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. Author manuscript; available in PMC: 2015 Jul 1.
Published in final edited form as: Cancer Epidemiol Biomarkers Prev. 2014 May 3;23(7):1187–1194. doi: 10.1158/1055-9965.EPI-13-1317

The global burden of disease for skin, lung and bladder cancer caused by arsenic in food

Shilpi Oberoi 1, Aaron Barchowsky 1,2, Felicia Wu 3
PMCID: PMC4082465  NIHMSID: NIHMS592791  PMID: 24793955

Abstract

Background

Arsenic is a ubiquitous, naturally occurring metalloid that poses a significant human cancer risk. While water consumption provides the majority of human exposure, millions of individuals worldwide are significantly exposed to arsenic through naturally occurring levels of arsenic in grains, vegetables, meats and fish, as well as through food processed with water containing arsenic. Thus, we estimated the global burdens of disease for bladder, lung and skin cancers attributable to inorganic arsenic in food.

Methods

To determine foodborne inorganic arsenic exposures worldwide, we used World Health Organization estimates of food consumption in thirteen country clusters, in conjunction with reported measurements of total and inorganic arsenic in different foods. We estimated slope factors for arsenic related bladder and lung cancers, and used the US Environmental Protection Agency skin cancer slope factor, to calculate the annual risk of the cancer incidence in males and females within each country cluster.

Results

We estimated that each year 9,129 to 119,176 additional cases of bladder cancer, 11,844 to 121,442 of lung cancer, and 10,729 to 110,015 of skin cancer worldwide are attributable to inorganic arsenic in food.

Conclusions

These estimates indicate that foodborne arsenic exposure causes a significant global burden of human disease.

Impact

Estimating the global cancer burden caused by arsenic exposure in food will support policies that reduce exposure to disease promoting environmental hazards.

Keywords: arsenic, bladder cancer, food, global disease burden, lung cancer, skin cancer

Introduction

Arsenic is a naturally occurring metalloid found in drinking water and certain foods. The International Agency for Research on Cancer (IARC) classifies arsenic as a Group 1 carcinogen based on evidence that inorganic arsenic (iAs) causes bladder, lung and non-melanoma skin cancer in humans (1). Additionally, arsenic exposure increases risk of mortality from cardiovascular (2, 3) and respiratory diseases (4, 5).

Naturally occurring levels of arsenic in vegetables, grains, meats and fish present a significant source of arsenic exposure worldwide (68). The arsenic comes from uptake by food crops from the soil and irrigation water (6, 912). In addition, arsenic in water can contaminate food during processing and cooking (e.g., in boiling rice, making breads or pasta) (7, 13). According to a recent World Health Organization (WHO) background document on global arsenic exposure (14), arsenic in contaminated water is completely bioavailable and provides the majority of daily arsenic dose (15). However, as water arsenic concentrations decrease, the relative contribution of dietary sources becomes more significant to human arsenic exposures (7, 8, 16).

As indicated by its IARC classification, arsenic exposure increases the risk for a number of important cancers. Numerous epidemiological studies indicate an association between arsenic exposure and an increased risk for lung cancer mortality (1, 1720), and lung cancer may be the leading cause of arsenic-associated cancer deaths. Meta-analysis of available epidemiological studies performed in Bangladesh, Chile, Argentina, Taiwan and the United States (21), estimated about 4.51 additional lung cancer cases per 100,000 people for a maximum contamination level of 10ug/ l of arsenic in drinking water. An association between arsenic exposure and bladder cancer has been substantiated by multiple ecological, as well as case-control and cohort studies (reviewed in (1, 17,18, 22). In addition, an extensive body of literature definitively links the ingestion of arsenic to increased incidence of non-melanoma skin cancer i.e. basal cell and squamous cell carcinoma (1). Multiple ecological studies based on mortality from skin cancer in Chile, Taiwan, and Bangladesh found consistent gradients of increasing risk with average level of arsenic in drinking water (1, 23). Cohort studies from IARC, 2012 reported risks of skin cancer to be significantly related to average concentration of arsenic in drinking water and index for cumulative exposure to arsenic (1, 2325).

The objective of the current study was to use quantitative risk assessment to estimate the global burden of foodborne arsenic-induced bladder cancer, lung cancer and skin cancers. Global burden of disease (GBD) is a widely accepted parameter that provides a frame of reference for comprehensive analysis of health gaps. It relies on use of all available mortality and health data by appropriate methods to confirm the comparability and consistency of estimates of demographic and epidemiological importance worldwide. This risk estimate was made as part of the WHO Foodborne Disease Burden Epidemiology Reference Group (FERG) efforts to estimate the GBD from foodborne chemical exposures, including dietary iAs exposure. A partial risk assessment was made previously by the Joint FAO/WHO Expert Committee on Food Additives (JECFA) who reviewed the PTWI of iAs with an emphasis on the speciation and occurrence of iAs in food (26). In addition, the human health risks in European countries from foodborne arsenic was assessed by the EFSA Panel on Contaminants in the Food Chain (27). However, the global burden of cancers caused by foodborne arsenic exposure has not been investigated, nor the extent of iAs content in different diets worldwide.

