Abstract
Arsenic exposure from drinking water is considered to be a risk factor for skin and internal cancers. Animal studies suggest a potential antagonism between As and Se in the body. We performed a case-cohort analysis to prospectively evaluate the association between As-related premalignant skin lesions and prediagnostic blood Se levels in 303 cases of skin lesions newly-diagnosed from November 2002 to April 2004 and 849 subcohort members randomly-selected from the 8,092 participants in the Health Effects of As Longitudinal Study with available baseline blood and urine samples collected in 2000. Incidence rate ratios for skin lesions in increasing blood Se quintiles were 1.00 (ref), 0.68 (95% confidence interval (CI): 0.39, 1.18), 0.51 (95% CI: 0.29, 0.87), 0.52 (95% CI: 0.30, 0.91), and 0.53 (95% CI: 0.31, 0.90). Effect estimates remained similar with adjustments for age, sex, BMI, smoking status, excessive sunlight exposure (in men), well water As concentration at baseline, and nutritional intakes of folate, iron, protein, Vitamin E, and B Vitamins. At any given As exposure level, the risk of premalignant skin lesions was consistently greater among participants with blood Se lower than the average level. The findings support the hypothesis that dietary Se intake may reduce the incidence of As-related premalignant skin lesions among populations exposed to As exposure from drinking water.
Keywords: Arsenic, Bangladesh, Case-cohort study, Premalignant skin lesions, Selenium
Background
The presence of inorganic arsenic (As) in groundwater has been recognized as a public health hazard in many countries. The International Agency for Research on Cancer has classified arsenic as a group 1 human carcinogen. Epidemiologic studies have documented associations between As exposure from drinking water and elevated risks of premalignant skin lesions, skin and internal cancers, and cardiovascular diseases (1–3). In Bangladesh, more than 50 million people have been chronically exposed to drinking groundwater with As concentrations exceeding the WHO standard (10 μg/L) (4). We have estimated the cancer burden to be doubling in Bangladesh (5). Clearly, As mitigation and cancer preventive programs are urgently needed to reduce As toxicity in the population.
Cutaneous abnormalities are well known early signs of chronic inorganic As poisoning. Melanosis is considered as early-stage skin lesions. Keratosis is the most frequent manifestation preceding the appearance of As-related skin cancer (6). Unlike As-related internal cancers that could have long latencies, these premalignant skin lesions may appear with shorter periods of As exposure (7). They give rise to the majority of As-induced basal and squamous cell skin cancers (6, 8, 9). In 428 cases of skin cancer in an As-exposed population in Taiwan, 90% were associated with hyperpigmentation and 72% were associated with keratosis (6). In other historical case series, 81–100% of As-related skin cancer cases were related to keratosis (10, 11).
It has been hypothesized that susceptibility to As toxicity differs by dietary selenium (Se) intake levels (12, 13). Se is an essential human dietary trace element required for synthesis of a variety of Se-containing proteins, some of which are selenoproteins that incorporate Se in the form of the amino acid selenocysteine (SeCys) during translation (14). Selenoproteins and their metabolites are critical in maintaining antioxidant/anti-inflammatory homeostasis. In experimental studies, As exposure has been associated with a greater production of free radicals and increased oxidative stress (15) that may be reduced by selenoproteins. Additionally, animal studies have demonstrated an interaction between Se and As, such that uptake of one of these elements causes release, redistribution, or elimination of the other element by urinary and/or biliary routes (16, 17). However, findings from epidemiologic studies about the protective effect of Se intake on risks of As-related diseases such as premalignant skin lesions and blackfoot disease (a unique peripheral vascular disease in lower extremities related to high levels of As exposure) in populations exposed to As exposure have been inconclusive (13, 18–21). Limitations of these studies include small sample sizes, unavailability of prediagnostic Se levels (in observational studies), and methodological shortcomings such as the lack of blindness in randomization (in intervention studies).
We conducted a case-cohort study nested in the Health Effects of As Longitudinal Study (HEALS) to prospectively assess the association between prediagnostic levels of Se in whole blood and the subsequent risk of premalignant skin lesions. We also evaluated whether the relationship between long-term As exposure from drinking water and risk of skin lesions is modifiable by blood Se levels.
Methods
The Health Effects of As Longitudinal Study (HEALS)
The parent study HEALS is an ongoing prospective cohort study in Araihazar, Bangladesh. Details of the study methodologies have been presented elsewhere (22, 23). Briefly, prior to subject recruitment, water samples and geographic positional system data were collected for 5,966 contiguous wells in a well-defined geographic area of 25 square km in Araihazar. Between October, 2000 and May, 2002, 11,746 men and women aged 18 years and above were recruited, with a participation rate of 97.5% (22). The cohort is being followed with in-person visits at two year intervals. Verbal consent was obtained from study participants. The study procedures were approved by the Columbia University Institutional Review Board and the Ethical Committee of the Bangladesh Medical Research Council.
