Evaluation of the Association Between Arsenic and Diabetes

A National Toxicology Program Workshop Review

Elizabeth A. Maull; Habibul Ahsan; Joshua Edwards; Matthew P. Longnecker; Ana Navas-Acien; Jingbo Pi; Ellen K. Silbergeld; Miroslav Styblo; Chin-Hsiao Tseng; Kristina A. Thayer; Dana Loomis

Disclosures

Environ Health Perspect. 2012;120(12):1658-1670. 

In This Article

Epidemiological Studies

The first epidemiological studies reporting associations between arsenic and diabetes were published in the mid-1990s. These early studies were conducted in populations exposed to high levels of arsenic in drinking water in Taiwan and Bangladesh or were occupational studies of copper smelter and glass workers in the United States and Europe exposed to dust and particulates as distinct from water. Previous reviews of studies published before 2008 concluded that arsenic exposure was most consistently associated with diabetes in areas of Taiwan and Bangladesh with high arsenic contamination of drinking water in the past, whereas results from occupational studies and studies of populations with low-to-moderate arsenic levels in drinking water were inconsistent (Chen et al. 2007; European Food Safety Authority 2009; Longnecker and Daniels 2001; Navas-Acien et al. 2006; Tseng et al. 2002). More than 10 new epidemiological studies of arsenic exposure and diabetes have been published since 2007.

Detailed descriptions of all of the epidemiological studies considered for the review can be found in the technical literature review document prepared for the NTP workshop (NTP 2011b). Eight occupational studies also were considered as part of the review [see Supplemental Material, Table S1 (http://dx.doi.org/10.1289/ehp.1104579)] but are not considered further in this report because of concerns about diabetes assessment, exposure misclassification, and limited power. Most of the occupational studies ascertained diabetes based on death certificates, which are well known to have low sensitivity and specificity for diabetes (Cheng et al. 2008). In addition, arsenic exposure was determined based on job title, and with one exception (Lubin et al. 2000) the sample size or number of individuals with diabetes was small. This assessment of the occupational studies is consistent with other reviews of arsenic (Longnecker and Daniels 2001; Navas-Acien et al. 2006).

Environmental Exposure Settings

Of the 27 eligible nonoccupational publications that met our inclusion criteria, 9 were classified as high exposure ( Table 1 ), 15 were classified as non-NHANES studies with low-to-moderate exposure ( Table 2 ), 1 was classified as both low and high exposure (Chen et al. 2010), and 4 were classified as analyses of NHANES data ( Table 2 ). Two high-exposure studies used a prospective design (Tseng et al. 2000a, 2000b), and the rest were cross-sectional (n = 12, excluding the NHANES studies), case–control (n = 5), or retrospective (n = 4). Three studies did not report risk estimates for diabetes, but compared the levels of arsenic in persons with diabetes (diabetics) and nondiabetics (Afridi et al. 2008; Kolachi et al. 2010; Serdar et al. 2009).

Diabetes ascertainment differed among studies. Four studies used death certificates to ascertain diabetes (Lewis et al. 1999; Meliker et al. 2007; Tollestrup et al. 2003; Tsai et al. 1999) and three others used exclusively self-reported history of diabetes (Afridi et al. 2008; Chen et al. 2010; Zierold et al. 2004). Two studies used diagnosis of diabetes but did not report the basis of diabetes diagnosis (Ruiz-Navarro et al. 1998; Ward and Pim 1984). Seven studies, generally those conducted more recently, incorporated diagnostic indicators such as fasting glucose or oral glucose tolerance test (OGTT) results (Coronado-González et al. 2007; Del Razo et al. 2011; Ettinger et al. 2009; Kolachi et al. 2010; Rahman et al. 1998; Tseng et al. 2000b; Wang et al. 2007). Two other studies reported risk estimates for metabolic syndrome (Wang et al. 2007) and impaired glucose tolerance (Ettinger et al. 2009) rather than diabetes. Many of the studies were conducted in Bangladesh [n = 4 (Chen et al. 2010; Nabi et al. 2005; Rahman et al. 1998, 1999)] or Taiwan [n = 5 (Lai et al. 1994; Tsai et al. 1999; Tseng et al. 2000b; Wang et al. 2003, 2007)]. Other countries included the United States (Ettinger et al. 2009; Lewis et al. 1999; Meliker et al. 2007; Navas-Acien et al. 2008, 2009a; Steinmaus et al. 2009a, 2009b; Tollestrup et al. 2003; Zierold et al. 2004), Mexico (Coronado-González et al. 2007; Del Razo et al. 2011), Pakistan (Afridi et al. 2008; Kolachi et al. 2010), Turkey (Serdar et al. 2009), Spain (Ruiz-Navarro et al. 1998), China (Wang et al. 2009), and the United Kingdom (Ward and Pim 1984).

