trip to Pakistan

Relationship between diet, micronutrients, and patterns of arsenic methylation in an exposed population in West Bengal, India

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The work was performed at the Institute of Postgraduate Medical Education and Resaerch, Calcutta, India

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[Last worked on: 04/23/09 ]

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Ruunning Title

Diet, micronutrients, and Arsenic methylation

Key words

Arsenic. Methylation, West Bengal, India, Diet, Micronutrients, Linear Model, Dietary Animal Fat

Acknowledgements

Staff of the Arsenic Research Group

...

Competing Interests

? None

Abbreviations and Definitions used in the manuscript

InAs% = Percentaage of Inorganic Arsenic in Urine

MMA% = Monomethyl Arsonous Acid

DMA% = Dimethyl Arsinic Acid

Abstract

Background

Objectives

Methods

Results

Conclusions

Abstract

Background: Methylation may have an important role in arsenic toxicity based on evidence that the monomethylated trivalent metabolite (MMA3) is more toxic than inorganic arsenic itself. It is therefore important to identify factors that might increase concentrations of MMA by reducing further methylation to dimethylated arsenic (DMA).

Objectives: To investigate the relationship of nutritional factors with methylation patterns of arsenic reflected in urine in what we believe is the first comprehensive joint assessment of dietary factors and micronutrients in a study of arsenic methylation

Methods: We conducted a a cross-sectional survey including 192 persons with arsenic-caused skin lesions and 213 persons without, who had been identified in a cross-sectional survey of 7638 people in an arsenic-exposed population in West Bengal, India. We assessed 30 dietary factors obtained from 24-hour recall, and 16 blood micronutrients. We used multivariate regression models were used to identify associations of indicators of arsenic methylation in urine with dietary factors and serum micronutrients, adjusting for age, gender, socioeconomic variables, body mass index, total urinary arsenic and skin lesion status.

Results: [needs to be rewritten ]

Conclusions: This study provides further evidence that folate and selenium intake are associated with lower MMA excretion. The finding that high intake of animal fat was positively related to MMA concentrations is new.

Background

Diet and micronutrients may play important roles in methylation of inorganic arsenic and in the mechanisms of arsenic-caused skin lesions affecting millions of people worldwide who are exposed to high concentrations of inorganic arsenic through their drinking water. Inorganic arsenic in drinking water exists in trivalent (As3), and pentavalent (As5) forms. After ingestion, As5 is reduced to As3, followed by sequential reduction-methylation reaction steps resulting in formation of trivalent and pentavalent methylated arsenicals – first monomethyl arsonous acids (MMA3 and MMA5), and subsequently trivalent and pentavalent dimethyl arsinic acids (DMA3 and DMA5) – which are excreted through urine. However, this process is incomplete, and populations show variations in the proportions of urinary inorganic arsenic and the methylated arsenicals (Hopenhayn-Rich, Biggs et al. 1996; Aposhian 1997; Healy, Zakharyan et al. 1997; Concha, Vogler et al. 2002; Hughes 2002; Drobna, Waters et al. 2004).

Isolation and characterization of MMA3 indicates that the first step of methylation which produces MMA should really be thought of as an activation step and is cytotoxic, while the conversion of MMA to DMA in a second methylation step would reduce toxicity. Epidemiological studies have found evidence of increased risks of skin and bladder cancer among persons with higher proportions of MMA in their urine (Steinmaus, Yuan et al. 2003) Therefore, factors that influence arsenic methylation may be important determinants of susceptibility to health effects resulting from exposure to inorganic arsenic in drinking water .  Arsenic toxicity is a major public health problem in the Bengal delta, affecting milions of people in West Bengal state of India and adjoining areas in Bangladesh. In view of widespread poor nutrition prevalent in West Bengal and Bangladesh, dietary factors which might increase arsenic toxicity through by influencing methylation of arsenic are important for study in this population.

