Environmental Research 140 (2015) 300–307

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Non-monotonic relationships between arsenic and selenium excretion and its implication on arsenic methylation pattern in a Bangladeshi population Nao Yoshida a, Tsukasa Inaoka b, Nayar Sultana a, Sk. Akhtar Ahmad c, Akihiko Mabuchi d, Hana Shimizu a, Chiho Watanabe a,n a Department of Human Ecology, School of International Health, Graduate School of Medicine, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 1130033, Japan b Department of Human Ecology, Faculty of Agriculture, Saga University, 1 Honjo-machi, Saga 840-8502, Japan c Department of Occupational and Environmental Medicine, Bangladesh University of Health Sciences, Mirpur, Dhaka 1216, Bangladesh d Department of Human Genetics, School of International Health, Graduate School of Medicine, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 1130033, Japan

art ic l e i nf o

a b s t r a c t

Article history: Received 20 April 2014 Received in revised form 16 March 2015 Accepted 19 March 2015

The toxicity of arsenic differs markedly between individuals and populations, which might be related to the metabolism (methylation) of inorganic arsenic (As), as well as the selenium (Se) nutritional status. Urinary excretion of As (u-As) and Se (u-Se) was examined in an adult population (n ¼ 128) living in an As-contaminated area in Bangladesh. Although there was a significant negative correlation between u-Se and u-As (median 137; range 49–927 μg/g creatinine), closer examination revealed a non-monotonous relationship between them. A quadratic curve with an axis of As at 155 μg/g Cre gave a better fit, and u-As and u-Se were positively or negatively correlated depending on whether the As concentration was lower or higher than 155 μg As/g Cre, respectively. Likewise, the relationships between the As methylation pattern and glutathione-S-transferase (GST) polymorphism, body mass index (BMI), and u-Se differed depending on the u-As range; i.e., higher or lower than 155 μg/g Cre. Although we did not determine the causal mechanism for these observations, the non-monotonic relationship between As exposure and the variables examined suggested the existence of a threshold at which the handling of As by human body is qualitatively changed. The possible importance of Se nutrition for As toxicity is also discussed. & 2015 Elsevier Inc. All rights reserved.

Keywords: Arsenic Urinary arsenic speciation Selenium Genetic polymorphism Non-monotonic relationship

1. Introduction The contamination of ground water by arsenic (As) has been observed in many Asian and Latin American countries. Exposure to inorganic arsenic (iAs) from drinking water has been associated with cancers of the skin and internal organs (including the bladder and lungs), diabetes, hypertension, and neonatal development (NRC, 2001), generating major public health concerns worldwide. Bangladesh has been confronted with severe As groundwater contamination problems because the vast majority of Bangladeshi people use tube wells to obtain water for drinking and cooking on a daily basis. The population at risk, as defined by use of tube wells where water exceeds the Bangladesh standard of 50 μg As/L, is estimated to be around 50 million (Bae, 2003). This number is n

Corresponding author. E-mail address: [email protected] (C. Watanabe).

http://dx.doi.org/10.1016/j.envres.2015.03.019 0013-9351/& 2015 Elsevier Inc. All rights reserved.

much larger if the WHO-recommended value of 10 μg As/L, adopted by many developed countries, is considered. A distinctive feature of As toxicity is its remarkable inter-individual and inter-population variability (Chen et al., 2003; Mead, 2005), which is at least partly due to the fact that inorganic As undergoes metabolism after ingestion. The process is influenced by a variety of factors, resulting in several metabolites with varying toxicity. Thus, the modification of As toxicity by age (Bae, 2003; Guifan Sun et al., 2007), sex (Watanabe et al., 2001; Rahman et al., 2006), pregnancy (Concha et al., 1998) as well as dietary (Maharjan et al., 2007) and genetic (Engström et al., 2007; Lin et al., 2007; Steinmaus et al., 2007; Li et al., 2008; Agusa et al., 2010) factors has been reported in human populations and in many experimental studies. Among the dietary factors, Se is one of the most extensively examined nutrients in relation to As toxicity and metabolism. However, the reported findings are mixed. One line of evidence suggests that Se has a protective effect against As toxicity in terms