Measures of exposure are highly variable between these studies, ranging from areawide exposure estimates based on measurement of arsenic from drinking-water sources to individual-level exposure estimates based on detailed water consumption history, work history, or actual biomarkers of exposure. These variations in study design constitute irreducible sources of heterogeneity and present interpretive challenges in evaluating the results observed in this collection of studies. Specifically, exposure was assessed by arsenic concentrations in drinking water within a geographic area (Del Razo et al. 2011; Meliker et al. 2007; Zierold et al. 2004), as cumulative exposure index based on residence time × average drinking-water level (Chen et al. 2010; Lai et al. 1994; Lewis et al. 1999; Rahman et al. 1999; Tseng et al. 2000b), residence time in an arsenicosis-endemic region (Tollestrup et al. 2003; Tsai et al. 1999; Wang et al. 2003) or presence or absence of arsenicosis or keratosis as a surrogate for long-term exposure to arsenic (Nabi et al. 2005; Rahman et al. 1998) or by biomarkers including blood/plasma arsenic levels (Ettinger et al. 2009; Serdar et al. 2009; Ward and Pim 1984) and arsenic concentration in urine (Coronado-González et al. 2007; Navas-Acien et al. 2008, 2009a; Ruiz-Navarro et al. 1998; Steinmaus et al. 2009a, 2009b; Wang et al. 2009) or hair (Afridi et al. 2008; Kolachi et al. 2010; Wang et al. 2007). Three studies did not report risk estimates, but compared the levels of arsenic in diabetics and nondiabetics. Afridi et al. (2008) measured higher levels of arsenic in the hair, blood, and urine of 196 diabetics participating in a study that included a total of 434 men from Hyderabad, Pakistan. Higher arsenic urine, blood, and hair levels were also found in diabetics compared to nondiabetics in another study conducted in Pakistan by Kolachi et al. (2010). Levels of hair arsenic were significantly higher in a group of 76 new mothers with insulin-dependent diabetes compared to a group of 68 nondiabetic mothers, although hair is not considered the preferred matrix for arsenic [National Research Council (NRC) 1999]. Serdar et al. (2009) did not detect any statistically significant differences in plasma arsenic in diabetes cases (n = 31, mean ± SD = 1.22 ± 0.57 μg/L) compared to controls [n = 22; mean (range) = 0.86 (0.64–1.59 μg/L)] in a study based in Turkey, although this study may have been underpowered to detect differences.

Environmental Exposure, High Arsenic Areas (≥150 μg/L Drinking Water)

Table 1 summarizes the high-arsenic environmental exposure studies from Bangladesh (Chen et al. 2010; Nabi et al. 2005; Rahman et al. 1998, 1999) and Taiwan (Lai et al. 1994; Tsai et al. 1999; Tseng et al. 2000a, 2000b; Wang et al. 2003). There is limited to sufficient evidence for an association between arsenic and diabetes in populations from high-arsenic areas, primarily occurring in Bangladesh or Taiwan. Support for an association was strongest in studies where arsenic drinking-water levels were > 500 μg/L (Lai et al. 1994; Nabi et al. 2005; Rahman et al. 1998, 1999; Tsai et al. 1999; Tseng et al. 2000b; Wang et al. 2003). Eight of the nine studies conducted in Taiwan or Bangladesh reported positive associations between arsenic and diabetes ( Table 1 ) (Lai et al. 1994; Nabi et al. 2005; Rahman et al. 1998, 1999; Tsai et al. 1999; Tseng et al. 2000a, 2000b; Wang et al. 2003). The only prospective study within this group also reported a positive association [adjusted relative risk (RR) = 2.1 (95% CI: 1.1, 4.2)] for development of diabetes over a 4-year follow-up period among individuals with ≥ 17 mg/L-years compared with < 17 mg/L-years cumulative arsenic exposure (Tseng et al. 2000b). Those studies relying on clinically accepted measures of disease (e.g., fasting blood glucose, OGGT) (Lai et al. 1994; Rahman et al. 1998; Tseng et al. 2000a, 2000b) reported risk estimates ranging from 2.1 (RR; 95% CI: 1.1, 4.2) to 10.05 [adjusted odds ratio (adjOR); 95% CI: 1.3, 77.9]. Some of the studies might not be completely independent if they were surveying the same population, and perhaps the same individuals. Of the studies conducted in Taiwan, several (Lai et al. 1994; Tsai et al. 1999; Tseng et al. 2000b; Wang et al. 2003) derived their study populations from the Southwestern Blackfoot or arseniasis-endemic region of Taiwan. Furthermore, several papers specifically included the village of Pu-Tai (Lai et al. 1994; Tseng et al. 2000a, 2000b). Data presented by Tseng et al. (2000a, 2000b) represent a follow-up to the Lai et al. (1994) study and therefore likely included many of the same participants. Studies conducted in Bangladesh have focused on the same geographical area for their exposed populations: Dhaka, Rajshahu, and Khulna Divisions (Chen et al. 2010; Nabi et al. 2005; Rahman et al. 1998, 1999). While none of the Bangladesh studies indicated that they were follow-up activities related to previous studies, participants may have overlapped.

