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Effect of the interaction between noise exposure and AGT gene polymorphisms in steelworkers on essential hypertension
Miao Liu1, Ying Wang2, Peishuai Li1
1School of Public Health, North China University of Science and Technology, No. 21 Bohai Avenue, Caofeidian Area, Tangshan, 063210, Hebei Province, People's Republic of China.
Purpose:
Although steelworkers are exposed to occupational noise, few studies have investigated the interaction between noise and genetic polymorphisms in relation to essential hypertension (EH). This study aimed to investigate the interaction between noise exposure and angiotensinogen(AGT) gene polymorphisms (A-20 C and T174M) on EH risk among steelworkers.
Methods:
The study included 725 male steelworkers, with 224 assigned to the EH group and 501 to the control group. The exposure to environmental factors such as high temperature and workplace noise levels were measured. Binary multifactorial logistic regression combined with crossover analysis and generalized multifactor dimensionality reduction (GMDR) were employed to examine the interaction effects between AGT gene polymorphisms and noise exposure on the incidence of EH among steelworkers.
Results:
Compared with nonexposed controls, individuals who experienced noise exposure exhibited a 1.636-fold increased risk of developing EH (95% CI: 1.127-2.375). Genetic analysis revealed that compared with those with the TT genotype, steelworkers with the TM genotype at the T174M locus of the AGT gene exhibited a 1.802-fold increased risk of EH (95% CI: 1.208-2.689). Gene-noise interaction analysis using a multiplicative model revealed that compared with individuals with the TT genotype without noise exposure, individuals with the TM genotype at the T174M locus who experienced noise exposure exhibited a 2.02-fold increased risk of EH (OR = 2.020; 95% CI: 1.064-3.835). Analysis of the GMDR gene‒gene‒noise interaction model revealed that the T174M‒A-20C-noise three-factor model is the best.
Conclusion:
Noise and AGT gene polymorphisms (especially T174M) interact to increase EH risk among steelworkers, with the three-factor model showing the strongest association.
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