Urinary biomarkers of exposure to 57 xenobiotics and its association with oxidative stress in a population in Jeddah, Saudi Arabia

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Urinary biomarkers of exposure to 57 xenobiotics and its association with oxidative stress in a population in Jeddah, Saudi Arabia Alexandros G. Asimakopoulos a,1, Jingchuan Xue a,f,1, Bruno Pereira De Carvalho a, Archana Iyer b, Khalid Omer Abualnaja c, Soonham Sami Yaghmoor d, Taha Abdullah Kumosani e, Kurunthachalam Kannan a,e,f,n a

Wadsworth Center, New York State Department of Health, Empire State Plaza, P.O. Box 509, Albany, New York 12201-0509, United States Biochemistry Department, Faculty of Science, Production of Bioproducts for Industrial Applications Research Group, Vitamin D Pharmacogenomics Research Group and Experimental Biochemistry Unit, King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia c Biochemistry Department, Faculty of Science and Bioactive Natural Products Research Group, King Abdulaziz University, Jeddah, Saudi Arabia d Experimental Biochemistry Unit, King Fahd Medical Research Center and Production of Bioproducts for Industrial Applications Research Group, King Abdulaziz University, Jeddah, Saudi Arabia e Biochemistry Department, Faculty of Science, Production of Bioproducts for Industrial Applications Research Group and Experimental Biochemistry Unit, King Fahd Medical Research, Center King Abdulaziz University, Jeddah, Saudi Arabia f Department of Environmental Health Sciences, School of Public Health, State University of New York at Albany, Albany, New York, United States b

art ic l e i nf o

a b s t r a c t

Article history: Received 4 August 2015 Received in revised form 12 November 2015 Accepted 23 November 2015

Oxidative stress arises from excessive free radicals in the body and is a trigger for numerous diseases, such as cancer and atherosclerosis. Elevated exposure to environmental chemicals can contribute to oxidative stress. The association between exposure to xenobiotics and oxidative stress, however, has rarely been studied. In this study, urinary concentrations of 57 xenobiotics (antimicrobials, parabens, bisphenols, benzophenones, and phthalates metabolites) were determined in a population from Jeddah, Saudi Arabia, to delineate association with the oxidative stress biomarker, 8-hydroxy-2′-deoxyguanosine (8OHDG). We collected 130 urine samples and analyzed for 57 xenobiotics using liquid chromatographytandem mass spectrometry (LC/MS/MS) methods. The association between unadjusted and creatinine- or specific gravity-adjusted concentrations of xenobiotics and 8OHDG was examined by Pearson correlations and multiple regression analysis. High concentrations of mCPP (a metabolite of di-n-octyl phthalate; DnOP) and mCMHP (a metabolite of diethylhexyl phthalate; DEHP) were found in urine. In addition, the concentrations of bisphenol S (BPS) were higher than those of bisphenol A (BPA). The concentrations of metabolites of DEHP, phthalic acid, BPA, BPS, and methyl-protocatechuic acid were significantly associated with 8OHDG. This is the first biomonitoring study to report exposure of the Saudi population to a wide range of environmental chemicals and provides evidence that environmental chemical exposures contribute to oxidative stress. & 2015 Elsevier Inc. All rights reserved.

Keywords: Phthalates Phenolics Parabens Oxidative stress Biomonitoring

1. Introduction Oxidative stress is a condition that arises from an imbalance in the redox state and an overload of reactive oxygen species in cells and tissues. Oxidative stress can disrupt normal cellular signaling and can act as a trigger for numerous diseases, such as cancer, infertility, and Alzheimer's. Urinary concentrations of 8-hydroxy2′-deoxyguanosine (8OHDG) have been reported as a biomarker of oxidative stress (Ravanat et al., 1998). Oxidation of DNA occurs n Corresponding author at: Wadsworth Center, Empire State Plaza, P.O. Box 509, Albany, NY 12201-0509. Fax: þ1 518 473 2895. E-mail address: [email protected] (K. Kannan). 1 Co-first author contributed to this study equally.

normally but that increases with elevated exposure to oxidizing agents (Guo et al., 2014). Exposure to a range of synthetic environmental chemicals can augment oxidative damage to DNA. Exposure of humans to environmental chemicals, such as antimicrobials, p-hydroxybenzoic acid esters (parabens; preservatives), bisphenols (BPs; intermediates in the production of epoxy resins and polycarbonate plastics), benzophenone-type UV filters (BzPs; sunscreen agents); bisphenol A diglycidyl ethers (BADGEs; industrial ethers), bisphenol F diglycidyl ethers (BFDGEs; industrial ethers), novolac glycidyl ethers (NOGEs; industrial ethers), phthalates (plasticizers), and benzothiazoles/ benzotriazoles (BTHs/BTRs; anti-corrosive agents), has been reported in populations from around the world (Asimakopoulos et al., 2012, 2013a, b, 2014a,b; Asimakopoulos and Thomaidis,

http://dx.doi.org/10.1016/j.envres.2015.11.029 0013-9351/& 2015 Elsevier Inc. All rights reserved.

