Arsenic exposure and type 2 diabetes: a systematic review of the experimental and epidemiologic evidence.

Chronic arsenic exposure has been suggested to contribute to diabetes development. We performed a systematic review of the experimental and epidemiologic evidence on the association of arsenic and type 2 diabetes. We identified 19 in vitro studies of arsenic and glucose metabolism. Five studies reported that arsenic interfered with transcription factors involved in insulin-related gene expression: upstream factor 1 in pancreatic [beta]-cells and peroxisome proliferative-activated receptor [gamma] in preadipocytes. Other in vitro studies assessed the effect of arsenic on glucose uptake, typically using very high concentrations of arsenite or arsenate. These studies provide limited insight on potential mechanisms. We identified 10 in vivo studies in animals. These studies showed inconsistent effects of arsenic on glucose metabolism. Finally, we identified 19 epidemiologic studies (6 in high-arsenic areas in Taiwan and Bangladesh, 9 in occupational populations, and 4 in other populations). In studies from Taiwan and Bangladesh, the pooled relative risk estimate for diabetes comparing extreme arsenic exposure categories was 2.52 (95% confidence interval, 1.69-3.75), although methodologic problems limit the interpretation of the association. The evidence from occupational studies and from general populations other than Taiwan or Bangladesh was inconsistent. In summary, the current available evidence is inadequate to establish a causal role of arsenic in diabetes. Because arsenic exposure is widespread and diabetes prevalence is reaching epidemic proportions, experimental studies using arsenic concentrations relevant to human exposure and prospective epidemiologic studies measuring arsenic biomarkers and appropriately assessing diabetes should be a research priority. Key words: arsenic, diabetes, glucose metabolism, meta-analysis, systematic review.

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Type 2 diabetes mellitus is a metabolic disorder characterized by hyperglycemia, insulin resistance in peripheral tissues, and altered insulin secretory capacity of pancreatic [beta]-cells. Type 2 diabetes accounts for 90-95% of all cases of diabetes and is a major public health problem worldwide (Wild et al. 2004). Established risks factors of type 2 diabetes include older age, obesity, physical inactivity, family history, and genetic polymorphisms. In addition, environmental toxicants, including arsenic, have been suggested to play an etiologic role in diabetes development (Longnecker and Daniels 2001).

Arsenic is a recognized toxicant and carcinogen. Nonoccupational exposure occurs mainly through drinking water, affecting millions of people worldwide. Exposure to levels of arsenic in drinking water well above 100 ppb has been associated with an increased risk of type 2 diabetes in the high-arsenic areas of Taiwan and Bangladesh (Lai et al. 1994; Rahman et al. 1998). The biological mechanisms for an association between chronic arsenic exposure and increased diabetes risk are not known [National Research Council (NRC) 1999, 2001; Tseng 2004].

Previous reviews of the role of arsenic in diabetes have questioned the quality of the evidence but were supportive of the possibility of an association [NRC 1999, 2001; Ng 2001; Tseng 2004; Tseng et al. 2000, 2002; World Health Organization (WHO) 2001]. These reviews, however, did not use systematic review criteria and may be subject to biased selection of the evidence. Our objective was to perform a systematic review of the experimental and epidemiologic evidence on arsenic and type 2 diabetes. We examined experimental studies (in vitro or in vivo) to synthesize available information on plausible mechanisms for the effect of arsenic on glucose metabolism, as well as epidemiologic studies to synthesize the association of arsenic exposure with diabetes risk in humans.

Materials and Methods

Search strategy and study selection. We searched the Medline database (http:// www.ncbi.nlm.nih.gov/entrez/query. fcgi?db=PubMed) and the TOXNET database [consisting of TOXLINE, GENETOX, and DART/ETIC (Developmental and Reproductive Toxicology/Environmental Teratogen Information Center); http:// toxnet.nlm.nih.gov/] from 1966 through July 2005 using free text and the medical subject headings (MESH) arsenic, arsenite, arsenate, arsenicals, diabetes, glucose, glycosylated hemoglobin, insulin, and mortality. In addition, we manually reviewed the reference lists from relevant original research and review articles.

For experimental studies, we identified in vitro or in vivo studies of the administration of arsenic or arsenic compounds, including inorganic arsenite (trivalent arsenic), inorganic arsenate (pentavalent arsenic), and others, and outcomes related to diabetes status or glucose and insulin metabolism. For epidemiologic studies, we identified studies assessing arsenic exposure through measures of environmental samples, biomarkers, or indirect measures (e.g., job titles reflecting occupational exposure or living in areas with known exposure via drinking water) and diabetes status or markers of glucose metabolism.

The exclusion criteria for experimental and epidemiologic studies were a) no original research (reviews, editorials, nonresearch letters); b) studies performed only on people with diabetes, including case reports; c) lack of outcomes related to diabetes or glucose metabolism; d) no data on arsenic exposure; e) experiments in nonmammalian cells, or noncellular experiments; f) animal studies administering a single dose of arsenic; and g) culture cell experiments using lewisite or oxophenylarsine. Figure 1 summarizes the study selection process.

[FIGURE 1 OMITTED]

Two investigators (A.N.-A., R.A.S.) independently abstracted the articles that met the selection criteria. Discrepancies were resolved by consensus. We converted all arsenic concentrations to parts per million or parts per billion, including concentrations from in vitro studies, which were usually reported in molar units of arsenic (1 [micro]M of arsenic = 74.9 [micro]g/L = 74.9 ppb).

Statistical methods. Measures of association in epidemiologic studies (odds ratios, prevalence ratios, standardized mortality ratios, relative risks, relative hazards, comparisons of means) and their SE values were abstracted or derived using data reported in the articles (Greenland 1987). Within each study, we used the model adjusted for the most covariates. Adjustment did not substantially modify the conclusions of any individual study. For five studies, we used data available in the original articles to derive relative risk estimates. For one study (Lagerkvist and Zetterlund 1994), because there were no cases among the unexposed, we added 0.5 to each cell to estimate the relative risk and the 95% confidence interval (CI). For Jensen and Hansen (1998), we compared the proportion of subjects with glycosylated hemoglobin above 7% across occupational exposure categories. For Ward and Pim (1984) and Ruiz-Navarro et al. (1998), we used the linear discriminant function method to estimate relative risks from comparisons of means (Greenland 1987). Finally, for Lewis et al. (1999), we estimated the relative risk of diabetes mortality comparing the highest with the lowest category of exposure within the cohort from the published standardized mortality ratios.

We grouped the studies in three categories: studies in general populations exposed to high arsenic levels, corresponding to studies in Taiwan and Bangladesh with average levels in drinking water well above 100 ppb; studies in occupational populations exposed to high arsenic levels most commonly in ambient air; and studies in general populations exposed to low or moderate levels of arsenic in drinking water (< 100 ppb), food, or ambient air. Because of substantial heterogeneity and methodologic limitations, we present a qualitative systematic review, and we used only meta-analysis techniques for studies from Taiwan and Bangladesh. For descriptive purposes, we report the range and the unweighted medians of the relative risk of diabetes comparing extreme categories of arsenic exposure in each study.

Results

In Vitro Experimental Studies

Nineteen in vitro studies published between 1965 and 2004 met our inclusion criteria (Figure 1, Table 1). None of the experimental studies were conducted in human cell lines. Five experiments investigated the effect of arsenic on insulin signal transduction and gene expression. Three studies were performed in transfected mouse pancreatic [beta]-cells, where exposure to high arsenite concentrations was similar to high glucose in stimulating insulin upstream factor 1 (IUF-1) (Macfarlane et al. 1997) and in stimulating the translocation of IUF-1 from the cytoplasm to the nucleus (Elrick and Docherty 2001; Macfarlane et al. 1999). IUF-1, also called homeodomain transcription factor PDX1, is a transcription factor that binds to the human insulin gene promoter and increases insulin messenger RNA levels in response to glucose. The effect of high glucose or arsenite was prevented by SB 203580, a specific inhibitor of stress-activated protein kinase-2 (SAPK2)/p38, whereas the effect of high glucose but not of arsenite was prevented by substances that specifically inactivate phosphatidylinositol-3 kinase (wortmannin and LY294002). Two other studies (Salazard et al. 2004; Wauson et al. 2002) investigated the role of arsenite in adipocyte differentiation and peroxisome proliferative-activated receptor [gamma] (PPAR[gamma]) expression. PPAR[gamma] is a transcription factor that regulates key gene expression for insulin sensitivity. These two experiments used different concentrations and lengths of exposure and produced opposite results. In the study by Salazard et al. (2004), the incubation of 3T3-F442A preadipocytes with 1.7 and 3 ppb (0.25 and 0.5 [micro]M) arsenite for 3 days induced the expression of PPAR[gamma] and CCAAT/ enhancer binding protein. In study by Wauson et al. (2002), the incubation of C3H 101T1/2 cells with 450 ppb (6 [micro]M) arsenite for 2 months prevented adipocyte differentiation through the inhibition of the PPAR[gamma]. Arsenite also inhibited the differentiating effect induced by pioglitazone, a PPAR[gamma] agonist used to reduce insulin resistance.

