A Predictive Model For Heart Attack Risk In Patients With Metabolic Syndrome Integrating Genetic Variant Information

Objective:To establish a predictive model for heart attack risk including genetic factors in patients with metabolic syndrome to more accurately identify patients at high risk of postoperative heart attack at an early stage to facilitate more targeted measures to increase surgical and anesthetic safety.Methods:In this study,380 patients with metabolic syndrome who underwent elective surgery in the First Hospital of Shanxi Medical University were screened and grouped according to the occurrence of postoperative myocardial infarction.SNP genotyping was performed using MassARRAY genetic analysis system,and genotyping was compared between groups using X2 test.The prediction model was established by binary logistic regression analysis.Results:The rs46522,rs17600940 and rs126172 loci are closely associated with the risk of metabolic syndrome and can be used as susceptibility genes for the risk of AMI in the metabolic syndrome population.rs46522 locus CT genotype is a risk factor for the development of AMI and is 2.308 times more likely to develop AMI than CC and CT genotypes(P=0.036),while rs17609940 locus GG genotype is 2.324 times more likely to develop AMI than CC and CG genotypes.The GG genotype at locus rs 17609940 was a risk factor for the development of AMI and was 2.324-fold higher than the CG genotype,while the GG genotype at locus rs216172 was 3.580-fold higher than the CC and CG genotypes for the risk of AMI.Conclusion:A postoperative infarction risk prediction model containing rs46522,rs 17600940 and rs126172 loci information and other traditional information was developed to predict the postoperative infarction risk of patients with metabolic syndrome more accurately than existing models.