Please cite this article as: Asimakopoulos, A.G., et al., Urinary biomarkers of exposure to 57 xenobiotics and its association with oxidative stress in a population in Jeddah, Saudi Arabia. Environ. Res. (2015), http://dx.doi.org/10.1016/j.envres.2015.11.029i

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2015; Callan et al., 2012; Casas et al., 2011; Colacino et al., 2010; Fisher et al., 2015; Frederiksen et al., 2013, 2014; Goldstone et al., 2015; Geens et al., 2014; Guo et al., 2014; Högberg et al., 2008; Itoh et al., 2007, 2009; Koch et al., 2003; López-Carrillo et al., 2010, Liao and Kannan, 2012; Liao et al., 2012; Philippat et al., 2012; QuirósAlcalá et al., 2013; Wang et al., 2013; Xue et al., 2015; Ye et al., 2008; Zhang et al., 2011; Zhou et al., 2014; Ji et al., 2010). However, the relationship between exposure to environmental xenobiotics and oxidative stress is not well established. Analysis of trace levels of environmental chemicals in human specimens for the establishment of association between exposure and effect biomarkers can be challenging. For population-based biomonitoring studies, a time- and cost-effective method for the analysis of human specimens, without compromising the data quality, is required. Further, the data analysis should take into consideration the methods to analyze censored data (i.e., nondetects; NDs). The use of appropriate statistical models is required to establish the relationship between chemical exposures in populations and biomarkers of adverse health effects. In particular, appropriate methods to analyze censored data continue to be a challenging issue. With this background, the present study aimed to establish urinary levels (total concentrations) of 57 xenobiotics in a general population from Jeddah, Saudi Arabia, to assess exposures and to delineate the association with oxidative stress in that population. We examined inter-correlations between xenobiotics and the association between xenobiotics and 8OHDG.

2. Materials and methods Urine samples were analyzed for two antimicrobials (triclosan, TCS; and triclocarban, TCC), 10 parabens (methyl-, MeP; ethyl-, EtP; propyl-, PrP; butyl-, BuP; benzyl-, BzP; heptyl-paraben, HeP; 4-hydroxy benzoic acid, 4HB; 3,4-dihydroxy benzoic acid, 3,4DHB; methyl-protocatechuic acid, OHMeP; and ethyl-protocatechuic acid, OHEtP), eight BPs (2,2-bis(4-hydroxyphenyl)propane, BPA; 4,4′-(hexafluoroisopropylidene)-diphenol, BPAF; 4,4′-(1-phenylethylidene)bisphenol, BPAP; 4,4′-sulfonyldiphenol, BPS; 4,4′-dihydroxydiphenylmethane, BPF; 4,4′-(1,4-phenylenediisopropylidene)bisphenol, BPP; 4,4′-cyclo-hexylidenebisphenol, BPZ; and 2,2-bis(4-hydroxyphenyl)butane, BPB), five BzPs (2-hydroxy-4methoxybenzophenone, BP3; 2,4-dihydroxybenzophenone, BP1; 2,2′-dihydroxy-4-methoxybenzophenone, BP8; 2,2′,4,4′-tetrahydroxybenzophenone, BP2; and 4-hydroxybenzophenone, 4OHBP), six BADGEs (bisphenol A diglycidyl ether, BADGE; bisphenol A (2,3-dihydroxypropyl) glycidyl ether, BADGE  H2O; bisphenol A (3-chloro-2-hydroxypropyl) glycidyl ether, BADGE  HCl; bisphenol A bis(2,3-dihydroxypropyl) glycidyl ether, BADGE  2H2O; bisphenol A bis(3-chloro-2-hydroxypropyl) glycidyl ether, BADGE  2HCl; and bisphenol A (3-chloro-2-hydroxypropyl) (2,3-dihydroxypropyl) glycidyl ether, BADGE  H2O  HCl), three BFDGEs (bisphenol F diglycidyl-ether, BFDGE; bisphenol F bis(3chloro-2-hydroxypropyl)glycidylether, BFDGE  2HCl; and bisphenol F bis(2,3-dihydroxypropyl)glycidylether, BFDGE  2H2O), 2 NOGEs (3-ring novolac glycidyl ether, 3RNOGE; and 4-ring novolac glycidyl ether, 4RNOGE), 18 phthalates metabolites (mono(2-ethyl-5-carboxypentyl) phthalate, mECPP; mono-[(2-carboxymethyl) hexyl] phthalate, mCMHP; mono-(2-ethyl-5-oxohexyl) phthalate, mEOHP; mono-(2-ethyl-5-hydroxyhexyl) phthalate, mEHHP; mono-(3-carboxypropyl) phthalate, mCPP; mono-2-isobutyl phthalate, mIBP; mono-cyclohexyl phthalate, mCHP; monoisononyl phthalate, mINP; phthalic acid, PA; mono-(8-methyl-1nonyl) phthalate, mIDP; mono-octyl phthalate, mOP; mono-nbutyl phthalate, mBP; mono-hexyl phthalate, mHxP; mono-2heptyl phthalate, mHpP; mono-methyl phthalate, mMP; monoethyl phthalate, mEP; mono-benzyl phthalate, mBzBP; and mono-

