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- Original Article Association of weight and dietary habits with high blood mercury levels in Korean adolescents: data from the KoNEHS cycle 4, 2018-2020
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Ji Hoon Kim1
, Minju Jung1, Jaewon Mun1, Dong-Jae Seo1, Jong-Han Leem1,2, Shin-Goo Park1,2, Dong-Wook Lee1,2, Hyung Doo Kim1, Hwan-Cheol Kim1,2,* -
Annals of Occupational and Environmental Medicine 2025;37:e5.
DOI: https://doi.org/10.35371/aoem.2025.37.e5
Published online: February 21, 2025
1Department of Occupational and Environmental Medicine, Inha University Hospital, Incheon, Korea
2Department of Social and Preventive Medicine, Inha University College of Medicine, Incheon, Korea
- *Corresponding author: Hwan-Cheol Kim Department of Occupational and Environmental Medicine, Inha University College of Medicine, 27 Inhang-ro, Jung-gu, Incheon 22332, Korea E-mail: cheol17@hanmail.net, carpediem@inha.ac.kr
• Received: December 4, 2024 • Revised: January 31, 2025 • Accepted: January 31, 2025
© 2025 Korean Society of Occupational & Environmental Medicine
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Abstract
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Background Humans are exposed to mercury primarily in its highly toxic form, methyl mercury, which is known to have adverse effects on various organs and systems. The negative impact of mercury exposure on the growth, development, and mental health of children, from infancy to adolescence, is well-documented. However, there are no internationally standardized safe limits for mercury exposure. This study investigated the impact of dietary habits and higher body mass index (BMI) on blood mercury levels in adolescents.
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Methods This study analyzed the data from the 4th Korean National Environmental Health Survey (KoNEHS) 2018–2020. The focus was on 825 middle and high school students aged 13–18 years, whose blood mercury levels were measured. A survey on dietary and lifestyle habits was also conducted. Blood mercury levels were categorized by geometric median values, and associations with overweight status and seafood consumption were examined using a generalized linear model.
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Results The geometric mean blood mercury level for the entire sample was 1.37 µg/L, with levels of 1.31 µg/L in normal-weight individuals and 1.43 µg/L in overweight individuals, showing a statistically significant difference between the two groups. After adjusting for other variables, blood mercury levels were significantly associated with overweight status (estimate: 0.084; p = 0.018; 95% confidence interval [CI]: 0.015–0.153), consumption of large fish and tuna more than once a week (estimate: 0.18; p = 0.001; 95% CI: 0.077–0.284), and consumption of fish once a week or more (estimate: 0.147; p = 0.004; 95% CI: 0.043–0.250).
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Conclusions In adolescents, a higher BMI and an increased consumption of large fish, tuna, and fish were associated with higher blood mercury levels. Notably, a stronger association was found between large fish consumption and blood mercury levels in the overweight group. These findings suggest the need to moderate seafood consumption and establish more proactive mercury exposure standards for adolescents.
BACKGROUND
Mercury exists both naturally in the earth’s environment, and as a man-made contaminant. Therefore, human exposure to mercury is widespread.1 The release of processed mercury can lead to its progressive increase in the environment and its entry into the atmospheric-soil-water distribution cycle where it can remain in circulation for years, leading to continuous human exposure.2 Among the primary forms of mercury, methylmercury is particularly of concern due to its bioaccumulation in marine and freshwater fish, which serves as a major source of human exposure.1 This issue is especially significant in regions where seafood consumption is high, such as South Korea.
The health risks of mercury exposure are well-documented.2-4 Historically, there have been reports of serious incidents of high-level exposure, such as the Minamata disease outbreak in Japan and the methylmercury poisoning event in Iraq. While these events highlighted the dangers of acute mercury toxicity, more recent research underscores the risks associated with low-level exposure. Even at levels previously considered safe, mercury exposure during critical developmental periods5—such as fetal development,6 childhood, and adolescence—has been linked to neurodevelopmental delays,4 cognitive impairment, and mental health disorders like autism spectrum disorder.7 Specifically, adolescence is a crucial period for neurodevelopment and physical growth, making this population especially vulnerable to the effects of mercury exposure.
Low-level mercury exposure has also been associated with early-onset puberty,8,9 indicating the complexity of its impact on public health. At the same time, fish, a significant source of mercury exposure, is an important dietary component, providing essential nutrients such as omega-3 fatty acids, which are beneficial for cardiovascular health.10 Balancing these benefits with the risks of mercury exposure presents a significant challenge for the development of effective dietary recommendations.
South Korea’s high seafood consumption has led to blood mercury levels that are notably higher than those in countries like the United States or Canada, where seafood consumption is lower. According to a survey conducted by the Korean National Environmental Health Survey (KoNEHS), the average mercury concentrations in the blood of Korean middle and high school students from 2015 to 2020 reached 1.38 μg/L. On the other hand, according to the U.S. Environmental Protection Agency (EPA), the blood mercury concentration for 11–15-year-old adolescents from 2015 to 2018 was only 0.3 μg/L, and for 16–17-year-olds was 0.4 μg/L. This merits concern, given the accumulating evidence of health risks of mercury even at low concentrations. Even if blood mercury levels do not exceed global reference standards, adolescents may still face potential risks to neurodevelopment and overall health, raising significant concerns.