The rest of the in vitro studies assessed the effect of arsenic on glucose uptake, typically using very high concentrations of arsenite as general inducers of cellular stress. Ten studies measured basal glucose uptake (in the absence of insulin) in cell lines exposed to arsenite or other compounds (Table 1, Figure 2). Four of the studies also exposed the cells simultaneously to insulin and arsenite (Table 2). Compared with insulin alone, simultaneous exposure to insulin and arsenite decreased glucose uptake in insulin-sensitive cells (Bazuine et al. 2003; Walton et al. 2004). One of the studies (Walton et al. 2004) measured basal and insulin-stimulated glucose uptake in cells exposed to arsenate and to methylated arsenic compounds. Methylarsine oxide ([MAs.sup.III]O) inhibited insulin-stimulated glucose uptake at the concentration of 75 ppb after 4- or 24-hr exposure (Walton et al. 2004). For arsenite, because the concentrations used in glucose uptake studies were extremely high, their relevance to diabetes development in humans is questionable.

[FIGURE 2 OMITTED]

Overall, in vitro studies provided limited insight into potential mechanisms that may explain an etiologic role of arsenic on diabetes.

In Vivo Experimental Studies

Ten experimental studies in mice, rats, goats, and guinea pigs published between 1979 and 2004 met our inclusion criteria (Figure 1, Table 3). Arsenite was evaluated in 6 studies (Biswas et al. 2000; Cobo and Castineira 1997; Ghafghazi et al. 1980; Pal and Chatterjee 2004a, 2004b, 2005), and arsenate in 2 studies (Aguilar et al. 1997; Hughes and Thompson 1996). Other compounds were methanearsonate (Judd 1979) and monomethylarsenic (Arnold et al. 2003). Six studies administered arsenic in water or food for lengths of time ranging from 4 weeks to 2 years, and 5 studies involved intraperitoneal exposure to arsenic for 5-30 days. The doses of arsenic were high or very high in most studies, with a lowest dose of 5.55 ppm arsenite (Pal and Chatterjee 2004a) and 0.025 ppm arsenate (Hughes and Thompson 1996).

Although all studies measured glucose levels in blood, plasma, or serum, only one study provided information on potential mechanisms (Cobo and Castineira 1997). In this study, the oral administration of arsenite did not affect insulin levels in vivo. However, a glucose stimulus applied ex vivo produced greater insulin release from the isolated pancreas cells of rats treated with arsenite in vivo compared with the insulin release from isolated pancreas cells of control rats.

Epidemiologic Studies

Study characteristics. Nineteen epidemiologic studies met our inclusion criteria (Figure 1, Table 4). Three studies were published between 1980 and 1984 (Enterline and Marsh 1982; Mabuchi et al. 1980; Ward and Pim 1984), and the other 15 were reported between 1994 and 2004. Only 2 studies used a prospective cohort design (Lewis et al. 1999; Wang et al. 2003). The rest used cross-sectional, case-control, or retrospective cohort designs. Two studies used the WHO diabetes definition based on oral glucose tolerance tests and/or self-reported medication to define diabetes, whereas the other studies used death certificates, medical or insurance records, urine tests for glucosuria, self-reported diabetes symptoms such as polyuria confirmed by two positive urine tests and a positive oral glucose tolerance test, glycosylated hemoglobin, or self-reported diagnosis. Two studies did not specify the diagnostic criteria. The number of diabetes cases ranged from 2 (Mabuchi et al. 1980) to 27,543 (Wang et al. 2003), but most studies had fewer than 100 cases. Studies in general populations included adult men and women, whereas occupational studies included mostly men.

There were substantial differences in arsenic exposure ascertainment. Most studies in general populations assessed exposure indirectly, using measurements of total arsenic levels in community drinking water sources. Two studies from Taiwan (Lai et al. 1994; Tseng et al. 2000), one from Bangladesh (Rahman et al. 1999), and one from the United States (Lewis et al. 1999) estimated a cumulative arsenic exposure index (ppm-year) by multiplying the number of years that individuals lived in a specific village/area by the average arsenic level in drinking water in that village/area (usually, in each area, several measurements were performed once in time). Other studies in Taiwan and Bangladesh assigned exposure on the basis of residence in an area determined to be endemic for arseniasis (Rahman et al. 1998; Tsai et al. 1999; Wang et al. 2003). None of the studies from Taiwan or Bangladesh obtained individual measures of arsenic exposure either from household tap water measures or more directly by using biomarkers of exposure. None of these studies assessed potential sources of exposure other than drinking water.

In occupational studies, exposure was based on job tide or on estimated arsenic levels in air for different job categories as assessed by a safety engineer (Rahman and Axelson 1995). One study in an occupationally exposed area assessed arsenic exposure based on years of residence within 4 km of a copper smelter during childhood (Tollestrup et al. 2003). Some occupational studies (Enterline and Marsh 1982; Jensen and Hansen 1998; Lagerkvist and Zetterlund 1994; Lubin et al. 2000) also measured arsenic in urine or air to confirm exposure, but this information was not linked to diabetes in the analyses. Only two studies used biomarkers of exposure: Ward and Pim (1984) measured total arsenic in plasma, and Ruiz-Navarro et al. (1998) measured total arsenic in urine, without speciation of inorganic and methylated compounds.

Quality assessment. In the epidemiologic studies we abstracted information to evaluate study quality, adapting the criteria proposed for observational studies by Longnecker et al. (1988). As shown in Table 5, most studies failed to fulfill important quality criteria such as individual measures of arsenic exposure using biomarkers, standard criteria to diagnose diabetes, or information on established risk factors for diabetes.

Relative risk estimates. The relative risk estimates comparing the highest with the lowest arsenic exposure categories are shown in Table 4. Studies in Taiwan and Bangladesh consistently identified an increased risk of diabetes with increased arsenic exposure, with relative risks ranging from 1.46 to 10.1 (median, 2.40) and with a pooled relative risk estimate using and inverse variance weighted random-effects model of 2.52 (95% CI, 1.69-3.75; p heterogeneity < 0.001). Occupational studies were small and showed no consistent pattern, with relative risks ranging from 0.34 to 9.61 (median, 1.40). We identified only 4 studies in general populations from countries with low or moderate arsenic exposure. These studies were small and did not show an increased risk of diabetes with increasing arsenic levels (relative risks ranged from 0.65 to 1.09; median, 0.95).

Five studies provided information on the dose response in diabetes risk by cumulative arsenic exposure in drinking water (Figure 3). Diabetes risk tended to increase with increasing cumulative exposure in studies from Taiwan (Lai et al. 1994; Tseng et al. 2000) and Bangladesh (Rahman et al. 1999). No trend was observed in the U.S. studies (Lewis et al. 1999; Zierold et al. 2004).

[FIGURE 3 OMITTED]

Discussion

Summary of findings. The evidence on the association of arsenic exposure with diabetes risk summarized in this systematic review is inconclusive. Evidence from in vitro studies suggests that arsenic interferes with signal transduction and transcription factors that are related to insulin pathways such as IUF-1 in pancreatic cells or PPAR[gamma] in preadipocytes. In vitro glucose uptake experiments and in vivo studies did not provide evidence on potential mechanisms that may explain a diabetogenic effect of arsenic. In general, experimental studies were limited by the use of arsenic concentrations that were much higher than those relevant to human exposure. For example, the current U.S. Environmental Protection Agency recommended standard for arsenic in drinking water is 10 ppb. The lowest concentration of arsenite used in studies of cultured cells investigating glucose uptake was 750 ppb (Bazuine et al. 2003), and the lowest concentration of arsenite in animal studies was 5,550 ppb (Pal and Chatterjee 2004a, 2004b).

In epidemiologic studies, the association between arsenic exposure and diabetes across different populations and different sources of exposure was inconsistent. In populations exposed to high arsenic levels via drinking water in Taiwan and Bangladesh, diabetes risk was consistently increased. In occupational settings, diabetes mortality was increased in some studies and decreased in others. Finally, no association with diabetes was observed in four studies of general populations outside of Taiwan or Bangladesh. Overall, the quality of the epidemiologic evidence was limited by methodologic problems, particularly in assessing arsenic exposure and diabetes outcomes.

Mechanisms for arsenic-related diabetes. Acute arsenite toxicity, including its effects on glucose metabolism, is generally attributed to its reactivity toward thiol (SH) groups (Aposhian 1989; NRC 1999). During acute poisoning, arsenite inhibits pyruvate and [alpha]-ketoglutarate dehydrogenases (Aposhian 1989), essential enzymes for gluconeogenesis and glucolysis. The interference of arsenic with pyruvic acid metabolism was described by Krebs in the early 1930s (Krebs 1933). Arsenate, on the other hand, can replace phosphate in energy transfer pathways of phosphorylation and also uncouples oxidative phosphorylation (Kennedy and Lehninger

1949). However, these toxic effects of acute arsenic exposure are unlikely to occur as a result of chronic exposure to environmentally relevant doses (Tseng 2004).

The influence of arsenic on the expression of gene transcription factors may be related to diabetes risk. However, the effects of arsenite on IUF-1 and PPAR[gamma] were contradictory in terms of diabetes development. The differential effects may reflect a complex dose-response pattern for arsenic or differences in length of exposure to arsenic across studies. Further studies with wide ranges and durations of arsenic exposure are needed to investigate the effect of arsenic on these and other insulin-related events at the cellular and molecular levels. For instance, interference with the glucocorticoid receptor is another potential mechanism for arsenic-related diabetes that deserves further investigation. Arsenic shows a complex dose-response effect on glucocorticoid receptor mediated transcription (Bodwell et al. 2004), with a stimulatory effect at very low concentrations (6-120 ppb) and an inhibitory effect at doses greater than 120 ppb. The glucocorticoid receptor is a member of the steroid receptor superfamily that among other metabolic processes regulates gluconeogenesis. Reduction of glucocorticoid receptor expression in hepatic and adipose tissue has been shown to improve hyperglycemia in diabetic rodents (Watts et al. 2005).