We have already reported on the relationship of dietary constituents and blood micronutrients with arsenic-caused skin lesions in West Bengal (Mitra, Mazumder et al. 2004; Chung, Haque et al. 2005). Here, we report the findings concerning ?? dietary constituents and blood micronutrients with arsenic methylation patterns in urine in what we believe is the first comprehensive joint assessment of dietary and micronutrients.

Methods

Study participants

We investigated 180 individuals with arsenic-caused skin lesions and 192 individuals without, who lived in the North 24-Parganas district of the West Bengal state in India. Participants had been selected for a previously completed case-control study of arsenic-caused skin lesions (Haque, Mazumder et al. 2003). The study base for selection of cases and controls involved 7683 individuals who participated in a 1995-1996 population based cross-sectional survey of the South 24 Parganas, a rural district located south of Kolkata. The survey identified 415 individuals with signs of arsenic-induced skin lesions(Guha Mazumder, Haque et al. 2003 reference might be better here1998)  . Because of our interest in examining effects at low doses, we based the case-control study on survey participants whose primary drinking water sources contained less than 500 micrograms/liter of inorganic arsenic (N = 4815, 2160 females and 2025 males).

Cases had a positive skin lesion classification, with either hyperpigmentation (mottled dark brown pigmentation bilaterally distributed on the trunk) or keratoses (diffuse thickening of palms or soles, with or without nodules) at the time of survey. Controls were randomly selected from the study base, and matched to the cases by gender and age within 4 years. The study protocol was approved by the Institutional Review Boards of the Institute of Post Graduate Medical Education and Research, Kolkata and the University of California, Berkeley. Informed consent was obtained from all participants.

The current investigation we report here uses cross-sectional data from the cases and controls including dietary assessment, measurement of blood micronutrients, and measurement of methylated arsenic species in urine samples. The controls were selected to be representative of the source population as described above. Cases with skin lesions were included in the present analysis to increase the numbers of participants. As explained below, we checked for possible distortion of associations between nutritional factors and arsenic methylation patterns due to the inclusion of both cases and controls.

In India, socioeconomic status is commonly measured by the types of dwellings, which are correlated with household economic status (Mishra, Retherford et al. 1999) . In this study, we determined socioeconomic status based on the materials used to construct the house where the respondent lived. We considered three types of houses: pucca houses – built with high quality materials, eg bricks or concrete; semi-pucca houses – constructed partly with clay and bricks, and kacha – mud houses. We classified educational status as non-formal (participant never attended school), primary education (up to four years of education), high school education (between 8 and 12 years of education), and beyond high school education (more than 12 years of formal education).

Assessment of dietary intake and blood micronutrients

For each participant, we measured weight and height and calculated body mass index (BMI). We ascertained food intake for each participant with a detailed questionnaire based primarily on 24-hour recall. We have previously presented the methods used for dietary assessment(Mitra, Mazumder et al. 2004). In brief, the senior woman, who in this population directs preparing food for the family, was interviewed. The senior woman, who was usually the mother in the family or the eldest daughter-in-law, was questioned about each meal from the previous day’s lunch through to the breakfast on the day of the interview. The volume of each cooked food was assessed by questioning the senior woman using standard cups and plates. Standard sized spoons were used to assess the intake of sugar and oil. We asked about weekly consumptions of meat, fish, eggs, milk, and fruit because these items were not consumed on a daily basis. The 1-week intake of these food items was then divided by 7 to compute the average intake per day. We calculated total 24 hour intake of the followingeach nutrients using a spreadsheet program based on food composition tables: .... ...., ...., .....

