Influence of Genetic Variance on Biomarker Levels After Occupational Exposure to 1,6-Hexamethylene Diisocyanate Monomer and 1,6-Hexamethylene Diisocyanate Isocyanurate - Taylor, Laura W.; French, John E.; Robbins, Zachary G.; Boyer, Jayne C.; Nylander-French, Leena A. (Frontiers in Genetics, 2020)
We evaluated the impact of genetic variance on biomarker levels in a population of workers in the automotive repair and refinishing industry who were exposed to respiratory sensitizers 1,6-hexamethylene diisocyanate (HDI) monomer and one of its trimers, HDI isocyanurate. The exposures and respective urine and plasma biomarkers 1,6-diaminohexane (HDA) and trisaminohexyl isocyanurate (TAHI) were measured in 33 workers, and genome-wide microarrays (Affymetrix 6.0) were used to genotype the workers’ single nucleotide polymorphisms (SNPs). Linear mixed model analyses have indicated that inter-individual variations in both inhalation and skin exposures influenced these biomarker levels. Using exposure values as covariates and a false discovery rate <0.10 to assess biological relevance and statistical significance we observed that seven SNPs were associated with HDA in plasma, five were associated with HDA in urine, none reached significance for TAHI in plasma, and eight were associated with TAHI levels in urine. The different genotypes for the 20 significant SNPs accounted for 4 – 16-fold changes observed in biomarker levels. Associated gene functions include transcription regulation, calcium ion transport, vascular morphogenesis, and transforming growth factor beta signaling pathway, which may impact toxicokinetics indirectly by altering inflammation levels. Additionally, in an expanded analysis using a minor allele cut-off of 0.05 instead of 0.10, there were biomarker-associated SNPs within three genes that have been associated with isocyanate-induced asthma. We demonstrate that genetic variance impacts the biomarker levels in workers exposed to HDI monomer and HDI isocyanurate and that genetics can be used to refine exposure predictions in small cohorts when quantitative personal exposure and biomarker measurements are included in the models.