Environmental Research 130 (2014) 14–19
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Associations between blood mercury levels and subclinical changes in liver enzymes among South Korean general adults: Analysis of 2008–2012 Korean national health and nutrition examination survey data Heun Lee a, Yangho Kim a, Chang-Sun Sim a, Jung-O Ham b, Nam-Soo Kim c, Byung-Kook Lee d,n a
Department of Occupational and Environmental Medicine, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan 682-060, South Korea Department of Occupational and Environmental Medicine, Soonchunhyang University Hospital, Cheonan 330-721, South Korea Institute of Environmental & Occupational Medicine, Soonchunhyang University, Asan 336-745, South Korea d Korean Industrial Health Association, Hyesan Bldg., #1490-32 Seocho-3-Dong, Seocho-Gu, Seoul 153-801, South Korea b c
art ic l e i nf o
a b s t r a c t
Article history: Received 1 September 2013 Received in revised form 10 November 2013 Accepted 15 January 2014 Available online 10 February 2014
Introduction: We herein used data from the Korean National Health and Nutritional Examination Survey (KNHANES) 2008–2012 to examine the associations between blood mercury levels and subclinical changes of liver function in a representative sample of the adult Korean population. Methods: This study was based on data obtained from KNHANES, in which a rolling sampling design was used to perform a complex, stratified, multistage probability cluster survey of a representative sample of the non-institutionalized civilian population in South Korea. The associations between subclinical hepatic changes and blood mercury levels were assessed after adjustment for various demographic and lifestyle factors. Results: Multivariate linear regression analyses revealed that each doubling of blood mercury increased serum aspartate transaminase (AST) by 0.676 U/L and serum alanine transaminase (ALT) by 1.067 U/L. The mean differences (95% CI) in serum AST and ALT between the lowest and highest quartiles were statistically significant at 1.249 (0.263–2.235) U/L and 2.248 (0.648–3.848), respectively. Logistic regression analysis showed that the odd ratios for having serum AST and ALT levels above the median were statistically significant in both the models according to the increase of blood mercury. The risks of having serum AST and ALT levels higher than the median among subjects in 4th quartile of blood mercury were 1.524 and 1.947, respectively. Discussion: The present findings show that subclinical changes of liver function are associated with blood mercury levels. This is the first study to show an association between blood mercury levels and mild liver dysfunction, as a possible proxy measure of nonalcoholic fatty liver disease (NAFLD), in Asian population. & 2014 Elsevier Inc. All rights reserved.
Keywords: Liver Mercury Fatty
1. Introduction Mercury, which is a well-known toxic metal, may be found worldwide in the environment, natural products, and industrial activities, such as medicine, agriculture and manufacturing (ATSDR, 1999; WHO, 1990). Workers could be exposed by inhalation of elemental or inorganic mercury, but the major source of mercury in the general population is organic mercury found in the diet (especially seafood). Elemental and inorganic mercury exposure may also arise through dental amalgams, thermometers, batteries, pesticides, mines, incineration plants, and so on.
n
Corresponding author. Fax: þ82 2586 2412. E-mail address: [email protected] (B.-K. Lee).
0013-9351/$ - see front matter & 2014 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.envres.2014.01.005
Several animal studies have suggested that acute inorganic mercury exposure can affect the liver (Ashe et al., 1953; Jonker et al., 1993; NTP, 1993; Rana and Boora, 1992; Ung et al., 2010), but only limited information is available regarding the hepatic effects of mercury exposure in humans. Autopsies of four adults and four infants who died from methyl mercury poisoning in Iraq in 1972 revealed fatty changes in the most of the livers (Al-Saleem and Clinical Committee on Mercury Poisoning, 1976). Other information came from a case study describing the acute poisoning of a young child who was exposed to vapors from the heating of an unknown quantity of mercury (Jaffe et al., 1983). Hepatocellular effects were noted, including biochemical changes (e.g., elevated serum alanine aminotransferase (ALT), ornithine carbamyltransferase, and serum bilirubin levels) and decreased synthesis of hepatic coagulation factors. Similarly, hepatomegaly and central
H. Lee et al. / Environmental Research 130 (2014) 14–19
lobular vacuolation were observed in a man who died following acute-duration exposure to high levels of elemental mercury vapors (Kanluen and Gottlieb, 1991; Rowens et al., 1991). However, the prevalence of liver disease in a population from the Minamata area was not found to be significantly increased compared to unexposed controls (Futatsuka et al., 1992). Recently, Cave et al. (2010) evaluated the association of total blood with ALT levels as a measure of liver function, and specifically the risk of NAFLD, in a representative US population sample. After adjustment for possible confounders the authors found a positive association between total blood mercury biomarkers and ALT levels in this population. However, no other studies have reported similar findings. Here, we conducted a cross-sectional study of a representative sample of the adult South Korean population using data from the Korean National Health and Nutrition Examination Survey (KNHANES)(2008–2012) to test (1) whether low-level mercury exposure is associated with mild liver dysfunction such as aspartate aminotransferase (AST) and ALT and (2) which species of mercury have an association with mild liver dysfunction.
2. Materials and methods 2.1. Design and data collection This study used data obtained from the KNHANES for 2008–2012; this represented the second and third years of KNHANES IV (2007–2009) and the first and second years of KNHANES V (2010–2012). KNHANES is conducted annually, using a rolling sampling design that involves a complex, stratified multistage probability cluster survey of a representative sample of the non-institutionalized civilian population in South Korea. The survey was performed by the Korean Centers for Disease Control and Prevention of the Korean Ministry of Health and Welfare, and had three components: a health interview survey, a health examination survey, and a nutrition survey. The survey was approved by the IRB of the Korean Centers for Disease Control and Prevention (approval no. 2010-02CON-21-C and 2011-02CON-06). The target population of the survey consisted of noninstitutionalized South Korean civilians aged 1 year or older. Detailed information regarding the survey design was provided previously (Lee and Kim, 2012). Briefly, the survey consisted of three components: a health interview survey, a health examination survey, and a nutrition survey. The present cross-sectional analysis was restricted to participants Z 20 years of age who completed the health examination survey, which included blood metal measurements, and the nutrition survey. We excluded individuals with other liver diseases (e.g., viral hepatitis, liver cirrhosis and liver cancer), and analyzed a total of 6686 participants. Information on age, education, smoking history, and alcohol intake was collected during the health interview. Height and weight measurements were performed with the participants wearing light clothing and no shoes. Body mass index (BMI) was calculated as weight in kilograms divided by the square of the height in meters. Obesity was categorized into three groups: lean (BMI o 18.5), normal (18.5 r BMIo 25), and obese (BMI Z 25). Age, as reported at the time of the health interview, was categorized into five groups (20–29, 30–39, 40–49, 50–59, and Z60 years). Education level was categorized into three groups: below high school, high school, and college or higher. Smoking status was divided into three categories based on self-reported cigarette use: current smoker, past smoker, and neversmoker. Never-smokers had smoked o100 cigarettes in their lifetime, and participants who smoked Z 100 cigarettes were classified as past or current smokers based on their current use. Alcohol consumption was assessed by asking 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 mL) of alcoholic beverages ingested on a single occasion. The responses were converted into the amount of pure alcohol (in g) 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 ( 430 g). Information about the frequency of seafood consumption, including fish, shellfish, and seaweed, was obtained from the nutrition survey of KNHANES 2008– 2012, which was performed separately on different dates after the health examination. The nutrition survey listed nine types of seafood that are consumed most frequently in Korea: mackerel, tuna, yellow fish, pollock, anchovy, seafood paste, squid, clam, and pickled seafood. 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 (low), once a week (moderate), and more than once a week (high).
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2.2. Measurement of serum AST and ALT Blood samples were obtained in the morning following an overnight fast. The serum concentrations of AST and ALT were measured using a Hitachi automatic analyzer 7600 (Tokyo, Japan). All clinical analyses were performed by the Neodin Medical Institute (Seoul, Korea), a laboratory certified by the Korean Ministry of Health and Welfare. 2.3. Measurement of total mercury levels in whole blood To assess total mercury levels in whole blood, 3-mL blood samples were drawn into BD Vacutainers tubes containing EDTA for trace-element determination (K2 EDTA tube, Vacutainers). Blood mercury levels were measured using the goldamalgam collection method with a DMA-80 (Milestone, Bergamo, Italy) at the Neodin Medical Institute. Commercial reference materials were used for internal quality assurance and control (Lyphocheks Whole Blood Metals Control; Bio-Rad, Hercules, CA, USA). The coefficient of variation for blood mercury was 1.59–4.86% across the three reference samples. With respect to external quality assurance and control, the institute had passed both the German External Quality Assessment Scheme operated by Friedrich-Alexander University and the Quality Assurance Program operated by the Korea Occupational Safety and Health Agency. The institute was also certified by the Ministry of Employment and Labor as one of the designated laboratories for analysis of specific chemicals, including heavy metals and certain organic chemicals. The detection limit for blood mercury in the present study was 0.158 µg/L. No tested sample exhibited a value below the detection limit. Methyl mercury constitutes the largest share of total blood mercury in general populations (Clarkson and Magos, 2006), and total blood mercury is not a validated measure for inorganic mercury from sources, for which exposure is generally measured using urinary mercury biomarkers. However, KNHANES data are not available for urinary mercury. To evaluate the effect of inorganic mercury rather than organic mercury on liver enzymes, the frequency of fish consumption was utilized as an adjustment factor of organic mercury. 2.4. Statistical analysis Statistical analyses were performed using SAS software (ver. 9.3; SAS Institute, Cary, NC) and SUDAAN (Release 11.0; Research Triangle Institute, Research Triangle Park, NC), the latter of which 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 non-institutionalized civilian Korean population. The unadjusted geometric means (GMs) (95% confidence interval, CI) of blood mercury and the arithmetic means (AMs) of serum AST and ALT were calculated by gender, age group, residence location, educational level, smoking status, drinking status, BMI, frequency of fish consumption, and quartile of blood mercury, using the Proc Descript function in SUDAAN. The adjusted means (95% CI) were also obtained by analysis of covariance, with the data adjusted for all of the other variables in the tables, using the Proc Regress function of SUDAAN. Multivariate linear regression analyses were used to determine the differences (95% CI) in serum AST and ALT by blood mercury level after covariate adjustment. The log2-transformed blood mercury level was taken as a continuous independent variable due to its skewed distribution, and regressed on serum AST and ALT. The quartile of blood mercury was used as a categorical independent variable for regression on serum AST and ALT. The covariates used in these analyses were age, residence area, BMI, smoking status, drinking status, education level, obesity, and frequency of fish consumption. To calculate the odds ratios (ORs), we set cut-off values of 440 IU/L for AST, and 4 35 IU/L for ALT. Next, we set the criteria for subclinical increases of AST and ALT as being over the median values of 21.1/21.2 IU/L for men, and 17.6/13.9 IU/L for women, respectively. Using logistic regression analysis, we then determined ORs and 95% CIs for elevated AST and ALT or subclinical increases in AST and ALT by blood mercury level, after adjusting for the same covariates used in the regression analyses.
