriate quantitative indicator of diazinon exposure, especially in the urine of young children (Eaton et al., 2008; Morgan et al., 2011). The higher levels of DEP obtained in children whose parents did not have university degree studies compared with those with university degree studies are in agreement with previously published data (Barr et al., 2004). These authors pointed out that this fact could be related with a higher vegetable consumption frequency and more hygienic food habits, regarding the cleaning of vegetables, in the former group compared with the latter. On the other hand, the high concentrations of some dimethyl phosphate metabolites (Table 2) might be explained by the frequent use of some of their parent compounds in Valencia Region for citrus and fruit crop protection (Agricultural Department, 2012). Parent compounds include dimethoate, methyl chlorpyrifos or fosmet. Their presence in fruits and marketed vegetables in the area has also been reported before (Berrada et al., 2010).

M. Roca et al. / Environmental Research 131 (2014) 77–85

The lower detection frequencies calculated for pyrethroid metabolites compared with the rest of metabolites may correspond to low residential uses of these compounds in participants' houses, gardens, and pets. In the case of herbicides, many of them are not currently approved for use in agriculture, so it is not surprising that they were not detected in the studied population. Recently, the German Human Biomonitoring Commission has established reference values (RV95) for some DAP compounds and pyrethroids metabolites, for children aged between 3 and 14 years in urine derived from the 95th percentile population. Although these are not health-based reference benchmarks and consequently cannot be used for risk characterization, these reference values can be used to compare exposure levels amongst studies and populations. The 95th percentile obtained in our study for DEP was of 7.67 ng/mL (non-adjusted data), well below the RV95 (30 ng/mL) specified by the German Commission (Schulz et al., 2011). The inter-individual variation in urinary levels of metabolites among children was explained by the multiple linear regression models with squared multiple correlations ranging between 13.1% and 17.6%. There was a significant increase in IMPY concentrations in children from 6 to 8 years old. With the gathered information, the cause related with the high exposure to diazinon in this group of children could not be established. However, some authors suggest that higher OP levels in young children can be explained by inadequate hygienic habits, less activity, slower metabolism or a reduced expression of detoxifying enzymes compared with older children or adults (Bouchard et al., 2010; Muñoz-Quezada et al., 2012; Valcke and Bouchard, 2009). On the other hand, children with low residential use of pesticides were related with high exposure to IMPY. This indicates the presence of other sources of exposure to diazinon different from the residential use of pesticides in the studied population. Nevertheless, the low number of children with high residential use of pesticides (n ¼ 18) may have influenced the results obtained for this variable. Vegetable consumption was a contributor to DEP and TCPy metabolites in urine. This fact could be related with the frequent intake of contaminated fruits and vegetables with OP insecticides, highly used in Valencia Region for crop protection. Although a similar dietary exposure to pesticides both in the agricultural and urban area could be expected, higher levels of DEP were obtained in children from the agricultural location compared with the urban location. This suggests that differences obtained between locations could result from other routes of exposure, such as water ingestion or inhalation. A high positive relationship between creatinine levels and PNP and TCPy concentrations was obtained in this study. These results are in accordance with previously published results by other authors (Becker et al., 2006; Morgan et al., 2011). This relationship indicates that the excretion of these metabolites in urine can depend on the metabolic behavior of pesticides in the body, such as the kidney filtration rate of metabolites. Moreover, it highlights the need for calculating metabolite levels with a correction factor taking into account kidney filtration rate of metabolites, to compare individuals from the same or similar populations. Results from PCA showed that TCPy (chlorpyrifos metabolite) was present only in samples where IMPY (diazinon metabolite) was not detected. This can be explained because chlorpyrifos and diazinon (which was banned since 2007) were both recommended in similar crops (vegetables and grapes) in Valencia Region (Agricultural Department, 2012). Therefore, it is probable that farmers did not use them for crop protection simultaneously. Furthermore, these results may also be a consequence of the replacement of diazinon by other approved insecticides recommended for similar crops, such as chlorpyrifos. In addition, our results indicate that inhalation of polluted air is not the most probable route of exposure to diazinon. Although diazinon was present in ambient air (Hart et al., 2012), children