Specifically, we focused on adverse effects associated with inorganic arsenic exposure, since foodborne organic arsenical exposures pose little human health risk (7, 8, 23, 26, 27). We estimated the numbers of additional cases of cancers per year due to iAs through food in different diets worldwide, based on data adapted from WHO Global Environment Monitoring System (GEMS)/ Food Consumption Cluster Diets database (28). GEMS/Food Consumption Cluster Diets database divides countries of the world into 13 groups based on diets.

Materials and Methods

Quantitative cancer risk assessment

To assess the quantitative cancer risk for a given population, the dietary arsenic exposure was multiplied by the cancer potency factor (slope factor) for a given cancer endpoint. The global estimate for burden of a particular arsenic-induced cancer was then obtained by summing across different populations.

Dose-response assessment

Cancer potency factors for bladder cancer and lung cancer were derived using data adapted from Morales et al Table 8. Model 1, in which the relative risk of mortality at any time is assumed to increase exponentially, with a linear function of dose and a quadratic function of age; no external comparison population was used (29). EPA has used the same model for the development of arsenic water standard (2001) as it best fit the data based on the Akaike information criterion.

This study was selected as the best estimates of the cancer potency factor despite concerns that it may not be representative of risk worldwide. However, a recent review found that there are no other currently published studies that provide a more powerful estimate (17). This table provides the concentration of arsenic in drinking water (µg/L) estimated to cause bladder or lung cancer in 1% of males and females in a cohort in southwestern Taiwan. The cancer potency factor was transformed to be relevant to human doses by assuming a daily consumption of 2 liters of water per adult. For skin cancer caused by iAs, the slope factor was adapted from the United States EPA IRIS database (30). EPA developed dose-response for skin cancer using data from Taiwan on about 40,000 persons exposed to arsenic in drinking water and 7500 relatively unexposed controls (31, 32).

The dose response assessment included the following assumptions: (i) that the southwestern Taiwanese population that provides the dose-response data (29) used for estimation of the cancer potency factors are reasonably representative of global populations in terms of adverse effects of arsenic (based on IARC 2012(1)). This allowed the same cancer potency factor to be applied in other parts of the world; (ii) that dose-response curves for arsenic-induced cancers can be linearized and driven through (0, 0); (iii) that the average human consumption of water per day is 2 liters (28); (iv) that iAs in food and water has the same potency and efficacy for cancer promotion; and (v) that the slope factors for arsenic-related bladder cancer and lung cancer, would not change appreciably as a result of infections or co-exposures in the Taiwanese population from which Morales et al (28) derived the data.

Exposure assessment

Exposure to arsenic via food depends on the concentration of arsenic in individual foods and the rate of consumption of these food items. The range of iAs content including a range of uncertainty for different food groups that represents content in crops worldwide was adapted from literature values (26, 27, 33) to derive the mean portion of iAs relative to the total food arsenic. Using a common range of arsenic content for food crops grown in different parts of the world has the advantages of demonstrating the effect of dietary patterns on arsenic exposure via food, and allowing uniformity in calculations across all nations. For each cluster of countries, a lower and an upper bound value of iAs content was modeled at 50% and 100% bioavailability respectively to take into account a factor of uncertainty. JECFA noted the need for improved data on occurrence of different species of arsenic in, and their bioavailability from, different foods in order to improve the estimates of dietary and systemic exposure (26).

To estimate the total bioavailable iAs in the diet worldwide, these exposure assessment calculations were then consolidated for each relevant population, across all of the different foods consumed in different proportions. The GEMS Food Consumption Cluster Diets database (28) was used to gather information on the dietary patterns (amounts of specific foods consumed) in different parts of the world, as it divides the world into 13 clusters of countries based on dietary similarities. The GEMS database uses data from the FAOSTAT to divide the countries of the world into thirteen clusters on the basis of similarities in dietary pattern. In the final step, the populations of individual nations across each of the GEMS cluster were summed to estimate the global population.

The primary assumption in the exposure assessment was that the values reported in literature for total foodborne exposure to arsenic and the proportion of iAs in different foodstuffs (6, 26, 30) are reasonably accurate. In addition, it was assumed that the rough upper and lower bounds for bioavailability of iAs in foods is 50–100% (26), with beverages being 100% as seen with drinking water. For calculations based on populations within each GEMS cluster, it was assumed that (i) roughly an equal number of men and women comprise each GEMS dietary cluster of nations; and (ii) that the individuals within each GEMS cluster consume roughly comparable amounts of the foodstuffs that are presented in the GEMS database, including across age groups and genders.

Risk characterization

To characterize the risk of bladder, lung and skin cancer due to foodborne arsenic, the data from dose–response and exposure assessment were integrated to quantify the burden of arsenic related cancers across the world. For each cancer type the respective slope factor was multiplied with the estimated range of daily dietary iAs exposure, and the population size of the individual GEMS cluster to obtain an annual gender-specific estimate of the additional number of foodborne arsenic related cancers. The life span per individual was assumed to be 70 years.

