Association between urinary arsenic and diabetes mellitus in the Korean general population according to KNHANES 2008

Science of the Total Environment 409 (2011) 4054–4062

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Science of the Total Environment j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / s c i t o t e n v

Association between urinary arsenic and diabetes mellitus in the Korean general population according to KNHANES 2008☆ Yangho Kim a, Byung-Kook Lee b,⁎ a b

Department of Occupational and Environmental Medicine, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Republic of Korea Institute of Environmental & Occupational Medicine, Soonchunhyang University, 646 Eupnae-ri, Shinchang-myun, Asan-si, Choongnam, 336–745 Republic of Korea

a r t i c l e

i n f o

Article history: Received 11 October 2010 Received in revised form 31 May 2011 Accepted 1 June 2011 Available online 1 July 2011 Keywords: Diabetes Arsenic Hypertension Exposure

a b s t r a c t Introduction: We present data from the Korean National Health and Nutrition Examination Survey (KNHANES) 2008 on the associations between urinary arsenic and diabetes mellitus in a representative sample of the adult Korean population. Methods: This study was based on data obtained in KNHANES 2008, which was conducted for three years (2007–2009) using a rolling sampling design involving a complex, stratified, multistage, probability-cluster survey of a representative sample of the noninstitutionalized civilian population of South Korea. Results: Geometric means of total urinary arsenic concentration in females and total participants with diabetes mellitus were significantly higher than in participants without diabetes mellitus after adjustment for covariates, including age, seafood consumption, body mass index (BMI), hypertension, area of residence, regional area, education level, and smoking and drinking status. Multiple regression analysis after similar adjustment showed that total urinary arsenic concentration was associated with diabetes status in the females and total participants. In addition, after similar adjustment, the odds ratios (ORs) for diabetes mellitus in female participants and all participants were 1.502 (95% CI, 1.038–2.171) and 1.312 (95% CI, 1.040–1.655), respectively, for doubling of the level of urinary total arsenic concentration. Conclusion: This study showed an association between total urinary arsenic concentration and the prevalence of diabetes mellitus in a representative sample of the adult population, especially women, with environmental arsenic exposure after adjustment for seafood intake and relevant diabetes risk factors. © 2011 Elsevier B.V. All rights reserved.

1. Introduction Increasing rates of type 2 diabetes mellitus in the USA and worldwide suggest that the condition is related to environmental factors (Longnecker and Daniels, 2001). Arsenic is abundant in the Earth's crust and can be released into groundwater under certain conditions. It has been estimated that 13 million Americans have been exposed to public water supplies with 10–50 μg/L arsenic (US EPA 2000, 2001). Chronic arsenic intoxication is still a major health problem in some areas in which arsenic exposure is endemic, especially in Asia, such as Taiwan, China, and Bangladesh (Tseng et al., 2002; Xia and Liu, 2004). In epidemiological studies from Taiwan, Bangladesh, and Mexico, high chronic exposure to inorganic arsenic in drinking water (N100 μg/L) was shown to be associated with diabetes mellitus (Chen et al., 2007; Coronado-González et al., 2007; Lai et al., 1994; Navas-Acien et al. 2006;

☆ The authors declare that there are no conflicts of interest. ⁎ Corresponding author at: Institute of Environmental and Occupational Medicine, College of Medicine, Soonchunhyang University, 646 Eupnae-ri, Shinchang-myun, Asan, Choongnam 336–745, Republic of Korea. Tel.: + 82 41 530 1760. E-mail address: [email protected] (B.-K. Lee). 0048-9697/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.scitotenv.2011.06.003

Rahman et al., 1998; Tseng et al., 2000; Wang et al., 2003). High chronic exposure to inorganic arsenic in occupational settings was also related to higher levels of glycated hemoglobin, a marker of blood glucose levels (Jensen and Hansen, 1998). However, the effect of lower levels of exposure to inorganic arsenic on diabetes risk is controversial (Lamm et al., 2006; Lewis et al., 1999; Longnecker and Daniels, 2001; Navas-Acien et al. 2006; Navas-Acien et al. 2008; Ruiz-Navarro et al., 1998; Wang et al., 2007; Zierold et al., 2004). Therefore, it is important to perform studies in general populations with low arsenic exposure to evaluate the associations between urinary arsenic and diabetes mellitus. In contrast to some Asian countries, such as Taiwan, China, and Bangladesh, there are no regions in which arsenic exposure is endemic in Korea. However, Korea is a mountainous country, and there are approximately 930 closed metal mines across South Korea, which were abandoned without strict environmental controls. Arsenic is a major contaminant around closed metal mines, and concerns regarding environmental arsenic exposure in the vicinity of these closed metal mines have recently been raised in Korea (Jung and Jung, 2006; Ko et al., 2003). The Third Korean National Health and Nutrition Examination Survey (KNHANES III), performed in 2005, included human biomonitoring of blood lead, cadmium, and mercury levels for the first time

Y. Kim, B.-K. Lee / Science of the Total Environment 409 (2011) 4054–4062

in an Asian region to provide national estimates of the blood levels of selected heavy metals (Kim and Lee, 2010, 2011). The second-year survey of KNHANES IV (KNHANES 2008) (Korean Ministry of Health and Welfare, 2010) included blood manganese and urinary arsenic in addition to the three heavy metals included in the previous study. KNHANES 2008 yielded national estimated data for the five heavy metals stratified by demographic and lifestyle characteristics using a representative national sample. Here, we present data from KNHANES 2008 regarding the associations between urinary arsenic and diabetes mellitus in a representative sample of the adult Korean population. 2. Methods 2.1. Design and data collection This study was performed using data obtained in KNHANES 2008, which was the second year of the ongoing KNHANES IV 2007–2009 survey. KNHANES IV was conducted for three years (2007–2009) using a rolling sampling design that involved a complex, stratified, multistage, probability cluster survey of a representative sample of the noninstitutionalized civilian population in South Korea. The survey was performed by the Korean Ministry of Health and Welfare and had three components: the health interview survey, the health examination survey, and the nutrition survey. The target population of the survey was all noninstitutionalized civilian Korean individuals aged 1 year or older. The survey employed stratified multistage probability sampling units based on geographic area, sex, and age, which were determined based on the household registries of the 2005 National Census Registry, the most recent 5-year national census in Korea. The survey sample pool ultimately consisted of 264,186 primary sampling units, each consisting of approximately 60 households. For KNHANES 2008, 200 sampling units were randomly selected from the 264,186 primary sampling units encompassing the target population in Korea, with 20–23 households selected from each primary sampling unit to yield 4600 households. The field survey in KNHANES 2008 was conducted by specially trained interviewers during the entire year of 2008 at mobile centers and in the participants' households. The health interview and health examination surveys were performed in specially designed and equipped mobile centers, which traveled to locations throughout the country. These surveys were completed by 9308 participants (74.3% of the total target population of 12,528). Eighty-two percent (8641) of the total eligible target population of 10,528 completed the nutrition survey. The interviewer was not given any information about specific participants before performing the interviews, and all participants provided written consent to participate in the study. A total of 7108 adults completed the health examination survey. Ten participants were randomly selected from each of the 200 sampling units according to sex and six age groups (20–29, 30–39, 40–49, 50–59, 60–69, and ≥70 years and older), yielding a total of 2005 participants for measurement of the five heavy metals as part of ongoing biomonitoring of the general adult population. As 324 of these participants did not complete the nutrition survey, 1681 participants were included in this study. Four participants whose total urinary arsenic levels were extremely high (≥1000 μg/g creatinine) were excluded from the analysis; therefore, the final total number of participants included in this study was 1677. Age as reported at the time of the health interview was categorized into six groups as described above. Education level was categorized into three groups: below high school, high school, and college or higher. Residence area was categorized into urban areas (administrative divisions of a city) and rural areas (not classified as administrative divisions of a city). Regional area was categorized into six geographic areas: Seoul, Incheon/Kyunggi/Kangwon, Daejeon/Choongchung, Daegu/Kyungbuk, Busan/Kyungnam, and Kwangju/Honam/Jeju.

