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Association of volatile organic compound exposure with metabolic syndrome and its components: a nationwide cross-sectional study

医学 代谢综合征 全国健康与营养检查调查 流行病学 横断面研究 逻辑回归 内科学 环境卫生 人口 肥胖 病理
作者
Rui Dong,Dongchun Chang,Chao Shen,Ya Shen,Zhengkai Shen,Ting Tian,Jie Wang
出处
期刊:BMC Public Health [BioMed Central]
卷期号:24 (1) 被引量:11
标识
DOI:10.1186/s12889-024-18198-2
摘要

Abstract Background Metabolic syndrome (MetS) is a health issue consisting of multiple metabolic abnormalities. The impact of exposure to volatile organic compounds (VOCs) on MetS and its components remains uncertain. This study aimed to assess the associations of individual urinary metabolites of VOC (mVOCs) and mVOC mixtures with MetS and its components among the general adult population in the United States. Methods A total of 5345 participants with eligible data were filtered from the 2011–2020 cycles of the National Health and Nutrition Examination Survey. Multivariate logistic regression models were applied to assess the associations of individual mVOCs with MetS and its components. The least absolute shrinkage and selection operator (LASSO) regression models were constructed to identify more relevant mVOCs. The weight quantile sum regression model was applied to further explore the links between mVOC co-exposure and MetS and its components. Results The results indicated positive associations between multiple mVOCs and MetS, including CEMA, DHBMA, and HMPMA. CEMA was found to be positively correlated with all components of MetS. HMPMA was associated with elevated triglyceride (TG), reduced high-density lipoprotein, and fasting blood glucose (FBG) impairment; 3HPMA was associated with an elevated risk of high TG and FBG impairment; and DHBMA had positive associations with elevated TG and high blood pressure. The co-exposure of LASSO-selected mVOCs was associated with an increased risk of elevated TG, high blood pressure, and FBG impairment. Conclusion Positive associations of certain individual urinary mVOCs and mVOC mixtures with MetS and its components were observed by utilizing multiple statistical models and large-scale national data. These findings may serve as the theoretical basis for future experimental and mechanistic studies and have important implications for public health.
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