Field physicians interviewed participants using a structured questionnaire, conducted a general examination, and obtained blood samples from each participant when they were visited in their homes. We have previously presented detailed information concerning storage and analysis of blood samples  In brief, nonfasting blood samples were collected and stored in an ice chest in the field. Aliquots were prepared within 24 hours, frozen at -20 deg C in India, later transported to the United States on dry ice where they were stored at -70 deg C until laboratory analysis. Pacific Biometrics (Seattle, WA) conducted most serum and plasma analyses for the micronutrients and biochemical indicators, or in some instances arranged for them to be done at a different laboratory. Plasma measurements included homocysteine, glutathione, cysteine, methionine, vitamin B6, retinol (Vitamin A), alpha-tocopherol (Vitamin E), alpha-carotene, beta-carotene, lycopene, lutein-zeaxanthin, and beta-cryptodextrin (Chung, Haque et al. 2005). Serum measurements included glucose, cholesterol, vitamin B12, folate, transthyretin, and selenium.

We analyzed a total of 18 dietary variables and 15 micronutrients to identify associations with InAs%, MMA%, and DMA% excreted in urine. We adjusted each dietary variable for individual total calorie intake by dividing total daily dietary intake with the total calorie intake.

Measurement of urinary arsenic

Spot urine sampes were collected form each participant and stored frozen for later analysis at the University of Washignton (Kalman DA). The urinary concentrations of arsenic were measured using hydride generation atomic absorption spectroscopy. In this technique, InAs, MMA, and DMA were reduced to the corresponding arsine in a batch reactor using sodium borohydride in 5 ml samples. The volatile reduction products (arsenic, methyl arsine, and dimethylarsine) were removed by sparging with helium. Entrained arsines were concentrated in a chromosorb-filled cryogenic trap in liquid nitrogen temperatures until all arsine-forming arsenic in the sample had reacted. The cryotrap was then allowed to warm, and the collected arsines were separated on the basis of differential volatilization. The separated volatile arsenic species were detected with a hydrogen microburner combustion cell to convert arsines to elemental arsenic. To prevent interference by other compounds, each urine sample was acidified with 2 M HCl and allowed to sit for at least 4 hours. Total arsenic was determined by flow injection analysis/atomic fluorescence spectrometry and the result was compared with the sum of the species detected. If a significant amount of arsenic remained undetected, additional digestion or assay for asenobetaine was performed. Detection limits for InAS, MMA, and DMA were 0.5, 1, and 2 micrograms/L respectively. Concentrations below the detection limit were set at one half the detection limit. The MMA and DMA measured in this study were in the pentavalent forms. The trivalent forms, MMA3 and DMA3, are rapidly oxidized to MMA5 and DMA5 during storage.

Indicators of arsenic methylation

Urinary arsenic concentrations were measured in micrograms per liter of urine; percentages of inorganic arsenic (InAs%), MMA (MMA%), and DMA (DMA%) were calculated using the sum of InAs, MMA and DMA as denominators as follows:

InAs% = (InAs / InAs + MMA + DMA) * 100

MMA% = (MMA /InAs + MMA + DMA) * 100

DMA% = (DMA /InAs + MMA + DMA) * 100

Steps of statistical modeling

For each dietary variable and micronutrient, we compared the differences in mean urinary InAs%, MMA%, and DMA% corresponding to the highest and lowest tertiles using unpaired one way t-test. We calculated the difference in the mean percentages of urinary methylated arsenicals keeping the the lowest tertiles of the dietary variables and micronutrients as baseline. For each methylated arsenical, we ranked the dietary or micronutrient variables in the descending order of the magnitude of the absolute value of their differences in mean. The dietary variables and micronutrients that showed high differences in means and low p-values were then entered into a series of linear regressions.

In these models, InAs%, MMA%, and DMA% were response variables, and tertiles of selected dietary or micronutrient variables were explanatory variables with the lowest tertile of the dietary variable or micronutrient as the reference category. We considered beta coefficients corresponding to the highest tertile of the diet/micronutrient as a measure to reflect the change in urinary metabolite excretion in response to dietary intakes or change in micronutrient levels. The other variables in the model were age, gender, housing, education, skin lesions, BMI, & urinary arsenic.

Results

, 63% of the respondents were men, 70% were less than 45 years, less than 5% of the study population had studied beyond high school, and about 14% lived in houses made of bricks (Table 1).