Results

The essential steps of risk assessment are hazard identification, dose-response relationship, exposure assessment and risk characterization. For the present work, we relied on the hazard identification by IARC 2012 (1) that clearly identifies arsenic as a human carcinogen with increased risk for bladder, lung, and non-melanoma skin cancers. To establish the dose-response relationship, we converted the dose response estimates for water exposure to human dose and the data in Table 1 include the imputed slope factors for each of the cancers. For bladder and lung cancers gender-specific slope factors are reported based on the data adapted from Morales et al (29). However for skin cancer the slope factors are the same for both the genders (30). The total increased risk in the population of each of the cancers for every incremental unit of foodborne arsenic was estimated on the basis of the slope factors.

Table 1.

Slope factors, or cancer potency factors, for incidence of each arsenic-related cancer.

Cancer type Slope factor (increased
population risk per µg iAs/day)
Males Females
Bladder* 0.0000127 0.0000198
Lung* 0.0000137 0.0000194
Skin^ 0.000015 0.000015
*

slope factor derived by using data adapted from Morales et al (2000)

^

slope factor was adapted from the United States EPA IRIS database (2001)

For exposure estimation, the data in Table 2 provide the mean adjusted total arsenic content of foods used in the EFSA (27) dietary exposure estimates along with the conversion factors from total arsenic to iAs in each of the different foodstuffs provided in JECFA (26). In contrast to water exposures, not all of the arsenic in food is bioavailable and Table 3 presents the estimated levels of bioavailable iAs for the 13 GEMS food consumption clusters as well as the population size for each cluster. For each of these clusters, the GEMS food consumption database provides an estimate of the amount of cereals, vegetables, fruits, beverages, meat, nuts, and oilseeds consumed. Rice and rice products appear to be a major source of exposure to iAs, especially in GEMS cluster G comprised of Asian countries.

Table 2. Mean adjusted total arsenic content of foods and the reported conversion factors from total arsenic to inorganic arsenic used in the dietary exposure estimates.

Data adapted from EFSA (2009) and FAO/WHO JECFA Monographs 8, 2011.

Food group Total arsenic
lower bound
mean level (mg/kg)
Total arsenic
upper bound
mean level (mg/kg)
Mean % inorganic
Arsenic
01. All cereal & cereal products 0.0671 0.0848 30–100^
01.A Cereal-based dishes 0.0157 0.0283
01.B Cereal & cereal products 0.0825 0.1017
02. Sugar products and chocolate 0.0135 0.0320 30–100^
03. Fats (vegetable and animal) 0.0063 0.0245 30–100^
04. All vegetables, nuts, pulses 0.0121 0.0212 30–100^
04.A Vegetable soups 0.0050 0.0110
04.B Vegetables, nuts, pulses 0.0122 0.0213
05. Starchy roots and tubers 0.0031 0.0142 30–100^
06. Fruits 0.0051 0.0155 30–100^
07. Juices, soft drinks and bottled water 0.0030 0.0068 30–100^
07.A Fruit and vegetable juices 0.0048 0.0129
07.B Soft drinks 0.0044 0.0132
07.C Bottled water 0.0023 0.0041
08. Coffee, tea, cocoa 0.0034 0.0051 30–100^
09. Alcoholic beverages 0.0055 0.0151 30–100^ [this category not detailed in GEMS diets database and hence was not used for calculations]
09.A Beer and substitutes 0.0054 0.0161
09.B Wine and substitutes 0.0061 0.0110
09.C Other alcoholic beverages 0.0085 0.0155
10. All meat and meat products, offal 0.0044 0.0138 100*
10.A Meat and meat products 0.0042 0.0137
10.B Edible offal and offal products 0.0044 0.0139
10.C Meat-based preparations 0.0121 0.0185
11. All fish and seafood 1.6136 1.6159 Standard ratio
0.015 – 0.10 mg/kg^
11.A Seafood and seafood products 5.5537 5.5545
11.B Fish and fish products 1.4426 1.4549
11.C Fish-based preparations 1.1524 1.1573
12. Eggs 0.0042 0.0117 41*
13. Milk and milk-based products 0.0044 0.0139 26*
13.A Milk and dairy-based drinks 0.0026 0.0104
13.B Dairy-based products 0.0068 0.0184
13.C Cheese 0.0065 0.0188
14. Miscellaneous/special dietary products 0.3993 0.4187 30–100^

Category not detailed in GEMS
14.A Miscellaneous products 0.2449 0.2658
14.B Foods for special dietary uses 0.4383 0.4573
^

Data adapted from EFSA (2009),

*

Reference: Yost, Schoof and Aucoin (1998)

Table 3.

Range of food-borne total and inorganic arsenic exposure at 50 –100% bioavailability for 13 WHO - GEMS clusters of countries¥.

