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Arsenic exposure at low-to-moderate levels and skin lesions, arsenic metabolism,
neurological functions, and biomarkers for respiratory and cardiovascular diseases:
Review of recent findings from the Health Effects of Arsenic Longitudinal Study
(HEALS) in Bangladesh
Yu Chen
a
, Faruque Parvez
b
, Mary Gamble
b
, Tariqul Islam
c
, Alauddin Ahmed
c
, Maria Argos
d
,
Joseph H. Graziano
b
, Habibul Ahsan
d,
⁎
a
Departments of Environmental Medicine and Medicine and New York University Cancer Institute, New York University School of Medicine, New York, NY, USA
b
Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York City, NY, USA
c
Columbia University Arsenic Research Project, Dhaka, Bangladesh
d
Departments of Health Studies, Medicine and Human Genetics and Cancer Research Center, The University of Chicago, Chicago, IL, USA
abstractarticle info
Article history:
Received 26 September 2008
Revised 5 December 2008
Accepted 19 January 2009
Available online 27 January 2009
Keywords:
Arsenic exposure
Epidemiology
Bangladesh
The contamination of groundwater by arsenic in Bangladesh is a major public health concern affecting 35–75
million people. Although it is evident that high levels (N300 μg/L) of arsenic exposure from drinking water are
related to adverse health outcomes, health effects of arsenic exposure at low-to-moderate levels (10–300 μg/L)
are not well understood. We established the Health Effects of Arsenic Longitudinal Study (HEALS) with more
than 20,000 men and women in Araihazar, Bangladesh, to prospectively investigate the health effects of arsenic
predominately at low-to-moderate levels (0.1 to 864 μg/L, mean 99 μg/L) of arsenic exposure. Findings to date
suggest adverse effects of low-to-moderate levels of arsenic exposure on the risk of pre-malignant skin lesions,
high blood pressure, neurological dysfunctions, and all-cause and chronic disease mortality. In addition, the
data also indicate that the risk of skin lesion due to arsenic exposure is modifiable by nutritional factors, such as
folate and selenium status, lifestyle factors, including cigarette smoking and body mass index, and genetic
polymorphisms in genes related to arsenic metabolism. The analyses of biomarkers for respiratory and
cardiovascular functions support that there may be adverse effects of arsenic on these outcomes and call for
confirmation in large studies. A unique strength of the HEALS is the availability of outcome data collected
prospectively and data on detailed individual-level arsenic exposure estimated using water, blood and
repeated urine samples. Future prospective analyses of clinical endpoints and related host susceptibility will
enhance our knowledge on the health effects of low-to-moderate levels of arsenic exposure, elucidate disease
mechanisms, and give directions for prevention.
© 2009 Elsevier Inc.
Introduction
Arsenic is abundant in the earth's crust and can be released into
groundwater under certain conditions. In many parts of the world
where groundwater is an important source of drinking water, long-
term exposure to arsenic from drinking water has been considered a
public health hazard. In addition to be classified as a Class I human
carcinogen by the International Agency for Research on Cancer (IARC)
for its association with an increase in skin cancer risk, arsenic
exposure has also been linked to increased risks of internal cancers,
diabetes, cardiovascular disease, adverse pregnancy outcomes, and a
decrease in children's intellectual function.
However, epidemiologic evidence on many of these health effects
of arsenic exposure has not been well-established, with uncertainties
in latency, dose–response relationships and population differences. In
particular, although it is evident that high levels of arsenic exposure
(N300 μg/L) are related to internal cancer and cardiovascular disease,
epidemiologic evidence of the effects of arsenic exposure from
drinking water at low-to-moderate levels (b300 or b10 0 μg/L)
remains inconclusive. Most existing studies include limited sample
size (Chiou et al., 2001; Karagas et al., 2004; Steinmaus et al., 2003)
and/or unreliable long-term measures of arsenic exposure (Engel and
Smith, 1994; Lewis et al., 1999; Meliker et al., 2007; Zierold et al.,
2004) with exposure measured ecologically or only cross-sectionally.
Ecologic exposure measurement that uses the mean or median values
in a county or community as the exposure level for individuals could
Toxicology and Applied Pharmacology 239 (2009) 184–192
⁎Corresponding author. Center for Cancer Epidemiology and Prevention, The
University of Chicago, 5841 South Maryland Avenue, Suite N102, Chicago, IL 60637,
USA. Fax: +1 773 834 0139.
E-mail address: habib@uchicago.edu (H. Ahsan).
0041-008X © 2009 Elsevier Inc.
doi:10.1016/j.taap.2009.01.010
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journal homepage: www.elsevier.com/locate/ytaap
Open access under CC BY-NC-ND license.
Open access under CC BY-NC-ND license.
result in considerable measurement errors. Since the effects of low-to-
moderate levels of arsenic on disease are likely to be modest in
magnitude, these studies are particularly susceptible to measurement
errors in exposure ascertainment, which, in most cases, would lead to
bias towards the null, but could also generate spurious associations
under certain conditions (Greenland and Robins, 1994). In addition,
the heterogeneity of drinking water resources, the limited exposure
range, and the relatively high migration rates within the nation
altogether pose a challenge for epidemiologic studies in the U.S. or
other populations with surface water as the main source of drinking
water that aim to investigate the impact of low-to-moderate levels of
arsenic exposure to have a valid long-term arsenic measure at the
individual level.
The contamination of groundwater by arsenic in Bangladesh is the
largest poisoning of a population in history (Smith et al., 2000). As 95%
of the country's 140 million population rely on well water, an
estimated 57 million people have been chronically exposed to
drinking water with arsenic levels exceeding the WHO standard of
10 μg/L, and 35 million people were exposed to arsenic levels above
the country's government standard of 50 μg/L (British Geological
Survey, 1999). Given the known health consequences of arsenic
exposure, there is an imminent need to develop arsenic mitigation
programs. Although there have been several case–control and cross-
sectional studies published on arsenic exposure in Bangladesh
(McCarty et al., 2007; Milton et al., 2005; Mitra et al., 2004; Tondel
et al., 1999; Rahman et al., 1998), large epidemiologic studies are
needed to assess the health effects of arsenic exposure in the
population.
