Low-density lipoprotein triglyceride predicts outcomes in patients with chronic coronary syndrome following percutaneous coronary intervention according to inflammatory status

CLINICA CHIMICA ACTA(2023)

引用 0|浏览14
暂无评分
摘要
Background: Low-density lipoprotein-triglyceride (LDL-TG), a novel lipid marker, has been reported to be associated with cardiovascular events (CVEs). However, whether inflammatory status has a combined effect with LDL-TG on CVEs in patients with chronic coronary syndrome (CCS) receiving percutaneous coronary intervention (PCI) remains uncertain.Methods: A total of 4,415 patient with coronary angiography were primarily enrolled. Among them, 2,215 patients undergoing PCI were finally classified into subgroups according to LDL-TG and high-sensitivity C-reactive protein (hs-CRP) concentrations. Patients were followed up for up to 7 y for CVEs. The associations between LDLTG, hs-CRP and CVEs were analyzed.Results: Patients with CVEs showed higher concentrations of LDL-TG compared to those without. In Cox regression analysis, LDL-TG was independently associated with CVEs (hazard ratio [HR]: 2.003, 95 % confidence intervals [CI]: 1.365-2.940, p < 0.001). Interestingly, when patients were further categorized into six subgroups according to hs-CRP and LDL-TG concentrations, LDL-TG was correlated with increased events only in patients with high hs-CRP concentrations (HR: 1.726, 95 %CI: 1.055-2.826, p = 0.030). Moreover, the Kaplan-Meier survival curves indicated that patients in the higher plasma concentrations of hs-CRP in combination with the highest LDL-TG concentrations were associated with the highest risk of CVEs.Conclusions: LDL-TG was associated with increased CVEs among patients receiving PCI with increased hs-CRP concentrations, suggesting that measurement of LDL-TG combined with hs-CRP facilitates prognostic utility for cardiovascular risks.
更多
查看译文
关键词
Low-density lipoprotein triglyceride,High-sensitivity C-reactive protein,Percutaneous coronary intervention,Cardiovascular events
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要