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Unsupervised cardiometabolic phenotyping unmasks residual MACCE risk beyond LDL-C in acute myocardial infarction
Wendong Xu1, Xiaoxu Zhang2, Lu Er1
1Department of Cardiology, Hebei Key Laboratory of Precision Medicine Translational Research on Cardiovascular Diseases, Hebei General Hospital, Shijiazhuang, China.
Insights
New cardiometabolic phenotypes in acute myocardial infarction (AMI) patients after percutaneous coronary intervention (PCI) better predict outcomes than LDL-C alone. A decompensated inflammatory-catabolic phenotype shows highest risk, highlighting the need for multi-dimensional profiling.
Area of Science:
- Cardiovascular Medicine
- Biomarker Discovery
- Metabolic Health
Background:
- Outcomes after percutaneous coronary intervention (PCI) for acute myocardial infarction (AMI) are variable despite current therapies.
- Existing risk stratification using low-density lipoprotein cholesterol (LDL-C) may not capture all high-risk patients.
- Novel approaches are needed to identify residual risk in post-PCI AMI patients.
Purpose of the Study:
- To identify novel clinical phenotypes in AMI patients post-PCI using unsupervised clustering of cardiometabolic biomarkers.
- To evaluate the association of these phenotypes with major adverse cardiovascular and cerebrovascular events (MACCE).
- To compare the predictive performance of phenotyping versus LDL-C targets for MACCE risk.
Main Methods:
- K-means clustering was applied to 13 cardiometabolic variables in 668 AMI patients undergoing successful PCI.
- Cluster stability was confirmed using bootstrap resampling.
- Kaplan-Meier analysis, multivariable Cox regression, and ROC curves were used to assess outcomes and discriminative performance.
Main Results:
- Three distinct phenotypes were identified: Metabolically Balanced (58.5%), Hyperglycemic-Dyslipidemic (16.0%), and Decompensated Inflammatory-Catabolic (25.4%).
- The Decompensated Inflammatory-Catabolic phenotype had the highest MACCE rate (49.4%) despite the lowest LDL-C levels.
- Phenotype classification (AUC 0.626) significantly outperformed LDL-C target attainment (AUC 0.503) in predicting MACCE.
Conclusions:
- Unsupervised phenotyping reveals a high-risk 'Decompensated Inflammatory-Catabolic' subgroup post-PCI for AMI.
- This phenotype is associated with significantly higher MACCE risk, independent of other factors.
- Multi-dimensional metabolic profiling offers a superior approach to LDL-C targets for identifying residual risk in this patient population.
Background:
Post-percutaneous coronary intervention (PCI) outcomes in acute myocardial infarction (AMI) remain heterogeneous despite guideline-directed therapy and lower low-density lipoprotein cholesterol (LDL-C) targets. We employed unsupervised clustering of cardiometabolic biomarkers to uncover novel clinical phenotypes that conventional stratifications overlook.
Methods:
K-means clustering was applied to 13 standardized cardiometabolic variables in 668 AMI patients who underwent successful PCI. Cluster stability was assessed by bootstrap resampling. Clinical outcomes were evaluated by Kaplan-Meier analysis and multivariable Cox regression. Receiver operating characteristic curves and DeLong testing compared discriminative performance between phenotype classification and LDL-C target attainment.
Results:
Three phenotypes were identified: Ph0 Metabolically Balanced (n = 391, 58.5%), Ph1 Hyperglycemic-Dyslipidemic (n = 107, 16.0%), and Ph2 Decompensated Inflammatory-Catabolic (n = 170, 25.4%). Over a median follow-up of 31.9 months, major adverse cardiovascular and cerebrovascular events (MACCE) occurred in 19.4%, 31.8%, and 49.4%, respectively (P < 0.001). After full covariate adjustment, Ph2 remained independently associated with MACCE (hazard ratio 2.97, 95% confidence interval 2.00-4.41, P < 0.001), while Ph1 was attenuated to non-significance (P = 0.136). Despite having the lowest LDL-C, Ph2 carried the highest event rate. LDL-C target attainment (<1.8 mmol/L) did not discriminate MACCE risk [area under the curve (AUC) 0.503], whereas phenotype classification yielded an AUC of 0.626 (DeLong P < 0.001).
Conclusions:
Unsupervised cardiometabolic phenotyping identified a decompensated inflammatory-catabolic phenotype that carried the highest MACCE risk despite having the lowest LDL-C, representing a high-risk subgroup unrecognizable by conventional lipid-centric stratification. These findings suggest that multi-dimensional metabolic profiling may complement LDL-C targets for residual risk identification in post-PCI AMI patients.
