Integrative Modeling of Urinary Metabolomics and Metal Exposure Reveals Systemic Impacts of Electronic Waste in Exposed Populations
<b>Background</b>: Informal electronic waste (e-waste) recycling practices release a complex mixture of pollutants, particularly heavy metals, into the environment. Chronic exposure to these contaminants has been linked to a range of health risks, but the molecular underpinnings remain p...
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2025-07-01
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| author | Fiona Hui Zhiqiang Pang Charles Viau Gerd U. Balcke Julius N. Fobil Niladri Basu Jianguo Xia |
| author_facet | Fiona Hui Zhiqiang Pang Charles Viau Gerd U. Balcke Julius N. Fobil Niladri Basu Jianguo Xia |
| author_sort | Fiona Hui |
| collection | DOAJ |
| description | <b>Background</b>: Informal electronic waste (e-waste) recycling practices release a complex mixture of pollutants, particularly heavy metals, into the environment. Chronic exposure to these contaminants has been linked to a range of health risks, but the molecular underpinnings remain poorly understood. In this study, we investigated the alterations in metabolic profiles due to e-waste exposure and linked these metabolites to systemic biological effects. <b>Methods</b>: We applied untargeted high-resolution metabolomics using dual-column LC-MS/MS and a multi-step analysis workflow combining MS1 feature detection, MS2 annotation, and chemical ontology classification, to characterize urinary metabolic alterations in 91 e-waste workers and 51 community controls associated with the Agbogbloshie site (Accra, Ghana). The impacts of heavy metal exposure in e-waste workers were assessed by establishing linear regression and four-parameter logistic (4PL) models between heavy metal levels and metabolite concentrations. <b>Results</b>: Significant metal-associated metabolomic changes were identified. Both linear and nonlinear models revealed distinct sets of exposure-responsive compounds, highlighting diverse biological responses. Ontology-informed annotation revealed systemic effects on lipid metabolism, oxidative stress pathways, and xenobiotic biotransformation. This study demonstrates how integrating chemical ontology and nonlinear modeling facilitates exposome interpretation in complex environments and provides a scalable template for environmental biomarker discovery. <b>Conclusions</b>: Integrating dose–response modeling and chemical ontology analysis enables robust interpretation of exposomics datasets when direct compound identification is limited. Our findings indicate that e-waste exposure induces systemic metabolic alterations that can underlie health risks and diseases. |
| format | Article |
| id | doaj-art-059c6816022d4d6cad8259cb25dc4e4a |
| institution | DOAJ |
| issn | 2218-1989 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metabolites |
| spelling | doaj-art-059c6816022d4d6cad8259cb25dc4e4a2025-08-20T02:47:09ZengMDPI AGMetabolites2218-19892025-07-0115745610.3390/metabo15070456Integrative Modeling of Urinary Metabolomics and Metal Exposure Reveals Systemic Impacts of Electronic Waste in Exposed PopulationsFiona Hui0Zhiqiang Pang1Charles Viau2Gerd U. Balcke3Julius N. Fobil4Niladri Basu5Jianguo Xia6Faculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC H9X 3V9, CanadaFaculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC H9X 3V9, CanadaFaculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC H9X 3V9, CanadaMetacom Program Center, Leibniz Institute of Plant Biochemistry, 06120 Halle, GermanySchool of Public Health, University of Ghana, Accra P.O. Box LG 13, GhanaFaculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC H9X 3V9, CanadaFaculty of Agricultural and Environmental Sciences, McGill University, Ste-Anne-de-Bellevue, QC H9X 3V9, Canada<b>Background</b>: Informal electronic waste (e-waste) recycling practices release a complex mixture of pollutants, particularly heavy metals, into the environment. Chronic exposure to these contaminants has been linked to a range of health risks, but the molecular underpinnings remain poorly understood. In this study, we investigated the alterations in metabolic profiles due to e-waste exposure and linked these metabolites to systemic biological effects. <b>Methods</b>: We applied untargeted high-resolution metabolomics using dual-column LC-MS/MS and a multi-step analysis workflow combining MS1 feature detection, MS2 annotation, and chemical ontology classification, to characterize urinary metabolic alterations in 91 e-waste workers and 51 community controls associated with the Agbogbloshie site (Accra, Ghana). The impacts of heavy metal exposure in e-waste workers were assessed by establishing linear regression and four-parameter logistic (4PL) models between heavy metal levels and metabolite concentrations. <b>Results</b>: Significant metal-associated metabolomic changes were identified. Both linear and nonlinear models revealed distinct sets of exposure-responsive compounds, highlighting diverse biological responses. Ontology-informed annotation revealed systemic effects on lipid metabolism, oxidative stress pathways, and xenobiotic biotransformation. This study demonstrates how integrating chemical ontology and nonlinear modeling facilitates exposome interpretation in complex environments and provides a scalable template for environmental biomarker discovery. <b>Conclusions</b>: Integrating dose–response modeling and chemical ontology analysis enables robust interpretation of exposomics datasets when direct compound identification is limited. Our findings indicate that e-waste exposure induces systemic metabolic alterations that can underlie health risks and diseases.https://www.mdpi.com/2218-1989/15/7/456urine metabolomicsMS2environmental healthheavy metalselectronic waste |
| spellingShingle | Fiona Hui Zhiqiang Pang Charles Viau Gerd U. Balcke Julius N. Fobil Niladri Basu Jianguo Xia Integrative Modeling of Urinary Metabolomics and Metal Exposure Reveals Systemic Impacts of Electronic Waste in Exposed Populations Metabolites urine metabolomics MS2 environmental health heavy metals electronic waste |
| title | Integrative Modeling of Urinary Metabolomics and Metal Exposure Reveals Systemic Impacts of Electronic Waste in Exposed Populations |
| title_full | Integrative Modeling of Urinary Metabolomics and Metal Exposure Reveals Systemic Impacts of Electronic Waste in Exposed Populations |
| title_fullStr | Integrative Modeling of Urinary Metabolomics and Metal Exposure Reveals Systemic Impacts of Electronic Waste in Exposed Populations |
| title_full_unstemmed | Integrative Modeling of Urinary Metabolomics and Metal Exposure Reveals Systemic Impacts of Electronic Waste in Exposed Populations |
| title_short | Integrative Modeling of Urinary Metabolomics and Metal Exposure Reveals Systemic Impacts of Electronic Waste in Exposed Populations |
| title_sort | integrative modeling of urinary metabolomics and metal exposure reveals systemic impacts of electronic waste in exposed populations |
| topic | urine metabolomics MS2 environmental health heavy metals electronic waste |
| url | https://www.mdpi.com/2218-1989/15/7/456 |
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