(2-ethylhexyl) phthalate, mEHP), two BTHs (benzothiazole, BTH; and 2-hydroxy-benzothiazole, 2OHBTH), one BTR (xylyltriazole, XTR), 8OHDG, specific gravity (SG), and creatinine (CR). The urine samples were collected from 130 individuals from the general population in Jeddah, Saudi Arabia, in May and June 2014. The samples were collected from healthy individuals who visited King Abdulaziz University Hospital for routine examination. Of the 130 samples collected, age and gender information were available for 67 individuals (31 males and 36 females; 63 unknown). The ages of donors ranged from 1 to 87 years with a median value of 35 years (mean: 37 years, standard deviation: 25). All samples were stored at  20 °C until analysis. The study was approved by the Institutional Review Boards of Wadsworth Center, New York State Department of Health, and King Abdulaziz University, Jeddah, Saudi Arabia. For the preparation of matrix-matched calibration curve, pooled urine samples were obtained by mixing equal volumes of urine from 6 individuals (3 male and 3 female donors). Urine samples were extracted using liquid-liquid extraction (LLE) (Asimakopoulos et al., 2014a,b; Xue et al., 2015) for the analysis of all xenobiotics listed above, except for phthalates metabolites (Fig. S1). The chromatographic separation of parabens and antimicrobials 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 (ESI–MS/MS; Applied Biosystems, Foster City, CA, U.S.) under the negative ionization mode. A Kinetex C18 column (2.1 mm  50 mm, 1.3 mm; Phenomenex Inc., Torrance, CA, U.S.) serially connected to a SecurityGuard ULTRA C18 guard column (2.1 mm, sub-2 mm core-shell column; Phenomenex Inc.) was used for separation of target chemicals. The mobile phase comprised methanol (A) and Milli-Q water that contained 1% (v/v) formic acid (B). Low limits of detection (LODs) on the order of sub-nanogram per liter (ppt) were obtained (see Table 1 for details). The chromatographic separation of BPs and BzPs was achieved using a Shimadzu Prominence™ Modular HPLC system (Shimadzu Corporation, Kyoto, Japan). Identification and quantification of BPs and BzPs were performed with an API 3200™ electrospray triple quadrupole mass spectrometer (ESI-MS/MS; Applied Biosystems) under the negative ionization mode. A Betasil C18 column (2.1 mm  100 mm, 5 mm; Thermo Electron Corp., Waltham, MA, U. S.) serially connected to a Javelin guard column (Betasil C18, 2.1 mm  20 mm, 5 mm; Thermo Electron Corp.) was used for separation. The mobile phase comprised methanol (A) and Milli-Q water that contained 0.1% (v/v) ammonium hydroxide (B). The use of ammonium hydroxide in the mobile phase and periodic injection of acetone (as a blank solvent) in the LC-MS/MS system were critical to enhancing instrumental sensitivity and signal stability. The use of ammonium hydroxide in the mobile phase enabled BPA analysis possible with a simple LLE sample preparation. The LODs for BPs and BzPs were in the ranges of 0.035 (BPB)–0.57 (BPF) ng/mL and 0.004 (BP1) –0.28 (BP3) ng/mL, respectively (Table 1). The chromatographic separation of BTHs and XTR was performed using the instrument as described above for BPs and BzPs with methanol (A) and Milli-Q water (B) as mobile phases. The chromatographic separation of BADGEs, BFDGEs, and NOGEs was carried out using an Agilent 1100 Series HPLC system (Agilent Technologies Inc., Santa Clara, CA, U.S.). Identification and quantification of BADGEs, BFDGEs, and NOGEs were performed with an Applied Biosystems API 2000™ ESI-MS/MS under the positive ionization mode, and the chromatographic column used was similar to that described for BPs and BzPs. The mobile phase comprised methanol (A) and Milli-Q water/Methanol (90:10, % v/v) that

Please cite this article as: Asimakopoulos, A.G., et al., Urinary biomarkers of exposure to 57 xenobiotics and its association with oxidative stress in a population in Jeddah, Saudi Arabia. Environ. Res. (2015), http://dx.doi.org/10.1016/j.envres.2015.11.029i

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Table 1 Urinary concentrations (ng/mL) of environmental chemicals in individuals from Jeddah, Saudi Arabia. General statistics for censored (both Ds and NDs) and detected data (only Ds). Chemicals

General statistics for censored data set (with NDs) using Kaplan-Meier (KM) General statistics for raw data set using Ds Only method Max ND* KM Mean Min D Max D Mean Median Num Ds Min ND*

