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Chromium (Cr) exposure is a widespread environmental concern, yet its molecular effects in adolescents remain insufficiently understood. Here, we applied an integrative multi-level framework combining metabolomics, network toxicology, transcriptomic analysis, machine learning, molecular docking, and molecular dynamics simulations to characterize Cr-associated biological perturbations in Chinese adolescents. Untargeted metabolomics using an extreme quantile comparison (Q4 vs Q1) identified 48 differential metabolites, predominantly upregulated, suggesting activation-driven metabolic remodeling; notably, leucylphenylalanine was significantly elevated, indicating altered protein turnover and amino acid homeostasis. Although pathway enrichment was limited after multiple-testing correction, the implicated processes were consistent with lipid metabolism, antioxidant responses, and detoxification. Integration of toxicogenomic and transcriptomic data identified 27 key genes, with enrichment analyses highlighting oxidative stress, reactive oxygen species metabolism, mitochondrial function, and neurodevelopment-related pathways, including those implicated in autism spectrum disorder. Machine learning further identified six core candidate genes (AKR1C3, ERBB2, GPX3, JUN, MYH9, and RPS4Y1). At the structural prediction level, molecular docking suggested favorable binding potential between leucylphenylalanine and multiple targets, particularly AKR1C3, while molecular dynamics simulations supported the stability of the predicted AKR1C3–ligand complex. Collectively, these findings show that serum Cr exposure was associated with metabolomic alterations in adolescents, while integrative computational analyses further suggested potential involvement of oxidative stress- and mitochondrial function-related pathways. These results provide a hypothesis-generating systems-level perspective on the biological perturbations potentially associated with Cr exposure.
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Zhou et al. (2026) studied this question.
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