In Bangladesh, where 95% of the population exclusively drinks
groundwater through wells and the population is remarkably stable,
long-term arsenic exposure can be measured and tracked in large
cohort at the individual level. We have established the Health Effects
of Arsenic Longitudinal Study (HEALS) in Araihazar, Bangladesh, a
prospective cohort with more than 20,000 participants, to investigate
the health effects of arsenic exposure, avoiding many of the
limitations in previous studies. In particular, the study population
has been exposed to a wide range of arsenic exposure at low-to-
moderate levels, providing us with a unique opportunity to evaluate
health effects of arsenic exposure at these levels and avoiding many of
the limitations of previous studies. We summarize findings from the
HEALS to date and discuss the implications of our findings and future
directions.
Health Effects of Arsenic Longitudinal Study (HEALS)
The HEALS was established as part of the Columbia University's
Superfund Basic Research Program (CU-SBRP). Details of the HEALS
have been presented elsewhere (Ahsan et al., 2006a; Parvez et al.,
2006). Briefly, prior to subject recruitment, water samples and
geographic positional system data were collected for a set of 5966
contiguous wells in a well-defined geographic area of 25 km
2
in
Araihazar. As a baseline measurement, well owners were interviewed
to create an initial roster list of 65,876 regular well users and residents
in the area. From October, 2000 to May, 2002, 12,050 potential
participants meeting eligibility criteria were approached, and 11,746 of
those eligible agreed to participate (97.5% response rate) (Ahsan et al.,
2006a). In 2007, an additional 8000 participants were recruited using
the same methodologies, with a roster established based on arsenic
measurement of a different set of 5000 well with a response rate of
95%. In order to update changes in arsenic exposure and the detection
of a variety of health outcomes of interest, all cohort members were
actively followed every two years through in-person home visits by
trained physicians and interviewers.
Water samples from all the tube wells in the study area were
collected and tested for total arsenic concentrations first by graphite
furnace atomic-absorption spectrometry (GFAA) with a Hitachi Z-
8200 system at the Lamont-Doherty Earth Observatory (LDEO) of
Columbia University. 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.
The study population has been exposed to a wide range of arsenic
exposure at low-to-moderate levels, ranging from 0.1 to 864 μg/L, with
a mean of 99 μg/L.
At baseline recruitment, venous whole blood samples and a spot
urine sample were collected for N90% cohort participants. At follow-up
visits, a spoturine sample is also collected. Bothblood and urine samples
were kept in portable coolers immediately after collection. All samples
were kept frozen and shipped to USA on dry ice within 1–2 months. All
urine samples collected at baseline and at follow-up visits were
analyzed for total arsenic concentration by GFAA, using the Analyst
600 graphite furnace system at Columbia University, as previously
described (Nixon et al., 1991).
At baseline and in each in-person biennial follow-up visit,
participants undergo a comprehensive clinical examination conducted
by trained physicians. Information onpatterns and history of well use,
demographics and lifestyle characteristics are also collected at every
visit. At baseline, the questionnaire also included a validated food
frequency questionnaire (FFQ) designed specifically for the study
population (Chen et al., 2004b). In addition, a field clinic was
established solely for cohort study participants and their family
members to passively follow up participants between their biennial in-
person visits to augment the detection of study outcomes through
active follow-up.
Given its biologicalsamples and its data on arsenic exposure, dietary
factors and lifestyle habits, the HEALS provides a unique opportunity to
study the health effectsof arsenic exposure, host susceptibility and pre-
clinical disease states measured using biomarkers. In addition,based on
findings from observational studies, a series of intervention studies has
been initiated, including those pertaining to health education and
chemoprevention trials.
Key findings from heals, to date
Well arsenic concentration and biochemical measures of arsenic
exposure
Relationships among different measures of arsenic exposure
Although total urinary and blood arsenic levels have been
considered indicators of short-term internal dose of arsenic exposure,
with chronic and continuing exposure, steady-state concentrations in
blood and urine are can be achieved. Repeated measurements of these
indices may be used to depict long-term exposure level and changes in
exposure over time. Among 849 individuals randomly selected from
the HEALS participants, baseline urinary arsenic was highly correlated
with baseline blood arsenic (r= 0.85, p-value b0.01) and with water
arsenic levels (r=0.76, p-value b0.01) (Hall et al., 2006).
Using urinary arsenicdata collected during the first three visits from
10,224 participants, we estimated that the pair-wise correlation
between urinary arsenic measured at each visit was high (all ≥0.60,
p-values b0.01). The correlation of baseline urinary arsenic levels
with urinary arsenic levels at follow-up visits somewhat decreased
from 0.66 for Visit 2 to 0.60 for Visit 3, suggesting a change in arsenic
exposure among some participants; however, the change in arsenic
exposure is not substantial in the overall cohort. On average, urinary
arsenic levels decreased by 62 μg/g creatinine from baseline to the first
follow-up visit(∼−20 μg/L in well arsenic), remained at the same level
at Visit 3, with a high correlation (0.74, p-value b0.01) between urinary
arsenic at Visits 2 and 3.
These data suggest that the level of urinary arsenic does not
fluctuate greatly overtime, and urinary arsenic can serve as a long-
term biomarker of arsenic exposure to track arsenic exposure levels
during the follow-up time in the cohort.
185Y. Chen et al. / Toxicology and Applied Pharmacology 239 (2009) 184–192
Measures and determinants of arsenic metabolism
Arsenic in drinking water is present as inorganic arsenic (InAs), i.e.,
arsenite (As
III
) and arsenate (As
V
). In Bangladesh, As
III
is the
predominant form to which people are exposed from groundwater.
