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The effects of chemical mixtures on lipid profiles in the Korean adult population: threshold and molecular mechanisms for dyslipidemia involved

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Abstract

A scarcity of research assesses the effects of exposure to a combination of chemicals on lipid profiles as well as molecular mechanisms related to dyslipidemia. A cross-sectional study of 3692 adults aims to identify the association between chemical mixtures, including blood and urine 26 chemicals, and lipid profiles among Korean adults (aged ≥ 18) using linear regression models, weighted quantile sum (WQS) regression, quantile g-computation (qgcomp), and Bayesian kernel machine regression (BKMR). In silico toxicogenomic data-mining, we assessed molecular mechanisms linked with dyslipidemia, including genes, miRNAs, pathways, biological processes, and diseases. In the linear regression models, heavy metals, volatile organic compound metabolites, and phthalate metabolites were found to be related to HDL-C, triglycerides, LDL-C, total lipids, and total cholesterol, and significant trends were observed for these chemical quartiles (p < 0.01). The WQS index was significantly linked with HDL-C, triglycerides, LDL-C, total cholesterol, and total lipids. The qgcomp index also found a significant association between chemicals and HDL-C, triglycerides, and total lipids. In BKMR analysis, the overall effect of the chemical mixture was significantly associated with HDL-C, triglycerides, total cholesterol, and total lipids. We found that mixed chemicals interacted with the PPARA gene and were linked with dyslipidemia. Several pathways (“SREBF and miR33 in cholesterol,” “estrogen receptor pathway and lipid homeostasis,” and “regulation of PGC-1α”), “negative regulation of hepatocyte apoptotic process,” “negative regulation of sequestering of triglycerides,” “regulation of hepatocyte apoptotic process,” and “negative regulation of cholesterol storage,” and “abdominal obesity metabolic syndrome” were identified as key molecular mechanisms that may be affected by mixed chemicals and implicated in the development of dyslipidemia. The highest interaction and expression of miRNAs involved in the process of dyslipidemia were also described. Especially, the cutoff levels for chemical exposure levels related to lipid profiles were also provided.

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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, http://www.nier.go.kr/NIER/kor/openapi/getEcoHealth.do?menuNo=14018.

Abbreviations

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-isononyl phthalate

MCPP:

Mono (3-carboxypropyl) phthalate

ttMA:

t,t-Muconic acid

BMA:

Benzylmercapturic acid

1OHP:

1-Hydroxypyrene

2NAP:

2-Naphthol

2OHFlu:

2-Hydroxyfluorene

1OHPhe:

1-Hydroxyphenanthrene

BPA:

Bisphenol A

BPF:

Bisphenol F

BPS:

Bisphenol S

TCS:

Triclosan

MeP:

Methylparaben

EtP:

Ethylparaben

PrP:

Propylparaben

3PBA:

3-Phenoxybenzoic acid

COT:

Cotinine

BPb:

Blood lead

BHg:

Blood mercury

UHg:

Urine mercury

UCd:

Urine cadmium

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 is supported by grants National Research Foundation of Korea (NRF) grant funded by the Korean government (MEST) (grant nos. NRF2013R1A1A3008851, and 2018R1D1A1B07049610).

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Authors and Affiliations

  1. Department of Pharmacy, College of Pharmacy and Research Institute of Life and Pharmaceutical Sciences, Sunchon National University, Sunchon, Jeonnam, Republic of Korea

    Hai Duc Nguyen, Hojin Oh & Min-Sun Kim

Authors
  1. Hai Duc Nguyen
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  2. Hojin Oh
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Contributions

Hai Duc Nguyen: conceptualization, methodology, formal analysis, investigation, resources, data curation, writing—original draft, writing—review and editing, visualization.

Min-Sun Kim: visualization, project administration, supervision.

Hojin Oh: visualization.

Corresponding author

Correspondence to Min-Sun Kim.

Ethics declarations

Ethics approval and consent to participate

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).

Consent for publication

Not applicable.

Conflict of interest

The authors declare no competing interests.

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Responsible Editor: Lotfi Aleya

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Highlights

- The combined effect of 26 chemicals on lipid profiles was identified.

- A mixture of chemical exposures was associated with the risk of dyslipidemia.

- HDL-C, TG, total cholesterol, and lipids were related to mixture of chemical exposure.

- Molecular mechanisms related to dyslipidemia were provided.

- The cutoff levels for chemical levels related to dyslipidemia were reported.

Supplementary Information

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Nguyen, H., Oh, H. & Kim, MS. The effects of chemical mixtures on lipid profiles in the Korean adult population: threshold and molecular mechanisms for dyslipidemia involved. Environ Sci Pollut Res 29, 39182–39208 (2022). https://doi.org/10.1007/s11356-022-18871-2

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  • DOI: https://doi.org/10.1007/s11356-022-18871-2

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