Metabolic effects of exposure to potentially toxic elements in workers - a metabolomics approach applied to health technology

Abstract

Contamination by potentially toxic elements (PTE) is a growing public health and environmental problem. Some metals can be harmful to human health even in low doses, and their health effects include kidney damage and cardiovascular, liver, neurological, reproductive, and immunological diseases. This project focuses on the metabolomic analysis of the blood of individuals exposed to PTEs (Mn, Ni, Cd, Sb, Sn, Cu, Zn, As, Cr, and Pb) to detail the association between their levels and the metabolic profile. Analytical techniques such as gas chromatography coupled to mass spectrometry (GC-MS), and nuclear magnetic resonance (NMR) will be used to obtain the metabolomic profiles of individuals exposed to PTEs. The population of Limeira, SP, a production center for jewelry and costume jewelry, and the population of Volta Redonda, RJ, which includes workers at Companhia Siderúrgica Nacional (CSN), will be studied in a total of 267 participants. The metabolic profiles of workers from both regions will be compared with participants who are not occupationally exposed, residents of the same region as the workers, but who do not work in the productive processes involving chemical exposure. The results obtained will be used to investigate potential biomarkers of exposure/effect and prediction of clinical complications before the onset of symptoms. In order to maximize the information gain related to PTE exposure, multi-omics analysis will be promoted by the integration of transcriptomics data, previously acquired and analyzed by the group, and the metabolomics data generated in this project. Additionally, artificial intelligence will be trained for the similarity assessment of the metabolic profile of the participants with the metabolic profile of different diseases linked to PTEs exposure. The construction of the artificial intelligence will be conducted using online available data from multiple metabolomics studies. The application of the trained AI to the participants' metabolomics data will enable the assessment of their predisposition to different diseases. The association between analytical chemistry, omics, and data science makes this project highly innovative, with a high potential for generating a new health technology applicable to public and collective health.