N. Yoshida et al. / Environmental Research 140 (2015) 300–307

of the motor function of children (Parvez et al., 2011) and the prevention of skin lesions (Lin et al., 2007), while As and Se both exert global hypomethylation of genomic DNA, a mechanism considered to contribute to As toxicity (Pilsner et al., 2011). Se and As are thought to kinetically interact with each other, although the nature and mechanism of the interaction have not been determined (Wu et al., 2001). For example, the urinary excretion of these metals have been reported to have a negative correlation in Bangladesh (Miyazaki et al., 2003) and a positive correlation in Taiwan (Chen et al., 2003) and in Chile (Christiana et al., 2006). In plasma, there is a negative correlation between Se and As, and the methylation of As is affected by Se (Pilsner et al., 2011), although some studies have not observed this relationship (Lindberg et al., 2008). It should be noted that in the urinary excretion studies mentioned above, the Bangladeshi population had a much higher level of As exposure than the other two populations (Hsueh et al., 2003; Miyazaki et al., 2003; Christiana et al., 2006), suggesting that the differences observed might be due to a difference in the exposure levels. With regard to the genetic factors, various genetic polymorphisms that can modify As metabolism and/or toxicity have been proposed, including, arsenic (III) methyltransferase (AS3MT) (Engström et al., 2007; Lin et al., 2007), isozymes of glutathione-Stransferases (GSTs), and purine nucleoside phosphorylase (PNP) (Watanabe, 2012). GSTs comprise a large family of ubiquitous and multifunctional enzymes, of which GSTO1 has been shown to reduce pentavalent As(V) to the trivalent form (Zakharyan et al., 2001) using glutathione (GSH). GSTT1 and GSTM1 are members of the GST family, with both having deletion polymorphisms that are devoid of enzyme activities (Pemble et al., 1994; Xu et al., 1998). Unlike with GSTO1, the role of these isozymes in As metabolism has not been confirmed, but the deletion of either of them is associated with an increased risk of cancers (Hayes and Strange, 2000 ). Furthermore, some polymorphisms in these enzymes have been associated with As metabolism/toxicity, although the results are somewhat mixed (Chiou, 1997; Engström et al., 2007; Lin et al., 2007; Steinmaus et al., 2007). Most of the studies on the genetic effects are conducted at relatively lower exposure levels, and the studies conducted in Bangladesh and West Bengal in India, with very high exposure levels (Ghosh, 2006; McCarty et al., 2007b), did not examine the As metabolism in relation to the polymorphisms. These mixed observations led us to hypothesize that the relationship between As metabolism/ toxicity and potential confounders is As-dose dependent. A recent theoretical study using simulation model of As metabolism indicated that the major determinants of As metabolism in hepatocytes can differ substantially according to As exposure level (Stamatelos et al., 2011). To clarify the practical relevance of such theoretical findings, this study examined two modifying factors (Se and genetic polymorphisms) in terms of their effects on As metabolism, together with several other factors known to influence As metabolism. We selected a population in Bangladesh with a wide within-population range of exposure to As through the consumption of groundwater. For genetic polymorphisms, we focused on the effect of GSTT1 or GSTM1 deletion, which are found with a relatively high frequency in this population and might be associated with As metabolism.

2. Subjects and methods 2.1. Study area and population The study areas were two rural communities, i.e., Sherpur Vandar and Sadashibpur in Nawabganj district, northwestern

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Bangladesh, located around 5 km from each other. The environmental setting and subsistence of the both communities were similar to each other; both communities were located in a plain area, mostly covered by paddy field and crop lands, having no major upland areas, and therefore sharing common weather/climatic patterns. Approximately 50% of the residents in both communities were engaged in farming, cultivating rice, jute, vegetables, and mango and additional 25–30% were engaged in smallscale retailing. The structure of houses and their variation as well as possession of the livestocks and other assets were also similar. The inhabitants relied on groundwater for drinking and cooking on a daily basis. The tube wells they used provided water contaminated by arsenic, with a concentration varying from less than 10 to more than 500 μg/L (Watanabe, 2001, 2004). The study was supported by the Ministry of Environment and Ministry of Education, Culture, Sports, Science, and Technology, Japan. The study protocol was approved by the Research Ethics Committee at Graduate School of Medicine in the University of Tokyo (approval number 1948) and by the Ethical Review Committee of the National Institute of Preventive and Social Medicine (NIPSOM) in Dhaka, Bangladesh. 2.2. Sample collection and interviews The field survey was conducted by a team of Bangladeshi and Japanese researchers in September 2007. In each community, a health station was established, and local staffs made door-to-door visit to invite residents to voluntary participate in the survey. Before participation, the local staff explained to the potential participants the purpose and the protocol of the survey, and obtained written consent. In response to the invitation, 128 adults (68 males and 60 females, 18–58 years old) participated in the study. At the health station, well-trained Bangladeshi staff interviewed participants to obtain basic demographic information. Blood samples were collected by Bangladeshi nurse in a tube containing EDTA, and all samples were immediately centrifuged to separate blood cell and plasma. Urine samples were collected in polypropylene tubes. Samples were kept in cooler box with dry ice and later transported to the University of Tokyo, Japan, where they were stored at  80 °C until analyses. 2.3. Chemicals Sodium arsenite (NaAsO2), sodium arsenate (Na2HAsO4Na  7H2O), and monomethyl arsonic acid ((CH3)AsO(OH)2) were purchased from Wako Pure Chemical Industries Ltd., Japan., Dimethylarsenic acid ((CH3)2AsO(OH)) from Tri Chemicals Co. Ltd., Japan. Stock solutions for all As compounds were prepared at a concentration of 1000 μg As/L, and kept at  80 °C. Working solutions at concentration of 10 μg As/L were prepared daily from the stock solutions. 2.4. As and Se measurement and chemical speciation of As in urine Urinary total As (u-As) and Se (u-Se) were determined by inductively-coupled plasma mass spectrometry (ICP-MS: Agilent 7500ce, Agilent technologies Japan, Tokyo, Japan) using the helium gas mode. The urine samples were diluted 20-fold with MilliQ water containing 1% nitric acid and 2% buthanol before measurement. Speciation analysis was performed by HPLC coupled with ICP-MS (HPLC-ICP-MS). For the HPLC system, JASCO Gulliver Series (PU-980) equipped with a polymer-based anion exchange column (Gelpack GL-IC-A15, 150 mm  4.6 mm i.d., Hitachi-kasei, Japan) was used. Mobile phase was 0.2 mM EDTA–2Na/2.0 mM phosphate buffer, pH 6.0. Four peaks identified as As (III), As (V), monomethyl-arsonic acid (MMA), and dimethylarsinic acid (DMA)