Methylation of InAs
III
, which is primarily hepatic, relies on folate-
dependent, one-carbon metabolism; folate contributes methyl groups
used in generating s-adenosylmethionine (SAM). Arsenic (+3 oxida-
tion state) methyltransferase (AS3MT) transfers the methyl group
from SAM to InAs
III
to generate monomethylarsonic acid (MMA
V
).
After the reduction of MMA
V
to monomethylarsonous acid (MMA
III
),
AS3MT can catalyze a second methylation, generating dimethylarsinic
acid (DMA
V
)(Yang et al., 2002). The relative distribution of urinary
arsenic metabolites varies from person to person and has been
interpreted to reflect biologically effective doses of exposure to the
various arsenic metabolites (Chen et al., 2005) as well as arsenic
methylation efficiency, with some evidence suggesting that increased
proportions of MMA and InAs species in urine may be associated with a
higher risk of cancer (Chen et al., 2003a; Huang et al., 2008).
Among 98 HEALS cohort members participating in the placebo arm
of a double-blind randomized folate supplementation trial (Gamble
et al., 2006), the intraclass correlations (ICCs) of three urine samples
collected over a three-month period were all N0.65 for urinary arsenic
metabolites. Of particular note, the ICCs for %MMA, %DMA, and the
ratio of MMA-to-DMA were 0.84 (95% CI, 0.78–0.89), 0.82 (95% CI,
0.74–0.87), and 0.82 (95% CI, 0.75–0.88), respectively (Ahsan et al.,
2007). The data suggest that, in the absence of interventions, there is
little within-subject variability of urinary arsenic metabolite profiles
and thus justifies the use of single measurements in epidemiologic
studies.
Factors that are related to the composition of urinary arsenic
metabolites may contribute to the susceptibility to health effects of
arsenic exposure. Table 1 summarizes several studies that utilized the
resources of the HEALS with a focus on inter-individual variability in
the distribution of urinary arsenic metabolites. In 1041 individuals
randomly selected from the HEALS participants who were free of skin
lesions at baseline, water arsenic concentration was positively
associated with urinary %MMA and inversely associated with urinary
%DMA (Ahsan et al., 2007), suggesting that either methylation of
arsenic to DMA is saturable or, alternatively, that arsenic inhibits the
arsenic methyltransferase enzyme. Body mass index (BMI) was
positively related to urinary %MMA and inversely related to urinary
%DMA, however the association was not statistically significant
(Ahsan et al., 2007). Specific dietary food/nutrient intakes may be
more relevant to arsenic metabolism. In the same study population,
higher intakes of cysteine, methionine, calcium, protein and vitamin
B-12 measured by the FFQ were associated with higher %MMA in
urine. Additionally, higher intakes o f niacin (beta = 0.22, p-
value=0.02) and choline (beta= 0.10, p-value= 0.02) were asso-
ciated with higher DMA-to-MMA ratios (Heck et al., 2007).
In a sample of 1650 HEALS cohort members who were recruited
into the Nutritional Influences of Arsenic Toxicity (NIAT) study, an
unusually high prevalence of hyperhomocysteinemia was found,
particularly among males (Gamble et al., 2005a). In subsequent
analyses of 300 randomly selected participants from the NIAT study,
urinary %DMA was positively associated with plasma folate (r=0.14,
p=0.02) and negatively associated with total homocysteine (tHcys;
r=−0.14, p=0.01). Conversely, urinary %MMA was negatively
associated with folate (r=−0.12, p=0.04) and positively associated
with tHcys (r=0.21, pb0.01) (Gamble et al., 2005b). An unanticipated
finding was that urinary creatinine, a breakdown product of creatine,
is negatively associated with %InAs and positively associated with %
DMA in urine. Creatine is synthesized endogenously in a process that
relies on one-carbon metabolism; it also consumes methyl groups,
and is itself consumed in the diet, predominantly from meat. The
finding warrants further investigation. Collectively, these findings
indicate that nutritional factors involved in one-carbon metabolism
contribute to the variability in arsenic methylation.
Recently, the CU-SBRP Trace Metals Laboratory has developed the
technology to measure arsenic metabolites in blood. This has led to
the observation that there is relatively more MMA and less DMA in
blood than in urine both in adults (Gamble et al., 2007) and in
children (Hall et al., 2007). This is not surprising, given that DMA has a
shorter circulating half-life than InAs, and is consistent with the role of
methylation in facilitating arsenic elimination. This finding has
important implications with regard to risk assessment in epidemio-
logic studies that traditionally rely on measures of arsenic metabolites
in urine.
Pre-malignant skin lesions
Cutaneous abnormalities are well-known early signs of chronic
inorganic arsenic poisoning. Melanosis is considered as an early-stage
skin lesions, while keratosis is the most frequent manifestation
preceding the appearance of arsenic-related skin cancer (Tseng et al.,
196 8). They give rise to the majority of arsenic-induced basal and
squamous cell skin cancers (Tseng et al., 1968). Unlike arsenic-related
internal cancers that could have long latencies, these premalignant
skin lesions may appear with shorter periods of arsenic exposure
(Saha, 2003). We have conducted several cross-sectional, case–
control, case-cohort, and nested case–control studies of skin lesions
to evaluate the full dose–response relationship between arsenic
exposure and the risk of skin lesions, as well as relevant nutritional
and genetic susceptibility factors (Table 2).
Arsenic exposure at low-to-moderate levels and skin lesions
At baseline, we identified 714 cases of premalignant skin lesions. In
cross-sectional analyses of baseline data comparing cases and non-
cases of skin lesions, we observed a dose–response effect of arsenic on
Table 1
Findings from HEALS on determinants of urinary or blood arsenic metabolites
Reference Study design Characteristics of subjects Main findings
Heck et al. (2007) Cross-sectional 1041 randomly selected subjects
free of skin lesions
Positive associations of intakes of cysteine, methionine,
calcium, protein and vitamin B-12 with urinary %MMA
Ahsan et al. (2007) Positive associations of intakes of niacin, choline with
urinary DMA-to-MMA ratios
Positive associations between water arsenic and urinary %MMA,
and inverse association between water arsenic and urinary %DMA
Gamble et al. (2005b) Cross-sectional 300 randomly selected
participants
Positive correlation between plasma folate and urinary %DMA,
between total homocysteine (tHcys) and urinary %MMA,
and between urinary creatinine and urinary %DMA
Inverse correlation tHcys and urinary %DMA, and between plasma
folate and urinary %MMA
Gamble et al. (2005a, b) Randomized, double-blind,
placebo-controlled trial
200 folate-deficient cohort
participants
Greater increase of urinary %DMA and reduction in urinary %MMA
in the folic acid group than in the placebo group
Gamble et al. (2007) Significant reduction in total blood arsenic and of MMA in the blood in folic acid group.
