Low-Density Lipoprotein Cholesterol 4: The Notable Risk Factor of Coronary Artery Disease Development

Dongmei Wu; Qiuju Yang; Baohua Su; Jia Hao; Huirong Ma; Weilan Yuan; Junhui Gao; Feifei Ding; Yue Xu; Huifeng Wang; Jiangman Zhao; Bingqiang Li

doi:10.3389/fcvm.2021.619386

Low-Density Lipoprotein Cholesterol 4: The Notable Risk Factor of Coronary Artery Disease Development

Front Cardiovasc Med. 2021 Apr 16:8:619386. doi: 10.3389/fcvm.2021.619386. eCollection 2021.

Authors

Dongmei Wu¹, Qiuju Yang², Baohua Su³, Jia Hao¹, Huirong Ma¹, Weilan Yuan⁴, Junhui Gao⁴, Feifei Ding⁴, Yue Xu⁴, Huifeng Wang¹, Jiangman Zhao⁴, Bingqiang Li²

Affiliations

¹ Department of Cardiovascular Medicine, General Hospital of Tisco, Sixth Hospital of Shanxi Medical University, Shanxi, China.
² Department of Cardiovascular Medicine, The First People's Hospital of Pingdingshan, Pingdingshan, China.
³ Department of Cardiovascular Medicine, Mianxian Hospital, Hanzhong, China.
⁴ Shanghai Zhangjiang Institue of Medical Innovation, Shanghai Biotecan Pharmaceuticals Co., Ltd., Shanghai, China.

Abstract

Background: Coronary artery disease (CAD) is the leading cause of death worldwide, which has a long asymptomatic period of atherosclerosis. Thus, it is crucial to develop efficient strategies or biomarkers to assess the risk of CAD in asymptomatic individuals. Methods: A total of 356 consecutive CAD patients and 164 non-CAD controls diagnosed using coronary angiography were recruited. Blood lipids, other baseline characteristics, and clinical information were investigated in this study. In addition, low-density lipoprotein cholesterol (LDL-C) subfractions were classified and quantified using the Lipoprint system. Based on these data, we performed comprehensive analyses to investigate the risk factors for CAD development and to predict CAD risk. Results: Triglyceride, LDLC-3, LDLC-4, LDLC-5, LDLC-6, and total small and dense LDL-C were significantly higher in the CAD patients than those in the controls, whereas LDLC-1 and high-density lipoprotein cholesterol (HDL-C) had significantly lower levels in the CAD patients. Logistic regression analysis identified male [odds ratio (OR) = 2.875, P < 0.001], older age (OR = 1.018, P = 0.025), BMI (OR = 1.157, P < 0.001), smoking (OR = 4.554, P < 0.001), drinking (OR = 2.128, P < 0.016), hypertension (OR = 4.453, P < 0.001), and diabetes mellitus (OR = 8.776, P < 0.001) as clinical risk factors for CAD development. Among blood lipids, LDLC-3 (OR = 1.565, P < 0.001), LDLC-4 (OR = 3.566, P < 0.001), and LDLC-5 (OR = 6.866, P < 0.001) were identified as risk factors. To predict CAD risk, six machine learning models were constructed. The XGboost model showed the highest AUC score (0.945121), which could distinguish CAD patients from the controls with a high accuracy. LDLC-4 played the most important role in model construction. Conclusions: The established models showed good performance for CAD risk prediction, which can help screen high-risk CAD patients in asymptomatic population, so that further examination and prevention treatment might be taken before any sudden or serious event.

Keywords: LDL-C subfractions; coronary angiography; coronary artery disease; machine learning; risk factors.