Lipoprotein(a) molar concentrations rather than genetic variants better predict coronary artery disease risk and severity in Han Chinese population

脂蛋白(a)摩尔浓度而非基因变异更能预测汉族人群冠状动脉疾病的风险和严重程度。

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Abstract

BACKGROUND: It is well established that increased lipoprotein(a) [Lp(a)] is a significant risk factor for coronary artery disease (CAD). Plasma Lp(a) levels are genetically determined and vary widely between different races, regions and individuals. However, most studies on Lp(a) associated genetic variants have focused on the Caucasian population currently. Our study aimed to test the associations among LPA genetic variants, Lp(a) concentrations, and CAD in a Han Chinese cohort. METHODS: A total of 3779 patients undergoing coronary angiography were recruited from Tongji Hospital. LPA Kringle IV type 2 (KIV-2) copies were detected using TaqMan probe real-time quantitative polymerase chain reaction (qPCR) analysis and fifteen single nucleotide polymorphisms (SNPs) within the LPA gene were detected using TaqMan probe genotyping analysis. LPA genetic risk score (GRS) was computed based on seven SNPs associated with Lp(a). Associations of LPA genetic variants with Lp(a) and CAD were evaluated using linear regression analyses and Logistic regression analyses, respectively. RESULTS: Compared with the first quartile of Lp(a), the fourth quartile exhibited a significant association with CAD [odds ratio (OR): 2.08, 95% confidence interval (CI): 1.67-2.59, p < 0.001], multivessel CAD [OR: 2.54, 95% CI: 2.06-3.12, p < 0.001], and high Gensini scores [OR: 2.17, 95% CI: 1.77-2.66, p < 0.001] after multivariable adjustment for cardiovascular risk factors. Both LPA GRS and KIV-2 quartiles were associated with Lp(a) concentrations (both p for trend < 0.001). However, after false discovery rate (FDR) correction, there were no significant associations of LPA genetic variants with CAD, multivessel CAD or high Gensini scores. CONCLUSIONS: Our findings indicate LPA genetic variants can affect Lp(a) levels, but do not exceed Lp(a) molar concentrations to predict CAD incidence and severity usefully, highlighting the importance of Lp(a) detection and management.

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