Exposure to arsenic in drinking water is associated with increased prevalence of diabetes: a cross-sectional study in the Zimapán and Lagunera regions in Mexico
© Del Razo et al; licensee BioMed Central Ltd. 2011
Received: 24 May 2011
Accepted: 24 August 2011
Published: 24 August 2011
Human exposures to inorganic arsenic (iAs) have been linked to an increased risk of diabetes mellitus. Recent laboratory studies showed that methylated trivalent metabolites of iAs may play key roles in the diabetogenic effects of iAs. Our study examined associations between chronic exposure to iAs in drinking water, metabolism of iAs, and prevalence of diabetes in arsenicosis-endemic areas of Mexico.
We used fasting blood glucose (FBG), fasting plasma insulin (FPI), oral glucose tolerance test (OGTT), glycated hemoglobin (HbA1c), and insulin resistance (HOMA-IR) to characterize diabetic individuals. Arsenic levels in drinking water and urine were determined to estimate exposure to iAs. Urinary concentrations of iAs and its trivalent and pentavalent methylated metabolites were measured to assess iAs metabolism. Associations between diabetes and iAs exposure or urinary metabolites of iAs were estimated by logistic regression with adjustment for age, sex, hypertension and obesity.
The prevalence of diabetes was positively associated with iAs in drinking water (OR 1.13 per 10 ppb, p < 0.01) and with the concentration of dimethylarsinite (DMAsIII) in urine (OR 1.24 per inter-quartile range, p = 0.05). Notably, FPI and HOMA-IR were negatively associated with iAs exposure (β -2.08 and -1.64, respectively, p < 0.01), suggesting that the mechanisms of iAs-induced diabetes differ from those underlying type-2 diabetes, which is typically characterized by insulin resistance.
Our study confirms a previously reported, but frequently questioned, association between exposure to iAs and diabetes, and is the first to link the risk of diabetes to the production of one of the most toxic metabolites of iAs, DMAsIII.
KeywordsArsenic drinking water diabetes urinary metabolites of arsenic dimethylarsinite
Results of several epidemiologic studies have suggested that chronic environmental or occupational exposures to inorganic arsenic (iAs) increase risk of diabetes. These studies, as well as, other studies examining the diabetogenic effects of iAs exposure were recently reviewed by Navas-Acien and coworkers . The association between iAs exposure and diabetes has been frequently questioned because of methodological problems associated with some of these studies and because these studies focused mainly on populations exposed to high levels of iAs . In addition, results of studies examining effects of low exposures to iAs did not support this association or yielded conflicting results . For example, a recent study utilizing data collected by the National Health and Nutrition Examination Survey found a significant association between urinary arsenic and diabetes in the general U.S. population . Another U.S. study found significant associations between cumulative exposure to iAs and risk of depression or high blood pressure, but not diabetes . Conflicting information has also been provided by studies that examined the diabetogenic effects of iAs in laboratory animals. These studies were recently reviewed [1, 4]. In contrast, research using ex vivo or tissue culture models has consistently shown that exposures to subtoxic concentrations of trivalent iAs or trivalent methylated arsenicals produce effects consistent with diabetes, including inhibition of insulin production by pancreatic β-cells and inhibition of basal or insulin-stimulated glucose uptake by skeletal muscle, cultured adipocytes, or kidney cells . The tissue culture studies have also provided clues about molecular mechanisms of the diabetogenic effects of iAs exposure and have highlighted the role of specific metabolites of iAs in these mechanisms.
The pathway for iAs metabolism in humans involves the reduction of AsV-species to AsIII-species followed by oxidative methylation of AsIII-species, yielding mono- and dimethylated metabolites that contain either AsIII or AsV . Arsenic (+3 oxidation state) methyltransferase (AS3MT) is the key enzyme in this pathway [5, 6]. The trivalent iAs, arsenite (iAsIII), and its methylated metabolites containing trivalent As, methylarsonite (MAsIII) and dimethylarsinite (DMAsIII), are generally more toxic and reactive than their pentavalent counterparts, arsenate (iAsV), methylarsonate (MAsV) and dimethylarsinate (DMAsV) . These trivalent arsenicals are also potent inhibitors of insulin-stimulated glucose uptake by cultured murine adipocytes [8, 9]. Notably, MAsIII and DMAsIII are more potent than iAsIII as inhibitors of key steps in the insulin-activated signal transduction pathway, specifically, the insulin-dependent phosphorylation of protein kinase-B (PKB) or PKB-mediated translocation of the insulin-sensitive glucose transporter, GLUT4, from cytoplasm to the plasma membrane of adipocytes. MAsIII, and particularly DMAsIII is unstable in human urine [10, 11] Therefore, analysis of these metabolites requires a special sample handling and optimized analytical techniques. Indeed, both MAsIII and DMAsIII were detected in urines collected from individuals exposed to iAs in drinking water and from acute promyelocytic leukemia patients undergoing arsenic trioxide treatment [12–17]. It has been hypothesized that because of their exceptional toxicities, MAsIII and DMAsIII contribute to the adverse effects of iAs exposure. Results of numerous laboratory studies support this hypothesis [18–20]. However, to date only one study examined association between the production of MAsIII and DMAsIII and the adverse effects of iAs exposure in humans. This study by Valenzuela and associates showed that carriers of AS3MT/287Thr polymorph who have higher concentrations of MAsIII in urine could be more susceptible to adverse effects of iAs exposure than carriers of wild-type AS3MT (AS3MT/287Met) .
