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Brief Communication
Levels of exposure markers among residents in environmentally vulnerable areas in Korea, the general population in Korea, and Asians in the United States
Kyung-Hwa Choi, Dahee Han, Sang-Yong Eom, Yong Min Cho, Young-Seoub Hong, Woo Jin Kim
Epidemiol Health. 2025;47:e2025007.   Published online February 25, 2025
DOI: https://doi.org/10.4178/epih.e2025007
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  • 56 Download
AbstractAbstract AbstractSummary PDFSupplementary Material
Abstract
This study compares biomarker levels among environmentally vulnerable residents in Korea, the general Korean population, and Asians in the United States. We selected 953 exposed residents and 204 controls from the Forensic Research via Omics Markers in Environmental Health Vulnerable Areas (FROM) study (2021-2023), 4,239 participants from the fourth Korean National Environmental Health Survey (2018-2020), and 996 Asians from the U.S. National Health and Nutrition Examination Survey (2017-March 2020). The analyzed biomarkers included blood and urinary metals, urinary metabolites of polycyclic aromatic hydrocarbons, nicotine, volatile organic compounds, and serum perfluorocarbon metabolites. The highest median biomarker levels varied by pollution source among older adults. In refineries, blood lead and cadmium (Cd), as well as urinary Cd and 2-hydroxyfluorene, were highest. Abandoned metal mines exhibited the highest blood and urinary mercury, urinary Cd, total arsenic (As), 2-naphthol, and cotinine levels. Coal-fired power plants showed the highest urinary 1- hydroxyphenanthrene levels, while cement factories had the highest urinary As<sup>3+</sup> levels. Sprawls demonstrated the highest urinary monomethylarsonic acid, 1-hydroxypyrene, and phenylglyoxylic acid levels, and industrial areas recorded the highest levels of trans, trans-muconic acid, benzylmercapturic acid, and 2-methylhippuric acid. In general, biomarker levels were higher among exposed residents in the FROM study than in the general population; however, urinary 2-hydroxyfluorene and As<sup>5+</sup> levels did not differ significantly. Exposure to pollution sources in environmentally vulnerable areas may elevate biomarker levels in residents.
Summary
Korean summary
본 연구는 환경보건 취약지역 거주자, 대한민국의 일반인구집단, 미국에 거주하는 아시아인의 체내 환경유해물질 바이오마커 농도를 비교하였다. 체내 바이오마커의 농도는 석유정제시설, 폐금속광산, 화력발전소 등 환경보건 취약지역의 유형에 따라 차이를 보였다. 본 연구가 가지는 과학적, 역학적 의미는 환경보건 취약지역 유형별 환경유해물질 노출의 차이와 그로 인한 잠재적 건강영향을 알아봄으로써 환경보건 취약계층을 위한 특이적인 중재가 이루어질 수 있도록 하는데 있다.
Key Message
This study examines environmental health risks for vulnerable populations by comparing biomarker levels among exposed residents in Korea, the general Korean population, and Asians in the United States. Biomarker levels were found to be elevated near pollution sources such as refineries, metal mines, and power plants, with variations based on pollutant types. The scientific and epidemiological significance lies in revealing disparities in exposure and potential health effects, thereby contributing to targeted interventions for environmentally vulnerable groups.