Considering these concerns, this study investigated the relationship between dietary habits, body weight, and blood mercury levels among Korean adolescents. By reanalyzing the strong association between fish consumption and blood mercury levels established in previous studies, this study aimed to provide insights into the public health implications of mercury exposure for Korean adolescents. Moreover, it highlights the need for the development of informed dietary guidelines that minimize mercury exposure while preserving the nutritional benefits of seafood. The findings of this study will serve as a foundation for raising awareness about the health risks of mercury risks and establishing evidence-based recommendations for adolescents.
METHODS
This study was conducted using data from the 4th KoNEHS, published by the National Institute of Environmental Research (NIER) during the period 2018 to 2020. The study population comprised 825 Korean adolescents aged 12 to 18 years. Information on blood mercury levels, anthropometric measurements, dietary habits, and overweight status was obtained from participants through physical and blood examinations and based on a questionnaire survey.
Blood samples were collected from participants in the early morning following an overnight fast. Samples were drawn into anticoagulant-treated tubes and stored at temperatures below −20°C until analysis. Mercury concentrations in the whole blood were determined using the gold amalgamation method combined with cold vapor atomic absorption spectrometry. Analyses were performed using a Direct Mercury Analyzer (DMA-80, Milestone, Sorisole, Italy), employing thermal decomposition, gold amalgamation, and atomic absorption detection. The method detection limit (MDL) was 0.1 µg/L, and values below this threshold were replaced with half the MDL (0.05 µg/L) for statistical analysis. Calibration and quality control were ensured by using certified reference materials to maintain the accuracy and precision of the measurements.
Anthropometric measurements were obtained by trained health professionals using standardized protocols. Height was measured to the nearest 0.1 cm with a calibrated stadiometer, with participants standing barefoot in an upright position with their heads aligned to the Frankfurt horizontal plane. Weight was measured to the nearest 0.1 kg using a digital scale, with participants wearing light clothing and no shoes. Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared (kg/m2).
The basic survey comprised a questionnaire. The questionnaire was divided into two parts: one for the guardians and one for individual students, to identify the exposure pathways to environmental pollutants. The guardian questionnaire included questions about monthly household income and home environment, while middle and high school students completed sections on dietary habits, lifestyle habits (alcohol consumption, smoking), and recent living patterns. Additional questions regarding transportation methods were also included for middle and high school students.
In this study, blood mercury levels were classified as ‘high’ or ‘low’ based on the geometric mean value.
The levels of blood mercury concentrations in the study group were presented as the geometric mean, geometric standard deviation, and quartiles for the entire group, the normal-weight group, and the overweight group. The independent t-test was used to examine the relationship between overweight status and blood mercury levels.
The confounders included age, sex (male, female), overweight status (BMI [kg/m2] was calculated based on the participant's weight and height, and being overweight was defined according to the criteria of being at or above the 85th percentile for age and sex (Supplementary Table 1). The growth chart for children and adolescents is based on the pediatric growth charts from the Korea Disease Control and Prevention Agency (KDCA), dietary habits related to seafood consumption (categorized as ‘rarely eat,’ ‘once a week or more,’ or ‘once a month or more’). The confounders included smoking, alcohol consumption, monthly household income over the past year (categorized as ‘≤2 million won’, ‘2–5 million won’, or ‘>5 million won’ based on the 2020 first quarter household trend survey by the Korean Statistical Information Service (KOSIS), with an average monthly household income of 5,358,000 won and first quintile of 1,488,000 won), parent’s education level (less than elementary school, elementary school to high school, high school and above) and traditional medicine use (traditional medicines often have unspecified ingredients, and previous studies have revealed that they contain significant amounts of mercury11,12). Pearson’s chi-square test was performed to determine whether an overweight status and various seafood consumption habits were associated with higher blood mercury levels.
In this study, a linear regression model was then used to determine the association between seafood consumption habits and blood mercury levels in the normal-weight and overweight groups. The p-value and the 95% confidence interval (CI) were used to assess the association between blood mercury levels, dietary habits, and overweight status. The p-value and CI were adjusted in two stages; one for the unadjusted group and one for the adjusted group, which included adjustments for age, sex, smoking, alcohol consumption, monthly household income over the past year, and intake of traditional medicines. Finally, the association between overweight status, dietary habits, and blood mercury levels was assessed for the entire study population, with the p-value and CI adjusted in the same two stages. To make it easier to interpret the results, the variables were standardized before performing the generalized linear model analysis. All statistical analyses were conducted using SPSS for Windows version 25.0 (IBM Corp., Armonk, NY, USA), with significance tested at p < 0.05.
