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Diagnostic value of small dense low-density lipoprotein and trace elements in coronary artery disease
Na Zhang1, Yue Xu2, Hao Liang1
1Affiliated Hospital of Chengde Medical University, Department of Cardiology, Hebei Sheng, Chengde Prefecture, China.
Insights
Early diagnosis of coronary artery disease (CAD) is crucial. This study identified specific low-density lipoprotein subfractions and trace elements as potential biomarkers for detecting CAD using machine learning models.
Area of Science:
- Cardiovascular Medicine
- Biochemistry
- Computational Biology
Background:
- Coronary artery disease (CAD) is a leading global cause of mortality.
- A significant percentage of CAD patients experience a prolonged asymptomatic phase of atherosclerosis, necessitating early diagnostic strategies.
- Identifying novel biomarkers for early CAD detection is a critical unmet clinical need.
Purpose of the Study:
- To investigate the potential of serum low-density lipoprotein (LDL) subfractions and trace elements as biomarkers for early coronary artery disease (CAD) detection.
- To evaluate the efficacy of various machine learning algorithms in constructing diagnostic models for CAD based on these biomarkers.
- To identify specific LDL subfractions and trace elements that differentiate CAD patients from non-CAD controls.
Main Methods:
- An observational, case-control study involving 489 CAD patients and 75 controls.
- Quantification of LDL subfractions (LDLC1-7) using the Quantimetrix Lipoprint LDL system.
- Measurement of 18 trace elements via inductively coupled plasma mass spectrometry.
- Development and comparison of six machine learning algorithms (Logistic Regression, K Neighbors, GaussianNB, Random Forest, Decision Tree, XGBoost) for CAD diagnosis.
Main Results:
- Significant differences were observed in the serum levels of specific LDL subfractions (LDLC-1, LDLC-3, LDLC-4, LDLC-5) and trace elements (chromium, manganese, cobalt, strontium, lead) between CAD patients and controls.
- LDLC-3, LDLC-4, and lead were identified as risk factors for CAD, while LDLC-1, chromium, manganese, cobalt, and strontium showed protective effects.
- The XGBoost algorithm demonstrated the highest diagnostic performance among the evaluated machine learning models.
Conclusions:
- Specific LDL subfractions and trace elements serve as significant differentiating biomarkers between individuals with and without CAD.
- Machine learning algorithms, particularly XGBoost, can effectively integrate these biomarkers for improved CAD diagnostic capabilities.
- These findings suggest a promising avenue for developing non-invasive, early diagnostic tools for coronary artery disease.
Abstract:
Coronary artery disease (CAD) is a worldwide leading cause of death. Considering that 20%-40% of patients with CAD have a long asymptomatic period of atherosclerosis, it has become urgent to explore the feasibility of diagnosing CAD at an early stage. This is an observational, case-control study, a total of 489 consecutive CAD patients and 75 non-CAD controls were recruited. The levels of low-density lipoprotein subfractions (LDLC1-7) in serum were measured by the Quantimetrix Lipoprint LDL system. The levels of 18 trace elements (vanadium, chromium, manganese, cobalt, nickel, copper, zinc, gallium, arsenic, selenium, strontium, cadmium, tin, antimony, barium, mercury, thallium, and lead) were tested using inductively coupled plasma mass spectrometry. Six machine learning algorithms (Logistic Regression, K Neighbors, GaussianNB, Random Forest, Decision Tree and XGBoost) were used to construct CAD diagnostic models. The levels of LDLC-3, LDLC-4, LDLC-5, and lead were significantly higher in CAD patients, while the levels of LDLC-1, chromium, manganese, cobalt, and strontium were lower (p < 0.05 for all). Univariate logistic regression analysis indicates that LDLC-3, LDLC-4, and lead were the risk factors for CAD development (odds ratio >1 and p < 0.05 for all), while LDLC-1, chromium, manganese, cobalt, and strontium were the protective factors for CAD (odds ratio < 1 and p < 0.05 for all). XGBoost had the best overall diagnostic performance among the six algorithms. There are significant differences in the levels of several LDL subfractions and trace elements between non-CAD controls and CAD patients. These biomarkers may help the diagnostic of CAD while applying machine learning algorithms.
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