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were quantified with a software for chromatographic data; no other major peaks including arsenobetaine were detected. For the As speciation, the urine samples were diluted, filtered through a 0.45-μm membrane filter, and injected into the HPLC. For quality assurance of the As analyses, NIES #18 (Human urine, National Institute of Environmental Studies, Tsukuba, Japan) was used. The observed values (0.143 70.007 mg/L, n ¼3) fell within the ranges of certified values (0.137 70.011 mg/L). Likewise, for Se anlyses, Seronorm Trace Element Urine Blank (Sero AS, Billingstad, Norway) was used. The observed and certified values were 21.67 1.0 μg/L (n ¼3) and 21.7 72.8 μg/L, respectively. Urinary creatinine was determined by Jaffe's method using a commercial kit. When the creatinine-adjusted value of u-As (μg/g creatinine) was used for analysis, it did not affect the results for the As metabolism as evaluated in the study (data not shown). The u-As was used as an indicator of As exposure. For the evaluation of As metabolism, the percentages and ratios of each As compound were used. Total arsenic was calculated as the sum of these four peaks, which showed a good correlation with the u-As measured by the ICP-MS (β ¼0.84, r¼ 0.973, po 0.001). The inorganic As (iAs) was calculated as sum of arsenite and arsenate, and the percentage of each metabolite was calculated against the total As. The ratios of each As metabolites were also calculated as indicators of primary and secondary methylation (McCarty et al., 2007a). 2.5. Genetic typing of GST isozymes DNA was extracted from the blood samples using QIAmp DNA Mini kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions. Genetic polymorphisms in GSTT1 and GSTM1 were examined by multiprex PCR technique described in a previous study (Zhong et al., 1993). The GSTT1 and GSTM1 present group included the homozygous wildtype ( þ/þ ) and heterozygote ( þ/  ). 2.6. Statistical analysis Because the distributions of u-As and u-Se were log-normal, the logarithm of these variables were used for statistics. JMP 7.0.1 (SAS Institute Inc., Cary, NC, USA) software was used for statistical calculation. A p-Value less than 0.05 was considered to indicate statistical significance. While the u-As levels from the two communities differed significantly (see Section 3), because both communities had similar environmental and subsistence levels (see Section 2.1), ages, and BMIs, data from the two communities were pooled and analyzed. A t-test or ANOVA was used to test betweengroup differences due to genetic polymorphisms, sex, or exposure levels. Multiple regression analysis was used to evaluate the contribution of factors to the pattern of metabolite excretion. Age, sex, BMI, u-As and u-Se (per creatinine, log converted), as well as GSTT1, M1 polymorphisms were included in the models.

3. Results Table 1 (left column) shows the basic characteristics of the participants. About 25% (33) of the participants had BMI less than 18.5, indicating the poor nutritional status of this population. The u-As levels ranged widely from 49 to 927 μg/g Cre. While there were significant between-community difference in the u-As, with Serpur Vandar residents having a higher level than the Sadashibpur residents (217 71.96, 118 71.63 μg/g Cre, respectively, values are geometric mean 7SD), the ranges of u-As were largely overlapped (49–927 and 51–698 μg/g Cre in Serpur Vandar and Sadasibpur, respectively). There was less variation in u-Se levels,

Table 1 Basic characteristics of participants by As exposure level.

N (female/male) Age [yr] BMI Urinary As [mg/g Cre]b Urinary Se [mg/g Cre]b GSTM1 (null/wild) [%null/% wild] GSTT1 (null/wild) [%null/% wild] %iAs %MMA %DMA MMA/iAsc DMA/MMAd

Whole

Low Asa

High Asa

128 (61/67) 35 711 20.2 72.6 162 (1.94) 15 (1.3) 42/86 (33/67)

71 (28/43) 35711 20.0 72.3 100 (1.28) 15 (1.3) 26/45 (36/64)

57 (33/24) 357 11 20.3 7 3.0 302 (1.58) 14 (1.3) 16/41 (28/72)

16/112 (13/87) 10/61 (14/86)

6/51 (11/89)

19 711 11 75 697 11 0.777 0.51 7.4 7 3.6

197 13 107 4** 707 12 0.75 7 0.51 8.2 7 3.7*

197 8 127 5 69 710 0.79 70.50 6.6 73.4

Unless otherwise described, arithmetic mean 7 SD are shown. **

Significant difference between the “high” and “low”; p o 0.01. Significant difference between the “high” and “low”; p o0.05. a Low and high levels are based on the axis of quadratic regression curve, i.e., 155 mg/g Cre of As. b Creatinine-adjusted concentrations are shown (geometric mean and SD in parentheses). c Primary methylation index (McCarty, 2007a). d Secondary methylation index (McCarty, 2007a). *

