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Distinct immune-metabolic phenotypes underlie poor coronary collateral circulation
Zi-Tong Guo1, Hong-Mei Lai2, Run-Xuan Hu1
1First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, China.
Insights
Poor coronary collateral circulation (CCC) in heart disease patients reveals two distinct molecular phenotypes: Complement-Driven Vascular Remodeling and Immuno-Thrombotic Myocardial Dysfunction. This discovery aids in personalized risk stratification and treatment strategies.
Area of Science:
- Cardiology
- Molecular Biology
- Genomics
Background:
- Coronary collateral circulation (CCC) is crucial for myocardial perfusion in coronary artery disease (CAD).
- The molecular basis of poor CCC remains poorly understood.
- Understanding CCC heterogeneity is vital for improving patient outcomes.
Purpose of the Study:
- Identify distinct molecular phenotypes in patients with poor CCC.
- Validate these phenotypes using clinical data and assess their prognostic value.
- Evaluate the potential for personalized therapeutic strategies.
Main Methods:
- Proteomic profiling of 149 patients (69 with poor CCC).
- Unsupervised clustering to identify molecular subtypes within poor CCC.
- Machine learning (XGBoost) for clinical data modeling and SHAP value interpretation.
- External validation using the MIMIC database and survival analysis for MACE.
Main Results:
- Two phenotypes identified: Complement-Driven Vascular Remodeling (CDVR) and Immuno-Thrombotic Myocardial Dysfunction (ITMD).
- An XGBoost model using fasting glucose, eosinophil percentage, and HbA1c achieved high discrimination (AUC > 0.91).
- ITMD phenotype showed significantly higher MACE incidence and upregulated platelet activation, diabetic cardiomyopathy, and metabolic pathways.
Conclusions:
- Poor CCC is characterized by distinct immune-metabolic phenotypes.
- Integrated proteomic-clinical modeling accurately classifies these phenotypes.
- This classification improves risk stratification and may guide personalized therapies for CAD patients with inadequate collateralization.
Background:
Coronary collateral circulation (CCC) significantly impacts myocardial perfusion and clinical outcomes in coronary artery disease patients, yet the underlying molecular heterogeneity remains inadequately characterized.
Objective:
To identify distinct molecular phenotypes in patients with poor CCC, validate these phenotypes using clinical parameters, and evaluate their prognostic implications.
Methods:
This study enrolled 149 patients (80 with good CCC and 69 with poor CCC) for high-throughput proteomic profiling. Unsupervised consensus clustering identified molecular subtypes within poor CCC patients, followed by differential expression analysis and KEGG pathway enrichment. Boruta feature selection was implemented, and multiple machine learning algorithms were tested on clinical data, with XGBoost optimization (accuracy 80.0%, F1-score 80.31%) and SHAP value interpretation. External validation was performed using the MIMIC database. Kaplan-Meier analysis and Cox regression models assessed major adverse cardiovascular events (MACE).
Results:
Two distinct phenotypes emerged among poor CCC patients: Cluster 1 (n = 39, Complement-Driven Vascular Remodeling [CDVR]) and Cluster 2 (n = 30, Immuno-Thrombotic Myocardial Dysfunction [ITMD]). An XGBoost model incorporating fasting glucose, eosinophil percentage, and HbA1c achieved excellent discrimination (AUC > 0.91). External validation confirmed the phenotype-specific clinical patterns. Notably, Cluster 2 demonstrated significantly higher MACE incidence compared to Cluster 1 (Log-rank p < 0.05), with KEGG analysis revealing significant upregulation of platelet activation, diabetic cardiomyopathy, and metabolic pathways in the ITMD phenotype.
Conclusion:
Poor CCC encompasses distinct immune-metabolic phenotypes that can be accurately classified using integrated proteomic-clinical modeling. This classification enables more precise risk stratification and may guide personalized therapeutic strategies for coronary artery disease patients with inadequate collateralization.
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