The main goals of our study were (i) to reexamine the link between chronic exposure to iAs and diabetes, using multiple diabetes and exposure indicators, and (ii) to characterize the role of iAs metabolism, including MAsIII and DMAsIII production, in development of diabetes by individuals exposed to iAs in drinking water. Results of our work show that risk of diabetes significantly increases with iAs exposure and that urinary DMAsIII is positively associated with this risk.
Study Subjects and Sample Collection
Study subjects were recruited among residents of the Zimapán and Lagunera regions (Mexico), using information on current and historical levels of iAs in local drinking water supplies [22–26]. Potential participants who gave written informed consent were invited for an interview during which they completed a detailed questionnaire focusing on residence in the study areas, use and sources of drinking water, history of disease, and potential occupational exposures to As. The subjects with a minimum of two-year residency in the Zimapán or Lagunera regions and without occupational exposures to As were recruited for the study. Pregnant women, alcoholics, and individuals with chronic or acute diseases of the urinary tract or with type I diabetes were excluded from this study. In order to facilitate future analyses of the genotype-phenotype associations, we recruited only genetically unrelated individuals. All procedures included in this study were approved by Institutional Review Boards of Cinvestav-IPN, Mexico City, Mexico and University of North Carolina at Chapel Hill, North Carolina, USA.
All subjects were examined by an experienced physician and body weight, height, blood pressure, body mass index (BMI), and waist-to-hip ratio were recorded. A spot urine sample was collected from each subject during the medical exam. The urines were aliquoted and snap-frozen in dry ice immediately after collection. Each subject also provided a sample of water he or she had typically used for drinking. For diagnosis of diabetes, a sample of fasting blood was collected into EDTA-vacutainers (Becton, Dickinson and Co., Franklin Lakes, NJ), followed by oral glucose tolerance test (OGTT). Here, each subject drank a glass of water (~200 mL) containing 75 g of anhydrous glucose (Mallinckrodt Baker, Inc., Xalostoc Mexico). A sample of venous blood was collected two hours after glucose administration. Plasma was prepared from both fasting and two-hour blood samples and stored at -80°C.
Analyses of As in Urine and Drinking Water
The concentrations of iAs in drinking water and concentrations of total As (tAs) in urines were determined by HG-atomic fluorescence spectrometry (HG-AFS) as previously described [27, 29]. The Trace Elements in Water standard reference material (SRM 1643e, National Institute of Standards and Technology, Gaithersburg, MD) containing 60.4 ± 0.7 ng As/mL was used for quality control. The concentration of iAs in water were expressed as ng As/mL or ppb; tAs concentration in urine were expressed as ng As/mL or as μg As/mg creatinine.
Analyses in Blood and Plasma
1. Glucose concentrations were determined in the fasting and 2-hour blood by HemoCue Glucose 201RT glucometer (HemoCue, Lake Forest, CA) that reports results in plasma glucose equivalents.
2. Concentration of glycated hemoglobin HbA1c was measured in fasting blood, using a GDX A1c test cartridges (Cholestech Corp., Hayward, CA).
3. Insulin concentration was determined in fasting blood plasma using the Human Insulin ELISA Kit (Linco Research, Inc., St. Charles, MO).
