Potential Interactions Between Genetic Polymorphisms of the Transforming Growth Factor-β Pathway and Environmental Factors in Abdominal Aortic Aneurysms

Objective/BackgroundEvidence has accumulated that multiple polymorphisms in the transforming growth factor (TGF)-β pathway and renin–angiotensin system play important roles in determining susceptibility to abdominal aortic aneurysm (AAA). Few studies have considered interactions between these gene polymorphisms and environmental factors. The aim of this study was to evaluate the contribution of single nucleotide polymorphisms (SNPs) and complex gene–environment interactions in AAA.MethodsSix SNPs located in TGFB, TGFBR1, TGFBR2 and AGTR1 were selected. Genotyping of blood samples and collection of lifestyle factors were performed in 155 unrelated participants with AAAs and 310 non-AAA controls. Unconditional logistic regression was performed to assess the effects of SNPs on the risk of AAA. Generalized multifactor dimensionality reduction (GMDR) was used to evaluate gene–gene and gene–environment interactions.ResultsParticipants carrying TGFB1 rs1800469 TT (odds ratio [OR] 1.83, 95% confidence interval [CI] 1.18–2.85) or AGTR1 rs12695895 TT (OR 4.21, 95% CI 1.41–12.53) genotypes had a higher risk of AAA than those with the common CC genotype. The gene–gene interaction of AGTR1 rs5182, TGFBR1 rs1626340, and TGFB1 rs1800469 was found to be the best model according to the results of the GMDR analysis (cross validation consistency [CVC]) 10/10; p = .010). Smoking, dyslipidemia, and rs1800469 together contributed to the risk of AAA, which demonstrated a potential and complex gene–environment interaction among the three variants that might affect AAA risk (CVC 6/10; p = .001).ConclusionIn this study of the Chinese population, homozygosity of TGFB1 rs1800469-T and AGTR1 rs12695895-T might be associated with increased risk of AAA. The complex gene–gene and gene–environment interactions might contribute to the risk of AAA. As a small study, the preliminary results need extensive validation and replication in larger populations