with a smaller range of 9–30 μg/g Cre. Serpur Vandar residents had a lower value than the Sadashibpur (147 1.3, 16 71.3 μg/g Cre, respectively). About one third of the participants were GSTM1 null, while only 13% were GSTT1 null. The proportion of the participants that were GST null did not significantly differ between the two communities for both M1 and T1 (chi-square test; p 40.1). Table 2 shows the result of multiple regression models for each of the arsenic metabolites and the two methylation indexes (%iAs is not shown because this variable is not independent from the % MMA and %DMA). Models were significant only for %MMA and DMA/MMA. Sex had a significant effect on MMA/iAs, with males having a higher first methylation than females. None of the genetic variables or u-Se had any significant effects. The relationship between the u-As and u-Se was examined using their logarithm-converted values. While the two elements had a significant negative linear correlation (r ¼  0.18, p o0.05), the regression was much improved when the square term of the u-As, (u-As)2, was added to the regression equation. The resultant quadratic curve, with an upward convex shape, gave a much better fit (R2 ¼ 0.097, p o0.01), with the vertex corresponding to u-As ¼155 and u-Se ¼16 μg/g Cre, suggesting a non-monotonous relationship (Fig. 1a). The regression coefficient remained significant after adjusting for BMI, age, and sex (results not shown). To confirm the non-monotonicity, the correlation of the two elements was calculated separately for the “left” (lower As) and “right” (higher As) side of the vertical axis of the quadratic curve. Fig. 1b shows that the two elements were positively correlated (r ¼0.29, p o0.01, n ¼71) at the lower exposure level, but negatively correlated (r ¼  0.38, p o0.01, n ¼57) at the higher exposure level, supporting the notion of a non-monotonous relationship. To further explore the importance of this relationship, analyses for the lower and higher exposure groups were conducted separately. Table 1 (middle and right columns) shows the basic characteristics of the two groups with different exposure levels. With the exception of u-As, most of the other variables, including u-Se, GSTM1, and GSTT1 null frequencies did not differ between the low and high exposure groups. A slightly but significantly lower proportion of MMA was observed in the high As exposure group compared to the low As exposure group, which was in turn reflected in the difference in DMA/MMA.

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Table 2 Effects of genetic and physiological factors on urinary As metabolites. Index

%MMA

%DMA

MMA/iAsa

DMA/MMAb

N R2 Sex [female] Age [yr] BMI Urinary Ase Urinary See GSTM1f [wild] GSTT1f [wild]

123 0.140c 0.774 (  0.076, 1.623)  0.050 (  0.125, 0.026)  0.146 (  0.457, 0.165)  3.16 (  6.18,  0.15) 5.16 (  2.19, 12.5)  0.589 (  1.814, 0.636) 0.182 (  0.690, 1.054)

123 0.090 1.89 (  0.19, 3.97) 0.083 (  0.102, 0.269) 0.670 (  0.093, 1.433) 7.54 (0.14, 14.94) 5.13 (  12.90, 23.16) 0.555 (  1.585, 2.694)  0.328 (  3.332, 2.677)

123 0.107d 0.159 (0.066, 0.252)  0.003 (  0.011, 0.005) 0.010 (  0.024, 0.044) 0.180 (  0.152, 0.511) 0.510 (  0.298, 1.317) 0.031 (  0.065, 0.127)  0.048 (  0.183, 0.086)

123 0.141c  0.560 (  1.207, 0.088) 0.050 (  0.007, 0.108) 0.134 (  0.103, 0.371) 2.58 (0.28, 4.87)  1.40 (  7.00, 4.20)  0.173 (  0.838, 0.492) 0.457 (  0.476, 1.391)

In the parentheses, lower and upper limits of 95% confidence interval are shown. The number shows the change in each metabolites corresponding to a 1 unit increase in the independent variable. For categorical variables, referent group is shown in […]. a

Primary methylation index (McCarty et al., 2007a). Secondary methylation index (McCarty et al., 2007a) For R2 values, p o0.05. d For R2 values, p o0.1. e Both u-As and u-Se were adjusted for creatinine and log converted. f Effect of the absence of the isozyme is shown; thus, a positive value indicates the variable has larger value in the null than in the wild type. b c

Table 3 shows the results of multiple regression models for each of the arsenic metabolites and the two methylation indexes for the low and high As exposure groups. The table shows that the coefficient of determinant (R2) was higher and significant in the high As exposure group, while the R2 for the low As exposure group was significant only for MMA/iAs, and a different set of

significant variables were selected for high As exposure models compared to low As exposure models, suggesting that the determinants of the methylation pattern depended on the As exposure level. In the high As exposure group, a higher %MMA was associated with being male and a lack of GSTM1, while a higher %DMA was

Fig. 1. (a) Relationship between the urinary concentrations of As (abscissa) and Se (ordinate). A quadratic fit is shown, where R2 ¼ 0.097, p o0.01. (b) Relationship between the urinary concentrations of As (abscissa) and Se (ordinate) for low (left) and high As (right) exposure groups. The axis of the quadric regression (155 μg/g creatinine) defined the border of the two groups. Both scales are logarithmic. Both regression lines are statistically significant (see text for the equations).

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Table 3 Effects genetic and physiological factors on As methylation pattern in the groups by different urinary As level. MMA/iAsa

%DMA

DMA/MMAb

%MMA

Arsenic exposure levelc

Low

High

Low

High

Low

High

Low

High

R2 Sexd [female] Age BMI Urinary Ase Urinary See GSTM1 [wild]d GSTT1 [wild]d

0.147 0.368 ( 0.930, 1.665)  0.056 ( 0.170, 0.058) 0.014 (  0.507, 0.534)  5.83 (  17.70, 6.06) 3.57 (  8.24, 15.37)  0.906 (  2.175, 0.364)  1.97 (  3.69,  0.25)

0.289** 0.930 (  0.155, 2.016)  0.056 (  0.154, 0.041)  0.275 ( 0.630, 0.079)  1.36 ( 6.93, 4.21) 6.56 ( 2.74, 15.86) 1.35 (0.17, 2.52) 0.908 (  0.825, 2.640)

