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Mixtures modeling identifies heavy metals and pyrethroid insecticide metabolites associated with obesity

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Abstract

We aim to examine the association between chemical mixtures and obesity. Blood and urinary levels of tween-six chemicals were measured in adults who participated in the KoNEHS. We identified the associations of chemicals with obesity using linear regression models. Weighted quantile sum (WQS) regression, quantile g-computation (qgcomp), and Bayesian kernel machine regression (BKMR) were conducted as secondary analyses. Of the 3,692 participants included in the analysis, 18.0% had obesity. In the logistic regression model, mercury (Hg), lead (Pb), and 3PBA levels were associated with obesity, and significant trends were observed for these chemical tertiles (p < 0.001). Hg, Pb, and 3PBA levels were also associated with BMI. The WQS index was significantly associated with both obesity (OR = 2.15, 95% CI: 2.11–2.20) and BMI (β = 0.39, 95% CI: 0.37–0.51). The qgcomp index also found a significant association between chemicals and both obesity (OR = 1.70, 95% CI: 1.56–1.85) and BMI (β = 0.40, 95% CI: 0.39–0.41). Hg, Pb, and 3PBA were the most heavily weighed chemicals in these models. In BKMR analysis, the overall effect of the mixture was significantly associated with obesity. Hg, Pb, and 3PBA showed positive trends and were observed as the most important factors associated with obesity. Given increasing exposure to chemicals, there is a need to investigate the associations between chemical exposures, either separately or together, and incident obesity risk factors in well-characterized cohorts of different populations, and to identify potential approaches to chemical exposure prevention.

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Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

Change history

  • 26 November 2021

    Correct ORCID for the 1st Author

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

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Acknowledgements

The authors are grateful to all research staff that contributed to the data collection required for this study.

Funding

This work was supported by National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (grant nos. NRF2013R1A1A3008851 and 2018R1D1A1B07049610).

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Authors and Affiliations

Authors

Contributions

Hai Duc Nguyen: conceptualization; methodology; formal analysis; investigation; resources; data curation; writing, original draft; writing, review and editing; visualization. Min-Sun Kim: conceptualization; resources; data curation; writing, review and editing; visualization; supervision; project administration. Hojin Oh: validation, visualization. Ngoc Hong Minh Hoang and Won Hee Jo: investigation, visualization.

Corresponding author

Correspondence to Min-Sun Kim.

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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).

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Not applicable.

Competing interests

The authors declare no competing interests.

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Nguyen, H.D., Oh, H., Jo, W.H. et al. Mixtures modeling identifies heavy metals and pyrethroid insecticide metabolites associated with obesity. Environ Sci Pollut Res 29, 20379–20397 (2022). https://doi.org/10.1007/s11356-021-16936-2

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Keywords

  • Obesity
  • Lead
  • Mercury
  • 3PBA
  • Chemical mixture