Key result
Specific genotypes of inflammatory genes, a SNP in HMGCR, middle age, male gender, low HDL, and diabetes were identified by an Auto Contractive Map algorithm as predictors of AMI risk.
Why the study?
Do specific SNPs in inflammatory and vascular genes interact with clinical factors to predict the risk of acute myocardial infarction?
Case-Control
Do specific SNPs in inflammatory and vascular genes interact with clinical factors to predict the risk of acute myocardial infarction?
A novel mathematical algorithm identified specific genotypes in inflammatory and vascular genes, along with clinical factors, as key predictors of acute myocardial infarction risk.
Should not yet inform AMI risk stratification; hypothesis-generating for gene-clinical interactions in prospective cohorts.
AIMS: The complex pathogenesis of acute myocardial infarction (AMI) implicates phenotypic and genetic heterogeneity. In this pilot case-control study single nucleotide polymorphism (SNP) in several inflammatory genes, such as interleukin (IL)-1beta, IL-6, IL-10, alpha-1-antichymotrypsin (ACT), tumor necrosis factor alpha (TNF)-alpha and interferon gamma (IFN)-gamma genes along with SNPs of genes regulating vascular functions (vascular endothelial growth factor; VEGF) and cholesterol synthesis (hydroxy-methyl-glutaryl CoA reductase; HMGCR) were investigated. METHODS: Patients were genotyped with RT-PCR technique and data were analyzed with a new mathematical algorithm named Auto Contractive Map. RESULTS: The Auto Contractive Map (AutoCM), was applied in AMI patients with the aim to detect and evaluate the relationships among genetic factors, clinical variables and classical risk factors. Genes were selected because their strong regulatory effect on inflammation and SNP in these gene were located in the promoter region. In the connectivity map generated by AutoCM a group of variables was directly linked with the AMI status; these were: gender (male), early age at onset (50-65 years), HMGCR gene (CC wild type genotype), IL-1betaCT, IL-6 GG and VEGF CC genotypes. This direct link suggested a possible pathogenetic association with AMI. Other genetic, clinical and phenotypic variables were associated to the disease under a statistically defined hierarchy showed in the new connectivity map generated by AutoCM. CONCLUSION: These analyses suggested that genotypes of few inflammatory genes, a SNP in HMGCR gene, middle age, gender, low HDL and diabetes were very informative variables to predict the risk of AMI.
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Licastro et al. (2010) conducted a case-control in Acute Myocardial Infarction. Auto Contractive Map (AutoCM) algorithm was evaluated on Relationships among genetic factors, clinical variables and classical risk factors with AMI status. Specific genotypes of inflammatory genes, a SNP in HMGCR, middle age, male gender, low HDL, and diabetes were identified by an Auto Contractive Map algorithm as predictors of AMI risk.
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