GEMS
Cluster
Lower
boundary
of total
As*
(ug/kg
bw/day)a
Upper
boundary
of total
As*
(ug/kg
bw/day)
Lowest
boundary of
iAsb (50%
bioavailable)
(ug/day)^
Upper boundary
of iAsc (100%
bioavailable)
(ug/day)^
Range of iAs
exposure via
rice and rice
products
(ug/ day)
Population
mid-2012
(millions)#
A 0.91 1.26 4.8 53.4 0.92 to 6.95 302.5
B 2.87 3.47 10.37 108.35 0.32 to 2.41 224.9
C 1.38 1.79 9.09 85.46 0.95 to 7.22 263.7
D 1.32 1.72 6.71 66.95 0.33 to 2.53 408
E 1.41 1.83 5.75 63.45 0.13 to 0.97 339.2
F 1.84 2.19 5.25 57.27 0.13 to 0.97 26.7
G 2.08 2.42 7.82 75.14 3.79 to 28.78 3544.5
H 1.15 1.55 6.44 66.54 0.65 to 4.9 213.5
I 0.87 1.18 5.02 52.2 0.38 to 2.9 256.8
J 0.97 1.28 5.01 51.88 0.75 to 5.67 357
K 1.04 1.48 6.6 66.13 2.39 to 18.19 335.7
L 2.69 3.05 7.88 79.1 3.84 to 29.1 307.4
M 1.35 1.83 6.44 70.56 0.35 to 2.64 436.8

Data are adapted from GEMS/Food Consumption Cluster Diets database (FAOSTAT 2006).

¥

Listing of countries within each cluster is available at http://www.who.int/foodsafety/chem/gems/en/index1.html.

a

Assuming 60 kg body weight per individual

b

Lower bound for iAs content assumes Non detect equals zero

c

Upper bound for iAs content assumes non-detect equals the limit of detection

*

Calculations based on Table 13, FAO/WHO JECFA Monographs 8, 2011 for range of total arsenic content in food items.

^

Calculations based on Table 15, FAO/WHO JECFA Monographs 8, 2011 for range of mean % inorganic arsenic content in food items.

#

Data source: “Population Data sheet 2012” by the Population Reference Bureau (www.prb.org). PRB has derived the data from International Programs Center of the U.S. Census Bureau; the United Nations (UN) Population Division; the Institut national d’etudes démographiques (INED), Paris; and the World Bank.

Risk characterization of the total estimated cases of bladder, lung and skin cancers attributable to foodborne arsenic annually, worldwide was calculated from the slope factors in Table 1 and the exposure data in Tables 2 and 3. These estimates are listed in Table 4 and further resolved by GEMS cluster and gender to yield the number of expected additional cases of bladder, lung and skin cancer from foodborne iAs exposures per year in Table 5 with the assumption of 70 years life span per individual. Overall, the data indicate that arsenic in food causes a small, but significant burden of the three major cancers that is distributed throughout the world.

Table 4.

Global burden of cancers caused by foodborne arsenic.

Cancer Male Female Total burden (global) by
foodborne arsenic
Bladder 4,527 to 46,420 7,096 to 72,756 9,129 to 119,176
Lung 4,913 to 50,373 6,931 to 71,069 11,844 to 121,442
Skin (Non melanoma) 5,365 to 55,007 5,365 to 55,007 10,730 to 110,014

Table 5.

Annual expected burden of cancers caused by foodborne arsenic, by GEMS cluster and gender, lower bounds (LB) and upper bounds (UB)a.

GEMS
cluster
Bladder cancer Lung cancer Skin cancer
Male Female Male Female Male Female
LB UB LB UB LB UB LB UB LB UB LB UB
A 195 2001 306 3137 212 2172 299 3064 231 2371 231 2371
B 145 1488 227 2332 157 1614 222 2278 172 1763 172 1763
C 170 1744 267 2734 185 1893 260 2671 202 2067 202 2067
D 263 2699 413 4230 286 2929 403 4132 312 3199 312 3199
E 219 2244 343 3517 237 2435 335 3436 259 2659 259 2659
F 17 177 27 277 19 192 26 270 21 209 21 209
G 2287 23449 3584 36753 2482 25446 3502 35901 2710 27787 2710 27787
H 138 1412 216 2214 149 1533 211 2162 163 1674 163 1674
I 166 1699 260 2663 180 1843 254 2601 196 2013 196 2013
J 230 2362 361 3702 250 2563 353 3616 273 2799 273 2799
K 217 2221 339 3481 235 2410 332 3400 257 2632 257 2632
L 198 2034 311 3187 215 2207 304 3114 235 2410 235 2410
M 282 2890 442 4529 306 3136 431 4424 334 3424 334 3424
Total 4527 46420 7097 72756 4913 50373 6932 71069 5365 55007 5365 55007
a