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Information about smoking and alcohol consumption was collected during the health interview survey. Smoking status was divided into three categories: current smoker, past smoker, and nonsmoker. Alcohol consumption was assessed by questioning the participants about their drinking behavior during the month prior to the interview. The participants were asked about their average frequency (days per month) of alcoholic beverage consumption and amount (in milliliters) of alcoholic beverages ingested on a single occasion. The responses were converted into the amount of pure alcohol (in grams) consumed per day. Alcohol consumption status was categorized into four groups according to average daily alcohol consumption: nondrinker, light drinker (1–15 g), moderate drinker (16–30 g), and heavy drinker (N30 g). Information about the frequency of seafood consumption, including fish, shellfish, and seaweed, was obtained from the nutrition survey of KNHANES 2008, which was performed separately on different dates after the health examination. The nutrition survey listed eleven types of seafood that are consumed most frequently in Korea: mackerel, tuna, yellow fish, pollock, anchovy, seafood paste, squid, clam, pickled seafood, brown seaweed, and laver. The overall consumption frequency was categorized into three groups based on the consumption of at least one type of seafood on the nutrition survey checklist: less than once a week, once a week, and more than once a week. Diabetes mellitus was defined as a fasting serum glucose level ≥126 mg/dL, a self-reported physician diagnosis of diabetes mellitus, or self-reported use of insulin or oral hypoglycemic medication. Hypertension was defined as a diastolic blood pressure ≥90 mmHg, systolic blood pressure ≥140 mmHg, or self-reported use of antihypertensive medication. Body mass index (BMI) was calculated by dividing measured weight in kilograms by measured height in meters squared. 2.2. Clinical laboratory Fasting blood samples were taken in the morning after at least an 8-h fast. Blood samples were centrifuged, refrigerated at the examination site, and transferred in iceboxes to a central laboratory in Seoul on the day on which they were obtained. Plasma glucose level was measured using an autoanalyzer (Hitachi 7600 autoanalyzer; Hitachi, Tokyo, Japan). 2.3. Determination of arsenic in urine Spot urine samples for total arsenic in urine were collected at the time of health checkups. Total arsenic in urine was measured in 0.1mL samples of urine by graphite furnace atomic absorption spectrometry with Zeeman background correction (Perkin Elmer AAS 600; Perkin Elmer, Singapore) (Nixon et al., 1991). Total urinary arsenic was calculated according to urine creatinine level. Urinary arsenic metabolites were measured in 200 participants with total urinary arsenic concentration. All analyses of total urinary arsenic were carried out by Neodin Medical Institute (NMI), a laboratory certified by the Korean Ministry of Health and Welfare. For internal quality assurance and control, commercial reference materials were obtained from Bio-Rad (Lyphochek ® Whole Blood Metals Control; Bio-Rad, Hercules, CA). The coefficient of variation was within 0.57%–3.20% for 3 reference samples of total arsenic (reference values: 66.0, 160.0, and 278.0 μg/L). As part of external quality assurance and control, the institute passed the German External Quality Assessment Scheme operated by Friedrich-Alexander University and also passed the Quality Assurance Program operated by the Korea Occupational Safety and Health Agency. The institute also held a certified license from the Ministry of Labor as one of the designated laboratories for special chemicals, including heavy metals and certain organic chemicals. The method detection limit for total arsenic in urine

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in the present study was 2.584 μg/L. None of the urine samples had arsenic levels below the limit of detection. Urine arsenic species (arsenite, arsenate, monomethylarsonate [MMA], dimethylarsinate [DMA], and arsenobetaine) were measured by inductively coupled plasma-mass spectrometry using highperformance liquid chromatography (Perkin-Elmer ELAN DRC-e, Series 200 HPLC; Perkin-Elmer, Singapore) (Reuter et al., 2003).

2.4. Statistical analysis Statistical analyses were performed using SAS (Version 9.22; SAS Institute, Cary, NC) and SUDAAN (Release 10.0; Research Triangle Institute, Research Triangle Park, NC), a software package that incorporates sample weights and adjusts analyses for the complex sample design of the survey. Survey sample weights were used in all analyses to produce estimates that were representative of the noninstitutionalized civilian Korean population. The levels of total urinary arsenic were log-transformed because their distributions were skewed, and the unadjusted geometric mean (GM) [95% confidence interval (CI)] and selected percentiles were calculated by sex, age group, BMI, smoking status, drinking status, seafood consumption, area of residence, regional area, education level, diabetic status, and hypertensive status using the Proc Descript function in SUDAAN. To compare the GMs of the total urinary arsenic levels among different groups according to demographics, seafood consumption, hypertensive status, and diabetic status while controlling for covariates (age, sex, smoking status, BMI, drinking status, area of residence, and regional area), adjusted GMs and 95% CIs were calculated by analysis of covariance (ANCOVA) calculated by the Proc Regress function.

