Advanced data mining approaches in the assessment of urinary concentrations of bisphenols, chlorophenols, parabens and benzophenones in Brazilian children and their
association to DNA damage
Bruno A. Rochaa,b, Alexandros G. Asimakopoulosb,c, Masato Hondab, Nattane L. da Costad, Rommel M. Barbosad, Fernando Barbosa Jra, Kurunthachalam Kannanb,e*
aLaboratório de Toxicologia e Essencialidade de Metais, Faculdade de Ciências Farmacêuticas de Ribeirão Preto, Universidade de São Paulo, Ribeirão Preto, São Paulo, 14040-903, Brazil.
bWadsworth Center, New York State Department of Health, and Department of Environmental Health Sciences, School of Public Health, State University of New York at Albany, New York 12201, United States.
cDepartment of Chemistry, The Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway.
dInstituto de Informática, Universidade Federal de Goiás, Goiânia, Goiás, 74690-900, Brazil.
eBiochemistry Department, Faculty of Science, King Abdulaziz University, Jeddah, Saudi Arabia.
* Corresponding author at: Wadsworth Center, Empire State Plaza, P. O. Box 509, Albany, NY 12210-0509, United States. [email protected] (K. Kannan).
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Abstract
Human exposure to endocrine disrupting chemicals (EDCs) has received considerable attention over the last three decades. However, little is known about the influence of co-exposure to multiple EDCs on effect-biomarkers such as oxidative stress in Brazilian children. In this study, concentrations of 40 EDCs were determined in urine samples collected from 300 Brazilian children of ages 6–14 years and data were analyzed by advanced data mining techniques.
Oxidative DNA damage was evaluated from the urinary concentrations of 8-hydroxy-2’- deoxyguanosine (8OHDG). Fourteen EDCs, including bisphenol A (BPA), methyl paraben (MeP), ethyl paraben (EtP), propyl paraben (PrP), 3,4-dihydroxy benzoic acid (3,4-DHB), methyl-protocatechuic acid (OH-MeP), ethyl-protocatechuic acid (OH-EtP), triclosan (TCS), triclocarban (TCC), 2-hydroxy-4-methoxybenzophenone (BP3), 2,4-dihydroxybenzophenone (BP1), bisphenol A bis(2,3-dihydroxypropyl) glycidyl ether (BADGE·2H2O),2,4-dichlorophenol (2,4-DCP), and 2,5-dichlorophenol (2,5-DCP) were found in >50% of the urine samples analyzed. The highest geometric mean concentrations were found for MeP (43.1 ng/mL), PrP (3.12 ng/mL), 3,4-DHB (42.2 ng/mL), TCS (8.26 ng/mL), BP3 (3.71 ng/mL), and BP1 (4.85 ng/mL), and exposures to most of which were associated with personal care product (PCP) use.
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BPA, MeP, 3,4-DHB, OH-MeP, OH-EtP, TCS, BP3, 2,4-DCP, and 2,5-DCP. After clustering the data on the basis of i) 14 EDCs (exposure levels), ii) demography (age, gender and geographic location), and iii) 8OHDG (effect), two distinct clusters of samples were identified.
8OHDG concentration was the most critical parameter that differentiated the two clusters, followed by OH-EtP. When 8OHDG was removed from the dataset, predictability of exposure variables increased in the order of: OH-EtP > OH-MeP > 3,4-DHB > BPA> 2,4-DCP> MeP>
TCS > EtP > BP1 > 2,5-DCP. Our results showed that co-exposure to OH-EtP, OH-MeP, 3,4- DHB, BPA, 2,4-DCP, MeP, TCS, EtP, BP1, and 2,5-DCP was associated with DNA damage in children. This is the first study to report exposure of Brazilian children to a wide range of EDCs and the data mining approach further strengthened our findings of chemical co-exposures and biomarkers of effect.
Keywords:
Endocrine disrupting chemicals; human co-exposure; children; data mining; oxidative stress
Highlights:
40 endocrine disrupting chemicals were measured in children’s urine from Brazil.
High urinary concentrations of EDCs were associated with personal care products’ use.
Urinary concentrations of EDCs were influenced by sex, age and region.
Co-exposures to BPA, parabens, and dichlorophenols is associated with DNA damage.
1. Introduction
Populations throughout the world are exposed to a wide range of synthetic environmental 45
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chemicals, which are harmful to human health (Naidu et al., 2016; Scognamiglio et al., 2016;
Woodruff, 2015; Zidek et al., 2017). Many of these chemicals are endocrine disrupting chemicals (EDCs), for which exposure has been associated with the progression of metabolic disorders including obesity, diabetes, cancer and endometriosis (Diamanti-Kandarakis et al., 2009; Giulivo et al., 2016; Jimenez-Diaz et al., 2015; Naidu et al., 2016; Smarr et al., 2016; Xue et al., 2015). Exposure to EDCs has been linked to oxidative stress in human populations (Asimakopoulos et al., 2016; Bledzka et al., 2014; Ferguson et al., 2016; Franken et al., 2017; Lu et al., 2016; Lv et al., 2017; Rocha et al., 2017; Tavares et al., 2016; Watkins et al., 2015; Zhang et al., 2016). Oxidative stress is a condition that arises from an imbalance between the endogenous formation of reactive oxygen species (ROS), and the organism's capacity to detoxify or eliminate the ROS or to repair damage caused by the ROS. This condition can disrupt normal cellular signaling and can act as a trigger for numerous diseases, such as cancer, cardiovascular disease, and infertility. Oxidized DNA repair products are excreted in urine, and therefore urinary 8OHDG is considered an important marker of oxidative stress (Asimakopoulos et al., 2016; Bisht et al., 2017; Di Minno et al., 2016; Kelly and Fussell, 2017; Reuter et al., 2010;
Rocha et al., 2017; Zhang et al., 2016).
Human biomonitoring programs, implemented by public health agencies in various countries, assess exposure of populations to EDCs. Due to the smaller body weight and higher calorie intake per kilogram, children can be exposed to greater levels of EDCs than adults.
Several studies have reported the occurrence of EDCs in children (Asimakopoulos et al., 2016;
Calafat et al., 2017; CDC, 2012; Covaci et al., 2015; Frederiksen et al., 2013; Health Canada, 2013; Heffernan et al., 2015; Jiménez-Díaz et al., 2016; Larsson et al., 2014; Myridakis et al., 2015; Xue et al., 2015). However, to the best of our knowledge, no data exist on the exposure of 68
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Brazilian children to these EDCs. Furthermore, data analysis models often associate exposure to a single chemical to health outcomes. Current developments in data mining techniques enable analysis of co-exposure to multiple chemicals on health outcomes. Such an approach would enable understanding of effects from co-exposures to multiple chemicals. The present study was conducted with the aim to elucidate urinary concentrations of EDCs in Brazilian children and to examine association between urinary concentrations of EDCs and 8OHDG.
2. Materials and methods
2.1. Study population and sample collection
Urine samples were collected from 300 urban resident Brazilian school children aged 6 to 14 years from five geographic regions in Brazil (Southeast, South, Central-West, Northeast, and North) in 2012–2013 (Rocha et al., 2017). The demographic characteristics (distribution by gender, age, and region) of the population studied are shown in Table S1. Spot urine samples were collected in polypropylene conical tubes from healthy donors and stored at -80° C until analysis. Informed consent was obtained from the legal guardian(s) of every child. The study was approved by the Institutional Ethical Review Board of the School of Pharmaceutical Sciences of Ribeirão Preto, University of São Paulo, Brazil.
