The R93C Variant of PCSK9 Reduces the Risk of Premature MI in a Chinese Han Population.

Abstract

Background: Dyslipidemia is a common risk factor for premature myocardial infarction (PMI). Our previous work has shown that single-nucleotide polymorphisms (SNPs) of LDLR, APOB, and PCSK9 are associated with dyslipidemia, but how these SNPs correlate with risk for PMI is unknown. Objective: This study aims to evaluate the association between SNPs of LDLR, APOB, and PCSK9 and risk of PMI in Chinese Han population. Methods: Two cohorts were established. In Cohort 1 (413 in the PMI group and 1,239 in the control group), SNPs of APOB, LDLR, and PCSK9 with minor allele frequency (MAF) > 1%, which has been shown to impact the risk of PMI in a Chinese Han population, were thoroughly examined, and gene–environment interactions were analyzed. A model for PMI risk prediction was developed in Cohort 1 and externally validated in Cohort 2 (577 in the PMI group and 270 in the control group). Results: The distribution of the T allele at the PCSK9 R93C variant (rs151193009, C > T) was lower in the PMI group than that in the control group (PMI vs. Control in Cohort 1, 0.8% vs. 2.3%, P adjust < 0.05; in Cohort 2, 1.0% vs. 2.4%, P adjust < 0.05). The T allele at PCSK9 R93C variant (rs151193009, C > T) reduced the risk of PMI by ∼60% regardless of adjusting for confounding factors (in Cohort 1, adjusted odds ratio (OR) 0.354, 95% confidence interval (CI) 0.139–0.900, p = 0.029; in Cohort 2, adjusted OR 0.394, 95% CI 0.157–0.987, p = 0.047). No gene–environment interactions were observed between the R93C variant and diabetes/hypertension/smoking in PMI occurrence in this Chinese Han population. Our model showed good performance in predicting the risk of PMI in Cohort 1 (AUC 0.839, 95% CI 0.815–0.862, p < 0.001) and in an external cohort (AUC 0.840, 95% CI 0.810–0.871, p < 0.001). Conclusions: The PCSK9 R93C variant was associated with significantly reduced risk of PMI in the Chinese Han population, and the model we developed performed well in predicting PMI risk in this Chinese Han population.