Association of Urinary Benzene Metabolite and the Ratio of Triglycerides to High-Density Lipoprotein Cholesterol: A Cross-Sectional Study Using the Korean National Environmental Health Survey (2018-2020).
Seungju BaekEunjung ParkEun Young ParkPublished in: Toxics (2023)
The aim of this study was to investigate the association between benzene and toluene, and the ratio of triglycerides to high-density lipoprotein cholesterol (TG/HDL-C). This cross-sectional study analyzed 1928 adults using nationally representative data from the Korean National Environmental Health Survey (KoNEHS) Cycle 4 (2018-2020). Urinary trans, trans-muconic acid (t,t-MA) and benzylmercapturic acid (BMA) were measured by high-performance liquid chromatography-mass spectrometry (HPLC-MS/MS), and high-density lipoprotein cholesterol (HDL-C) and triglycerides (TGs) were analyzed by colorimetry. Survey logistic regression analysis was applied to examine the association between urinary t,t-MA and BMA and the TG/HDL-C ratio. Urinary t,t-MA is significantly associated with an elevated TG/HDL-C ratio in both men and women (for men, OR [95% (CI)]: 2nd quartile: 2.10 [1.04, 4.22]; 3rd quartile: 2.13 [0.98, 4.62]; 4th quartile: 2.39 [1.05, 5.45]; for women, OR [95% (CI)]: 2nd quartile: 1.21 [0.71, 2.06]; 3rd quartile: 1.65 [0.94, 2.90]; 4th quartile: 1.78 [1.01, 3.11]), with significant dose-response relationships (P for trend: for men, 0.029; women, 0.024). This study shows that environmental exposure to benzene is associated with the TG/HDL-C ratio in the Korean general population. This suggests that more stringent environmental health policies are needed to reduce benzene exposure.
Keyphrases
- high performance liquid chromatography
- mass spectrometry
- ms ms
- human health
- public health
- simultaneous determination
- tandem mass spectrometry
- healthcare
- polycystic ovary syndrome
- solid phase extraction
- high density
- life cycle
- mental health
- quality improvement
- type diabetes
- pregnant women
- liquid chromatography tandem mass spectrometry
- high resolution
- big data
- risk assessment
- ultra high performance liquid chromatography
- deep learning
- machine learning
- cross sectional
- health information
- health promotion
- breast cancer risk