2.2. Chemical analysis
2.2.1. Sample preparation and instrumental analysis
Urine samples were analyzed for 40 EDCs, 8OHDG, creatinine and specific gravity. The list of all EDCs analyzed is shown in Table 1. Individual stock solutions of each compound and internal standards were prepared by dissolution in methanol (MeOH) and stored in amber glass 91
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vials at -20 °C. The calibration and working standard solutions were prepared daily from the stock solutions through serial dilution with MeOH, and stored in amber glass vials at 4 °C until analysis. The methods for the analysis of EDCs in urine samples have been described in Asimakopoulos et al. (2016). EDCs were extracted after enzymatic deconjugation of urine samples, followed by liquid-liquid extraction. The extracts were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) methods. Details of the analytical methods are presented elsewhere (Asimakopoulos et al., 2016). Urinary concentrations of 8OHDG, creatinine, and specific gravity were determined as reported elsewhere (Rocha et al., 2017). However, the previous methods did not analyze chlorophenols, which is described below.
The chromatographic separation of dichlorophenols and trichlorophenols was carried out using a Waters Acquity™ ultra performance liquid chromatography (UPLC) system (Waters, Milford, MA, U.S.), which consisted of a binary pump and an auto sampler. Identification and quantification of target analytes were accomplished with an Applied Biosystems API 5500™
electrospray triple quadrupole mass spectrometer (APCI–MS/MS; Applied Biosystems, Foster City, CA, U.S.) under the negative ionization mode. An ACQUITY UPLC® BEH C18 column (2.1 mm × 50 mm, 1.7 mm; Waters, Milford, MA, U.S.) was used for the separation of target compounds. The mobile phase comprised MeOH (0.01% ammonium hydroxide) and Milli-Q water (0.01% ammonium hydroxide) with gradient elution at a flow rate of 300 μL/min starting at 5% MeOH which was held for 0.5 min, increased to 45% MeOH within 0.1 min, then increased again to 99% MeOH within 2 min, held for 0.7 min, then decreased to 5% MeOH within 0.10 min, held for 0.70 min, for a total run time of 5.0 min. The ionization voltage was -4500 V. The curtain and collision gas (nitrogen) flow rates were set at 25 and 10 psi, respectively, and the source heater was set at 300° C. The nebulizer gas (ion source gas 1) was 114
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set at 30 psi, and the heater gas (ion source gas 2) was set at 40 psi. The mass spectrometer was operated in multiple reaction monitoring (MRM) negative ionization mode. Detailed information regarding MS transitions for each target chemical and internal standard are presented in the supplementary material (Table S2).
2.2.2. Quality assurance/quality control
EDCs were determined after enzymatic deconjugation. Quality assurance and quality control parameters included procedural blanks, matrix spikes and analysis of Standard Reference Materials (SRM). Labeled internal standards were spiked into all samples and quantification was by isotope dilution method (Table S2). Contamination that arises from laboratory materials and solvents was monitored by the analysis of procedural blanks. A 20-point instrumental calibration curve was prepared in MeOH at concentrations that ranged from 0.01 to 100 ng/mL, except for chlorophenols that ranged from 0.01 to 20.0 ng/mL. The regression coefficients of the calibration curves were >0.99. For each batch of 25 samples analyzed, two procedural blanks and two pre- extraction matrix spikes (prepared by spiking known concentrations [40 ng/mL] of target compounds) were analyzed by passing them through the entire analytical procedure. In addition, SRMs 3672 (Organic Contaminants in Smokers’ Urine) and 3673 (Organic Contaminants in Non-Smokers’ Urine) from the National Institute of Standards & Technology (Gaithersburg, MD, U.S.), which were certified for select EDCs, were analyzed with every 50 samples to assure accuracy of the analytical method.Our results for NIST SRMs were within ±15% of the certified values. A calibration check standard and methanol were injected after every 25 samples as a check for drift in instrumental sensitivity and carry-over between samples, respectively. The limits of detection (LODs) and the limits of quantification (LOQs) were calculated based on 137
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Asimakopoulos et al. (2016). Briefly, the LODs and LOQs were calculated as 3 and 10 times the standard deviation of 6 replicate analyses of the lowest calibration standard (or at the concentration found in procedural blanks, if the target analyte maintained measurable background levels) divided by the value of the slope of regression, after adjusting for recovery/loss during extraction and matrix effects, except for chlorophenols, for which the lowest calibration standard that fitted the calibration curve divided by 3. The LODs of EDCs in urine varied from 0.004 to 0.60 ng/mL (Table S2).
2.3. Statistical analysis 2.3.1. Data analysis
Data analysis was performed using SPSS, version 20 and Microsoft Excel 2013®. Median, mean, geometric mean and percentiles of urinary EDC concentrations were calculated on a volume-based, creatinine-adjusted and specific-gravity (SG) adjusted basis. The results reported herein were for volume-based concentrations (i.e., ng/mL); creatinine and SG-adjusted concentrations are provided in the supporting information. For EDCs concentrations below the LOD, we used a value equal to the LOD divided by the square root of 2 (Hornung and Reed, 1990). We only calculated geometric means for EDCs detected in > 50% of the urine samples.
Only concentrations above the LOD were used to statistical analysis and comparisons. Since the urinary concentrations were not normally distributed (as determined by Shapiro-Wilk test), data were log-transformed for statistical analysis. The nonparametric Mann-Whitney U test was used to examine the difference between two groups of data, whereas the nonparametric Kruskal- Wallis test was used to test the differences among three or more groups. Moreover, multiple linear regression analysis was conducted to elucidate statistical association between each urinary 160
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EDC concentration and demographic characteristics, after adjusting for covariates (gender, age and region and interaction between age/gender). Natural log transformations were performed on all EDC concentrations because of the skewed distribution of data. Log-transformed data were normally distributed. To examine the relationship among EDCs, Spearman's correlation was applied. Analytes with detection rates below 50% were excluded from statistical analysis. All statistical tests were considered significant if the p-value was < 0.05.
2.3.2. Cluster analysis
Cluster analysis is a family of unsupervised pattern recognition techniques that identifies a finite set of categories or clusters in such a way that those within a group are more similar to each other than to objects in another group (Maione et al., 2017; Zidek et al., 2017). We used k- medoids, a technique based on partitioning around medoids (PAM) algorithm developed by Kaufman and Rousseuw (1990). The objective of this algorithm is to partition the dataset into k clusters with each cluster having one representative object known as medoid. A medoid is defined as the most centrally located object within a cluster, i.e., the object that has the minimum sum of distances to other points. We used the Euclidean distance as a measure of distance. The algorithm is defined by the following steps: 1. Choose k objects at random to be the initial cluster medoids called representative objects. 2. Assign each object to the cluster associated with the closest medoid. 3. Randomly select a non-representative object O. 4. Calculate the total cost S of swapping the medoid M with O (sum of distances of points to medoid M minus the sum of distance of points to medoid O). 5. If S<0, then swap M with Oi to form the new set of medoids.
6. Repeat steps 3 to 5 until the medoids become fixed. One of the advantages of using k- medoids instead of the popular k-means is that the k-medoids algorithm is more robust with 183
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respect to outliers (Kaufman and Rousseuw, 1990; Reynolds et al., 2004).
2.3.3. Classification algorithms and model evaluation
Decision trees have become a powerful and popular approaches in data science due to their simplicity, low computational costs, and quick generalization of data (Rokach, 2016).The structure of a decision tree consists of internal nodes, which represents a test on a variable, branches representing the outcome of the test, and each leaf node representing a class label. We used C4.5 decision tree implemented as J48 decision from R software for the analysis of our data (Hornik et al., 2009).
The Random Forest (RF) algorithm is a classifier that generates multiple decision trees using bootstrap samples from the original training data. Around one third of data (called out-of- bag [OOB]) is separated to test the respective tree constructed from the bootstrap sample. Each tree in the RF is a Classification and Regression Tree (CART). Each node in the tree corresponds to a variable, and each edge originating from a node x represents a value, or a range of values, for the variable x. The tree chooses which variable will be a node based on the Gini criteria and the node splits into new nodes until a stopping criterion is met or until the terminal nodes are pure. The classification occurs according to the most voted class among the trees. The RF algorithm is an effective tool in prediction due to the high level of accuracy (Breiman, 2001).
