Background: A high level of lipoprotein(a) can lead to a high risk of cardiovascular events or mortality. However, the association of moderately elevated lipoprotein(a) levels (≥15 mg/dL) with long-term prognosis among patients with coronary artery disease (CAD) is still uncertain. Hence, we aim to systematically analyzed the relevance of baseline plasma lipoprotein(a) levels to long-term mortality in a large cohort of CAD patients. Methods: We obtained data from 43,647 patients who were diagnosed with CAD and had follow-up information from January 2007 to December 2018. The patients were divided into two groups (<15 and ≥15 mg/dL). The primary endpoint was long-term all-cause death. Kaplan-Meier curve analysis and Cox proportional hazards models were used to investigate the association between moderately elevated baseline lipoprotein(a) levels (≥15 mg/dL) and long-term all-cause mortality. Results: During a median follow-up of 5.04 years, 3,941 (18.1%) patients died. We observed a linear association between lipoprotein(a) levels and long-term all-cause mortality. Compared with lipoprotein(a) concentrations <15 mg/dL, lipoprotein(a) ≥15 mg/dL was associated with a significantly higher risk of all-cause mortality [adjusted hazard ratio (aHR) 1.10, 95%CI: 1.04-1.16, P-values = 0.001). Similar results were found for the subgroup analysis of non-acute myocardial infarction, non-percutaneous coronary intervention, chronic heart failure, diabetes mellitus, or non-chronic kidney diseases. Conclusion: Moderately elevated baseline plasma lipoprotein(a) levels (≥15 mg/dL) are significantly associated with higher all-cause mortality in patients with CAD. Our finding provides a rationale for testing the lipoprotein(a)-reducing hypothesis with lower targets (even <15 mg/dL) in CAD outcome trials.
Keywords: baseline lipoprotein(a); coronary angiography; coronary artery disease; long-term all-cause mortality; percutaneous coronary intervention.
Copyright © 2021 Liu, Liu, Wang, Chen, Liu, Liang, Huang, Li, Lun, Ying, Chen, Huang, Xu, Yan, Zhu, Tadesse, Tan, Chen and Liu.