83

living in areas close to intensive agriculture land presented lower levels of diazinon than those living in urban areas, in our study. Hence, food intake can be the most probable route of exposure to diazinon. Our results, however, did not show significant results related with the vegetable consumption. Further research on the sources of exposure of this pesticide is necessary to explain the results obtained. Similar results were found in the PCA analysis for DEAMPY and PNP metabolites. The DEAMPY was present only in those samples where PNP was not detected. In addition DEAMPY, pirimiphosmethyl metabolite, was not related with any of the other metabolites. These results can be explained by the different uses of each compound. While pirimiphos-methyl is only recommended for use in stores and agricultural facilities, PNP as well as other OP insecticides, are generally used for spraying crops. On the other hand, PNP was present in samples from children from both agricultural and urban locations, indicating a similar source of exposure to parathion and methyl-parathion in both the populations. Nevertheless, the explanation for the negative correlation between PNP and DEAMPY in the PCA analysis found in this study still remains unclear. Different scientific efforts are currently in progress in order to derive health related Human Biomonitoring values based on epidemiological studies or derived from toxic kinetic extrapolation. The HBM values (HBM-I, HBM-II) from the German Human Biomonitoring Commission (Schulz et al., 2011) and Biomonitoring equivalents (BEs) (Hays and Aylward, 2009) are the most relevant ones. However, there are not health-related values established for pesticide metabolites and consequently, it is not possible to conclude what the health outcomes may be, if any, related with the levels detected in this study. In conclusion, to our knowledge this is the first HBM study developed in Spain reporting the exposure to non-persistent pesticides in children population. Therefore, the results of this pilot study have allowed us to determine the feasibility of the methodology employed as well as an evaluation of the limitations and “lessons learned” to take them into consideration in future larger scale biomonitoring studies. Regarding the study design, the sample size will be defined to ensure the representativeness of the target population and to determine possible statistical differences between exposure concentrations in population subgroups. The strategy employed in this pilot study for the data collection has been appropriate to comply with the objectives. Nevertheless the results obtained showed that food consumption may be an important route of exposure for some OP pesticides. Therefore it would be appropriate to collect more dietary information in future studies by using a validated semi-quantitative food frequency questionnaire (FFQ) and a 24-h recall, for an in-depth evaluation of the exposure to diet-derived contemporary pesticides. Regarding the results obtained in the multivariate regression analysis, we cannot exclude some confounders influencing the association between the concentrations of metabolites and their determinants. Taking into consideration the wide range of factors of pesticides exposure among children, further studies are needed in order to identify the true predictors and their cofounders. Moreover, as the results obtained in biomonitoring studies are not sufficient to differentiate amongst the sources and routes of exposure for the different pesticides, it is necessary to combine them with environmental monitoring studies in order to have a comprehensive knowledge of the exposure and pathways involved.

Scientific Committee Please find attached a copy of the document that proves that the study and sampling protocol were properly approved by the

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M. Roca et al. / Environmental Research 131 (2014) 77–85

Scientific Ethic Committee of the Centre for Public Health Research of Valencia (CSISP-FISABIO) of the Valencian Government (Dirección General de Salud Pública, DGSP).