In contrast to the relative strength and consistency of results in many of the high-exposure studies, the most recent and largest study in Bangladesh did not find any significant associations between urinary arsenic or time-weighted average water arsenic and self-reported diabetes, glucosuria, or hemoglobin A1c (HbA1c) levels in a population-based cross-sectional study of 11,319 Bangladeshi men and women participating in the Health Effects of Arsenic Longitudinal Study (HEALS) (Chen et al. 2010). Diagnosis of diabetes was based on self-report of physician diagnosis prior to baseline, glucosuria (excluding 90 individuals who were taking medications for diabetes), or, in a smaller subset of 2,100 participants, HbA1c. Although the Chen et al. (2010) cohort is large, statistical power was limited by the small number of diabetes cases (241 of 11,078; about 2% of the total cohort reported a diagnosis of diabetes prior to baseline, including 45 diabetes cases in the highest quintile category for time-weighted average arsenic). Nonetheless, while a number of explanations for the findings of Chen et al. (2010) exist, no definitive conclusions could be drawn regarding aspects of the status, obesity, genetic differences) or exposure history (i.e., the relatively short duration of exposure for some study participants compared with the experiences of individuals in the arsenic-contaminated areas of Taiwan) that could explain the difference between this and the other studies.

Environmental Exposure, Low-to-Moderate Arsenic Areas

Excluding the NHANES studies, 12 of the 15 identified epidemiologic studies reported risk estimates related to diabetes, glycemic control, or metabolic syndrome in populations under conditions of low-to-moderate arsenic exposure from drinking water (< 150 μg/L drinking water) ( Table 2 ). Two studies (Lewis et al. 1999; Meliker et al. 2007) evaluated SMRs for each sex separately. The highest categories of drinking-water exposure in these studies were lower than the arsenic-exposed population studies in Bangladesh and Taiwan. Overall, the current literature provides insufficient evidence to conclude that arsenic is associated with diabetes at these levels of exposure. Recent studies with better measures of outcome (fasting blood glucose levels or OGTT) reported more consistent associations between arsenic and diabetes (Coronado-González et al. 2007; Del Razo et al. 2011) or impaired glucose tolerance (Ettinger 2009) within this range of exposure. Some of the differences among the studies may be due to variation in sample sizes and to differences in study populations and methods used to classify diabetes (e.g., death certificates vs. self-report or blood glucose level) or to estimate arsenic exposure (e.g., urine levels vs. drinking-water surveys).

Four publications based on analyses of data from NHANES cohorts, which are representative of the U.S. population and generally include participants with low-to-moderate exposure, were considered in our review (Navas-Acien et al. 2008, 2009a; Steinmaus et al. 2009a, 2009b). However, the results of these studies should not be considered independent because the main focus of several of the publications was to compare the methodological strategies used to assess the association between urinary arsenic and diabetes. In brief, differences in interpretation of the association between arsenic and diabetes can be reached based on different methodological approaches used to account for organic arsenic due to seafood consumption and whether to include urinary creatinine as an adjustment factor in the statistical model. Results of two of the NHANES analyses supported an association between arsenic exposure and diabetes (Navas-Acien et al. 2008, 2009a), but results based on two alternative analyses did not (Steinmaus et al. 2009a, 2009b). Differences in methodological approaches used to characterize arsenic exposure in these studies are discussed in more detail below under "Urinary arsenic."