Data Profile
Introduction to the forensic research via omics markers in environmental health vulnerable areas (FROM) study
Jung-Yeon Kwon, Woo Jin Kim, Yong Min Cho, Byoung-gwon Kim, Seungho Lee, Jee Hyun Rho, Sang-Yong Eom, Dahee Han, Kyung-Hwa Choi, Jang-Hee Lee, Jeeyoung Kim, Sungho Won, Hee-Gyoo Kang, Sora Mun, Hyun Ju Yoo, Jung-Woong Kim, Kwan Lee, Won-Ju Park, Seongchul Hong, Young-Seoub Hong
Epidemiol Health. 2024;46:e2024062.   Published online July 12, 2024
DOI: https://doi.org/10.4178/epih.e2024062
  • 3,817 View
  • 140 Download
  • 2 Web of Science
  • 2 Crossref
AbstractAbstract AbstractSummary PDF
Abstract
This research group (forensic research via omics markers in environmental health vulnerable areas: FROM) aimed to develop biomarkers for exposure to environmental hazards and diseases, assess environmental diseases, and apply and verify these biomarkers in environmentally vulnerable areas. Environmentally vulnerable areas—including refineries, abandoned metal mines, coal-fired power plants, waste incinerators, cement factories, and areas with high exposure to particulate matter—along with control areas, were selected for epidemiological investigations. A total of 1,157 adults, who had resided in these areas for over 10 years, were recruited between June 2021 and September 2023. Personal characteristics of the study participants were gathered through a survey. Biological samples, specifically blood and urine, were collected during the field investigations, separated under refrigerated conditions, and then transported to the laboratory for biomarker analysis. Analyses of heavy metals, environmental hazards, and adducts were conducted on these blood and urine samples. Additionally, omics analyses of epigenomes, proteomes, and metabolomes were performed using the blood samples. The biomarkers identified in this study will be utilized to assess the risk of environmental disease occurrence and to evaluate the impact on the health of residents in environmentally vulnerable areas, following the validation of diagnostic accuracy for these diseases.
Summary
Korean summary
환경보건 취약지역 주민을 대상으로 실시한 현장 역학 조사에서 혈액과 소변 시료를 안정적으로 확보하였다. 현장에서 확보한 시료는 즉시 이송하여 오믹스 분석을 통해 환경유해인자별, 환경성질환별 특이적인 바이오마커를 개발한다.
Key Message
Blood and urine samples were stably obtained from on-site epidemiological investigations done on residents in environmental health vulnerable areas, and samples obtained from the sites were immediately transported to the omics laboratory after separation under biobank system. Through this analysis, we aimed to develop biomarkers specific to each environmental hazard and disease.

Citations

Citations to this article as recorded by  
  • Heavy metal exposure and its effects on APOC3, CFAI, and ZA2G
    Nam-Eun Kim, Min Heo, Hyeongyu Shin, Ah Ra Do, Jeeyoung Kim, Hee-Gyoo Kang, Sora Mun, Hyun Ju Yoo, Mi Jeong Kim, Jung-Woong Kim, Chul-Hong Kim, Young-Seoub Hong, Yong Min Cho, Heejin Jin, Kyungtaek Park, Woo Jin Kim, Sungho Won
    Journal of Hazardous Materials.2025; 482: 136574.     CrossRef
  • A Comparative Study on the Paradoxical Relationship Between Heavy Metal Exposure and Kidney Function
    Jee Hyun Rho, Seungho Lee, Jung-Yeon Kwon, Young-Seoub Hong
    Diagnostics.2025; 15(1): 86.     CrossRef
Original Article
Dynamic changes in clinical biomarkers of cardiometabolic diseases by changes in exercise behavior, and network comparisons: a community-based prospective cohort study in Korea
JooYong Park, Jaesung Choi, Ji-Eun Kim, Sang-Min Park, Joo-Youn Cho, Daehee Kang, Miyoung Lee, Ji-Yeob Choi
Epidemiol Health. 2023;45:e2023026.   Published online February 16, 2023
DOI: https://doi.org/10.4178/epih.e2023026
  • 6,327 View
  • 90 Download
  • 1 Web of Science
  • 1 Crossref
AbstractAbstract AbstractSummary PDFSupplementary Material
Abstract
OBJECTIVES
Lifestyles, including exercise behaviors, change continually over time. This study examined whether the clinical biomarkers (CBs) related to cardiometabolic diseases (CMDs) and their relationships differed with changes in exercise behavior.