This study used data from the KoNEHS Cycle 4 (2018–2020) carried out by the NIER (2018-01-01-001). Participants provided written informed consent before participation in the KoNEHS Cycle 4 (2018–2020). The survey was conducted by the NIER following ethical guidelines and was approved by the relevant Institutional Review Board (IRB).
RESULTS
The demographic characteristics of the study subjects based on their blood mercury concentrations were compared (Table 1). The group with high blood mercury levels had a higher proportion of males. Additionally, blood mercury concentrations increased with the frequency of consumption of large fish and tuna, and fish in general. However, there were no statistically significant variations in the different age groups. Similarly, smoking and alcohol consumption did not result in statistically significant changes in blood mercury levels. The blood mercury concentrations increased with an increase in the monthly income levels of the households. The frequency of consumption of crustaceans, seaweed, mollusks, and other seafood was also associated with higher blood mercury levels, although these associations were not statistically significant.
The study involved a total of 825 participants. As shown in Table 2, the geometric average blood concentration of the participants was 1.37 µg/L. Among the groups, the blood mercury concentrations were higher in the overweight group, with a concentration of 1.43 µg/L, compared to the normal-weight group, which had a concentration of 1.31 µg/L. The distribution of mercury levels, including the minimum value, 25th percentile, median, 75th percentile, and maximum value, was consistently higher in the overweight group compared to the normal-weight group. These differences were deemed to be statistically significant by the independent t-test.
Table 3 presents the associations between log blood mercury concentrations, seafood consumption habits, and overweight status, as analyzed using linear regression. In the unadjusted model, it was observed that among the normal-weight group, individuals who consumed large fish and tuna or fish more than once a week had higher log blood mercury concentrations compared to those who did not consume these types of seafood had higher log blood mercury concentrations compared to those who did not consume these types of seafood. In the overweight group, individuals who consumed large fish and tuna more than once a week or fish more than once a month had higher log blood mercury concentration compared with those who did not consume these types of seafood. When all the participants were analyzed together, consuming large fish and tuna or fish more than once a week was associated with higher log blood mercury concentrations, and the log concentrations increased with the frequency of consumption.
In adjusted model, which included additional confounders such as smoking, alcohol consumption, household income, parent’s education level, and the use of traditional medicine, the following observations were made. In the normal-weight group, consuming large fish and tuna more than once a month or fish more than once a week was associated with higher log blood mercury concentrations. In the overweight group, consuming large fish and tuna more than once a month and more than once a week were associated with increased log blood mercury concentrations, and consuming fish more than once a month also resulted in higher log blood mercury concentrations. When analyzing all participants together, consuming large fish and tuna or fish more than once a month and more than once a week were associated with increased log blood mercury concentrations, with the effect size increasing as the frequency of consumption increased. Additionally, overweight individuals had a stronger association with higher log blood mercury concentrations in the case of large fish and tuna compared to the normal-weight group across all models.
DISCUSSION
This study investigated the relationship between dietary habits, overweight status, and blood mercury levels in Korean adolescents, with a focus on seafood consumption as the primary source of mercury exposure. The findings confirmed a significant association between seafood consumption and blood mercury levels, consistent with previous research.13,14 Moreover, the results of the adjusted model demonstrated stronger associations for large fish and tuna consumption patterns in the overweight group compared to the normal-weight group.15-17
In the adjusted model, adjusted for confounders including smoking, alcohol consumption, household income, and traditional medicine use, consuming fish more than once a month resulted in higher blood mercury concentrations in both normal-weight and overweight groups. Notably, the effect size was 0.096 in the normal-weight group and 0.178 in the overweight group, indicating a stronger association for the overweight group. Similarly, for large fish and tuna, the effect sizes were 0.105 (normal weight) and 0.132 (overweight) for monthly consumption, and 0.127 (normal weight) and 0.176 (overweight) for weekly consumption. Only weekly fish consumption showed a higher effect size in the normal-weight group (0.215 vs. 0.137). However, in the normal-weight group, the consumption of large fish and tuna more than once a week and fish more than once a month, as well as the consumption of fish more than once a week in the overweight group, were not statistically significant. These results align with prior research and highlight stronger associations between seafood consumption and mercury levels in overweight populations.
However, the interaction analysis between fish consumption and overweight status did not reveal any statistically significant findings (Supplementary Table 2), suggesting that body weight status may not uniformly modify the effect of fish consumption on mercury levels. This non-significant interaction may be due to the limited statistical power or other unmeasured variables influencing the relationship. Despite this, the observed pattern of higher mercury levels associated with large fish intake in the overweight group remains biologically plausible and warrants further investigation.