3. Results Adjusted GMs and their 95% CI for blood mercury and AMs and their 95% CI for serum AST and ALT are shown in Table 1. The adjusted GM (95% CI) of blood mercury, and the AMs (95% CI) of serum AST and serum ALT in men were 4.392 (4.250–4.535) mg/L, 24.15 (23.59–24.71) U/L, and 26.60 (25.70–27.50) U/L, respectively; these values were significantly higher than the corresponding values in women (3.632: 3.525–3.739 mg/L; 20.34: 19.89–20.80 U/L; and 18.08: 17.37–18.79 U/L, respectively). The adjusted means of blood
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Table 1 Mean and 95% confidence interval (CI) of blood mercury, serum AST and ALT of adult population by classification variables, KNHANES 2008–2012. Classification
No
Blood mercury Crude
AST Adjusted#
Crude
ALT Adjusted
Adjusted
6689
3.987 (3.894–4.080)
22.25 (21.71–22.80)
3109 3580
4.756 (4.613–4.898) 3.366 (3.273–3.460)n
4.392 (4.250–4.535) 3.632 (3.525–3.739)n
24.76 (24.19–25.33) 19.76 (19.41–20.11)n
24.15 (23.59–24.71) 20.34 (19.89–20.80)n
27.39 (26.52–28.27) 17.32 (16.70–17.94)n
26.60 (25.70–27.50) 18.08 (17.37–18.79)n
Age groups 20–29 30–39 40–49 50–59 60–69
1240 1335 1322 1347 1445
3.344 (3.219–3.469) 3.963 (3.825–4.101)nn 4.499 (4.330–4.667)nn 4.773 (4.591–4.955)nn 3.547 (3.352–3.742)nn
3.089 (2.959–3.221) 3.732 (3.603–3.869)nn 4.357 (4.208–4.513)nn 5.002 (4.816–5.196)nn 4.055 (3.853–4.267)nn
19.77 (19.05–20.48) 21.02 (20.36–21.68)n 22.73 (21.85–23.61)nn 24.30 (23.49–25.10)nn 23.44 (22.76–24.12)nn
20.00 (19.22–20.79) 20.82 (20.16–21.47)nn 22.37 (21.49–23.25)nn 23.99 (23.18–24.79)nn 24.10 (23.30–24.91)nn
20.27 22.88 23.66 24.33 20.08
21.38 (20.08–22.67) 22.83 (21.42–24.24) 22.96 (21.35–24.56) 23.51 (22.43–24.59)nn 20.58 (19.61–21.54)
Residence area Urban Rural
5287 1402
3.939 (3.833–4.044) 4.183 (3.924–4.442)
3.935 (3.838–4.034) 4.195 (3.959–4.441)
21.83 (21.48–22.17) 23.70 (22.72–24.68)nn
21.94 (21.59–22.29) 23.24 (22.27–24.21)n
21.94 (21.36–22.52) 23.49 (22.02–24.97)nn
22.06 (21.48–22.65) 23.00 (21.51–24.49)
Education level Less than high school High school College and more
3031 1465 2193
3.751 (3.619–3.882) 4.227 (4.079–4.374)nn 4.160 (4.023–4.298)nn
3.621 (3.504–3.743) 4.229 (4.083–4.379)nn 4.353 (4.208–4.504)nn
23.05 (22.56–23.54) 22.30 (21.50–23.09) 21.01 (20.49–21.54)nn
22.41 (21.86–22.96) 22.72 (21.96–23.49) 21.59 (21.02–22.16)
21.91 (21.10–22.72) 22.43 (21.43–23.43) 22.59 (21.52–23.67)nn
22.28 (21.27–23.29) 22.44 (21.50–23.38) 22.09 (21.00–23.18)
Smoking status Non-smoker Past smoker Current smoker
4436 751 1502
3.582 (3.485–3.680) 5.057 (4.765–5.350)nn 4.738 (4.559–4.917)nn
3.830 (3.724–3.939) 4.445 (4.212–4.697)nn 4.203 (4.043–4.375)nn
20.89 (20.54–21.24) 23.30 (22.50–24.10)nn 25.14 (24.18–26.10)nn
21.90 (21.47–22.32) 21.54 (20.67–22.41) 23.31 (22.37–24.25)n
19.77 (19.16–20.37) 24.92 (23.54–26.29)nn 27.53 (26.10–28.96)nn
21.81 (21.17–22.46) 21.56 (20.22–22.90) 23.70 (22.25–25.15)n
Drinking status No drink Mild drink Moderate drink Heavy drink
1628 1684 1080 2297
3.372 (3.222–3.522) 3.713 (3.581–3.845)nn 4.055 (3.882–4.227)nn 4.643 (4.481–4.804)nn
3.539 3.907 4.083 4.327
(3.404–3.680) (3.777–4.039)nn (3.919–4.250)nn (4.178–4.486)nn
21.17 (20.60–21.74) 20.66 (20.13–21.19) 21.84 (20.92–22.75) 24.09 (23.41–24.77)nn
21.45 (20.75–22.15) 21.43 (20.87–21.99) 22.08 (21.15–23.02) 23.28 (22.62–23.95)nn
20.62 (19.40–21.84) 19.22 (18.42–20.01) 21.81 (19.96–23.67) 25.55 (24.67–26.44)nn
23.09 (21.49–24.68) 21.41 (20.58–22.25)n 22.21 (20.31–24.11) 22.26 (21.34–23.19)
Obesity Lean Normal Obese
292 4280 2117
3.183 (2.975–3.392) 3.788 (3.686–3.889)nn 4.552 (4.383–4.720)nn
3.560 (3.326–3.811) 3.822 (3.724–3.919)nn 4.401 (4.250–4.553)nn
19.32 (17.96–20.68) 21.29 (20.87–21.72)nn 24.41 (23.81–25.01)nn
21.40 (20.01–22.79) 21.52 (21.09–21.94) 23.69 (23.11–24.26)nn
14.56 (13.47–15.65) 19.54 (18.88–20.20)nn 28.68 (27.68–29.69)nn
16.70 (15.46–17.94) 19.88 (19.21–20.55)nn 27.73 (26.76–28.69)nn
Frequency of fish consumption Low 556 Moderate 2214 High 3919
3.742 (3.437–4.046) 3.793 (3.663–3.922) 4.140 (4.026–4.254)
3.769 (3.507–4.051) 3.830 (3.709–3.955) 4.112 (4.006–4.216)n
23.03 (21.76–24.29) 21.92 (21.34–22.50) 22.25 (21.81–22.69)
22.22 (21.05–23.40) 22.08 (21.51–22.65) 22.28 (21.84–22.71)
21.88 (20.42–23.34) 21.80 (20.81–22.79) 22.57 (21.86–23.28)
21.41 (20.05–22.77) 22.15 (21.16–23.13) 22.43 (21.77–23.10)
Quartile of blood mercury (μg/L) 1621 1st Q¥ 2nd Q 1662 3rd Q 1676 4th Q 1730
– – – –
21.14 (20.45–21.83) 21.7 (21.09–22.30) 22.41 (21.71–23.10)n 23.60 (22.88–24.31)nn
21.55 (20.84–22.27) 21.90 (21.32–22.49) 22.41 (21.75–23.07) 22.97 (22.29–23.65)nn
19.67 (18.45–20.90) 21.90 (20.99–22.82) 23.12 (21.81–24.43)n 24.36 (23.42–25.31)nn
20.60 22.08 22.91 23.46
(19.09–21.45) (21.45–24.31)nn (22.13–25.19)nn (23.27–25.38)nn (19.33–20.84)nn
(19.31–21.88) (21.22–22.94) (21.68–24.15)n (22.58–24.33)nn
Covariates: sex, age, residence area, smoking status, drinking status, educational level, obesity, and frequency of fish consumption. Quartile¥: 1st Q ( r 3.212 for men, r 2.340 for women), 2nd Q ( 43.212–4.768 for men, 42.340– 3.290 for women), 3rd Q ( 44.768–7.052 for men, 4 3.290–4.651 for women), 4th Q ( 47.052 for men, 44.651 for women). AST; aspartate transaminase, ALT; alanine transaminase. n
po 0.05. po 0.01.