At baseline recruitment, venous whole blood samples were collected in 3 ml Vacutainers containing EDTA as anticoagulant for 91.8% of the overall 11,746 cohort participants. At baseline and the follow-up visits, a spot urine sample was collected in 50 ml acid-washed tubes for 95.6% and 94.5% of the cohort participants, respectively. Both blood and urine samples were kept in portable coolers immediately after collection. Within 2–8 hours, blood and urine samples were processed and transferred to −20° C freezers in the study office located in Dhaka city. All samples were kept frozen and shipped to Columbia University on dry ice within 1–2 months.
Trained physicians completed a comprehensive physical examination at baseline and follow-up visits. Details of the clinical examination protocol for premalignant skin lesion diagnosis were previously described (22). We instituted a structured protocol adapting the method for quantitative assessment of the extent of body surface involvement in burn patients. The principle is based on dividing the entire body skin surface into 11 segments and assigning percentages to each of them based on their size relative to the whole body surface. This method requires a physician to record presence/absence, type, size, shape of skin lesions and extent of skin involvement. Physicians were blind to information on the As level in participants’ drinking wells. In the present study, presence of premalignant skin lesions was defined as existence of any melanosis and/or keratosis.
Selection of cases and subcohort
A case-cohort study design (24) was used to evaluate the relationship between blood Se level and risk of skin lesions. The case-cohort study design has been used to analyze cohort data efficiently when most observations are censored (non-diseased) (24). It provides the advantages of a cohort study in that it allows the direct calculation of a rate ratio without the collection and analysis of full information on every member of the cohort. A random sample of the cohort, or “subcohort,” is designated as the comparison group for the newly-diagnosed cases of skin lesion observed in the overall cohort.
Among the 9,727 participants who gave both urine and blood samples and completed the physical examination at baseline, 712 were prevalent cases of skin lesions. They were excluded from the current analysis. Additionally excluded from the study were 923 randomly selected subjects whose blood samples were previously consumed in a study of genetic susceptibility. The present analysis included a 10.5% random sample of the remaining 8,092 participants (n=849) and 303 cases of newly-diagnosed skin lesions. The 303 cases of skin lesions were diagnosed at the first two-year follow up from the 8,092 participants between November, 2002 and April, 2004; 221 of the cases had only melanosis, while the remaining 82 had both hyperkeratosis and melanosis. Among the 303 newly-diagnosed cases, 31 were also part of the 849 subcohort members.
Measurements of As exposure
At baseline, water samples from all 5,966 tube wells in the study area were collected in 50 ml acid-washed tubes following well pumping for 5 minutes (25, 26). Total As concentration was determined by graphite furnace atomic-absorption spectrometry (GFAA) with a Hitachi Z-8200 system at the Lamont-Doherty Earth observatory of Columbia University (25). Samples that fell below the detection limit of GFAA (5 μg/L) were subsequently analyzed by inductively coupled plasma mass spectrometry (ICP-MS), with a detection limit of 0.1 μg/L (27). Analyses for time-series samples collected from 20 tube wells in the study area showed that the As concentration in well water is relatively stable over time (27). Therefore, we derived a time-weighted As concentration (TWA) as a function of drinking durations and well As concentrations (28, 29). The TWA represents the average As exposure that accrued for 9 years on average in the cohort members prior to the time of baseline visits. Total urinary As concentration in urine samples collected at both baseline and follow-up visits was measured by GFAA, using a Perkin-Elmer AAnalyst 600 graphite furnace system, as previously described (30). Urinary creatinine was analyzed using a method based on the Jaffe reaction for adjustment of urinary total As concentration (31).
Measurements of Se and As in whole blood
Whole blood samples collected at baseline were analyzed for blood Se and As concentrations using a Perkin-Elmer Elan DRC II ICP-MS equipped with an AS 93+ autosampler. ICP-MS-DRC methods for metals in whole blood were developed (with modifications) based on published methods (32). Whole blood samples were thawed, thoroughly mixed, diluted 50 times with diluent containing 1% HNO3 + 0.2% Triton-X-100 + 0.5% NH4OH, and centrifuged for 10 minutes at 3500 rpm with the supernatant reserved for analysis. A multi-element standard solution was used for instrument calibration, with Se and As concentrations chosen to cover the expected ranges of analyte in the blood samples. We used iridium to correct matrix-induced interferences. A stock internal standard spiking solution was added to all calibrators and samples in the same concentration, 10 ng iridium per tube. Polyatomic interferences were suppressed with the instrument’s Dynamic Reaction Cell (DRC) technology feature, utilizing oxygen as a second gas. Interclass correlation coefficient between the expected and observed concentrations in quality control samples (blood samples with known analyte concentrations obtained from the Laboratory for ICP-MS Comparison Program in Quebec), was 0.99 and 0.90 for blood Se and As, respectively.