0.088 2.37 (  0.40, 5.14)  0.076 (  0.319, 0.167) 0.403 (  0.710, 1.515) 6.20 ( 19.2, 31.6) 12.1 (  13.1, 37.3) 1.63 (  1.08, 4.35) 1.87 (  1.81, 5.54)

0.250** 2.16 ( 1.15, 5.46) 0.340 (0.044, 0.637) 0.917 ( 0.163, 1.99) 16.5 (  0.51, 33.4) 3.09 (  25.2, 31.4)  0.378 (  3.96, 3.20)  4.29 (  9.57, 0.99)

0.213* 0.126 (  0.003, 0.254)  0.009 (  0.020, 0.002) 0.031 (  0.020, 0.083)  0.641 (  1.818, 0.535) 1.08 (  0.085, 2.253)  0.054 (  0.0180, 0.071)  0.146 (  0.317, 0.024)

0.264* 0.190 (0.054, 0.325) 0.006 (  0.006, 0.018)  0.003 (  0.047, 0.042) 0.694 (  0.001,1.389) 0.226 (  0.935, 1.386) 0.143 (  0.004, 0.289)  0.010 (  0.226, 0.206)

0.115  0.322 (  1.226, 0.583) 0.036 (  0.043, 0.115)  0.107 (  0.469, 0.256) 1.95 (  6.33, 10.23) 2.33 (  5.90, 10.56) 0.533 (  0.352, 1.418) 1.36 (0.17, 2.56)

0.310**  0.690 (  1.647, 0.256) 0.093 (0.008, 0.177 0.333 (0.023, 0.642) 2.58 (  2.28, 7.44)  3.99 (  12.09, 4.13)  0.821 (  1.846, 0.204)  0.780 (  2.291, 0.732)

In the parentheses, lower and upper limits of 95% confidence interval are shown. The number shows the change in each metabolites corresponding to a 1 unit increase in the independent variable. **

For R2, p o 0.01. For R2, po 0.05. a Primary methylation index (McCarty, 2007). b Secondary methylation index (McCarty, 2007). c Low (N ¼ 67) and high (N¼ 56) levels are based on the axis of quadratic regression curve, i.e., 155 mg/g Cre of As. d […] ¼indicates the referent group for categorical variables. e Both u-As and u-Se were adjusted for creatinine and log converted. *

N. Yoshida et al. / Environmental Research 140 (2015) 300–307

Index

N. Yoshida et al. / Environmental Research 140 (2015) 300–307

associated with an older age and higher BMI, and u-As. In contrast, in the low As exposure group, a higher %MMA was associated with the presence of GSTT1. This was reflected in the primary and secondary methylation indexes.

4. Discussion This study showed that the association between u-Se and u-As was a distinct non-monotonous relationship. Depending on the As exposure level, the two elements had either a positive or negative correlation. Several factors that are known to influence the As metabolism (methylation), including genetic polymorphisms, age, and BMI, also had different effects at different exposure levels. The toxicological implications of these observations are discussed below. As noted earlier, there is a discrepancy in existing reports regarding the u-As and u-Se relationship. Studies in Chile, China, and Taiwan have reported positive correlations, while our previous study in Bangladesh (Watanabe et al., 2001) found negative correlations. In this study, while the data as a whole showed a significantly negative correlation, which was consistent with our previous observation, a quadratic regression gave a better fit, indicating that the relationship was not monotonous but rather biphasic. As a result, the linear correlation coefficient was positive or negative when the As exposure level was low or high, respectively. This observation is also consistent with previous reports. The [arithmetic] means of u-As were 281, 97 and 56 ng/mL in Bangladesh, Taiwan, and Chile, respectively (Hsueh et al., 2003; Miyazaki et al., 2003; Christiana et al., 2006). While such a discrepancy might be explained by the differences in genetic background of the population, or the potential sources and chemical forms of the arsenic, our observations, obtained in a relatively homogenous population living in geographically close areas, strongly suggest that the exposure level is an important determinant of the As–Se relationship. A simulation study of the As metabolism of hepatocytes demonstrates that metabolite concentrations are influenced by transportation and biotransformation in low and high dose ranges, respectively (Stamatelos et al., 2011). What was the potential mechanism(s) controlling the nonmonotonous relationship between u-As and u-Se? It was speculated that the positive correlation between u-As and u-Se, as observed at the lower As exposure level, was due to the facilitation of As excretion by Se, presumably through the formation of a GSH– As–Se complex (Chen et al., 2003). Based on the regression line in the low As group, where u-As ranged between 49 and 154 μg/g Cre, u-Se increased by only 6.2 μg/g Cre. Even assuming that all of the u-Se was excreted as an Se–As complex, the increase of As excretion in the form of this complex was only 5.9 μg/g Cre, which accounted for only 6% of the increase in u-As. A more plausible explanation is that u-Se excretion was facilitated by the large excess of u-As (relative to that of Se) through the formation of the complex. With a further increase in As exposure, u-As excretion would increase, but u-Se cannot increase indefinitely because the intake of Se would not change. The u-Se at the vertex of the quadratic regression was calculated to be 16.2 μg/g Cre. Using the mean creatinine concentration in this population (0.95 g/L) and assuming an average urine volume of 1.5 L/day, urinary Se excretion at the vertex was around 23 μg/day/person, which was close to the estimated daily Se intake in the Bangladeshi population of 17– 20 μg/day/person (Spallholz et al., 2005). Because urine is not the sole route of Se excretion, a further increase in Se excretion might lead to a negative Se balance and would eventually result in Se deficiency. Previous studies of As-exposed populations have

