ArticleHeliyon2024
Association between heavy metal exposure and biomarkers for non-alcoholic fatty liver disease in Korean adolescents.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Reliability Assessment of Metal Quantification in Formalin-fixed, Paraffin-embedded Human Liver Tissue for Studying Liver Diseases.Research square · 2026Article
- Inflammatory and metabolic markers mediate the association between urinary metals and non-alcoholic fatty liver disease in U.S. adults: a cross-sectional study.Frontiers in public health · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: The global prevalence of non-alcoholic fatty liver disease (NAFLD) in adolescents has increased. In addition to childhood obesity, environmental risk factors, such as heavy metals that are known to be involved in hepatotoxicity, play role in NAFLD occurrence. However, their association with NAFLD remains unclear. This study aimed to investigate the association between heavy metal exposure and NAFLD biomarkers in adolescents. Methods: In this cross-sectional study, we used the data of a total of 1505 adolescents aged 12-17 years who participated in the Korean National Environmental Health Survey III (2015-2017) and IV (2018-2020). The presence of blood lead (BPb), blood mercury (BHg), urinary mercury (UHg), and urinary cadmium (UCd) were measured. Liver enzymes including serum aspartate aminotransferase (AST), alanine aminotransferase (ALT), and gamma-glutamyl transferase (GGT) were evaluated. For NAFLD biomarkers, the hepatic steatosis index (HSI) was calculated. Multivariate linear regression models, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) model were used to investigate the association between heavy metals and NAFLD biomarkers. Results: Among heavy metals, mercury presence showed a significant association with NAFLD biomarkers. Two-fold increases in BHg and UHg were associated with 0.21 points (95 % confidence interval [CI]: 0.08-0.35) and 0.19 points (95 % CI: 0.09-0.30) higher HSI, respectively. In the WQS model, heavy metal mixture was significantly associated with increased HSI (β = 0.06, 95 % CI: 0.01-0.11). Similarly, in the BKMR model, heavy metal mixture was positively associated with NAFLD biomarkers, and BHg was the most important contributor in the association. Conclusions: BHg and UHg were significantly associated with NAFLD biomarkers in adolescents, indicating that organic and inorganic mercury exposure could potentially be a risk factor for NAFLD. To mitigate and address the risk of NAFLD associated with heavy metal exposure, it is imperative to take measure to reduce avoidable mercury exposure is necessary.
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