nn
H. Lee et al. / Environmental Research 130 (2014) 14–19
All Gender Men Women
#
22.21 (21.87–22.54)
Crude
H. Lee et al. / Environmental Research 130 (2014) 14–19
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Table 2 Beta coefficients and 95% CI of log-transformed blood mercury in multiple linear regression analysis in Korean adult after adjustment of covariates#. Independent Variables
Model 1 Per doubling of blood mercury level (μg/L) Model 2 Quartile of blood mercury (μg/L) 1st Q¥ 2nd Q 3rd Q 4th Q
Beta coefficients (95% CI) AST
p-value
ALT
p-value
0.676 (0.272–1.079)
0.001
1.067 (0.462–1.671)
0.000
0.000 (Ref.) 0.293 ( 0.620 to 1.210) 0.755 ( 0.380 to 1.741) 1.249 (0.263–2.235)
. 0.530 0.132 0.013
0.000 (Ref.) 1.184 ( 0.380 to 2.750) 1.950 (0.149–3.751) 2.248 (0.648–3.848)
. 0.138 0.033 0.006
#
Covariates: sex, age, residence area, smoking status, drinking status, educational level, obesity, and frequency of fish consumption. Quartile¥: 1st Q (r 3.212 for men, r 2.340 for women), 2nd Q (4 3.212–4.768 for men, 42.340–3.290 for women), 3rd Q (4 4.768–7.052 for men, 43.290–4.651 for women), 4th Q ( 47.052 for men, 44.651 for women). AST; aspartate transaminase, ALT; alanine transaminase
Table 3 Odd ratios (95% CI) for elevated AST and ALT, and AST and ALT higher than the median by blood mercury level in adult population after covariate adjustment#. Independent variables
Model 1 Per doubling of blood mercury level (μg/L) Model 2 Quartile of blood mercury (μg/L) 1st Q¥ 2nd Q 3rd Q 4th Q
Odd ratios (95% CI) Elevated AST
Elevated ALT
AST higher than the median
ALT higher than the median
1.041 (0.786–1.379)
1.004 (0.819–1.231)
1.299 (1.152–1.465)
1.448 (1.288–1.629)
1.000 (Ref.) 1.031 (0.701–1.518) 1.020 (0.703–1.480) 1.061 (0.747–1.507)
1.000 (Ref.) 0.927 (0.506–1.697) 1.013 (0.551–1.865) 0.970 (0.558–1.685)
1.000 (Ref.) 1.249 (1.035–1.506) 1.330 (1.100–1.609) 1.524 (1.256–1.849)
1.000 (Ref.) 1.485 (1.252–1.761) 1.499 (1.249–1.799) 1.947 (1.625–2.334)
#
Covariates: sex, age, residence area, smoking status, drinking status, educational level, obesity, and frequency of fish consumption. Quartile¥: 1st Q (r 3.212 for men, r 2.340 for women), 2nd Q ( 43.212 4.768 for men, 42.340–3.290 for women), 3rd Q ( 44.768–7.052 for men, 43.290–4.651 for women), 4th Q ( 4 7.052 for men, 44.651 for women). AST; aspartate transaminase, ALT; alanine transaminase.
mercury and serum ALT were significantly higher in the middle age group (50–59 years), while the adjusted mean of serum AST was significantly higher with age. While the adjusted mean of serum AST in rural residents was significantly higher than that for urban residents, there was no difference in blood mercury or serum ALT between residential areas. The adjusted means of blood mercury in non-smokers were significantly lower than those in past and current smokers, whereas the adjusted means of serum AST and ALT in nonsmokers were significantly lower than those of smokers. Drinking status was a significant predictor of increased blood mercury, and it was also a predictor of serum AST, but not ALT. Education level was also a significant predictor for blood mercury, but not serum AST or ALT. The adjusted GM of blood mercury was significantly increased according to obesity, and those of serum AST and ALT were also significantly increased according to obesity. The adjusted GM of blood mercury in the high fish-consumption groups was significantly higher than in the low fish-consumption group, whereas the adjusted means of serum AST and ALT were not different among the three groups. The AMs of serum AST and ALT were also significantly increased by each increase in the quartile of blood mercury. Multivariate linear regression analyses were used to calculate the mean differences in serum AST and ALT by blood mercury, and to determine the significance of blood mercury as a predictor of serum AST and ALT after we adjusted for sex, age, residential area, education level, smoking status, drinking status, obesity, and frequency of fish consumption (Table 2). We found that log2transformed blood mercury as a continuous independent variable was a significant predictor for serum AST and ALT; each doubling of blood mercury was associated with a 0.676 U/L increase in serum AST and a 1.067 U/l increase in serum ALT. The mean
differences (95% CI) in serum AST and ALT between lowest quartile and highest quartile were statistically significant at 1.249 (0.263– 2.235) U/l and 2.248 (0.648–3.848) U/L, respectively. Using logistic regression analysis, we calculated ORs and 95% CI values for having elevated or higher than the median serum AST and ALT for log2-transformed blood mercury and for quartiles of blood mercury after covariate adjustment (Table 3). The covariates used in the logistic regression analysis were the same as those used in the regression analysis. While the ORs for elevated serum AST and ALT were not significant for either the continuous or categorical models, the ORs for having serum AST and ALT levels above the median were statistically significant in both models according to the increase of blood mercury. The risks of having serum AST and ALT levels above the median among subjects in the 4th quartile of blood mercury were 1.524 and 1.947, respectively.
4. Discussion In the present study, the overall GM of blood mercury levels in Korean adults was 3.987 μg/L, which was much higher than the 0.814 μg/L reported in the recent US NHANES data (CDC, 2009). However, a survey of New York City residents found that the blood mercury level was highest in the Asian population at 4.11 mg/L (McKelvey et al., 2007), which is very similar to the value obtained in the present study. Total blood mercury levels are known to increase with fish consumption, which is higher in Korea compared with Western countries (Jarup, 2003; Mozaffarian, 2009). Inorganic mercury exposure such as tooth amalgam treatment or other environmental exposure, could also contribute to these differences.
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H. Lee et al. / Environmental Research 130 (2014) 14–19
Most of the existing case reports have shown hepatic injury following high-level exposure to mercury via inhalation or ingestion (Al-Saleem and Clinical Committee on Mercury Poisoning 1976; Jaffe et al., 1983; Kanluen and Gottlieb, 1991; Murphy et al., 1979; Rowens et al., 1991; Samuels et al., 1982). However, the prevalence of liver disease in a population from the Minamata area was not significantly increased when compared to unexposed controls (Futatsuka et al., 1992). Cave et al. (2010) evaluated the association of total blood mercury (as well as blood markers of PCBs and lead) with ALT levels as a measure of liver function in a representative US population sample (US NHANES). After adjustment for possible confounders the authors found a positive association between total blood mercury biomarkers and ALT levels in this population. We also report that subclinical changes in liver function are associated with low-level exposure to mercury in a representative Korean population. Multivariate linear regression analyses showed that blood mercury was a significant predictor of serum AST and ALT. Doubling of blood mercury was associated with a 0.676 U/L increase in serum AST and a 1.067 U/L increase in serum ALT. The mean differences (95% CI) in serum AST and ALT between the lowest and highest quartiles were 1.249 (0.263–2.235) U/L and 2.248 (0.648–3.848) U/L, respectively. The odds ratios for having serum AST and ALT levels above the median were statistically significant in both the models according to the doubling of blood mercury. The risks of having serum AST and ALT levels above the median among subjects in the 4th quartile of blood mercury were 1.524 and 1.947, respectively. Taken together, our results show that increased blood mercury levels were associated with liver enzyme levels (serum ALT, in particular) in the general adult population of South Korea. The present study showed that ALT elevation was associated with BMI and middle age, but not drinking status. Clark et al. (2003) also noted that aminotransferase (AST and ALT) elevation was associated with BMI and middle age (50–59 years). In their study, aminotransferase elevation unexplained by viral hepatitis, alcohol consumption, or hemochromatosis was strongly associated with adiposity and other features of the metabolic syndrome, and thus may represent NAFLD. Cave et al. (2010) showed that total blood mercury was associated with ALT levels which is a measure of liver function, and specifically the risk of NAFLD. Clark (2006) noted that ALT elevation (above normal laboratory reference ranges) is the most common laboratory manifestation of NAFLD, and ALT elevation unexplained by viral hepatitis, ethanol, or iron overload has been used as a surrogate biomarker for NAFLD in NHANES. Diet-induced obesity probably plays the primary role in the pathogenesis of most cases of NAFLD (Cave et al., 2007), but nutrient–toxicant interactions and genetic susceptibility to environmental pollution may be important cofactors (Cave et al., 2010). Diet-induced obesity and fatty liver may decrease antioxidant defenses and impair xenobiotic metabolism and disposition, which could sensitize the liver to chemical injury (Fisher et al. 2009; Kirpich et al., 2011). Taken together, the present study suggests a possible association between low-level mercury exposure and mild liver dysfunction, and then suspected NAFLD. In the present study, the changes in aminotransferase level are smaller and subclinical (within normal laboratory reference ranges) contrasted with previous studies (Cave et al., 2010; Clark, 2006). In fact, some authors have suggested using lower laboratory cutoffs of serum ALT (Prati et al. 2002; Ruhl and Everhart 2009). Importantly, ALT may be normal in NAFLD. Thus, low-level blood mercury may pose a risk for mild liver dysfunction which is possibly linked to NAFLD even within normal laboratory reference ranges. Furthermore, our results indicate that while adjusted total blood mercury concentrations were positively associated with various measures of AST and ALT, seafood intake was not