Measurements of Dietary Intakes
Dietary intakes were measured at baseline with a validated semi-quantitative food frequency questionnaire (FFQ) designed for the study population. Detailed information on the design and the validation of the FFQ has been published elsewhere (33). Briefly, to assess the validity of the FFQ, two 7-day food diaries (FD) were completed in two separate seasons by trained interviewers for 189 of the 200 participants randomly selected from the overall HEALS study population. Correlations for macronutrients and common micronutrients including total fat, monounsaturated fat, polyunsaturated fat, saturated fat, protein, carbohydrate, dietary fiber, sodium, potassium, vitamin B6, vitamin B12, riboflavin, manganese, thiamin, and iron ranged from 0.30 to 0.76 (33). We used both the United States Department of Agriculture (USDA) Nutrient Database for Standard Reference (abbreviated version) (34) and an Indian food nutrient database (35) to convert food intakes to nutrient intake values (33).
Statistical Analysis
Incidence rate ratios (RRs) for skin lesions were estimated using Cox proportional hazards models with the PROC PHREG procedure in SAS. Standard errors were estimated using the robust variance estimator proposed by Barlow (36). The random cohort was weighted by the inverse of the sampling fraction from the source population. Follow-up time, defined for each person as the time of baseline visit to the time of the first follow-up visit, was 1.9 years on average with a range of 0.9 to 3.5 years. Risk sets were created with age at the time of follow-up visit as a matching variable. For each case, members of the random subcohort whose age at the time of follow-up were older than that of the case by ≤ 3 years were included as the comparison for the case, i.e. those who had not been diagnosed with skin lesions at the age the case was diagnosed. Blood Se categories were determined according to quintile values in the subcohort. Previous studies from our group have suggested that age, sex, body mass index (BMI), and tobacco smoking may modify the risk of premalignant skin lesions (28, 29). These factors, along with well As concentration, were considered the primary potential confounders in evaluating the main effect of blood Se level because these factors may also be related to Se intake level. Other risk factors of premalignant skin lesions including indicators of short-term changes in As exposure (well switching status since baseline and total urinary As level at the time of follow-up), excessive sunlight exposure (in men) (28), and nutrient intakes that have been related to As toxicity in the literature (37–39) were also considered. These were evaluated in a separate model (model 2) because values were not available for all the study participants.
RRs in relation to joint effects of long-term As exposure and blood Se were also estimated. Since RRs for the main effect of blood Se did not differ by additional adjustments, RRs for joint effect of As exposure and Se were adjusted for primary potential confounders (except for As exposure) only. We further calculated relative excess risk due to interaction (RERI) to assess the additivity of the joint effects (40).
The subcohort is a good representation of the underlying source population. We performed linear regression models to evaluate the relationships of blood Se with various socio-demographics, lifestyles, As exposure-related variables, food intakes that have been shown to be related to blood Se, and nutrient intakes that have been associated with modification of As toxicity in the literature. In addition, we evaluated the cross-sectional relationships of blood Se with blood As and total urinary As (all measured at baseline) in the subcohort. Factors such as well As level and water consumption that may be related to As intake were additionally adjusted for in this analysis.
Results
Cases were more likely to be male, older, less educated, and ever to have smoked at baseline (Table 1). Total urinary As, well water As level, blood As level, and the time-weighted well As level measured at baseline were all higher in cases than in the subcohort. Cases were more likely to have switched to another well water source since baseline. Nevertheless, total urinary As measured two years later was higher in cases.
Table 1.