The implementation of the RF algorithm that we used in this study define the number of trees in 500, and the number of randomly selected variables chosen at each node was defined as the square root of the number of variables in the dataset (in our case, the number is approximately 4) (Liaw and Wiener, 2002). To evaluate the performance of the classification model, we used k- fold cross validation with k=10. This method splits the data into k subsets and uses k-1 fold to 206
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train data and one-fold to test data. The relationship of correct and incorrect classifications is organized in a confusion matrix (Table S3) to obtain the measure performances of accuracy, sensitivity and specificity. The matrix values are true positive (TP) for samples correctly classified as positive; true negative (TN) for samples correctly classified as negative; false negative (FN) for the positive samples that were classified as negative; and false positive (FP) for negative samples that were classified as positive.
3. Results
Geometric mean, arithmetic mean, percentiles and range for volume-based concentrations of 40 EDCs (ng/mL) measured in 300 urine samples of Brazilian children are summarized in Table 1. The results for creatinine-adjusted and SG-adjusted concentrations of EDCs are summarized in Tables S4 and S5, respectively. Among the 40 EDCs measured, 14 were commonly detected in Brazilian children with the detection rates above 50%. Nineteen chemicals were detected in <50% of the samples whereas 7 chemicals remained undetected.
Correlations among urinary EDCs (Table S6) ranged from negative (r = -0.2) to positive (r = 0.8). Positive correlations (r > 0.6) were observed between BP3 and BP1; MeP and PrP; OH- MeP and OH-EtP; TCS and 2,4-DCP. Strong positive correlations between chemicals often suggest common sources and co-exposures.
Among bisphenols, BPA was the major compound found in 98% of the samples analyzed at concentrations ranging from < LOD to 35.9 ng/mL with a GM value of 1.74 ng/mL. The other BPA analogues were found at much lower detection rates: BPS (23%), BPP (16%), BPF (9%), BPAP (5%) BPB (3%) and BPAF (1%). All BADGE derivatives were found in urine samples, but only BADGE.2H2O was frequently detected (57%) at concentrations that ranged from <LOD 229
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to 33.8 ng/mL. with a GM concentration of 0.30 ng/mL. Other BADGE derivatives were found in less than 20% of the samples. BFDGEs and NOGE derivatives were not detected.
Parabens and their metabolites were found at detection rates that ranged from 14% to 100%. MeP was detected in all samples at a GM concentration of 43.1 ng/mL. PrP and EtP were also frequently detected with GM concentrations of 3.12 and 0.19 ng/mL, respectively. Paraben metabolites, 3,4-DHB, OH-MeP and OH-EtP, were frequently detected with detections rates
>70% and at GM concentrations of 8.24, 2.17 and 0.22 ng/mL, respectively.
BP3 and BP1 were detected at rates of 100% and 95%, respectively, with GM concentrations of 3.71 and 4.85 ng/mL, respectively. The detection rates of BP8, BP2 and 4- OHBP were <40%. TCS and TCC were detected in 90 and 70% of the samples, respectively, at concentrations ranging from <LOD to 874 ng/mL (GM: 8.26) for TCS and <LOD to 0.94 (GM:
0.02) for TCC.
Dichlorophenols were frequently detected in urine. The GM concentrations of 2,4-DCP and 2,5-DCP were 2.60 ng/mL and 4.59 ng/mL, respectively. 2,4,5-TCP and 2,4,6-TCP were less frequently detected.
Unadjusted urinary concentrations of EDCs stratified by gender and age are summarized in Table 2 and urinary concentrations of EDCs adjusted for creatinine and SG are listed in Tables S7 and S8. When children were grouped by gender, statistically significant differences (p < 0.05;
nonparametric Mann-Whitney U test) were observed for the concentrations of MeP, PrP, TCS, BP3 and BP1. The results of the multiple linear regression analysis are presented in Table S9, which suggested that the urinary concentrations of MeP, EtP, PrP BP3, and BP1 differed significantly by sex as observed in non-parametric comparison methods (except for EtP). The urinary concentrations of MeP, EtP, PrP, TCS, BP3 and BP1 were higher in females than males.
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Furthermore, significant differences (p < 0.05; nonparametric Kruskal-Wallis test) were observed in the concentrations of MeP, PrP, and BP1 between age and gender groups of 6-10 and 11-14 year old female and male children (Table S9). In addition, MLR analysis shows that the urinary concentrations of MeP, PrP, OH-MeP, TCS, BP1 and, BP3 significantly different (p < 0.05) by the interaction terms of age and gender. Higher concentrations of these compounds (except for OH-MeP) were observed in female older children.
The urinary concentrations of EDCs, except for MeP, PrP, parabens metabolites and TCS, showed significant differences (p < 0.05; nonparametric Kruskal-Wallis test) among the geographic regions of Brazil (Table S10). The median concentrations of EDCs were lower in Southern and Southeastern regions than in the other regions of Brazil. Furthermore, MLR analysis shows that the urinary concentrations of BPA, BADGE.2H2O and MeP EtP, antimicrobials and dichlorophenols are significantly different by region.
8OHDG was detected in 94.6% of the samples at concentrations ranging from 0.40 to 29.5 ng/mL (GM: 4.40). Correlations (r = 0.145–0.359) were found between log-transformed urinary concentrations of 8OHDG and BPA, MeP, 3,4-DHB, OH-MeP, OH-EtP, TCS, BP3, 2,4- DCP and 2,5-DCP. No significant differences were found in the urinary concentrations of 8OHDG among various demographic (i.e., age, gender and region) groups. However, multiple linear regression analysis revealed the influence of age and region (Table S11) on the concentrations of 8OHDG. Only the urinary concentrations of OH-MeP was significantly correlated (p = 0.004) with 8OHDG after adjusting for covariates (gender, age and region).
Urinary concentrations of 8OHDG and 14 commonly detected EDCs were subjected to cluster analysis (k-medoids) and classification algorithms (RF and J48) to identify common groups within the dataset. To estimate the number of clusters, NbClust package, from the R 275
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software was used (Charrad et al., 2014). This program implements 30 distinct, efficient, and widely used indexes for estimating the number of clusters. The function was programmed to determine the best number of clusters from two to ten possible groups. The indexes combine information about intracluster compactness and intercluster isolation, as well as other factors, such as geometric or statistical properties of the data, the number of data objects and dissimilarity or similarity in measurements to determine the optimal number of clusters (Charrad et al., 2014). Among all the indexes, 10 proposed 2 as the best number of cluster; 7 proposed 3 as the best number of clusters; 1 proposed 5 as the best number of clusters; 4 proposed 7 as the best number of clusters; and 2 proposed 10 as the best number of clusters. We decided to use two clusters to perform cluster analysis as this value was frequently suggested to be the best number for cluster analysis. Then, k-medoids algorithm on standardized data was carried out (all features have a mean 0 and a standard deviation of 1) to search for the two clusters. It was found that 65.7% of the samples (197 samples) were grouped as Cluster 1, and the remainder (34.3%) of the samples were grouped as Cluster 2.
After clustering the data, classification algorithms RF and J48 decision tree were applied to investigate the clusters characteristics. The RF algorithm was selected because it uses decision trees to classify the samples and provides a feature importance ranking, allowing us to analyze how the EDCs affects the clusters. The J48 algorithm was selected to construct a decision tree that represented the results of the feature importance ranking obtained by RF. The performance measures for RF algorithm implemented on 10-fold cross validation using all variables (8OHDG, BPA, BADGE.2H2O, MeP, EtP, PrP, 3,4-DHB, OH-MeP, OH-EtP, TCS, TCC, BP3, BP1, 2,5- DCP, 2,4-DCP, region, age, and gender) are shown in Table 3. The RF algorithm classified 94.3% of the samples correctly. However, RF provides a variable importance for measures based 298
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on the classification accuracy of the OOB data called Random Forest Importance (RFI) (Breiman, 2001). Fig. S1 shows the importance values achieved by RFI. The higher the value the more significant the variables are in the classification scheme.