Next, logarithmically transformed total urinary arsenic concentration was regressed against age group and diabetic status after adjusting for sex, BMI, smoking and drinking status, seafood consumption, education level, residential area, and regional area using multiple regression analysis (Proc Regress, SUDAAN) to examine whether these variables were independently associated with total urinary arsenic. Further regression analyses were performed separately by sex. The beta coefficient and 95% CI are presented as exponentiated values providing ratio. To test the statistical significance of each categorical variable as an independent variable, the Wald χ 2 test with Satterthwaite correction was also performed. Odds ratio (OR) and 95% CI values for having diabetes mellitus were calculated based on the log-transformed total urinary arsenic level (base 2), age, BMI, smoking status, drinking status, and the frequency of seafood consumption while controlling for covariates (sex, residential area, regional area, and education level) using the Proc Rlogist function to incorporate the sample weights and adjust the analyses for the complex sample design of the survey. The OR and 95% CI values for having diabetes mellitus were calculated based on the log-transformed total urinary arsenic level (base 2), before (model 1) and after adjusting for age, sex, BMI (model 2), smoking status, drinking status, residential area, regional area, education level, hypertensive status (model 3), and the frequency of seafood consumption (model 4). 3. Results The total urinary arsenic concentrations of the study participants are listed by age, residential area, and regional area in Table 1. Unadjusted and adjusted GMs of total urinary arsenic levels calculated by ANCOVA are presented in Table 1 along with their 95% CIs. Overall, the GMs of total urinary arsenic levels of female participants (n = 891),

Table 1 Geometric mean (GM) and 95% confidence interval (CI) values of total arsenic in urine (μg/g creatinine) by age group, type of residence area and regional area. Classification variable

Females N

All subjects, age 20+ 891 years Age group 20-29§ 165

95%CI

GM

95%CI

128.4– 145.5

133.5

125.8– 141.6

786 99.7

85.9

76.1– 95.7 98.5– 121.7 132.3– 168.0 154.7– 194.5 168.7– 214.5

85.4

73.3– 99.5 100.8– 125.5 132.9– 166.8 152.6– 192.5 165.2– 219.3

141 59.6

53.1– 66.1 151 86.1⁎⁎ 76.3– 95.8 151 110.8⁎⁎ 97.8– 123.8 156 136.1⁎⁎ 121.8– 150.3 187 130.5⁎⁎ 117.9– 143.1

54.75– 68.65 88.2⁎⁎ 78.56– 99.08 111.4⁎⁎ 99.88– 124.3 130.7⁎⁎ 116.6– 146.5 126.3⁎⁎ 110.1– 144.9

129.5– 145.9 114.7– 154.5

598 97.7

99.0

110.1⁎⁎

40-49

180

150.2⁎⁎

50-59

184

174.6⁎⁎

60+

180

191.6⁎⁎

693

136.9

198

137.5

149

128.0

Regional area Seoul§

All subjects Adjusted#

Crude

137.0

182

Rural

N

GM

30-39

Residence area Urban§

Males Adjusted#

Crude

127.6– 146.2 115.4– 159.4

112.1– 143.8 Incheon, Kyunggi & 261 127.4 113.4– Kangwon 141.3 DaeJeon & 104 106.8 82.86– Choongchung 130.7 Daegu & Kyungbuk 101 127.3 109.9– 144.6 Pusan & Kyungnam 142⁎⁎ 169.6⁎⁎ 140.2– 199.0 Kwanju, Honam & Jeju 134⁎⁎ 176.8⁎⁎ 148.5– 204.9

112.5⁎⁎ 149.0⁎⁎ 171.4⁎⁎ 190.4⁎⁎

137.5 133.2

131.7

117.9– 146.9 123.6 110.7– 137.9 107.3 88.8– 129.6 130.6 113.2– 150.5 169.5⁎⁎ 146.3– 196.4 172.3⁎⁎ 149.6– 198.4

GM

188 108.7

130 90.1

95%CI

GM

95%CI

94. 5– 104.9

102.3

96.51– 108.5

92.2– 103.1 92.1– 125.3

79.4– 100.7 226 91.3 81. 6– 101.1 100 90.9 77.9– 103.8 84 97.2 84.3– 110.2 126 117.5⁎⁎ 102.6– 132.3 120 130.3⁎⁎ 111.7– 148.9

*p b 0.05; **p b 0.01; §:reference; #: adjusted for all categorical variables in the Tables 1 and 2.

61.3

101.9

91.7

94.1– 104.2 88.7– 117.1

81.6– 103.1 90.6 82.3– 99.7 83.9 73.8– 95.3 99.6 86.0– 115.3 117.3⁎⁎ 105.5– 130.2 132.8⁎⁎ 114.9– 153.4

N

GM 1677 118.4

306 333 331 340 367

66.2– 78.3 98.5⁎⁎ 90. 7– 106.2 ⁎⁎ 129.9 118.2– 141.6 155.8⁎⁎ 142.4– 169.1 162.1⁎⁎ 148.7– 175.3

386

123.4

279

108.7

204 185 268 254

95%CI

GM

95%CI

73.7

67.2– 81.0 92.7– 108.8 119.4– 142.3 138.2– 165.0 143.1– 174.3

112.9– 123.8

72.2

1291 117.2

487

Adjusted#

Crude

111.3– 123.0 106.9– 139.9

99.0– 118.2 109.6 99.3– 119.8 98.8 83.3– 114.3 112.8 102.7– 122.8 143.4⁎⁎ 127.5– 159.3 153.7⁎⁎ 134.8– 172.4

100.5⁎⁎ 130.4⁎⁎ 151.1⁎⁎ 158.0⁎⁎

118.4 117.3

111.9

113.5– 123.4 103.9– 132.5

103.2– 121.3 106.6 98.0– 116.0 96.5 85.2– 109.3 114.4 104.3– 125.4 144.0⁎⁎ 130.9– 158.3 153.2⁎⁎ 136.8– 171.4

Y. Kim, B.-K. Lee / Science of the Total Environment 409 (2011) 4054–4062

male participants (n = 786), and all participants (n = 1677) representing adult Koreans aged ≥20 years were 137.0 μg/g creatinine (95% CI, 128.4–145.5 μg/g creatinine), 99.7 μg/g creatinine (95% CI, 94.5– 104.9 μg/g creatinine), and 118.4 μg/g creatinine (95% CI, 112.9– 123.8 μg/g creatinine), respectively. The total urinary arsenic levels were significantly higher in female participants and in older participants (40 and over) than in males and younger participants (20– 39 years), respectively, in both crude and adjusted analyses. There were no significant differences in GM between urban and rural area, whereas significant differences in GM were observed among regional areas, with the highest GM concentration of total urinary arsenic in Kwangju or Honam, which are close to the southwestern coast, and Jeju area, which is a large island. Crude GM analysis of total urinary arsenic revealed significant differences in GM according to educational level, but these differences disappeared in adjusted GM analysis (Table 2). In the adjusted GM analysis of total urinary arsenic according to smoking and drinking