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,mMen had lower InAs% (men: 22.2%, women: 24.5%; p=0.05) and higher MMA% than women (men: 8.88, women: 7.02% ; p=0.01). Those with skin lesions had lower InAs% (21.3% versus 24.3% for those with no €​skin lesions; p=0.03), similar MMA%, and higher DMA% in urine (70.4% versus 67.3% for those with no skin lesions). (Table 2).

Compared to the lowest tertiles,

On multivariate analysis, dietary Animal Fat, Serum Folate and Serum Selenium were independently associated with MMA%. Serum Lutein Zeaxanthine and dietary riboflavine were independently associated with urinary DMA%.

Discussion

In summary, the study population was predominantly male, rural, and belonged to low socioeconomic status. Being male was associated with higher MMA%, as was the following dietary and micronutrient variables: he population was male dominant, with low socioeconomic and educational status, conforming to the characteristic rural population in India, Bangladesh and much of other third world countries, (2) Male gender was associated with increased MMA%,

essential findings of this study were that, while male gender and high dietary intakes of Animal Fat were associated with increased MMA% (but no significant association was found with InAs% or DMA%), Serum Selenium, and Folate were associated with reduced MMA%. MMA% , however, had no association with skin lesions, and dietary animal fat intake was higher among women, who in turn had lower MMA%, but dietary fat intake was also higher among those with higher socioeconomic status (measured in terms of high schoold education and standard of housing). These findings need to be interpreted in the light of complex biosocial connotations of arsenic toxicity in West Bengal

Biologically, methylation of inorganic arsenic is a two-step process, therefore, use of InAs%, MMA%, and DMA% as indicators of methylation indicate four emergent patterns with respect to factors that affect either step I or step II or both steps of methylation (Table 6). In the first pattern – factors that may increase both steps of methylation will likely to be positively associated with InAs%, while negatively associated with both MMA% and DMA% (Cell A, Table 6). In the second pattern, factors associated with increased Step I methylation but decreased Step II methylation will be positively associated with MMA% while negatively associated with both InAs% and DMA% (Cell B of Table 6). In the third pattern, factors associated with low Step I but high Step II methylation (or increased clearance of accumulated MMA) would be negatively associated with MMA% but positively associated with both InAs% and DMA% (Cell C of Table 6). Finally, factors that increase both steps of methylation may be positively associated with DMA% but negatively associated with InAs% and MMA% (Cell D of Table 6).

In vivo methylation of inorganic arsenic uses S-adenosyl methionine (SAM) as the methyl group donor. As a result, SAM gets converted to s-adenosyl homocysteine (SAH). Regeneration of SAM from SAH goes through a pathway involving generation of Homocysteine from SAH, and transfer of methyl groups from Folate metabolites. SAH also act as rate limiting inhibitor of the methylation, by tightly binding to methyltransferases. In the metabolic pathway, Folate is a key agent for the removal of Homocysteine that accumulates as a result of conversion from SAH (which is the result of conversion of SAM to SAH during methylation),. Thus, downstream removal of Homocysteine allows for further conversion of SAH to Homocysteine and in turn, releases the step-limiting inhibition of methylation (Gamble, Liu et al. 2005). This model would also predict serum Homocysteine would be positively correlated with MMA%, and negatively correlated with serum Folate. This was supported in our study where we found negative association with serum Folate.