Assuming 70 years life span per individual

Discussion

Using quantitative risk assessment, we estimated the increased incidence of cancers that can be attributed to arsenic in food. The most difficult aspect of this risk assessment was estimating the highly variable levels of iAs in the varied foods consumed by the different populations contained in the GEMS clusters. There is uncertainty in whether arsenic in food is equivalent to arsenic in water for disease promotion given the many other food constituents, such as folate (34) and selenium (35) that may modulate arsenic pathogenesis. Additionally, the assumption of linear dose-response relationships of arsenic-related cancers is controversial, particularly regarding the mode of carcinogenicity of skin cancer, despite the EPA IRIS derivation of a single slope factor for arsenic-related skin cancer (30). There are no studies that present the effects of low dose arsenic exposures on skin cancer, which reduces certainty regarding the shape of the lower end of the dose response curve. Thus, it is conservative to default to the linear model for determining the skin cancer potency factor. Accounting for these uncertainties, we provide estimates that levels of iAs found in food cause a low but significant increase in the burden of lung, bladder, and non-melanoma skin cancers worldwide.

There are a limited number of epidemiological studies that examine the health effects of the levels of arsenic commonly found in food. Much of the available data on disease risk come from studies of arsenic in drinking water and often the populations studied are exposed to higher levels of arsenic (>100 µg/L drinking water). However, as levels of arsenic in water decrease, the contribution of arsenic from food to total arsenic exposure becomes greater and more significant (7, 36). While human biomarkers for arsenic exposure, such as arsenic and metabolite levels in urine, blood, hair, or nails are available (36), it is not possible to determine the proportion of the measurements attributable to arsenic in drinking water or food. For the purposes of estimating human health consequences associated with arsenic consumption, knowing the overall population arsenic exposure matters more than knowing the relative contribution from different routes of exposure. However, for the purpose of recommending interventions, it can be helpful to understand the separate contributions.

There are several additional unavoidable constraints with estimating health risks from arsenic in food. The bioavailability of arsenic in different foods varies with the food group or method of processing and the complexity of influence of other food constituents on arsenic toxicity and adverse health effects. We focused our exposure estimates and risk characterization on both the range of iAs content and the range of predicted bioavailability of iAs in different foods. This approach is limited by using the GEMS cluster data for food consumption, since it contains an inherently broad range of dietary variation between the countries within each cluster (37). For example, the daily consumption of rice in Bangladesh (GEMS cluster G country) was reported as 445gm/day (38); however for GEMS cluster G the average rice consumed daily is 380gm. Using the cluster values may underestimate arsenic exposure via rice in Bangladesh. On the other hand, for the USA (GEMS cluster M country) the actual daily consumption is 18 gm (38) while overall for cluster M, it is almost double that level at 35gm/day. Moreover one of the major assumptions in the current analysis is that the speciation and arsenic content of rice cultivated in different regions of the world would be the same. However, there are conflicting reports indicating a large range in the levels of iAs in rice from developing and developed countries (38, 39). To overcome these limitations and obtain a realistic estimate for iAs levels, we used data from studies that provide actual measured levels (27) in different categories of food items (6, 12, 40).

The GEMS cluster data also does not provide specific details of the consumption of certain miscellaneous food items with reported high levels of iAs (e.g. seaweed hijiki and edible algae (27) Table 2, miscellaneous items). In certain Asian countries, such as Japan, the consumption of seaweed is a relatively important part of diet and can add substantially to the daily exposure levels of iAs (26, 41).

Despite the complexity of assessing foodborne arsenic exposures, the estimates for global burden of cancers caused by the estimated range of exposures appear feasible. We found that human exposures to iAs through food is substantial (see Table 2) and can be roughly comparable with lower levels of arsenic in drinking water. It was reasonable to convert the data from that of Morales et al (29) to dietary consumption, and calculate slope factors for lung and bladder cancer to estimate risk of foodborne iAs. Using this data set reduces the concern about issues of low-dose extrapolations of arsenic’s carcinogenic effects; although, the estimates would be improved by including additional epidemiological studies that focus on low dose consumption. A recent review (17) emphasized the need for such studies on bladder and lung cancer that address adequacy of the sample size, as well as the synergistic relationship of arsenic and smoking, duration of arsenic exposure, age when exposure began and ended, and histologic subtype of cancer (17). This review observed that many recent studies that examine the risk ratio of bladder cancer from low arsenic concentration (<100ug/L) drew cases and controls from arsenic-endemic areas that may reduce the difference in arsenic exposure, requiring a larger sample size to determine whether an excess risk exists for a given exposure. The potential for arsenic from smoking and the different patterns for smoking worldwide to confound the risk estimates attributable to food consumption would likely be true for lung cancer estimates as well. Additionally, exposure misclassification probably further reduced the difference between groups and epidemiological studies focused on low-arsenic levels have a greater need to control for confounders (17).