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status, there were differences in GM in current smokers and heavy drinkers compared with nonsmokers and nondrinkers, respectively. Although there were no differences in GM of total urinary arsenic according to seafood consumption frequency in the crude analysis, the GM of total urinary arsenic in the moderate- and high-consumption groups was significantly higher than in the low-frequency group in adjusted analyses of male and all participants. The low BMI group showed significant low GM of total urinary arsenic compared with normal and high BMI groups, but the differences disappeared in the adjusted analysis. The GM of total urinary arsenic level in diabetics was significantly higher than that in normal subjects in females and all participants. On the other hand, the GM of total urinary arsenic in hypertensives was significantly higher than that in normal subjects, but after adjustment for covariates, only the male hypertensives had a significantly higher GM of total urinary arsenic compared with normal subjects.

Table 2 Geometric mean (GM) and 95% confidence interval (CI) values of arsenic in urine (ug/g_cr) by classification of study variables. Classification variable

Females N

Males Adjusted#

Crude GM

95%CI

158.6– 193.3 283 133.1⁎⁎ 120.3– 145.9 259 103.1⁎⁎ 93.7– 112.5

137.8

122.4– 155.1 128.2– 153.3 117.9– 146.5

239 130.6

764 140.6

131.5– 149.7 86.2– 139.4 96.4– 143.3

137.9

130.0– 146.2 105.8– 147.6 110.6– 158.5

147 102.3

134.5– 162.5 121.4– 152.4 111.2– 147.5 105.9– 136.3

131.2

119.6– 144.0 118.2– 144.1 127.1– 162.7 137.2– 175.4

146 121.3

108.4– 159.6 120.1– 151.6 128.3– 147.5

114.9

96.00– 137.5 124.8– 153.8 130.8– 147.0

90

80.3– 119.5 Normal (18.5 = b BMI 578 129.6⁎⁎ 120.8– b25.0) 138.4 High (BMI = b25.0) 249 168.8⁎⁎ 148.2– 189.3 Diabetes § No 812 131.3 123.4– 139.1 Yes 79 208.8⁎⁎ 167.7– 249.8 Hypertension No§ 696 127.1 118.3– 135.9 Yes 195 179.8⁎⁎ 160.0– 199.5

125.1

103.0– 152.0 125.8– 142.7 132.1– 165.1

31

High school College and more Smoking status Never smoked§

349 176.0

Past smoker

60

112.8

Current smoker

66

119.9

Drinking status No drink§

328 148.6

Mild drink

269 137.0

Moderate drink

143 129.4

Heavy drink

151 121.1

Seafood consumption Less than once a week§ 75 Once a week

134.1

265 135.9

More than once a week 551 138.0 Body mass index (BMI) Low (BMI b 18.5)§

51

100.0

All subjects Adjusted#

Crude

GM

Education level Less than high school§

95%CI

N

95%CI

121.1

579

146.1– 169.5 117.2⁎⁎ 108.2– 126.2 92.6⁎⁎ 86.6–98.7

111.5– 131.6 111.6– 128.4 106.6– 122.6

103.2– 128.2 97.86– 114.9 81.49– 95.21

911

133.5

124.9

401

125.3– 141.6 115.2⁎⁎ 106.2– 124.0 91.3⁎⁎ 84.2–98.4

85.38– 108.4 79.00– 103.2 86.24– 105.7 97.22– 112.4

474

140.4

129.3– 151.3 123.1 111.3– 134.9 113.1⁎⁎ 100.1– 126.0 102.4⁎⁎ 95.3– 109.5

112.3

69.23– 97.33 99.4⁎⁎ 90.91– 108.5 103.1⁎⁎ 97.28– 109.2

165

106.4

98.7

490

118.9

59.3– 97.50 476 103.5⁎⁎ 96.6– 110.4 271 96.0⁎⁎ 87.8– 104.2

88.2

73.5– 105.8 96.4– 109.8 88.6– 102.9

82

76.19– 106.6 1054 117.6⁎⁎ 111.3– 123.7 520 125.0⁎⁎ 115.0– 134.9

106.9

126.9– 141.4 165.9⁎ 137.6– 200.0

709 97.6

92.2– 103.1 102.6– 149.9

98.9

93.96– 104.0 89.37– 128.8

1521 114.5

116.7

156

134.8⁎

136.8

576 93.0

87.35– 98.67 210 123.2⁎⁎ 110.9– 135.4

96.6

91.06– 102.3 99.07– 120.6

1272 110.5

116.4

405

124.6

131.4

125.0 132.4

130.6 143.9 155.2

138.6 138.7

134.1 147.7

134.0

136.0

127.9– 146.2 121.5– 152.3

227 100.1⁎ 320 85.0⁎⁎

303 115.7 335 86.4

119 96.0⁎⁎ 105 93.6⁎⁎ 416 96.5⁎⁎

87.6

225 100.2 471 101.8

77

95%CI

GM

95%CI

119.5– 141.7 89.2– 111.0 77.6–92.3

103.3

93.08– 114.7 90.42– 109.9 90.00– 105.3

588

89.5– 115.0 106.3– 124.9 79.1–93.8

115.1

107.1– 135.4 83.4– 108.6 80.1– 107.0 89.1– 103.8

96.2

70.3– 104.9 90.9– 109.4 95.2– 108.4

82.1

Adjusted#

Crude

GM

140.3

GM

N

78.4

126.3⁎

*p b 0.05; **p b 0.01; §:reference; #:adjusted for all categorical variables in the Tables 1 and 2.

99.7 97.4

106.0 88.1⁎⁎

90.3 95.5 104.6

102.9 95.5

107.3

109.3⁎

GM

510

363

388 248 567

95%CI

157.8

1022 120.1

91.1– 121.7 109.1– 128.6 113.4– 126.7

91.4

109.3– 119.6 168.1⁎⁎ 144.3– 191.8 105.1– 115.8 149.2⁎⁎ 137.3– 161.0

119.8 114.4

118.1– 132.1 119.0 110.1– 128.5 103.0⁎⁎ 95.4– 111.2 104.2– 121.0 110.0 101.2– 119.4 119.0 109.1– 129.7 129.3⁎⁎ 120.7– 138.5

86.5– 112.6 119.2⁎⁎ 110.6– 128.3 120.9⁎⁎ 115.6– 126.4

118.6 119.7

92.8– 123.2 113.0– 124.3 111.7– 128.1 112.1– 121.4 118.4– 153.2 111.3– 121.6 115.3– 134.5

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Table 3 Species analysis of urinary arsenic metabolites in 200 participants. Unit: μg/g creatinine. Species

Arsenite Arsenate MMA

Concentration 1.20 ±1.11

2.64 ±1.17

2.56 ±1.10

DMA

Arsenobetaine Total arsenic

25.66 ±1.09

71.00 ± 1.11

116.72 ±1.10

GM ± GSD, As; arsenic, MMA; monomethyl arsonic acid, DMA; dimethyl arsenic acid.