Experimental studies on glucose uptake showed that arsenite increases uptake independently of the earlier steps of the insulin transduction pathway, although when co-administered with insulin, arsenite inhibited insulin-stimulated glucose uptake in 3T3-L1 adipocytes. The purpose of most of these studies was to investigate the role of stress in glucose uptake, which is unrelated to the possibility that arsenic could affect diabetes risk. Under these designs, cultured cells were exposed to high arsenic levels for a few hours, whereas humans are chronically exposed to lower concentrations. Only one study investigated methylated arsenical compounds and their interference in insulin signaling in adipocytes (Walton et al. 2004). For these reasons, the relevance of in vitro glucose uptake findings to diabetes etiology is uncertain.

Arsenic could influence diabetes development by other mechanisms, including oxidative stress, inflammation, or apoptosis, nonspecific mechanisms that have been implicated in the pathogenesis of type 2 diabetes. Arsenic exposure can enhance the production of reactive oxygen species (Barchowsky et al. 1999; Chen et al. 1998; Tseng 2004; Wang et al. 1996), interfere with the activity of key antioxidant enzymes such as glutathione reductase, glutathione S-transferase, glutathione peroxidase, and glucose 6-phosphate dehydrogenase (Maiti and Chatterjee 2000; Santra et al. 2000), and induce lipid peroxidation (Santra et al. 2000). In individuals from Taiwan, increasing blood levels of arsenic correlated with increasing levels of reactive oxygen species and with decreasing levels of antioxidant capacity in plasma (Wu et al. 2001). Arsenic may also up-regulate interleukin-6 and other inflammatory cytokines (Wu et al. 2003), and it may induce the release of tumor necrosis factor-[alpha] from mononuclear cells (Yu et al. 2002). Finally, arsenic is well known for inducing apoptosis in multiple cell lines (Waalkes et al. 2000). Future research should evaluate whether these mechanisms mediate the role of arsenic in diabetes development.

The in vivo experimental studies were mostly uninformative. The diversity of species studied probably reflects that there are no good animal models to study the effects of arsenic on diabetes development. Indeed, the classification of arsenic as a human carcinogen, although recently supported by animal models (Waalkes et al. 2004), was for a long time based on human data. Progress in the study of the role of arsenic in diabetes requires the identification of appropriate animal models.

Arsenic and diabetes in human studies. Suggestive evidence links chronic exposure to high arsenic levels in drinking water with increased diabetes risk in Taiwan and Bangladesh. Methodologic problems, however, limit the causal interpretation of this association. The use of average drinking water and the lack of individual measures of arsenic make it possible to underestimate exposure due to between-subject variability in water consumption and to other sources of arsenic exposure in these areas, such as contaminated food and cooking water. On the other hand, because arsenic exposure was assessed at the village level and diabetes diagnosis was often not performed according to standard procedures, this ecologic association could reflect the uncertain comparability of exposure groups in terms of socioeconomic development, access to care, study selection factors and other diabetes risk factors. The use of urine tests and of administrative data to identify diabetes makes it likely that only severe or symptomatic cases were identified, and it is uncertain whether the procedures and frequency for diabetes testing were similar across areas with different arsenic exposure. In addition, the use of administrative data can be affected by surveillance and diagnostic biases. For example in Taiwan, arsenic-related health problems in the endemic areas are well known, hence, subjects in these areas may have received different medical care, including different diagnostic services, compared with subjects in areas with lower arsenic levels.

It is also possible that the findings from Taiwan and Bangladesh may not be generalizable to other populations. Some reasons for this include variations in the distribution of polymorphisms in genes involved in arsenic metabolism or response (Loffredo et al. 2003), differences in arsenic species to which populations were exposed (Chen et al. 1995), other co-exposures (Chen et al. 1995), and dietary deficiencies that may interact with arsenic. For example, selenium and zinc levels in Taiwan and Bangladesh are among the lowest worldwide (Lin and Yang 1988), and poor dietary selenium has been suggested as an underlying factor for arsenic and cancer in Bangladesh and West Bengal in India (Spallholz et al. 2004). In guinea pigs, selenium and arsenic counteract each other in glucose metabolism (Das et al. 1989), and the joint effect of high arsenic and low selenium could play a role in diabetes development. Exposure to arsenic, selenium, other nutrients, and other diabetes risk factors were not measured in epidemiologic studies.

We found no reports of diabetes in populations known to be exposed to high levels of arsenic in drinking water in Chile and Argentina. This lack of information on diabetes could reflect a lack of research, but it has also been suggested to be related to publication bias (Longnecker and Daniels 2001).

The evidence from general populations outside of Taiwan or Bangladesh was inconclusive because of the small number of cases, limitations in study design, and misclassification of diabetes status. Occupational studies, on the other hand, could not be interpreted in favor or against an association because of uncertain comparability of study participants with the general population used as reference, limitations in exposure assessment, lack of information on concomitant exposures, lack of information on major diabetes risk factors, and the possibility of a healthy worker survivor effect.

An important conclusion we derived from the epidemiologic review is the limited quality of the evidence base. This finding is consistent with previous reviews, including those by U.S. and international panels (NRC 1999, 2001; Ng 2001; WHO 2001). These panels determined that the available evidence on arsenic and diabetes suffered from uncertainties in study design and exposure assessment. Our review further refines these reports and identifies the lack of biomarker data and the lack of standard criteria for diabetes assessment as major limitations of the evidence base. Current uncertainties in the role of arsenic in diabetes development could be reduced by conducting carefully planned epidemiologic studies in populations exposed to a wide range of arsenic levels. Future studies should a) measure appropriate arsenic biomarkers that integrate all sources of exposure (e.g., urine or toenails); b) carefully collect information on current and past sources of arsenic exposure and on potential confounders and modifiers, including known determinants of diabetes development; c) and prospectively ascertain diabetes using standard definitions.

Conclusion

The possibility of an association between chronic arsenic exposure and diabetes has implications for research and public health. Millions of people are exposed worldwide to moderate or high levels of arsenic in drinking water. Because diabetes is also a major public health problem, the public health consequences of a causal association could be serious. Methodologic problems limit the causal interpretation of the moderately strong association between high arsenic exposure and diabetes in Taiwan and Bangladesh. Overall, the experimental and epidemiologic evidence is at present insufficient and inadequate to establish causality. Experimental studies that use arsenic concentrations relevant to human exposures, and high-quality prospective epidemiologic studies that use appropriate methods for exposure assessment as well as rigorous criteria for outcome definitions should be research priorities.

CORRECTION

Table 1 has been modified from the original manuscript published online. The table now includes information on the 24-hr study by Walton et al. (2004).

Received 1 August 2005; accepted 15 December 2005.

REFERENCES

Aguilar MV, Martinez-Para MC, Gonzalez MJ. 1997. Effects of arsenic (V)-chromium (Ill) interaction on plasma glucose and cholesterol levels in growing rats. Ann Nutr Metab 41:189-195.

Aposhian HV. 1999. Biochemical toxicology of arsenic. In: Reviews in Biochemical Toxicology (Hodgson E, Bend JR, Philpot RM, eds). Vol 10. Amsterdam:Elsevier, 265-289.

Arnold LL, Eldan M, van Gemert M, Capen CC, Cohen SM. 2003. Chronic studies evaluating the carcinogenicity of monomethylarsonic acid in rats and mice. Toxicology 100:197-219.

Barchowsky A, Klei LR, Dudek EJ, Swartz HM, James PE. 1999. Stimulation of reactive oxygen, but not reactive nitrogen species, in vascular endothelial cells exposed to low levels of arsenite. Free Radic Biol Med 27:1405-1412.

Bartoli O, Battista G, De Santis M, Iaia TE, Orsi D, Tarchi M, et al. 1998. Cohort study of art glass workers in Tuscany, Italy: mortality from non-malignant diseases. Occup Med (Lond) 48:441-445.

Bazuine M, Carlotti F, Tafrechi RS, Hoeben RC, Maassen JA. 2004. Mitogen-activated protein kinase (MAPK) phosphatase-1 and -4 attenuate p38 MAPK during dexamethasone-induced insulin resistance in 3T3-L1 adipocytes. Mol Endocrinol 18:1697-1707.

Bazuine M, Ouwens DM, Gomes de Mesquita DS, Maassen JA. 2003. Arsenite stimulated glucose transport in 3T3-L1 adipocytes involves both Glut4 translocation and p38 MAPK activity. Eur J Biochem 270:3891-3903.

Biswas U, Sarkar S, Bhowmik MK, Samanta AK, Biswas S. 2000. Chronic toxicity of arsenic in goats: clinicobiochemical changes, pethomorphology and tissue residues. Small Rumin Res 38:229-235.

Bodwell JE, Kingsley LA, Hamilton JW. 2004. Arsenic at very low concentrations alters glucocorticoid receptor (GR)-mediated gene activation but not GR-mediated gene repression: complex dose-response effects are closely correlated with levels of activated GR and require a functional GR DNA binding domain. Chem Res Toxicol 17:1064-1076.

Brazy PC, Balaban RS, Gullans SR, Mandel LJ, Dennis VW. 1980. Inhibition of renal metabolism. Relative effects of arsenate on sodium, phosphate, and glucose transport by the rabbit proximal tubule. J Clin Invest 66:1211-1221.