The negative association between Serum Folate and MMA% is supported by findings from other epidemiological studies. Using data from a cross-sectional survey in a comparable Bangladeshi population (N = 300), Gamble reported that Serum Folate was negatively associated with InAs% (Spearman r = -0.12,, p < 0.05), and MMA% (r = -0.12; p = 0.04) and was positively associated with serum Homocysteine (r = 0.21, p < 0.05) and urinary DMA% (r = 0.21; p < 0.001). Gamble and colleagues reported univariate correlation estimates of associations and did not report resutls of multivariate associations after adjustment of possible confounding variables (Gamble, Liu et al. 2005). On the same population, Gamble and colleagues conducted a follow up double blind randomized controlled trial in the same population (N = 200) comparing 12 week administration of Folate versus placebo and found that after 12 weeks administration of Folic Acid Supplementation, the increase in the proportion of total urinary arsenic excreted as DMA in the Folate group (72% at baseline and 79% after supplementation, p < 0.0001) was greater than that in the placebo group, as was the reduction in the proportions of total urinary arsenic excreted as MMA (13% and 10%, respectively; p < 0.0001) and as InAs (15% and 11%, respectively; p < 0.001). However, in this study, the intervention group and the placebo group were exposed to Arsenic during the time of the trial and the authors concluded that Folate Supplementation enhanced arsenic methylation (Gamble, Liu et al. 2006) . Since this study was conducted among individuals concomitantly exposed to high concentrations of inorganic arsenic, it was unclear from this study if Folate supplementation would have comparable effects if the individuals were removed from exposure, or under conditions of no exposure to arsenic – the true nature of the association between Folate and methylation.

The positive association between dietary Animal Fat and MMA%, and consequently, their being associated with high Step I methylation were new findings not reported elsewhere. In a study on dietary intake and arsenic methylation based on a US population (N = 30), Steinmaus et.al., had found positive correlations between calorie adjusted dietary protein intake and DMA% (beta = 0.088, p = 0.12), but negative associations with InAs% (beta = -0.012, p = 0.76) and MMA% (beta = -0.075, p = 0.02). The authors concluded on the basis of their small effect sizes that there could be other factors that explained the variability of methylation better compared to dietary variables alone. A possible reason why our findings were different from the US study could be attributed to the different levels & patterns of nutrition between a US population and this rural Indian population, in addition to difference in age, where the US population studied older adults and our population consisted of younger individuals (Steinmaus, Carrigan et al. 2005).

The findings of this study need be interpreted in the light of its several limitations. First, data for this study came from a cross-sectional survey. Information on the dietary variables were collected at the same time as blood was sampled for estimation of serum micronutrients and urine samples were collected for estimation of urinary InAs%, MMA%, and DMA%. This provided snapshots of inter-relationships between diet, micronutrients and arsenic methylation variables averaged over the members of this population. Because of the complex inter-relationships between arsenic methylation reactions and micronutrients including Folate (serum), and Homocysteine (serum), involving transmission of methyl groups, it could not be ascertained only on the basis of a cross-sectional study whether the changes in either dietary intake of Animal Fat, or levels of Homocysteine, Folate (serum), or serum Selenium preceded the changes in the levels of urinary arsenic metabolites. Second, we obtained data on dietary factors using a diet based food frequency questionnaire based on 24 hours recall, with the tacit assumption that the dietary patterns of people in this population remained unchanged over periods of time, and that the methylation pattern was invariant over time. While this assumption may have been a valid assumption in view of this stable, rural population, we cannot comment if the results of this study be applicable to other, non-comparable population. Tbird, we did not measure MMA3 as part of this study as it was not possible to measure MMA3 reliably in the field when this study was done. Based on the evidence of high toxicity of MMA3 and links between total MMA and arsenic-caused cancer risks, future studies that investigate association between dietary variables, serum nutrients and trivalent MMA are warranted (Del Razo, Garcia-Vegas et al. 1997; Hsueh, Chiou et al. 1997; Yu, Hsu et al. 2000; Chen, Li et al. 2004). Finally, this study was conducted in an exposed population in rural West Bengal, who had low socioeconomic status; their food pattern was stable over the years but their diet was mixed – the main source of Animal Fat was egg and meat. For both cases and controls, information about the dietary variables came from a diet survey from an interview of the elder member of the household who prepared the food, and levels of serum micronutrients were measured separately. This may be a limiting factor for the generalizability of this study findings.

