Association between phenolic compound exposure and dyslipidemia in the population

Qizhe Song; Zizi LI; Di MU; Huijun Wang; Chang SU; Zhenyu WU

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Association between phenolic compound exposure and dyslipidemia in the population

VernacularTitle:酚类化合物暴露与人群血脂异常的关联性研究
Author: Qizhe SONG ¹ ; Zizi LI ² ; Di MU ² ; Huijun WANG ² ; Chang SU ² ; Zhenyu WU ¹
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1. School of Public Health, Fudan University, Shanghai 200032, China.
2. National Institute for Nutrition and Health/Key Laboratory of Trace Element Nutrition of National Health Commission, Chinese Center for Disease Control and Prevention, Beijing 100050, China.
Publication Type:Investigation
Keywords: dyslipidemia; phenolic compound; principal component analysis; random forest; National Health and Nutrition Examination Survey
From: Journal of Environmental and Occupational Medicine 2023;40(5):565-570
CountryChina
Language:Chinese
Abstract: Background Phenolic compounds may adversely affect human health, but the current relevant studies are mostly limited to the impact of single phenolic compound exposure on human health, and there is still a lack of studies on the population-based association between combined exposure to multiple common phenolic compounds and dyslipidemia. Objective To explore the association of phenolic compound combined exposure and dyslipidemia based on principal component analysis-random forest (PCA-RF) strategy. Methods The data were from the National Health and Nutrition Examination Survey (2013–2016). A total of 1301 adult residents aged ≥ 20 years with complete information on demographics and lifestyle, urine phenol concentrations (bisphenol A, bisphenol F, bisphenol S, triclocarban, benzophenone, and triclosan), and serum concentrations of total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) were included in this study. The concentrations of six urinary phenolic compounds were determined by solid phase extraction coupled with high performance liquid chromatography and tandem mass spectrometry, and the lipid indicators were determined by enzymatic methods. Principal component analysis combined with random forest model was used for model construction. First, principal component analysis was performed on 18 original variables including 6 phenolic compounds and 12 basic characteristic indicators, and then random forest model was established with dyslipidemia and its four evaluation indicators as dependent variables and the extracted principal components as independent variables, respectively. Results The PCA-RF analysis showed that bisphenol A, bisphenol F, and benzophenone may be important factors for dyslipidemia in the study subjects; bisphenol A, bisphenol F, and triclosan may be important factors for TC level in the study subjects; bisphenol A, bisphenol F, triclocarban, and benzophenone may be important factors for TG level in the study subjects; bisphenol A may be an important factor for LDL-C level in the study subjects; bisphenol F and benzophenone may be important factors for HDL-C level in the study subjects. Conclusion Phenolic compound exposure may be an important risk factor for the development of dyslipidemia. PCA-RF strategy can be effectively used to explore the association between phenolic compound exposure and dyslipidemia in the population.