早发性冠心病患者MTHFR、PAI-1、ACE、PON1和eNOS基因多态性的多因素降维分析

M Agirbasli, A I Guney, H S Ozturhan, D Agirbasli, K Ulucan, D Sevinc, D Kirac, K K Ryckman, S M Williams
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引用次数: 34

摘要

背景:土耳其人群的关联研究调查了不同基因多态性对冠状动脉疾病(CAD)的单位点效应。CAD是一种复杂的多基因疾病,涉及多种遗传和环境条件之间的复杂相互作用。设计:我们评估了五种候选遗传多态性(亚甲基四氢叶酸还原酶C677T,纤溶酶原激活物抑制剂4G/5G,内皮一氧化氮合酶(eNOS) 3-27碱基对重复,血管紧张素I转换酶的287 bp Alu重复序列多态性的插入或缺失,以及对氧核糖核酸酶Gln192Arg PON1多态性)与早发性CAD的存在和程度的关联。方法:从连续90例经血管造影证实的早发性CAD患者(男性41±5岁,女性49±7岁)和90例无明显冠状动脉阻塞的对照受试者(男性42±5岁,女性48±6岁)中分离并扩增DNA。采用多因素降维分析(MDR)确定了基于遗传和常规危险因素的CAD模型。结果:MDR分析检测到包含四个基因的显著模型(预测成功率为61%,p = 0.03)。当使用候选多态性分析常规风险因素的总数时,确定了一个不同的模型,该模型包含上述模型中四个基因中的三个,并实现了与仅基因模型相似的CAD预测。结论:这些数据表明基因-基因和基因-环境风险相互作用是预测早发性CAD的重要模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multifactor dimensionality reduction analysis of MTHFR, PAI-1, ACE, PON1, and eNOS gene polymorphisms in patients with early onset coronary artery disease.

Background: Association studies in the Turkish population have investigated the single locus effects of different gene polymorphisms on coronary artery disease (CAD). CAD is a complex polygenic disease that involves complex interactions among multiple genetic and environmental conditions.

Design: We evaluated associations of five candidate genetic polymorphisms (methylene tetrahydrofolate reductase C677T, plasminogen activator inhibitor 4G/5G, endothelial nitric oxide synthase (eNOS) 3-27 base pair repeat, insertion, or deletion of a 287 bp Alu repeat sequence polymorhism of angiotensin I converting enzyme, and paraoxonase Gln192Arg PON1 polymorphisms) with the presence and extent of early onset CAD.

Methods: DNA was isolated and amplified from 90 consecutive patients with angiographically proven early onset CAD (ages 41 ± 5 for men, 49 ± 7 for women) and also from 90 control subjects with no significant coronary obstruction angiographically (ages 42 ± 5 for men, 48 ± 6 for women). Multifactor dimensionality reduction (MDR) analysis was performed to identify a model of CAD based on both genetic and conventional risk factors.

Results: MDR analysis detected a significant model with four genes (prediction success ∼ 61%, p = 0.03). When the total number of the conventional risk factors is analysed with the candidate polymorphisms, a different model is identified that includes three of the four genes from the above model and achieves a similar prediction of CAD as the gene only model.

Conclusion: These data indicate that gene-gene and gene-environmental risk interactions form significant models in predicting early onset CAD.

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