One explanation for the stronger association between seafood intake and blood mercury in the overweight group is the higher mercury content typically found in large fish. Overweight individuals may consume larger portions or different types of fish with high mercury content.18 Additionally, metabolic differences in overweight individuals may influence mercury absorption and retention. Mercury is known to accumulate in adipose tissue,19 and the altered liver function in overweight individuals may affect mercury detoxification and excretion.20 The increased liver size seen in individuals with higher BMI, in the presence of co-morbid conditions such as non-alcoholic fatty liver disease, may contribute to the storage of mercury and reduction in blood mercury concentrations.20 Additionally, a decrease in cortisol levels, a stress hormone, and an increase in acute-phase immune proteins are associated with increased blood mercury levels.21 It is known that with increasing BMI, the cortisol levels in the blood decrease while those of acute-phase immune proteins increase.22 These changes can lead to stress-related physical disorders and may contribute to inflammatory cytokine activity and acute-phase responses, potentially leading to the development of chronic diseases.23 In individuals with obesity, the immune and inflammatory systems may have an impact on mercury levels. Furthermore, other potential sources of mercury exposure in obese individuals should be considered.
Despite socioeconomic status (SES) typically being associated with increased seafood consumption and thus higher mercury exposure,24-26 this study did not find a significant relationship between SES and blood mercury levels. This discrepancy could result from protective behaviors among higher SES groups, such as limiting fish intake due to awareness of the risks of mercury, or from unmeasured confounding factors influencing the relationship between SES and dietary patterns. Given that SES was included as a confounding variable rather than a primary outcome, its role in the analysis is minimized. Additionally, parental education level was analyzed as an additional SES indicator, but no significant association with blood mercury levels was observed.
Interestingly, the results also show that, while both normal-weight and overweight groups exhibit positive associations between fish consumption and mercury levels, the type of fish consumed may explain some differences. In normal-weight individuals, fish consumption had a more prominent effect, whereas large fish consumption was more strongly associated with mercury levels in overweight individuals. This pattern suggests that the higher mercury content in large fish exerts a more substantial impact on mercury accumulation in the overweight population.
This underscores the importance of weight-specific dietary patterns in understanding mercury exposure. Overweight adolescents, despite their increased association, still consume seafood as a significant source of dietary mercury. Public health efforts should therefore consider the unique dietary habits and nutritional needs of this group.
The confirmed relationship between seafood consumption and mercury exposure in overweight adolescents highlights the need for tailored dietary interventions. While promoting low-mercury seafood options is critical for all adolescents, overweight individuals may require additional guidance to balance their broader dietary habits with mercury exposure risks.
Educational campaigns should address the potential for unhealthy dietary substitutions that could obscure the impact of seafood on blood mercury levels. Overweight adolescents may benefit from integrated interventions that not only manage weight but also promote informed seafood consumption practices.
While current international guidelines for mercury exposure may not have been exceeded in the populations studied, the cumulative effects of chronic low-level exposure during critical developmental stages, such as adolescence, may present long-term health risks. This suggests a need for greater awareness and more stringent recommendations regarding seafood consumption in adolescents, particularly for those who are overweight.
This study has several strengths, including its use of data that are representative of the adolescent population in South Korea, and its consideration of the various types of seafood intake that could affect blood mercury levels. This study is the first, to our knowledge, to explore the relationship between seafood consumption, overweight status (based on the Korean adolescent growth chart), and blood mercury levels specifically in Korean adolescents. Furthermore, the study clarified the impact of overweight status and dietary habits on blood mercury levels by including potential confounders such as traditional medicine consumption and monthly household income.
However, this study also has some limitations. As a cross-sectional study, it cannot establish causal relationships between seafood consumption, overweight status, and mercury levels. Additionally, the half-life of mercury in blood is relatively short, making it difficult to capture long-term exposure through blood samples alone. Furthermore, the study did not consider regional differences in seafood consumption or cooking methods, both of which could influence mercury exposure.
CONCLUSIONS
In conclusion, the frequent consumption of seafood and an overweight status are both associated with elevated blood mercury levels in adolescents. Notably, the study highlights a stronger association between the consumption of large fish and tuna and blood mercury levels in the overweight group compared to the normal-weight group. Even though the levels of mercury in this study did not exceed the levels of exposure indicated in international guidelines, even low levels of mercury exposure can pose health risks, especially for adolescents in critical stages of development. More research is needed to evaluate the long-term effects of low-level mercury exposure on adolescent health, as well as to develop clearer guidelines for seafood consumption in this vulnerable group.
Abbreviations
BMI
body mass index
CI
confidence interval
EPA
U.S. Environmental Protection Agency
KDCA
Korea Disease Control and Prevention Agency
KoNEHS
Korean National Environmental Health Survey
KOSIS
Korean Statistical Information Service
MDL
method detection limit
NIER
National Institute of Environmental Research
SES
socioeconomic status
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Funding
This research was supported by the Inha University Hospital’s Environmental Health Center for Training Environmental Medicine Professionals, funded by the Ministry of Environment, Republic of Korea (2024).