Chen CJ, Hsueh YM, Lai MS, Shyu MP, Chen SY, Wu MM, et al. 1995. Increased prevalence of hypertension and long-term arsenic exposure. Hypertension 25:53-60.

Chert YC, Lin-Shiau SY, Lin JK. 1998. Involvement of reactive oxygen species and caspase 3 activation in arsenite-induced apoptosis. J Cell Physiol 177:324-333.

Cobo JM, Castineira M. 1997. Oxidative stress, mitochondrial respiration, and glycemic control: clues from chronic supplementation with Cr[3.sup.+] or As[3.dup.+] to male Wistar rats. Nutrition 13:965-970.

Das PM, Sadana JR, Gupta RK, Kumar K. 1989. Experimental selenium toxicity in guinea pigs: biochemical studies. Ann Nutr Metab 33:57-63.

Dixit PK, Lazarow A. 1967. Effects of metal ions and sulfhydryl inhibitors on glucose metabolism by adipose tissue. Am J Physiol 213:849-856.

Elrick LJ, Docherty K. 2001. Phosphorylation-dependent nucleocytoplasmic shuttling of pancreatic duodenal homeebox-1. Diabetes 50:2244-2252.

Enterline PE, Marsh GM. 1982. Cancer among workers exposed to arsenic and other substances in a copper smelter. Am J Epidemiol 116:895-911.

Fladeby C, Serek-Hanssen G. 1999. Stress-induced glucose uptake in bovine chromaffin cells: a comparison of the effect of arsenite and anisomycin. Biochim Biophys Acta 1452:313-321.

Ghafghazi T, Ridlington JW, Fowler BA. 1980. The effects of acute and subacute sodium arsenite administration on carbohydrate metabolism. Toxicol Appl Pharmacol 55:126-130.

Greenland S. 1987. Quantitative methods in the review of epidemiologic literature. Epidemiol Rev 9:1-30.

Hughes MF, Thompson DJ. 1996. Subchronic dispositional and toxicological effects of arsenate administered in drinking water to mice. J Toxicol Environ Health 49:177-196.

Hunder G, Nguyen PT, Schumann K, Fichtl B. 1993. Influence of inorganic and organic arsenicals on intestinal transfer of nutrients. Res Commun Chem Pathol Pharmacol 80:83-92.

Jensen GE, Hansen ML. 1998. Occupational arsenic exposure and glycosylated haemoglobin. Analyst 123:77-60.

Judd FW. 1979. Acute toxicity and effects of sublethal dietary exposure of monosodium methanearsonate herbicide to Peromyscus Ieucopus (Rodentia: Cricetidae). Bull Environ Contam Toxicol 22:143-150.

Kennedy EP, Lehninger AL. 1949. Oxidation of fatty acids and tricarboxylic acid cycle intermediates by isolated rat liver mitochondria. J Biol Chem 179:957-972.

Krebs HA. 1933. Untersuchugen uber den stoffwechsel der aminosauren im tierkorper [in German]. Z Physiol Chem 218:191-227.

Lagerkvist BJ, Zetterlund B. 1994. Assessment of exposure to arsenic among smelter workers: a five-year follow-up. Am J Ind Med 25:477-488.

Lai MS, Hsueh YM, Chen CJ, Shyu MP, Chen SY, Kuo TL, et al. 1994. Ingested inorganic arsenic and prevalence of diabetes mellitus. Am J Epidemiol 139:484-492.

Lewis DR, Southwick JW, Ouellet-Hellstrom R, Rench J, Calderon RL. 1999. Drinking water arsenic in Utah: a cohort mortality study. Environ Health Perspect 107:359-365.

Liebl B, Muckter H, Doklea E, Fichtl B, Forth W. 1992. Influence of organic and inorganic arsenicals on glucose uptake in Madin-Darby canine kidney (MDCK) cells. Analyst 117:681-684.

Lin SM, Yang MH. 1988. Arsenic, selenium, and zinc in patients with blackfoot disease. Biol Trace Elem Res 15:213-221.

Loffredo CA, Aposhian HV, Cebrian ME, Yamauchi H, Silbergeld EK. 2003. Variability in human metabolism of arsenic. Environ Res 92:85-91.

Longnecker MP, Berlin JA, Orza MJ, Chalmers TC. 1988. A meta-analysis of alcohol consumption in relation to risk of breast cancer. JAMA 260:852-658.

Longnecker MP, Daniels JL. 2001. Environmental contaminants as etiologic factors for diabetes. Environ Health Perspect 109(suppl 6):871-876.

Lubin JH, Pottern LM, Stone BJ, Fraumeni JF Jr. 2000. Respiratory cancer in a cohort of copper smelter workers: results from more than 50 years of follow-up. Am J Epidemiol 151:554-565.

Mabuchi K, Lilienfeld AM, Snell LM. 1980. Cancer and occupational exposure to arsenic: a study of pesticide workers. Prev Med 9:51-77.

Macfarlane WM, McKinnon CM, Felton-Edkins ZA, Cragg H, James RF, Docherty K. 1999. Glucose stimulates translocation of the homeodomain transcription factor PDX1 from the cytoplasm to the nucleus in pancreatic beta-cells. J Biol Chem 274:1011-1016.

Macfarlane WM, Smith SB, James RF, Clifton AD, Doza YN, Cohen P, et al. 1997. The p38/reactivating kinase mitogen-activated protein kinase cascade mediates the activation of the transcription factor insulin upstream factor 1 and insulin gene transcription by high glucose in pancreatic beta-cells. J Biol Chem 272:20936-20944.

Maiti S, Chatterjee AK. 2000. Differential response of cellular antioxidant mechanism of liver and kidney to arsenic exposure and its relation to dietary protein deficiency. Environ Toxicol Pharmacol 8:227-235.

McDowell HE, Walker T, Hajduch E, Christie G, Batty IH, Downes CP, et al. 1997. Inositol phospholipid 3-kinase is activated by cellular stress but is not required for the stress-induced activation of glucose transport in L6 rat skeletal muscle cells. Eur J Biochem 247:306-313.

Ng J. 2001. Arsenic and Arsenic Compounds. 2nd ed. Environmental Health Criteria 224. Geneva:World Health Organization.

NRC (National Research Council). 1999. Arsenic in Drinking Water. Washington, DC:National Academy Press.

NRC (National Research Council). 2001. Arsenic in Drinking Water. 2001 Update. Washington DC:National Academy Press.

Pal S, Chatterjee AK. 2004a. Protective effect of methionine supplementation on arsenic-induced alteration of glucose homeostasis. Food Chem Toxicol 42:737-742.

Pal S, Chatterjee AK. 2004b. Protective effect of N-acetylcysteine against arsenic-induced depletion in vivo of carbohydrate. Drug Chem Toxicol 27:179-189.

Pal S, Chatterjee AK. 2905. Prospective protective role of melatonin against arsenic-induced metabolic toxicity in Wistar rats. Toxicology 208:25-33.

Pasternak CA, Aiyathurai JE, Makinde V, Davies A, Baldwin SA, Konieczko EM, et al. 1991. Regulation of glucose uptake by stressed cells. J Cell Physiol 1149:324-331.

Rahman M, Axelson O. 1995. Diabetes mellitus and arsenic exposure: a second look at case-control data from a Swedish copper smelter. Occup Environ Med 52:773-774.

Rahman M, Tondel M, Ahmad SA, Axelson O. 1998. Diabetes mellitus associated with arsenic exposure in Bangladesh. Am J Epidemiol 148:198-203.

Rahman M, Tondel M, Ahmad SA, Chowdhury IA, Faruquee MH, Axelson O. 1999. Hypertension and arsenic exposure in Bangladesh. Hypertension 33:74-78.

Rahman M, Wingren G, Axelson O. 1996. Diabetes mellitus among Swedish art glass workers--an effect of arsenic exposure? Scand J Work Environ Health 22:146-149.

Ruiz-Navarro ML, Navarro-Alarcon M, Lopez Gonzalez-de la Serrana, Perez-Valero V, Lopez-Martinez MC. 1998. Urine arsenic concentrations in healthy adults as indicators of environmental contamination: relation with some pathologies. Sci Total Environ 216:55-61.

Salazard B, Bellon L, Jean S, Maraninchi M, El Yazidi C, Orsiere T, et al. 2004. Low-level arsenite activates the transcription of genes involved in adipose differentiation. Cell Biol Toxicol 20:375-385.

Santra A, Maiti A, Das S, Lahiri S, Charkaborty SK, Mazumder DN. 2000. Hepatic damage caused by chronic arsenic toxicity in experimental animals. J Toxicol Clin Toxicol 38:395-405.

Short AL, Wright FE, Whitney JE. 1965. Effect of anaerobiosis and cell poisons on glucose uptake of hemidiaphragms and epididymal fat pads in vitro. Diabetes 14:128-131.

Spallholz JE, Mallory BL, Rhaman MM. 2004. Environmental hypothesis: is poor dietary selenium intake an underlying factor for arsenicosis and cancer in Bangladesh and West Bengal, India? Sci Total Environ 323:21-32.

Sviderskaya EV, Jazrawi E, Baldwin SA, Widnell CC, Pasternak CA. 1996. Cellular stress causes accumulation of the glucose transporter at the surface of cells independently of their insulin sensitivity. J Membr Biol 149:133-140.