186 Y. Chen et al. / Toxicology and Applied Pharmacology 239 (2009) 184–192
the risk of skin lesions (Ahsan et al., 2006b). In particular, arsenic
exposure appears to increase the risk of skin lesions, even at the low
end of exposure in this population. Compared with drinking water
containing b8.1 μg/L of arsenic, drinking water containing 8.1–40.0,
40.1–91.0, 91.1–175.0 and 175.1–864.0 μg/L of arsenic was associated
with adjusted prevalence odds ratios of skin lesions of 1.91 (95%
confidence interval (CI): 1.26, 2.89), 3.03 (95% CI: 2.05, 4.50), 3.71
(95% CI: 2.53, 5.44) and 5.39 (95% CI: 3.69, 7.86), respectively (Ahsan
et al., 2006b). A prospective case-cohort analysis of skin lesion cases
diagnosed during two years of follow-up also showed a similar dose–
response relationship (Hall et al., 2006).
Modifiable determinants of effects of arsenic on skin lesions
In our analyses, we found that males and older participants were
more likely to be affected by arsenic exposure (Ahsan et al., 2006b).
Additionally, a synergistic effect between the highest level of arsenic
exposure (N113 μg/L) and tobacco smoking on risk of skin lesions was
observed in men (Chen et al., 2006a). Furthermore, the risk of skin
lesions associated with any given level of arsenic exposure was greater
in men with excessive sun exposure (Chen et al., 2006a). The effect of
arsenic was also modified by land ownership on a multiplicative scale,
with an increased risk among non-land owners associated with well
water arsenic (Argos et al., 2007). Part of the modification effect due to
socioeconomic status may be explained by nutritional status. In
particular, participants with a comparatively high BMI (Ahsan et al.,
2006b) or with comparatively high intake levels of riboflavin,
pyridoxine, folate, and vitamins A, C and E were less likely to be
affected by arsenic exposure (Zablotska et al., 2008). In a prospective
case-cohort analysis of 303 incident skin lesion cases and 849
subcohort members, there was an inverse association between
baseline blood selenium status and the incidence of skin lesions
(Chen et al., 2007a).
In another nested case–control study of 274 skin lesion cases
identified two years after recruitment and 274 controls matched to
cases for gender, age, and water arsenic, we found that folate deficiency,
hyperhomocysteinemia, low urinary creatinine–each associated with
decreased arsenic methylation–are risk factors for arsenic-induced skin
lesions. The positive association between DNA methylation andarsenic
exposure that we previously observed in the NIAT study (Pilsner et al.,
2007) was confirmed among the controls in this study. However, we
believe that this may be an adaptive change, as hypomethylation of
leukocyte DNA was found to be associated with an increased risk for
skin lesions (Pilsneret al., in press). These data suggest that lifestyle and
nutritional factors may modify the risk of arsenic-related skin lesions.
Genetic susceptibility to effects of arsenic on skin lesions
Interindividual variability in arsenic metabolism capacity may also
contribute to the variation in susceptibility to the effect of arsenic.
Glutathione S-transferase 1 (GSTO1) and methylenetetrahydrofolate
reductase (MTHFR) are enzymes involved in arsenic metabolism
pathways. In a case–control study of 594 skin lesion cases and 1041
controls, the dose–response relationship of skin lesion risk with
urinary monomethylarsonous acid percentage (%MMA) was more
apparent than those with other methylation indices (Ahsan et al.,
2007). Individuals with the MTHFR 677TT/1298AA and 677CT/
1298AA diplotypes were 1.66 (95% CI, 1.00–2.77) and 1.77 (95% CI,
0.61–5.14) times more likely to have skin lesions, compared with
those carrying 677CC/1298CC diplotype. The OR for skin lesions in
relation to the GSTO1 diplotype containing all at-risk alleles was 3.91
(95% CI, 1.03–14.79) (Ahsan et al., 2007). These findings reiterate that
arsenic-induced health effects may be especially deleterious in
subsets of the population carrying susceptible variants of genes
relevant to arsenic metabolism. Based on the risk estimates observed
in this study, the proportion of skin lesions in our study population
attributable to the MTHFR 677TT/1298AA and 677CT/1298AA diplo-
types was estimated to be 7.5%. The corresponding estimated
attributable proportion for the GSTO1 at-risk diplotype was 8.9%.
Peripheral neuropathy and children's intellectual function
Among 137 HEALS participants randomly selected from those
visited the field clinic over eight weeks, peripheral neuropathy was
assessed by a vibration sensitivity tester (Vibratron II) (Hafeman et
al., 2005). Arsenic exposure was associated with elevated toe
vibration threshold (TVT). Specifically, urinary arsenic was signifi-
cantly associated with elevated TVT (p-value b0.01) after adjustment
for age and gender.
In cross-sectional analyses of 201 children at 10 years of age (whose
parents participate in HEALS), children's intellectual function was
assessed using tests drawn from the Wechsler Intelligence Scale for
Table 2
Findings from HEALS on the risk of arsenic-related pre-malignant skin lesions
Reference Study design Characteristics of subjects Arsenic measurement Main findings
Ahsan et al. (2006b) Cross-sectional 714 cases of skin lesion and
10,724 non-skin lesion subjects
Water, urinary arsenic,
time-weighted arsenic
concentration
Dose–response relationship between arsenic
exposure and risk of skin lesions
Synergistic effect between high levels of arsenic
exposure and male gender, low BMI, and old age.