Characteristics of the 849 Subcohort Members and 303 Newly Diagnosed Skin Lesion Cases in the HEALS Cohort
| Characteristic* | Skin lesion cases | Subcohort | Quintile of blood selenium levels in the Subcohort
|
p-value for trend | ||||
|---|---|---|---|---|---|---|---|---|
| Q1 | Q2 | Q3 | Q4 | Q5 | ||||
| No. of participants | 303 | 849 | 170 | 173 | 167 | 171 | 168 | |
| Range of blood Se levels, μg/L | 88.5–258.8 | 69.8–262.6 | 69.8–132.4 | 132.3–145.0 | 145.1–156.6 | 156.7–169.8 | 169.9–262.6 | |
| Mean blood Se, μg/L | 150.1 | 152.3 | 120.9 | 139.3 | 150.5 | 163.4 | 188.1 | |
| Baseline characteristic | ||||||||
| Males, % | 70.3 | 37.0 | 26.5 | 35.8 | 37.7 | 39.8 | 45.2 | <0.01 |
| Mean Age | 45.0 | 36.6 | 36.4 | 35.7 | 37.2 | 35.7 | 37.8 | 0.27 |
| Mean BMI | 19.4 | 19.9 | 19.2 | 19.6 | 20.1 | 20.2 | 20.5 | <0.01 |
| Cigarettes smoking status | ||||||||
| Ever-smokers in men, % | 81.7 | 70.7 | 62.2 | 72.3 | 65.1 | 73.4 | 76.3 | 0.12 |
| Ever-smokers in women, % | 11.1 | 5.6 | 5.6 | 9.0 | 4.8 | 2.9 | 5.4 | 0.36 |
| Excessive sunlight exposure in men, % † | 8.5 | 5.1 | 8.9 | 3.2 | 7.9 | 1.5 | 5.3 | 0.32 |
| Mean educational level, years | 2.9 | 3.7 | 3.0 | 3.7 | 3.6 | 3.6 | 4.6 | <0.01 |
| Mean baseline well As, μg/L | 157.4 | 103.1 | 96.7 | 103.1 | 93.0 | 117.4 | 104.9 | 0.27 |
| Mean time-weighted well As, μg/L | 147.4 | 101.8 | 93.8 | 106.6 | 95.9 | 109.2 | 103.2 | 0.44 |
| Mean baseline total urinary As, μg/L | 172.0 | 137.3 | 137.0 | 134.1 | 132.9 | 142.2 | 140.0 | 0.71 |
| Mean urinary creatinine, g/L | 60.6 | 58.1 | 54.7 | 55.9 | 54.6 | 60.2 | 65.1 | 0.02 |
| Mean blood As, μg/L | 14.3 | 10.8 | 10.2 | 10.7 | 10.7 | 11.1 | 11.1 | 0.20 |
| Follow-up characteristic | ||||||||
| Mean total urinary As, μg/L | 139.1 | 119.9 | 115.5 | 122.7 | 126.2 | 111.3 | 123.9 | 0.89 |
| Mean urinary creatinine, g/L | 67.8 | 63.8 | 58.1 | 62.0 | 68.1 | 61.6 | 69.1 | 0.05 |
| Switched to other well since baseline, % | 52.2 | 40.5 | 34.7 | 39.8 | 40.1 | 43.3 | 44.5 | 0.06 |
| Mean daily food or nutrient intake ‡ | ||||||||
| No. of participants | 292 | 824 | 162 | 167 | 161 | 164 | 164 | |
| Protein, g/day | 91.6 | 86.9 | 84.9 | 85.7 | 86.4 | 89.6 | 88.7 | 0.02 |
| Iron, mg/day | 26.7 | 25.1 | 24.3 | 24.2 | 25.0 | 26.8 | 25.2 | <0.01 |
| Folate, μg/day | 137.2 | 131.7 | 126.2 | 129.7 | 129.7 | 139.7 | 134.5 | 0.05 |
| Vitamin B2, mg/day | 1.1 | 1.1 | 1.02 | 1.05 | 1.05 | 1.10 | 1.07 | 0.01 |
| Vitamin B12, mg/day | 1.9 | 1.9 | 1.92 | 1.89 | 1.77 | 1.98 | 1.92 | 0.82 |
| Vitamin B6, mg/day | 3.7 | 3.5 | 3.56 | 3.54 | 3.55 | 3.48 | 3.49 | 0.12 |
| Vitamin E, mg/day | 5.7 | 5.5 | 5.2 | 5.5 | 5.5 | 5.6 | 5.5 | 0.36 |
| Big fish, g/day | 23.5 | 22.5 | 21.5 | 21.6 | 19.8 | 23.0 | 27.9 | 0.02 |
| Small fish, g/day | 34.5 | 32.2 | 34.7 | 36.0 | 27.7 | 29.6 | 30.5 | 0.18 |
| Bread, g/day | 25.5 | 14.5 | 7.0 | 10.5 | 17.2 | 15.0 | 22.8 | <0.01 |
| Dried Beans, g/day | 86.7 | 81.0 | 69.8 | 74.8 | 80.0 | 89.4 | 89.8 | <0.01 |
| Beans, g/day | 42.8 | 37.1 | 33.8 | 40.4 | 36.1 | 39.6 | 35.1 | 0.80 |
| Beef lamb, g/day | 13.6 | 15.4 | 14.2 | 13.5 | 16.9 | 19.5 | 13.5 | 0.51 |
| Poultry, g/day | 3.4 | 4.0 | 3.2 | 5.0 | 4.7 | 3.3 | 4.3 | 0.89 |
| Milk, g/day | 32.3 | 25.7 | 21.6 | 22.6 | 22.2 | 31.1 | 30.1 | 0.01 |
| Eggs, g/day | 6.7 | 8.5 | 7.6 | 8.9 | 8.4 | 8.4 | 8.7 | 0.72 |
Data on body mass index were missing for 4 cases skin lesions and 7 subcohort members. Data were also missing on time-weighted As for, respectively, 18 and 36 subjects; on follow-up total urinary As for 0 and 27 subjects; and on switching status for 0 and 26 subjects.