Tollestrup K, Frost FJ, Harter LC, McMillan GP. 2003. Mortality among children residing near the American Smelting and Refining Company (ASARCO) copper smelter in Ruston, Washington. Arch Environ Health 58:683-691.

Tsai SM, Wang TN, Ko YC. 1999. Mortality for certain diseases in areas with high levels of arsenic in drinking water. Arch Environ Health 54:186-193.

Tseng CH. 2004. The potential biological mechanisms of arsenic-induced diabetes mellitus. Toxicol Appl Pharmacol 197:67-83.

Tseng CH, Tai TY, Chong CK, Tseng CP, Lai MS, Lin BJ, et al. 2000. Long-term arsenic exposure and incidence of noninsulin-dependent diabetes mellitus: a cohort study in arseniasis-hyperendemic villages in Taiwan. Environ Health Perspect 108:847-851.

Tseng CH, Tseng CP, Chiou HY, Hsueh YM, Chong CK, Chen CJ. 2002. Epidemiologic evidence of diabetogenic effect of arsenic. Toxicol Lett 133:89-76.

Waalkes MP, Fox DA, States JC, Patierno SR, McCabe MJ, Jr. 2000. Metals and disorders of cell accumulation: modulation of apoptosis and cell proliferation. Toxicol Sci 56:255-261.

Waalkes MP, Liu J, Ward JM, Diwan BA. 2004. Animal models for arsenic carcinogenesis: inorganic arsenic is a transplacental carcinogen in mice. Toxicol Appl Pharmacol 198:377-384.

Walton FS, Harmon AW, Paul DS, Drobna Z, Patel YM, Styblo M. 2004. Inhibition of insulin-dependent glucose uptake by trivalent arsenicals: possible mechanism of arsenic-induced diabetes. Toxicol Appl Pharmacol 198:424-433.

Wang SL, Chioo JM, Chen CJ, Tseng CH, Chou WL, Wang CC, et al. 2003. Prevalence of non-insulin-dependent diabetes mellitus and related vascular diseases in southwestern arseniasis-endemic and nonendemic areas in Taiwan. Environ Health Perspect 111:155-159.

Wang TS, Kuo CF, Jan KY, Huang H. 1996. Arsenite induces apoptosis in Chinese hamster ovary cells by generation of reactive oxygen species. J Cell Physiol 169:256-268.

Ward NI, Pim B. 1984. Trace element concentrations in blood plasma from diabetic patients and normal individuals. Biol Trace Elem Res 6:469-487.

Warren AP, James MH, Menzies DE, Widnell CC, Whitaker-Dowling PA, Pasternak CA. 1985. Stress induces an increased hexose uptake in cultured cells. J Cell Physiol 128:383-388.

Watts LM, Manchem VP, Leedom TA, Rivard AL, McKay RA, Bao D, et al. 2005. Reduction of hepatic and adipose tissue glucocorticoid receptor expression with antisense oligonucleotides improves hyperglycemia and hyperlipidemia in diabetic rodents without causing systemic glucocorticoid antagonism. Diabetes 54:1846-1853.

Wauson EM, Langan AS, Vorce RL. 2002. Sodium arsenite inhibits and reverses expression of adipogenic and fat cell-specific genes during in vitro adipogenesis. Toxicol Sci 65:211-219.

WHO. 2001. United Nations Synthesis Report on Arsenic Drinking Water. Developed on behalf of the United Nations Administrative Committee on Cooperation Sub-Committee on Water Resources, with active participation of UNICEF, UNIDO, IAEA and the World Bank. World Health Organization. Available: http://www.who.int/water_sanitation health/dwq/arsenic3/en/[accessed 1 December 2005].

Widnell CC, Baldwin SA, Davies A, Martin S, Pasternak CA. 1990. Cellular stress induces a redistribution of the glucose transporter. FASEB J 4:1634-1637.

Wild S, Roglic G, Green A, Sieree R, King H. 2004. Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. Diabetes Care 27:1047-1053.

Wu MM, Chiou HY, Ho IC, Chen C J, Lee TC. 2003. Gene expression of inflammatory molecules in circulating lymphocytes from arsenic-exposed human subjects. Environ Health Perspect 111:1429-1438.

Wu MM, Chiou HY, Wang TW, Hsueh YM, Wang IH, Chen C J, et al. 2001. Association of blood arsenic levels with increased reactive oxidants and decreased antioxidant capacity in a human population of northeastern Taiwan. Environ Health Perspect 109:1011-1017.

Yu HS, Lian WT, Chang KL, Yu CL, Chen GS. 2002. Arsenic induces tumor necrosis factor alpha release and tumor necrosis factor receptor I signaling in T helper cell apoptosis. J Invest Dermatol 119:812-819.

Zierold KM, Knobeloch L, Anderson H. 2004. Prevalence of chronic diseases in adults exposed to arsenic-contaminated drinking water. Am J Public Health 94:1935-1937.

Ana Navas-Acien, (1,2,3,4) Ellen K. Silbergeld, (4) Robin A. Streeter, (1,3) Jeanne M. Clark, (1,2,5) Thomas A. Burke, (3,6) and Eliseo Guallar (1,2,3)

(1) Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, (2) Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Medical Institutions, (3) Johns Hopkins Center for Excellence in Environmental Public Health Tracking, Johns Hopkins University Bloomberg School of Public Health, (4) Department of Environmental Health Sciences, Johns Hopkins University Bloomberg School of Public Health, (5) Department of Medicine, Johns Hopkins School of Medicine, and (6) Department of Health Policy and Management, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA

Address correspondence to A. Navas Acien, Department of Environmental Health Sciences, Johns Hopkins University Bloomberg School of Public Health, 615 N. Wolfe St., Office W7033B, Baltimore, MD 21205-2223 USA. Telephone: [phone]. Fax: [phone]. E-mail: [e-mail]

This work was supported by National Institute of Environmental Health Sciences grant 1R01 ES012673-01. A.N.-A., R.A.S., T.A.B., and E.G. were supported by the Johns Hopkins Center of Excellence in Environmental Public Health Tracking (Centers for Disease Control and Prevention grant U50CCU322417).

The authors declare they have no competing financial interests.

Table 1. In vitro studies of arsenic exposure and
glucose metabolism outcomes.

Source                       Type of cell/tissue       Compound

Signal transduction and gene expression

Macfarlane et al. 1997       Pancreatic [beta]-cells   Arsenite
Macfarlane et al. 1999       Pancreatic [beta]-cells   Arsenite
Elrick and Docherty 2001     Pancreatic [beta]-cells   Arsenite
Wauson et al. 2002           C3H 10T1/2                Arsenite
                             preadipocytes
Salazard et al. 2004         3T3-F442A                 Arsenite
                             preadipocytes

Glucose uptake in cultured cells

Warren et al. 1986           BHK-21 cells              Arsenite
Widnell et al. 1990          BHK-21 cells              Arsenite
Pasternak et al. 1991        BHK-21 cells              Arsenite
Liebl et al. 1992            MDCK dog cells            Arsenite
Sviderskaya et al. 1996      BHK cells                 Arsenite
                             3T3-L1 adipocytes
McDowell et al. 1997         L6 rat muscle cells       Arsenite
Fladeby and Serck-           Bovine adrenal cells      Arsenite
  Hanssen 1999
Bazuine et al. 2003          3T3-L1 adipocytes         Arsenite
Bazuine et al. 2004          3T3-L1 adipocytes         Arsenite
Walton et al. 2004           3T3-L1 adipocytes         Arsenite
                                                       [MAs.sup.III]O
                                                       [DMAs.sup.III]I
                                                       Arsenate
                                                       [MAs.sup.V]
                                                       [DMAs.sup.V]

                                                       Arsenite
                                                       [MAs.sup.III]O
                                                       [DMAs.sup.III]I

Miscellaneous experiments

Short et al. 1965            Rat hemidiaphragms        Arsenite
                             Epidydimal fat pads       Arsenate
Dixit and Lazarow 1967       Epidydimal fat pads       Arsenite
Brazy et al. 1980            Rabbit kidney tubules     Arsenate
Hunder et al. 1993           Rat jejunal segments      Arsenite
                                                       Arsenate

Source                             Dose (ppm)            Incubation

Signal transduction and gene expression

Macfarlane et al. 1997                37.5                 0.33 hr
Macfarlane et al. 1999                 75                  0.5 hr
Elrick and Docherty 2001               75                  0.5 hr
Wauson et al. 2002                    0.45                2 months
Salazard et al. 2004                 0.0017,               3 days
                                      0.003

Glucose uptake in cultured cells

Warren et al. 1986                    3.75                  2 hr
Widnell et al. 1990                    15                   2 hr
Pasternak et al. 1991                4.5-7.5                2 hr
Liebl et al. 1992                    37.5-75                1 hr
Sviderskaya et al. 1996             7.5-22.5                2 hr
McDowell et al. 1997                7.5-112.5              0.5 hr
Fladeby and Serck-                  1.88-18.8               1 hr
  Hanssen 1999
Bazuine et al. 2003                  0.75-75               0.5 hr
Bazuine et al. 2004                 3.75-750               0.5 hr
Walton et al. 2004                  1.57, 7.5               4 hr
                                    0.08, 0.4
                                   0.15, 0.75
                                     7.5, 75
                                     7.5, 75
                                     7.5, 75
                                  0.4, 0.8, 1.5             24 hr
                                0.02, 0.04, 0.08
                                0.04, 0.08, 0.15

