Environmental phenol exposure associates with urine metabolome alteration in young Northeast Indian females

Urinary biomonitoring delivers the most accurate environmental phenols exposure assessment. However, envi-ronmental phenol exposure-related biomarkers are required to improve risk assessment to understand the in-ternal 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-hydrox-ybenzoic 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 meta-bolism, 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 pre-dictive 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.

Automated summary

Urinary biomonitoring is the most accurate method for assessing environmental phenols exposure. This study aimed to investigate the association between environmental phenols exposure and the metabolome of young adult females from India. The study identified seven potential biomarkers and revealed the metabolic pathways altered by environmental phenol exposure. Additionally, an innovative strategy combining urinary metabolomics and machine learning algorithms was proposed for early disease prediction.