associated with AST and ALT levels. Thus, the association of mercury biomarkers and liver enzymes may be due to exposure to inorganic mercury such as tooth amalgam treatment or other environmental exposure. Many of the animal hepatoxicity studies (Ashe et al., 1953; Jonker et al., 1993; NTP, 1993; Rana and Boora, 1992; Ung et al., 2010) and human case studies (Jaffe et al., 1983; Kanluen and Gottlieb, 1991; Rowens et al., 1991) involving inorganic exposure support our findings. Mercury undergoes extensive biliary-hepatic cycling (Dutczak and Ballatori, 1994; Clarkson and Magos, 2006). It is secreted into bile, partly reabsorbed into the portal circulation, and from there returns to the liver. The high mobility of mercury in the body is attributed to the formation of water-soluble mercury complexes that are mainly (if not exclusively) attached to the sulfur atoms of thiol groups such as glutathione (Clarkson, 2002). The glutathione moiety is degraded in the bile duct and gall bladder to a dipeptide and finally to an L-cysteine mercury complex before entering the circulatory system (Dutczak and Ballatori, 1994). However, despite the extensive biliary-hepatic cycling of mercury and some evidence suggesting that the liver plays a role in renal tubule-mediated uptake of mercury (Zalups, 2000), little is known regarding the possible mechanism(s) of mercury-induced hepatotoxicity. In an animal experiment using zebra fish, Ung et al. (2010) found dosage-dependent lipid accumulation (as indicated by more and larger red-stained lipid vesicles) in the livers of mercurytreated fish, prompting us to speculate that mercury-induced fatty liver conditions may cause subclinical changes in liver function. They also showed that mercury-induced hepatotoxicity was triggered by oxidative stress, intrinsic apoptotic pathways, and deregulation of nuclear receptor and kinase activities (Ung et al., 2010). The present findings have toxicological implications. First, our results underscore the need to monitor and reduce mercury exposure in the general population and to clinically assess modifiable exposures in liver disease patients. Second, as the liver is mainly responsible for detoxification processes and the regulation of metabolic pathways, it is important to gain a better understanding of mercury-induced hepatotoxicity. Such information could provide new insights into metabolic disorders induced by some toxicants in liver in vivo. Third, low-level mercury exposure may have significant public health implications because low-level blood mercury may pose a risk for mild liver dysfunction, possibly linked to NAFLD, even within normal laboratory reference ranges. This study has several important strengths. First, we used a representative sample of the general South Korean population. Second, rigorous quality controls were applied to the KNHANES. However, this study also has some limitations. First, as in many of the previous reports, our associations were obtained by crosssectional analysis, barring us from determining any causality for these associations. We cannot completely exclude the possibility of reverse causality (i.e., mild liver dysfunction could have caused increased blood mercury). Mild liver dysfunction (evidenced by slightly higher liver enzymes) may impede smooth elimination of methyl mercury through the biliary-hepatic route, leading to slightly higher circulating blood mercury in those with elevated enzymes. Sheehan et al. (2012) tested the hypothesis of liver disease slowing methyl mercury elimination through the enterohepatic cycle by examining the association of viral hepatitis markers and blood mercury using the US NHANES database; these authors found significantly increased blood mercury in women with markers of hepatitis B, with more serious disease associated with higher mercury. An unknown third factor might be a common link responsible for an observed association. Prospective studies in which mercury levels are determined before the development of abnormal findings will be required to establish a causal basis for these associations. Second, the KNHANES did not include
H. Lee et al. / Environmental Research 130 (2014) 14–19
an occupation/job code, so we were unable to screen for mercuryexposure-prone jobs, such as the manufacturing of thermostats, fluorescent light bulbs, barometers, glass thermometers, and some blood pressure devices. Third, KNHANES adopted gold amalgam direct method as a standard method for blood mercury according to Korean standard method analysis which is not sensitive enough to measure low level of blood mercury as the ICP–MS method. Fourth, KNHANES measured total blood mercury only because KNHANES did not have enough funding to specify the blood mercury into organic and inorganic mercury in the survey. Methyl mercury constitutes the largest share of total blood mercury in general populations (Clarkson and Magos, 2006). Kim et al. (2012) provide estimates of the mean methyl mercury share (78.3%) of total blood mercury in three coastal cities in South Korea. Total blood mercury is a validated biomarker for methyl mercury exposure from seafood. As an adjustment factor of organic mercury, the frequency of fish consumption was utilized, and thus the effect of inorganic mercury rather than organic mercury on liver enzymes was suggested. Fifth, blood mercury is not a validated measure for inorganic mercury from sources such as dental amalgams, for which exposure is generally measured using urinary mercury biomarkers. However, KNHANES data are not available for urinary mercury. Follow up research using a urinary mercury should be undertaken in future. Finally, the exact implication of ALT for liver disease in KNHANES is unknown because hepatitis B surface antigen, hepatitis C antibody, or liver biopsies were not available in KNHANES although we suggested an association between low blood mercury levels and mild liver function, as a possible proxy measure of NAFLD. Future studies should be performed to confirm the potential role of mercury exposure in NAFLD. In conclusion, we herein demonstrate an association between low blood mercury levels and subclinical changes of liver function, as a possible proxy measure of NAFLD in the general population of South Korea. Acknowledgment This study was supported by a Grant of the Korean Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea (HI13C0713). References Agency for Toxic Substances and Disease Registry (ATSDR), 1999. Toxicological Profile for Mercury (Update). US Department of Health and Human Services. Public Health Services, Atlanta Al-Saleem, T., 1976. Clinical Committee on Mercury Poisoning, 1976. Levels of mercury and pathologic changes in patients with organomercury poisoning. Bull. World Health Organ. 53 (Suppl), S99–S104. Ashe, W., Largent, E., Dutra, F., Hubbard, D., Blackstone, M., 1953. Behavior of mercury in the animal organism following inhalation. Arch. Ind. Hyg. Occup. Med. 17, 19–43. Cave, M., Appana, S., Patel, M., Falkner, K.C., McClain, C.J., Brock, G., 2010. Polychlorinated biphenyls, lead, and mercury are associated with liver disease in American adults: NHANES 2003–2004. Environ. Health Perspect. 118, 1735–1742. Cave, M., Deaciuc, I., Mendez, C., Song, Z., Joshi-Barve, S., Barve, S., McClain, C., 2007. Nonalcoholic fatty liver disease: predisposing factors and the role of nutrition. J. Nutr. Biochem 18, 184–195. Centers for Disease Control and Prevention (CDC), 2009. Fourth National Report on Human Exposure to Environmental Chemicals. CDC. Available from: 〈http:// www.cdc.gov/exposurereport〉 (accessed 24.05.12.). Clark, J.M., 2006. The epidemiology of nonalcoholic fatty liver disease in adults. J. Clin. Gastroenterol. 40 (Suppl 1), S5–S10.
