Coronary Plaque Characteristics Assessed by Optical Coherence Tomography and Plasma Lipoprotein(a) Levels in Patients With Acute Coronary Syndrome.

BACKGROUND

Lipoprotein(a) [Lp(a)] is associated with increased cardiovascular risk, but its influence on plaque characteristics at optical coherence tomography (OCT) evaluation is not fully understood.

AIMS

This study seeks to explore the impact of Lp(a) levels on plaque morphology as assessed by OCT in a very high-risk subset of patients.

METHODS

Consecutive patients admitted for acute coronary syndrome (ACS) and undergoing OCT-guided percutaneous coronary intervention (PCI) at a large tertiary care center between 2019 and 2022 were deemed eligible for the current analysis. The overall population was categorized into two subgroups according to baseline Lp(a) levels: (1) lower Lp(a) (Lp(a) ≤ 300 mg/L) and (2) elevated Lp(a) (Lp(a)   300 mg/L). Predictors of lipid-rich plaques were identified using multivariable logistic regression with stepwise selection of candidate covariates.

RESULTS

A total of 202 patients were included in this study. OCT findings revealed that patients with elevated Lp(a) had a higher prevalence of lipid-rich plaques, a significantly greater mean lipid arc, along with increased macrophage infiltration and thin-cap fibroatheroma (TCFA). In contrast, calcific plaque prevalence was higher in the lower Lp(a) group. Multivariable regression analysis identified low-density lipoprotein cholesterol ≥ 70 mg/dL, and elevated Lp(a) as strong predictors of lipid-rich plaques at OCT.

CONCLUSION

In this observational study including ACS patients undergoing OCT-guided PCI, those with elevated Lp(a) levels exhibited a higher prevalence of lipid-rich plaques, increased macrophage infiltration, and TCFA, thereby indicating a more vulnerable plaque phenotype. Additionally, elevated Lp(a) levels and LDL-C levels ≥ 70 mg/dL were each independently associated with lipid enrichment of coronary plaques. These findings suggest Lp(a), beyond other well-known risk factors, as a key marker for risk stratification, potentially informing optimal medical management strategies.