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Competing interests
Dong-Wook Lee and Hwan-Cheol Kim contributing editors of the Annals of Occupational and Environmental Medicine, were not involved in the editorial evaluation or decision to publish this article. All remaining authors have declared no conflicts of interest.
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Author contributions
Conceptualization: Kim JH, Kim HD. Data curation: Kim HD. Formal analysis: Kim JH, Kim HD. Methodology: Jung M, Kim HD, Kim HC. Supervision: Kim HC, Leem JH, Park SG, Kim HD. Writing - original draft: Kim JH. Writing - review & editing: Jung M, Mun J, Seo DJ, Kim HC, Leem JH, Park SG, Lee DW, Kim HD.
NOTES
SUPPLEMENTARY MATERIAL
Supplementary Table 1.
Age- and sex-specific BMI over 85 percentile thresholds for overweight classification
Supplementary Table 2.
Interaction between a fish diet and obesity
Table 1.
General characteristics and whole blood mercury levels of the study population
| Characteristics | Total | Blood mercury level | p-valuea | p-valueb | ||
|---|---|---|---|---|---|---|
| Geometric mean (μg/L) | Low (<1.367 μg/L) | High (≥1.367 μg/L) | ||||
| Total | 825 (100) | 1.367 | 431 | 394 | ||
| Sex | <0.001 | <0.001 | ||||
| Male | 383 (46.4) | 1.485 | 165 (38.3) | 218 (55.3) | ||
| Female | 442 (53.6) | 1.274 | 266 (61.7) | 176 (44.7) | ||
| Mean age (years) | 14.64±1.685 | 14.69±1.685 | 14.58±1.685 | 0.849 | 0.901 | |
| Monthly household income (Won) | 0.264 | 0.329 | ||||
| <2,000,000 | 54 (6.5) | 1.312 | 34 (7.9) | 20 (5.1) | ||
| ≥2,000,000, <5,000,000 | 577 (69.9) | 1.345 | 305 (70.8) | 272 (69.0) | ||
| ≥5,000,000 | 147 (17.8) | 1.445 | 76 (17.6) | 71 (18.0) | ||
| Mother's education level | 0.22 | 0.683 | ||||
| Less than elementary school | 21 (2.5) | 1.649 | 9 (2.1) | 12 (3.0) | ||
| Elementary school to high school | 333 (40.4) | 1.36 | 175 (40.6) | 158 (40.1) | ||
| High school and above | 471 (57.1) | 1.362 | 247 (57.3) | 224 (56.9) | ||
| Father's education level | 0.904 | 0.839 | ||||
| Less than elementary school | 20 (2.4) | 1.341 | 11 (2.6) | 9 (2.3) | ||
| Elementary school to high school | 308 (37.3) | 1.382 | 157 (36.4) | 151 (38.3) | ||
| High school and above | 497 (60.2) | 1.361 | 263 (61.0) | 234 (59.4) | ||
| Smoker | 0.025 | 0.197 | ||||
| Non-smoker | 775 (93.9) | 1.352 | 411 (95.4) | 364 (92.4) | ||
| Past smoker | 24 (2.9) | 1.621 | 10 (2.3) | 14 (3.6) | ||
| Current smoker | 26 (3.2) | 1.669 | 10 (2.3) | 16 (4.1) | ||
| Alcohol consumption | 0.549 | 0.388 | ||||
| Never drinking | 540 (65.5) | 1.337 | 288 (66.8) | 252 (64.0) | ||
| Ever once | 285 (34.5) | 1.429 | 143 (33.2) | 142 (36.0) | ||
| Intake of herbal medicine | 0.028 | 0.095 | ||||
| Never | 732 (88.7) | 1.349 | 390 (90.5) | 342 (86.8) | ||
| Ever once | 93 (11.3) | 1.522 | 41 (9.5) | 52 (13.2) | ||
| Large fish and tuna | <0.001 | 0.002 | ||||
| Almost never consumed | 417 (50.5) | 1.264 | 243 (56.4) | 174 (44.2) | ||
| > Once per month | 280 (33.9) | 1.459 | 130 (30.2) | 150 (38.1) | ||
| > Once per week | 128 (15.5) | 1.54 | 58 (13.5) | 70 (17.8) | ||
| Fish | <0.001 | 0.003 | ||||
| Almost never consumed | 162 (19.6) | 1.175 | 101 (23.4) | 61 (15.5) | ||
| > Once per month | 409 (49.6) | 1.385 | 215 (49.9) | 194 (49.2) | ||
| > Once per week | 254 (30.8) | 1.477 | 115 (26.7) | 139 (35.3) | ||
| Crustaceans | 0.001 | 0.245 | ||||
| Almost never consumed | 279 (33.8) | 1.245 | 155 (36.0) | 124 (31.5) | ||
| > Once per month | 434 (52.6) | 1.426 | 224 (52.0) | 210 (53.3) | ||
| > Once per week | 112 (13.6) | 1.477 | 52 (12.0) | 60 (15.2) | ||
| Seaweeds | 0.124 | 0.443 | ||||
| Almost never consumed | 118 (14.3) | 1.255 | 68 (15.8) | 50 (12.7) | ||
| > Once per month | 292 (35.4) | 1.394 | 151 (35.0) | 141 (35.8) | ||
| > Once per week | 415 (50.3) | 1.384 | 212 (49.2) | 203 (51.5) | ||
| Mollusks | 0.002 | 0.079 | ||||
| Almost never consumed | 386 (46.8) | 1.287 | 213 (49.4) | 173 (43.9) | ||
| > Once per month | 351 (42.5) | 1.424 | 181 (42.0) | 170 (43.1) | ||
| > Once per week | 88 (10.7) | 1.528 | 37 (8.6) | 51 (12.9) | ||
| Other seafood | 0.007 | 0.068 | ||||
| Almost never consumed | 268 (32.5) | 1.269 | 152 (35.3) | 116 (29.4) | ||
| > Once per month | 414 (50.2) | 1.402 | 215 (49.9) | 199 (50.5) | ||
| > Once per week | 143 (17.3) | 1.471 | 64 (14.8) | 79 (20.1) | ||
| Overweight status | 0.014 | 0.004 | ||||
| Normal weight | 385 (46.7) | 1.307 | 222 (51.5) | 163 (41.4) | ||
| Overweight | 440 (53.3) | 1.425 | 209 (48.5) | 231 (58.6) | ||
Table 2.