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Clark, J.M., Brancati, F.L., Diehl, A.M., 2003. The prevalence and etiology of elevated aminotransferase levels in the United States. Am. J. Gastroenterol. 98, 960–967. Clarkson, T.W., 2002. The three modern faces of mercury. Environ. Health Perspect. 110 (Suppl 1), S11–S23. Clarkson, TW, Magos, L., 2006. The toxicology of mercury and its chemical compounds. Crit. Rev. Toxicol. 36, 609–662. Dutczak, W.J., Ballatori, N., 1994. Transport of the glutathione methylmercury complex across liver canalicular membranes on reduced glutathione carriers. J. Bio. Chem. 269, 9746–9751. Fisher, C.D., Lickteig, A.J., Augustine, L.M., Oude Elferink, R.P., Besselsen, D.G., Erickson, R.P., Cherrington, N.J., 2009. Experimental nonalcoholic fatty liver disease results in decreased hepatic uptake transporter expression and function in rats. Eur. J. Pharmacol. 613, 119–127. Futatsuka, M., Kitano, T., Nagano, M., Inaoka, T., Arimatsu, Y., Ueno, T., Wakamiya, J., Miyamoto, K., 1992. An epidemiological study with risk analysis of liver diseases in the general population living in a methyl mercury polluted area. J. Epidemiol. Community Health 46, 237–240. Jarup, L., 2003. Hazards of heavy metal contamination. Br. Med. Bull. 68, 167–182. Jaffe, K.M., Shurtleff, D.B., Robertson, W.O., 1983. Survival after acute mercury vapor poisoning–role of intensive supportive care. Am. J. Dis. Child 137, 749–751. Jonker, D., Jones, M.A., van Bladeren, P.J., Woutersen, R.A., Til, H.P., Feron, V.J., 1993. Acute (24 h) toxicity of a combination of four nephrotoxicants in rats compared with the toxicity of the individual compounds. Food Chem. Toxicol. 31, 45–52. Kanluen, S., Gottlieb, C.A., 1991. A clinical pathologic study of four adult cases of acute mercury inhalation toxicity. Arch. Pathol. Lab. Med. 115, 56–60. Kim, B.G., Jo, E.M., Kim, G.Y., Kim, D.S., Kim, Y.M., Kim, R.B., Suh, B.S., Hong, Y.S., 2012. Analysis of methylmercury concentration in the blood of Koreans by using cold vapor atomic fluorescence spectrophotometry. Ann. Lab. Med. 32, 31–37. Kirpich, I.A., Gobejishvili, L.N., Bon Homme, M., Waigel, S., Cave, M., Arteel, G., Barve, S.S., McClain, C.J., Deaciuc, I.V., 2011. Integrated hepatic transcriptome and proteome analysis of mice with high-fat diet-induced nonalcoholic fatty liver disease. J. Nutr. Biochem. 22, 38–45. Lee, B.K., Kim, Y., 2012. Iron deficiency is associated with increased levels of blood cadmium in the Korean general population: analysis of 2008–2009 Korean National Health and Nutrition Examination Survey data. Environ. Res. 112, 155–163. McKelvey, W., Gwynn, R.C., Jeffery, N., Kass, D., Thorpe, L.E., Garg, R.K., Palmer, C.D., Parsons, P.J., 2007. A biomonitoring study of lead, cadmium, and mercury in the blood of New York City adults. Environ. Health Perspect. 115, 1435–1441. Mozaffarian, D., 2009. Fish, mercury, selenium and cardiovascular risk: current evidence and unanswered questions. Int. J. Environ. Res. Public Health 6, 1894–1916. Murphy, M.J., Culliford, E.J., Parsons, V., 1979. A case of poisoning with mercuric chloride. Resuscitation 7, 35–44. NTP, 1993. Toxicology and carcinogenesis studies of mercuric chloride (CAS no. 7487-94-7) in F344/N rats and B6C3F1 mice (gavage studies). National Toxicology Program, U.S. Department of Health and Human Services, Public Health Service, National Institutes of Health, Research Triangle Park. NC. NTP TR 408, NIH publication no. 91-3139. Prati, D., Taioli, E., Zanella, A., Della Torre, E., Butelli, S., Del Vecchio, E., Vianello, L., Zanuso, F., Mozzi, F., Milani, S., Conte, D., Colombo, M., Sirchia, G., 2002. Updated definitions of healthy ranges for serum alanine aminotransferase levels. Ann. Intern. Med. 137, 1–10. Rana, S.V.S., Boora, P.R., 1992. Antiperoxidative mechanisms offered by selenium against liver injury caused by cadmium and mercury in rat. Bull. Environ. Contam. Toxicol. 48, 120–124. Rowens, B., Guerrero-Betancourt, D., Gottlieb, C.A., Boyes, R.J., Eichenhorn, M.S., 1991. Respiratory failure and death following acute inhalation of mercury vapor: a clinical and histologic perspective. Chest 99, 185–190. Ruhl, C.E., Everhart, J.E., 2009. Elevated serum alanine aminotransferase and gamma-glutamyltransferase and mortality in the United States population. Gastroenterology 136, 477–485. Samuels, E.R., Heick, H.M.C., McLaine, P.N., Farant, J.P., 1982. A case of accidental inorganic mercury poisoning. J. Anal. Toxicol. 6, 120–122. Sheehan, M.C., Burke, T.A., Breysse, P.N., Navas-Acien, A., McGready, J., Fox, M.A., 2012. Association of markers of chronic viral hepatitis and blood mercury levels in US reproductive-age women from NHANES 2001–2008: a cross-sectional study. Environ. Health 11, 62. 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Associations between blood mercury levels and subclinical changes in liver enzymes among South Korean general adults: Analysis of 2008–2012 Korean national health and nutrition examination survey data Heun Lee a, Yangho Kim a, Chang-Sun Sim a, Jung-O Ham b, Nam-Soo Kim c, Byung-Kook Lee d,n a
Department of Occupational and Environmental Medicine, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan 682-060, South Korea Department of Occupational and Environmental Medicine, Soonchunhyang University Hospital, Cheonan 330-721, South Korea Institute of Environmental & Occupational Medicine, Soonchunhyang University, Asan 336-745, South Korea d Korean Industrial Health Association, Hyesan Bldg., #1490-32 Seocho-3-Dong, Seocho-Gu, Seoul 153-801, South Korea b c
art ic l e i nf o
a b s t r a c t
Article history: Received 1 September 2013 Received in revised form 10 November 2013 Accepted 15 January 2014 Available online 10 February 2014
Introduction: We herein used data from the Korean National Health and Nutritional Examination Survey (KNHANES) 2008–2012 to examine the associations between blood mercury levels and subclinical changes of liver function in a representative sample of the adult Korean population. Methods: This study was based on data obtained from KNHANES, in which a rolling sampling design was used to perform a complex, stratified, multistage probability cluster survey of a representative sample of the non-institutionalized civilian population in South Korea. The associations between subclinical hepatic changes and blood mercury levels were assessed after adjustment for various demographic and lifestyle factors. Results: Multivariate linear regression analyses revealed that each doubling of blood mercury increased serum aspartate transaminase (AST) by 0.676 U/L and serum alanine transaminase (ALT) by 1.067 U/L. The mean differences (95% CI) in serum AST and ALT between the lowest and highest quartiles were statistically significant at 1.249 (0.263–2.235) U/L and 2.248 (0.648–3.848), respectively. Logistic regression analysis showed that the odd ratios for having serum AST and ALT levels above the median were statistically significant in both the models according to the increase of blood mercury. The risks of having serum AST and ALT levels higher than the median among subjects in 4th quartile of blood mercury were 1.524 and 1.947, respectively. Discussion: The present findings show that subclinical changes of liver function are associated with blood mercury levels. This is the first study to show an association between blood mercury levels and mild liver dysfunction, as a possible proxy measure of nonalcoholic fatty liver disease (NAFLD), in Asian population. & 2014 Elsevier Inc. All rights reserved.
Keywords: Liver Mercury Fatty
1. Introduction Mercury, which is a well-known toxic metal, may be found worldwide in the environment, natural products, and industrial activities, such as medicine, agriculture and manufacturing (ATSDR, 1999; WHO, 1990). Workers could be exposed by inhalation of elemental or inorganic mercury, but the major source of mercury in the general population is organic mercury found in the diet (especially seafood). Elemental and inorganic mercury exposure may also arise through dental amalgams, thermometers, batteries, pesticides, mines, incineration plants, and so on.
n
Corresponding author. Fax: þ82 2586 2412. E-mail address: [email protected] (B.-K. Lee).
0013-9351/$ - see front matter & 2014 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.envres.2014.01.005
Several animal studies have suggested that acute inorganic mercury exposure can affect the liver (Ashe et al., 1953; Jonker et al., 1993; NTP, 1993; Rana and Boora, 1992; Ung et al., 2010), but only limited information is available regarding the hepatic effects of mercury exposure in humans. Autopsies of four adults and four infants who died from methyl mercury poisoning in Iraq in 1972 revealed fatty changes in the most of the livers (Al-Saleem and Clinical Committee on Mercury Poisoning, 1976). Other information came from a case study describing the acute poisoning of a young child who was exposed to vapors from the heating of an unknown quantity of mercury (Jaffe et al., 1983). Hepatocellular effects were noted, including biochemical changes (e.g., elevated serum alanine aminotransferase (ALT), ornithine carbamyltransferase, and serum bilirubin levels) and decreased synthesis of hepatic coagulation factors. Similarly, hepatomegaly and central
H. Lee et al. / Environmental Research 130 (2014) 14–19
lobular vacuolation were observed in a man who died following acute-duration exposure to high levels of elemental mercury vapors (Kanluen and Gottlieb, 1991; Rowens et al., 1991). However, the prevalence of liver disease in a population from the Minamata area was not found to be significantly increased compared to unexposed controls (Futatsuka et al., 1992). Recently, Cave et al. (2010) evaluated the association of total blood with ALT levels as a measure of liver function, and specifically the risk of NAFLD, in a representative US population sample. After adjustment for possible confounders the authors found a positive association between total blood mercury biomarkers and ALT levels in this population. However, no other studies have reported similar findings. Here, we conducted a cross-sectional study of a representative sample of the adult South Korean population using data from the Korean National Health and Nutrition Examination Survey (KNHANES)(2008–2012) to test (1) whether low-level mercury exposure is associated with mild liver dysfunction such as aspartate aminotransferase (AST) and ALT and (2) which species of mercury have an association with mild liver dysfunction.
2. Materials and methods 2.1. Design and data collection This study used data obtained from the KNHANES for 2008–2012; this represented the second and third years of KNHANES IV (2007–2009) and the first and second years of KNHANES V (2010–2012). KNHANES is conducted annually, using a rolling sampling design that involves a complex, stratified multistage probability cluster survey of a representative sample of the non-institutionalized civilian population in South Korea. The survey was performed by the Korean Centers for Disease Control and Prevention of the Korean Ministry of Health and Welfare, and had three components: a health interview survey, a health examination survey, and a nutrition survey. The survey was approved by the IRB of the Korean Centers for Disease Control and Prevention (approval no. 2010-02CON-21-C and 2011-02CON-06). The target population of the survey consisted of noninstitutionalized South Korean civilians aged 1 year or older. Detailed information regarding the survey design was provided previously (Lee and Kim, 2012). Briefly, the survey consisted of three components: a health interview survey, a health examination survey, and a nutrition survey. The present cross-sectional analysis was restricted to participants Z 20 years of age who completed the health examination survey, which included blood metal measurements, and the nutrition survey. We excluded individuals with other liver diseases (e.g., viral hepatitis, liver cirrhosis and liver cancer), and analyzed a total of 6686 participants. Information on age, education, smoking history, and alcohol intake was collected during the health interview. Height and weight measurements were performed with the participants wearing light clothing and no shoes. Body mass index (BMI) was calculated as weight in kilograms divided by the square of the height in meters. Obesity was categorized into three groups: lean (BMI o 18.5), normal (18.5 r BMIo 25), and obese (BMI Z 25). Age, as reported at the time of the health interview, was categorized into five groups (20–29, 30–39, 40–49, 50–59, and Z60 years). Education level was categorized into three groups: below high school, high school, and college or higher. Smoking status was divided into three categories based on self-reported cigarette use: current smoker, past smoker, and neversmoker. Never-smokers had smoked o100 cigarettes in their lifetime, and participants who smoked Z 100 cigarettes were classified as past or current smokers based on their current use. Alcohol consumption was assessed by asking 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 mL) of alcoholic beverages ingested on a single occasion. The responses were converted into the amount of pure alcohol (in g) 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 ( 430 g). Information about the frequency of seafood consumption, including fish, shellfish, and seaweed, was obtained from the nutrition survey of KNHANES 2008– 2012, which was performed separately on different dates after the health examination. The nutrition survey listed nine types of seafood that are consumed most frequently in Korea: mackerel, tuna, yellow fish, pollock, anchovy, seafood paste, squid, clam, and pickled seafood. 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 (low), once a week (moderate), and more than once a week (high).