Determining Exposure and Internal Dose in Studies of Arsenic

Arsenic Concentrations in Drinking Water. Measurement of total arsenic in drinking-water supplies is often used to assess arsenic exposure, but this approach is not appropriate for research questions pertaining to individual exposures, including research concerning the effects of individual variation in arsenic metabolism on internal dose. Individual-level information on the magnitude, duration, and timing of exposure is critical, especially for estimating cumulative exposure. One alternative has been to combine historical measurements of arsenic concentrations in drinking water with self-reported residential and water-use histories. This approach usually requires an assumption that arsenic concentrations in drinking water are stable over time and that study subjects do not consume water from other sources. Support for these assumptions has been found in several study populations (Navas-Acien et al. 2009b; Ryan et al. 2000).

Arsenic Levels in Blood, Nails, and Hair. The literature review revealed a number of arsenic exposure biomarkers in need of further characterization and validation. Whole blood and plasma are emerging exposure matrices that reflect a shorter half-life (i.e., about 1 hr) compared to arsenic levels in urine (4 days) (NRC 1999). Hair and nail arsenic levels are noninvasive measures that reflect mean arsenic levels for exposures that occurred several months (for hair) to over a year (for nails) before sampling (Orloff et al. 2009). Moreover, arsenic levels in nails generally reflect exposure to inorganic arsenic and seem to be less affected by seafood arsenicals (see below). While sometimes useful, hair is not a recommended exposure matrix for arsenic (NRC 1999). One limitation of measuring arsenic in hair and nails is that arsenic speciation is difficult to conduct. Also, the time period of exposure captured by hair and nail measurements depends on the specific segments collected and analyzed. Other target tissues (e.g., urothelial cells) and buccal and saliva samples have also been suggested (Bartolotta et al. 2011; Hernández-Zavala et al. 2008; Lew et al. 2010). Although these emerging biomarkers deserve additional attention, a more expanded knowledge of toxicokinetic data and information on correlations with existing biomarkers and intake doses is needed before they are adopted for use in research.

Urinary Arsenic. One of the most commonly used measures of arsenic exposure is urine. However, measurements of total urinary arsenic will not distinguish between inorganic and organic forms of arsenic unless a speciated analysis is conducted. Distinguishing between the inorganic and organic forms of arsenic is important because the inorganic forms are generally accepted as being of greater toxicological concern than the organic forms [Agency for Toxic Substances and Disease Registry (ATSDR) 2007; Vahter and Concha 2001]. The metabolism of inorganic arsenic is complex and results in a number of metabolites, including some that are chemically unstable. Inorganic arsenic occurs in two oxidation states: arsenite (AsIII) and arsenate (AsV), where the Roman numeral refers to the oxidation state. In the process of forming more water-soluble molecules, inorganic arsenic goes through alternating reduction and methylation reactions and fluctuates between oxidation states of III (regarded as more toxic) and V (less toxic) (ATSDR 2007; Vahter and Concha 2001). The general characterization of oxidation state III as less toxic than V is primarily based on acute toxicity studies, and this issue has not been adequately assessed in long-term toxicological studies.

In any case, total urinary arsenic reflects the number of arsenic ions generated from all arsenic species in the urine, including inorganic arsenic (AsIII, AsV), the tri- and pentavalent methylated metabolites of inorganic arsenic [monomethylarsonite (MMAIII), dimethylarsinite (DMAIII), monomethylarsonate (MMAV), dimethylarsinate (DMAV)] and the less toxic organic arsenic compounds commonly associated with dietary exposures, particularly in seafood (mainly arsenobetaine, arsenosugars, and arsenolipids) (Caldwell et al. 2009; Navas-Acien et al. 2009b) [Figure 1; for detailed information on common forms of arsenic, see Supplemental Material, Table S2 (http://dx.doi.org/10.1289/ehp.1104579)]. Because it is currently assumed that both the inorganic forms of arsenic and their methylated metabolites may be associated with diabetes and other health risks, speciation analysis, including specification of the arsenic oxidation state, is recommended. Studies that do include a speciated analysis often do not include an oxidative state analysis to distinguish between tri- and pentavalent metabolites of inorganic arsenic. In particular, there is a need to improve the ability to measure methylated trivalent species because they are regarded as more toxic (ATSDR 2007; Vahter and Concha 2001) and concentrations may be underestimated unless the appropriate speciation analysis is conducted. Although technically challenging and not typically done, it is possible to conduct analyses of these metabolites at the point of collection.

