The atherogenic index of plasma (AIP) and remnant-like particle cholesterol (RLP-C) as prognostic biomarkers of post-PCI adverse cardiovascular events

Abstract

Background/Synopsis

AIP is a lipid index correlating with lipoprotein particle size and atherogenicity. RLP-C is a subset of triglyceride-rich lipoproteins, associated with residual atherogenic risk observed despite taking lipid-lowering medications. Several studies have identified AIP and RLP-C as potential predictors of post-percutaneous coronary intervention (PCI) prognosis; however, contradicting results exist.

Objective/Purpose

To investigate and compare the predictive capacities of AIP and RLP-C for post-PCI adverse cardiovascular (CV) events.

Methods

This was a secondary data analysis on patients undergoing PCI from 2015 to 2021. AIP was calculated as log [TG / HDL-C] and RLP-C as [TC – HDL-C – LDL-C]. The primary outcome was the first episode of MACCE (defined as all-cause mortality, myocardial infarction (MI), stroke, target vessel revascularization, target lesion revascularization, and coronary artery bypass graft). Secondary outcomes were the first incidence of MI, all-cause mortality, and stroke. Parametric survival models with Gompertz and Weibull distributions were used. Model 1 was crude. Model 2 was adjusted for age, gender, smoking, and BMI. Model 3 was additionally adjusted for hypertension, diabetes, dyslipidemia, previous ACS, previous PCI, and creatinine levels. Time-dependent receiver operating characteristic (ROC) curve analysis was performed.

Results

A total of 13,392 patients were included and followed for a median time of 376 days. In model 3, no significant associations were observed between the indices and MACCE or stroke. While RLP-C showed significant association with MI in model 3 (HR = 1.36, 95% CI: 1.03 to 1.80, P = 0.029) the same association for AIP was insignificant by a small margin (HR = 1.32, 95% CI: 0.99 to 1.75, p-value = 0.058). However, MI was best predicted by AIP (AUC: 0.659 vs 0.650) in ROC curve analysis. Both AIP and RLP-C were significantly associated with all-cause mortality as continuous variables (AIP: HR = 1.83, 95% CI: 1.02 to 3.28, P = 0.043; RLP-C: HR = 1.38, 95% CI: 1.11 to 1.72, P = 0.003) as opposed to categorical. RLP-C exhibited higher prediction ability for mortality in ROC curve analysis (AUC: 0.650 vs 0.641).

Conclusions

To the best of our knowledge this is the first study to compare the predictive utility of both AIP and RLP-C for post-PCI adverse CV events. Both AIP and RLP-C had significant associations with post-PCI mortality, and RLP-C showed better predictive ability. RLP-C was also significantly associated with post-PCI MI, however, AIP had superior predictive ability.