15
2.2. Measurement of serum AST and ALT Blood samples were obtained in the morning following an overnight fast. The serum concentrations of AST and ALT were measured using a Hitachi automatic analyzer 7600 (Tokyo, Japan). All clinical analyses were performed by the Neodin Medical Institute (Seoul, Korea), a laboratory certified by the Korean Ministry of Health and Welfare. 2.3. Measurement of total mercury levels in whole blood To assess total mercury levels in whole blood, 3-mL blood samples were drawn into BD Vacutainers tubes containing EDTA for trace-element determination (K2 EDTA tube, Vacutainers). Blood mercury levels were measured using the goldamalgam collection method with a DMA-80 (Milestone, Bergamo, Italy) at the Neodin Medical Institute. Commercial reference materials were used for internal quality assurance and control (Lyphocheks Whole Blood Metals Control; Bio-Rad, Hercules, CA, USA). The coefficient of variation for blood mercury was 1.59–4.86% across the three reference samples. With respect to external quality assurance and control, the institute had passed both the German External Quality Assessment Scheme operated by Friedrich-Alexander University and the Quality Assurance Program operated by the Korea Occupational Safety and Health Agency. The institute was also certified by the Ministry of Employment and Labor as one of the designated laboratories for analysis of specific chemicals, including heavy metals and certain organic chemicals. The detection limit for blood mercury in the present study was 0.158 µg/L. No tested sample exhibited a value below the detection limit. Methyl mercury constitutes the largest share of total blood mercury in general populations (Clarkson and Magos, 2006), and total blood mercury is not a validated measure for inorganic mercury from sources, for which exposure is generally measured using urinary mercury biomarkers. However, KNHANES data are not available for urinary mercury. To evaluate the effect of inorganic mercury rather than organic mercury on liver enzymes, the frequency of fish consumption was utilized as an adjustment factor of organic mercury. 2.4. Statistical analysis Statistical analyses were performed using SAS software (ver. 9.3; SAS Institute, Cary, NC) and SUDAAN (Release 11.0; Research Triangle Institute, Research Triangle Park, NC), the latter of which 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 non-institutionalized civilian Korean population. The unadjusted geometric means (GMs) (95% confidence interval, CI) of blood mercury and the arithmetic means (AMs) of serum AST and ALT were calculated by gender, age group, residence location, educational level, smoking status, drinking status, BMI, frequency of fish consumption, and quartile of blood mercury, using the Proc Descript function in SUDAAN. The adjusted means (95% CI) were also obtained by analysis of covariance, with the data adjusted for all of the other variables in the tables, using the Proc Regress function of SUDAAN. Multivariate linear regression analyses were used to determine the differences (95% CI) in serum AST and ALT by blood mercury level after covariate adjustment. The log2-transformed blood mercury level was taken as a continuous independent variable due to its skewed distribution, and regressed on serum AST and ALT. The quartile of blood mercury was used as a categorical independent variable for regression on serum AST and ALT. The covariates used in these analyses were age, residence area, BMI, smoking status, drinking status, education level, obesity, and frequency of fish consumption. To calculate the odds ratios (ORs), we set cut-off values of 440 IU/L for AST, and 4 35 IU/L for ALT. Next, we set the criteria for subclinical increases of AST and ALT as being over the median values of 21.1/21.2 IU/L for men, and 17.6/13.9 IU/L for women, respectively. Using logistic regression analysis, we then determined ORs and 95% CIs for elevated AST and ALT or subclinical increases in AST and ALT by blood mercury level, after adjusting for the same covariates used in the regression analyses.
3. Results Adjusted GMs and their 95% CI for blood mercury and AMs and their 95% CI for serum AST and ALT are shown in Table 1. The adjusted GM (95% CI) of blood mercury, and the AMs (95% CI) of serum AST and serum ALT in men were 4.392 (4.250–4.535) mg/L, 24.15 (23.59–24.71) U/L, and 26.60 (25.70–27.50) U/L, respectively; these values were significantly higher than the corresponding values in women (3.632: 3.525–3.739 mg/L; 20.34: 19.89–20.80 U/L; and 18.08: 17.37–18.79 U/L, respectively). The adjusted means of blood
16
Table 1 Mean and 95% confidence interval (CI) of blood mercury, serum AST and ALT of adult population by classification variables, KNHANES 2008–2012. Classification
No
Blood mercury Crude
AST Adjusted#
Crude
ALT Adjusted
Adjusted
6689
3.987 (3.894–4.080)
22.25 (21.71–22.80)
3109 3580
4.756 (4.613–4.898) 3.366 (3.273–3.460)n
4.392 (4.250–4.535) 3.632 (3.525–3.739)n
24.76 (24.19–25.33) 19.76 (19.41–20.11)n
24.15 (23.59–24.71) 20.34 (19.89–20.80)n
27.39 (26.52–28.27) 17.32 (16.70–17.94)n
26.60 (25.70–27.50) 18.08 (17.37–18.79)n
Age groups 20–29 30–39 40–49 50–59 60–69
1240 1335 1322 1347 1445
3.344 (3.219–3.469) 3.963 (3.825–4.101)nn 4.499 (4.330–4.667)nn 4.773 (4.591–4.955)nn 3.547 (3.352–3.742)nn
3.089 (2.959–3.221) 3.732 (3.603–3.869)nn 4.357 (4.208–4.513)nn 5.002 (4.816–5.196)nn 4.055 (3.853–4.267)nn
19.77 (19.05–20.48) 21.02 (20.36–21.68)n 22.73 (21.85–23.61)nn 24.30 (23.49–25.10)nn 23.44 (22.76–24.12)nn
20.00 (19.22–20.79) 20.82 (20.16–21.47)nn 22.37 (21.49–23.25)nn 23.99 (23.18–24.79)nn 24.10 (23.30–24.91)nn
20.27 22.88 23.66 24.33 20.08
21.38 (20.08–22.67) 22.83 (21.42–24.24) 22.96 (21.35–24.56) 23.51 (22.43–24.59)nn 20.58 (19.61–21.54)
Residence area Urban Rural
5287 1402
3.939 (3.833–4.044) 4.183 (3.924–4.442)
3.935 (3.838–4.034) 4.195 (3.959–4.441)
21.83 (21.48–22.17) 23.70 (22.72–24.68)nn
21.94 (21.59–22.29) 23.24 (22.27–24.21)n
21.94 (21.36–22.52) 23.49 (22.02–24.97)nn
22.06 (21.48–22.65) 23.00 (21.51–24.49)
Education level Less than high school High school College and more
3031 1465 2193
3.751 (3.619–3.882) 4.227 (4.079–4.374)nn 4.160 (4.023–4.298)nn
3.621 (3.504–3.743) 4.229 (4.083–4.379)nn 4.353 (4.208–4.504)nn
23.05 (22.56–23.54) 22.30 (21.50–23.09) 21.01 (20.49–21.54)nn
22.41 (21.86–22.96) 22.72 (21.96–23.49) 21.59 (21.02–22.16)
21.91 (21.10–22.72) 22.43 (21.43–23.43) 22.59 (21.52–23.67)nn
22.28 (21.27–23.29) 22.44 (21.50–23.38) 22.09 (21.00–23.18)
Smoking status Non-smoker Past smoker Current smoker
4436 751 1502
3.582 (3.485–3.680) 5.057 (4.765–5.350)nn 4.738 (4.559–4.917)nn
3.830 (3.724–3.939) 4.445 (4.212–4.697)nn 4.203 (4.043–4.375)nn
20.89 (20.54–21.24) 23.30 (22.50–24.10)nn 25.14 (24.18–26.10)nn
21.90 (21.47–22.32) 21.54 (20.67–22.41) 23.31 (22.37–24.25)n
19.77 (19.16–20.37) 24.92 (23.54–26.29)nn 27.53 (26.10–28.96)nn
21.81 (21.17–22.46) 21.56 (20.22–22.90) 23.70 (22.25–25.15)n
Drinking status No drink Mild drink Moderate drink Heavy drink
1628 1684 1080 2297
3.372 (3.222–3.522) 3.713 (3.581–3.845)nn 4.055 (3.882–4.227)nn 4.643 (4.481–4.804)nn
3.539 3.907 4.083 4.327
(3.404–3.680) (3.777–4.039)nn (3.919–4.250)nn (4.178–4.486)nn
21.17 (20.60–21.74) 20.66 (20.13–21.19) 21.84 (20.92–22.75) 24.09 (23.41–24.77)nn
21.45 (20.75–22.15) 21.43 (20.87–21.99) 22.08 (21.15–23.02) 23.28 (22.62–23.95)nn
20.62 (19.40–21.84) 19.22 (18.42–20.01) 21.81 (19.96–23.67) 25.55 (24.67–26.44)nn
23.09 (21.49–24.68) 21.41 (20.58–22.25)n 22.21 (20.31–24.11) 22.26 (21.34–23.19)
Obesity Lean Normal Obese
292 4280 2117
3.183 (2.975–3.392) 3.788 (3.686–3.889)nn 4.552 (4.383–4.720)nn
3.560 (3.326–3.811) 3.822 (3.724–3.919)nn 4.401 (4.250–4.553)nn
19.32 (17.96–20.68) 21.29 (20.87–21.72)nn 24.41 (23.81–25.01)nn
21.40 (20.01–22.79) 21.52 (21.09–21.94) 23.69 (23.11–24.26)nn
14.56 (13.47–15.65) 19.54 (18.88–20.20)nn 28.68 (27.68–29.69)nn
16.70 (15.46–17.94) 19.88 (19.21–20.55)nn 27.73 (26.76–28.69)nn
Frequency of fish consumption Low 556 Moderate 2214 High 3919
3.742 (3.437–4.046) 3.793 (3.663–3.922) 4.140 (4.026–4.254)
3.769 (3.507–4.051) 3.830 (3.709–3.955) 4.112 (4.006–4.216)n
23.03 (21.76–24.29) 21.92 (21.34–22.50) 22.25 (21.81–22.69)
22.22 (21.05–23.40) 22.08 (21.51–22.65) 22.28 (21.84–22.71)
21.88 (20.42–23.34) 21.80 (20.81–22.79) 22.57 (21.86–23.28)
21.41 (20.05–22.77) 22.15 (21.16–23.13) 22.43 (21.77–23.10)
Quartile of blood mercury (μg/L) 1621 1st Q¥ 2nd Q 1662 3rd Q 1676 4th Q 1730
– – – –
21.14 (20.45–21.83) 21.7 (21.09–22.30) 22.41 (21.71–23.10)n 23.60 (22.88–24.31)nn
21.55 (20.84–22.27) 21.90 (21.32–22.49) 22.41 (21.75–23.07) 22.97 (22.29–23.65)nn
19.67 (18.45–20.90) 21.90 (20.99–22.82) 23.12 (21.81–24.43)n 24.36 (23.42–25.31)nn
20.60 22.08 22.91 23.46
(19.09–21.45) (21.45–24.31)nn (22.13–25.19)nn (23.27–25.38)nn (19.33–20.84)nn
(19.31–21.88) (21.22–22.94) (21.68–24.15)n (22.58–24.33)nn
Covariates: sex, age, residence area, smoking status, drinking status, educational level, obesity, and frequency of fish consumption. Quartile¥: 1st Q ( r 3.212 for men, r 2.340 for women), 2nd Q ( 43.212–4.768 for men, 42.340– 3.290 for women), 3rd Q ( 44.768–7.052 for men, 4 3.290–4.651 for women), 4th Q ( 47.052 for men, 44.651 for women). AST; aspartate transaminase, ALT; alanine transaminase. n
po 0.05. po 0.01.