Men who worked outside with a bare upper body were categorized as having excessive sun exposure (28). As women in Bangladesh universally wear traditional dresses that almost completely cover the skin of their trunk, sunlight exposure of female respondents was considered minimal and therefore was not assessed in the study.
Dietary intakes were measured with a validated FFQ at baseline. A total of 824 subcohort members and 292 cases completed the FFQ. Mean values shown by quintile of blood Se in the subcohort were adjusted for age, sex, BMI, and total energy intake.
In the subcohort, the proportion of men was higher among participants with higher levels of blood Se (p-trend <0.01) (Table 1). Average baseline BMI and educational attainment were higher in higher quintiles of blood Se (p-trend <0.05). There were no apparent associations of blood Se with age, cigarettes smoking status, and all of the As exposure measures. The proportion of participants who switched to a different well since baseline was greater among participants with higher levels of blood Se (p-trend = 0.06). Adjusted average intakes of large fresh water fish, bread, dried beans, and milk were higher in participants with higher levels of blood Se. No significant associations were observed between blood Se level and intakes of meats, small fish, eggs, or any specific vegetables (data not shown). Average intakes of protein, iron, folate, and Vitamin B2 were positively related to blood Se levels (p-trend ≤ 0.05); spearman correlations of blood Se with these nutritional parameters were ≤ 0.12.
Blood Se level was inversely related to risk of premalignant skin lesions (Table 2). Comparing the higher four quintiles to the bottom quintile of blood Se, age- and sex-adjusted RRs ranged from 0.56 to 0.81. The inverse association remained apparent with additional adjustments for BMI, cigarettes smoking status, and baseline well As level; RRs were 0.51 (95% confidence interval (CI): 0.29, 0.87), 0.52 (95% CI: 0.30, 0.91), and 0.53 (95% CI: 0.30, 0.91) comparing the third, fourth, and fifth quintile to the bottom quintile, respectively (model 1). Additional adjustments for well switching status, total urinary As and urinary creatinine at the time of follow-up, total energy intake, excessive sunlight exposure in men, and intakes of protein, folate, iron, Vitamins E, B2, B6, and B12 did not change the estimates appreciably (model 2).
Table 2.
Adjusted Rate Ratios for Skin Lesions by Quintile of Blood Selenium Level
| Model 1 | Model 2 | |||||||
|---|---|---|---|---|---|---|---|---|
| Blood Se Quintile (μg/L) | Mean Blood Se Level (μg/L) | No. of subcohort (%) | No. of cases (%) | Age- & Sex-adjusted Rate Ratios (95% CI) * | No. of subcohort/cases | Multivariate Adjusted Rate Ratios (95% CI) † | No. of subcohort/cases | Multivariate Adjusted Rate Ratios (95% CI) ‡ |
| 68.8–132.4 | 121.0 | 170 (20.0) | 72 (23.8) | 1.00 | 168/72 | 1.00 | 158/68 | 1.00 |
| 132.5–145.0 | 139.0 | 173 (20.4) | 62 (20.5) | 0.81 (0.49–1.34) | 172/60 | 0.68 (0.39–1.18) | 160/58 | 0.60 (0.33–1.10) |
| 145.1–156.6 | 150.7 | 167 (19.7) | 52 (17.2) | 0.58 (0.35–0.96) | 166/51 | 0.51 (0.29–0.87) | 156/50 | 0.52 (0.30–0.89) |
| 156.7–169.8 | 163.4 | 171 (20.1) | 59 (19.5) | 0.62 (0.37–1.04) | 169/58 | 0.52 (0.30–0.91) | 157/57 | 0.53 (0.30–0.96) |
| 169.9–262.6 | 187.3 | 168 (19.8) | 58 (19.0) | 0.56 (0.33–0.93) | 167/58 | 0.53 (0.31–0.90) | 160/55 | 0.51 (0.29–0.89) |
Rate Ratios were adjusted for age and sex
Rate Ratios were adjusted for age, sex, BMI, smoking status, and baseline well As. A total of 11 subjects with unknown BMI were excluded from the analysis
Rate Ratios were adjusted for age, sex, BMI, smoking status, baseline well As, well switching status at follow-up, urinary As at follow-up, excessive sunlight exposure in men, total energy intake, and dietary intakes of folate, iron, protein, Vitamin E, B2, B12, and B6. A total of 83 subjects with unknown information on BMI, well switching status since baseline, urinary As level at the time of follow-up, or dietary intakes of As-related nutrients were excluded from the analysis
The cross-sectional relationship between baseline blood Se and baseline urinary As in the subcohort is presented in Table 3. Partial spearman correlation controlling for age, well As level, BMI, and urinary creatinine was −0.10 (p = 0.02) between blood Se and urinary As and 0.07 (p = 0.05) between blood Se and blood As. Participants with higher blood Se levels had lower urinary As levels, adjusting for urinary creatinine, age, sex, BMI, smoking status, baseline well As concentration, and daily water consumption. The inverse association was statistically significant in multiple linear regression (p for trend = 0.03). On the other hand, no apparent association was observed between Se and As concentrations in the blood.