Evaluation of iAs Exposure and Metabolism
The following indicators were used to estimate individual exposure to iAs in drinking water:
1. The concentration of iAs in drinking water samples provided by study subjects.
2. The concentration of tAs in spot urines collected during medical exams.
3. Cumulative exposure estimated from measurements of current and historical concentrations of iAs in drinking water supplies in Zimapán and Lagunera and information from the questionnaires about each subject's length of residence in these areas [22, 25, 26]. The measurements of iAs concentration were used to estimate the average annual exposure at each location in the study area during the years 1993 to 2008. When a location had no measurements in a given year, the concentration of iAs was assumed to have remained constant since the last year for which data was available. Cumulative exposure in ppm.years was estimated for each subject by summing the annual exposures over the years during which the subject had lived in the study area. Cumulative exposure could not be estimated for the period before 1993 because only scattered measurements for a few locations were available from 1978 to 1992 and no data were available for 1977 and before. Estimates of cumulative exposures were therefore generated for the 1993-2008 period and, to examine temporal effects, for the 5-year segments 1993-1997, 1998-2002 and 2003-2007.
The metabolic patterns for iAs were characterized by the concentration or relative amount (% of tAs) of each of the tri- and pentavalent metabolites of iAs in urine and by the ratios of DMAs/MAs, MAs/iAs, and (DMAs+MAs)/iAs.
Classification of Diabetes Status
Diabetic individuals were identified by fasting blood glucose level (FBG) ≥126 mg/dL, by the two-hour blood glucose level (2HBG) ≥200 mg/dL determined by OGTT, and by self-reported doctor diagnosis of diabetes or use of anti-diabetic medication as indicated in the questionnaires. Individuals who reported previous diagnoses of diabetes or taking diabetes medication were not administered OGTT. Classification by 2HBG and FBG gave nearly identical results, so in the text we report results for both indicators combined. Results for separate analyses based on FBG and 2HBG are given in Additional Material. Fasting plasma insulin (FPI) and homeostasis model assessment - insulin resistance (HOMA-IR = FPI [μU/mL] × FBG [mmol/L]/22.5) were used to characterize insulin production and insulin resistance.
Standard descriptive analyses were carried out using means and standard deviations for continuous variables and frequencies for categorical variables. The distributions of continuous variables were examined graphically using histograms and normal probability plots. Measurements of iAs in water and most biological indicators, including iAs and its metabolites in urine were approximately log-normally distributed and for these we report the geometric mean and standard deviation. Measured values of MAsIII and DMAsIII that were below the limit of detection (LOD) were replaced with LOD/21/2 for statistical analysis. The LOD for both metabolites was 0.1 ng/mL As. Associations of diabetes status with iAs and urinary metabolites of iAs were examined using logistic regression to estimate odds ratios (ORs) and 95% confidence intervals (CIs). iAs concentration in drinking water and concentrations of tAs and iAs metabolites in urine were examined both as continuous variables and in categorical form. To control for potential confounding, year of age, sex, hypertension (systolic blood pressure ≥140, diastolic blood pressure ≥90 or reported use of anti-hypertensive drugs) and obesity (BMI > 30) were included as covariates. Although the effects of adjustment were negligible (< 10% change in the OR), all covariates were retained in the models because of their known association with diabetes. Associations of continuous variables for 2HBG, FBG, FPI, and HOMA-IR with measures of tAs and iAs metabolites were analyzed by linear regression, with log-transformation of tAs in water and of tAs and iAs metabolites in urine. Age, sex, hypertension and obesity, classified as described above, were included as covariates in these models. The assumption of linear exposure-response was assessed graphically and by fitting alternative models with flexible spline functions and was found to be adequate. Because of the different concentration ranges of urinary metabolites, we report ORs, regression coefficients, and CIs for those indicators for a one interquartile range (IQR) increment of exposure to facilitate comparison. Analyses of urinary metabolites of iAs were conducted both with and without urinary creatinine concentration as a covariate to assess possible effects of variation in urine volume. Statistical adjustment for creatinine concentrations changed the numerical values of some regression coefficients, but the interpretation and statistical significance of the findings remained the same. Statistical analyses reported here were performed in Stata/MP version 11.2 for Mac OS × (StataCorp, College Station, TX).
