Jala Aishwarya, Dutta Ratul, Josyula Jhansi Venkata Nagamani, Mutheneni Srinivasa Rao, Borkar Roshan M
Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research, Guwahati, 781101, Assam, India.
Down Town Hospital, Guwahati, Assam, 781106, India.
Chemosphere. 2023 Mar;317:137830. doi: 10.1016/j.chemosphere.2023.137830. Epub 2023 Jan 11.
Urinary biomonitoring delivers the most accurate environmental phenols exposure assessment. However, environmental phenol exposure-related biomarkers are required to improve risk assessment to understand the internal processes perturbed, which may link exposure to specific health outcomes. This study aimed to investigate the association between environmental phenols exposure and the metabolome of young adult females from India. Urinary metabolomics was performed using liquid chromatography-mass spectrometry. Environmental phenols-related metabolic biomarkers were investigated by comparing the low and high exposure of environmental phenols. Seven potential biomarkers, namely histidine, cysteine-s-sulfate, 12-KETE, malonic acid, p-hydroxybenzoic acid, PE (36:2), and PS (36:0), were identified, revealing that environmental phenol exposure altered the metabolic pathways such as histidine metabolism, beta-Alanine metabolism, glycerophospholipid metabolism, and other pathways. This study also conceived an innovative strategy for the early prediction of diseases by combining urinary metabolomics with machine learning (ML) algorithms. The differential metabolites predictive accuracy by ML models was >80%. This is the first mass spectrometry-based metabolomics study on young adult females from India with environmental phenols exposure. The study is valuable in demonstrating multiple urine metabolic changes linked to environmental phenol exposure and a better understanding of the mechanisms behind environmental phenol-induced effects in young female adults.
尿液生物监测可提供最准确的环境酚类暴露评估。然而，需要与环境酚类暴露相关的生物标志物来改进风险评估，以了解受到干扰的内部过程，这些过程可能将暴露与特定的健康结果联系起来。本研究旨在调查印度年轻成年女性的环境酚类暴露与代谢组之间的关联。使用液相色谱-质谱法进行尿液代谢组学分析。通过比较环境酚类的低暴露和高暴露情况，研究了与环境酚类相关的代谢生物标志物。确定了七种潜在的生物标志物，即组氨酸、半胱氨酸-硫代硫酸盐、12-酮二十碳四烯酸、丙二酸、对羟基苯甲酸、磷脂酰乙醇胺（36:2）和磷脂酰丝氨酸（36:0），这表明环境酚类暴露改变了组氨酸代谢、β-丙氨酸代谢、甘油磷脂代谢等代谢途径。本研究还构思了一种创新策略，通过将尿液代谢组学与机器学习（ML）算法相结合来早期预测疾病。ML模型对差异代谢物的预测准确率>80%。这是第一项基于质谱的针对印度年轻成年女性环境酚类暴露的代谢组学研究。该研究对于证明与环境酚类暴露相关的多种尿液代谢变化以及更好地理解年轻成年女性中环境酚类诱导效应背后的机制具有重要价值。