LOD

1. ANTIMICROBIALS 1 TCS 2 TCC

108 35

0 0

0.018 0.002

4.34 0.099

0.032 0.004

177 4.02

5.22 0.37

0.51 0.075

0.019 0.004

2. PARABENS 1 MeP 2 EtP 3 PrP 4 BuP 5 BzP 6 HeP 7 4HB 8 3,4DHB 9 OHMeP 10 OHEtP

130 114 111 15 26 10 130 116 128 96

N/A 0 0 0 0 0 N/A 0 0 0

N/A 0.002 0 0.011 0.005 0.003 N/A 0 0 0.040

69.7 2.36 17.7 0.27 0.015 0.001 150 64.2 35.6 2.41

0.18 0.004 0.011 0.063 0.006 0.004 2.30 0.95 0.62 0.050

588 58.8 267 18.4 0.68 0.040 996 1242 921 73.7

69.7 2.69 20.7 2.34 0.073 0.013 150 72.0 36.2 3.26

11.7 0.23 1.66 0.15 0.033 0.010 97.0 37.6 12.0 1.40

0.017 0.003 0.010 0.022 0.006 0.004 0.59 0.13 0.031 0.046

3. BISPHENOLS 1 BPF 2 BPA 3 BPB 4 BPS 5 BPZ 6 BPAP 7 BPAF 8 BPP

12 112 41 130 48 91 4 66

0 0 0 N/A 0 0 0 0

0.55 0.27 0.063 N/A 0.007 0.061 0.31 0.086

0.19 4.92 0.050 13.3 0.058 0.30 0.05 0.093

0.72 0.30 0.089 0.077 0.059 0.063 0.48 0.091

4.56 177 0.44 630 1.66 12.6 3.39 0.45

2.04 5.71 0.16 13.3 0.16 0.43 1.52 0.18

2.16 2.01 0.12 4.92 0.10 0.21 1.10 0.16

0.57 0.27 0.082 0.035 0.049 0.062 0.44 0.087

4. BENZOPHENONES 1 BP3 2 4OHBP 3 BP1 4 BP8 5 BP2

111 130 130 64 84

0 N/A N/A 0 0.040

0.27 N/A N/A 0 0.26

17.4 0.45 4.85 0.052 0.68

0.28 0.16 0.24 0.010 0.27

1848 3.23 360 0.64 8.50

20.4 0.45 4.85 0.11 1.03

1.02 0.30 0.46 0.060 0.56

0.28 0.005 0.004 0.005 0.27

5. BADGEs 1 BADGE  2H2O 2 BADGE  H2O 3 BADGE 4 BADGE  HCl  H2O 5 BADGE  HCl 6 BADGE  2HCl

35 5 0 7 0 2

0 0 0 0 0 0

0.67 0.29 0.37 0.24 0 0.53

1.58 0.013 N/A 0.032 N/A 0.018

0.68 0.31 N/A 0.25 N/A 0.71

82.4 0.45 N/A 1.55 N/A 1.65

5.87 0.35 N/A 0.60 N/A 1.18

2.00 0.34 N/A 0.32 N/A 1.18

0.68 0.31 1.05 0.25 1.23 0.67

6. BFDGEs 1 BFDGE 2 BFDGE  2HCl 3 BFDGE  2H2O

22 1 5

0 0 0

0.77 0.25 0.33

0.76 0.004 0.055

0.85 0.47 0.39

14.2 0.47 3.80

4.49 0.47 1.42

3.43 0.47 0.45

0.81 0.31 0.35

7. NOGEs 1 3RNOGE 2 4RNOGE

0 0

0 0

0 0.43

N/A N/A

N/A N/A

N/A N/A

N/A N/A

N/A N/A

0.020 0.60

N/A N/A 0.035 N/A N/A N/A N/A N/A N/A 0 0 0 0 0 0 0

N/A N/A 0.035 N/A N/A N/A N/A N/A N/A 0.48 0.98 0.063 22.8 0.82 0.55 0.30

14.4 261 34.6 46.0 66.4 71.4 60.4 29.9 42.8 3.28 9.81 0.11 15.4 0.61 19.3 0.32

0.60 1.30 0.30 0.70 0.50 1.40 0.90 0.50 0.20 0.49 1.03 0.066 24.1 0.94 0.80 0.35

110 5136 571 263 388 1714 1692 479 2096 126 199 3.33 641 24.7 205 12.7

14.4 261 34.9 46.0 66.5 71.4 60.4 29.9 42.8 4.96 16.4 0.42 133 4.95 20.1 2.17

8.65 47.5 9.80 32.9 38.5 27.2 26.4 14.5 11.7 1.36 7.26 0.17 37.0 1.93 10.2 0.86

0.20 0.009 0.060 0.21 0.001 0.02 0.15 0.10 0.13 0.49 1.01 0.066 23.1** 0.84 0.56 0.31

8. PHTHALATES METABOLITES 1 mMP 130 2 mEP 130 3 mCPP 129 4 mIBP 130 5 mBP 130 6 mECPP 130 7 mCMHP 130 8 mEHHP 130 9 mEOHP 130 10 mBzBP 86 11 mNP 78 12 mCHP 34 13 mEHP 15 14 mOP 16 15 PA 125 16 mIDP 19

Please cite this article as: Asimakopoulos, A.G., et al., Urinary biomarkers of exposure to 57 xenobiotics and its association with oxidative stress in a population in Jeddah, Saudi Arabia. Environ. Res. (2015), http://dx.doi.org/10.1016/j.envres.2015.11.029i

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Table 1 (continued ) Chemicals

General statistics for censored data set (with NDs) using Kaplan-Meier (KM) General statistics for raw data set using Ds Only method Max ND* KM Mean Min D Max D Mean Median Num Ds Min ND*

LOD

17 mHxP 18 mHpP

16 4

0 0

0.04 0.12

0.10 0.011

0.05 0.19

9.32 0.57

0.85 0.37

0.13 0.36

0.05 0.15

9. BTHs and BTRs 1 BTH 2 XTR 3 2OHBTH

5 2 20

0 0 0

2.61 0 0.17

0.62 0.094 0.12

5.71 4.99 0.22

38.2 7.17 3.10

16.1 6.08 0.80

10.4 6.08 0.61

45 0.14 0.22

0.13

1.19

0.16

9.40

1.33

0.99

0.14

10. IN VIVO OXIDATIVE STRESS BIOMARKER 1 8OHDG 116 0.010 *

Minimum and maximum concentrations for which the response of the target analyte was found below the LOD and cannot be reliably distinguished from the method blank or baseline noise. NDs are referred to as censored values. ** High background.

contained 1.5% (w/v) ammonium acetate (B). Urine samples were extracted using a solid-phase extraction (SPE) method for the analysis of 18 phthalates metabolites (Guo et al., 2011) (Fig. S1). The chromatographic separation of phthalates metabolites was accomplished using an Agilent 1100 Series HPLC system. Identification and quantification of 18 phthalates metabolites were performed with an ABSCIEX 4500™ QTRAP mass spectrometer (Applied Biosystems) under the negative ionization mode. Chromatographic separation was achieved using an Ultra AQ C18 column (100 mm  2.1 mm, 3 μm; Restek, Bellefonte, PA, U.S.) serially connected to a Javelin guard column (Betasil C18, 2.1 mm  20 mm, 5 mm; Thermo Electron Corp.). The mobile phase comprised 0.1% (v/v) acetic acid in acetonitrile (A) and 0.1% (v/v) acetic acid in Milli-Q water (B). 8OHDG and CR were analyzed in urine following dilution with Milli-Q water, under the positive ionization mode using Shimadzu Prominence Modular HPLC system interfaced with an Applied Biosystems API 3200™ ESI-MS/MS. The mobile phase for 8OHDG analysis was the same as that for phthalates metabolites, whereas the mobile phase for CR comprised 50% methanol containing 0.1% (v/v) formic acid. A Zorbax SB-Aq column (150 mm  2.1 mm, 3.5 μm; Santa Clara, CA, U.S.) serially connected to a Javelin guard column (Betasil C18, 2.1 mm  20 mm, 5 mm; Thermo Electron Corp.) was used for 8OHDG, and the chromatographic column used for CR analysis was similar to that described for BPs and BzPs, and BADGEs, BFDGEs, and NOGEs. The SG of urine was measured with a refractometer, Atago PAL-3 (Atago, Tokyo, Japan) (Asimakopoulos et al., 2013b). Further details of the HPLC mobile phase gradients, sample preparation protocols, injection volumes, typical multi-residue MS/MS chromatograms (Figs. S2–S5), MS ion source parameters, and tandem MS transitions (Table S1) are provided in the Supplementary material.