Characteristics of the Study Population and Exposure to iAs
Descriptive characteristics of the study population
Population (N, %)
Female (N, %)
iAs concentration in drinking water (ppb)
Water consumption (L/day)
Bottled water for drinking (N, %)
BMI > 30 (N, %)
Hypertensive (N, %)
Descriptive statistics for exposure to iAs in drinking water and iAs metabolites in urine
Exposure to iAs in Water
iAs, current concentration (ppb)
iAs, cumulative exposure 1993-2008 (ppm.years)
iAs, cumulative exposure 1993-1997 (ppm.years)
iAs, cumulative exposure 1998-2002 (ppm.years)
iAs, cumulative exposure 2002-2007 (ppm.years)
Metabolites of iAs in Urine (ng/mL)
Urinary Metabolites of iAs
Most of the urine samples contained both tri- and pentavalent metabolites of iAs, including iAsIII, iAsV, MAsIII, MAsV, DMAsIII, and DMAsV (Table 2). TMAsO was not detected. MAsIII was detected in 67% of urines collected in Zimapán and 100% of urines collected in Lagunera; DMAsIII was detected in 75% and 100% of urines collected in Zimapán and Lagunera, respectively. In these urines, MAsIII represented 0.1% to 18% (median 0.9%) and DMAsIII 0.2% to 81% (median 8.6%) of tAs. We found no statistically significant associations between the concentration or percentage of DMAsIII or MAsIII in urine and either age or gender of the study subjects. However, the young (≤18-year old) subjects had a lower average DMAs/MAs ratio (4.7) than either subjects older than 18 years (5.9, p < 0.01) or subjects older than 50 years (6.1, p < 0.01). We also found statistically significant positive correlations (p < 0.01) between DMAs/MAs ratio and iAs exposure represented by either iAs concentration in drinking water or tAs concentration in urine. In contrast, the MAs/iAs ratio was negatively associated with iAs concentration in drinking water (R = -0.19, p < 0.01) and tAs concentration in urine (R = -0.22, p < 0.01).
Associations of Diabetes with iAs Exposure and Metabolism
Association of diabetesa with exposure to iAs in drinking water and iAs metabolites in urine, adjusted for age, sex, obesity and hypertension
Exposure to iAs in Water
iAs, current concentration
iAs, cumulative exposure 1993-2008
iAs, cumulative exposure, 2003-2007
iAs, cumulative exposure, 1998-2002
iAs, cumulative exposure, 1993-1997
Metabolites of iAs in Urine
Diabetes was not notably associated with tAs or with most of the metabolites of iAs in urine (Table 3). However, a marginally significant association (OR 1.24, 95% CI 1.00-1.55, p = 0.05) was found with the urinary concentration of DMAsIII. The association of diabetes with urinary DMAsIII was statistically significant (p = 0.04) when diabetes was classified only by 2HBG≥200 and reported diagnosis or medication, but and on the borderline of significance (p = 0.05) when FBG≥126 mg/dL was used (Additional file 4). The OR for diabetes increased slightly after controlling for creatinine (OR 1.37 per IQR 95% CI 1.10-1.72, p = 0.01, data not shown).
Association of log-transformed HbA1c, FPI and HOMA-IR with log-transformed concentrations of iAs in drinking water and of iAs metabolites in urine, adjusted for age, sex, obesity and hypertension
Urinary DMAs/MAs ratio
The arsenicosis-endemic areas of Zimapán and Lagunera were chosen for this study because tens of thousands of local residents are currently exposed to a wide range of iAs concentrations in drinking water and because relatively detailed information exists about historical levels of iAs in the local drinking water supplies. Unlike most previous studies, we used multiple biomarkers to classify diabetes and to characterize exposure to iAs. In spite of a relatively small number of subjects recruited for this study we were able to show that the exposure to iAs was positively associated with all three diabetes indicators. Our findings contradict results of a recent study that found no significant association of iAs exposure with diabetes in another arsenicosis area located in Bangladesh . This study used only glycated hemoglobin (HbA1c) and glucosuria to classify diabetic individuals. Although the HbA1c level in blood is now an accepted measure for diagnosis of diabetes in the US, it is unclear whether it was also validated for the Bangladeshi population. Glucosuria is not a reliable indicator of diabetes . Thus, using the validated diabetes indicators may be essential for linking iAs exposure to risk of diabetes. Notably, our data on the negative associations between iAs exposure and FPI and HOMA-IR suggest that the mechanisms of iAs-induced diabetes differ from those underlying type-2 diabetes, which is typically characterized by insulin resistance (i.e., increased HOMA-IR) and hyperinsulinemia .
In our study, FPG and 2HBG were significantly associated with iAs concentration in drinking water, but not with tAs or sum of iAs metabolites in urine. Samples of water provided for iAs analysis represented the types and sources of drinking water that were used by study subjects on a daily basis for an extensive period of time. Thus, it is plausible that iAs concentrations in these samples are more representative of the current exposures to iAs than are tAs levels in spot urines, which could be affected by changes in drinking water sources or consumption during days or even hours before urine collection. Our results show that diabetes was not associated with cumulative exposure to iAs over the last fifteen years. Historical records show that levels of iAs in drinking water supplies in both Zimapán and Lagunera areas have changed in recent years as a result of interventions by local governments. These changes may explain the low correlation between estimated cumulative exposure and the current concentrations of iAs in drinking water. In addition, the use of bottled water for drinking and cooking has become common, particularly in Zimapán, which historically had the higher levels of iAs in the municipal water supplies.