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shown a linear decrease in plasma Se (a better indicator of the Se nutritional status than u-Se) with increasing exposure to As (Parvez et al., 2011; Pilsner et al., 2011). In addition, relative proportion of urinary excretion of Se is lowered under Se deficient conditions (Hogberg and Alexander, 2007). Taken together, one might expect a decrease of u-Se with increasing exposure to (and excretion of) high levels of As. In other words, the negative correlation between u-As and u-Se suggests that the high As exposure groups in the present study might have a negative Se balance. In view of the many epidemiological and experimental studies that have suggested the importance of Se nutrition in As toxicity, this hypothesis warrants further study. To examine the possible toxicological relevance of these observations, we examined whether the relationship between the As methylation pattern and factors known to be related to As metabolism (including u-Se) differed in the low vs. high As exposure groups. As already described, the metabolism of As is associated with its toxicity. Typically a higher proportion of MMA is associated with enhanced toxicity (Steinmaus et al., 2007). The dosestratified analyses revealed different determinants of As metabolism (methylation) between the low and high As groups (Table 3), which are described below. First, the effects of the GST polymorphisms were dependent on the dose level; e.g., the M1 null increased the %MMA in the high As exposure group, while the T1 null variants decreased the %MMA only in the low As exposure group. The result for M1 deletion agreed with previous studies, in that the deletion was associated with a higher proportion of MMA (Marcos et al., 2006; Engström et al., 2007). In contrast, the T1 null was associated with a higher % DMA (Chiou, 1997). Information regarding the influence of the T1 genotype on As toxicity/metabolism is scarce, although the T1 wild type has been shown to be associated with increased toxicity (high rate of skin lesions) (McCarty et al., 2007a). Several studies have demonstrated differential or even opposing effects of GSTT1 vs. GSTM1 on some biological outcomes, such as the plasma proteomic profile, cancer risks (Franca et al., 2012), and cancer progression (Liu et al., 2005). For example, the effect of GSTM1 deletion on the risk of pediatric leukemia relapse is the opposite to the effect of GSTT1 deletion (Franca et al., 2012). Likewise, the two GST isozymes may have differential roles in As metabolism and its toxicity. Interestingly, Engström et al. (2007) reported statistically significant interactions between both the GST (M1 and T1) genotypes (wild/null) and As exposure level on the effect of As methylation, which is similar to our observations. Second, the effect of BMI appeared only in the high As exposure group, where a higher BMI was associated with higher secondary methylation (DMA/MMA). Because a quarter of the population had a BMI of less than 18.5, and only 6% had a BMI exceeding 25, the observed relationship should be interpreted as underweight individuals having less secondary methylation than individuals with a normal weight, which is unfavorable in terms of As toxicity (Steinmaus et al., 2007). In the Bangladeshi population, u-As is negatively associated with the percentage body fat, after adjusting for the exposure level (i.e., As concentration in the drinking water) (Watanabe et al., 2001). In Nepal, a lower BMI was associated with a higher susceptibility to As skin lesions (Maharjan et al., 2007). These examples suggest that leanness or undernutrition might be associated with enhanced As toxicity. Together with the previous observations, which have demonstrated that exposure to a higher level of As would lead to a lower BMI (Maharjan et al., 2007), it is possible that a vicious cycle may be generated involving high As exposure, lowered BMI, and an unfavorable As metabolism, which might have practical implications if supported by further evidence. Some potential drawbacks of the study need to be noted. We have not examined the toxic endpoint and did not try to demonstrate how such a non-monotonous relationship is relevant for As

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toxicity. The relatively small sample size in the study, together with potentially complex blood relationship of the participants, which would be difficult to completely exclude, might prevent us from determining exact relationship. Because there were significant differences between the u-As and u-Se levels in the two communities, we cannot completely exclude the possibility that the observed difference between the high and low As exposure groups might be explained in part by some unidentified community differences. Despite these limitations, the data obtained suggest the existence of a threshold at around 150 μg As/g Cre in this population, at which various biological responses to As, including u-Se excretion as well as genetic and physiological influences on As methylation, changed qualitatively. Closer examination of the relationship between u-Se and u-As suggested that poor Se nutritional status might be associated with this threshold. The existence of such a threshold, if proven in other populations with a wide range of exposure levels, would be useful for determining the toxic mechanism of As.

Funding source This work has been financially supported by Ministry of Environment, Japan (Global environment Research Fund, H-063) and Ministry of Education, Culture, Sports, Science, and Technology, Japan (Grant-in-aid for Scientific Research, 23310045).

Ethics The study protocol was approved by the Research Ethics Committee at Graduate School of Medicine in the University of Tokyo (approval number 1948) and by the Ethical Review Committee of the National Institute of Preventive and Social Medicine (NIPSOM) in Dhaka, Bangladesh.

Acknowledgements The authors thank all the participants of the survey for their cooperation and kindness, Mr. Hoque Bokul and Mr. Zakir Hossain for their continuous cooperation and support, Dr. Munjoo Bae for her technical advices for ICP-MS measurement.