Blood mercury concentrations of the study population
| Blood mercury concentration (μg/L) | Total (n = 825) | Normal weight (n = 385) | Overweight (n = 440) | p-valuea |
|---|---|---|---|---|
| Geometric mean | 1.37 ± 1.64 | 1.31 ± 1.59 | 1.43 ± 1.69 | <0.001*** |
| Percentile | ||||
| Min | 0.07 | 0.42 | 0.07 | |
| 25th | 1 | 0.97 | 1.01 | |
| 50th | 1.34 | 1.27 | 1.42 | |
| 75th | 1.85 | 1.76 | 1.95 | |
| Max | 17.35 | 9.58 | 17.35 |
Table 3.
Associations between log blood mercury concentrations and seafood eating habits
| Unadjusted model a | Adjusted model b | |||
|---|---|---|---|---|
| Estimate (95% CI) | p-value | Estimate (95% CI) | p-value | |
| Normal weight (n = 385) | ||||
| Large fish and tuna | ||||
| Almost never consumed | Reference | |||
| >Once per month | 0.098 (–0.004 to 0.201) | 0.059 | 0.105 (0.000 to 0.210) | 0.049 |
| >Once per week | 0.142 (0.008 to 0.277) | 0.038 | 0.127 (–0.01 to 0.265) | 0.07 |
| Fish | ||||
| Almost never consumed | Reference | |||
| >Once per month | 0.092 (–0.033 to 0.218) | 0.149 | 0.096 (–0.035 to 0.227) | 0.152 |
| >Once per week | 0.217 (0.084 to 0.349) | 0.001 | 0.215 (0.078 to 0.353) | 0.002 |
| Overweight (n = 440) | ||||
| Large fish and tuna | ||||
| Almost never consumed | Reference | |||
| >Once per month | 0.136 (0.025 to 0.246) | 0.016 | 0.132 (0.016 to 0.247) | 0.025 |
| >Once per week | 0.181 (0.035 to 0.327) | 0.015 | 0.176 (0.023 to 0.330) | 0.024 |
| Fish | ||||
| Almost never consumed | Reference | |||
| >Once per month | 0.165 (0.035 to 0.294) | 0.013 | 0.178 (0.042 to 0.314) | 0.011 |
| >Once per week | 0.142 (–0.004 to 0.288) | 0.056 | 0.137 (–0.017 to 0.29) | 0.081 |
| Total (n = 825) | ||||
| Large fish and tuna | ||||
| Almost never consumed | Reference | |||
| >Once per month | 0.116 (0.041 to 0.192) | 0.002 | 0.141 (0.046 to 0.236) | 0.004 |
| >Once per week | 0.158 (0.058 to 0.258) | 0.003 | 0.180 (0.077 to 0.284) | 0.001 |
| Fish | ||||
| Almost never consumed | Reference | |||
| >Once per month | 0.133 (0.041 to 0.224) | 0.004 | 0.116 (0.038 to 0.195) | 0.006 |
| >Once per week | 0.182 (0.083 to 0.281) | 0.000 | 0.147 (0.043 to 0.250) | 0.004 |
| Overweight | ||||
| Normal-weight | Reference | |||
| Overweight | 0.091 (0.025 to 0.158) | 0.007 | 0.084 (0.015 to 0.153) | 0.018 |
Blood mercury concentrations (µg/L) were log-transformed for analysis. Estimates represent changes in the log-transformed outcome variable. To interpret results in the original scale, use exp(Estimate).
