Integrative predictive model of coronary artery calcification in atherosclerosis.
Integrative predictive model of coronary artery calcification in atherosclerosis.
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DOI：
10.1161/circulationaha.109.865501
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发表时间：
2009-12-15
期刊：
影响因子：
37.8
通讯作者：
Ramoni MF
中科院分区：
文献类型：
--
作者：
McGeachie M;Ramoni RL;Mychaleckyj JC;Furie KL;Dreyfuss JM;Liu Y;Herrington D;Guo X;Lima JA;Post W;Rotter JI;Rich S;Sale M;Ramoni MF
许多不同的遗传和临床因素已被确定为动脉粥样硬化的原因或贡献者。在多种族动脉粥样硬化研究(MESA)中，我们提出了一个基于遗传和临床数据的临床前动脉粥样硬化模型，该模型预测了年龄在45岁至84岁的欧洲裔健康美国人中冠状动脉钙化的存在。我们评估了712名有或没有冠状动脉钙化的个体，以及他们2882个单核苷酸多态(SNPs)的基因类型。利用这些SNPs和相关的临床数据，构建了一个预测冠状动脉钙化存在的贝叶斯网络。该模型包含13个SNP(来自基因AGTR1、ALOX15、INSR、PRKAB1、IL1R2、ESR2、KCNK1、FBLN5、PPARA、VEGFA、PON1、TDRD6、PLA2G7和一个祖先信息标记)和5个临床变量(性别、年龄、体重、吸烟和糖尿病)，通过ROC曲线下面积(AUC)衡量，预测准确率达到85%。这是对仅使用SNP数据或仅使用临床变量的模型的显著改进(p<；0.001)。我们提出了一项与动脉粥样硬化相关的关节遗传和临床因素的调查，显示了对这两种情况的预测结果，并提高了组合的性能。
Many different genetic and clinical factors have been identified as causes or contributors to atherosclerosis. We present a model of preclinical atherosclerosis based on genetic and clinical data that predicts the presence of coronary artery calcification in healthy Americans of European descent aged 45 to 84 in the Multi-Ethnic Study of Atherosclerosis (MESA). We assessed 712 individuals for the presence or absence of coronary artery calcification, and their genotypes for 2882 single-nucleotide polymorphisms (SNPs). Using these SNPs and relevant clinical data, a Bayesian network that predicts the presence of coronary calcification was constructed. The model contains 13 SNPs (from genes AGTR1, ALOX15, INSR, PRKAB1, IL1R2, ESR2, KCNK1, FBLN5, PPARA, VEGFA, PON1, TDRD6, PLA2G7, and one ancestry informative marker) and 5 clinical variables (sex, age, weight, smoking, and diabetes) and achieves 85% predictive accuracy, as measured by area under the ROC curve (AUC). This is a significant (p < 0.001) improvement upon models using just the SNP data or using just the clinical variables. We present an investigation of joint genetic and clinical factors associated with atherosclerosis that shows predictive results for both cases, and enhanced performance for the combination.