References Agusa, T., Iwata, H., Fujihara, J., Kunito, T., Takeshita, H., Minh, T.B., Trang, P.T., Viet, P. H., Tanabe, S., 2010. Genetic polymorphisms in glutathione S-transferase (GST) superfamily and arsenic metabolism in residents of the Red River Delta, Vietnam. Toxicol. Appl. Pharmacol. 242 (3), 352–362. Bae, M.-J., 2003. Assessment of Exposure to Goundwater-Derived Inorganic Arsenic in a Rural Population of Bangladesh (Ph.D. dissertation). University of Tokyo. Chen, Y.-C., Su, H.-J.J., Guo, Y.-L.L., Hsueh, Y.-M., Smith, T.J., Ryan, L.M., Lee, M.-S., Christiani, D.C., 2003. Arsenic methylation and bladder cancer risk in Taiwan. Cancer Causes Control 14 (4), 303–310. Chiou, H.Y., 1997. Arsenic methylation capacity, body retention, and null genotypes of glutathione S-transferase M1 and T1 among current arsenic-exposed residents in Taiwan. Mutat. Res. 386 (3), 197–207. Christiana, W., Hopenhaynb, C., Centenoc, J.A., Todorovc, T., 2006. Distribution of urinary selenium and arsenic among pregnant women exposed to arsenic in drinking water. Environ. Res. 100, 115–122. Concha, G., Vogler, G., Lezcano, D., Nermell, B., Vahter, M., 1998. Exposure to inorganic arsenic metabolites during early human development. Toxicol. Sci. 44 (2), 185–190. Engström, K., Broberg, Karin, Concha, Gabriela, Nermell, Barbro, Warholm, Margareta, Vahter, Marie, 2007. Genetic polymorphisms influencing arsenic metabolism: evidence from Argentina. Environ. Health Perspect. 115 (4), 599–605. Franca, R., Paola, R., Giuseppe, B., Andrea, B., Giovanni, C., Sergio, C., Giuliana, D., Franca, F., Emanuela, G., Franco, L., Vincenzo, P., Maria, G.V., Marco, R., 2012. Glutathione S-transferase homozygous deletions and relapse in childhood

acute lymphoblastic leukemia: a novel study design in a large Italian AIEOP cohort. Pharmacogenomics 13 (16), 1905–1916. Ghosh, P., 2006. Cytogenetic damage and genetic variants in the individuals susceptible to arsenic-induced cancer through drinking water. Int. J. Cancer 118 (10), 2470–2478. Guifan Sun, Y.X., Li, Xin, Jin, Yaping, Li, Bing, Sun, Xiance, 2007. Urinary arsenic metabolites in children and adults exposed to arsenic in drinking water in inner Mongolia, China. Environ. Health Perspect. 115 (4), 648–652. Hayes, J.D., Strange, R.C., 2000. Glutathione S-Transferase polymorphisms and their biological consequences. Pharmacology 61 (3), 154–166. Hogberg, J., Alexander, J., 2007. In: Nordberg, G., Fowler, B., Nordberg, M., Friberg, L. (Eds.), Selenium Handbook on the Toxicology of Metals, 3rd ed. Elsevier, Amsterdam, pp. 784–808. Hsueh, Y., Koa, Y., Huang, Y., Chen, H., Chiou, H., Huang, Y., Yang, M., Chen, C., 2003. Determinants of inorganic arsenic methylation capability among residents of the Lanyang Basin, Taiwan: arsenic and selenium exposure and alcohol consumption. Toxicol. Lett. 137, 49–63. Li, L., Ekstrom, E.-C., Goessler, W., Lonnerdal, B., Nermell, B., Yunus, M., Rhaman, A., El Arifeen, S., Persson, L.A., Vahter, M., 2008. Nutritional status has marginal influence on the metabolism of inorgnia carsenic in pregnant Bangladeshi women. Environ. Health Perspect. 116, 315–321. Lin, G.F., Du, H., Chen, J.G., Lu, H.C., Kai, J.X., Zhou, Y.S., Guo, W.C., Zhang, X.J., Lu, D.R., Golka, K., Shen, J.H., 2007. Glutathione S-transferases M1 and T1 polymorphisms and arsenic content in hair and urine in two ethnic clans exposed to indoor combustion of high arsenic coal in Southwest Guizhou, China. Arch. Toxicol. 81 (8), 545–551. Lindberg, A., Rahman, M., Persson, L., Vahtar, M., 2008. the risk of arsenic induced skin lesions in Bangladeshi men and women is affected by arsenic metabolism and the age at first exposure. Toxicol. Appl. Pharmacol. 230, 9–16. Liu, C.-J., Chang, C.-S., Lui, M.-T., Dang, C.-W., Shih, Y.-H., Chang, K.-W., 2005. Association of GST genotypes with age of onset and lymphnode metastasis in oral squamous cell carcinoma. J. Oral Pathol. Med. 34, 473–477. Maharjan, M., Watanabe, C., Ahmad, S.A., Umezaki, M., Ohtsuka, R., 2007. Mutual interaction between nutritional statsua and chronic arsenic toxicity due to groundwater contamination in an area of Terai, lowland Nepal. J. Epidemiol. Commun. Health 61, 389–394. Marcos, R., Martinez, V., Hernandez, A., Creus, A., Sekaran, C., Tokunaga, H., Quinteros, D., 2006. Metabolic profile in workers occupationally exposed to arsenic: role of GST polymorphisms. J. Occup. Environ. Med. 48 (3), 334–341. McCarty, K.M., Chen, Y.C., Quamruzzaman, Q., Rahman, M., Mahiuddin, G., Hsueh, Y. M., Su, L., Smith, T., Ryan, L., Christiani, D.C., 2007a. Arsenic methylation, GSTT1, GSTM1, GSTP1 polymorphisms, and skin lesions. Environ. Health Perspect. 115, 341–345. McCarty, K.M., Ryan, L., Houseman, E.A., Williams, P.L., Miller, D.P., Quamruzzaman, Q., Rahman, M., Mahiuddin, G., Smith, T., Gonzalez, E., Su, L., Christiani, D.C., 2007b. A case–control study of GST polymorphisms and arsenic related skin lesions. Environ. Health 6, 5. Mead, M., 2005. Arsenic – in search of an antidote to a global poison. Environ. Health Perspect. 113, A379–A386. Miyazaki, K., Ushijima, K., Kadono, T., Inaoka, T., Watanabe, C., Ohtsuka, R., 2003. Negative correlation between urinary selenium and arsenic levels of the residents liing in an arsenic-contaminated area in Bangladesh. J. Health Sci. 49, 239–242. NRC, 2001. Arsenic in Drinking Water: 2001 Update. National Academy Press, Washinton, DC. Parvez, F., Wasserman, G.A., Factor-Litvak, P., Liu, X., Slavkovich, V., Siddique, A.B., Sultana, R., Sultana, R., Islam, T., Levy, D., Mey, Jacob L., Geen, A.V., Khan, K., Kline, J., Ahsan, H., Graziano, J.H., 2011. Arsenic exposure and motor function among children in Bangladesh. Environ. Health Perspect. 119, 1665–1670. Pilsner, J.R., Hall, M.N., Liu, X., Ahsan, H., Ilievski, V., Slavkovich, V., Levy, D., FactorLitvak, P., Graziano, J.H., Gamble, M.V., 2011. Associations of plasma selenium with arsenic and genomic methylation of leukocyte DNA in Bangladesh. Environ. Health Perspect. 119, 113–118. Rahman, M., Vahter, M., Sohel, N., Yunus, M., Wahed, M., Streatfield, P., Ekström, E.C., Persson, L.Å., 2006. Arsenic exposure and Age- and sex-specific risk for skin lesions: a population-based case–referent study in Bangladesh. Environ. Health Perspect. 114 (12), 1847–1852. Pemble, S., Schroeder, K.R., Spencer, S.R., Meyer, D.J., Hallier, E., Bolt, H.M., Taylor, J. B., Ketterer, B., 1994. Human glutathione S-transferase theta (GSTT1): cDNA cloning and the characterization of a genetic polymorphism. Biochem. J. 300 (Pt 1), 271–276. Spallholz, J., Byolan, L., Palace, V., Chen, J., Smith, L., Rahman, M., Robertson, J., 2005. Arsenic and selenium in human hair. A comparison of five countries with and without arsenicosis. Biol. Trace Elem. Res. 106, 133–144. Stamatelos, S., Brinkerhoff, C., Isukapalli, S., Georgopoulos, P., 2011. Mathematical model of uptake and metabolism of arsenic(III) in human hepatocytes-Incorporation of cellular antioxidant response and thresholddependent behavior. BMC Syst. Biol. 5, 16. Steinmaus, C., Moore, L.E., Shipp, M., Kalman, D., Rey, O.A., Biggs, M.L., Hopenhayn, C., Bates, M.N., Zheng, S., Wiencke, J.K., Smith, A.H., 2007. Genetic polymorphisms in MTHFR 677 and 1298, GSTM1 and T1, and metabolism of arsenic. J. Toxicol. Environ. Health A 70 (2), 159–170. Watanabe, C., 2004. Water intake in an Asian population living in arsenic contaminated area. Toxicol. Appl. Pharmacol. 198, 272–282. Watanabe, C., 2012. Role of health sciences in tackling with groundwater contamination In: Lee, K.-M., Kauffmann, J. (Eds.), Handbook of Sustainable