Miscellaneous experiments

Short et al. 1965                      75                  1-3 hr
                                       75
Dixit and Lazarow 1967             0.75-7,500               3 hr
Brazy et al. 1980                   0.75-375               0.5 hr
Hunder et al. 1993                  0.19-18.9               2 hr
                                   0.19-187.5

                                         Outcomes and results
Source                                 (compared with controls)

Signal transduction and gene expression

Macfarlane et al. 1997         [up arrow] IUF-1 dependent gene
                                          expression PI-3 kinase
                                          independent, SAPK2/p38
                                          involved

Macfarlane et al. 1999         [up arrow] IUF-1 translocation from
                                          cytoplasm to nucleus PI-3
                                          kinase independent; SAPK2/p38
                                          involved

Elrick and Docherty 2001       [up arrow] IUF-1 translocation from
                                          cytoplasm to nucleus PI-3
                                          kinase independent, SAPK2/p38
                                          involved

Wauson et al. 2002           [down arrow] PPAR[gamma] mRNA
                             [down arrow] Pioglitazone-stimulated
                                          adipocyte differentiation

Salazard et al. 2004           [up arrow] Expression of PPAR[gamma] and
                                          C/EBP[alpha] (genes with
                                          important roles in adipose
                                          determination)

Glucose uptake in cultured cells

Warren et al. 1986             [up arrow] Basal glucose uptake; =
                                          insulin-stimulated glucose
                                          uptake = amino acid uptake

Widnell et al. 1990            [up arrow] Basal glucose uptake;
                               [up arrow] glucose transporter
                                          translocation (reversible)

Pasternak et al. 1991          [up arrow] Basal glucose uptake
                                          (reversible when arsenite
                                          removed) Fast and reversible
                                          translocation of glucose
                                          receptor

Liebl et al. 1992            [down arrow] Basal glucose uptake, dose
                                          dependent

Sviderskaya et al. 1996        [up arrow] Basal glucose uptake, dose
                                          dependent
                               [up arrow] Glucose transporter
                                          translocation in both types
                                          of cells

McDowell et al. 1997           [up arrow] Basal glucose uptake, dose
                                          dependent but maximal with
                                          37.5 ppm
                               [up arrow] GLUT1 and GLUT4 in cell
                                          membrane, PI-3 kinase
                                          independent
                               [up arrow] Insulin-stimulated glucose
                                          uptake

Fladeby and Serck-             [up arrow] Basal glucose uptake up to
  Hanssen 1999                            7.5 ppm, then plateau PI-3
                                          kinase independent, SAPK2/p38
                                          partly involved

Bazuine et al. 2003            [up arrow] Basal glucose uptake up to
                                          37.5 ppm, then [down arrow]
                             [down arrow] Insulin-stimulated glucose
                                          uptake
                               [up arrow] GLUT4 and GLUT1 translocation
                                          (but less than insulin) PI-3
                                          kinase independent; no
                                          changes in IR[beta], IRS-1,
                                          IRS-2 No phosphorylation of
                                          PKB; PKC-[lambda]/[zeta] and
                                          SAPK2/p38 involved

Bazuine et al. 2004            [up arrow] Basal glucose uptake up to
                                          37.5 ppm Dexamethasone
                                          [down arrow] arsenite glucose
                                          uptake SAPK2/p38 involved
Walton et al. 2004           = basal glucose uptake at 1.50 ppm,
                               [down arrow] at 7.5 ppm, [down arrow]
                               insulin-stimulated
                             = basal glucose uptake at 0.08 ppm,
                               [down arrow] at 0.4 ppm, [down arrow]
                               insulin-stimulated
                             = basal glucose uptake all doses,
                               [down arrow] insulin-stimulated
                               [up arrow] basal glucose uptake at
                               7.5 ppm, [down arrow] at
                               75 ppm, = insulin-stimulated
                             = basal glucose uptake all doses,
                               [down arrow] insulin-stimulated
                             = basal and insulin-stimulated glucose
                               uptake all doses,
                               PI-3 kinase independent. No changes in
                               IR[beta] and IRS-2
                               [MAs.sup.III]O and [DAs.sup.III]I,
                               but not arsenite IRS-1, [up arrow]
                               phosphorylation of IRS-1
                               Arsenite, [MAs.sup.III]O and
                               [DAs.sup.III]I [down arrow]
                               phosphorylation of PKB/Akt
                               Arsenite, [MAs.sup.III]O and
                               [DAs.sup.III]I [down arrow] GLUT4
                               translocation in insulin-treated cells
                               Dose-dependent [down arrow]
                               insulin-stimulated glucose uptake
                             = insulin-stimulated glucose uptake at
                               0.02 ppm, [down arrow] at 0.04 and 0.08
                             = insulin-stimulated glucose uptake all
                               doses

Miscellaneous experiments

Short et al. 1965              [up arrow] Basal glucose uptake in
                                          hemidiaphragms; [up arrow]
                                          uptake with arsenate in
                                          fat pads = insulin stimulated
                                          glucose uptake in
                                          hemidiaphragm; [down arrow]
                                          uptake with arsenite in fat
                                          pad

Dixit and Lazarow 1967         [up arrow] Basal glucose oxidation up
                                          to 7.5 ppm

Brazy et al. 1980            [down arrow] Fluid, phosphate, and glucose
                                          absorption (lumen to bath)

Hunder et al. 1993           [down arrow] Intestinal glucose transfer
                                          dose dependent (= arsenate
                                          < 7.5 ppm)

Abbreviations: [up arrow], increase; [down arrow], decrease; = similar
levels; BHK-21 cells, baby hamster kidney cells (contain predominantly
GLUT1); C/EBP[alpha], CCAAT/enhancer binding protein; [DAs.sup.III]I,
iodo-dimethylarsine; [DMAs.sup.V], dimethylarsinic acid; GLUT, glucose
transporter; IR[beta]: insulin receptor [beta]; IRS, insulin receptor
substrate; IUF-1, insulin upstream factor-1 (also called homeodomain
transcription factor PDX1); [MAs.sup.III]O, methylarsine oxide;
[MAs.sup.V], monosodium methyl arsenate; MDCK dog cells, Madin-Darby
canine kidney cells; PI-3 kinase, phosphatydilinositol-3 kinase;
PKB, protein kinase B; PKC, protein kinase C; PPAR[gamma], peroxisome
proliferative-activated receptor [gamma], SAPK2, stress activator
protein kinase 2 (also called p38 mitogen-activated protein kinase).
1 ppm = 13.35 [micro]M. Basal glucose uptake, glucose uptake in the
absence of insulin.

Table 2. Experimental characteristics and ratio of glucose uptake in
peripheral cell lines exposed to arsenite and insulin compared with
insulin and arsenite alone.

                                  Experiment characteristics

                                         Incubation  Arsenite  Insulin
Source                Type of cell          (hr)      (ppm)     (nM)

Warren et al. 1986    BHK-21 cells          2           3.75       100
McDowell et al. 1997  L6 rat muscle         0.5        37.5        100
                        cells
Bazuine et al. 2003   3T3-L1 adipocytes     0.5        37.5        100
Walton et al. 2004    3T3-L1 adipocytes     4           1.50     1,000
Walton et al. 2004    3T3-L1 adipocytes     4           7.49     1,000

                              Ratio of glucose
                                 uptake vs.

Source                     Insulin        Arsenite

Warren et al. 1986          0.94            0.91
McDowell et al. 1997        1.42            1.21
Bazuine et al. 2003         0.57            1.33
Walton et al. 2004          0.60            0.55
Walton et al. 2004          0.20            0.33

BHK-21 cells, baby hamster kidney cells. For arsenite,
1 ppm = 13.35 [micro]M.

Table 3. In vivo studies of arsenic exposure and glucose metabolism.

                                  Experimental
Source                               animal                n

Judd 1979                     Field mice                   19
Ghafghazi et al. 1980         Rats                         12
Hughes and Thompson 1996      B6C3[F.sub.1] mice           72
Aguilar et al. 1997           Wistar rats                  20
Cobo and Castineira 1997      Wistar rats                  21
Biswas et al. 2000            Bengal goats                 12
Arnold et al. 2003            Fischer rats                480
Pal and Chatterjee 2004a      Wistar rats                  18
Pal and Chatterjee 2004b      Wistar rats                  18
Pal and Chatterjee 2005       Wistar rats                  18

Source                             Compound (route)

Judd 1979                     Methanearsonate (po in water)
Ghafghazi et al. 1980         Arsenite (ip)
Hughes and Thompson 1996      Arsenate (po in water)
Aguilar et al. 1997           Arsenate (po in food)
Cobo and Castineira 1997      Arsenite (po in water)
Biswas et al. 2000            Arsenite (po in capsule)
Arnold et al. 2003            Monomethylarsenic (po in food)
Pal and Chatterjee 2004a      Arsenite (ip)
Pal and Chatterjee 2004b      Arsenite (ip)
Pal and Chatterjee 2005       Arsenite (ip)

                                 Daily dose
Source                             (ppm)          Duration

Judd 1979                       1,000             30 days
Ghafghazi et al. 1980           5-10              7 days
Hughes and Thompson 1996      0.025-2.5           28 days
Aguilar et al. 1997              5                10 weeks
Cobo and Castineira 1997        17.75             1st week
                              up to 100           8th week
Biswas et al. 2000               25               12 weeks
Arnold et al. 2003            50-1,300            2 years
Pal and Chatterjee 2004a        5.55              21 days
Pal and Chatterjee 2004b        5.55              30 days
Pal and Chatterjee 2005         5.55              30 days

                                    Outcomes and results
Source                            (compared with controls)

Judd 1979                     [down arrow] Blood glucose, = fluid and
                                           food consumption
Ghafghazi et al. 1980           [up arrow] Glucose levels after glucose
                                           tolerance test, dose
                                           dependent
Hughes and Thompson 1996      [down arrow] Plasma glucose, = fluid and
                                           food consumption
Aguilar et al. 1997                      = Plasma glucose levels
Cobo and Castineira 1997                   Delayed glucose clearance
                                           after glucose tolerance test
                                         = Basal insulin levels in vivo
Biswas et al. 2000              [up arrow] Blood glucose at week 6 and
                                           [up arrow][up arrow] at
                                           week 12
Arnold et al. 2003                       = Blood glucose levels up to
                                           400 ppm, [down arrow] with
                                           1,300 ppm
Pal and Chatterjee 2004a      [down arrow] Blood glucose (reversed with
                                           methionine)
                                         = Body, liver, kidney weight
Pal and Chatterjee 2004b      [down arrow] Blood glucose (reversed with
                                           N-acetylcysteine)
Pal and Chatterjee 2005       [down arrow] Blood glucose (reversed with
                                           methionine)

Abbreviations: ip, intraperitonea1; po, per oral; [up arrow], increase;
[down arrow], decrease.