Table 3.
Relationships of blood Se with Urinary and Blood As in the Subcohort at Baseline
| Adjusted means of baseline urinary As (μg/L) * |
Adjusted means of of baseline urinary As (μg/L) † |
||||
|---|---|---|---|---|---|
| Blood Se Quintile (μg/L) | n | Means (SD) | p-value for trend | Means (SD) | p-value for trend |
| 68.8–132.4 | 170 | 142.94 (9.03) | 0.03 | 10.81 (0.48) | 0.66 |
| 132.5–145.0 | 173 | 135.58 (8.88) | 10.68 (0.48) | ||
| 145.1–156.6 | 167 | 142.37 (8.96) | 11.22 (0.48) | ||
| 156.7–169.8 | 171 | 126.08 (8.88) | 10.57 (0.48) | ||
| 169.9–262.6 | 168 | 125.41 (8.95) | 11.06 (0.48) | ||
Adjustments were made for baseline age, sex, smoking status, BMI, well As concentration, daily water consumption, and urinary creatinine.
Adjustments were made for baseline age, sex, smoking status, BMI, well As concentration, daily water consumption.
Low blood Se was associated with a greater risk for skin lesions at each level of As exposure (Table 4). The increased risk associated with low blood Se appeared to be additive to the risk related to higher levels of As exposure. The pattern of effect estimates was consistent with all four As exposure measurements. Additional adjustment for well switching status since baseline did not change the pattern of RRs. An RERI estimate significantly greater or lower than zero (perfect additivity) indicates that the joint effects are significantly greater or lesser than additivity, respectively. All the RERI estimates were close to zero, ranging from −0.35 to 0.5 (data not shown). For instance, the RERI for joint effects of low blood Se and well As 25.1–117.0 μg/L is −0.26 (2.56-1.70–2.12+1). Therefore, there is no evidence that the joint effect of As exposure and low blood Se departs from additivity.
Table 4.
Joint Effect of As Exposure and Low Blood Se on Risk of Skin Lesion
| As exposure measures § (Tertiles) | Blood Se > 150.2 μg/L† |
Blood Se ≤ 150.2 μg/L† |
||||
|---|---|---|---|---|---|---|
| N (Cases/Subcohort) | Median As level‡ | Rate Ratios (95% CI)* | N (Cases/Subcohort) | Median As level‡ | Rate Ratios (95% CI)* | |
| Baseline well As levels (μg/L) | ||||||
| 0.1–25.0 | 25/129 | 7.2 | 1.00 | 37/153 | 7.2 | 2.12 (1.09–4.10) |
| 25.1–117.0 | 36/140 | 67.7 | 1.70 (0.86–3.36) | 45/140 | 62.1 | 2.56 (1.33–4.94) |
| 117.1–564.0 | 87/157 | 231.7 | 3.38 (1.86–6.17) | 69/130 | 237.8 | 4.15 (2.24–7.67) |
| Time-weighted water As levels (TWA) (μg/L) | ||||||
| 0.1–29.0 | 24/123 | 8.4 | 1.00 | 34/146 | 9.0 | 2.11 (1.01–4.34) |
| 29.1–116.0 | 35/138 | 68.2 | 1.85 (0.92–3.74) | 45/131 | 63.9 | 2.62 (1.30–5.28) |
| 116.1–564.0 | 79/148 | 223.8 | 3.40 (1.75–6.63) | 64/127 | 232.2 | 4.58 (2.33–8.99) |
| Baseline blood As (μg/L) | ||||||
| 1.6–6.8 | 25/118 | 5.0 | 1.00 | 36/171 | 4.9 | 1.55 (0.82–2.92) |
| 6.9–11.3 | 39/146 | 8.9 | 1.36 (0.71–2.59) | 45/127 | 8.9 | 2.18 (1.16–4.09) |
| 11.4–63.9 | 84/162 | 17.8 | 2.50 (1.40–4.46) | 70/125 | 19.2 | 3.55 (1.94–6.50) |
| Baseline total urinary As (μg/L) ‡‡ | ||||||
| 3–54 | 27/142 | 30.6 | 1.00 | 38/141 | 30.1 | 1.68 (0.89–3.16) |
| 55–138 | 44/125 | 88.0 | 1.46 (0.78–2.71) | 49/154 | 88.7 | 2.15 (1.16–3.96) |
| 139–1220 | 77/159 | 281.2 | 2.67 (1.42–5.03) | 64/128 | 301.3 | 3.12 (1.66–5.84) |
RRs were adjusted for age, BMI, sex, and smoking status. RRs in relation to urinary As were additionally adjusted for urinary creatinine. A total of 11 subjects with unknown information on BMI were excluded from the analysis. A total of 51 subjects with unknown information on the TWA were also excluded from the calculation of RRs in relation to TWA.
