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Low-Density Lipoprotein Cholesterol 4: The Notable Risk Factor of Coronary Artery Disease Development
Dongmei Wu1, Qiuju Yang2, Baohua Su3
1Department of Cardiovascular Medicine, General Hospital of Tisco, Sixth Hospital of Shanxi Medical University, Shanxi, China.
Insights
This study identifies specific low-density lipoprotein cholesterol (LDL-C) subfractions as key indicators for coronary artery disease (CAD) risk. Machine learning models, particularly XGboost, accurately predict CAD, aiding early detection in asymptomatic individuals.
Area of Science:
- Cardiology
- Biochemistry
- Data Science
Background:
- Coronary artery disease (CAD) is a leading global cause of mortality, often progressing asymptomatically.
- Early risk assessment in asymptomatic individuals is critical for timely intervention.
- Identifying novel biomarkers for CAD risk stratification is a significant clinical need.
Purpose of the Study:
- To investigate the association between low-density lipoprotein cholesterol (LDL-C) subfractions and CAD.
- To develop and evaluate machine learning models for predicting CAD risk.
- To identify key predictors for CAD development in a diverse patient cohort.
Main Methods:
- Recruitment of 356 CAD patients and 164 non-CAD controls diagnosed via coronary angiography.
- Quantification of LDL-C subfractions using the Lipoprint system and analysis of clinical data.
- Development and comparison of six machine learning models for CAD risk prediction.
Main Results:
- Elevated levels of triglycerides, LDLC-3, LDLC-4, LDLC-5, LDLC-6, and total small and dense LDL-C were observed in CAD patients.
- Male sex, older age, higher BMI, smoking, drinking, hypertension, and diabetes mellitus were significant clinical risk factors.
- LDLC-3, LDLC-4, and LDLC-5 were identified as significant lipid-based risk factors, with LDLC-4 being a primary predictor in the XGboost model (AUC 0.945).
Conclusions:
- Machine learning models, especially XGboost, demonstrate high accuracy in predicting CAD risk.
- Specific LDL-C subfractions, particularly LDLC-4, are valuable biomarkers for identifying individuals at high risk of CAD.
- These predictive models can facilitate early screening and preventive strategies for asymptomatic populations, potentially averting severe cardiovascular events.
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.
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