3. Quality assurance/Quality control Contamination that arose from laboratory materials and solvents was evaluated by the analysis of procedural blanks. For those chemicals that were extracted by LLE and SPE, nine and seven procedural blanks, respectively, were analyzed. Throughout the analysis, 14 pre-extraction matrix spikes (in urine) were prepared by spiking known concentrations of target analytes and passing them through the entire analytical procedure. A quality control chart (QCC) that demonstrated recoveries (absolute or relative to a specific internal standard for each target analyte) was used, and the progress of analysis was assessed by a moving range control chart; both charts maintained control and warning levels. A typical

QCC generated for 4HB through the analysis is shown in Figs. S6 and S7. A calibration check standard and methanol were injected after every 20–30 samples as a check for drift in instrumental sensitivity and carry-over between samples, respectively. The limits of detection (LODs) were calculated as 3 times the standard deviation (SD) of 6 replicate analyses at the lowest concentration of the standard (or at the concentration 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. The limits of quantification (LOQs) were set at 3.3 times higher than the corresponding LODs. The recoveries of all 57 xenobiotics through the analytical procedure are provided in Table S2. Low absolute recoveries were observed for BADGE  2HCl, BFDGE, BADGE, 3RNOGE, and 4RNOGE but were compensated effectively with the isotope dilution method by using the internal standard 2D6-BADGE (which demonstrated similar absolute recovery values to those of the corresponding target analytes). The quantification was accomplished by both the internal standard and external standard methods and with a matrix-matched calibration standard, which was prepared by spiking target analytes into a matrix prior to extraction. Quantitation and confirmation of target analytes in samples was performed by using matrix-matched calibration curves (when 13C-labeled standards were not available) and/or isotopically-labeled internal standards. Data were acquired with Analyst software (Applied Biosystems, Foster City, CA, U.S.). Statistical treatment was performed with the ProUCL Version 5.0.00 (U.S.EPA), Statgraphics Centurion XV software package (Stat Point, Inc., Version 2002) and Excel (Microsoft, 2010).

4. Analysis of censored data In population-based biomonitoring studies that involve measurements of multiple chemicals in a single human specimen, measured values can vary widely, depending on the chemical. Treatment of the values below the LOD can skew the analysis and can bias the mean and standard deviation of the data (Newman et al., 1989). In this study, for those values that were below the LOD, the non-parametric Kaplan-Meier (KM) method was used, instead of substitution of non-detects (NDs) by values at LOD, LOD/ 2, LOD/√2, or 0. The substitution method can yield reasonable estimates of the general statistics only if the proportion of NDs is small ( o 15%) (Warren and Nussbaum, 2009). If the NDs exceed 15%, then the substitution method introduces significant biases in the general statistics (Helsel, 1990; 2005). The KM method is appropriate when NDs range up to 50% of the population and has

Please cite this article as: Asimakopoulos, A.G., et al., Urinary biomarkers of exposure to 57 xenobiotics and its association with oxidative stress in a population in Jeddah, Saudi Arabia. Environ. Res. (2015), http://dx.doi.org/10.1016/j.envres.2015.11.029i

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Fig. 1. Distribution of various metabolites and derivatives of parabens, antimicrobials (TCC/TCS), BADGEs, BFDGEs, bisphenols (BPs), benzophenones (BzPs), and phthalate metabolites in urine (based on Kaplan-Meier mean) from a Saudi population.

been used for the analysis of left censored environmental data sets (Helsel, 1990, 2005; Warren and Nussbaum, 2009). The KM method does not employ underlying assumptions about the data and can be used with multiple LOD values (e.g., the LOD of a chemical may fluctuate over time in LC-MS/MS analysis) (Beal, 2010). If the frequency of NDs exceeds 50%, however, the KM method does not provide statistically reliable estimates (Warren and Nussbaum, 2009). It has been suggested that, when the NDs exceed 80%, the 90th or 95th percentiles from the censored data be used (Helsel, 2005).

gender as independent variables to assess relationships between chemicals (with a DR of Z50%) and 8OHDG, with p o0.05 indicating statistical significance. Optimization (or desirability) functions (or plots) were created as a function of two parameters (by holding the remaining parameters constant) and are illustrated graphically as response surfaces (Asimakopoulos and Thomaidis, 2015). Logistic regression was used to estimate the odds ratio and 95% confidence intervals for each chemical and 8OHDG.