Chen et al. (2006a) Cross-sectional 714 cases of skin lesion and
10,724 non-skin lesion subjects
Water, urinary arsenic,
time-weighted arsenic
concentration
Synergistic effect between the highest level of arsenic
exposure and tobacco smoking in men.
Additive effects of sun exposure and arsenic
exposure in men
Argos et al. (2007) Cross-sectional 714 cases of skin lesion and
10,724 non-skin lesion subjects
Water, urinary arsenic Effect-modification by land ownership on the
multiplicative scale
Zablotska et al. (2008) Cross-sectional 714 cases of skin lesion and
10,724 non-skin lesion subjects
Water, urinary arsenic Effect-modification by high intake of riboflavin,
pyridoxine, folate, and vitamins A, C and E
Ahsan et al. (2007) Case–control 594 skin lesion cases and
1041 controls
Water, urinary arsenic,
urinary %MMA, %DMA,
MMA/DMA
Stronger dose–response relationship with urinary
%MMA compared with other methylation indices
Effect differs by MTHFR and GSTO1 diplotypes
Chen et al. (2007a) Prospective
case-cohort
303 incident skin lesion cases
and 849 subcohort members
Water, urinary arsenic An inverse association between baseline blood
selenium status and the incidence of skin lesions
Additive effect of high arsenic exposure and low selenium status.
Hall et al. (2006) Prospective
case-cohort
303 incident skin lesion cases
and 849 subcohort members
Water, urinary, and
blood arsenic
Dose–response relationship between baseline blood
and well arsenic and incidence of skin lesions
Pilsner et al. (2007) Prospective nested
case–control study
274 skin lesion cases 274
matched controls
Water, blood, urinary arsenic Positive relationships of folate deficiency, hyperhomocysteinemia,
low urinary creatinine, with skin lesion risk
187Y. Chen et al. / Toxicology and Applied Pharmacology 239 (2009) 184–192
Children, version III (WISC-III). Children provided urine specimens for
the measurement of urinary As and creatinine. Information on the
primary source of drinking water was obtained from the child's
mother. Exposure to arsenic from drinking water was associated with
reduced intellectual function after adjustment for sociodemographic
covariates in a dose–response manner, such that children exposed to
water arsenic levels N50 μg/L achieved significantly lower Perfor-
mance and Full-Scale scores than did children exposed to lower water
arsenic levels (Wasserman et al., 2004). In a similar investigation of
301 randomly selected six-year-olds, water arsenic exposure was
significantly negatively associated with both Performance and Proces-
sing Speed raw scores of the Wechsler Preschool and Primary Scale of
Intelligence, version III (WPPSI-III). Although, these associations were
less strong than in our previously studied 10-year-olds, this second
cross-sectional study of arsenic exposure expands the concerns about
arsenic neurotoxicity to a younger age group (Wasserman et al., 2007).
Arsenic exposure at low-to-moderate levels and blood pressure
Using baseline data in 10,910 participants, we assessed the
association between arsenic exposure from drinking water and
blood pressure. We observed a positive association between low-to-
moderate levels of arsenic exposure from drinking water and high
pulse pressure (pulse pressure ≥55 mm Hg) (Chen et al., 2007b), an
indicator of arterial stiffness, which is associated with an increased
risk of atherosclerosis. In addition, among participants with lower-
than-average dietary intake levels of B vitamins and folate, those with
a higher well arsenic concentration were 1.83–1.89 times more likely
to have a pulse pressure ≥55 mm Hg, compared with those in the
bottom quintile of well arsenic concentration (b8μg/L) (Chen et al.,
2007b). These findings indicate that the effect of low-level arsenic
exposure on blood pressure is nonlinear and may be more pronounced
in persons with a lower intake of nutrients related to arsenic
metabolism and cardiovascular health.
Intermediate biomarkers of early biological effect or altered structures
and functions of target organs
Arsenic and pre-clinical markers of cardiovascular diseases
Previous publications have documented dose–response relation-
ships between Carotid Artery Intima-Medial Thickness (IMT), which is
measuredusing ultrasound imaging,stroke, angina pectoris, myocardial
infarction (MI), intermittent claudication and essential hypertension
(Bots et al., 1997). These relationships indicate that IMT is a valid
surrogate marker for clinical endpoints.
In a pilot cross-sectional study of 66 healthy, normotensive,
relatively young cohort members, the ORs for carotid IMT N0.75 mm
were 1.61 (95% CI: 0.29–8.81) and 2.84 (95% CI: 0.39–20.86)
comparing middle and highest levels with the lowest level of total
urinary arsenic, respectively (Chen et al., 2006b). Although the
observed associations were not statistically significant, the trend in
effect estimates suggests a possible role of low-to-moderate levels of
arsenic exposure in atherosclerosis, which warrants future investiga-
tion in a larger study.
Adhesion of circulating leucocytes to the endothelial cell and
subsequent transendothelial migration is an important step in the
initiation of atherosclerosis. In part, this process is mediated by cellular
adhesion molecules (CAMs) (Cybulsky and Gimbrone, 1991; Adams
and Shaw, 1994), expressed on the endothelial membrane, in response
to inflammatory stimuli. Circulating markers of systemic inflammation
and endothelial dysfunction, such as soluble intercellular adhesion
molecule-1 (sICAM-1) and soluble vascular adhesion molecule-1
(sVCAM-1), have been shown to predict future cardiovascular disease
(CVD) (Hwang et al., 1997). In a subgroup of 115 individuals with
arsenic-related skin lesions, there was a positive association of urinary
arsenic and well arsenic concentration with plasma levels of sICAM-1
and sVCAM-1 (Chen et al., 2007c). The positive associations of well
arsenic with baseline and changes in plasma sVCAM-1 and sICAM-1
suggest a potential pathway underlying the effect of long-term arsenic
exposure on CVD. Future larger studies are required to furtherexamine
the associations of low-level arsenic exposure with markers of vascular
inflammation and endothelial dysfunction in healthy persons.
