Oxidized low-density lipoprotein associates with cardiovascular disease by a vicious cycle of atherosclerosis and inflammation: A systematic review and meta-analysis

FRONTIERS IN CARDIOVASCULAR MEDICINE(2023)

引用 6|浏览21
暂无评分
摘要
Background Low-density lipoprotein cholesterol (LDL-C) is an established marker for cardiovascular disease (CVD) and a therapeutic target. Oxidized LDL (oxLDL) is known to be associated with excessive inflammation and abnormal lipoprotein metabolism. Chronic inflammatory diseases confer an elevated risk of premature atherosclerosis and adverse cardiovascular events. Whether oxLDL may serve as a potential biomarker for CVD stratification in populations with chronic inflammatory conditions remains understudied.Objective To perform a systematic review and meta-analysis evaluating the relationship between oxLDL and CVD (defined by incident CVD events, carotid intima-media thickness, presence of coronary plaque) in patients with chronic inflammatory diseases.Methods A systematic literature search was performed using studies published between 2000 and 2022 from PubMed, Cochrane Library, Embase (Elsevier), CINHAL (EBSCOhost), Scopus (Elsevier), and Web of Science: Core Collection (Clarivate Analytics) databases on the relationship between oxLDL and cardiovascular risk on inflamed population. The pooled effect size was combined using the random effect model and publication bias was assessed if P < 0.05 for the Egger or Begg test along with the funnel plot test.Results A total of three observational studies with 1,060 participants were ultimately included in the final meta-analysis. The results demonstrated that oxLDL is significantly increased in participants with CVD in the setting of chronic inflammatory conditions. This meta-analysis suggests that oxLDL may be a useful biomarker in risk stratifying cardiovascular disease in chronically inflamed patients.
更多
查看译文
关键词
cardiovascular disease,atherosclerosis,inflammation,oxidized low-density lipoprotein,lipids
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