Zhao Yi-Jing, Li Yong, Wang Feng-Xiang, Lv Hao, Qu Yaoyao, Qi Lian-Wen, Xiao Pingxi
State Key Laboratory of Natural Medicines School of Traditional Chinese Pharmacy China Pharmaceutical University, Nanjing, China.
Department of Cardiology Pukou Hospital of Chinese Medicine Affiliated to China Pharmaceutical University, Nanjing, China.
Cardiovasc Ther. 2024 Aug 30;2024:9935805. doi: 10.1155/2024/9935805. eCollection 2024.
Patients with stable coronary artery disease (CAD) are at an increased risk of acute myocardial infarction (AMI), particularly among older individuals. Developing a reliable model to predict AMI occurrence in these patients holds the potential to expedite early diagnosis and intervention. This study is aimed at establishing a circulating amino acid-assisted model, incorporating amino acid profiles alongside clinical variables, to predict AMI risk. A cohort of 874 CAD patients from two independent centers was analyzed. Plasma amino acid levels were quantified using liquid chromatography tandem mass spectrometry (LC-MS/MS) employing a targeted metabolomics approach. This methodology incorporated C isotope-labeled internal standards for precise quantification of 27 amino acids. Univariate logistic regression was applied to identify differentially expressed amino acids that distinguished between stable CAD and AMI patients. To assess prediction performance, receiver operating characteristic (ROC) curve and nomogram analyses were utilized. Five amino acids-lysine, methionine, tryptophan, tyrosine, and N6-trimethyllysine-emerged as potential biomarkers ( < 0.05), exhibiting significant differences in their expression levels across the two centers when comparing stable CAD with AMI patients. For AMI risk prediction, the base model, utilizing 12 clinical variables, achieved areas under the curve (AUC) of 0.7387 in the discovery phase ( = 623) and 0.8205 in the external validation set ( = 251). Notably, the integration of these five amino acids into the prediction model significantly enhanced its performance, increasing the AUC to 0.7651 in the discovery phase (Delong's test, = 1.43e-02) and to 0.8958 in the validation set (Delong's test, = 8.91e-03). In conclusion, the circulating amino acid-assisted model effectively enhances the prediction of AMI risk among CAD patients, indicating its potential clinical utility in facilitating early detection and intervention.
稳定型冠状动脉疾病（CAD）患者发生急性心肌梗死（AMI）的风险增加，尤其是在老年人群中。开发一种可靠的模型来预测这些患者发生AMI的情况，有可能加快早期诊断和干预。本研究旨在建立一种循环氨基酸辅助模型，将氨基酸谱与临床变量相结合，以预测AMI风险。对来自两个独立中心的874例CAD患者队列进行了分析。采用靶向代谢组学方法，通过液相色谱串联质谱（LC-MS/MS）对血浆氨基酸水平进行定量。该方法采用C同位素标记的内标物对27种氨基酸进行精确定量。应用单因素逻辑回归来识别区分稳定型CAD患者和AMI患者的差异表达氨基酸。为了评估预测性能，采用了受试者操作特征（ROC）曲线和列线图分析。五种氨基酸——赖氨酸、蛋氨酸、色氨酸、酪氨酸和N6-三甲基赖氨酸——成为潜在的生物标志物（<0.05），在比较稳定型CAD患者和AMI患者时，其在两个中心的表达水平存在显著差异。对于AMI风险预测，利用12个临床变量的基础模型在发现阶段（=623）的曲线下面积（AUC）为0.7387，在外部验证集（=251）中为0.8205。值得注意的是，将这五种氨基酸整合到预测模型中显著提高了其性能，在发现阶段将AUC提高到0.7651（德龙检验，=1.43e-02），在验证集中提高到0.8958（德龙检验，=8.91e-03）。总之，循环氨基酸辅助模型有效地增强了对CAD患者AMI风险的预测，表明其在促进早期检测和干预方面具有潜在的临床应用价值。