Species analysis of arsenic in the urine of 200 participants with total urinary arsenic concentrations showed that the concentrations of arsenite, arsenate, MMA, DMA, and arsenobetaine were 1.2, 2.6, 2.6, 25.7, and 71.0 μg/g creatinine, respectively (Table 3). The diabetics were significantly older than the normal subjects among males and females. BMI among the diabetics was significantly higher than in the normal subjects among females. The proportion of smokers among diabetics was significantly lower than among the normal subjects for both males and females. The proportion of drinkers among diabetics was significantly lower than among normal female subjects. The proportion of diabetics with hypertension was significantly higher than in the normal subjects for both men and women. However, seafood consumption among the diabetics was not different from that in the normal subjects for both males and females (Table 4). To evaluate the associations of total urinary arsenic with diabetic status and other selected categorical variables in multiple regression analysis after controlling for covariates, including smoking status, drinking status, BMI, educational level, residence area, and regional area, the beta coefficient and its 95% CI were presented as exponentiated values in all participants (Table 5) and in female and male participants separately (Table 6). The sex and age groups were the most significant predictors of the increase in log-transformed total urinary arsenic. The beta coefficients and 95% CIs as exponentiated values for diabetics based on the log-transformed total urinary arsenic levels were 1.154 (1.014–1.314) and 1.238 (1.025–1.494) in all participants and female participants, respectively. By contrast, the beta coefficient of hypertensives for log-transformed total urinary arsenic was significant only in male participants.

The OR and 95% CI values for having diabetes mellitus with logtransformed total urinary arsenic and other related categorical variables after adjustment of covariates are listed in Table 7. Logtransformed total urinary arsenic (base 2) showed ORs of 1.502 and 1.312 in females and all participants, respectively, suggesting that doubling of total urinary arsenic increases the risk of diabetes mellitus by 1.502- and 1.312-fold, respectively. BMI was a predictor of diabetes mellitus in all participants only, and not in female or male participants separately. The ORs increased with age in all analyses, and statistically significant differences were only observed in males and all participants. Smoking was found to be a predictor of diabetes mellitus in all participants and male participants, but not in female participants. However, seafood consumption was not found to be a predictor of diabetes mellitus. The log-transformed total urinary arsenic levels (base 2) gave ORs for diabetes of 1.500 and 1.293 in females and all participants, respectively, after adjusting for age, sex, and BMI (Table 8). The OR for diabetes remained significant after further adjusting for smoking status, drinking status, hypertensive status, education level, regional area, and residential area. The OR of diabetes for log urinary arsenic before and after adjusting for seafood intake was not significantly different. 4. Discussion Previous studies in Taiwan, Bangladesh, and Mexico showed that high levels of inorganic arsenic in the drinking water are associated with increased risk of type 2 diabetes (Chen et al., 2007; Lai et al., 1994; Navas-Acien et al. 2006; Rahman et al., 1998; Tseng et al., 2000; Wang et al., 2003). However, only a few studies have examined the association between low-level exposure to inorganic arsenic and diabetes risk, and the results reported to date are conflicting (Chen et al., 2010; Coronado-González et al., 2007; Lewis et al., 1999; NavasAcien et al. 2008; Ruiz-Navarro et al., 1998; Zierold et al., 2004). In a cross-sectional study of 1185 residents in Wisconsin, USA, Zierold et al. (2004) calculated ORs for diabetes mellitus of 1.4 (95% CI, 0.8–2.3) and 1.1 (95% CI, 0.5–2.2) for arsenic exposure levels of 2–10 μg/L and N10 μg/L, respectively, with the referent group b2 μg/L. Compared with that of the general population in Utah, USA, Lewis et al. (1999) found that the standard mortality ratio for diabetes mellitus was not

Table 4 Characteristics of study variables by diabetic status and sex. Female N Age (years) Body mass index (kg/m2) Education level Less than high school High school College and more Smoking status Never smoked Past smoker Current smoker Drinking status No drink Mild drink Moderate drink Heavy drink Seafood consumption Less than once a week Once a week More than once a week Hypertension No Yes

Male Non-diabetic (n = 812)

Diabetic (n = 79)

p-value

41.7 ± 0.5 22.8 ± 0.1

59.4 ± 2.3 25.0 ± 0.4

p b 0.01 p b 0.01 p b 0.01

349 283 259

292(81.7) 268(95.9) 252(97.0)

57(18.3) 15(4.1) 7(3.0)

764 60 66

693(90.4) 55(89.0) 63(97.4)

71(9.6) 5(11.0) 3(2.6)

328 269 143 151

287(85.4) 244(91.3) 139(98.1) 142(95.0)

41(14.6) 25(8.7) 4(1.9) 9(5.0)

75 265 551

68(89.0) 241(90.6) 503(91.2)

7(11.0) 24(9.4) 48(8.8)

696 195

663(95.6) 149(73.7)

33(4.4) 46(26.3)

N

Non-diabetic (n = 709)

Diabetic (n = 77)

p-value

41.8 ± 0.5 23.8 ± 0.1

54.3 ± 1.6 24.4 ± 0.4

p b 0.01 NS p b 0.01

239 227 320

200(85.3) 211(94.8) 298(93.6)

39(14.7) 16(5.2) 22(6.4)

147 303 335

141(96.4) 270(91.2) 297(90.3)

6(3.6) 33(8.8) 38(9.7)

146 119 105 416

122(84.8) 105(91.4) 97(94.4) 385(93.6)

24(15.2) 14(8.6) 8(5.6) 3(6.4)

90 225 471

79(88.9) 201(92.8) 429(92.0)

11(11.1) 24(7.2) 42(8.0)

576 210

533(93.8) 176(86.1)

43(6.2) 34(13.9)

p b 0.05

p b 0.05

p b 0.01

NS

NS

NS

p b 0.01

t-test for age and BMI, X2 test for categorical variables, mean ± SD, (%).

