Abstract
There is a scarcity of research on the effects of a mixture of chemicals on liver function biomarkers and non-alcoholic fatty liver disease (NAFLD) indices, including FSI, HIS, and FBI-4. Thus, we aimed to explore whether there is an association between chemical mixtures, including 26 chemicals found in blood and urine, liver function biomarkers, and non-alcoholic fatty liver disease (NAFLD) indices in Korean adults. The effects of exposure to chemical mixtures on liver function biomarkers and NAFLD indices were investigated using linear regression models, weighted quantile sum (WQS) regression, quantile g-computation (qgcomp), and Bayesian kernel machine regression (BKMR) among 3669 adults. In silico toxicogenomic data-mining, we evaluated molecular mechanisms associated with NAFLD, including pathways, diseases, genes, miRNAs, and biological processes. The linear regression models showed blood or urine Hg levels were the most important factors associated with AST, ALT, GGT, FSI, and HSI levels, and significant trends were observed for these chemical quartiles (p < 0.01). The WQS index was significantly associated with ALT, GGT, FSI, and HSI. The qgcomp index also found an association between chemicals and AST, ALT, GGT, and FSI. In the BKMR model, the overall effect of the mixture was significantly related to ALT, GGT, FSI, and HSI. In silico analysis, we found mixed chemicals interacted with the CYP1A2 gene and were associated with NAFLD. Seventy-eight percent of interactions were identified as physical interactions in the CYP1A2 gene related to NAFLD. Transcription factor regulation in adipogenesis and lipid metabolic processes are fundamental molecular mechanisms that could be influenced by NAFLD-related mixed chemicals. Cutoff thresholds for chemical exposure levels associated with liver function indicators and NAFLD indices were also reported. The strongest interactions and expression of miRNAs involved in NAFLD development were also identified.
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Data availability
The datasets examined during this study are available on request at the National Institute of Environmental Research, Environmental Health Research Department by dialing + 82–032–560–7129.
Abbreviations
- BPb:
-
Blood lead
- BHg:
-
Blood mercury
- UHg:
-
Urine mercury
- UCd:
-
Urine cadmium
- ttMA:
-
T,t-muconic acid
- BMA:
-
Benzylmercapturic acid
- 1OHP:
-
1-Hydroxypyrene
- 2NAP:
-
2-Naphthol
- 2OHFlu:
-
2-Hydroxyfluorene
- 1OHPhe:
-
1-Hydroxyphenanthrene
- MEHHP:
-
Mono-(2-ethyl-5-hydroxyhexyl) phthalate
- MEOHP:
-
Mono-(2-ethyl-5-oxohexyl) phthalate
- MnBP:
-
Mono-n-butyl phthalate
- MBzP:
-
Mono-benzyl phthalate
- MECPP:
-
Mono-(2-ethyl-5-carboxypentyl) phthalate
- MCOP:
-
Mono-carboxyoctyl phthalate
- MCNP:
-
Mono-carboxy-isononly phthalate
- MCPP:
-
Mono (3-carboxypropyl) phthalate
- BPA:
-
Bisphenol A
- BPF:
-
Bisphenol F
- BPS:
-
Bisphenol S
- TCS:
-
Triclosan
- MeP:
-
Methylparaben
- EtP:
-
Ethylparaben
- PrP:
-
Propylparaben
- 3PBA:
-
3-Phenoxybenzoic acid
- COT:
-
Cotinine
- EDCs:
-
Endocrine disrupting chemicals
- VOCs:
-
Volatile organic compounds
- PAH:
-
Polycyclic aromatic hydrocarbon
- BKMR:
-
Bayesian kernel machine regression
- QWS:
-
Weighted quantile sum
- Qgcomp:
-
Quantile g-computation
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Acknowledgements
The authors are grateful to all research staff for their excellent contributions in data collection in the survey.
Funding
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MEST) (grant nos. NRF2013R1A1A3008851, and 2018R1D1A1B07049610).
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Hai Duc Nguyen: Conceptualization, methodology, formal analysis, investigation, resources, data curation, writing (original draft), writing (review and editing), visualization. Min-Sun Kim: Visualization.
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All participants in KoNEHS provided written informed consent. The KoNEHS dataset has been de-identified and made publicly available. This survey was approved by the institutional review board of the NIER in Korea (NIER-2016-BR-003–01, NIER-2016-BR-003–03).
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The authors declare no competing interests.
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Highlights
• Mixed chemicals were linked with the risk of non-alcoholic fatty liver disease (NAFLD).
• CYP1A2 gene was linked with mixed chemicals and NAFLD development.
• Key molecular mechanisms affected by NAFLD-related mixed chemicals were provided.
• Chemicals’ cutoff thresholds linked with the risk of suspected NAFLD were reported.
• miRNAs involved in NAFLD development were also identified.
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Nguyen, H.D., Kim, MS. Effects of chemical mixtures on liver function biomarkers in the Korean adult population: thresholds and molecular mechanisms for non-alcoholic fatty liver disease involved. Environ Sci Pollut Res 29, 78555–78587 (2022). https://doi.org/10.1007/s11356-022-21090-4
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DOI: https://doi.org/10.1007/s11356-022-21090-4