In conclusion, while this study had several limitations, the findings suggested that dietary variables were not the only factors that determined patterns of methylation in people exposed to high concentration of inorganic arsenic. While Folate, Selenium, and dietary intakes of Animal Fat may partially explain variability in methylation capacities, their implication in addressing the larger public health problems of arsenic poisoning is unclear. At best, dietary modification and metabolic manipulation by micronutrient supplementation may be beneficial in conjunction with provision of safe arsenic-free water to the affected population

Explanaiton of the peculiar values

When comparing the means for the levels of a factor in an analysis of variance, a simple comparison using t-tests will inflate the probability of declaring a significant difference when it is not in fact present. This because the intervals are calculated with a given coverage probability for each interval but the interpretation of the coverage is usually with respect to the entire family of intervals.

John Tukey introduced intervals based on the range of the sample means rather than the individual differences. The intervals returned by this function are based on this Studentized range statistics.

Technically the intervals constructed in this way would only apply to balanced designs where there are the same number of observations made at each level of the factor. This function incorporates an adjustment for sample size that produces sensible intervals for mildly unbalanced designs.

References

Aposhian, H. (1997). " Enzymatic methylation of arsenic species and other new approaches to arsenic toxicity. ." Annu Rev Pharmacol Toxicol 37: 397-419.

Bhattacharyya, R., D. Chatterjee, et al. (2003). "High arsenic groundwater: mobilization, metabolism and mitigation-an overview in the Bengal Delta Plain." Mol Cell Biochem 253(1-2): 347-355.

Chen, H., S. Li, et al. (2004). "Chronic inorganic arsenic exposure induces hepatic global and individual gene hypomethylation: implications for arsenic hepatocarcinogenesis." Carcinogenesis 25(9): 1779-86.

Chung, J., R. Haque, et al. (2005). "Blood concentrations of methionine, selenium, betacarotene and other micronutrients in a case-control study of arsenic induced skin lesions in West Bengal, India." Environ Res In Press(In Press): In Press.

Concha, G., G. Vogler, et al. (2002). "Intra-individual variation in the metabolism of inorganic arsenic." Int Arch Occup Environ Health 75(8): 576-80.

Das, D., G. Samanta, et al. (1996). "Arsenic in groundwater in six districts of West Bengal, India." Environ Geochem Health 18(0): 5-15.

Del Razo, L., G. Garcia-Vegas, et al. (1997). "Altered profile of urinary arsenic metabolites in adults with chronic arsenicism." Arch Toxicol 71: 211-217.

Drobna, Z., S. Waters, et al. (2004). " Interindividual variation in the metabolism of arsenic in cultured primary human hepatocytes. ." Toxicol Appl Pharmacol 201(2): 166-77.

Gamble, M., X. Liu, et al. (2005). "Folate, homocysteine, and arsenic metabolism in arsenic-exposed individuals in Bangladesh. ." Environ Health Perspect 113(12): 1683-1688.

Gamble, M. V., X. Liu, et al. (2006). "Folate and arsenic metabolism: a double-blind, placebo-controlled folic acid-supplementation trial in Bangladesh." The American journal of clinical nutrition 84(5): 1093-101.

Gamble, M. V., X. Liu, et al. (2005). "Folate, homocysteine, and arsenic metabolism in arsenic-exposed individuals in Bangladesh." Environmental health perspectives 113(12): 1683-8.

Guha Mazumder, D., R. Haque, et al. (1998). "Arsenic levels in drinking water and the prevalence of skin lesions in West Bengal, India. ." Int J Epidemiol 27(5): 871-7.

Haque, R., D. Mazumder, et al. (2003). "Arsenic in drinking water and skin lesions: dose-response data from West Bengal, India. ." Epidemiology 14(2): 174-82.

Healy, S., R. Zakharyan, et al. (1997). "Enzymatic methylation of arsenic compounds: IV. In vitro and in vivo deficiency of the methylation of arsenite and monomethylarsonic acid in the guinea pig. ." Mutat Res 386(3): 229-39.

Hopenhayn-Rich, C., M. Biggs, et al. (1996). "Methylation study of a population environmentally exposed to arsenic in drinking water. ." Environ Health Perspect 104(6): 620-8.

