Wu Xiaoying, He Zhijian, Sun Runlu, Xie Xiangkun, Chen Qingqun, Wang Junjie, Bao Jinlan, Huang Jingjing, Jiang Yuan, Zhang Yuling, Wang Jingfeng
Cardiovascular Medicine Department, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Cardiovascular Medicine Department, The First Affiliated Hospital/School of Clinical Medicine, Guangdong Pharmaceutical University, Guangzhou, China.
Ann Transl Med. 2021 Apr;9(8):672. doi: 10.21037/atm-21-948.
This study investigated whether combinations of high-density lipoprotein (HDL) subfractions and inflammatory markers would add value to coronary artery disease (CAD) prediction.
Non-CAD subjects (n=245) were stratified into low/moderate/high-Framingham risk (L/M/H-FR) groups and 180 CAD patients were enrolled. Levels of HDL-C, HDL, HDL, monocyte chemoattractant protein-1 (MCP-1), and high-sensitivity C-reactive protein (hsCRP) were measured. Multivariable logistic models for CAD were estimated with a single parameter or all parameters together after adjustment for conventional risk factors (CRFs), and Z statistics, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to compare discrimination among different models.
The results show that HDL-C, HDL and HDL gradually decreased, while MCP-1 and hsCRP gradually increased from L/M/H-FR to the CAD group. When applying a single factor in the CRFs-adjusted models, HDL-C (OR 0.011, 95% CI, 0.002-0.071, P<0.05) and HDL (OR 0.000072, 95% CI, 0.000001-0.004, P<0.05), but not HDL, were significantly related to CAD risk. Only HDL (OR 0.000072, 95% CI, 0.000001-0.004, P<0.001) remained significant when applying all HDL parameters. In the model including all HDL and inflammatory parameters, HDL (OR 0.001, 95% CI, 0.000027-0.051), MCP-1 (OR 1.066, 95% CI, 1.039-1.094), and hsCRP (OR 1.130, 95% CI, 1.041-1.227) showed significant differences (all P<0.05). This combined model showed improved discrimination over the models with a single factor (P<0.05) or all HDL parameters (Z=3.299, NRI =0.179, IDI =0.081, P<0.001).
Large HDL is superior to small HDL in the inverse association with CAD. The combination of HDL, MCP-1, and hsCRP with CRFs provides an optimal prediction for CAD.
本研究调查了高密度脂蛋白(HDL)亚组分与炎症标志物的组合是否会增加冠状动脉疾病(CAD)预测的价值。
将非CAD受试者(n = 245)分为低/中/高弗明翰风险(L/M/H-FR)组,并纳入180例CAD患者。测量HDL-C、HDL、HDL、单核细胞趋化蛋白-1(MCP-1)和高敏C反应蛋白(hsCRP)的水平。在调整传统风险因素(CRF)后,用单个参数或所有参数一起估计CAD的多变量逻辑模型,并使用Z统计量、净重新分类改善(NRI)和综合判别改善(IDI)来比较不同模型之间的判别能力。
结果显示,从L/M/H-FR组到CAD组,HDL-C、HDL和HDL逐渐降低,而MCP-1和hsCRP逐渐升高。在经CRF调整的模型中应用单个因素时,HDL-C(比值比[OR]0.011,95%置信区间[CI],0.002 - 0.071,P < 0.05)和HDL(OR 0.000072,95% CI,0.000001 - 0.004,P < 0.05)与CAD风险显著相关,但HDL不相关。应用所有HDL参数时,只有HDL(OR 0.000072,95% CI,0.000001 - 0.004,P < 0.001)仍具有显著性。在包括所有HDL和炎症参数的模型中,HDL(OR 0.001,95% CI,0.000027 - 0.051)、MCP-1(OR 1.066,95% CI,1.039 - 1.094)和hsCRP(OR 1.130,95% CI,1.041 - 1.227)显示出显著差异(均P < 0.05)。与单因素模型(P < 0.05)或所有HDL参数模型相比,该联合模型的判别能力有所提高(Z = 3.299,NRI = 0.179,IDI = 0.081,P < 0.001)。
大颗粒HDL在与CAD的负相关中优于小颗粒HDL。HDL、MCP-1和hsCRP与CRF的组合可为CAD提供最佳预测。