N. Yoshida et al. / Environmental Research 140 (2015) 300–307

Engineering. Springer, Netherland. Watanabe, C., Inaoka, Tsukasa, Kadono, Takefumi, Nagano, Megumi, Nakamura, Satoshi, Ushijima, Kayo, Murayama, Nobuko, Miyazaki, Kaori, Ohtsuka, Ryutaro, 2001. Males in rural Bangladeshi communities are more susceptible to chronic arsenic poisoning than females: analyses based on urinary arsenic. Environ. Health Perspect. 109, 1265–1270. Wu, Y., Linsheng, Y., Shaofan, H., T, J.A., L, H., 2001. Prevention of endemic arsenism with selenium. Curr. Sci. 81, 1215–1218. Xu, S.-j, Wang, Y.-p, Roe, B., Pearson, W.R., 1998. Characterization of the human class Mu glutathione S-transferase gene cluster and the GSTM1 deletion. J. Biol.

307

Chem. 273 (6), 3517–3527. Zakharyan, R.A., Sampayo-Reyes, A., Healy, S.M., Tsaprailis, G., Board, P.G., Liebler, D. C., Aposhian, H.V., 2001. Human monomethylarsonic acid (MMAV) reductase is a member of the glutathione-S-transferase superfamily. Chem. Res. Toxicol. 14 (8), 1051–1057. Zhong, S., Wyllie, A.H., Barnes, D., Wolf, C.R., Spurr, N.K., 1993. Relationship between the GSTM1 genetic polymorphism and susceptibility to bladder, breast and colon cancer. Carcinogenesis 14 (9), 1821–1824.

Non-monotonic relationships between arsenic and selenium excretion and its implication on arsenic methylation pattern in a Bangladeshi population.

The toxicity of arsenic differs markedly between individuals and populations, which might be related to the metabolism (methylation) of inorganic arse...
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