Immune Regulation Patterns in Response to Environmental Pollutant Chromate Exposure-Related Genetic Damage: A Cross-Sectional Study Applying Machine Learning Methods

Environ Sci Technol. 2024 Apr 30;58(17):7279-7290. doi: 10.1021/acs.est.4c00433. Epub 2024 Apr 17.

Abstract

Exposure to hexavalent chromium damages genetic materials like DNA and chromosomes, further elevating cancer risk, yet research rarely focuses on related immunological mechanisms, which play an important role in the occurrence and development of cancer. We investigated the association between blood chromium (Cr) levels and genetic damage biomarkers as well as the immune regulatory mechanism involved, such as costimulatory molecules, in 120 workers exposed to chromates. Higher blood Cr levels were linearly correlated with higher genetic damage, reflected by urinary 8-hydroxy-2'-deoxyguanosine (8-OHdG) and blood micronucleus frequency (MNF). Exploratory factor analysis revealed that both positive and negative immune regulation patterns were positively associated with blood Cr. Specifically, higher levels of programmed cell death protein 1 (PD-1; mediated proportion: 4.12%), programmed cell death ligand 1 (PD-L1; 5.22%), lymphocyte activation gene 3 (LAG-3; 2.11%), and their constitutive positive immune regulation pattern (5.86%) indirectly positively influenced the relationship between blood Cr and urinary 8-OHdG. NOD-like receptor family pyrin domain containing 3 (NLRP3) positively affected the association between blood Cr levels and inflammatory immunity. This study, using machine learning, investigated immune regulation and its potential role in chromate-induced genetic damage, providing insights into complex relationships and emphasizing the need for further research.

Keywords: NLRP3; blood chromium; costimulatory molecules; genetic damage; mediation analysis.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Biomarkers
  • Chromates*
  • Cross-Sectional Studies
  • DNA Damage
  • Environmental Pollutants
  • Female
  • Humans
  • Machine Learning*
  • Male
  • Middle Aged

Substances

  • Chromates
  • Environmental Pollutants
  • Biomarkers