Acknowledgments This study was part of the research project Control e impacto de los plaguicidas en la atmósfera de la Comunidad Valenciana (CIPAV) funded by the Consellería de Educación, Generalitat Valenciana (GV/2011/007) and had the support of dFP7-ENV2011 DENAMIC project (cod 282957). Authors thank the staff from the Clinical Analysis Laboratory of Hospital Doctor Peset in Valencia for the analysis of creatinine in urine samples. Authors also wish to thank the children and families from the public schools García Lorca located in Alzira and Padre Catalá located in Valencia, for their participation and collaboration in this study. Authors would also like to acknowledge Dr. Amparo Casals for her collaboration in the experimental design and in sample collection and Mario Murcia for the statistical analysis support. Members of the CIPAV project are also acknowledged. References AEPLA (Asociación Empresarial para la Protección de las Plantas), 2010. Memoria AEPLA 2009. 〈http://www.aepla.es/publicaciones〉. Agriculture Department (Conselleria de Presidencia, Agricultura, Pesca y Alimentación de la Generalitat Valenciana, Spain), 2010. Informe del sector agrario 2010. 〈http://www.agricultura.gva.es/web/〉. Agriculture Department (Conselleria de Agricultura, Pesca y Alimentación de la Generalitat Valenciana, Spain), 2012. Boltetín de avisos agrarios. Residuos de plaguicidas en cítricos. 〈http://www.agricultura.gva.es/web/〉. Alexander, B.H., Mandel, J.S., Baker, B.A., Bums, C.J., Bartels, M.J., Acquavella, J.F., Gustin, C., 2007. Biomonitoring of 2,4-dichlorophenoxyacetic acid exposure and dose in farm families. Environ. Health Perspect. 115, 370–376. Angerer, J., Ewers, U., Wilhelm, M., 2007. Human biomonitoring: state of the art. Int. J. Hyg. Environ. Health 210, 201–228. Arcury, T.A., Grzywacz, J.G., Talton, J.W., Chen, H., Vallejos, Q.M., Galvan, L., Barr, D.B., Quandt, S.A., 2010. Repeated pesticide exposure among North Carolina migrant and seasonal farm workers. Am. J. Ind. Med. 53, 802–813. Armstrong, J.L., Fenske, R.A., Yost, M.G., Galvin, K., Tchong-French, M., Yu, J., 2013. Presence of organophosphorus pesticide oxygen analogs in air samples. Atmos. Environ. 66, 145–150. Banerjee, K., Utture, S., Dasgupta, S., Kandaswamy, C., Pradhan, S., Kulkarni, S., Adsule, P., 2012. Multiresidue determination of 375 organic contaminants including pesticides, polychlorinated biphenyls and polyaromatic hydrocarbons in fruits and vegetables by gas chromatography–triple quadrupole mass spectrometry with introduction of semi-quantification approach. J. Chromatogr. a 1270, 283–295. Barr, D.B., Baker, S.E., Whitehead , R.D., Wong, L., Needham, L.L., 2008. Urinary concentrations of pyrethroid metabolites in the general US population, NHANES 1999–2002. Epidemiology 19, S192–S193. Barr, D.B., Olsson, A.O., Wong, L.Y., Udunka, S., Baker, S.E., Whitehead, R.D., Magsumbol, M.S., Williams, B.L., Needham, L.L., 2010. Urinary concentrations of metabolites of pyrethroid insecticides in the general U.S. population: National Health and Nutrition Examination Survey 1999–2002. Environ. Health Perspect. 118, 742–748. Barr, D., Bravo, R., Weerasekera, G., Caltabiano, L., Whitehead, R., Olsson, A., Caudill, S., Schober, S., Pirkle, J., Sampson, E., Jackson, R., Needham, L., 2004. Concentrations of dialkyl phosphate metabolites of organophosphorus pesticides in the US population. Environ. Health Perspect. 112, 186–200. Becker, K., Seiwert, M., Angerer, J., Kolossa-Gehring, M., Hoppe, H.W., Ball, M., Schulz, C., Thumulla, J., Seifert, B., 2006. GerES IV Pilot Study: assessment of the exposure of German children to organophosphorus and pyrethroid pesticides. Int. J. Hyg. Environ. Health 209, 221–233. Berman, T., Hochner-Celnikier, D., Barr, D.B., Needham, L.L., Amitai, Y., Wormser, U., Richter, E., 2011. Pesticide exposure among pregnant women in Jerusalem, Israel: results of a pilot study. 37. Environ. Int., pp. 198–203. Berrada, H., Fernandez, M., Ruiz, M.J., Molto, J.C., Manes, J., Font, G., 2010. Surveillance of pesticide residues in fruits from Valencia during twenty months (2004/05). Food Control 21, 36–44. Bolognesi, C., 2003. Genotoxicity of pesticides: a review of human biomonitoring studies. Mutat. Res. – Rev. Mutat. Res. 543, 251–272. Bouchard, M.F., Bellinger, D.C., Wright, R.O., Weisskopf, M.G., 2010. Attentiondeficit/hyperactivity disorder and urinary metabolites of organophosphate pesticides. Pediatrics 125, 270–277. Bouchard, M.F., Chevrier, J., Harley, K.G., Kogut, K., Vedar, M., Calderon, N., Trujillo, C., Johnson, C., Bradman, A., Barr, D.B., Eskenazi, B., 2011. Prenatal exposure to

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