Category-specific median values in the subcohort for each of the four As exposure measures in the left column.
Cut point was determined based on median value in the subcohort.
Cut points were determined based on tertile values in the subcohort.
RRs associated with total urinary arsenic were additionally adjusted for urinary creatinine level.
Discussion
To our knowledge, this is the first prospective study that evaluates the association between Se levels and risk of As-related disease in a population exposed to As from drinking water. Higher prediagnostic blood Se level was related to as much as a 50% reduction in risk of As-related premalignant skin lesions. This estimate did not change appreciably with adjustments for age, sex, BMI, smoking status, As exposure level, and dietary intakes related to As toxicity, including dietary folate, iron, protein, Vitamin E, and B Vitamins (37–39). The pattern of RRs suggests that the effects of As exposure and Se deprivation on risk of skin lesions are additive. These findings are in line with the hypothesis that dietary Se intakes may reduce the incidence of skin lesions among populations with As exposure from drinking water.
Findings from previous studies were mostly inconclusive on the relationship between Se intake and As toxicity. A case-control study in Taiwan found that patients with blackfoot disease had lower blood Se levels than controls, while a similar case-control study found that blood Se was higher in patients with late-stage blackfoot disease compared to that in controls (18, 19). In another case-control study in West Bengal, odds ratios for As-related skin lesions did not differ by blood Se levels (21). It is unclear, however, whether the blood Se levels observed in cases were a consequence or a contributing factor to blackfoot disease or As-related skin lesions in these case-control analyses. A placebo-controlled trial in Inner Mongolia found that Se supplementation significantly improved skin lesions (20). However, the trial was neither randomized nor double-blind, and the drop-out rates in both the placebo and the treatment groups were high. A pilot randomized, placebo-controlled trial conducted by our group found that Se supplementation slightly improved skin lesion status; however the sample size of the study was small and the improvement was not significant (13).
Our findings are consistent with several observational studies that found a protective association between plasma selenium level and the risk of nonmelanoma skin cancer (41–43). A large randomized clinical trial in patients who previously had nonmelanoma skin cancer, on the other hand, found that selenium supplementation increased the risk of skin cancer (44). There are several possible explanations. First, selenium supplementation may not offer benefits for secondary prevention of skin cancer in an older population (median age 65) (44). Second, the observed inverse association between blood Se and risk of skin lesions in the present analysis is likely due to both the chemopreventive effect of Se and the interaction between Se and As; the latter is absent in populations not exposed to As exposure. Third, it has been postulated that sub-clinical health effects of Se deficiency may be manifest at the low-end of “adequate” Se intake (45) and that physiological stressors may exert additional demand on Se-dependent systems. Indeed, the negative effects of selenium supplementation for secondary prevention of nonmelanoma skin cancer appear to be greater in those with high baseline plasma selenium (44). We observed that the risk associated with any given level of As exposure was consistently greater among persons with blood Se lower than the average level. Using the equation suggested by Yang et al (46), we estimated the average Se daily intake for participants with blood Se lower than the average level (150.2 μg/L) to be 61 μg/day, close to the low-end of the recommended daily intake (RDI) of Se (55 μg/day), which are established to maintain adequate levels of selenoenzymes. When the level of As exposure was statistically held constant, the reduced RRs associated with the higher three quintiles of blood Se were significant with similar magnitude, indicating that the Se dose-response curve may have a threshold above which no additional benefit occurs. Future As mitigation programs or randomized trials of Se supplementation may consider this finding. It should be noted that Se toxicity, although rare in human populations, has been observed at selenium intakes above 600 μg/day (47).
