Circulating chemokines accurately identify individuals with clinically significant atherosclerotic heart disease
Circulating chemokines accurately identify individuals with clinically significant atherosclerotic heart disease
复制标题
DOI：
10.1152/physiolgenomics.00104.2007
复制
发表时间：
2007-11-14
影响因子：
4.6
通讯作者：
Quertermous, Thomas
中科院分区：
文献类型：
--
作者：
Ardigo, Diego;Assimes, Themistocles L.;Quertermous, Thomas
血清炎症标志物与心血管疾病患者的预后和治疗反应相关。然而，目前的个体标记物缺乏诊断冠状动脉疾病（CAD）的特异性。我们假设，多标记蛋白质组学方法测量血管源性炎症生物标志物的血清水平，可以揭示一个“疾病的签名”，可以作为一个高度准确的方法来评估冠状动脉粥样硬化的存在。我们同时测量了来自ADVANCE研究的48名具有临床意义的CAD受试者（“病例”）和44名对照者的7种趋化因子[CXCL 10（IP-10）、CCL 11（嗜酸性粒细胞趋化因子）、CCL 3（MIP 1 α）、CCL 2（MCP 1）、CCL 8（MCP 2）、CCL 7（MCP 3）和CCL 13（MCP 4）]的血清水平。我们应用了三种分类算法来确定最能预测病例对照状态的变量组合，并使用受试者工作特征（ROC）曲线评估这些模型的诊断性能。病例组血清中6种趋化因子水平均显著高于对照组（P < 0.05）。所有三种分类算法在其最终模型中输入三种趋化因子，并且仅逻辑回归选择临床变量。三种算法中，Logistic回归产生的ROC最高（AUC = 0.95; SE = 0.03），明显优于传统CAD危险因素logistic回归模型的AUC（AUC = 0.67; SE = 0.06）或CRP（AUC = 0.68; SE = 0.06）。多种趋化因子的血清水平的组合以非常高的准确度鉴定具有临床显著的动脉粥样硬化性心脏病的受试者。这些结果需要在更大的横断面研究中重复，并探讨其预后价值。
Serum inflammatory markers correlate with outcome and response to therapy in subjects with cardiovascular disease. However, current individual markers lack specificity for the diagnosis of coronary artery disease (CAD). We hypothesize that a multimarker proteomic approach measuring serum levels of vascular derived inflammatory biomarkers could reveal a "signature of disease" that can serve as a highly accurate method to assess for the presence of coronary atherosclerosis. We simultaneously measured serum levels of seven chemokines [CXCL10 (IP-10), CCL11 (eotaxin), CCL3 (MIP1 alpha), CCL2 (MCP1), CCL8 (MCP2), CCL7 (MCP3), and CCL13 (MCP4)] in 48 subjects with clinically significant CAD ("cases") and 44 controls from the ADVANCE Study. We applied three classification algorithms to identify the combination of variables that would best predict case-control status and assessed the diagnostic performance of these models with receiver operating characteristic (ROC) curves. The serum levels of six chemokines were significantly higher in cases compared with controls (P < 0.05). All three classification algorithms entered three chemokines in their final model, and only logistic regression selected clinical variables. Logistic regression produced the highest ROC of the three algorithms (AUC = 0.95; SE = 0.03), which was markedly better than the AUC for the logistic regression model of traditional risk factors of CAD without (AUC = 0.67; SE = 0.06) or with CRP (AUC = 0.68; SE = 0.06). A combination of serum levels of multiple chemokines identifies subjects with clinically significant atherosclerotic heart disease with a very high degree of accuracy. These results need to be replicated in larger cross-sectional studies and their prognostic value explored.