6. Results 5. Statistical analysis

6.1. Detection rates and concentrations of xenobiotics

The urinary concentrations of the xenobiotics were rightskewed, and, therefore, the data were logarithm (Ln) transformed. Data were CR and SG normalized for general statistical analysis. We performed comparisons between volume-, CR-, and SGnormalized concentrations of xenobiotics with a detection rate (DR) of Z50.0%. Pearson correlations were performed to assess the relationships among xenobiotics and between xenobiotics and 8OHDG (on volume-, creatinine-, and SG-adjusted bases); herein, all demonstrated correlations are on the basis of the volume unless stated otherwise. It has been reported that the concentration expressed on a volumetric basis is more appropriate with CR as an independent variable (Barr et al., 2005). This approach allows the concentration of the target analyte appropriately adjusted for CR and the statistical significance of the other model variables to be independent of the effects of CR (Barr et al., 2005). In this study, the measured concentrations were appropriately adjusted not only for CR, but also for SG, by including both as independent variables. Multiple regression analysis was performed with CR, SG, age and

The volume-based concentrations of the 57 xenobiotics and 8OHDG are shown in Table 1 and detailed in Table S3. The CR- and SG-normalized concentrations of the 57 xenobiotics are shown in Table S4. The percentile distribution of xenobiotic concentrations (including CR and SG values) is shown in Table S5. TCS and TCC were found in urine at a DR of 83.1% and 26.9%, respectively. TCS was the predominant species between the two antimicrobials, which was in accordance with the literature. All parabens were found in urine, and the rank order of DR for the predominant derivatives (DR 450%) was: MeP ¼4HB (100%) 4OHMeP (98.5%) 43,4DHB (89.2%) 4 EtP (87.7%) 4PrP (85.4%) 4OHEtP (73.9%). The polar metabolites of parabens were found more frequently than were the parent compounds (Asimakopoulos et al., 2014b; Xue et al., 2015). BPs were ubiquitous in urine, with BPS and BPA showing a DR of 100% and 86.2%, respectively. All BzPs were found in urine, but concentrations varied widely, with median concentrations ranging from 0.060 (BP8) to 1.02 ng/mL (BP3). Only four of six BADGE derivatives were found

Please cite this article as: Asimakopoulos, A.G., et al., Urinary biomarkers of exposure to 57 xenobiotics and its association with oxidative stress in a population in Jeddah, Saudi Arabia. Environ. Res. (2015), http://dx.doi.org/10.1016/j.envres.2015.11.029i

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in urine at median concentrations that ranged from 0.32 (BADGE•HCl•H2O) to 2.00 ng/mL (BADGE2H2O). The median concentrations of BFDGEs ranged from 0.45 (BFDGE•2H2O) to 3.43 ng/mL (BFDGE). NOGE derivatives were not detected in urine. All phthalates metabolites were found in urine, and the rank order of occurrence of major metabolites (DR 450%) was: mMP ¼mEP ¼mIBP¼mBP ¼mECPP¼ mCMHP ¼mEHHP ¼mEOHP (100%) 4mCPP (99.2%) 4PA (96.2%) 4mBzBP (66.2%) 4mINP (60.0%). The distribution of concentrations of phthalates metabolites in urine samples was similar to that of parabens, whereas the frequency distribution of PA concentrations resembled that of 4-HB (Fig. S8). The median concentrations of BTH derivatives and XTR ranged from 0.61 (2 OHBTH) to 10.4 ng/mL (BTH). The urinary concentrations of 8OHDG were 3 times lower than those reported for the U.S. (Guo et al., 2014). The urinary profiles of parabens, antimicrobials, BADGEs, BFDGEs, BPs, BzPs, and phthalates metabolites in the Saudi population are presented in Fig. 1. 6.2. Correlations among xenobiotics Many xenobiotics analyzed in urine exhibited significant intercorrelations. A strong correlation that was previously reported in urine samples from other countries was demonstrated between MeP (Ln MeP) and PrP (Ln PrP) (r ¼0.75, p o0.0001). Other statistically significant relationships were found between the Lntransformed concentrations of: BP3 and BP1 (r¼ 0.73, p o0.0001); mCMHP and mECPP (r ¼0.80, p o0.0001); mIBP and mBP (r¼0.81, p o0.0001); mEOHP and mECPP (r ¼0.80, p o0.0001); PA and mMP (r ¼0.71, po 0.0001); mCMHP and mEHHP (r¼0.81, p o0.0001); mCMHP and mEOHP (r ¼0.76, p o0.0001); and mEHHP and mEOHP (r ¼ 0.92, p o0.0001). Moderate but statistically significant relationships (15 in total) were found between the Ln-transformed concentrations of: 3,4DHB (a product of paraben metabolism) and nine parabens (r¼ 0.46, po 0.0001); mMP and 4HB (r ¼0.46, p o0.0001); mEP and 4HB (r ¼ 0.41, p ¼0.001); mEP and OHEtP (r ¼0.41, p ¼0.0009); mIBP and 4HB (r ¼0.42, p ¼0.0008); mECPP and BPA (r ¼0.46, p ¼0.0002); PA and BPA (r ¼0.42, p ¼0.0006); mMP and mEP (r ¼0.46, p o0.0003); mMP and mCPP (r ¼0.47, po 0.0002); mEP and mIBP (r ¼0.50, p o0.0001); mMP and mIBP (r ¼0.47, p o0.0001); mCMHP and mECPP (r ¼0.49, p ¼0.0001); PA and mEP (r ¼0.53, p o0.0001); PA and mCPP (r ¼0.60, p o0.0001); and mCMHP and PA (r ¼0.50, p o0.0001). Several weak but statistically significant correlations also were noted among xenobiotics (see Supplementary material). These correlations remained consistent when the urinary concentrations were adjusted for SG and CR. 6.3. Correlation of xenobiotics with 8OHDG Moderate but statistically significant relationships were found between the Ln-transformed concentrations of: 8OHDG and BPs (r ¼0.43, p o0.0001), and 8OHDG and phthalates metabolites (r ¼0.45, po 0.0001). Among the 57 xenobiotics analyzed, eight chemicals showed the strongest correlations with 8OHDG (Ln values demonstrated on a CR-adjusted basis): OHMeP (r ¼0.36, p o0.0001); BPA (r¼0.38, p o0.0001); BPS (r ¼0.30, p ¼0.0005); mECPP (r ¼0.44, po 0.0001); mCMHP (r ¼0.48, p o0.0001); mEHHP (r ¼0.42, p o0.0001); mEOHP (r ¼0.38, p o0.0001); and PA (r ¼ 0.36, po 0.0001). Overall, DEHP metabolites (mECPP, mCMHP, mEHHP, and mEOHP), BPA, PA, OHMeP, and BPS, were highly correlated with 8OHDG. The Ln-transformed concentrations of the sum of these eight compounds showed a correlation coefficient of 0.53 (p o0.0001) to Ln-transformed values of 8OHDG (Fig. 2). Multiple linear regression analysis was performed for volumebased urinary concentrations of xenobiotics and Ln-transformed