Previous epidemiologic studies used MAs/iAs and DMAs/MAs ratios in urine to evaluate the efficiency of iAs methylation and detoxification by subjects exposed to environmental iAs. Some of these studies reported a negative association between iAs exposure and the DMAs/MAs ratio or %DMA in urine, suggesting saturation or inhibition of the methylation pathway for iAs at high exposure levels . Other reports have linked low DMAs/MAs ratio (or high MAs/DMAs ratio) and high %MAs (or low %DMAs) to an increased susceptibility to diseases associated with iAs exposure, including skin lesions [34–36]. However, our data suggest that with increasing iAs exposure the efficiency of iAs methylation increases as indicated by higher DMAs/MAs and lower MAs/iAs ratios in urine.
An important goal of the present study was to examine associations between the prevalence of diabetes and urinary concentrations of MAsIII and DMAsIII. All urine samples collected in Lagunera and analyzed in UJED by the advanced HG-CT-AAS system contained both MAsIII and DMAsIII. The failure to detect DMAsIII and, particularly MAsIII in a large number of urines from Zimapán was likely due to a lower sensitivity of the conventional HG-CT-AAS system used in Cinvestav-IPN. Notably, DMAsIII in urine was associated positively with FBG and 2HBG but negatively with FPI. These results suggest that individuals with high concentrations of DMAsIII in urine are at increased risk of developing diabetes. DMAsIII is a potent inhibitor of insulin-stimulated glucose uptake by adipocytes . DMAsIII also inhibits glucose-stimulated insulin secretion by isolated murine pancreatic islets (Styblo and Douillet, unpublished data). Thus, the mechanism of the diabetogenic effects of iAs exposure may involve inhibition by DMAsIII of insulin-dependent glucose uptake and metabolism in peripheral tissues and/or inhibition by DMAsIII of insulin production by pancreatic β-cells. Taken together these two mechanisms would produce fasting hyperglycemia and impaired glucose tolerance, as well as decreased FPI and HOMA-IR values, i.e., symptoms that are consistent with results of our study.
While this study had notable strengths, the interpretation of the results may be affected by several limitations. The study population was predominantly female because men in the area often emigrate to work. Although we adjusted for sex in the analysis, the ability to extend the findings to populations with a more balanced sex ratio may be limited to the extent that women and men may differ in susceptibility to As-related disease. As noted above, iAs exposures in the study area have changed in recent years as a result of modifications to municipal water systems and the use of bottled water. The negative correlation between current and cumulative exposures suggests that areas with historically high iAs levels were targeted for interventions to reduce exposure. Our data (not shown) suggest that individuals who had been told by a doctor that they had diabetes were not more likely to use bottled water, however. The recent changes in exposure add to the challenges of determining whether measurements in drinking water versus urine or current versus cumulative exposure are the best measure of biologically-relevant exposure to iAs. Although we were able to estimate exposure from 1993 onward, 64% of the subjects had lived in the study area before 1993 and had longer exposure histories than we could estimate. The sparse environmental data from the 1980s suggest that iAs levels in municipal water supplies may have been higher in that era than in later years. No measurements were available for years before 1978, but it is possible that iAs exposures were lower before the construction of municipal water systems served by deep wells began in the 1960s. In summary, there are plausible mechanisms for errors resulting in both under- and over-estimation of cumulative iAs exposure, but the magnitude and direction of bias in exposure-disease associations as a result of any such errors cannot be assessed without additional data.
The American Diabetes Association has recommended that FPG, OGTT or HbA1c be used to test for diabetes or to assess risk of future diabetes in asymptomatic patients . However, there is a potential for misclassification of diabetes status due to error in the field methods we used to measure these indicators. Any such error is likely to be random with respect to arsenic exposure, and so most likely to attenuate associations between diabetes status and exposure. We also lacked reliable indicators of social class and were consequently unable to adjust for it in the analysis. Both Zimapan and Lagunera are rural, low income areas of Mexico. Recent data suggest that type-2 diabetes is less prevalent among Mexican residents with low socioeconomic status . Nevertheless, the potential magnitude of confounding by social class is difficult to assess, because of the limited information about the extent to which it is a risk factor for diabetes in rural Mexican populations.
Finally, the cross-sectional design has well known limitations. In the present study, these relate primarily to the inability to measure the changes in exposure discussed above. Because we did not know when the onset of diabetes occurred, cumulative exposures may also have been overestimated for subjects who had the disease for a number of years. Such overestimation would tend to understate the association of exposure with disease. Cross-sectional studies can be susceptible to selection bias if exposed individuals withdraw from the population because of exposure or its early effects. However, exposure to iAs in drinking water was not recognized as a health concern until recently, so it is unlikely to have prompted more highly exposed individuals to move out of the study area. In addition, individuals with prevalent and new-onset diabetes may differ in the way they metabolize arsenic.