nn
H. Lee et al. / Environmental Research 130 (2014) 14–19
All Gender Men Women
#
22.21 (21.87–22.54)
Crude
H. Lee et al. / Environmental Research 130 (2014) 14–19
17
Table 2 Beta coefficients and 95% CI of log-transformed blood mercury in multiple linear regression analysis in Korean adult after adjustment of covariates#. Independent Variables
Model 1 Per doubling of blood mercury level (μg/L) Model 2 Quartile of blood mercury (μg/L) 1st Q¥ 2nd Q 3rd Q 4th Q
Beta coefficients (95% CI) AST
p-value
ALT
p-value
0.676 (0.272–1.079)
0.001
1.067 (0.462–1.671)
0.000
0.000 (Ref.) 0.293 ( 0.620 to 1.210) 0.755 ( 0.380 to 1.741) 1.249 (0.263–2.235)
. 0.530 0.132 0.013
0.000 (Ref.) 1.184 ( 0.380 to 2.750) 1.950 (0.149–3.751) 2.248 (0.648–3.848)
. 0.138 0.033 0.006
#
Covariates: sex, age, residence area, smoking status, drinking status, educational level, obesity, and frequency of fish consumption. Quartile¥: 1st Q (r 3.212 for men, r 2.340 for women), 2nd Q (4 3.212–4.768 for men, 42.340–3.290 for women), 3rd Q (4 4.768–7.052 for men, 43.290–4.651 for women), 4th Q ( 47.052 for men, 44.651 for women). AST; aspartate transaminase, ALT; alanine transaminase
Table 3 Odd ratios (95% CI) for elevated AST and ALT, and AST and ALT higher than the median by blood mercury level in adult population after covariate adjustment#. Independent variables
Model 1 Per doubling of blood mercury level (μg/L) Model 2 Quartile of blood mercury (μg/L) 1st Q¥ 2nd Q 3rd Q 4th Q
Odd ratios (95% CI) Elevated AST
Elevated ALT
AST higher than the median
ALT higher than the median
1.041 (0.786–1.379)
1.004 (0.819–1.231)
1.299 (1.152–1.465)
1.448 (1.288–1.629)
1.000 (Ref.) 1.031 (0.701–1.518) 1.020 (0.703–1.480) 1.061 (0.747–1.507)
1.000 (Ref.) 0.927 (0.506–1.697) 1.013 (0.551–1.865) 0.970 (0.558–1.685)
1.000 (Ref.) 1.249 (1.035–1.506) 1.330 (1.100–1.609) 1.524 (1.256–1.849)
1.000 (Ref.) 1.485 (1.252–1.761) 1.499 (1.249–1.799) 1.947 (1.625–2.334)
#
Covariates: sex, age, residence area, smoking status, drinking status, educational level, obesity, and frequency of fish consumption. Quartile¥: 1st Q (r 3.212 for men, r 2.340 for women), 2nd Q ( 43.212 4.768 for men, 42.340–3.290 for women), 3rd Q ( 44.768–7.052 for men, 43.290–4.651 for women), 4th Q ( 4 7.052 for men, 44.651 for women). AST; aspartate transaminase, ALT; alanine transaminase.
mercury and serum ALT were significantly higher in the middle age group (50–59 years), while the adjusted mean of serum AST was significantly higher with age. While the adjusted mean of serum AST in rural residents was significantly higher than that for urban residents, there was no difference in blood mercury or serum ALT between residential areas. The adjusted means of blood mercury in non-smokers were significantly lower than those in past and current smokers, whereas the adjusted means of serum AST and ALT in nonsmokers were significantly lower than those of smokers. Drinking status was a significant predictor of increased blood mercury, and it was also a predictor of serum AST, but not ALT. Education level was also a significant predictor for blood mercury, but not serum AST or ALT. The adjusted GM of blood mercury was significantly increased according to obesity, and those of serum AST and ALT were also significantly increased according to obesity. The adjusted GM of blood mercury in the high fish-consumption groups was significantly higher than in the low fish-consumption group, whereas the adjusted means of serum AST and ALT were not different among the three groups. The AMs of serum AST and ALT were also significantly increased by each increase in the quartile of blood mercury. Multivariate linear regression analyses were used to calculate the mean differences in serum AST and ALT by blood mercury, and to determine the significance of blood mercury as a predictor of serum AST and ALT after we adjusted for sex, age, residential area, education level, smoking status, drinking status, obesity, and frequency of fish consumption (Table 2). We found that log2transformed blood mercury as a continuous independent variable was a significant predictor for serum AST and ALT; each doubling of blood mercury was associated with a 0.676 U/L increase in serum AST and a 1.067 U/l increase in serum ALT. The mean
differences (95% CI) in serum AST and ALT between lowest quartile and highest quartile were statistically significant at 1.249 (0.263– 2.235) U/l and 2.248 (0.648–3.848) U/L, respectively. Using logistic regression analysis, we calculated ORs and 95% CI values for having elevated or higher than the median serum AST and ALT for log2-transformed blood mercury and for quartiles of blood mercury after covariate adjustment (Table 3). The covariates used in the logistic regression analysis were the same as those used in the regression analysis. While the ORs for elevated serum AST and ALT were not significant for either the continuous or categorical models, the ORs for having serum AST and ALT levels above the median were statistically significant in both models according to the increase of blood mercury. The risks of having serum AST and ALT levels above the median among subjects in the 4th quartile of blood mercury were 1.524 and 1.947, respectively.
4. Discussion In the present study, the overall GM of blood mercury levels in Korean adults was 3.987 μg/L, which was much higher than the 0.814 μg/L reported in the recent US NHANES data (CDC, 2009). However, a survey of New York City residents found that the blood mercury level was highest in the Asian population at 4.11 mg/L (McKelvey et al., 2007), which is very similar to the value obtained in the present study. Total blood mercury levels are known to increase with fish consumption, which is higher in Korea compared with Western countries (Jarup, 2003; Mozaffarian, 2009). Inorganic mercury exposure such as tooth amalgam treatment or other environmental exposure, could also contribute to these differences.