The most important RFI variable identified by this analysis was 8OHDG. In order to investigate whether 8OHDG was the most important variable, we applied the decision tree algorithm for further interpretation of this finding. Fig. S2 shows the tree structure generated by J48 algorithm. According to this tree, given an arbitrary urine sample, if the urinary concentration of 8OHDG for this sample was ≤6.60 ng/mL, the sample was in Cluster 1. The accuracy of the J48 was 86.3%. As can be seen in Fig. S2, only 13.2% of the samples which had 8OHDG concentration ≤6.60 ng/mL were misclassified in Cluster 1. The remaining 86.8%
samples in Cluster 1 were correctly classified. Among the samples of Cluster 2, 14.8% of the samples with 8OHDG concentration >6.60 ng/mL were misclassified in this cluster. The remaining 78.4% samples in Cluster 2 were correctly classified. These results suggest that the variable 8OHDG has a major importance in the formation of the clusters.
To analyze the importance of the remaining variables, we used the RF algorithm to classify 17 subsets, generated by the i-th most important variable for all i = 1, 2, …17, according to the ranking as presented in Fig. S1 after removing 8OHDG from the list. The first subset had OH-EtP as a dominant variable and the second subset contained OH-EtP and OH-MeP (Table S13). The subset that showed higher prediction capability to classify the samples according to Cluster 1 and Cluster 2 was #RFI05, which comprised of OH-EtP, OH-MeP, 3,4-DHB, BPA, 2,4-DCP, MeP, TCS, EtP, and BP1, accounting for 81.7% precision. The descriptive statistics of these variables for each cluster are shown in Table 4.
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4. Discussion
BPA was the major bisphenol frequently detected in children’s urine from Brazil. The GM urinary concentrations of BPA in Brazilian children (1.74 ng/mL) was similar to those reported for children from the USA (1.58 ng/mL), Canada (1.3 ng/mL), and several European countries (1.48 to 2.35 ng/mL) but lower than those reported from India (5.08 ng/mL) and China (4.10 ng/mL)(CDC, 2012; Covaci et al., 2015; Health Canada, 2013; Li et al., 2013; Xue et al., 2015). BPA is gradually being replaced with other analogues (Asimakopoulos et al., 2016; Liao et al., 2012a, 2012b; Ye et al., 2015; Zhang et al., 2016). For instance, BPS has been used as an alternative for BPA in baby bottles and thermal papers, and other bisphenol analogues are used in the manufacture of certain consumer products (Liao et al., 2012a, 2012b; Rocha et al., 2015;
Simoneau et al., 2011; Ye et al., 2015). BPS was also detected in urine samples from other countries such as Saudi Arabia (100%), Japan (100%), USA (97%), China (82%), India (76%), and Korea (42%) at higher detection rates (Asimakopoulos et al., 2016; Liao et al., 2012a; Xue et al., 2015). However, BPS and other bisphenol analogues were less frequently detected in Brazilian children. These results are in line with a study from Brazil that showed low detection of BPS and other bisphenol analogues in adult urine samples (Rocha et al., 2016).
Humans are exposed to BADGEs mainly through canned foods and drinks. However, studies on human exposure to BADGEs are still limited. The occurrence of BADGEs and BFDGEs was shown in populations in the USA, China, India, and Greece (Asimakopoulos et al., 2014; Wang et al., 2012; Xue et al., 2015). The urinary GM concentration of BADGE.2H2O in Brazilian children was 0.30 ng/mL, which was closer than that reported for Chinese children (0.59 ng/mL) and much lower than that reported for Indian Children (12.2 ng/mL)(Wang et al., 2012; Xue et al., 2015).
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Chlorophenols have been used as pesticides or as intermediates in the production of pesticides, dyes, and pharmaceuticals. The high detection frequencies of dichlorophenols (99%) indicate widespread exposure to these chemicals among Brazilian children. The urinary GM concentrations of 2,4-DCP and 2,5-DCP (2.60 and 4.59 ng/mL, respectively) in Brazilian children were higher than those reported for American , Canadian children except for 2,5-DCP andAsian countries (CDC, 2012; Health Canada, 2013; Zidek et al., 2017). One possible source of 2,4-DCP is the degradation of TCS (Ye et al., 2014). We also found high concentrations of TCS in urine from these children and a correlation existed between urinary concentrations of 2,4- DCP and TCS (r= 0.690), which suggests that TCS is a major source of 2,4-DCP found in urine.
Parabens are widely used as antimicrobial preservatives in cosmetics, pharmaceuticals, and foodstuffs (Bledzka et al., 2014; Guo and Kannan, 2013; Liao et al., 2013).Urinary GM concentrations of MeP and PrP in Brazilian children were higher than those previously reported for Americans, Indians and Chinese children (CDC, 2012; Wang et al., 2013; Xue et al., 2015).A correlation among MeP, EtP and PrP (r = 0.275-0.593) in urine samples suggested that these compounds are used in combination in various consumer products (Asimakopoulos et al., 2014;
Jiménez-Díaz et al., 2016; Larsson et al., 2014; Wang et al., 2013).
Benzophenones (BPs) have been used as sunscreen agents in personal care products for the protection of skin and hair from UV irradiation. BP3 is the most commonly used sunscreen agent in a variety of cosmetics (Asimakopoulos et al., 2014; Gao et al., 2015; Heffernan et al., 2015; Kunisue et al., 2012; Liao and Kannan, 2014; Wang and Kannan, 2013). The GM urinary concentration of BP3 measured in the present study (3.71 ng/mL) was much higher than that previously reported for Indian (0.91 ng/mL) and Chinese children (0.62 ng/mL), which may be attributed to lower sunscreen usage in India and China than in Brazil (Wang and Kannan, 2013;
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Xue et al., 2015). However, urinary BP3 concentrations in Brazilian children were lower than those reported for the U.S. (18.7 ng/mL) and Australian children (26.2-96.2 ng/mL)(CDC, 2012;
Heffernan et al., 2015). The urinary GM concentration of BP1 (5.86 ng/mL) was higher than those reported for the U.S. and Chinese children (4.21 and 0.115 ng/mL, respectively)(Wang and Kannan, 2013). There was a correlation between BP3 and BP1 concentrations, suggesting concomitant exposure of children to these compounds.
TCS and TCC have been widely used as antimicrobial agents in PCPs such as soaps, toothpastes and deodorants (Larsson et al., 2014; H. Ma et al., 2013; Pycke et al., 2014).The urinary GM concentration of TCS in Brazilian children (8.26 ng/mL) was closer than those reported for the U.S. (7.2 ng/mL), Canadian (8.5 ng/mL), Indian (9.6 ng/mL) and Chinese children (7.5 ng/mL) but much lower than those reported for Australian children (59.8-106 ng/mL)(CDC, 2012; Health Canada, 2013; Heffernan et al., 2015; Li et al., 2013; Xue et al., 2015). TCC (70%) was more frequently detected in our study than in studies from Greece, Saudi Arabia, Canada and the USA (Asimakopoulos et al., 2016, 2014; CDC, 2012; Ye et al., 2016).
Several studies have associated the use of PCPs with the high concentrations of parabens, benzophenones, TCC and TCS in urine (Frederiksen et al., 2013; Gao et al., 2015; Guo and Kannan, 2013; Heffernan et al., 2015; Larsson et al., 2014; Liao and Kannan, 2014; W. L. Ma et al., 2013; Pycke et al., 2014; Schebb et al., 2011; Wang et al., 2013). Brazil is one of the leading countries in consumption of beauty and personal care products and it is the second largest market for children’s personal care products globally. Furthermore, Brazil is the biggest consumer market for fragrances and deodorants in the world (Euromonitor International, 2016a, 2016b;
Rocha et al., 2017). Therefore, high urinary concentrations of several EDCs may be associated with heavy usage of PCPs among the Brazilian population. Our results also showed significantly 390
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higher concentrations of MeP, PrP, TCS, BP3 and BP1 in females than males (Table 2). This could be related to more frequent use of personal care products and cosmetics by females.