Figure 1.

Arsenic exposure and metabolism in the human body: from source to urine (modified from Navas-Acien et al. 2009a).
aArsenic species measured in NHANES (Caldwell et al. 2009). Two other organic forms of arsenic considered to be minor contributors to arsenic in seafood were also measured in NHANES but were detected only in a small number of urine samples: arsenocholine (1.8%) and trimethylarsine oxide (0.3%). The predominant urinary metabolite of arsenocholine in rats, mice, and rabbits is arsenobetaine (Marafante et al. 1984).

Accounting for Arsenic of Seafood Origin. Most human biomonitoring studies report levels of total arsenic, which includes inorganic and organic arsenic compounds and their metabolites. Depending on location and diet of the population being studied, fish and other seafood can be a significant source of exposure to specific organic forms of arsenic such as arsenobetaine, arsenosugars, and arsenolipids (Figure 1). Although they have not been evaluated as risk factors for diabetes-related end points, these complex organic arsenic compounds are generally accepted as less toxic than either inorganic arsenic or their methylated metabolites (ATSDR 2007; Vahter and Concha 2001). Inorganic arsenic as well as methylated forms in oxidation state III are highly reactive, with a high affinity for sulfhydryl groups (Vahter and Concha 2001). Therefore, failure to distinguish organoarsenicals from inorganic arsenic and metabolites of inorganic arsenic in urine may result in misclassification of exposure to the most toxicologically relevant forms of arsenic, which in turn may lead to mischaracterization of the association between urinary arsenic and diabetes. This is less of a concern when study participants are exposed to higher levels of arsenic from drinking water or proximity to an industrial or mining site with arsenic contamination because it is reasonable to assume that urinary arsenic primarily reflects exposure to inorganic arsenic in these populations. However, in studies of the general population, such as NHANES, a larger portion of urinary arsenic may represent organic arsenic, mostly due to seafood consumption (Longnecker 2009; Navas-Acien et al. 2009a; Steinmaus et al. 2009a).

How to best adjust for organic arsenicals of seafood origin is a controversial topic [for a detailed discussion, see Supplemental Material, pp. 5–7 (http://dx.doi.org/10.1289/ehp.1104579)]. Inorganic forms, arsenite and arsenate, are metabolized to their methylated forms, MMA and DMA, and eliminated in the urine. Although DMA is the major metabolite of inorganic arsenic, it is also a metabolite of the organic arsenicals, arsenosugars and arsenolipids and therefore reflects both exposures to inorganic and organic forms of arsenic of seafood origin (Figure 1). Three published strategies have been used to address this issue using NHANES data: a) statistically adjusting models used to estimate the association between total urinary arsenic and diabetes for markers of seafood intake, such as levels of urinary arsenobetaine and blood mercury (Navas-Acien et al. 2008), b) restricting the analysis to participants with very low or nondetectable levels of arsenobetaine (Navas-Acien et al. 2009a), and c) subtracting any organic arsenicals (i.e., arsenobetaine and arsenocholine) above detection limits from the total urinary arsenic measurement (Steinmaus et al. 2009a). These strategies led to different conclusions regarding the association between inorganic arsenic and diabetes in NHANES, with the first two approaches resulting in statistically significant associations (Navas-Acien et al. 2009a, 2011), whereas the third suggested no association (Steinmaus et al. 2009a). Subtracting arsenobetaine from total urinary arsenic does not account for exposure misclassification due to the presence of other seafood arsenicals and their metabolites, which are included in total urinary arsenic measurements but cannot be specifically accounted for because they were not measured separately in the NHANES samples. Statistical adjustment for arsenobetaine and restriction to participants with low levels of arsenobetaine control for all seafood arsenic species, not only for arsenobetaine, and have shown consistent results (Navas-Acien et al. 2009a, 2011). However, statistical adjustment may not completely eliminate bias because it mixes the effects of relevant and irrelevant exposures, and exclusion of seafood consumers from analysis may lead to selection bias in populations where seafood consumption is common. The lack of consistency of findings based on the different analytical approaches described above warrants caution in interpreting results from NHANES studies and highlights the importance of having good analytical methods to distinguish inorganic arsenic and its methylated metabolites from organic arsenicals of seafood origin.