Fig. 2. Correlation between the natural logarithm-transformed concentrations of the sum of 8 xenobiotics (mECPP, mCMHP, mEHHP, and mEOHP, BPA, BPS, PA, and OHMeP) and 8-hydroxy-2′-deoxyguanosine (8OHDG) (CR-adjusted basis).

concentrations of 8OHDG as the dependent variable, and eight independent variables (covariates): age, gender, Ln [CR], Ln [SG], Ln [antimicrobials], Ln [phthalates metabolites], Ln [BPs], and Ln [BzPs]. The regression coefficient was 0.47 (p ¼0.0001). Higher CR and 8OHDG concentrations were associated with higher odds of being age 436 years, and higher BPs concentrations were associated with higher odds of being male (Table S6).

7. Discussion The median concentrations of TCS measured in urine from the population in Jeddah were 15 and 25 times lower than the geometric means reported for Greece (8 ng/mL) (Asimakopoulos et al., 2014b) and the U.S. (Calafat et al., 2008), respectively. Urinary TCC concentrations in Jeddah residents were low and were similar to those reported for the U.S. ( o0.1 ng/mL) (Calafat et al., 2008). The distribution profiles of parabens in Saudi Arabia were different from those reported in other countries. Urinary paraben concentrations in many countries were in the order of MeP » PrP 4EtP, whereas, in Saudi Arabia, EtP concentrations were higher than those of PrP. Moreover, the DR of BzP was 2 times higher than that of BuP (Asimakopoulos et al., 2014b; Wang et al., 2013). Nevertheless, the median concentrations of MeP and PrP in Saudi Arabia were 2–5 times lower than those reported for China, the U.S., and Denmark (Wang et al., 2013). The median urinary concentrations of 4HB and 3,4DHB in Saudi Arabia were 4–5 times lower than those reported for China (Wang and Kannan, 2013). Although the DRs for 4OHBP, BP1, BP8 and BP2 were higher in Saudi Arabia than those reported for Greece and the U.S. (Asimakopoulos et al., 2014b; Kunisue et al., 2012), the median concentrations of BP3 and BP1 were 6 and 12 times, respectively, lower than those reported for the U.S. (Kunisue et al., 2012), and BP8 and BP2 were 17 and 2 times lower, respectively, than those reported for Greece. Overall, the low abundance and concentrations of BzPs in human urine from Saudi Arabia suggested limited use of sunscreen products. Similarly, urinary BADGE concentrations in Saudi Arabia were lower than in populations from the U.S., China, and Greece (Asimakopoulos et al., 2014b; Wang et al., 2012). BADGE•2H2O was the predominant compound, with a DR of only 26.9%, whereas the DRs in Greece, the U.S., and China wereZ 90% (Asimakopoulos et al., 2014b; Wang et al., 2012). BPA and BPS were the two major BP derivatives found in urine from Saudi Arabia (Liao and Kannan, 2012; Liao et al., 2012).

Please cite this article as: Asimakopoulos, A.G., et al., Urinary biomarkers of exposure to 57 xenobiotics and its association with oxidative stress in a population in Jeddah, Saudi Arabia. Environ. Res. (2015), http://dx.doi.org/10.1016/j.envres.2015.11.029i

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Fig. 3. Urinary BPS and BPA concentrations (median; ng/mL) in Saudi Arabia, compared with those reported (median/geometrical mean) for several countries.

Concern regarding the health risks of BPA has increased in the recent past, and restrictions have been put forward to limit the use of BPA in some applications (Asimakopoulos et al., 2012; Liao and Kannan, 2012; Liao et al., 2012). Industries have begun replacing BPA with alternatives, such as BPS (Asimakopoulos et al., 2012; Liao and Kannan, 2012; Liao et al., 2012). The predominance of BPS in urine from Saudi Arabia (Fig. 3 and Fig. S8) indicates that this compound is used widely in products in this country and suggests the need for further studies to assess sources of exposures. BPS is relatively more heat and light stable than is BPA (Kang et al., 2014). It appears that, in tropical countries, BPS is more widely used than BPA. Among the 18 phthalates metabolites determined, concentrations of mEP, mBP, and mIBP were the highest in urine. MEHP concentrations were found elevated, but they were not used in further assessment because, based on the literature, MEHP represents a small percentage ( o1%) of DEHP metabolites (Silva et al. 2006), and it is not considered a reliable biomarker in some biological matrices (Lamoree, 2015). MEHP is a hydrolated monoester which can be produced from the hydrolysis of DEHP, while other DEHP metabolites, such as mEHHP, mEOHP, mCMHP and mECPP are oxidative metabolites that are produced only in vivo (Lamoree, 2015). The measured concentrations of phthalates in urine from Saudi Arabia were compared with those reported from China, Korea, Japan, Mexico, the Netherlands, Sweden, and the U.S. (Fig. 4) (Guo et al., 2011). Overall, the Saudi population is exposed to high levels