Table 4. Epidemiolopic studies of arsenic exposure and diabetes.

Source                 Design   Country   Population

General populations, high arsenic exposure

Lai et al. 1994        CS       Taiwan    Survey of participants
                                            in high-arsenic area
Tsai et al. 1999       RCO      Taiwan    Deaths in 1971-1994
Tseng et al.           CO       Taiwan    Survey of participants
  2000                                      in high-arsenic area
Wang et al.            CS       Taiwan    National Health
  2003                                      Insurance Database
Rahman et al.          CS       Bangla-   Survey participants
  1998                            desh      in high- and
                                            low-arsenic areas
Rahman et al.          CS       Bangla-   Survey participants
  1999                            desh      in high-arsenic area

Occupational populations, high arsenic exposure

Mabuchi et al.         RCO      U.S.      Pesticide workers,
  1980                                      Baltimore, MD
Enterline and          RCO      U.S.      Copper smelter
  Marsh 1982                                workers, Washington
                                            State
Lagerkvist and         CS       Sweden    Copper smelter
  Zetterlund 1994                           workers, other jobs
Rahman and             CC       Sweden    Copper smelter
  Axelson 1995                              workers
Rahman et al.          CC       Sweden    Deaths in glass
  1996                                      industry area
Jensen and             CS       Denmark   Taxidermists, wood
  Hansen 1998                               workers, other jobs
Bartoli et al.         RCO      Italy     Glass industry
  1998                                      workers
Lubin et al. 2000      RCO      U.S.      Copper smelter
                                            workers, Montana
Tollestrup et al.      RCO      U.S.      Children < 4 km of
  2003                                      Copper smelter

General populations, low to moderate arsenic exposure

Ward and Pim           CC       UK        Hospital based
  1984
Ruiz-Navarro           CC       Spain     Hospital based
  et al. 1998
Lewis et al.           CO       U.S.      Mormons
  1999
Zierold et al.         CC       U.S.      Survey participants
  2004                                      with private wells

                    Diabetes             Cases/
Source              diagnosis            noncases

General populations, high arsenic exposure

Lai et al. 1994     OGTT or              86/805
                      self-reported

Tsai et al. 1999    Death certificate    531 deaths
Tseng et al.        OGTT                 41/405
  2000
Wang et al.         ICD-9 250            27,543/
  2003                ICD-9 A181           678,791
Rahman et al.       Self-reported        46/971
  1998                symptoms +
                      glucosuria +
                      OGTT
Rahman et al.       Glucosuria           263/1,332
  1999

Occupational populations, high arsenic exposure

Mabuchi et al.      Death certificate    2 deaths
  1980
Enterline and       Death certificate    12 deaths
  Marsh 1982
Lagerkvist and      Self-reported        4/85
  Zetterlund 1994     type 2 diabetes
Rahman and          Death certificate,   12/31
  Axelson 1995        medical record
Rahman et al.       Death certificate    240/2,216
  1996
Jensen and          HbA1c                5/59
  Hansen 1998
Bartoli et al.      Death certificate    3 deaths
  1998
Lubin et al. 2000   Death certificate    54 deaths
Tollestrup et al.   Death certificate    16/3,116
  2003

General populations, low to moderate arsenic exposure

Ward and Pim        NR                   87/30
  1984
Ruiz-Navarro        NR                   38/49
  et al. 1998
Lewis et al.        Death certificate    55/4,003
  1999
Zierold et al.      Self-reported        67/1118
  2004

                    Men                  Age range
Source              (%)                  (year)

General populations, high arsenic exposure

Lai et al. 1994     43                   30-69
Tsai et al. 1999    35                   All ages
Tseng et al.        50                   Mean 47
  2000
Wang et al.         43                   25-65+
  2003
Rahman et al.       59                   30-60+
  1998
Rahman et al.       61                   30-60+
  1999

Occupational populations, high arsenic exposure

Mabuchi et al.      75                   < 20-40+
  1980                                     at hire
Enterline and       100                  < 20-69
  Marsh 1982                               at hire
Lagerkvist and      100                  Mean 57
  Zetterlund 1994
Rahman and          100                  30-74 at
  Axelson 1995                             death
Rahman et al.       100                  45-75+
  1996
Jensen and          87                   Mean 37
  Hansen 1998
Bartoli et al.      100                  < 40-65+
  1998
Lubin et al. 2000   100                  < 20-30+
                                           at hire
Tollestrup et al.   58                   < 14
  2003

General populations, low to moderate arsenic exposure

Ward and Pim        65                   18-78
  1984
Ruiz-Navarro        39                   NR
  et al. 1998
Lewis et al.        52                   < 50-80+
  1999
Zierold et al.      NR                   Mean 62
  2004

                        Arsenic            Levels, exposed
Source                 assessment           vs. reference

General populations, high arsenic exposure

Lai et al. 1994     CEI village          > 15 vs. 0 ppm-year
                      drinking water

Tsai et al. 1999    Living in HAA        HAA vs. no HAA
Tseng et al.        CEI village          > 17 vs. < 17
  2000                drinking water     ppm-year
Wang et al.         Living in HAA        HAA vs. no HAA
  2003
Rahman et al.       Living in HAA        Keratosis vs.
  1998                and keratosis      no keratosis
Rahman et al.       CEI village          > 10 vs. 0 ppm-year
  1999                drinking water

Occupational populations, high arsenic exposure

Mabuchi et al.      Job title            Workers vs. general
  1980                                     population
Enterline and       Job title            Workers vs. general
  Marsh 1982                               population
Lagerkvist and      Job title            Workers vs. other
  Zetterlund 1994                          workers
Rahman and          Air levels           ~ 5 vs. 0 mg/[m.sup.3]
  Axelson 1995
Rahman et al.       Job title            Workers vs. other
  1996                                     workers
Jensen and          Job title            Workers vs. general
  Hansen 1998                              population
Bartoli et al.      Job title            Workers vs. general
  1998                                     population
Lubin et al. 2000   Job title            Workers vs. general
                                           population
Tollestrup et al.   Years of             [greater than or
  2003                residency            equal to] l0 vs. < 1 year

General populations, low to moderate arsenic exposure

Ward and Pim        Plasma levels        75th vs. 25th
  1984                (NAA)                percentile
Ruiz-Navarro        Urinary levels       75th vs. 25th
  et al. 1998         (AAS)                percentile
Lewis et al.        CEI community        > 4 vs. < 1
  1999                drinking water       ppm-year
Zierold et al.      Subject              > 10 vs. < 2 ppb
  2004                drinking water

                    RR of diabetes
Source              (95% CI)             Adjusted for

General populations, high arsenic exposure

Lai et al. 1994     10.1 (1.30-77.9)     Age, sex, BMI,
                                         physical
                                         activity
Tsai et al. 1999    1.46 (1.28-1.67)     Age, sex
Tseng et al.        2.10 (1.10-4.20)     Age, sex, BMI
  2000
Wang et al.         2.69 (2.65-2.73)     Age, sex
  2003
Rahman et al.       5.90 (2.90-11.6)     Age, sex, BMI
  1998
Rahman et al.       2.10 (1.10-4.20)     Age, sex
  1999

Occupational populations, high arsenic exposure

Mabuchi et al.      0.47 (0.12-1.88)     Age, sex, period
  1980
Enterline and       0.85 (0.48-1.49)     Age
  Marsh 1982
Lagerkvist and      9.61 (0.53-173)      Crude
  Zetterlund 1994
Rahman and          3.30 (0.50-30.0)     Age
  Axelson 1995
Rahman et al.       1.40 (0.90-2.10)     Age
  1996
Jensen and          4.43 (0.47-42.0)     Age
  Hansen 1998
Bartoli et al.      0.34 (0.09-0.88)     Age
  1998
Lubin et al. 2000   0.83 (0.63-1.08)     Age
Tollestrup et al.   1.60 (0.36-1.16)     Crude
  2003

General populations, low to moderate arsenic exposure

Ward and Pim        1.09 (0.79-1.49)     Crude
  1984
Ruiz-Navarro        0.87 (0.50-1.53)     Crude
  et al. 1998
Lewis et al.        0.65 (0.34-1.24)     Age, sex
  1999
Zierold et al.      1.02 (0.49-2.15)     Age, sex, BMI,
  2004                                     smoking

Abbreviations: AAS, atomic absorption spectrometry; BMI, body mass
index; CC, case-control; CEI, cumulative exposure index: [SIGMA]
arsenic levels in drinking water; x time of exposure; (i indicates
specific village); CO, cohort; CS, cross-sectional; HAA, high-arsenic
area; HbA1c, hemoglobin A1c; ICD-9, International Classification of
Diseases, Ninth revision; NAA, neutron activation analysis; NR, not
reported; OGTT, oral glucose tolerance test, criteria for a positive
test based on the WHO criteria; RCO, retrospective cohort; RR, relative
risk.