The estimated global burden for arsenic induced bladder and lung cancers is highest for both males and females in cluster G for several possible reasons. First, cluster G comprises of countries in Asia where the arsenic content in the bedrock ranks among the highest in the world. This translates into high overall rate of exposure to arsenic through more than one route of exposure and on a consistent basis for an extended period- thus pre-disposing this population to develop arsenic induced cancers. Second, rice is the main food consumed in most of the countries in Cluster G. As depicted in table 3, rice contributes up to 68.1% of iAs exposure in cluster G countries. Perhaps related to the first reason or type of cultivar, rice grown in cluster G may contain higher levels of arsenic than rice grown elsewhere (38, 39). Finally, the population size is a chief component in our model for the estimation of the disease burden. Cluster G comprises nearly 50% of the world population with inclusion of China and India. For this reason, although the percentage of arsenic via rice is high in cluster L countries as well (up to 65.8%), this does not reflect in a high global burden of disease for this cluster owing to its small population size. Moreover, other recent studies have also reported rapidly rising cancer incidence and high cancer mortality rates in China and India contributing to a major portion of global cancer burden (42, 43).

In conclusion, the results of this quantitative risk assessment indicate that consumption of arsenic in food increases the incidence of bladder, lung and skin cancer. There are limitations with the estimates that are derived from the ranges of arsenic content in food and the interactions of arsenic with other foodborne constituents. Nonetheless, the risk estimates are valuable for informing policies to reduce the global burden of disease from arsenic exposures in food.

Acknowledgments

This work was funded by the World Health Organization Foodborne Disease Burden Epidemiology Group (A. Barchowsky), the National Institute of Environmental Health Sciences (R01ES0138781, A. Barchowsky) and the National Cancer Institute of the National Institutes of Health (R01CA153073, F. Wu).

Abbreviations and Definitions

EPA

United States Environmental Protection Agency

FAOSTAT

Food and Agricultural Organization Statistical database

GBD

Global burden of disease

GEMS

Global Environmental Monitoring Survey

IARC

International Agency for Research on Cancer

iAs

inorganic arsenic

IRIS

Integrated Risk Information System

JECFA

Joint FAO/WHO Expert Committee on Food Additives

PTWI

Provisional tolerable weekly intake

WHO

World Health Organization

Footnotes

The authors declare no competing financial interests.