Exposure to EDCs has been associated with oxidative stress (Asimakopoulos et al., 2016;
Bledzka et al., 2014; Franken et al., 2017; Hong et al., 2009; Lu et al., 2016; Lv et al., 2017;
Rocha et al., 2017; Zhang et al., 2016).In this study, urinary concentrations of BPA, MeP, 3,4- DHB, OH-MeP, OH-EtP, TCS, BP3, 2,4-DCP and 2,5-DCP exhibited a correlation with 8OHDG (r = 0.158–0.359). BPA has been shown to induce oxidative stress in both experimental (Bindhumol et al., 2003; Hassan et al., 2012) and epidemiological studies (Asimakopoulos et al., 2016; Ferguson et al., 2016; Hong et al., 2009; Lv et al., 2017; Zhang et al., 2016). Similarly, relationships between urinary paraben concentrations and oxidative DNA damage have been shown (8OHDG and/or malondialdehyde in human (Bledzka et al., 2014; Kang et al., 2013;
Watkins et al., 2015) and animal studies. (Bledzka et al., 2014; Popa et al., 2011). Statistically significant association between urinary concentrations of dichlorophenols and 8OHDG was found in our study. A recent study also reported a weak association between 2,5-DCP and 8OHDG (Franken et al., 2017). A correlation between urinary concentrations of BP3 and 8OHDG was found in our study. However, Watkins et al., (2015) did not observe similar relationship, although animal studies corroborate with our findings (Gao et al., 2013; Kato et al., 2006). Previous in vitro studies have shown that TCS can induce oxidative stress (H. Ma et al., 2013; Zeng et al., 2016).
The associations between EDC exposures and 8OHDG described above are based on conventional univariate statistics. However, univariate or even some classical multivariate regression statistical tools have limitations to identify realistic associations between exposure and outcome variables, mainly in scenarios in which coexposure multiple contaminants exists.
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Recently, data mining techniques or Knowledge Discovery from Databases (KDD) have been used as alternative and powerful mathematical tools to discover hidden patterns in data or correlations amongst data (Pang-Ning Tan, 2005). Data mining is an interdisciplinary subfield of computer science and defined as a computing process of discovering or searching for patterns in datasets (Pang-Ning Tan, 2005). Data mining is a vast area of research and there is an abundance of techniques and methods that can uncover new knowledge (Witten et al., 2016). It involves methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. A dataset is usually analyzed using different tools such as clustering, classification and feature selection. It is helpful in data classification and identification of any co-occurring sequences and in the knowledge of the correlation between any activities (Pang-Ning Tan, 2005).
Data mining has been used successfully in many fields of research and its application in the environmental sciences is on the rise (Lausch et al., 2015; Barbosa et al., 2014; Corstanje et al., 2016; Marvuglia et al., 2015; Tsai et al., 2017). According to Lausch et al (2015), the limited use of data mining in environmental science is due to the requirement of extensive programming expertise, making the data mining techniques currently used exclusively in the areas of computer science, physics and mathematics.
Data mining approaches were used in this study to evaluate or search for patterns in our EDC database. First, the whole dataset was subject to clustering (k-medoids) with the identification of two different clusters. These clusters presented some features of interest the data may have and were yet unknown. Moreover, when the samples were categorized by region, age, and gender, the distribution of samples along the three categories between the two clusters was found similar, denoting that the region, age, and gender did not influence EDC levels to determine the formation of clusters. Interestingly, the variable importance measurements 436
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identified 8OHDG as the most significant variable to separate the clusters with a “cutoff” value of 6.60 ng/mL (provided by the decision tree algorithm) in 86.3%. It denotes that subjects belonging to cluster 1 (i.e. 8OHDG ≤6.60 ng/mL) have lower DNA effects in 86.8% of cases, whereas the subjects belonging to cluster 2 have higher DNA effects in 85.2% of cases (i.e.
8OHDG >6.60 ng/mL). With this “cut off” value, we could identify target EDCs that were associated with cluster 2 that are linked to oxidative DNA damage. Thus, after removing 8OHDG values from the database and after applying the classification models (by using different subsets of variables rated according to the Random Forest Importance [RFI]), it was observed that exposure variables increased the differentiation of the two clusters in the following order:
OH-EtP > OH-MeP >3,4-DHB > BPA> 2,4-DCP> MeP> TCS > EtP > BP1 > 2,5-DCP. The order of the compounds was determined by RFI algorithm. This method of selection generated a ranking of importance in which the top variable was the most significant to differentiate between the classes of the data. The second variable had the second major significance and so on. This sequence of variables allowed us to classify the data interactively to identify which variable subset allowed for a better classification of the data. The combined use of the variables (OH-EtP, OH-MeP, 3,4-DHB, BPA, 2,4-DCP, MeP, TCS, EtP, BP1) resulted in the higher accuracy, indicating that this subset of variables was able to discriminate the classes better than the other sets of variables. The order of the compounds allowed identification of this variable subset. In terms of classification, the order of the compounds of a variable subset in the classifier did not change the results of the classification model because of the way RFI algorithm performs.
Interestingly, the levels of OH-EtP alone differentiated the two clusters with a 74.0% precision.
Moreover, mean levels of each of these 10 EDCs were higher in cluster 2 (higher 8OHDG levels) compared to that in cluster 1. In summary, our study suggests that coexposure to EDCs 459
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(mainly OH-EtP, OH-MeP, 3,4-DHB, BPA, 2,4-DCP, MeP, TCS, EtP, BP1, 2,5-DCP) is associated with DNA damage and OH-EtP is a major contributor to such effects followed by other paraben metabolites chlorophenols, TCS and benzophenone.
To our knowledge, this is the first study to examine the association between oxidative stress (DNA damage) and coexposure to multiple classes of EDCs through a data mining approach. This approach provides much more relevant information related to multiple EDC exposures than univariate statistical models. Overall, our findings suggest that coexposures to BPA, parabens, and dichlorophenols contribute to DNA damage in Brazilian children.
Conflict of interest
The authors declare no conflict of interest.
Acknowledgments
We thank all Brazilian children for providing urine samples for this study. This research was supported in part by São Paulo Research Foundation (FAPESP, grant numbers 2015/20928-3 and 2013/23710-3). The sample analysis was conducted at Wadsworth Center, New York State Department of Health. Research reported in this publication was supported in part by the National Institute of Environmental Health Sciences of the National Institutes of Health under Award Number U2CES026542-01. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
References
Asimakopoulos, A.G., Thomaidis, N.S., Kannan, K., 2014. Widespread occurrence of bisphenol 482
483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501
502 503
filters, triclosan, and triclocarban in human urine from Athens, Greece. Sci. Total Environ.
470–471, 1243–1249. https://doi.org/10.1016/j.scitotenv.2013.10.089
Asimakopoulos, A.G., Xue, J., De Carvalho, B.P., Iyer, A., Abualnaja, K.O., Yaghmoor, S.S., Kumosani, T.A., Kannan, K., 2016. Urinary biomarkers of exposure to 57 xenobiotics and its association with oxidative stress in a population in Jeddah, Saudi Arabia. Environ. Res.
150, 573–581. https://doi.org/10.1016/j.envres.2015.11.029
Barbosa, R.M., Batista, B.L., Varrique, R.M., Coelho, V.A., Campiglia, A.D., Barbosa, F., 2014.
The use of advanced chemometric techniques and trace element levels for controlling the authenticity of organic coffee. Food Res. Int. 61, 246–251.
https://doi.org/10.1016/j.foodres.2013.07.060
Bindhumol, V., Chitra, K.C., Mathur, P.P., 2003. Bisphenol A induces reactive oxygen species generation in the liver of male rats. Toxicology 188, 117–124.
https://doi.org/10.1016/S0300-483X(03)00056-8
Bisht, S., Faiq, M., Tolahunase, M., Dada, R., 2017. Oxidative stress and male infertility. Nat.