Table 5. Criteria for evaluating the design and data analysis of
epidemiologic studies on arsenic and diabetes. (a)

                                       Taiwan and Bangladesh

                                                 Tsai       Tseng
                                   Lai et al.   et al.      et al.
                                      1994       1999        2000

All studies (n = 19)

  Diabetes diagnosis based on          Y           N          Y
    fasting glucose levels or
    oral glucose tolerance tests
  Exposure assessed at the             N           N          N
    individual level
  Exposure assessed using a            N           N          N
    biomarker of exposure
  Control for established              Y           N          Y
    diabetes risk factors in
    addition to age

Case-control and cross-sectional studies (n = 11)

  Response rate among noncases         Y          --          --
    at least 70% (b)
  Noncases would have been cases       N          --          --
    if they had developed
    diabetes
  Data collected in a similar          Y          --          --
    manner for all participants
  Cases interviewed within             N          --          --
    6 months of diagnosis
  Interviewer blinded with             Y          --          --
    respect to the case status
    of the person
    interviewed (c)
  Time period during which all         Y          --          --
    participants were
    interviewed was the same (c)
  Same exclusion criteria              Y          --          --
    applied to all participants

Cohort studies (n = 8)

  Loss to follow-up was                --          N          Y
    independent of exposure
  Intensity of search of disease       --          N          Y
    independent of exposure
    status

                                       Taiwan and Bangladesh

                                      Wang      Rahman      Rahman
                                     et al.     et al.      et al.
                                      2003       1998        1999

All studies (n = 19)

  Diabetes diagnosis based on          N           N          N
    fasting glucose levels or
    oral glucose tolerance tests
  Exposure assessed at the             N           N          N
    individual level
  Exposure assessed using a            N           N          N
    biomarker of exposure
  Control for established              N           N          N
    diabetes risk factors in
    addition to age

Case-control and cross-sectional studies (n = 11)

  Response rate among noncases         Y           N          Y
    at least 70% (b)
  Noncases would have been cases       N           N          N
    if they had developed
    diabetes
  Data collected in a similar          Y           N          N
    manner for all participants
  Cases interviewed within             N           N          N
    6 months of diagnosis
  Interviewer blinded with             --          N          N
    respect to the case status
    of the person
    interviewed (c)
  Time period during which all         --          N          N
    participants were
    interviewed was the same (c)
  Same exclusion criteria              Y           N          N
    applied to all participants

Cohort studies (n = 8)

  Loss to follow-up was                --         --          --
    independent of exposure
  Intensity of search of disease       --         --          --
    independent of exposure
    status

                                       Occupational populations

                                                          Lagerkvist
                                    Mabuchi    Enferline     and
                                     et al.    and Marsh  Zetterlund
                                      1980       1982        1994

All studies (n = 19)

  Diabetes diagnosis based on          N           N          N
    fasting glucose levels or
    oral glucose tolerance tests
  Exposure assessed at the             N           N          N
    individual level
  Exposure assessed using a            N           N          N
    biomarker of exposure
  Control for established              Y           N          N
    diabetes risk factors in
    addition to age

Case-control and cross-sectional studies (n = 11)

  Response rate among noncases         --         --          N
    at least 70% (b)
  Noncases would have been cases       --         --          N
    if they had developed
    diabetes
  Data collected in a similar          --         --          N
    manner for all participants
  Cases interviewed within             --         --          N
    6 months of diagnosis
  Interviewer blinded with             --         --          N
    respect to the case status
    of the person
    interviewed (c)
  Time period during which all         --         --          N
    participants were
    interviewed was the same (c)
  Same exclusion criteria              --         --          N
    applied to all participants

Cohort studies (n = 8)

  Loss to follow-up was                N           N          --
    independent of exposure
  Intensity of search of disease       N           N          --
    independent of exposure
    status

                                       Occupational populations

                                   Rahman and   Rahman      Jensen
                                    Axelson     et al.    and Hansen
                                      1995       1996        1998

All studies (n = 19)

  Diabetes diagnosis based on          N           N          N
    fasting glucose levels or
    oral glucose tolerance tests
  Exposure assessed at the             Y           N          N
    individual level
  Exposure assessed using a            N           N          N
    biomarker of exposure
  Control for established              N           N          N
    diabetes risk factors in
    addition to age

Case-control and cross-sectional studies (n = 11)

  Response rate among noncases         --         --          N
    at least 70% (b)
  Noncases would have been cases       N           N          Y
    if they had developed
    diabetes
  Data collected in a similar          Y           Y          Y
    manner for all participants
  Cases interviewed within             N           N          N
    6 months of diagnosis
  Interviewer blinded with             N           N          Y
    respect to the case status
    of the person
    interviewed (c)
  Time period during which all         Y           Y          N
    participants were
    interviewed was the same (c)
  Same exclusion criteria              Y           N          N
    applied to all participants

Cohort studies (n = 8)

  Loss to follow-up was                --         --          --
    independent of exposure
  Intensity of search of disease       --         --          --
    independent of exposure
    status

                                       Occupational populations

                                    Bartoli      Lubin    Tollestrup
                                     et al.     et al.      et al.
                                      1998       2000        2003

All studies (n = 19)

  Diabetes diagnosis based on          N           N          N
    fasting glucose levels or
    oral glucose tolerance tests
  Exposure assessed at the             N           N          Y
    individual level
  Exposure assessed using a            N           N          N
    biomarker of exposure
  Control for established              N           N          N
    diabetes risk factors in
    addition to age

Case-control and cross-sectional studies (n = 11)

  Response rate among noncases         --         --          --
    at least 70% (b)
  Noncases would have been cases       --         --          --
    if they had developed
    diabetes
  Data collected in a similar          --         --          --
    manner for all participants
  Cases interviewed within             --         --          --
    6 months of diagnosis
  Interviewer blinded with             --         --          --
    respect to the case status
    of the person
    interviewed (c)
  Time period during which all         --         --          --
    participants were
    interviewed was the same (c)
  Same exclusion criteria              --         --          --
    applied to all participants

Cohort studies (n = 8)

  Loss to follow-up was                N           N          N
    independent of exposure
  Intensity of search of disease       N           N          N
    independent of exposure
    status

                                     Other populations

                                                 Ruiz-
                                      Ward      Navarro
                                    and Pim     et al.
                                      1984       1998

All studies (n = 19)

  Diabetes diagnosis based on          N           N
    fasting glucose levels or
    oral glucose tolerance tests
  Exposure assessed at the             Y           Y
    individual level
  Exposure assessed using a            Y           Y
    biomarker of exposure
  Control for established              N           N
    diabetes risk factors in
    addition to age

Case-control and cross-sectional studies (n = 11)

  Response rate among noncases         N           N
    at least 70% (b)
  Noncases would have been cases       N           N
    if they had developed
    diabetes
  Data collected in a similar          N           N
    manner for all participants
  Cases interviewed within             N           N
    6 months of diagnosis
  Interviewer blinded with             N           N
    respect to the case status
    of the person
    interviewed (c)
  Time period during which all         N           N
    participants were
    interviewed was the same (c)
  Same exclusion criteria              N           N
    applied to all participants

Cohort studies (n = 8)

  Loss to follow-up was                --         --
    independent of exposure
  Intensity of search of disease       --         --
    independent of exposure
    status

                                     Other populations

                                     Lewis      Zierold
                                     et al.     et al.
                                      1999       2004

All studies (n = 19)

  Diabetes diagnosis based on          N           N
    fasting glucose levels or
    oral glucose tolerance tests
  Exposure assessed at the             N           Y
    individual level
  Exposure assessed using a            N           N
    biomarker of exposure
  Control for established              N           Y
    diabetes risk factors in
    addition to age

Case-control and cross-sectional studies (n = 11)

  Response rate among noncases         --          N
    at least 70% (b)
  Noncases would have been cases       --          N
    if they had developed
    diabetes
  Data collected in a similar          --          Y
    manner for all participants
  Cases interviewed within             --          N
    6 months of diagnosis
  Interviewer blinded with             --          Y
    respect to the case status
    of the person
    interviewed (c)
  Time period during which all         --          Y
    participants were
    interviewed was the same (c)
  Same exclusion criteria              --          Y
    applied to all participants

Cohort studies (n = 8)

  Loss to follow-up was                Y          --
    independent of exposure
  Intensity of search of disease       Y          --
    independent of exposure
    status

Abbreviations: --, not applicable; N, no; Y, yes.

(a) Criteria modified from Longnecker et al. (1988). (b) Not applicable
to two case-control studies based only on deaths (Rahman and Axelson
1995; Rahman et al. (1996). (c) Not applicable to the study using the
National Health Insurance Database from Taiwan (Wang et al. 2003).