Our research links exposure to iAs in drinking water to an increased risk of developing diabetes which is characterized by fasting hyperglycemia and impaired glucose tolerance. However, unlike typical type-2 diabetes, iAs-related diabetes was not associated in this study with increased insulin resistance as measured by HOMA-IR. In addition, low FPI levels seem to suggest that β-cell function may be impaired by exposure to iAs or its toxic methylated metabolites. In summary, we believe that strengths of our research outweigh the above-described limitations, providing additional evidence about the diabetogenic effects of iAs exposure.
List of Abbreviations
2-hour blood glucose level determined by OGTT
atomic absorption spectrometry
atomic fluorescence spectrometry
arsenic (+3 oxidation state) methyltransferase
body mass index
Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional
- DMAsIII :
- DMAsV :
fasting blood glucose
fasting plasma insulin
glucose transporter - type 4
homeostasis model assessment - insulin resistance
- iAsIII :
- iAsV :
- MAsIII :
- MAsV :
oral glucose tolerance test
standard reference material.
This work was funded by U.S. EPA/STAR grant No. 832735 and by NIH grant No. 5R01 ES015326-02 to M.S. Support was also provided by the UNC Nutrition Obesity Research Center funded by the National Institute of Diabetes and Digestive and Kidney Diseases (grant No. DK056350). We thank Angel Barrera-Hernández (Departamento de Toxicología, CINVESTAV), Arturo Cebrian and Jaime Duarte (Facultad de Medicina, Universidad Juárez del Estado de Durango), and the staff of Jurisdiccion Sanitaria 5 de Zimapán, Hidalgo, for their enthusiastic support and help with subject recruitment, interviews, medical exams, and sample collection. We also thank Professor William R. Cullen (University of British Columbia) who provided us with methylated trivalent arsenic standards and Dr. David J. Thomas (US EPA) and Dr. John Buse (UNC, Chapel Hill) for their suggestions and comments regarding the diabetes diagnosis, and data analysis and interpretation. The Authors declare that they do not have any competing financial or other interests to disclose.
- Navas-Acien A, Silbergeld EK, Streeter RA, Clark JM, Burke TA, Guallar E: Arsenic Exposure and Type 2 Diabetes: A Systematic Review of the Experimental and Epidemiological Evidence. Environ Health Perspect. 2006, 114: 641-648.View ArticleGoogle Scholar
- Navas-Acien A, Silbergeld EK, Pastor-Barriuso R, Guallar E: Arsenic exposure and prevalence of type 2 diabetes in US adults. JAMA. 2008, 300: 814-822. 10.1001/jama.300.7.814.View ArticleGoogle Scholar
- Zierold KM, Knobeloch L, Anderson H: Prevalence of chronic diseases in adults exposed to arsenic-contaminated drinking water. Am J Public Health. 94: 1936-1937.
- Paul DS, Harmon AW, Devesa V, Thomas DJ, Styblo M: Molecular mechanisms of diabetogenic effects of arsenic: Inhibition of insulin signaling by arsenite and methylarsonous acid. Environ Health Perspect. 2007, 115: 734-742. 10.1289/ehp.9867.View ArticleGoogle Scholar
- Thomas DJ, Li J, Waters SB, Xing W, Adair BM, Drobna Z, Devesa V, Styblo M: Arsenic (+3 oxidation state) methyltransferase and methylation of arsenicals. Exp Biol Med. 2007, 232: 3-13.Google Scholar
- Lin S, Shi Q, Nix FB, Styblo M, Beck MA, Herbin-Davis KM, Hall LL, Simeonsson JB, Thomas DJ: A novel S-Adenosyl-L-methionine: arsenic(III) methyltransferase from rat liver cytosol. J Biol Chem. 2002, 277: 10795-10803. 10.1074/jbc.M110246200.View ArticleGoogle Scholar
- Styblo M, Drobna Z, Jaspers I, Lin S, Thomas DJ: The role of biomethylation in toxicity and carcinogenicity of arsenic. A research update. Environ Health Perspect. 2002, 110 (Suppl 5): 767-771.View ArticleGoogle Scholar