CI: confidence interval.
aGeneralized linear model (GLM) was calculated by linear regression for log blood mercury concentration;
bGLM was calculated by linear regression for log blood mercury concentration after adjusting for sex, age, smoking, alcohol consumption, monthly household income, parent’s education level, and intake of traditional medicine.
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XML DownloadAssociation of weight and dietary habits with high blood mercury levels in Korean adolescents: data from the KoNEHS cycle 4, 2018-2020
Association of weight and dietary habits with high blood mercury levels in Korean adolescents: data from the KoNEHS cycle 4, 2018-2020
| Characteristics | Total | Blood mercury level | p-value |
p-value |
||
|---|---|---|---|---|---|---|
| Geometric mean (μg/L) | Low (<1.367 μg/L) | High (≥1.367 μg/L) | ||||
| Total | 825 (100) | 1.367 | 431 | 394 | ||
| Sex | <0.001 | <0.001 | ||||
| Male | 383 (46.4) | 1.485 | 165 (38.3) | 218 (55.3) | ||
| Female | 442 (53.6) | 1.274 | 266 (61.7) | 176 (44.7) | ||
| Mean age (years) | 14.64±1.685 | 14.69±1.685 | 14.58±1.685 | 0.849 | 0.901 | |
| Monthly household income (Won) | 0.264 | 0.329 | ||||
| <2,000,000 | 54 (6.5) | 1.312 | 34 (7.9) | 20 (5.1) | ||
| ≥2,000,000, <5,000,000 | 577 (69.9) | 1.345 | 305 (70.8) | 272 (69.0) | ||
| ≥5,000,000 | 147 (17.8) | 1.445 | 76 (17.6) | 71 (18.0) | ||
| Mother's education level | 0.22 | 0.683 | ||||
| Less than elementary school | 21 (2.5) | 1.649 | 9 (2.1) | 12 (3.0) | ||
| Elementary school to high school | 333 (40.4) | 1.36 | 175 (40.6) | 158 (40.1) | ||
| High school and above | 471 (57.1) | 1.362 | 247 (57.3) | 224 (56.9) | ||
| Father's education level | 0.904 | 0.839 | ||||
| Less than elementary school | 20 (2.4) | 1.341 | 11 (2.6) | 9 (2.3) | ||
| Elementary school to high school | 308 (37.3) | 1.382 | 157 (36.4) | 151 (38.3) | ||
| High school and above | 497 (60.2) | 1.361 | 263 (61.0) | 234 (59.4) | ||
| Smoker | 0.025 | 0.197 | ||||
| Non-smoker | 775 (93.9) | 1.352 | 411 (95.4) | 364 (92.4) | ||
| Past smoker | 24 (2.9) | 1.621 | 10 (2.3) | 14 (3.6) | ||
| Current smoker | 26 (3.2) | 1.669 | 10 (2.3) | 16 (4.1) | ||
| Alcohol consumption | 0.549 | 0.388 | ||||
| Never drinking | 540 (65.5) | 1.337 | 288 (66.8) | 252 (64.0) | ||
| Ever once | 285 (34.5) | 1.429 | 143 (33.2) | 142 (36.0) | ||
| Intake of herbal medicine | 0.028 | 0.095 | ||||
| Never | 732 (88.7) | 1.349 | 390 (90.5) | 342 (86.8) | ||
| Ever once | 93 (11.3) | 1.522 | 41 (9.5) | 52 (13.2) | ||
| Large fish and tuna | <0.001 | 0.002 | ||||
| Almost never consumed | 417 (50.5) | 1.264 | 243 (56.4) | 174 (44.2) | ||
| > Once per month | 280 (33.9) | 1.459 | 130 (30.2) | 150 (38.1) | ||
| > Once per week | 128 (15.5) | 1.54 | 58 (13.5) | 70 (17.8) | ||
| Fish | <0.001 | 0.003 | ||||
| Almost never consumed | 162 (19.6) | 1.175 | 101 (23.4) | 61 (15.5) | ||
| > Once per month | 409 (49.6) | 1.385 | 215 (49.9) | 194 (49.2) | ||
| > Once per week | 254 (30.8) | 1.477 | 115 (26.7) | 139 (35.3) | ||
| Crustaceans | 0.001 | 0.245 | ||||
| Almost never consumed | 279 (33.8) | 1.245 | 155 (36.0) | 124 (31.5) | ||
| > Once per month | 434 (52.6) | 1.426 | 224 (52.0) | 210 (53.3) | ||
| > Once per week | 112 (13.6) | 1.477 | 52 (12.0) | 60 (15.2) | ||
| Seaweeds | 0.124 | 0.443 | ||||
| Almost never consumed | 118 (14.3) | 1.255 | 68 (15.8) | 50 (12.7) | ||
| > Once per month | 292 (35.4) | 1.394 | 151 (35.0) | 141 (35.8) | ||
| > Once per week | 415 (50.3) | 1.384 | 212 (49.2) | 203 (51.5) | ||
| Mollusks | 0.002 | 0.079 | ||||
