The Relationship between Metabolic Syndrome and Plasma Metals Modified by EGFR and TNF-α Gene Polymorphisms.
Tzu-Hua ChenWei-Shyang KungHung-Yu SunJoh-Jong HuangJia-Yi LuKuei-Hau LuoHung-Yi ChuangPublished in: Toxics (2021)
With the escalating global prevalence of metabolic syndrome (MetS), it is crucial to detect the high-risk population early and to prevent chronic diseases. Exposure to various metals has been indicated to promote MetS, but the findings were controversial, and the effect of genetic modification was not considered. Epidermal growth factor receptor (EGFR) was proposed to be involved in the pathway of metabolic disorders, and tumor necrotic factor-α (TNF-α) was regarded as an early inflammatory biomarker for MetS. This research aimed to analyze the impact of EGFR and TNF-α gene polymorphisms on the prevalence of MetS under environmental or occupational exposure to metals. We gathered data from 376 metal industrial workers and 639 non-metal workers, including physical parameters, biochemical data, and plasma concentrations of six metals. According to the genomic database of Taiwan Biobank, 23 single nucleotide polymorphisms (SNPs) on EGFR gene and 6 SNPs on TNF-α gene were incorporated in our research. We applied multivariable logistic regression to analyze the probability of MetS with various SNPs and metals. Our study revealed some susceptible and protective EGFR and TNF-α genotypes under excessive exposure to cobalt, zinc, selenium, and lead. Thus, we remind the high-risk population of taking measures to prevent MetS.
Keyphrases
- epidermal growth factor receptor
- tyrosine kinase
- human health
- genome wide
- rheumatoid arthritis
- metabolic syndrome
- advanced non small cell lung cancer
- small cell lung cancer
- health risk
- health risk assessment
- copy number
- risk assessment
- heavy metals
- risk factors
- dna methylation
- mental health
- physical activity
- climate change
- uric acid
- emergency department
- gene expression
- oxidative stress
- drinking water
- wastewater treatment
- cardiovascular risk factors
- type diabetes
- genome wide identification
- gold nanoparticles
- adipose tissue
- skeletal muscle
- deep learning
- data analysis
- weight loss