Fig. 4. Urinary concentrations (median; ng/mL) of phthalate metabolites in Saudi Arabia, compared with those (median / geometrical mean values) reported for several countries.

of di-n-octyl phthalate (mCPP is a metabolite) and DEHP (mCMHP is a metabolite). The high exposure levels of DEHP in Saudi Arabia are a concern and suggest the need for further studies (ATSDR, 2002). The urinary concentrations of two BTH derivatives were compared with those reported for the populations in the U.S., Vietnam, China, Japan, Korea, India, and Greece (Asimakopoulos et al., 2013b). BTH was found at a median concentration of 10.4 ng/mL and was comparable to those reported in other countries. Correlations among 57 environmental chemicals analyzed in this study showed nine strong, 15 moderate, and numerous weak but significant correlations. These results suggest concomitant exposure of humans to several xenobiotics, as these compounds are used in combination in various products. The collinearity among xenobiotics suggests that some individuals in the population are exposed to high levels of multiple xenobiotics. Several of these correlations, mainly between compounds that belong to different chemical classes, are reported for the first time in human specimens. For instance, the correlation between PA and BPA has been reported for the first time. Phthalates are esters of PA and are found mainly as plasticizers in numerous consumer products, while BPA is an industrial chemical used mainly in the production of polycarbonate plastics and epoxy resins. However, neither phthalates nor BPA is chemically bound to polymeric substrates and can potentially leach, migrate or evaporate into the environment. Thus, the correlation between PA (metabolite of phthalates) and BPA is an indication of common route of exposure to these chemicals by humans (Sakhi et al., 2014). When more than one independent variable, i.e., concentration of each of the five chemical classes studied (antimicrobials, parabens, BPs, BzPs, phthalates metabolites), SG, and CR (seven variables in total), is associated with 8OHDG, the combined effect can be visualized through the use of desirability plots. This approach has been used to assess how a combination of variables can affect the response, in our case, the 8OHDG. The desirability plots demonstrated that, when the concentrations of phthalates metabolites and BPs increased, the 8OHDG levels increased concomitantly (Fig. 5). Higher concentrations of SG or/and CR also were associated with higher concentrations of 8OHDG. Thus, SG, CR, phthalates metabolites, and BPs are associated with oxidative stress, and a simultaneous increase in one or two of these variables concurrently affects 8OHDG levels (Fig. 5). Urinary concentrations of DEHP metabolites, BPA, BPS, PA, and OHMeP exhibited significant correlations with 8OHDG. A positive correlation between DEHP metabolites and 8-OHDG has been reported. Laboratory animal exposure studies have shown that BPA

Please cite this article as: Asimakopoulos, A.G., et al., Urinary biomarkers of exposure to 57 xenobiotics and its association with oxidative stress in a population in Jeddah, Saudi Arabia. Environ. Res. (2015), http://dx.doi.org/10.1016/j.envres.2015.11.029i

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Fig. 5. Desirability plots of Ln-transformed concentrations of sum concentrations of individual chemical classes (i.e., function of two chemical classes) as a function of Lntransformed concentrations of 8-hydroxy-2′-deoxyguanosine (8OHDG). The red and blue color demonstrates the strongest and weakest effect of chemicals on 8OHDG concentrations, respectively.

and PA can induce oxidative stress (Bai et al., 2009; Song et al., 2014). The correlation of OHMeP with 8OHDG may be related to the reaction of methyl radicals (induced from oxidative stress) with 3,4DHB. It has been reported that 3,4DHB possesses high reactivity toward free radicals and produces a protective effect from oxidative stress (Guan et al., 2006). Overall, we found that mECPP, mCMHP, mEHHP, and mEOHP, BPA, BPS, PA, and OHMeP (out of the 57 xenobiotics) are more strongly correlated with oxidative stress, even though the concentrations of 8OHDG in this population were lower than that in a U.S. population (Guo et al., 2014). As with many population based studies, this study has several limitations. The urine samples were spot urine samples and may not represent life time exposures. The number of samples collected is limited. The chemicals were evaluated as chemical classes as a whole rather than individual chemicals (e.g., PA, 4-HB and 3,4DHB, represent only a sum parameter of exposure of phthalates and parabens metabolites) and this assumes additivity in the mechanism of action. Despite this, this is the first study to report exposure of the Saudi population to several xenobiotics. Further studies are needed to describe health outcomes from such exposures at a population level.

8. Conclusions Urinary concentrations of 57 xenobiotics and a biomarker of oxidative stress were measured in individuals from the general population in Saudi Arabia for the first time. Correlations among antimicrobials, parabens, BPs, BzPs, and phthalates metabolites have been established, and exposure to some of these environmental chemicals was strongly associated with oxidative stress. However, oxidative stress can be generated by various sources, such as nutrition, lifestyle (e.g., smoking) and age. Thus, exposure to chemicals and population descriptors must be taken into account in future studies as possible confounders. Multi-class biomonitoring studies of populations with detailed demographic information are deemed necessary to elucidate the sources of exposure and potential health effects.

Conflict of interest The authors declare no conflict of interest.

Acknowledgments This project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under Grant no. 176-1301435. The authors, therefore, acknowledge with thanks DSR technical and financial support.

Appendix A. Supplementary material Supplementary data associated with this article can be found in the online version at http://dx.doi.org/10.1016/j.envres.2015.11.029.

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Please cite this article as: Asimakopoulos, A.G., et al., Urinary biomarkers of exposure to 57 xenobiotics and its association with oxidative stress in a population in Jeddah, Saudi Arabia. Environ. Res. (2015), http://dx.doi.org/10.1016/j.envres.2015.11.029i