- Walton FS, Harmon AW, Paul DS, Drobna Z, Patel YM, Styblo M: Inhibition of insulin-dependent glucose uptake by trivalent arsenicals: possible mechanism of arsenic-induced diabetes. Toxicol Appl Pharmacol. 2004, 198: 424-433. 10.1016/j.taap.2003.10.026.View ArticleGoogle Scholar
- Paul DS, Hernández-Zavala A, Walton FS, Adair BM, Dedina J, Matousek T, Styblo M: Examination of the effects of arsenic on glucose homeostasis in cell culture and animal studies: Development of a mouse model for arsenic-induced diabetes. Toxicol Appl Pharmacol. 2007, 222: 305-314. 10.1016/j.taap.2007.01.010.View ArticleGoogle Scholar
- Del Razo LM, Styblo M, Cullen WR, Thomas DJ: Determination of trivalent methylated arsenicals in biological matrices. Toxicol Appl Pharmacol. 2001, 174: 282-293. 10.1006/taap.2001.9226.View ArticleGoogle Scholar
- Gong Z, Lu X, Cullen WR, Le XC: Unstable trivalent arsenic metabolites, monomethylarsonous acid and dimethylarsinous acid. J Anal At Spectrom. 2001, 16: 1409-1413. 10.1039/b105834g.View ArticleGoogle Scholar
- Gong Z, Lu X, Ma M, Wattand C, Le X: Arsenic speciation analysis. Talanta. 2002, 58: 77-96. 10.1016/S0039-9140(02)00258-8.View ArticleGoogle Scholar
- Mandal BK, Ogra Y, Suzuki KT: Identification of dimethylarsinous and monomethylarsonous acids in human urine of the arsenic-affected areas in West Bengal, India. Chem Res Toxicol. 2001, 14: 371-378. 10.1021/tx000246h.View ArticleGoogle Scholar
- Mandal BK, Ogra Y, Anzai K, Suzuki KT: Speciation of arsenic in biological samples. Toxicol Appl Pharmacol. 2004, 198: 307-318. 10.1016/j.taap.2003.10.030.View ArticleGoogle Scholar
- Valenzuela OL, Borja-Aburto VH, Garcia-Vargas GG, Cruz-Gonzalez MB, Garcia-Montalvo EA, Calderon-Aranda ES, Del Razo LM: Urinary trivalent methylated arsenic species in a population chronically exposed to inorganic arsenic. Environ Health Perspect. 2005, 113: 250-254. 10.1289/ehp.113-a250.View ArticleGoogle Scholar
- Xie R, Johnson W, Spayd S, Hall GS, Buckley B: Arsenic speciation analysis of human urine using ion exchange chromatography coupled to inductively coupled plasma mass spectrometry. Anal Chim Acta. 2006, 578: 186-194. 10.1016/j.aca.2006.06.076.View ArticleGoogle Scholar
- Wang Z, Zhou J, Lu X, Gong Z, Le XC: Arsenic speciation in urine from acute promyelocytic leukemia patients undergoing arsenic trioxide treatment. Chem Res Toxicol. 2004, 17: 95-103. 10.1021/tx0341714.View ArticleGoogle Scholar
- Thomas DJ, Li J, Waters SB, Xing W, Adair BM, Drobna Z, Devesa V, Styblo M: Arsenic (+3 oxidation state) methyltransferase and methylation of arsenicals. Exp Biol Med. 2007, 232: 3-13.Google Scholar
- Tseng CH: Arsenic methylation, urinary arsenic metabolites and human diseases: current perspective. J Environ Sci Health C, Environ Carcinog Ecotoxicol Rev. 2007, 25: 1-22.View ArticleGoogle Scholar
- Hughes MF, Beck BD, Chen Y, Lewis AS, Thomas DJ: Arsenic Exposure and Toxicology: A Historical Perspective. Tox Sci. 2011, (on line, PMID: 21750349)Google Scholar
- Valenzuela OL, Drobná Z, Hernández-Castellanos E, Sánchez-Peña LC, García-Vargas GG, Borja-Aburto VH, Stýblo M, Del Razo LM: Association of AS3MT polymorphisms and the risk of premalignant arsenic skin lesions. Toxicol Appl Pharmacol. 2009, 239: 200-207. 10.1016/j.taap.2009.06.007.View ArticleGoogle Scholar
- Armienta MA, Rodríguez CR: Evaluación de la presencia de Arsénico en el valle de Zimapan Hidalgo. 1993, Report. Universidad Nacional Autónoma de MéxicoGoogle Scholar
- Del Razo LM, Arellano MA, Cebrian ME: The oxidation states of arsenic in well water from a chronic arsenicism area of northern Mexico. Environ Pollut. 1990, 64: 143-153. 10.1016/0269-7491(90)90111-O.View ArticleGoogle Scholar