Rev. Urol. 14, 470–485. https://doi.org/10.1038/nrurol.2017.69
Bledzka, D., Gromadzinska, J., Wasowicz, W., 2014. Parabens. From environmental studies to human health. Env. Int 67, 27–42. https://doi.org/10.1016/j.envint.2014.02.007
Breiman, L., 2001. Random forests. Mach. Learn. 45, 5–32.
https://doi.org/10.1023/A:1010933404324
Calafat, A.M., Ye, X., Valentin-Blasini, L., Li, Z., Mortensen, M.E., Wong, L.Y., 2017. Co- 505
506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524
exposure to non-persistent organic chemicals among American pre-school aged children: A pilot study. Int. J. Hyg. Environ. Health 220, 55–63.
https://doi.org/10.1016/j.ijheh.2016.10.008
Centers for Disease Control and Prevention; National Center for Environmental Health; Division of Laboratory Sciences. Fourth National Report on Human Exposure to Environmental
Chemicals (Updated Tables, February, 2015).
https://www.cdc.gov/biomonitoring/pdf/fourthreport_updatedtables_feb2015.pdf (accessed September 15, 2016).
Charrad, M., Ghazzali, N., Boiteau, V., Niknafs, A., 2014. Charrad : An R Package for Determining the Relevant Number of Clusters in a Data Set. J. Stat. Softw. 61.
https://doi.org/10.18637/jss.v061.i06
Corstanje, R., Grafius, D.R., Zawadzka, J., Moreira Barradas, J., Vince, G., Ivanoff, D., Pietro, K., 2016. A datamining approach to identifying spatial patterns of phosphorus forms in the Stormwater Treatment Areas in the Everglades, US. Ecol. Eng. 97, 567–576.
https://doi.org/10.1016/j.ecoleng.2016.10.003
Covaci, A., Hond, E. Den, Geens, T., Govarts, E., Koppen, G., Frederiksen, H., Knudsen, L.E., Mørck, T.A., Gutleb, A.C., Guignard, C., Cocco, E., Horvat, M., Heath, E., Kosjek, T., Mazej, D., Tratnik, J.S., Castaño, A., Esteban, M., Cutanda, F., Ramos, J.J., Berglund, M., Larsson, K., Jönsson, B.A.G., Biot, P., Casteleyn, L., Joas, R., Joas, A., Bloemen, L., Sepai, O., Exley, K., Schoeters, G., Angerer, J., Kolossa-Gehring, M., Fiddicke, U., Aerts, D., Koch, H.M., 2015. Urinary BPA measurements in children and mothers from six European 525
526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545
https://doi.org/10.1016/j.envres.2014.08.008
Diamanti-Kandarakis, E., Bourguignon, J.P., Giudice, L.C., Hauser, R., Prins, G.S., Soto, A.M., Zoeller, R.T., Gore, A.C., 2009. Endocrine-disrupting chemicals: An Endocrine Society scientific statement. Endocr. Rev. 30, 293–342. https://doi.org/10.1210/er.2009-0002
Di Minno, A., Turnu, L., Porro, B., Squellerio, I., Cavalca, V., Tremoli, E., Di Minno, M.N.D., 2016. 8-Hydroxy-2-Deoxyguanosine Levels and Cardiovascular Disease: A Systematic Review and Meta-Analysis of the Literature. Antioxid. Redox Signal. 24, 548–555.
https://doi.org/10.1089/ars.2015.6508
Euromonitor International, 2016a. Fragrances in Brazil [WWW Document]. URL http://www.euromonitor.com/fragrances-in-brazil/report (accessed 11.15.16).
Euromonitor International, 2016b. Beauty and Personal Care in Brazil [WWW Document]. URL http://www.euromonitor.com/beauty-and-personal-care-in-brazil/report (accessed 11.15.16).
Ferguson, K.K., Cantonwine, D.E., McElrath, T.F., Mukherjee, B., Meeker, J.D., 2016. Repeated measures analysis of associations between urinary bisphenol-A concentrations and biomarkers of inflammation and oxidative stress in pregnancy. Reprod. Toxicol. 66, 93–98.
https://doi.org/10.1016/j.reprotox.2016.10.002
Franken, C., Koppen, G., Lambrechts, N., Govarts, E., Bruckers, L., Den Hond, E., Loots, I., Nelen, V., Sioen, I., Nawrot, T.S., Baeyens, W., Van Larebeke, N., Boonen, F., Ooms, D., Wevers, M., Jacobs, G., Covaci, A., Schettgen, T., Schoeters, G., 2017. Environmental exposure to human carcinogens in teenagers and the association with DNA damage.
547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566
Frederiksen, H., Nielsen, J.K.S., Mørck, T.A., Hansen, P.W., Jensen, J.F., Nielsen, O., Andersson, A.M., Knudsen, L.E., 2013. Urinary excretion of phthalate metabolites, phenols and parabens in rural and urban Danish mother-child pairs. Int. J. Hyg. Environ. Health 216, 772–783. https://doi.org/10.1016/j.ijheh.2013.02.006
Gao, C.J., Liu, L.Y., Ma, W.L., Zhu, N.Z., Jiang, L., Li, Y.F., Kannan, K., 2015.
Benzonphenone-type UV filters in urine of Chinese young adults: Concentration, source and exposure. Environ. Pollut. 203, 1–6. https://doi.org/10.1016/j.envpol.2015.03.036
Gao, L., Yuan, T., Zhou, C., Cheng, P., Bai, Q., Ao, J., Wang, W., Zhang, H., 2013. Effects of four commonly used UV filters on the growth, cell viability and oxidative stress responses of the Tetrahymena thermophila. Chemosphere 93, 2507–2513.
https://doi.org/10.1016/j.chemosphere.2013.09.041
Giulivo, M., de Alda, M., Capri, E., Barceló, D., 2016. Human exposure to endocrine disrupting compounds: Their role in reproductive systems, metabolic syndrome and breast cancer. A review. Environ. Res. 151, 251–264. https://doi.org/10.1016/j.envres.2016.07.011
Guo, Y., Kannan, K., 2013. A Survey of Phthalates and Parabens in Personal Care Products from the United States and Its Implications for Human Exposure. Environ. Sci. Technol. 47, 14442–14449. https://doi.org/10.1021/es4042034
Hassan, Z.K., Elobeid, M.A., Virk, P., Omer, S.A., Elamin, M., Daghestani, M.H., Alolayan, E.M., 2012. Bisphenol a induces hepatotoxicity through oxidative stress in rat model. Oxid.
Med. Cell. Longev. https://doi.org/10.1155/2012/194829 568
569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587
Results of the Canadian Health Measures Survey Cycle 2 (20097–2011). Health Canada, Ottawa, Ontario, Canada, 2013. Available at http://www.hc-sc.gc.ca/ewh- semt/pubs/contaminants/chms-ecms-cycle2/index-eng.php.
Heffernan, A.L., Baduel, C., Toms, L.M.L., Calafat, A.M., Ye, X., Hobson, P., Broomhall, S., Mueller, J.F., 2015. Use of pooled samples to assess human exposure to parabens, benzophenone-3 and triclosan in Queensland, Australia. Environ. Int. 85, 77–83.
https://doi.org/10.1016/j.envint.2015.09.001
Hong, Y.C., Park, E.Y., Park, M.S., Ko, J.A., Oh, S.Y., Kim, H., Lee, K.H., Leem, J.H., Ha, E.H., 2009. Community level exposure to chemicals and oxidative stress in adult population. Toxicol. Lett. 184, 139–144. https://doi.org/10.1016/j.toxlet.2008.11.001
Hornik, K., Buchta, C., Zeileis, A., 2009. Open-source machine learning: R meets Weka.
