Abstract

Although nanotoxicology studies have shown that respiratory exposure of titanium dioxide nanoparticles (TiO₂ NPs) could induce adverse health effects, limited biomarkers associated with occupational exposure of TiO₂ NPs were reported. The purpose of this study is to screen serum biomarkers among workers occupationally exposed to TiO₂ NPs using metabolomics. Compared with the control group, a total of 296 serum metabolites were differentially expressed in the TiO₂ NPs-exposed group, of which the relative expression of 265 metabolites increased, and the remaining 31 decreased. Three machine learning methods including random forest (RF), support vector machines (SVM), and boruta screened eight potential biomarkers and simultaneously selected a metabolite, Liquoric acid. Through multiple linear regression analysis to adjust the influence of confounding factors such as gender, age, BMI, smoking and drinking, occupational exposure to TiO₂ NPs was significantly related to the relative expression of the eight potential biomarkers. Meanwhile, the receiver operating characteristic curves (ROCs) of these potential biomarkers had good sensitivity and specificity. These potential biomarkers were related to lipid peroxidation, and had biological basis for occupational exposure to TiO₂ NPs. Therefore, it was demonstrated that the serum metabolites represented by Liquoric acid were good biomarkers of occupational exposure to TiO₂ NPs.

摘要

尽管纳米毒理学研究表明，二氧化钛纳米粒子 (TiO ₂ NPs) 的呼吸暴露会导致不利的健康影响，但与 TiO ₂ NPs 的职业暴露相关的生物标志物有限。本研究的目的是使用代谢组学筛选职业暴露于 TiO ₂ NPs 的工人的血清生物标志物。与对照组相比，共有296种血清代谢物在TiO ₂中差异表达NPs暴露组，其中265个代谢物的相对表达增加，其余31个减少。包括随机森林 (RF)、支持向量机 (SVM) 和 boruta 在内的三种机器学习方法筛选了八种潜在的生物标志物，同时选择了一种代谢物，即甘草酸。通过多元线性回归分析调整性别、年龄、BMI、吸烟饮酒、职业接触TiO ₂等混杂因素的影响NPs 与八种潜在生物标志物的相对表达显着相关。同时，这些潜在生物标志物的受试者工作特征曲线（ROCs）具有良好的敏感性和特异性。这些潜在的生物标志物与脂质过氧化有关，并且具有职业暴露于 TiO ₂ NPs 的生物学基础。因此，证明以甘草酸为代表的血清代谢物是TiO ₂ NPs职业暴露的良好生物标志物。