p b 0.01

Y. Kim, B.-K. Lee / Science of the Total Environment 409 (2011) 4054–4062 Table 5 Multiple linear regression model of association of log-transformed total urine arsenic with selected study variables in all subjects after adjustment of covariates.b Independent variables Sex Male Female Age groups 20–29 30–39

Beta coefficient (95% CI) a

p-value S-waite Adj p-value for St-test B = 0 Chisq waite ChiSq

1.000 1.304 (1.194– 1.424)

0.0000

1.000 1.362 (1.220– 1.521) 40–49 1.768 (1.563– 2.000) 50–59 2.048 (1.772– 2.367) 60~ 2.142 (1.851– 2.477) Seafood consumption Less than once 1.000 a week Once a week 1.207 (1.034– 1.409) M o r e t h a n 1.224 (1.065– once a week 1.408) Diabetes No. 1.000 Yes 1.154 (1.014– 1.314) Hypertension No. 1.000 Yes 1.070 (0.982– 1.167)

35.6719

0.0000

115.8852

0.0000

0.0000 0.0000 0.0000 0.0000

8.5435

0.0134

together, the findings of the present study showed an association between total urinary arsenic concentration and the prevalence of diabetes mellitus in a representative sample of the adult population. This study had several important strengths. First, the study was performed in a representative sample of the general Korean population. Second, diabetes mellitus was diagnosed according to standard procedures. Third, relevant diabetes risk factors and seafood intake were adjusted. Fourth, a rigorous quality control of study procedures in KNHANES was assured. Animal and in vitro model systems have indicated that arsenic exposure can potentially increase the risk of diabetes mellitus through inhibition of insulin-dependent glucose uptake (Walton et al. 2004) and insulin signaling (Paul et al. 2007), impairment of insulin secretion and transcription in pancreatic beta cells (Diaz-Villasenor Table 6 Multiple linear regression model of association of log-transformed total urine arsenic with selected study variables by sex after adjustment of covariates.b Independent variables

0.0173 0.0047

4.7860

0.0287

0.0300

0.0071

0.9330

0.1210

a

Exponentiated values to provide ratio. Covariates: smoking & drinking status, body mass index, educational level, residential area, regional area. b

elevated among members of a Mormon community in Millard County, Utah, with b200 μg/L of arsenic in the drinking water. In a small casecontrol study in southern Spain, 38 participants with diabetes mellitus had similar total urinary arsenic concentrations (mean level 3.44 μg/ L) compared with 49 control participants (mean level 3.68 μg/L), but the study was not adjusted for diabetes risk factors or for markers of seafood intake (Ruiz-Navarro et al., 1998). In a case-control study in Mexico, the ORs for diabetes mellitus were 1.9 (95% CI, 1.1–3.4) and 2.7 (95% CI, 1.5–4.6) for groups with total urinary arsenic levels of 64– 104 μg/L and N104 μg/L, respectively (Coronado-González et al., 2007). In a recent cross-sectional study from NHANES, Navas-Acien et al. (2008) reported that the OR for diabetes mellitus was 3.6 (95% CI, 1.2–10.8) when they compared participants at the 80th percentile with those at the 20th percentile for urinary arsenic (16.5 vs. 3.0 μg As/L). However, in a very recent cross-sectional study in Bangladesh, Chen et al. (2010) did not find an association between arsenic exposure in drinking water and a significantly increased risk of diabetes mellitus. In this study, GMs of total urinary arsenic concentration in the females and total participants with diabetes mellitus were significantly higher than in participants without diabetes mellitus after adjustment for covariates, including age, seafood consumption, BMI, hypertension, residential area, regional area, education level, and smoking and drinking status. Multiple regression analyses after similar adjustment showed that total urinary arsenic concentrations were associated with diabetes status in the females and all participants. In addition, after similar adjustment, the ORs for diabetes mellitus in female participants and all participants were 1.502 (95% CI, 1.038–2.171) and 1.312 (95% CI, 1.040–1.655), respectively, for the level of urinary total arsenic concentration. Thus, the doubling of total urinary arsenic increased the risk of diabetes mellitus by 1.502- and 1.312-fold in females and all participants, respectively. Taken

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Female subjects Age groups 20–29 30–39

Beta coefficient (95% CI) a

1.000 1.317 (1.130– 1.536) 40–49 1.744 (1.461– 2.083) 50–59 2.007 (1.606– 2.508) 60~ 2.230 (1.764– 2.819) Seafood consumption Less than once 1.000 a week Once a week 1.205 (0.985– 1.476) M o r e t h a n 1.206 (0.998– once a week 1.459) Diabetes No. 1.000 Yes 1.238 (1.025– 1.494) Hypertension No. 1.000 Yes 0.994 (0.865– 1.144) Male subjects Age groups 20–29 1.000 30–39 1.438 (1.226– 1.687) 40–49 1.817 (1.555– 2.123) 50–59 2.132 (1.794– 2.532) 60~ 2.060 (1.684– 2.520) Seafood consumption Less than once 1.000 a week Once a week 1.210 (1.005– 1.456) M o r e t h a n 1.255 (1.044– once a week 1.509) Diabetes No. 1.000 Yes 1.085 (0.894– 1.316) Hypertension No. 1.000 Yes 1.132 (1.004– 1.276)

a

p-value S-waite Adj p-value for St-test B = 0 Chisq waite ChiSq

55.4027

0.0000

3.8429

0.1464

5.0212

0.0251

0.0062

0.9375

72.2674

0.0000

6.8939

0.0294

0.6911

0.4058

4.1990

0.0405

0.0005 0.0000 0.0000 0.0000

0.0701 0.0532

0.0263

0.9376

0.0000 0.0000 0.0000 0.0000

0.0433 0.0155

0.4069

0.0420

Exponentiated values to provide ratio. Covariates: smoking & drinking status, body mass index, educational level, residential area, regional area. b

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Table 7 Odds ratio (OR) and 95% CI values for having diabetes with log-transformed total arsenic (base 2) in urine (μg/g creatinine) and other related categorical variables after adjustment of covariates#. Independent variables

Females Odds Ratio

Log-transformed total urine 1.502 arsenic (μg/g creatinine) Body mass index (kg/m2) 1.090 Age group 20–29 30–39

1.000 5.309

40–49

3.077

50–59

4.021

60+

6.920

Smoking status Never smoked Past smoker Current smoker Drinking status No Drink Mild drink