Comput. Stat. 24, 225–232. https://doi.org/10.1007/s00180-008-0119-7
Hornung, R.W., Reed, L.D., 1990. Estimation of average concentration in the presence of nondetectable values. Appl. Occup. Environ. Hyg. 5, 46–51Jiménez-Díaz, I., Artacho- Cordón, F., Vela-Soria, F., Belhassen, H., Arrebola, J.P., Fernández, M.F., Ghali, R., Hedhili, A., Olea, N., 2016. Urinary levels of bisphenol A, benzophenones and parabens in Tunisian women: A pilot study. Sci. Total Environ. 562, 81–88.
https://doi.org/10.1016/j.scitotenv.2016.03.203
Jimenez-Diaz, I., Vela-Soria, F., Rodriguez-Gomez, R., Zafra-Gomez, A., Ballesteros, O., Navalon, A., 2015. Analytical methods for the assessment of endocrine disrupting chemical exposure during human fetal and lactation stages: A review. Anal. Chim. Acta 892, 27–48.
589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609
https://doi.org/10.1016/j.aca.2015.08.008
Kang, S., Kim, S., Park, J., Kim, H.J., Lee, J., Choi, G., Choi, S., Kim, S., Kim, S.Y., Moon, H.B., Kim, S., Kho, Y.L., Choi, K., 2013. Urinary paraben concentrations among pregnant women and their matching newborn infants of Korea, and the association with oxidative
stress biomarkers. Sci. Total Environ. 461–462, 214–221.
https://doi.org/10.1016/j.scitotenv.2013.04.097
Kato, T., Tada-Oikawa, S., Takahashi, K., Saito, K., Wang, L., Nishio, A., Hakamada-Taguchi, R., Kawanishi, S., Kuribayashi, K., 2006. Endocrine disruptors that deplete glutathione levels in APC promote Th2 polarization in mice leading to the exacerbation of airway inflammation. Eur. J. Immunol. 36, 1199–1209. https://doi.org/10.1002/eji.200535140
Kaufman, L., Rousseuw, P.J., 1990. Finding Groups in Data, John Wiley & Sons, Wiley Series in Probability and Statistics. John Wiley & Sons, Inc., Hoboken, NJ, USA.
https://doi.org/10.1002/9780470316801
Kelly, F.J., Fussell, J.C., 2017. Role of oxidative stress in cardiovascular disease outcomes following exposure to ambient air pollution. Free Radic. Biol. Med. 110, 345–367.
https://doi.org/10.1016/j.freeradbiomed.2017.06.019
Kunisue, T., Chen, Z., Buck Louis, G.M., Sundaram, R., Hediger, M.L., Sun, L., Kannan, K., 2012. Urinary concentrations of benzophenone-type UV filters in U.S. women and their association with endometriosis. Environ. Sci. Technol. 46, 4624–4632.
https://doi.org/10.1021/es204415a 610
611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629
B.A.G., Berglund, M., 2014. Exposure determinants of phthalates, parabens, bisphenol A and triclosan in Swedish mothers and their children. Environ. Int. 73, 323–333.
https://doi.org/10.1016/j.envint.2014.08.014
Lausch, A., Schmidt, A., & Tischendorf, L., 2015. Data mining and linked open data–New perspectives for data analysis in environmental research. Ecol. Model. 295, 5–17.
https://doi.org/10.1016/j.ecolmodel.2014.09.018
Li, X., Ying, G.G., Zhao, J.L., Chen, Z.F., Lai, H.J., Su, H.C., 2013. 4-Nonylphenol, bisphenol- A and triclosan levels in human urine of children and students in China, and the effects of drinking these bottled materials on the levels. Environ. Int. 52, 81–86.
https://doi.org/10.1016/j.envint.2011.03.026
Liao, C., Chen, L., Kannan, K., 2013. Occurrence of parabens in foodstuffs from China and its implications for human dietary exposure. Environ. Int. 57–58, 68–74.
https://doi.org/10.1016/j.envint.2013.04.001
Liao, C., Kannan, K., 2014. Widespread Occurrence of Benzophenone-Type UV Light Filters in Personal Care Products from China and the United States: An Assessment of Human Exposure. Environ. Sci. Technol. 48, 4103–4109. https://doi.org/10.1021/es405450n
Liao, C., Liu, F., Alomirah, H., Loi, V.D., Mohd, M.A., Moon, H.B., Nakata, H., Kannan, K., 2012a. Bisphenol S in urine from the United States and seven Asian countries: Occurrence and human exposures. Environ. Sci. Technol. 46, 6860–6866.
https://doi.org/10.1021/es301334j 631
632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650
and currency bills and its association with bisphenol a residues. Environ. Sci. Technol. 46, 6515–6522. https://doi.org/10.1021/es300876n
Liaw, A, Wiener, M., 2002. Classification and Regression by randomForest. R news 2, 18–22.
https://doi.org/10.1177/154405910408300516
Lu, S. you, Li, Y. xi, Zhang, J. qing, Zhang, T., Liu, G. hua, Huang, M. zhi, Li, X., Ruan, J. jun, Kannan, K., Qiu, R. liang, 2016. Associations between polycyclic aromatic hydrocarbon (PAH) exposure and oxidative stress in people living near e-waste recycling facilities in China. Environ. Int. 94, 161–169. https://doi.org/10.1016/j.envint.2016.05.021
Lv, Y., Lu, S., Dai, Y., Rui, C., Wang, Y., Zhou, Y., Li, Y., Pang, Q., Fan, R., 2017. Higher dermal exposure of cashiers to BPA and its association with DNA oxidative damage.
Environ. Int. 98, 69–74. https://doi.org/10.1016/j.envint.2016.10.001
Ma, H., Zheng, L., Li, Y., Pan, S., Hu, J., Yu, Z., Zhang, G., Sheng, G., Fu, J., 2013. Triclosan reduces the levels of global DNA methylation in HepG2 cells. Chemosphere 90, 1023–
1029. https://doi.org/10.1016/j.chemosphere.2012.07.063
Ma, W.L., Wang, L., Guo, Y., Liu, L.Y., Qi, H., Zhu, N.Z., Gao, C.J., Li, Y.F., Kannan, K., 2013. Urinary concentrations of parabens in Chinese young adults: Implications for human exposure. Arch. Environ. Contam. Toxicol. 65, 611–618. https://doi.org/10.1007/s00244- 013-9924-2
Maione, C., de Oliveira Souza, V.C., Togni, L.R., da Costa, J.L., Campiglia, A.D., Barbosa, F., Barbosa, R.M., 2017. Using Cluster Analysis and ICP-MS to Identify Groups of Ecstasy 652
653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671
https://doi.org/10.1111/1556-4029.13448
Marvuglia, A., Kanevski, M., Benetto, E., 2015. Machine learning for toxicity characterization of organic chemical emissions using USEtox database: learning the structure of the input space. Environ. Int. 83, 72–85.https://doi.org/10.1016/j.envint.2015.05.011
Myridakis, A., Fthenou, E., Balaska, E., Vakinti, M., Kogevinas, M., Stephanou, E.G., 2015.
Phthalate esters, parabens and bisphenol-A exposure among mothers and their children in Greece (Rhea cohort). Environ. Int. 83, 1–10. https://doi.org/10.1016/j.envint.2015.05.014
Naidu, R., Arias Espana, V.A., Liu, Y., Jit, J., 2016. Emerging contaminants in the environment:
Risk-based analysis for better management. Chemosphere 154, 350–357.
https://doi.org/10.1016/j.chemosphere.2016.03.068
Tan, P. N.; Steinbach, M.; Kumar, V. Introduction to data mining. Boston, MA: Addison-Wesley Longman Publishing Co., Inc., 2005
Popa, D.-S., Kiss, B., Vlase, L., Pop, A., Iepure, R., Pǎltinean, R., Loghin, F., 2011. Study of oxidative stress induction after exposure to bisphenol a and methylparaben in rats. Farmacia 59, 539–549.
Pycke, B.F.G., Geer, L.A., Dalloul, M., Abulafia, O., Jenck, A.M., Halden, R.U., 2014. Human fetal exposure to triclosan and triclocarban in an urban population from Brooklyn, New York. Environ. Sci. Technol. 48, 8831–8838. https://doi.org/10.1021/es501100w
Reuter, S., Gupta, S.C., Chaturvedi, M.M., Aggarwal, B.B., 2010. Oxidative stress, inflammation, and cancer: How are they linked? Free Radic. Biol. Med. 49, 1603–1616.
