Mixed exposure to phenol, parabens, pesticides, and phthalates and insulin resistance in NHANES: A mixture approach.


Journal

The Science of the total environment
ISSN: 1879-1026
Titre abrégé: Sci Total Environ
Pays: Netherlands
ID NLM: 0330500

Informations de publication

Date de publication:
10 Dec 2022
Historique:
received: 11 05 2022
revised: 01 08 2022
accepted: 18 08 2022
pubmed: 27 8 2022
medline: 21 10 2022
entrez: 26 8 2022
Statut: ppublish

Résumé

The effects of environmental chemicals on insulin resistance have attracted extensive attention. Previous studies typically focused on the single chemical effects. This study adopted three different models to analyze the mixed effects of nine common chemicals (one phenol, two parabens, two chlorophenols and four phthalates) on insulin resistance. Urinary concentrations of chemicals were extracted from National Health and Nutrition Examination Survey (NHANES) 2009-2016. Insulin resistance was assessed using homeostatic model assessment (HOMA) and defined as HOMA-IR >2.6. The generalized linear regression (GLM), weighted quantile sum regression (WQS) and Bayesian kernel machine regression models (BKMR) were applied to assess the relationship between chemical mixture and HOMA-IR or insulin resistance. Of the 2067 participants included, 872 (42.19 %) were identified as insulin resistant. In single-chemical GLM model, di-2-ethylhexyl phthalate (DEHP) had the highest parameter (β/OR, 95 % CIs) of 0.21 (quartile 4, 0.12- 0.29) and 1.95 (quartile 4, 1.39- 2.74). Similar results were observed in the multi-chemical models, with DEHP (quartile 4) showing the positive relationship with HOMA-IR (0.18, 0.08- 0.28) and insulin resistance (1.76, 1.17- 2.64). According to WQS models, the WQS indices were significantly positively correlated with both HOMA-IR (β: 0.07, 95 % CI: 0.03- 0.12) and insulin resistance (OR: 1.25, 95 % CI: 1.03- 1.53). DEHP was the top-weighted chemical positively correlated with both HOMA-IR and insulin resistance. In the BKMR model, the joint effect was also positively correlated with both outcomes. DEHP remained the main contributor to the joint effect, consistent with WQS analysis. Our findings suggested that these chemical mixtures had the positive joint effects on both HOMA-IR and insulin resistance, with DEHP being the potentially predominant driver. The inter-validation of the three models may indicate that reducing the DEHP concentration could improve glucose homeostasis and reduce the risk of insulin resistance. However, further studies are recommended to deepen our findings and elucidate the mechanisms of insulin resistance and chemical mixture.

Identifiants

pubmed: 36028038
pii: S0048-9697(22)05317-7
doi: 10.1016/j.scitotenv.2022.158218
pii:
doi:

Substances chimiques

phthalic acid 6O7F7IX66E
Parabens 0
Diethylhexyl Phthalate C42K0PH13C
Pesticides 0
Phenol 339NCG44TV
Environmental Pollutants 0
Phenols 0
Insulin 0
Chlorophenols 0
Glucose IY9XDZ35W2

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

158218

Informations de copyright

Copyright © 2022. Published by Elsevier B.V.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Jianjun Bai (J)

Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, 185# Donghu Road, Wuhan 430072, China.

Yudiyang Ma (Y)

Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, 185# Donghu Road, Wuhan 430072, China.

Yudi Zhao (Y)

Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, 185# Donghu Road, Wuhan 430072, China.

Donghui Yang (D)

Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, 185# Donghu Road, Wuhan 430072, China.

Sumaira Mubarik (S)

Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, 185# Donghu Road, Wuhan 430072, China.

Chuanhua Yu (C)

Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, 185# Donghu Road, Wuhan 430072, China; Global Health Institute, Wuhan University, 185# Donghu Road, Wuhan 430072, China. Electronic address: yuchua@whu.edu.cn.

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Classifications MeSH