Ye Yongxin, Fan Jiahua, Chen Zhiteng, Li Xiuwen, Wu Maoxiong, Liu Wenhao, Zhou Shiyi, Rasmussen Morten Arendt, Engelsen Søren Balling, Chen Yangxin, Khakimov Bekzod, Xia Min
Department of Nutrition, School of Public Health, Sun Yat-sen University (Northern Campus), Guangzhou 510080, China.
Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Guangzhou 510080, China.
Metabolites. 2023 Feb 14;13(2):273. doi: 10.3390/metabo13020273.
Non-invasive detection of unstable angina (UA) patients with different severity of coronary lesions remains challenging. This study aimed to identify plasma lipoproteins (LPs) that can be used as potential biomarkers for assessing the severity of coronary lesions, determined by the Gensini score (GS), in UA patients. We collected blood plasma from 67 inpatients with angiographically normal coronary arteries (NCA) and 230 UA patients, 155 of them with lowGS (GS ≤ 25.4) and 75 with highGS (GS > 25.4), and analyzed it using proton nuclear magnetic resonance spectroscopy to quantify 112 lipoprotein variables. In a logistic regression model adjusted for four well-known risk factors (age, sex, body mass index and use of lipid-lowering drugs), we tested the association between each lipoprotein and the risk of UA. Combined with the result of LASSO and PLS-DA models, ten of them were identified as important LPs. The discrimination with the addition of selected LPs was evaluated. Compared with the basic logistic model that includes four risk factors, the addition of these ten LPs concentrations did not significantly improve UA versus NCA discrimination. However, thirty-two selected LPs showed notable discrimination power in logistic regression modeling distinguishing highGS UA patients from NCA with a 14.9% increase of the area under the receiver operating characteristics curve. Among these LPs, plasma from highGS patients was enriched with LDL and VLDL subfractions, but lacked HDL subfractions. In summary, we conclude that blood plasma lipoproteins can be used as biomarkers to distinguish UA patients with severe coronary lesions from NCA patients.
对不同冠状动脉病变严重程度的不稳定型心绞痛（UA）患者进行无创检测仍然具有挑战性。本研究旨在确定可作为评估UA患者冠状动脉病变严重程度潜在生物标志物的血浆脂蛋白（LPs），冠状动脉病变严重程度由Gensini评分（GS）确定。我们收集了67例冠状动脉造影正常（NCA）的住院患者和230例UA患者的血浆，其中155例GS较低（GS≤25.4），75例GS较高（GS>25.4），并使用质子核磁共振波谱对其进行分析，以量化112个脂蛋白变量。在针对四个众所周知的风险因素（年龄、性别、体重指数和降脂药物使用情况）进行调整的逻辑回归模型中，我们测试了每种脂蛋白与UA风险之间的关联。结合LASSO和PLS-DA模型的结果，其中十个被确定为重要的LPs。评估了添加选定LPs后的鉴别能力。与包含四个风险因素的基本逻辑模型相比，添加这十种LPs浓度并没有显著提高UA与NCA之间的鉴别能力。然而，三十二种选定的LPs在逻辑回归建模中显示出显著的鉴别能力，可区分高GS UA患者与NCA患者，受试者工作特征曲线下面积增加了14.9%。在这些LPs中，高GS患者的血浆富含低密度脂蛋白（LDL）和极低密度脂蛋白（VLDL）亚组分，但缺乏高密度脂蛋白（HDL）亚组分。总之，我们得出结论，血浆脂蛋白可作为生物标志物，用于区分患有严重冠状动脉病变的UA患者和NCA患者。