18
H. Lee et al. / Environmental Research 130 (2014) 14–19
Most of the existing case reports have shown hepatic injury following high-level exposure to mercury via inhalation or ingestion (Al-Saleem and Clinical Committee on Mercury Poisoning 1976; Jaffe et al., 1983; Kanluen and Gottlieb, 1991; Murphy et al., 1979; Rowens et al., 1991; Samuels et al., 1982). However, the prevalence of liver disease in a population from the Minamata area was not significantly increased when compared to unexposed controls (Futatsuka et al., 1992). Cave et al. (2010) evaluated the association of total blood mercury (as well as blood markers of PCBs and lead) with ALT levels as a measure of liver function in a representative US population sample (US NHANES). After adjustment for possible confounders the authors found a positive association between total blood mercury biomarkers and ALT levels in this population. We also report that subclinical changes in liver function are associated with low-level exposure to mercury in a representative Korean population. Multivariate linear regression analyses showed that blood mercury was a significant predictor of serum AST and ALT. Doubling of blood mercury was associated with a 0.676 U/L increase in serum AST and a 1.067 U/L increase in serum ALT. The mean differences (95% CI) in serum AST and ALT between the lowest and highest quartiles were 1.249 (0.263–2.235) U/L and 2.248 (0.648–3.848) U/L, respectively. The odds ratios for having serum AST and ALT levels above the median were statistically significant in both the models according to the doubling of blood mercury. The risks of having serum AST and ALT levels above the median among subjects in the 4th quartile of blood mercury were 1.524 and 1.947, respectively. Taken together, our results show that increased blood mercury levels were associated with liver enzyme levels (serum ALT, in particular) in the general adult population of South Korea. The present study showed that ALT elevation was associated with BMI and middle age, but not drinking status. Clark et al. (2003) also noted that aminotransferase (AST and ALT) elevation was associated with BMI and middle age (50–59 years). In their study, aminotransferase elevation unexplained by viral hepatitis, alcohol consumption, or hemochromatosis was strongly associated with adiposity and other features of the metabolic syndrome, and thus may represent NAFLD. Cave et al. (2010) showed that total blood mercury was associated with ALT levels which is a measure of liver function, and specifically the risk of NAFLD. Clark (2006) noted that ALT elevation (above normal laboratory reference ranges) is the most common laboratory manifestation of NAFLD, and ALT elevation unexplained by viral hepatitis, ethanol, or iron overload has been used as a surrogate biomarker for NAFLD in NHANES. Diet-induced obesity probably plays the primary role in the pathogenesis of most cases of NAFLD (Cave et al., 2007), but nutrient–toxicant interactions and genetic susceptibility to environmental pollution may be important cofactors (Cave et al., 2010). Diet-induced obesity and fatty liver may decrease antioxidant defenses and impair xenobiotic metabolism and disposition, which could sensitize the liver to chemical injury (Fisher et al. 2009; Kirpich et al., 2011). Taken together, the present study suggests a possible association between low-level mercury exposure and mild liver dysfunction, and then suspected NAFLD. In the present study, the changes in aminotransferase level are smaller and subclinical (within normal laboratory reference ranges) contrasted with previous studies (Cave et al., 2010; Clark, 2006). In fact, some authors have suggested using lower laboratory cutoffs of serum ALT (Prati et al. 2002; Ruhl and Everhart 2009). Importantly, ALT may be normal in NAFLD. Thus, low-level blood mercury may pose a risk for mild liver dysfunction which is possibly linked to NAFLD even within normal laboratory reference ranges. Furthermore, our results indicate that while adjusted total blood mercury concentrations were positively associated with various measures of AST and ALT, seafood intake was not
associated with AST and ALT levels. Thus, the association of mercury biomarkers and liver enzymes may be due to exposure to inorganic mercury such as tooth amalgam treatment or other environmental exposure. Many of the animal hepatoxicity studies (Ashe et al., 1953; Jonker et al., 1993; NTP, 1993; Rana and Boora, 1992; Ung et al., 2010) and human case studies (Jaffe et al., 1983; Kanluen and Gottlieb, 1991; Rowens et al., 1991) involving inorganic exposure support our findings. Mercury undergoes extensive biliary-hepatic cycling (Dutczak and Ballatori, 1994; Clarkson and Magos, 2006). It is secreted into bile, partly reabsorbed into the portal circulation, and from there returns to the liver. The high mobility of mercury in the body is attributed to the formation of water-soluble mercury complexes that are mainly (if not exclusively) attached to the sulfur atoms of thiol groups such as glutathione (Clarkson, 2002). The glutathione moiety is degraded in the bile duct and gall bladder to a dipeptide and finally to an L-cysteine mercury complex before entering the circulatory system (Dutczak and Ballatori, 1994). However, despite the extensive biliary-hepatic cycling of mercury and some evidence suggesting that the liver plays a role in renal tubule-mediated uptake of mercury (Zalups, 2000), little is known regarding the possible mechanism(s) of mercury-induced hepatotoxicity. In an animal experiment using zebra fish, Ung et al. (2010) found dosage-dependent lipid accumulation (as indicated by more and larger red-stained lipid vesicles) in the livers of mercurytreated fish, prompting us to speculate that mercury-induced fatty liver conditions may cause subclinical changes in liver function. They also showed that mercury-induced hepatotoxicity was triggered by oxidative stress, intrinsic apoptotic pathways, and deregulation of nuclear receptor and kinase activities (Ung et al., 2010). The present findings have toxicological implications. First, our results underscore the need to monitor and reduce mercury exposure in the general population and to clinically assess modifiable exposures in liver disease patients. Second, as the liver is mainly responsible for detoxification processes and the regulation of metabolic pathways, it is important to gain a better understanding of mercury-induced hepatotoxicity. Such information could provide new insights into metabolic disorders induced by some toxicants in liver in vivo. Third, low-level mercury exposure may have significant public health implications because low-level blood mercury may pose a risk for mild liver dysfunction, possibly linked to NAFLD, even within normal laboratory reference ranges. This study has several important strengths. First, we used a representative sample of the general South Korean population. Second, rigorous quality controls were applied to the KNHANES. However, this study also has some limitations. First, as in many of the previous reports, our associations were obtained by crosssectional analysis, barring us from determining any causality for these associations. We cannot completely exclude the possibility of reverse causality (i.e., mild liver dysfunction could have caused increased blood mercury). Mild liver dysfunction (evidenced by slightly higher liver enzymes) may impede smooth elimination of methyl mercury through the biliary-hepatic route, leading to slightly higher circulating blood mercury in those with elevated enzymes. Sheehan et al. (2012) tested the hypothesis of liver disease slowing methyl mercury elimination through the enterohepatic cycle by examining the association of viral hepatitis markers and blood mercury using the US NHANES database; these authors found significantly increased blood mercury in women with markers of hepatitis B, with more serious disease associated with higher mercury. An unknown third factor might be a common link responsible for an observed association. Prospective studies in which mercury levels are determined before the development of abnormal findings will be required to establish a causal basis for these associations. Second, the KNHANES did not include
H. Lee et al. / Environmental Research 130 (2014) 14–19
an occupation/job code, so we were unable to screen for mercuryexposure-prone jobs, such as the manufacturing of thermostats, fluorescent light bulbs, barometers, glass thermometers, and some blood pressure devices. Third, KNHANES adopted gold amalgam direct method as a standard method for blood mercury according to Korean standard method analysis which is not sensitive enough to measure low level of blood mercury as the ICP–MS method. Fourth, KNHANES measured total blood mercury only because KNHANES did not have enough funding to specify the blood mercury into organic and inorganic mercury in the survey. Methyl mercury constitutes the largest share of total blood mercury in general populations (Clarkson and Magos, 2006). Kim et al. (2012) provide estimates of the mean methyl mercury share (78.3%) of total blood mercury in three coastal cities in South Korea. Total blood mercury is a validated biomarker for methyl mercury exposure from seafood. As an adjustment factor of organic mercury, the frequency of fish consumption was utilized, and thus the effect of inorganic mercury rather than organic mercury on liver enzymes was suggested. Fifth, blood mercury is not a validated measure for inorganic mercury from sources such as dental amalgams, for which exposure is generally measured using urinary mercury biomarkers. However, KNHANES data are not available for urinary mercury. Follow up research using a urinary mercury should be undertaken in future. Finally, the exact implication of ALT for liver disease in KNHANES is unknown because hepatitis B surface antigen, hepatitis C antibody, or liver biopsies were not available in KNHANES although we suggested an association between low blood mercury levels and mild liver function, as a possible proxy measure of NAFLD. Future studies should be performed to confirm the potential role of mercury exposure in NAFLD. In conclusion, we herein demonstrate an association between low blood mercury levels and subclinical changes of liver function, as a possible proxy measure of NAFLD in the general population of South Korea. Acknowledgment This study was supported by a Grant of the Korean Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea (HI13C0713). References Agency for Toxic Substances and Disease Registry (ATSDR), 1999. Toxicological Profile for Mercury (Update). US Department of Health and Human Services. Public Health Services, Atlanta Al-Saleem, T., 1976. Clinical Committee on Mercury Poisoning, 1976. Levels of mercury and pathologic changes in patients with organomercury poisoning. Bull. World Health Organ. 53 (Suppl), S99–S104. Ashe, W., Largent, E., Dutra, F., Hubbard, D., Blackstone, M., 1953. Behavior of mercury in the animal organism following inhalation. Arch. Ind. Hyg. Occup. Med. 17, 19–43. Cave, M., Appana, S., Patel, M., Falkner, K.C., McClain, C.J., Brock, G., 2010. Polychlorinated biphenyls, lead, and mercury are associated with liver disease in American adults: NHANES 2003–2004. Environ. Health Perspect. 118, 1735–1742. Cave, M., Deaciuc, I., Mendez, C., Song, Z., Joshi-Barve, S., Barve, S., McClain, C., 2007. Nonalcoholic fatty liver disease: predisposing factors and the role of nutrition. J. Nutr. Biochem 18, 184–195. Centers for Disease Control and Prevention (CDC), 2009. Fourth National Report on Human Exposure to Environmental Chemicals. CDC. Available from: 〈http:// www.cdc.gov/exposurereport〉 (accessed 24.05.12.). Clark, J.M., 2006. The epidemiology of nonalcoholic fatty liver disease in adults. J. Clin. Gastroenterol. 40 (Suppl 1), S5–S10.
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