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Large HDL2 combined with inflammatory factors as superior predictors for coronary artery disease than small HDL3
Xiaoying Wu1, Zhijian He2, Runlu Sun1
1Cardiovascular Medicine Department, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Insights
High-density lipoprotein (HDL) subfraction HDL2, along with inflammatory markers monocyte chemoattractant protein-1 (MCP-1) and high-sensitivity C-reactive protein (hsCRP), significantly improves coronary artery disease (CAD) prediction when combined with conventional risk factors.
Area of Science:
- Cardiovascular Medicine
- Biochemistry
- Preventive Cardiology
Background:
- Coronary artery disease (CAD) prediction relies on conventional risk factors (CRFs).
- The added value of high-density lipoprotein (HDL) subfractions and inflammatory markers to CAD risk stratification requires further investigation.
Purpose of the Study:
- To determine if combinations of HDL subfractions and inflammatory markers enhance CAD prediction beyond CRFs.
- To compare the predictive value of different HDL subfractions (HDL2, HDL3) and inflammatory markers (MCP-1, hsCRP) in CAD risk assessment.
Main Methods:
- Stratified 245 non-CAD subjects into low/moderate/high-Framingham risk (L/M/H-FR) groups and enrolled 180 CAD patients.
- Measured levels of HDL-C, HDL2, HDL3, monocyte chemoattractant protein-1 (MCP-1), and high-sensitivity C-reactive protein (hsCRP).
- Utilized multivariable logistic regression, Z statistics, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) to compare model discrimination after adjusting for CRFs.
Main Results:
- HDL2 and HDL-C levels decreased, while MCP-1 and hsCRP increased from low to high risk groups.
- HDL2 was the only significant HDL subfraction associated with CAD risk when all HDL parameters were considered.
- A combined model including HDL2, MCP-1, and hsCRP significantly improved CAD prediction (NRI=0.179, IDI=0.081, P<0.001) compared to models with single factors or all HDL parameters.
Conclusions:
- Larger HDL2 subfractions demonstrate a stronger inverse association with CAD risk than smaller HDL3 subfractions.
- The combination of HDL2, MCP-1, and hsCRP with CRFs offers an optimal predictive model for coronary artery disease.
Background:
This study investigated whether combinations of high-density lipoprotein (HDL) subfractions and inflammatory markers would add value to coronary artery disease (CAD) prediction.
Methods:
Non-CAD subjects (n=245) were stratified into low/moderate/high-Framingham risk (L/M/H-FR) groups and 180 CAD patients were enrolled. Levels of HDL-C, HDL2, HDL3, monocyte chemoattractant protein-1 (MCP-1), and high-sensitivity C-reactive protein (hsCRP) were measured. Multivariable logistic models for CAD were estimated with a single parameter or all parameters together after adjustment for conventional risk factors (CRFs), and Z statistics, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to compare discrimination among different models.
Results:
The results show that HDL-C, HDL2, and HDL3 gradually decreased, while MCP-1 and hsCRP gradually increased from L/M/H-FR to the CAD group. When applying a single factor in the CRFs-adjusted models, HDL-C (OR 0.011, 95% CI, 0.002-0.071, P<0.05) and HDL2 (OR 0.000072, 95% CI, 0.000001-0.004, P<0.05), but not HDL3, were significantly related to CAD risk. Only HDL2 (OR 0.000072, 95% CI, 0.000001-0.004, P<0.001) remained significant when applying all HDL parameters. In the model including all HDL and inflammatory parameters, HDL2 (OR 0.001, 95% CI, 0.000027-0.051), MCP-1 (OR 1.066, 95% CI, 1.039-1.094), and hsCRP (OR 1.130, 95% CI, 1.041-1.227) showed significant differences (all P<0.05). This combined model showed improved discrimination over the models with a single factor (P<0.05) or all HDL parameters (Z=3.299, NRI =0.179, IDI =0.081, P<0.001).
Conclusions:
Large HDL2 is superior to small HDL3 in the inverse association with CAD. The combination of HDL2, MCP-1, and hsCRP with CRFs provides an optimal prediction for CAD.
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