| Almost never consumed | 386 (46.8) | 1.287 | 213 (49.4) | 173 (43.9) | ||
| > Once per month | 351 (42.5) | 1.424 | 181 (42.0) | 170 (43.1) | ||
| > Once per week | 88 (10.7) | 1.528 | 37 (8.6) | 51 (12.9) | ||
| Other seafood | 0.007 | 0.068 | ||||
| Almost never consumed | 268 (32.5) | 1.269 | 152 (35.3) | 116 (29.4) | ||
| > Once per month | 414 (50.2) | 1.402 | 215 (49.9) | 199 (50.5) | ||
| > Once per week | 143 (17.3) | 1.471 | 64 (14.8) | 79 (20.1) | ||
| Overweight status | 0.014 | 0.004 | ||||
| Normal weight | 385 (46.7) | 1.307 | 222 (51.5) | 163 (41.4) | ||
| Overweight | 440 (53.3) | 1.425 | 209 (48.5) | 231 (58.6) | ||
| Blood mercury concentration (μg/L) | Total (n = 825) | Normal weight (n = 385) | Overweight (n = 440) | p-value |
|---|---|---|---|---|
| Geometric mean | 1.37 ± 1.64 | 1.31 ± 1.59 | 1.43 ± 1.69 | <0.001*** |
| Percentile | ||||
| Min | 0.07 | 0.42 | 0.07 | |
| 25th | 1 | 0.97 | 1.01 | |
| 50th | 1.34 | 1.27 | 1.42 | |
| 75th | 1.85 | 1.76 | 1.95 | |
| Max | 17.35 | 9.58 | 17.35 |
| Unadjusted model |
Adjusted model |
|||
|---|---|---|---|---|
| Estimate (95% CI) | p-value | Estimate (95% CI) | p-value | |
| Normal weight (n = 385) | ||||
| Large fish and tuna | ||||
| Almost never consumed | Reference | |||
| >Once per month | 0.098 (–0.004 to 0.201) | 0.059 | 0.105 (0.000 to 0.210) | 0.049 |
| >Once per week | 0.142 (0.008 to 0.277) | 0.038 | 0.127 (–0.01 to 0.265) | 0.07 |
| Fish | ||||
| Almost never consumed | Reference | |||
| >Once per month | 0.092 (–0.033 to 0.218) | 0.149 | 0.096 (–0.035 to 0.227) | 0.152 |
| >Once per week | 0.217 (0.084 to 0.349) | 0.001 | 0.215 (0.078 to 0.353) | 0.002 |
| Overweight (n = 440) | ||||
| Large fish and tuna | ||||
| Almost never consumed | Reference | |||
| >Once per month | 0.136 (0.025 to 0.246) | 0.016 | 0.132 (0.016 to 0.247) | 0.025 |
| >Once per week | 0.181 (0.035 to 0.327) | 0.015 | 0.176 (0.023 to 0.330) | 0.024 |
| Fish | ||||
| Almost never consumed | Reference | |||
| >Once per month | 0.165 (0.035 to 0.294) | 0.013 | 0.178 (0.042 to 0.314) | 0.011 |
| >Once per week | 0.142 (–0.004 to 0.288) | 0.056 | 0.137 (–0.017 to 0.29) | 0.081 |
| Total (n = 825) | ||||
| Large fish and tuna | ||||
| Almost never consumed | Reference | |||
| >Once per month | 0.116 (0.041 to 0.192) | 0.002 | 0.141 (0.046 to 0.236) | 0.004 |
| >Once per week | 0.158 (0.058 to 0.258) | 0.003 | 0.180 (0.077 to 0.284) | 0.001 |
| Fish | ||||
| Almost never consumed | Reference | |||
| >Once per month | 0.133 (0.041 to 0.224) | 0.004 | 0.116 (0.038 to 0.195) | 0.006 |
| >Once per week | 0.182 (0.083 to 0.281) | 0.000 | 0.147 (0.043 to 0.250) | 0.004 |
| Overweight | ||||
| Normal-weight | Reference | |||
| Overweight | 0.091 (0.025 to 0.158) | 0.007 | 0.084 (0.015 to 0.153) | 0.018 |
Table 1. General characteristics and whole blood mercury levels of the study population
Values are presented as number (%).
Table 2. Blood mercury concentrations of the study population
***
Table 3. Associations between log blood mercury concentrations and seafood eating habits
Blood mercury concentrations (µg/L) were log-transformed for analysis. Estimates represent changes in the log-transformed outcome variable. To interpret results in the original scale, use exp(Estimate). CI: confidence interval. Generalized linear model (GLM) was calculated by linear regression for log blood mercury concentration; GLM was calculated by linear regression for log blood mercury concentration after adjusting for sex, age, smoking, alcohol consumption, monthly household income, parent’s education level, and intake of traditional medicine.