673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692
https://doi.org/10.1016/j.freeradbiomed.2010.09.006
Reynolds, A.P., Richards, G., Rayward-Smith, V.J., 2004. The Application of K-Medoids and PAM to the Clustering of Rules. pp. 173–178. https://doi.org/10.1007/978-3-540-28651- 6_25
Rocha, B.A., Asimakopoulos, A.G., Barbosa, F., Kannan, K., 2017. Urinary concentrations of 25 phthalate metabolites in Brazilian children and their association with oxidative DNA damage. Sci. Total Environ. 586, 152–162. https://doi.org/10.1016/j.scitotenv.2017.01.193
Rocha, B.A., Azevedo, L.F., Gallimberti, M., Campiglia, A.D., Barbosa, F., 2015. High Levels of Bisphenol A and Bisphenol S in Brazilian Thermal Paper Receipts and Estimation of Daily Exposure. J. Toxicol. Environ. Heal. - Part A Curr. Issues 78, 1181–1188.
https://doi.org/10.1080/15287394.2015.1083519
Rocha, B.A., Da Costa, B.R.B., De Albuquerque, N.C.P., De Oliveira, A.R.M., Souza, J.M.O., Al-Tameemi, M., Campiglia, A.D., Barbosa, F., 2016. A fast method for bisphenol A and six analogues (S, F, Z, P, AF, AP) determination in urine samples based on dispersive liquid-liquid microextraction and liquid chromatography-tandem mass spectrometry.
Talanta 154, 511–519. https://doi.org/10.1016/j.talanta.2016.03.098
Rokach, L., 2016. Decision forest: Twenty years of research. Inf. Fusion 27, 111–125.
https://doi.org/10.1016/j.inffus.2015.06.005
Schebb, N.H., Inceoglu, B., Ahn, K.C., Morisseau, C., Gee, S.J., Hammock, B.D., 2011.
Investigation of human exposure to triclocarban after showering and preliminary evaluation 693
694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712
Scognamiglio, V., Antonacci, A., Patrolecco, L., Lambreva, M.D., Litescu, S.C., Ghuge, S.A., Rea, G., 2016. Analytical tools monitoring endocrine disrupting chemicals. TrAC - Trends Anal. Chem. 80. https://doi.org/10.1016/j.trac.2016.04.014
Simoneau, C., Valzacchi, S., Morkunas, V., van den Eede, L., 2011. Comparison of migration from polyethersulphone and polycarbonate baby bottles. Food Addit. Contam. - Part A Chem. Anal. Control. Expo. Risk Assess. 28, 1763–1768.
https://doi.org/10.1080/19440049.2011.604644
Smarr, M.M., Ph, D., Kannan, K., Ph, D., Louis, G.M.B., Ph, D., S, M., Buck Louis, G.M., 2016.
Endocrine disrupting chemicals and endometriosis. Fertil. Steril. 106, 959–966.
https://doi.org/10.1016/j.fertnstert.2016.06.034
Tavares, R.S., Escada-Rebelo, S., Correia, M., Mota, P.C., Ramalho-Santos, J., 2016. The non- genomic effects of endocrine-disrupting chemicals on mammalian sperm. Reproduction 151, R1–R13. https://doi.org/10.1530/REP-15-0355
Tsai, W.P., Huang, S.P., Cheng, S.T., Shao, K.T., Chang, F.J., 2017. A data-mining framework for exploring the multi-relation between fish species and water quality through self-
organizing map. Sci. Total Environ. 579, 474–483.
https://doi.org/10.1016/j.scitotenv.2016.11.071
Wang, L., Kannan, K., 2013. Characteristic profiles of benzonphenone-3 and its derivatives in urine of children and adults from the United States and China. Environ. Sci. Technol. 47, 12532–12538. https://doi.org/10.1021/es4032908
714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733
-Hydroxybenzoic Acid and its Esters (Parabens) in Children and Adults from the United States and China. Environ. Sci. Technol. 47, 2069–2076. https://doi.org/10.1021/es304659r
Wang, L., Wu, Y., Zhang, W., Kannan, K., 2012. Widespread Occurrence and Distribution of Bisphenol A Diglycidyl Ether (BADGE) and its Derivatives in Human Urine from the United States and China. Environ. Sci. Technol. 46, 12968–12976.
https://doi.org/10.1021/es304050f
Watkins, D.J., Ferguson, K.K., Anzalota Del Toro, L. V., Alshawabkeh, A.N., Cordero, J.F., Meeker, J.D., 2015. Associations between urinary phenol and paraben concentrations and markers of oxidative stress and inflammation among pregnant women in Puerto Rico. Int. J.
Hyg. Environ. Health 218, 212–219. https://doi.org/10.1016/j.ijheh.2014.11.001
Witten, I.H., Frank, E., Hall, M.A., Pal, C.J., 2016. Data mining: Practical machine learning tools and techniques (4th edition) (4th ed.). Morgan Kaufmann.
Woodruff, T.J., 2015. Making it real--the environmental burden of disease. What does it take to make people pay attention to the environment and health? J. Clin. Endocrinol. Metab. 100, 1241–1244. https://doi.org/10.1210/jc.2015-1622
Xue, J., Wu, Q., Sakthivel, S., Pavithran, P. V., Vasukutty, J.R., Kannan, K., 2015. Urinary levels of endocrine-disrupting chemicals, including bisphenols, bisphenol A diglycidyl ethers, benzophenones, parabens, and triclosan in obese and non-obese Indian children.
Environ. Res. 137, 120–128. https://doi.org/10.1016/j.envres.2014.12.007
Ye, X., Wong, L.-Y., Dwivedi, P., Zhou, X., Jia, T., Calafat, A.M., 2016. Urinary Concentrations 735
736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754
Health and Nutrition Examination Survey. Environ. Sci. Technol. 50, 13548–13554.
https://doi.org/10.1021/acs.est.6b04668
Ye, X., Wong, L.Y., Kramer, J., Zhou, X., Jia, T., Calafat, A.M., 2015. Urinary Concentrations of Bisphenol A and Three Other Bisphenols in Convenience Samples of U.S. Adults during
2000-2014. Environ. Sci. Technol. 49, 11834–11839.
https://doi.org/10.1021/acs.est.5b02135
Ye, X., Wong, L.Y., Zhou, X., Calafat, A.M., 2014. Urinary concentrations of 2,4- dichlorophenol and 2,5-dichlorophenol in the U.S. population (National health and nutrition examination survey, 2003-2010): Trends and predictors. Environ. Health Perspect. 122, 351–355. https://doi.org/10.1289/ehp.1306816
Zeng, L., Ma, H., Pan, S., You, J., Zhang, G., Yu, Z., Sheng, G., Fu, J., 2016. LINE-1 gene hypomethylation and p16 gene hypermethylation in HepG2 cells induced by low-dose and long-term triclosan exposure: The role of hydroxyl group. Toxicol. Vitr. 34, 35–44.
https://doi.org/10.1016/j.tiv.2016.03.002
Zhang, T., Xue, J., Gao, C.Z., Qiu, R.L., Li, Y.X., Li, X., Huang, M.Z., Kannan, K., 2016.
Urinary Concentrations of Bisphenols and Their Association with Biomarkers of Oxidative Stress in People Living Near E-Waste Recycling Facilities in China. Environ. Sci. Technol.
50, 4045–4053. https://doi.org/10.1021/acs.est.6b00032
Zidek, A., Macey, K., MacKinnon, L., Patel, M., Poddalgoda, D., Zhang, Y., 2017. A review of human biomonitoring data used in regulatory risk assessment under Canada’s Chemicals Management Program. Int. J. Hyg. Environ. Health 220, 167–178.
756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776
https://doi.org/10.1016/j.ijheh.2016.10.007 777
778