Hsueh, Y., H. Chiou, et al. (1997). "Serum beta-carotene level, arsenic methylation capability, and incidence of skin cancer. ." Cancer Epidemiol Biomarkers Prev 6(8): 589-96.

Hughes, M. (2002). "Arsenic toxicity and potential mechanisms of action." Toxicol Lett 133(1): 1-16.

Mandal, B. and K. Suzuki (2002). "Arsenic around the world: a review." Talanta 58: 201-235.

Mishra, V. K., R. D. Retherford, et al. (1999). "Biomass cooking fuels and prevalence of tuberculosis in India." International Journal of Infectious Diseases 3(3): 119-129.

Mitra, S., D. Mazumder, et al. (2004). "Nutritional factors and susceptibility to arsenic-caused skin lesions in West Bengal, India. ." Environ Health Perspect 112(10): 1104-9.

Rahman, M., M. Sengupta, et al. (2005). "The magnitude of arsenic contamination in groundwater and its health effects to the inhabitants of the Jalangi-one of the 85 arsenic affected blocks in West Bengal, India." Sci Total Environ 338(3): 189-200.

Smith, A., E. Lingas, et al. (2000). "Contamination of drinking-water by arsenic in Bangladesh: a public health emergency." Bull World Health Organ 78(9): 1093-103.

Smith, A., P. Lopipero, et al. (2002). "Public health. Arsenic epidemiology and drinking water standards." Science 296(5576): 2145-6.

Steinmaus, C., K. Carrigan, et al. (2005). " Dietary intake and arsenic methylation in a U.S. population. ." Environ Health Perspect 113(9): 1153-9.

Steinmaus, C., Y. Yuan, et al. (2003). "Case-control study of bladder cancer and drinking water arsenic in the western United States." Am J Epidemiol 158(12): 1193-1201.

Yu, R., K. Hsu, et al. (2000). "Arsenic methylation capacity and skin cancer." Cancer Epidemiol Biomarkers Prev 9(11): 1259-62.

Tables and Figures

Table 1. DIescription of nformation on demographic, sociaoeconomic variables l, &and status of Arsenic skin lesions

Variable Category N Percent

Age in years

< 15 42 10.4

15--29 111 27.5

30--44 135 33.4

45--59 82 20.3

>= 60 34 8.42

Gender

Female 150 36.9

Male 256 63.1

Educational status

No formal 118 29.1

Primary 200 49.3

Secondary 68 16.8

Above high school 20 4.93

Type of dwelling

Kacha 210 51.7

Semi-pucca 138 33.9

Pucca 58 14.3

Skin Lesions

Absent 213 52.5

Present 192 47.4

Table 2. Association between demographic, social, and skin lesions with indicators of methylation. Here. p-values are based on chi-square test of trend across the values in the categories

Table 3. Comparison of mean values of indicators of methylation (InAs%, MMA%, and DMA%) in the lowest and highest tertiles of dietary intake and serum micronutrient. Values under Q1 indicate the average value for (work out the table again...) This heading is incomplete. The reader needs to exactly what Q1 and Q3 are here. The table might look better with the SEs omitted. All p-values need checking.

Table 4. Multivariable models of the association between MMA%, DMA%, and Dietary Animal Fat, Plasma Folate, & Dietary Retinol. The multivariate models are based on linear models were urinary excretion of MMA and DMA were regressed on tertiles of dietary/plasma level of nutrients. Other covariates in the models were age in years, gender, housing, educational status, and status of skin lesions. This table is very cumbersome. The T-statistic seems redundant. Rather than SE, the 95% CI would be better.

Table 5. Association between Age in years, gender, socioeconomic status (education and housing), and skin lesion status on Dietary Animal fat intake

Table 6. Relationship between Steps of Methylation and percentages of methylated metabolites in urine This table should be removed

Table 7. Comparison of InAs%, MMA%, and DMA% between the lowest versus the highest tertiles of dietary intakes of various dietary variables and micronutrients