1.000 2.098 0.342

1.000 0.979

Moderate drink

0.233

Heavy drink

1.191

Seafood consumption Less than once a week Once a week More than once a week

1.000 1.407 1.188

Male 95%CI

Odds Ratio

All subjects 95%CI

Odds Ratio

1.038– 1.126 2.171 0.979– 1.125 1.213

0.803– 1.312 1.577 0.800– 1.312 1.577

1.000 4.644

1.000 5.104

0.773– 36.45 0.349– 27.11 0.427– 37.81 0.703– 68.03

0.489– 44.02 15.960 1.642– 155.1 19.390 2.081– 180.6 35.630 3.373– 376.3

95%CI 1.040– 1.655 1.040– 1.655

1.164– 22.36 7.544 1.701– 33.44 8.900 1.896– 41.75 16.880 3.344– 85.23

1.000 0.770– 1.983 5.708 0.069– 4.336 1.687

1.000 0.709– 1.433 5.543 1.470– 2.226 12.78

0.742– 2.765 1.209– 4.099

1.000 0.507– 0.581 1.886 0.064– 0.454 0.852 0.416– 0.511 3.402

1.000 0.248– 0.820 1.361 0.160– 0.347 1.284 0.235– 0.650 1.108

0.487– 1.378 0.155– 0.772 0.375– 1.124

1.000 0.457– 0.561 4.331 0.410– 0.632 3.436

1.000 0.214– 0.886 1.467 0.252– 0.853 1.582

0.443– 1.769 0.423– 1.72

et al. 2006), and modification of the expression of genes involved in insulin resistance (Diaz-Villasenor et al. 2006). However, the concentrations used in most mechanistic experiments performed to date were very high. Individual variability in detoxification capability, nutritional status, and interactions with other trace elements could also influence the susceptibility of arsenic-exposed subjects to diabetes mellitus (Tseng, 2004). For a detailed discussion of the potential biological mechanisms of arsenic-induced diabetes mellitus, see Tseng (2004). Taken together, our findings support an association between exposure to inorganic arsenic and diabetes risk. Humans may be exposed to organic arsenic compounds and inorganic arsenic compounds. Contaminated drinking water and food, especially rice, are the main sources of inorganic arsenic, such as arsenite and arsenate (Smedley and Kinniburgh 2002; Schoof et al. 1999; Focazio et al., 2000; Yost et al., 1998), and organic arsenic such as arsenobetaine and arsenosugars are mainly derived from seafood (Francesconi and Edmonds, 1997; Cullen and Reimer, 1989). Inorganic arsenic compounds are metabolized to MMA and DMA and excreted in the urine together with unchanged inorganic arsenic (Aposhian and Aposhian 2006). Thus, the sum of inorganic arsenic, MMA, and DMA has commonly been used as a biomarker of inorganic arsenic exposure (ACGIH, 2007). Most fish and shellfish are rich in arsenobetaine, which is excreted rapidly unchanged in urine, contributing to the total urinary arsenic level. Seaweed and some seafood, such as scallops and mussels, are also rich in arsenosugars, which are metabolized to several compounds (mainly DMA) that also contribute to total urinary arsenic levels (Francesconi et al. 2002; Raml et al., 2005). Therefore, not only inorganic arsenic, but also

Table 8 Odds ratio (OR) and 95% CI value for having diabetes with log-transformed total arsenic (base 2) (μg/g creatinine). Model Model Model Model Model

1 2 3 4

Female

Male

1.914 1.500 1.513 1.502

1.425 1.056 1.116 1.126

(1.423–2.573) (1.051–2.139) (1.048–2.185) (1.038–2.171)

All subjects (1.083–1.876) (0.744–1.501) (0.797–1.561) (0.803–1.577)

1.659 1.293 1.306 1.312

(1.374–2.004) (1.025–1.630) (1.037–1.646) (1.040–1.655)

Model 1: crude OR. Model 2: adjusted with age, sex, and BMI. Model 3: adjusted for smoking status, drinking status, educational level, hypertensive status, regional area, and residential area as well as the factors in model 2. Model 4: adjusted for seafood consumption as well as the factors in model 3.

organic arsenic from seafood contributes to the total urinary arsenic level. Consequently, the total urinary arsenic concentration, including arsenobetaine, may show much greater differences between western countries and Japan or Korea. We found that greater seafood consumption increased the total urinary arsenic level which was attributable to organic arsenic and inorganic arsenic, in the Korean population. Arsenic concentrations in the general population, taken as the sum of inorganic arsenic, MMA, and DMA in the urine, were reported to be approximately 10 μg/L in European countries and the USA, whereas the concentration was around 50 μg/L in Japan (ACGIH, 2007). Some of the difference between western countries and Japan may be due to differences in consumption of seaweeds and some types of seafood, such as scallops and mussels, which are rich in arsenosugars that are metabolized to DMA (Yamauchi et al., 2004; Hata et al., 2007). Recently, Cascio et al. (2011) found that rice is an important contributor of DMA, especially in populations with high rice consumption. Therefore, inorganic arsenic concentration or monomethyl arsenic acid may be more useful for assessing inorganic arsenic exposure than is the sum of inorganic arsenic, MMA, and DMA (Hata et al., 2007). Recently Tseng et al. (2005) found that individuals with a higher arsenic exposure and a lower capacity to methylate inorganic arsenic to DMA were at increased risk of developing peripheral arterial disease in the blackfoot disease-hyperendemic area of Taiwan. The arsenic methylation capacity may also be an important predictor of clinical disease, including peripheral arterial disease. However, the DMA/MMA ratio as an indicatior of methylation capacity in populations consuming large quantities of rice should be applied with caution, since variation in the quantity and type of rice eaten may alter this ratio (Cascio et al., 2011). In this study, data on the arsenic species related to inorganic arsenic exposure were available for only 200 participants. GM of total urinary arsenic concentration was 118 μg/g creatinine in all participants. This value is close to the 149 μg/g creatinine found in 248 normal healthy Japanese subjects (controls) in whom the mean concentrations of inorganic arsenic, MMA, DMA, and trimethylarsenate were 3.6, 2.0, 40.0, and 103.0 μg/g creatinine, respectively (Yamauchi et al., 2004). Species analysis of arsenic in the urine of 200 participants with a total urinary arsenic concentration showed that the arsenite, arsenate, MMA, DMA, and arsenobetaine levels were 1.2, 2.6, 2.6, 25.7, and 71.0 μg/g creatinine, respectively. Therefore, organic arsenic mainly contributes to the total urinary arsenic concentration in the Korean general population as in Japan (Hata et al., 2007; Yamauchi et al., 2004). The total urinary arsenic concentrations in our study were much higher than the 7.1 μg/L or 8.24 μg/g creatinine reported in NHANES (Caldwell et al., 2009; Navas-Acien et al., 2008). DMA (25.7 μg/g creatinine) and arsenobetaine (71.01 μg/g creatinine) in our study were much higher than the 3.69 or 1.54 μg/g creatinine, respectively, reported in NHANES (Caldwell et al., 2009). This striking difference was due mainly to the remarkably different pattern of diet in Korea or Japan compared to that in the USA. Korean and Japanese people consume much larger amounts of seafood and seaweeds than do people in Western countries in their everyday meals (Hata et al., 2007; Yamauchi et al., 2004). Species analysis of