The primary interaction between Se and As is thought to be via a Se-As-glutathione conjugate formed in the liver and excreted into bile. In recent studies in rabbits, Gailer et al identified the compound excreted into bile as a selenobis (S-glutathionyl) arsinium ion, [(GS)2AsSe]− (17, 48). Our observation of an inverse association between blood Se level and urinary As is consistent with the hypothesis that Se-induced biliary excretion may occur in human. The association of blood As and blood Se, on the other hand, was not apparent. These findings require further investigation. Other direct Se/As interactions exist. Berry et al reported that Se decreased As toxicity via the formation of a selenide precipitate (As2Se) that is deposited into tissues (49). Oxidative stress reducing effects of selenoenzymes including glutathione peroxidases (GPx), iodothyronine deiodinases (ID) and thioredoxine reductases (TR) (50) may also reduce As toxicity. In the mouse model, a significant reduction in the formation of 8-oxo-2′-deoxyguanosine, an oxidative DNA damage biomarker, was observed in ultraviolet radiation (UVR) and As treated mice that were supplemented with Se, compared with those treated with UVR or As alone (51). The initiation of UVR-induced skin tumors has been shown to vary with the activity of GPx and TR (52).
The underlying source population represents those who gave both blood and urine samples, who underwent the baseline clinical examination, and who did not have skin lesions at baseline and thus had a lower level of As exposure. Donation of blood and urine samples and consent to physical examination were weakly associated with a higher educational attainment (22). While these differences do not affect the internal validity of our findings, compared to the study population, the overall cohort may have a somewhat higher As level and a lower blood Se level given the positive association between blood Se level and educational attainment. The risk difference associated with Se intake thus may be more significant in the overall cohort. Consistent with findings from another study (53), we found that the average blood Se in Bangladeshi population (150 μg/L) was not particularly lower than those reported from populations in developed countries (54), ranging from 87–107 μg/L in Germany, 134–138 μg/L in England, and 166 to 200 μg/L in non-seleniferous areas in the US.
Se levels measured in whole blood are considered as a useful measure for ranking subjects for long-term Se intake (55). The calculation of TWA was based on self-reported use of wells. However, validity of self-reported well use history was good since the correlation between arsenic concentration in the baseline well and baseline urinary arsenic was 0.70 (22). In addition, the patterns of RRs for the joint effects of As exposure and low blood Se were similar using multiple biologic measures of As exposure, which further strengthen the findings. In a separate analysis, we have also shown consistent dose–response relationships of the risk of skin lesions with TWA, baseline blood As, and baseline urinary As, and we demonstrated that blood As is a good biomarker of As exposure in this population (56). The three measures were highly correlated with one another (pairwise spearman correlation = 0.8) (56). Dietary intakes of other nutrients relevant to As toxicity were measured by FFQ, and therefore measurement errors are expected. The fact that RRs for skin lesions in relation to blood Se levels remained the same after controlling for dietary folate, iron, protein, Vitamin E, and B Vitamins excludes the possibility of strong confounding effect due to these dietary factors. Sharing of the wells in the study population was minimal; the 1121 subjects included in the present analysis were users of 908 wells at baseline. Therefore, the findings are not likely to have been affected by correlated As exposure among subjects. After the completion of baseline interviews, participants with well As > 50 μg/L were advised to change their drinking well, leading to the changes in As exposure during the 1.9 years period of time from baseline to the follow-up visit. However, the short-term changes in As exposure are less relevant to the risk of skin lesions, compared to the TWA, which is based on an average of 9 years of well use history. In addition, adjustments for switching status and urinary As at the time of follow-up did not change RR estimates for skin lesions in relation to blood Se.
In conclusion, our results are consistent with the notions that 1) higher dietary Se intake may reduce the risk of As-related skin lesions, and 2) Se RDI may not be adequate in the presence of physiological stressors such as chronic As exposure from drinking water. Future studies should continue to evaluate the effect of Se in treating As-related skin lesions and skin cancers, as well as the influence of Se on relationships between As exposure and other As-related disorders.
Acknowledgments
This research was supported by U.S. National Institute of Environmental Health Sciences Grants P42ES10349, P30ES09089, and National Cancer Institute Grants R01CA107431, and R01CA102484. The authors would like to thank the staff, field workers and study participants in Bangladesh without whom this work would not have been possible. The authors would also like to thank Dr. Wei-Yann Tsai for his helpful comments.
Abbreviations
- HEALS
Health Effects of Arsenic Longitudinal Study
- As
Arsenic
- Se
Selenium
- FFQ
Food frequency questionnaire
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