Arsenic and biomarkers of reno-vascular diseases
Microalbuminuria, a marker of glomerular hyperfiltration, has been
correlated with and may be a manifestation of impaired endothelial
function (Stehouwer et al., 2004). Impairment of endothelialfunction is
recognized as one of the initialmechanisms that lead to atherosclerosis.
In a study of arsenic and proteinuria (detected by dipstick analysis) in
11,121 persons in HEALS, we observed a dose–response relationship
between well arsenic concentration and prevalence of proteinuria. In
addition, cohort analysis with repeated measures of proteinuria and
urinary arsenicconcentrations revealed thata change in urinary arsenic
was positively related to incidence of proteinuria during the four years
of follow-up. The dose–response relationship between arsenic expo-
sure and proteinuria provides evidenceof the effect of low-to-moderate
arsenic exposure levels on a common causal intermediate of CVD and
kidney disease.
Arsenic and biomarkers of cancer
The development of skin lesions from arsenic exposure may be
mediated by increases in the expression of various growth factors,
including transforming growth factor-alpha (TGFα). To investigate this
association in humans, levels of total urinary arsenicand urinary TGFα
were determined in 41 individuals from HEALS, with and without
arsenic-associated skin lesions; all individuals had chronic exposure to
arsenic in their drinking water (Do et al., 2001). Linear regression
analyses suggest that total urinary arsenic explained a substantial
variation of urinary TGFα(R-squared value of the model= 0.40; p-
value b0.01), particularly in individuals with arsenic-associated skin
lesions (R-squared value of the model= 0.70; p-valueb0.01). The R-
squared values did not change appreciably with the adjustment for age
and gender. There was also a trend of increasing odds ratios for the
presence of arsenic-associated skin lesions with increasing urinary
TGFα, although this was not significant (p-value= 0.15). However, the
results were based on a small number of subjects.
In a follow-up study of 574 participants of the HEALS, the
extracellular domain of the epidermal growth factor receptor
(EGFR), to which TGFαbinds to promote carcinogenesis, was
measured by enzyme-linked immunosorbent assay in serum. Serum
EGFR was found to be positively associated with three different
measures of arsenic exposure (well water arsenic, urinary arsenic and
a cumulative arsenic index) at statistically significant levels (p≤0.034)
(Li et al., 2007). In addition, the risk of skin lesions for a given level of
arsenic increased with increasing levels of EGFR. These results indicate
that TGFα-/EGFR-dependent mechanisms may play a role in the
development of arsenic-related skin lesions and skin cancer.
Arsenic and molecular and clinical biomarkers of pulmonary diseases
Serum level of Clara cell protein (CC16), one of the 20 proteins
secreted by Clara cells in the lung's alveolar epithelium, has been
indicated as a novel biomarker for respiratory illnesses, with a reduced
CC16 concentration indicating damages in alveolar Clara cells. In cross-
sectional analyses of 241 nonsmoking individuals randomly selected
from the cohort, there was an inverse association between urinary
arsenic and serum CC16 among persons with skin lesions (β=−0.13,
p=0.01) (Parvez et al., 2008). There was also a positive association
between the ratio of DMA to MMA in the urine and CC16 levels
(β=0.12, p=0.05). In a subsample of study participants undergoing
spirometric measures (n= 31), urinary arsenic was inverselyassociated
with lung function measured by predictive FEV1 (forced expiratory
volume measured in 1 sec) (r=−0.37) (Parvez et al., 2008).
188 Y. Chen et al. / Toxicology and Applied Pharmacology 239 (2009) 184–192
Arsenic and biomarkers of gene expressions in peripheral blood
In a microarray-based gene expression analysis comparing skin
lesion cases with non-cases, Affymetrix HG-U133A GeneChip arrays
were used to measure the expression of 22,000 transcripts, using RNA
from peripheral blood lymphocytes (Argos et al., 2006). Down-
regulating of several genes, including superoxide dismutase 2 (SOD2)
gene, tumor necrosis factor (TNF) gene and chemokine factor CCL20
gene was observed in skin lesion cases. These findings suggest the
involvement of inflammation, oxidative stress defense and chemokine
response pathways in the development of skin lesions, or as a
consequence of skin lesion manifestation.
In a separate comparison of samples collected from skin lesions
cases before and after selenium supplementation, genes up-regulated
by selenium supplementation included TNF,IL1B,IL8,SOD2,CXCL2
and several other immunological and oxidative stress-related genes
(Kibriya et al., 2007). These findings suggest that selenium supple-
mentation may reverse some gene expression changes in individuals
with pre-malignant skin lesions.
Prevention of health effects of arsenic exposure
Based on our findings of potential nutritional influence on arsenic
toxicity (described above), we desired to assess whether dietary
supplementation with specific micronutrients could prevent the
occurrence of skin lesions, improve existing skin lesions and/or
prevent skin and other cancers. In a pilot randomized, placebo-
controlled, double-blind trial of 121 skin lesion cases, supplementa-
tion with vitamin E and selenium, either alone or in combination,
slightly improved skin lesion status, although the improvement was
not statistically significant (Verret et al., 2005). A large 2 × 2 factorial
randomized placebo control trial is currently underway to evaluate
whether supplementation with vitamin E or selenium can prevent the
risk of skin cancer among 6000 patients with skin lesions.
In a randomized, double-blind, placebo-controlled folic acid-
supplementation trial of 200 folate-deficient cohort participants, we
tested the hypothesis that folic acid supplementation could increase
arsenic methylation and lower blood arsenic concentrations (Gamble
et al., 2006). In the trial, the increase of urinary %DMA in the folic acid
group was significantly greater than that in the placebo group, as was
the reduction in urinary %MMA (Gamble et al., 2006). Furthermore,
the concentrations of total blood arsenic and of MMA in the blood
were significantly reduced, by 14% and 22%, respectively (Gamble
et al., 2007).