- Del Razo LM, Corona JC, García-Vargas G, Albores A, Cebrian ME: Fluoride levels in well-water from a chronic arsenicism area of northern Mexico. Environ Pollut. 1993, 80: 91-94. 10.1016/0269-7491(93)90015-G.View ArticleGoogle Scholar
- Del Razo LM, García-Vargas GG, García-Salcedo JJ, Sanmiquel MF, Rivera M, Hernandez MC, Cebrian ME: Arsenic levels in cooked food and assessment of adult dietary intake of arsenic in the Region Lagunera, México. Food Chem Toxicol. 2002, 40: 1423-1431. 10.1016/S0278-6915(02)00074-1.View ArticleGoogle Scholar
- SARH (Secretaría de Agricultura y Recursos Hidraúlicas): Estudio de Hidroarsenicismo en la Comarca Lagunera, México. 1989, Cinvestav-IPN, Comisión de Aguas del Valle de México de SARHGoogle Scholar
- Devesa V, Del Razo LM, Adair B, Drobna Z, Waters SB, Hughes MF, Styblo M, Thomas DJ: Comprehensive analysis of arsenic metabolites by pH-specific hydride generation atomic absorption spectrometry. J Anal At Spectrom. 2004, 19: 1460-1467. 10.1039/b407388f.View ArticleGoogle Scholar
- Hernández-Zavala A, Matoušek T, Drobná Z, Paul DS, Walton F, Adair BM, Thomas DJ, Styblo M: Speciation of arsenic in biological matrices by automated hydride generation-cryotrapping-atomic absorption spectrometry with multiple microflame quartz tube atomizer (multiatomizer). J Anal At Spectrom. 2008, 23: 342-351. 10.1039/b706144g.View ArticleGoogle Scholar
- Le XC, Ma M: Short-column liquid chromatography with hydride generation atomic fluorescence detection for the speciation of arsenic. Anal Chem. 1998, 70: 1926-1933. 10.1021/ac971247q.View ArticleGoogle Scholar
- Chen Y, Ahsan H, Slavkovich V, Peltier GF, Gluskin RT, Parvez F, Liu X, Graziano JH: No Association between Arsenic Exposure from Drinking Water and Diabetes Mellitus: A Cross-Sectional Study in Bangladesh. Environ Health Perspect. 2010, 118: 1299-1305. 10.1289/ehp.0901559.View ArticleGoogle Scholar
- Goldstein DE, Little RR, Lorenz RA, Malone JI, Nathan DM, Peterson CM: Tests of glycemia in diabetes. Diabetes Care. 2004, 27: 1761-1773. 10.2337/diacare.27.7.1761.View ArticleGoogle Scholar
- Mudaliar S, Edelman SV: Insulin therapy in type 2 diabetes. Endocrinol Metab Clin North Am. 2001, 30: 935-982. 10.1016/S0889-8529(05)70222-X.View ArticleGoogle Scholar
- Ahsan H, Chen Y, Kibriya MG, Slavkovich V, Parvez F, Jasmine F, Gamble MV, Graziano JH: Arsenic metabolism, genetic susceptibility, and risk of premalignant skin lesions in Bangladesh. Cancer Epidemiol Biomark Prev. 2007, 16: 1270-1278. 10.1158/1055-9965.EPI-06-0676.View ArticleGoogle Scholar
- Lindberg AL, Rahman M, Persson LA, Vahter M: The risk of arsenic induced skin lesions in Bangladeshi men and women is affected by arsenic metabolism and the age at first exposure. Toxicol Appl Pharmacol. 2008, 230: 9-16. 10.1016/j.taap.2008.02.001.View ArticleGoogle Scholar
- Su L, Cheng Y, Lin S, Wu C: Study of relationship between arsenic methylation and skin lesion in a population with long-term high arsenic exposure. Wei Sheng Yan Jiu. 2007, 36: 336-340.Google Scholar
- Yu RC, Hsu KH, Chen CJ, Froines JR: Arsenic methylation capacity and skin cancer. Cancer Epidemiol Biomarkers Prev. 2000, 9: 1259-1262.Google Scholar
- American Diabetes Association: Standards of Medical Care in Diabetes - 2011. Diabetes Care. 34 (Suppl 1): S11-S61.
- Villalpando S, de la Cruz V, Rojas R, Shamah-Levy T, Avila MA, Gaona B, Rebollar R, Hernández L: Prevalence and distribution of type 2 diabetes mellitus in Mexican adult population: a probabilistic survey. 2010, Salud Publica Mex, 52 (Suppl 1): S19-26.
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