Genetic Variation of Migration Inhibitory Factor Gene rs2070766 Is Associated With Acute Coronary Syndromes in Chinese Population - Zhang, Jin-Yu; Zhao, Qian; Liu, Fen; Li, De-Yang; Men, Li; Luo, Jun-Yi; Zhao, Ling; Li, Xiao-Mei; Gao, Xiao-Ming; Yang, Yi-Ning (Frontiers in Genetics, 2022)
Genetic variation of macrophage migration inhibitory factor (MIF) gene has been linked to coronary artery disease. We investigated an association between the polymorphism of MIF gene rs2070766 and acute coronary syndromes (ACS) and the predictive value of MIF gene variation in clinical outcomes. This study involved in 963 ACS patients and 932 control subjects from a Chinese population. All participants were genotyped for the single nucleotide polymorphism (SNP) of MIF gene rs2070766 using SNPscanTM. A nomogram model using MIF genetic variation and clinical variables was established to predict risk of ACS. Major adverse cardiovascular events (MACE) were monitored during a follow-up period. The frequency of rs2070766 GG genotype was higher in ACS patients than in control subjects (6.2% vs. 3.8%, P = 0.034). Multivariate logistic regression analysis revealed that individuals with mutant GG genotype had a 1.7-fold higher risk of ACS compared with individuals with CC or CG genotypes. Using MIF rs2070766 genotypes and clinical factors, we developed a nomogram model to predict risk of ACS. The nomogram model had a good discrimination with an area under the curve of 0.781 (95% CI: 0.759-0.804), concordance index of 0.784 (95% CI: 0.762-0.806) and well-fitted calibration. During the follow-up period of 25 months, Kaplan-Meier curves demonstrated that ACS patients carrying GG phenotype developed more MACE compared to CC or CG carriers (P < 0.05). GG genotype of MIF gene rs2070766 was associated with a higher risk of ACS in a Chinese population. The GG genotype carriers in ACS patients had worse clinical outcomes compared with those carrying CC or CG genotype. Together with rs2070766 genetic variant of MIF gene, we established a novel nomogram model that can provide individualized prediction for ACS.