Arsenic exposure reduction by mitigation program
Since the baseline recruitment of the HEALS, we implemented
interventions including: 1) person-to-person reporting of well test
results and health education; 2) well labeling and village-level health
education; and, 3) installations of 50 deep, low-arsenic community
wells in villages with the highest levels of arsenic exposure. Two years
after these interventions, 58% of the 6512 participants with unsafe
wells at baseline (arsenic ≥50 μg/L) had responded by switching to
other wells (Chen et al., 2007d). Urinary arsenic levels in participants
who switched to a well identified as safe (arsenic b50 μg/L) dropped
from an average of 375 to 200 μg arsenic/g creatinine, a 46% reduction
towards the average urinary arsenic content of 136 μg arsenic/g
creatinine for participants that used safe wells throughout(Chen et al.,
2007d). These findings suggest that interventions such as these can
effectively encourage switching to safe wells and lower arsenic
exposure.
All-cause mortality and chronic disease mortality
A total of 219 participants have passed on from 2000 to 2006 in
HEALS, with ages ranging from 30–70 years and a mean of 51 years.
Major causes of chronic disease death within the cohort included
diseases of the cardiovascular system (n=87), which primarily
included hypertensive diseases, ischemic heart diseases, and cerebro-
vascular diseases; neoplasms (n=32); and diseases of the respiratory
system (n=18), which primarily included asthma, chronic obstruc-
tive pulmonary disease, bronchiectasis, and respiratory infections.
Other causes of death were related to the nervous system (n=3), the
digestive system (n=14), the genitourinary system (n= 8), preg-
nancy complications (n= 5), diabetes (n=1), musculoskeletal dis-
orders (n=1), or infectious disease (n=18).
We estimated hazard ratios for all-cause mortality and chronic
disease mortality, the latter category included deaths due to
cardiovascular disease, cancers, and non-infectious diseases, in
relation to baseline well arsenic levels. The adjusted hazard ratios
for all-cause mortality are 1.00 (ref), 1.23 (95% CI: 0.80, 1.88), 0.98
(95% CI: 0.65, 1.49) and 1.71 (95% CI: 1.16, 2.53) in increasing levels of
well arsenic (b10, 10 –50, 51–150, and 151–864 μg/L), respectively. The
adjusted hazard ratios for chronic disease mortalityare 1.00 (ref), 1.23
(95% CI: 0.80, 1.88), 0.98 (95% CI: 0.65, 1.49) and 1.71 (95% CI: 1.16,
2.53) in increasing levels of well arsenic, respectively. The hazard
ratios for chronic disease mortality associated with a baseline well
arsenic concentration N50 and N100 μg/L were 1.40 (0.93–2.10) and
1.61 (1.06–2.44), respectively.
Discussion
Recent findings from the HEALS provide valuable information on
the effects of arsenic exposure at low-to-moderate levels on arsenic-
related skin lesions, children's intelligence function, blood pressure,
all-cause and chronic disease mortality, and an array of biomarkers for
early biological effects or altered structures and functions of target
organs.
Consistent with studies of bladder and lung cancer (Karagas et al.,
2004; Steinmaus et al., 2003; Chen et al., 2004a), we also found a
synergistic effect between the use of tobacco products and arsenic
exposure on the risk of skin lesions. Cigarette smoking is likely to
influence arsenic toxicity and should be taken into consideration in
any studies on low-to-moderate levels of arsenic exposure. In
addition, the data strongly suggest effect-modification roles of
nutritional factors (i.e., folate and selenium) that are either involved
in arsenic metabolism or have an antagonistic relationship with
arsenic. While mounting evidence suggests that folate has an effect in
arsenic metabolism, epidemiologic data on the mechanisms through
which selenium may influence arsenic toxicity is limited. Similar to
arsenic, methylation of selenium also uses SAM as the methyl donor.
In addition to continue the investigation of gene expression changes
due to selenium, future studies are needed to assess whether the
effect of arsenic on DNA methylation is modifiable by selenium status.
Intervention trials with clinical disease endpoints may be the next
step in evaluating the potential of using some of these agents as
treatments or preventive measures. In this regard, a large randomized
clinical trial of 6000 cases of skin lesions investigating the effects of
selenium and/or vitamin E supplementation with five years of follow-
up is ongoing. Future studies are needed to evaluate whether the
associations of arsenic exposure with cancer, cardiovascular disease or
other arsenic-related clinical and intermediate endpoints also differ
by these nutritional factors.
Our observation that at a given level of As exposure, %MMA was
most strongly associated with increased risk of As-induced skin
lesions is consistent with studies of skin cancer (Hsueh et al., 1997;
Chen et al., 2003b; Yu et al., 2000), urothelial carcinoma (Pu et al.,
2007), and bladder cancer (Chen et al., 2003c). These data suggest a
role of interindividual variability that may be regulated by arsenic
methylation capacity. There is also evidence that genes coding the
enzymes that catalyze the metabolism processes of arsenic can
influence the risk of arsenic-related skin lesions. Beside MTHFR and
189Y. Chen et al. / Toxicology and Applied Pharmacology 239 (2009) 184–192
GSTO1, it is likely that there are many other candidate genes that may
influence susceptibility to arsenic toxicity. Other GSTs may play a role
in cellular antioxidant defense mechanisms by catalyzing the reduc-
tion of potentially harmful peroxides. Several studies have indicated
that SNPs in genes for GST mu 1 (GSTM1) and theta 1 (GSTT1) were
associated with urinary arsenic metabolite profiles (Schlawicke et al.,
2007; Steinmaus et al., 2007). A case–control of 600 cases (with
average exposure level at 174 μg/L) and 600 controls in Bangladesh
(McCarty et al., 2007), and a cross-sectional study of IMT with 605
subjects in Taiwan (Wang et al., 2006), have found that effects of
arsenic on these outcomes were modifiable by polymorphisms in
glutathione S-transferase P1 gene. However, the studies did not have
enough sample size to evaluate the effect-modification at low levels of
exposure (b50 or b100 μg/L). More recently, arsenic (+III) methyl-
transferase (AS3MT) was characterized in rodents as an arsenic
methyltransferase capable of methylating inorganic arsenic to its
monomethyl form, and monomethylarsenic to its dimethylarsenic
form. In a study of 147 individuals in northern Argentina, three intronic
polymorphisms in the AS3MT gene were associated with a lower %
MMA and a higher %DMA in urine (Schlawicke et al., 2007). Although
studies of arsenic-related skin lesions and urinary arsenic metabolites
point to the role of genetic susceptibility, case–control studies of skin
or bladder cancer generated mixed results, most probably due to the
limited sample sizes (Karagas et al., 2005; Moore et al., 2004; Chen
et al., 2004c) (number of cases b400). Taken together, additional
studies with larger sample size and comprehensive genomic approach
are needed to systemically evaluate gene-arsenic interactions, not only
the risk of skin lesions but also on other arsenic-related disease
conditions.