Accounting for Urine Dilution. Typically, epidemiological studies that quantify exposure on the basis of spot urine measures for arsenic or other nonpersistent chemicals include adjustments for urine creatinine to account for variation in urine dilution. This may be accomplished by normalizing arsenic levels for creatinine as the exposure metric (i.e., micrograms of arsenic per gram urinary creatinine) or adjusting by using urinary arsenic as the measure of exposure (i.e., micrograms of arsenic per liter urine) but then including creatinine as a separate independent variable in the multiple regression analyses. Of the two approaches, the latter approach is recommended (Barr et al. 2005) because urinary creatinine concentrations are influenced by age, sex, health status, race/ethnicity, body mass index, fat-free mass, and time of day of collection and therefore can vary widely across individuals (Barr et al. 2005; Boeniger et al. 1993; Mahalingaiah et al. 2008). However, this strategy may not be appropriate for metals or other chemicals that compromise kidney function.

The decision on how, or whether, to adjust for urinary creatinine concentration is more complicated when the health effect under investigation can impact creatinine levels, as is the case with diabetes (Greenland 2003). Persons with diabetes tend to have lower urinary concentrations of creatinine, in part because muscle mass is reduced as a consequence of diabetes, which results in reduced creatinine excretion (Park et al. 2009). Diabetes also leads to increased glomerular filtration and increased water intake, which can cause urine to be more dilute, resulting in lower urinary creatinine concentrations (Jerums et al. 2010). Both physiological processes may lead to biased assessments on the association between urinary arsenic and diabetes, although it is not possible to predict the direction of the overall bias with confidence (i.e., systematic bias toward or away from identifying a positive association). The reasons for this are discussed in more detail in the literature review document prepared for the 2011 workshop (NTP 2011b). The situation is further complicated because arsenic exposure has also been associated with increased urine creatinine in persons living in an arsenic-endemic area of Bangladesh (Nermell et al. 2008) or participating in the HEALS study described above (Ahsan H, personal communication). Thus, if diabetes and arsenic affect creatinine production, as well as urine dilution, then adjustment for creatinine may introduce bias rather than controlling measurement error induced by urine dilution (Greenland 2003). Relative risk estimates for associations between arsenic and diabetes based on creatinine- adjusted urine are quantitatively higher than estimates based on urinary arsenic levels that are not adjusted for creatinine (Chen et al. 2010; Steinmaus et al. 2009b). However, given the issues discussed above, it may not be possible to fully understand the potential bias with respect to clarifying the association between arsenic and diabetes. While specific gravity has been suggested as an alternative method to normalize urinary arsenic for differences in urine dilution because it appears to be less affected than creatinine by age, sex, and body size (Mahalingaiah et al. 2008; Nermell et al. 2008), its use is not recommended in studies of diabetes because it is well established that specific gravity is not an accurate method if albumin or glucose is present in the urine (Chadha et al. 2001; Voinescu et al. 2002). One approach to address concerns about creatinine adjustment is to report both raw and adjusted values. Prospective evidence, that is, measuring arsenic and creatinine at baseline and then during diabetes development over the follow-up, remains the best strategy to eliminate potential bias related to the impact of diabetes in urine creatinine concentrations (i.e., before any potential renal or metabolic effect of the disease occurs in urine creatinine concentrations).

Emerging Issues Related to Arsenic Exposure. At present, there is very little exposure or toxicity information for other types of arsenicals. Roxarsone, an arsenic-based drug fed to chicken, turkeys, and pigs for growth promotion, feed efficiency, and improved pigmentation, may be a source of dietary exposure to inorganic arsenic (Food and Drug Administration 2011; Silbergeld and Nachman 2008). Thioarsenical metabolites in urine are emerging forms of concern but are difficult to measure and their interpretation is at present unclear (Naranmandura et al. 2010; Pinyayev et al. 2011). The significance of the gut microbiome in understanding arsenic toxicity is another new issue in the field. Available data suggest the impact of microbiome metabolism of arsenic prior to absorption into the human body may be important in terms of interpreting observed differences in patterns of arsenic metabolites in addition to differences in metabolic pathways within human organs (Proctor 2011; Sun et al. 2012; Van de Wiele et al. 2010).

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