Low-density lipoprotein-associated variables and the severity of coronary artery disease: an untreated Chinese cohort study

Abstract

Background and aims: Elevated low-density lipoprotein cholesterol (LDL-C) is causal risk for coronary artery disease (CAD) and LDL-associated variables including LDL-C, apolipoprotein B (apoB), non-high-density lipoprotein cholesterol (non-HDL-C), lipoprotein a [Lp(a)], small dense LDL (sd-LDL), and oxidized LDL (ox-LDL) have been widely used for predicting the risk of CAD. This study was aimed to compare the values of six LDL-related variables for predicting the severity of CAD using untreated patients undergoing coronary angiography (CAG).

Methods: A group of 1977 individuals were consecutively enrolled and divided into CAD (n = 1151) and non-CAD groups (n = 826) according to the results of CAG. LDL-C, apoB, non-HDL-C, Lp(a), sd-LDL and ox-LDL were measured, respectively. The numbers of stenotic arteries and Gensini Scores (GS) were used to calculate the severity of CAD and the associations of six variables with the severity of CAD and predicting value of these parameters were simultaneously examined.

Results: CAD patients had significantly higher concentrations of LDL-related variables than non-CAD ones (all p < 0.05). Importantly, all variables rose with the increase in the severity of CAD. The predicting value of CAD manifested as sd-LDL > ox-LDL > apoB > non-HDL-C > LDL-C > Lp(a) [area under curve (AUC): sd-LDL 0.641; ox-LDL 0.640; apoB 0.611; non-HDL-C 0.587; LDL-C 0.583; Lp(a) 0.554; respectively]. In multivariate logistic analysis, all variables showed as independent risk factors for the severity of CAD [odds ratio (OR): ox-LDL > sd-LDL > apoB > non-HDL-C > LDL-C > Lp(a)].

Conclusions: All of LDL-related variables could be useful marker for predicting the severity of CAD but sd-LDL and ox-LDL appeared to litter better. Further study may be needed to validate our results.

Keywords:

• Coronary artery disease
• low-density lipoprotein cholesterol
• Gensini scores
• marker

Acknowledgements

We thank the staff and participants of this study for their important contributions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was partially supported by the Capital Special Foundation of Clinical Application Research [Z121107001012015], the Capital Health Development Fund [201614035], the Beijing Natural Science Foundation [7131014] and CAMS Major Collaborative Innovation Project [2016-I2M-1-011] awarded to Dr. Jian-Jun Li, MD, PhD.