METHODS
The Ansan-Ansung cohort study (third to fifth phases; n=2,668) was used in the current study. Regular exercise behavior was investigated using a yes/no questionnaire. Changes in exercise behavior were classified into 4 groups: Y-N, N-Y, Y-Y, and N-N, with “Y” indicating that a participant regularly engaged in exercise at a given time point and “N” indicating that he or she did not. Fourteen CBs related to CMDs were used, and the associations between changes in exercise behavior and relative changes in CBs were examined. CB networks were constructed and topological comparisons were conducted.
RESULTS
Y-N was associated with increases in fasting blood sugar and insulin levels in men, and increased total cholesterol and low-density lipoprotein cholesterol levels in women. Meanwhile, N-Y was inversely associated with body fat percentage, visceral fat percentage, fasting insulin, and triglyceride level. Waist circumference played a central role in most networks. In men, more edges were found in the N-Y and Y-Y groups than in the N-N and Y-N groups, whereas women in the N-Y and Y-Y groups had more edges than those in the N-N and Y-N groups.
CONCLUSIONS
Consistent exercise or starting to engage in regular exercise had favorable effects on CBs related to CMDs, although their network patterns differed between the sexes.
Summary
Korean summary
한국 지역사회기반 코호트 자료를 이용하여, 운동 행태 변화에 따른 심혈관대사질환 관련 임상 생체 지표들의 변화가 남녀에 따라, 변화 행태에 따라 다르게 나타남을 확인하였다. 이런 변화와 차이는 네트워크 분석을 통한 구조적인 차이로도 확인되었다.
Key Message
This study examined that changes in the clinical biomarkers related to cardiometabolic diseases differed with changes in exercise behavior using a community-based prospective cohort study in Korea. Consistent exercise or change into exercise behavior had favorable effects on CB related to CMD, although their network patterns differed between the sexes.

Citations

Citations to this article as recorded by  
  • A 6-month exercise intervention clinical trial in women: effects of physical activity on multi-omics biomarkers and health during the first wave of COVID-19 in Korea
    JooYong Park, Jaemyung Kim, Jihyun Kang, Jaesung Choi, Ji-Eun Kim, Kyung-Joon Min, Seong-Woo Choi, Joo-Youn Cho, Miyoung Lee, Ji-Yeob Choi
    BMC Sports Science, Medicine and Rehabilitation.2024;[Epub]     CrossRef
Methods
The clinical meaning of the area under a receiver operating characteristic curve for the evaluation of the performance of disease markers
Stefano Parodi, Damiano Verda, Francesca Bagnasco, Marco Muselli
Epidemiol Health. 2022;44:e2022088.   Published online October 17, 2022
DOI: https://doi.org/10.4178/epih.e2022088
  • 9,294 View
  • 143 Download
  • 14 Web of Science
  • 16 Crossref
AbstractAbstract AbstractSummary PDFSupplementary Material
Abstract
OBJECTIVES
The area under a receiver operating characteristic (ROC) curve (AUC) is a popular measure of pure diagnostic accuracy that is independent from the proportion of diseased subjects in the analysed sample. However, its actual usefulness in the clinical context has been questioned, because it does not seem to be directly related to the actual performance of a diagnostic marker in identifying diseased and non-diseased subjects in real clinical settings. This study evaluates the relationship between the AUC and the proportion of correct classifications (global diagnostic accuracy, GDA) in relation to the shape of the corresponding ROC curves.
METHODS
We demonstrate that AUC represents an upward-biased measure of GDA at an optimal accuracy cut-off for balanced groups. The magnitude of bias depends on the shape of the ROC plot and on the proportion of diseased and non-diseased subjects. In proper curves, the bias is independent from the diseased/non-diseased ratio and can be easily estimated and removed. Moreover, a comparison between 2 partial AUCs can be replaced by a more powerful test for the corresponding whole AUCs.
