Serum Cytokines Predict the Severity of Coronary Artery Disease Without Acute Myocardial Infarction - Liu, Sheng; Wang, Chenyang; Guo, Jinzhu; Yang, Yunxiao; Huang, Mengling; Li, Li; Wang, Yu; Qin, Yanwen; Zhang, Ming (Frontiers in Cardiovascular Medicine, 2022)
Introduction: Various cytokines were involved in the process of atherosclerosis, and their serum levels were correlated with coronary artery disease (CAD) to varying degrees. However, there were limited reports about the correlation between serum cytokines and the severity of coronary atherosclerotic lesion in patients with non-acute myocardial infarction (AMI). The purpose of this study was to investigate the relationship between serum cytokines and the severity of CAD, and identify the predictors of severe CAD in patients suspected to have CAD but AMI had been ruled out Methods: A total of 502 patients who had suspected CAD and underwent coronary angiography were enrolled. The serum levels of IL-1β, IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12p70, IL-17, TNF-α, INF-α ,and INF-γ were determined by multiplexed particle-based flow cytometric assays technology. And the severity of CAD was evaluated by Gensini score (GS). Results: The serum levels of IL-4, IL-12p70, IL-17, and INF-α were significantly lower in the severe CAD group (GS≥30) than those in the non-severe CAD group (GS<30). And IL-12p70 and IL-17 were negatively correlated with the severity of CAD. Multivariate logistic regression analyses demonstrated that two serum cytokines (IL-12p70 and IL-17), one clinical protective factor (HDL-C), and two clinical risk factors (gender and diabetes) were the independent predictors of severe CAD. ROC curve analysis showed that multivariate mode combined these predictors had a good performance in predicting severe CAD. Conclusions: The combination of serum cytokines (IL-12p70 and IL-17) and clinical risk factors (HDL-C, gender, and diabetes) may help identify patients with more severe coronary artery lesions from those with suspected CAD but not AMI, and may contribute to guiding the risk stratification for patients with chest discomfort in health care facilities without sufficient medical resources (especially cardiac catheterization resources).