References

  • 1.International Agency for Research on Cancer. A review of human carcinogens. Part C: Arsenic, metals, fibres, and dusts/ IARC Working Group on the Evaluation of Carcinogenic Risks to Humans (2009: Lyon, France) IARC monographs on the evaluation of carcinogenic risks to humans ; v. 100C. (Lyon, France) 2012 Available from: http://monographs.iarc.fr/ENG/Monographs/vol100C/index.php. [PMC free article] [PubMed] [Google Scholar]
  • 2.Chen Y, Graziano JH, Parvez F, Liu M, Slavkovich, Kalra T, et al. Arsenic exposure from drinking water and mortality from cardiovascular disease in Bangladesh: prospective cohort study. BMJ. 2011;342:d2431. doi: 10.1136/bmj.d2431. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Moon K, Guallar E, Navas-Acien A. Arsenic exposure and cardiovascular disease: an updated systematic review. Curr Atheroscler Rep. 2012;14:542–555. doi: 10.1007/s11883-012-0280-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Parvez F, Chen Y, Brandt-Rauf PW, Slavkovich V, Islam T, Ahmed A, et al. A prospective study of respiratory symptoms associated with chronic arsenic exposure in Bangladesh: findings from the Health Effects of Arsenic Longitudinal Study (HEALS) Thorax. 2010;65:528–533. doi: 10.1136/thx.2009.119347. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.von Ehrenstein OS, Mazumder DN, Yuan Y, Samanta S, Balmes J, Si A, et al. Decrements in lung function related to arsenic in drinking water in west bengal, India. Am.J.Epidemiol. 2005;162:533–541. doi: 10.1093/aje/kwi236. [DOI] [PubMed] [Google Scholar]
  • 6.Schoof RA, Yost LJ, Eickhoff J, Crecelius EA, Cragin DW, Meacher DM, et al. A Market Basket Survey of Inorganic Arsenic in Food. Food and Chemical Toxicology. 1999;37:839–846. doi: 10.1016/s0278-6915(99)00073-3. [DOI] [PubMed] [Google Scholar]
  • 7.Kile ML, Houseman EA, Breton CV, Smith T, Quamruzzaman Q, Rahman M, et al. Dietary arsenic exposure in bangladesh. Environ Health Perspect. 2007;115:889–893. doi: 10.1289/ehp.9462. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Davis MA, Mackenzie TA, Cottingham KL, Gilbert-Diamond D, Punshon T, Karagas MR. Rice consumption and urinary arsenic concentrations in U.S. children. Environ Health Perspect. 2012;120:1418–1424. doi: 10.1289/ehp.1205014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Samal AC, Kar S, Bhattacharya P, Santra SC. Human exposure to arsenic through foodstuffs cultivated using arsenic contaminated groundwater in areas of West Bengal, India. Journal of Environmental Science and Health, Part A. 2011;46:1259–1265. doi: 10.1080/10934529.2011.598810. [DOI] [PubMed] [Google Scholar]
  • 10.Dittmar J, Voegelin A, Maurer F, Roberts LC, Hug SJ, Saha GC, et al. Arsenic in Soil and Irrigation Water Affects Arsenic Uptake by Rice: Complementary Insights from Field and Pot Studies. Environmental Science & Technology. 2010;44:8842–8848. doi: 10.1021/es101962d. [DOI] [PubMed] [Google Scholar]
  • 11.Biswas A, Biswas S, Santra SC. Risk from winter vegetables and pulses produced in arsenic endemic areas of Nadia District: field study comparison with market basket survey. Bull Environ Contam Toxicol. 2012;88:909–914. doi: 10.1007/s00128-012-0569-z. [DOI] [PubMed] [Google Scholar]
  • 12.Muñoz O, Diaz OP, Leyton I, Nuñez N, Devesa V, Súñer MA, et al. Vegetables Collected in the Cultivated Andean Area of Northern Chile: Total and Inorganic Arsenic Contents in Raw Vegetables. J Agric Food Chem. 2001;50:642–647. doi: 10.1021/jf011027k. [DOI] [PubMed] [Google Scholar]
  • 13.Signes A, Mitra K, Burló F, Carbonell-Barrachina AA. Effect of cooking method and rice type on arsenic concentration in cooked rice and the estimation of arsenic dietary intake in a rural village in West Bengal, India. Food Additives & Contaminants: Part A. 2008;25:1345–1352. doi: 10.1080/02652030802189732. [DOI] [PubMed] [Google Scholar]
  • 14.World Health Organization. Guidlines for Drinking Water Quality. Geneva, Switzerland: 2011. Arsenic in drinking water, background document for development of WHO. Available from: http://www.who.int/water_sanitation_health/dwq/chemicals/arsenic.pdf. [Google Scholar]
  • 15.Abernathy CO, Thomas DJ, Calderon RL. Health Effects and Risk Assessment of Arsenic. The Journal of Nutrition. 2003;133:1536S–1538S. doi: 10.1093/jn/133.5.1536S. [DOI] [PubMed] [Google Scholar]
  • 16.European Food Safety Association. EFSA Panel on Contaminants in the Food Chain (CONTAM): Scientific Opinion on Arsenic in Food. EFSA J. 2009;7:60–71. [Google Scholar]
  • 17.Gibb H, Haver C, Gaylor D, Ramasamy S, Lee JS, Lobdell D, et al. Utility of recent studies to assess the National Research Council 2001 estimates of cancer risk from ingested arsenic. Environ Health Perspect. 2011;119:284–290. doi: 10.1289/ehp.1002427. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Smith AH, Goycolea M, Haque R, Biggs ML. Marked increase in bladder and lung cancer mortality in a region of Northern Chile due to arsenic in drinking water. American Journal of Epidemiology. 1998;147:660–669. doi: 10.1093/oxfordjournals.aje.a009507. [DOI] [PubMed] [Google Scholar]
  • 19.Smith AH, Ercumen A, Yuan Y, Steinmaus CM. Increased lung cancer risks are similar whether arsenic is ingested or inhaled. J Expo Sci Environ Epidemiol. 2009;19:343–348. doi: 10.1038/jes.2008.73. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Ferreccio C, Yuan Y, Calle J, Benitez H, Parra RL, Acevedo J, et al. Arsenic, tobacco smoke, and occupation: associations of multiple agents with lung and bladder cancer. Epidemiology. 2013;24:898–905. doi: 10.1097/EDE.0b013e31829e3e03. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Begum M, Horowitz J, Hossain MI. Low-Dose Risk Assessment for Arsenic: A Meta-Analysis Approach. Asia Pac J Public Health. 2012 doi: 10.1177/1010539512466568. [DOI] [PubMed] [Google Scholar]