RESULTS
Applications to 3 real datasets are provided: a marker for a hormone deficit in children, 2 tumour markers for malignant mesothelioma, and 2 gene expression profiles in ovarian cancer patients.
CONCLUSIONS
The AUC is a measure of accuracy with potential clinical relevance for the evaluation of disease markers. The clinical meaning of ROC parameters should always be evaluated with an analysis of the shape of the corresponding ROC curve.
Summary
Key Message
The area under a ROC curve is a measure of diagnostic accuracy with potential clinical relevance, whose meaning should always be evaluated analysing the shape of the corresponding curve.

Citations

Citations to this article as recorded by  
  • Predicting lack of clinical improvement following varicose vein ablation using machine learning
    Ben Li, Naomi Eisenberg, Derek Beaton, Douglas S. Lee, Leen Al-Omran, Duminda N. Wijeysundera, Mohamad A. Hussain, Ori D. Rotstein, Charles de Mestral, Muhammad Mamdani, Graham Roche-Nagle, Mohammed Al-Omran
    Journal of Vascular Surgery: Venous and Lymphatic Disorders.2025; 13(3): 102162.     CrossRef
  • Identification and validation of five ferroptosis-related molecular signatures in keloids based on multiple transcriptome data analysis
    Zhen Sun, Yonghong Qin, Xuanfen Zhang
    Frontiers in Molecular Biosciences.2025;[Epub]     CrossRef
  • Manner of death prediction: A machine learning approach to classify suicide and non-suicide using blood metabolomics
    Witchayawat Sunthon, Thitiwat Sopananurakkul, Giatgong Konguthaithip, Yutti Amornlertwatana, Somlada Watcharakhom, Kanicnan Intui, Churdsak Jaikang
    Forensic Science International: Synergy.2025; 10: 100580.     CrossRef
  • Performance of the necker cranial injury scale as a predictor of prognosis in children with severe traumatic brain injury: A retrospective cohort study
    José Roberto Tude Melo, Isadora Araújo Santos Lobo, Luíza Malheiros Montagna, Sophia Totaro, Valentina Ponchio Vasques, Luciana Andrea Digieri Chicuto, Jean Gonçalves de Oliveira, José Carlos Esteves Veiga
    Neurochirurgie.2025; 71(3): 101657.     CrossRef
  • Prediction of potential habitat of Verbena officinalis in China under climate change based on optimized MaxEnt model
    Shimao Chen, Zixuan Jiang, Jia Song, Tao Xie, Yu Xue, Qingshan Yang
    Frontiers in Plant Science.2025;[Epub]     CrossRef
  • Interpretable machine learning for predicting chronic kidney disease progression risk
    Jin-Xin Zheng, Xin Li, Jiang Zhu, Shi-Yang Guan, Shun-Xian Zhang, Wei-Ming Wang
    DIGITAL HEALTH.2024;[Epub]     CrossRef
  • Current and future distribution of Forsythia suspensa in China under climate change adopting the MaxEnt model
    En Wang, Zongran Lu, Emelda Rosseleena Rohani, Jinmei Ou, Xiaohui Tong, Rongchun Han
    Frontiers in Plant Science.2024;[Epub]     CrossRef
  • Dysbiosis of intestinal microbiota and metabolism caused by acute patulin exposure in mice
    Ting Zhang, Zimeng Guo, Jiayin Che, Min Yan, Jingyimei Liang, Furong Wang, Jinhong Hu, Wei Song, Yahong Yuan, Tianli Yue
    Food Frontiers.2024; 5(4): 1819.     CrossRef
  • Knee-Loading Predictions with Neural Networks Improve Finite Element Modeling Classifications of Knee Osteoarthritis: Data from the Osteoarthritis Initiative
    Alexander Paz, Jere Lavikainen, Mikael J. Turunen, José J. García, Rami K. Korhonen, Mika E. Mononen
    Annals of Biomedical Engineering.2024; 52(9): 2569.     CrossRef
  • Navigating test accuracy metrics used in diagnostic evaluation
    Ahmad Hamdan, Lubna A. Zar, Suhail A. Doi, Tawanda Chivese, Muhammad N. Khan, Salma M. Khaled, Giridhara R. Babu, Habib Hasan Farooqui