Y. Kim, B.-K. Lee / Science of the Total Environment 409 (2011) 4054–4062

urinary arsenic in 200 participants with total urinary arsenic concentrations showed a mean inorganic arsenic concentration of 3.8 μg/g creatinine. Our inorganic arsenic level was higher than that in NHANES, where 96.1% of arsenite and 93.5% of arsenate were below the limits of detection (1.2 μg/L for arsenite, 1.0 μg/L for arsenate, respectively) (Navas-Acien et al. 2008). The present findings with regard to inorganic arsenic suggested that the general Korean population may be exposed to higher levels of inorganic arsenic than are people in the USA. Whereas seafood intake contributes markedly to total urinary arsenic levels in Korea and Japan (Hata et al., 2007; Yamauchi et al., 2004), it contributes very little to total urinary arsenic level in regions where arsenic exposure is endemic, such as Bangladesh. Arsenobetaine and arsenocholine accounted for only 3% of total urinary arsenic in Bangladesh (Chen et al., 2010), which is remarkably lower than the value of 69.1% in Japan (Yamauchi et al., 2004). The GMs of total urinary arsenic concentration in women, men, and all participants clearly increased with age in the present study. These finding were similar to those in normal healthy Japanese subjects reported previously (Hata et al., 2007; Yamauchi et al., 2004). Yamauchi et al. (2004) found that four species of arsenic (inorganic arsenic, MMA, DMA, and trimethylarsenate) and the total arsenic in urine increased with increasing age in these subjects. Compared with the 20- to 29-year-old group, those over 40 years old showed approximately two- to threefold increases in the levels of DMA, trimethylarsenate and total arsenic concentrations in urine (Yamauchi et al., 2004). Consumption of marine foods containing significant levels of organic arsenic compounds may account for some of the differences with age. In Korea, rapid industrialization or urbanization has changed the dietary patterns in young people from the traditional diet including consumption of high percentages of marine foods. The reason for the remarkable increase in arsenic levels in urine with age in this control population is not clear. The GMs of total urinary arsenic concentration in women were higher than those in men in our study. This sex difference was also reported by Yamauchi et al. (2004); they showed that the sum of urinary inorganic arsenic, MMA, and DMA concentrations in female subjects was significantly higher than that in males (51.7 vs. 39.9 μg/g creatinine), although the difference in total urinary arsenic concentration was not significant (159 vs. 139 μg/g creatinine). It is not clear whether this sex difference is due to differences in dietary pattern or metabolism between men and women. Further studies are required to determine the underlying causes of the observed sex differences in urinary arsenic concentrations. Our study had some limitations. First, we did not perform species analysis of arsenic and could not identify the proportions of inorganic or organic arsenic in total urinary arsenic. Instead, we adjusted for seafood consumption as the main sources of organic arsenic. We found that greater seafood consumption increased the total urinary arsenic level. However, the relationship of seafood consumption with diabetes was not observed in the Korean population. Further, the ORs of diabetes based on the log urinary arsenic level before and after adjusting for seafood intake were not significantly changed. Therefore, the prevalence of diabetes mellitus may be associated with inorganic arsenicals rather than organic arsenicals from seafood out of the total urinary arsenic, in the general Korean population, although organic arsenics such as arsenosugars or arsenobetaine may contribute to the total arsenic concentration in Koreans. A future study in which arsenic speciation is determined will be required to establish the association between inorganic arsenic and diabetes. Second, family history is a very important risk factor for diabetes, however, these data were not available in KNHANES 2008. Third, this study was cross-sectional and the temporal relationship between urinary arsenic levels and development of diabetes mellitus cannot be completely ensured. It is unknown whether diabetes mellitus alters the excretion and metabolism of arsenic. A prospective study, in

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which arsenic levels are determined before the development of disease, will be required to establish the causal nature of these associations. In conclusion, we found a positive association between total urinary arsenic and the prevalence of diabetes mellitus in a general population, especially women, with arsenic exposure after adjusting for seafood intake and relevant diabetes risk factors. References American Conference of Governmental Industrial Hygienists (ACGIH). Documentation of the Threshold Limit Values and Biological Exposure Indices. 7th Ed. Cincinnati, OH: ACGIH; 2007. Aposhian HV, Aposhian MM. Arsenic toxicology: five questions. Chem Res Toxicol 2006;19(1):1–15. Caldwell KL, Jones RL, Verdon CP, Jarrett JM, Caudill SP, Osterloh JD. Levels of urinary total and speciated arsenic in the US population: National Health and Nutrition Examination Survey 2003–2004. J Expo Sci Environ Epidemiol 2009;19(1):59–68. Cascio C, Raab A, Jenkins RO, Feldmann J, Meharg AA, Haris PI. The impact of a rice based diet on urinary arsenic. J Environ Monit 2011;13(2):257–65. Chen CJ, Wang SL, Chiou JM, Tseng CH, Chiou HY, Hsueh YM, et al. Arsenic and diabetes and hypertension in human populations: a review. Toxicol Appl Pharmacol 2007;222(3):298–304. Chen Y, Ahsan H, Slavkovich V, Peltier GL, Gluskin RT, Parvez F, et al. No association between arsenic exposure from drinking water and diabetes mellitus: a crosssectional study in Bangladesh. Environ Health Perspect 2010;118(9):1299–305. Coronado-González JA, Del Razo LM, Garcia-Vargas G, Sanmiguel-Salazar F, Escobedode la Peña J. Inorganic arsenic exposure and type 2 diabetes mellitus in Mexico. Environ Res 2007;104(3):383–9. Cullen WR, Reimer KJ. Arsenic speciation in the environment. Chem Rev 1989;89(4): 713–64. Diaz-Villaserior A, Sanchez-Soto MC, Cebrian ME, Ostrosky-Wegman P, Hiriart M. Sodium arsenite impairs insulin secretion and transcription in pancreatic betacells. Toxicol Appl Pharmacol 2006;214(1):30–4. 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