The studies of biomarkers for early biological effects or altered
structures may aid in the recognition of early effects of arsenic
exposure, as well as the elucidation of underlying pathogenic
mechanisms. For instance, the study of serum CC16 shows promise
as a biomarker for assessing early respiratory damage induced by
arsenic. Consistent with studies that have shown arsenic to be an
inducer of TNF (Das et al., 2005; Germolec et al., 1997), a trigger of
chemokine signal responses including chemokine factor CCL20
(Spiekstra et al., 2005), we observed down-regulation of CCL20 and
TNF genes in skin lesion cases. ICAM-1 and VCAM-1 expression in
human umbilical vein endothelial cells (HUVEC) was higher after
stimulation with arsenic (Hou et al., 2005). In mice tumors, treatment
with As trioxide was associated with a clear increase in expression of
ICAM-1 and VCAM-1 (Griffin et al., 2000). To our knowledge, our
studies on EGFR and CC16 are the first published studies that link
arsenic exposure with these biomarkers. However, aspects of the data
are consistent with other reports in the literature. For example,
individuals at risk for cancer from carcinogen exposures are shown to
have increased levels of serum EGFR ECD years prior to their clinical
diagnosis of malignancy (Partanen et al., 1994). Serum CC16
concentrations are decreased in individuals with compromised lung
condition induced by chronic environmental exposures such as
cigarette smoking or ozone (Bernard et al., 1994; Lagerkvist et al.,
2004). Reliable biomarkers may also serve as intermediate endpoints
for intervention trials, arsenic mitigation programs, screening and/or
large observational epidemiologic studies. Analyses of participants
with available data for both biomarkers and disease endpoints may be
useful to further support biological plausibility of the putative causal
pathways linking arsenic exposure to disease.
The observed positive associations between arsenic exposure and
biomarkers relevant to cardiovascular disease and respiratory illness
underscore the potentially adverse effects of arsenic exposure at low-
to-moderate levels on these conditions. In a study of 463 subjects from
the high-exposure area in southwestern Taiwan, a dose–response
relationship was observed between arsenic exposure and carotid
atherosclerosis assessed by duplex ultrasonography (Wang et al.,
2002). The only epidemiologic evidence of low-to-moderate levels of
arsenic exposure on cardiovascular disease came from the results of a
cross-sectional study of N8000 subjects in northeastern coast of
Taiwan that showed an elevated risk of cerebrovascular disease
associated with arsenic exposure level at b50 and 50–300 μg/L (Chiou
et al., 1997). Because IMT is a reliable marker of atherosclerosis, which
is a strong risk factor of both coronary heart disease and stroke, larger
studies of IMT are needed to assess the influence of the low-to-
moderate levels of arsenic.
Using the hazard ratios of chronic disease mortality for a baseline
well arsenic concentration N50 and N100 μg/L and the prevalence of
these exposure levels, we estimated population attributable risk (PAR
%); 18.1% and 13.1% of chronic disease mortality in the population can
be attributable to arsenic exposure N50 and N100 μg/L, respectively.
Apparently, arsenic exposure from drinking water is associated with a
significant proportion of chronic disease mortality in the overall
population, especially in those with comparatively high exposure
levels. In future, with a longer follow-up of the HEALS, we will be able
to further assess the dose–response relationship between arsenic
exposure and cause-specific mortality. The datawill be valuable, given
that existing studies of arsenic exposure at low-to-moderate levels
and disease endpoints mostly include limited sample size (Chiou et al.,
2001; Karagas et al., 2004; Steinmaus et al., 2003) and unreliable or
group-level long-term measures of arsenic exposure (Engel and
Smith, 1994; Lewis et al., 1999; Meliker et al., 2007; Zierold et al.,
2004).
Importantly, the use of multiple exposure measures, including well
arsenic, urinary arsenic and blood arsenic, with repeated measure-
ments in HEALS, improves the ascertainment of long-term exposure
levels. The availability of detailed exposure data also provides a unique
opportunity to evaluate the health effects of changes in exposure
status over time and the joint status of difference exposure measures.
In light of complex data, future analyses will need to employ modern
epidemiology techniques, such as longitudinal analyses methods,
survival analyses methods with time-dependent variables and/or
marginal structural models to enhance causal inferences.
In conclusion, findings from the HEALS to date indicate that arsenic
exposure has a profound influence on the health of the arsenic-
exposed population in Bangladesh. Our findings on skin lesions,
biomarkers and susceptibility altogether indicate that health effects of
arsenic exposure at low-to-moderate levels may be cause for further
investigation. Future analyses of clinical endpoints of cancer and
cardiovascular disease are likely to provide useful knowledge on
dose–response relationships. The HEALS will continue to be a valuable
resource for the investigation of the health effects of arsenic exposure
for years to come.
Conflict of interest
The authors declare that there are no conflicts of interest.
Acknowledgments
The research is supported by NIH grants: P42ES010349,
R01CA102484, R01CA107431, R01ESO11601, P30ES09089, CA016087,
ES000260, and CA014599.
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