    Current Opinion in Epidemiology and Public Health.2024; 3(3): 45.     CrossRef
  • Assessment of urine metabolite biomarkers for the detection of S. haematobium infection in pre-school aged children in a rural community in Zimbabwe
    Herald Midzi, Thajasvarie Naicker, Arthur Vengesai, Lucy Mabaya, Petros Muchesa, Tariro L. Mduluza-Jokonya, Aaron Garikai Katerere, Donald Kapanga, Maritha Kasambala, Francisca Mutapi, Takafira Mduluza
    Acta Tropica.2024; 258: 107327.     CrossRef
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    Hexin Li, Negin Ashrafi, Chris Kang, Guanlan Zhao, Yubing Chen, Maryam Pishgar, Upaka Rathnayake
    PLOS ONE.2024; 19(9): e0309383.     CrossRef
  • Predicting the potential distribution of Astragali Radix in China under climate change adopting the MaxEnt model
    Zixuan Wen, Ke Yan, Man Zhang, Ruiqing Ma, Xiaoyan Zhu, Qing Duan, Xiaolin Jiang
    Frontiers in Plant Science.2024;[Epub]     CrossRef
  • Electromyographic Diagnostic Ranges Defining Temporomandibular Disorders and Healthy Individuals’ Results in Functional Clenching Index
    Grzegorz Zieliński, Michał Ginszt
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  • Identification of key signaling pathways and hub genes related to immune infiltration in Kawasaki disease with resistance to intravenous immunoglobulin based on weighted gene co-expression network analysis
    Yue Wang, Yinyin Cao, Yang Li, Meifen Yuan, Jin Xu, Jian Li
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  • Escherichia coli triggers α-synuclein pathology in the LRRK2 transgenic mouse model of PD
    Dongxiao Liang, Han Liu, Ruoqi Jin, Renyi Feng, Jiuqi Wang, Chi Qin, Rui Zhang, Yongkang Chen, Jingwen Zhang, Junfang Teng, Beisha Tang, Xuebing Ding, Xuejing Wang
    Gut Microbes.2023;[Epub]     CrossRef
Review
Relationship between Stress and Biomarkers.
Sang Baek Koh
Korean J Epidemiol. 2002;24(2):137-147.
  • 7,173 View
  • 36 Download
AbstractAbstract PDF
Abstract
Stress can induce modifications in the central nervous(CNS), autonomic nervous and neuroendocrine system. Thus, the stress response has long been measured in laboratory experiments by biochemical changes in the hormone systems that are referred to as the sympathetic nervous system(SNS) and pituitary-adrenocortical axes(HPA). These axes react to acute stress or chronic stress. The activation of these two particular pathways result in elevated serum levels of catecholamines, cortisol, ACTH, dopamine, and others hormones. But there is considerable debate about the relevance of traditional laboratory stress findings to real-life situation. The neurobiology of stress is a key step to the understanding of stress-induced changes of immune functions. The immune system operates in communication with brain and endocrine system. Because of this extensive communication, the immune system can influence how we feel and behave. The stress are associated with endocrine and autonomic changes that can inhibit immune system function. The concept of neurocardiology renders plausible the various theoretical constructs of stress as they relate to circulatory vascular disease. Detailed reviews of the anatomic connections between the brain and the heart and of experimental and clinical data on the role of the CNS in cardiac dysfunction can be found elsewhere. In this study, we reviewed that stress was associated with cardiovascular disease mortality through the known cardiovascular risk factors(hypertension, heart rate variability, homocycteine, and clotting system).
Summary
Epidemiol Health : Epidemiology and Health
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