  • 22.Christoforidou EP, Riza E, Kales SN Hadjistavrou K, Stoltidi M, Kastania AN, et al. Bladder cancer and arsenic through drinking water: a systematic review of epidemiologic evidence. J Environ Sci Health A Tox Hazard Subst Environ Eng. 2013;48:1764–1775. doi: 10.1080/10934529.2013.823329. [DOI] [PubMed] [Google Scholar]
  • 23.Hughes MF, Beck BD, Chen Y, Lewis AS, Thomas DJ. Arsenic Exposure and Toxicology: A Historical Perspective. Toxicological Sciences. 2011;123:305–332. doi: 10.1093/toxsci/kfr184. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Wu MM, Kuo TL, Hwang YH, Chen CJ. Dose-response relation between arsenic concentration in well water and mortality from cancers and vascular diseases. American Journal of Epidemiology. 1989;130:1123–1132. doi: 10.1093/oxfordjournals.aje.a115439. [DOI] [PubMed] [Google Scholar]
  • 25.Hsueh YM, Cheng GS, Wu MM, Yu HS, Kuo TL, Chen CJ. Multiple risk factors associated with arsenic-induced skin cancer: effects of chronic liver disease and malnutritional status. Br J Cancer. 1995;71:109–114. doi: 10.1038/bjc.1995.22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Joint FAO/WHO Expert Committtee on Food Additives (JECFA) WHO Food Additives Series: 63. Geneva, Switzerland: FAO JECFA Monographs; 2011. Safety evaluation of certain contaminants in food. Available from: http://whqlibdoc.who.int/trs/who_trs_959_eng.pdf. [Google Scholar]
  • 27.EFSA. EFSA panel on contaminants in the food chain (contam): Scientific opinion on arsenic in food. EFSA J. 2009;7:60–71. [Google Scholar]
  • 28.World Health Organization. Global Environment Monitoring System-Food Contamination Monitoring and Assessment Programme (GEMS/Food) Geneva, Switzerland: 2006. Available from: http://www.who.int/foodsafety/chem/gems/en/index1.html. [Google Scholar]
  • 29.Morales KH, Ryan L, Kuo TL, Wu MM, Chen CJ. Risk of internal cancers from arsenic in drinking water. Environ.Health Perspect. 2000;108:655–661. doi: 10.1289/ehp.00108655. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.United States Environmental Protecion Agency Integrated Risk Information System (IRIS) Arsenic, inorganic. Washington DC: 1998. Available from: http://www.epa.gov/iris/subst/0278.htm. [Google Scholar]
  • 31.Tseng WP, Chu HM, How SW, Fong JM, Lin CS, Yeh S. Prevalence of skin cancer in an endemic area of chronic arsenicism in Taiwan. J Natl Cancer Inst. 1968;40:453–463. [PubMed] [Google Scholar]
  • 32.Tseng WP. Effects and dose--response relationships of skin cancer and blackfoot disease with arsenic. Environ Health Perspect. 1977;19:109–119. doi: 10.1289/ehp.7719109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Yost LJ, Schoof RA, Aucoin R. Intake of inorganic arsenic in the North American diet. Human and Ecological Risk Assessment. 1998;4:137–152. [Google Scholar]
  • 34.Hall MN, Gamble MV. Nutritional manipulation of one-carbon metabolism: effects on arsenic methylation and toxicity. J Toxicol. 2012;2012:595307. doi: 10.1155/2012/595307. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Chen Y, Hall M, Graziano JH, Slavkovich V, van Geen A, Parvez F, et al. A prospective study of blood selenium levels and the risk of arsenic-related premalignant skin lesions. Cancer Epidemiol Biomarkers Prev. 2007;16:207–213. doi: 10.1158/1055-9965.EPI-06-0581. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Kurzius-Spencer M, Burgess JL, Harris RB, Hartz V, Roberge J, Huang S, et al. Contribution of diet to aggregate arsenic exposures-An analysis across populations. J Expo Sci Environ Epidemiol. 2014;24:156–162. doi: 10.1038/jes.2013.37. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Liu Y, Wu F. Global burden of aflatoxin-induced hepatocellular carcinoma: a risk assessment. Environ Health Perspect. 2010;118:818–824. doi: 10.1289/ehp.0901388. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Meharg AA, Williams PN, Adomako E, Lawgali YY, Deacon C, Villada A. Geographical variation in total and inorganic arsenic content of polished (white) rice. Environ Sci Technol. 2009;43:1612–1617. doi: 10.1021/es802612a. [DOI] [PubMed] [Google Scholar]
  • 39.Carey AM, Lombi E, Donner E, de Jonge MD, Punshon T, Jackson BP, et al. A review of recent developments in the speciation and location of arsenic and selenium in rice grain. Anal Bioanal Chem. 2012;402:3275–3286. doi: 10.1007/s00216-011-5579-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Diaz OP, Leyton I, Munoz O, Nunez N, Devesa V, Suner MA, et al. Contribution of water, bread, and vegetables (raw and cooked) to dietary intake of inorganic arsenic in a rural village of Northern Chile. J Agric Food Chem. 2004;52:1773–1779. doi: 10.1021/jf035168t. [DOI] [PubMed] [Google Scholar]
  • 41.Uneyama C, Toda M, Yamamoto M, Morikawa K. Arsenic in various foods: cumulative data. Food Addit Contam. 2007;24:447–534. doi: 10.1080/02652030601053121. [DOI] [PubMed] [Google Scholar]
  • 42.Goss PE, Strasser-Weippl K, Lee-Bychkovsky BL, Fan L, Li J, Chavarri-Guerra Y, et al. Challenges to effective cancer control in China, India, and Russia. Lancet Oncol. 2014;15:489–538. doi: 10.1016/S1470-2045(14)70029-4. [DOI] [PubMed] [Google Scholar]
  • 43.Collingridge D, et al. Three countries-half of the global cancer burden. Lancet Oncol. 2014;15:483. doi: 10.1016/S1470-2045(14)70107-X. [DOI] [PubMed] [Google Scholar]

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