Metabolic effects of chemical exposure in forest firefighters from the Pará State: a metabolomic approach applied to health technology

• Grant number: 24/01326-1
• Support Opportunities: Regular Research Grants
• Start date: December 01, 2024
• End date: November 30, 2026
• Field of knowledge: Physical Sciences and Mathematics - Chemistry - Analytical Chemistry
• Principal Investigator: Kelly Polido Kaneshiro Olympio
• Grantee: Kelly Polido Kaneshiro Olympio
• Host Institution: Faculdade de Saúde Pública (FSP). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated research grant: 23/04212-4 - Evaluation of occupational exposure and potential health impacts of forest firefighters in the State of Pará, AP.R
• Associated scholarship(s): 24/17990-8 - Metabolic effects of chemical exposure in wildland firefighters from the State of Pará - A metabolomics approach applied to health technology, BP.JD

Abstract

The growing impact of climate change has triggered a concerning increase in wildfires in the Brazilian Amazon region, making it imperative to investigate the metabolic effects of exposure to smoke from these fires. The emissions from these fires contain a complex composition, involving a mixture of particles and chemicals such as polycyclic aromatic hydrocarbons (PAHs). In the state of Pará, the epicenter of many of these events, forest firefighters routinely face this exposure. However, little is known about the impacts of this exposure on the health of those affected and the potential effects on the health and metabolism of these individuals. Thus, this project aims to conduct a metabolomic analysis of the blood of firefighters exposed daily to forest fire smoke. Analytical techniques such as gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance (NMR) will be used to obtain the metabolomic profiles of individuals exposed to fires. The metabolomes of the firefighters will be compared with those of occupationally unexposed participants, specifically administrative workers in the corporation. The results obtained will be used to investigate potential exposure/effect biomarkers and predict clinical complications before the onset of symptoms of potential diseases caused by intoxication. Additionally, an artificial intelligence model will be trained to assess the similarity of participants' metabolic profiles to the metabolic profiles of different diseases related to exposure to compounds present in the smoke. The construction of the artificial intelligence model will be based on available online data from multiple metabolomic studies. The application of the trained artificial intelligence to participants' metabolomic data will allow the assessment of their predisposition to different diseases. The combination of analytical chemistry, omics tools, and data science makes this proposal innovative, with high potential for generating a new health technology applicable to public and collective health. (AU)