ArticleFrontiers in cardiovascular medicine2026
Unsupervised cardiometabolic phenotyping unmasks residual MACCE risk beyond LDL-C in acute myocardial infarction after revascularization.
Article in Frontiers in cardiovascular medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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 ( 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.
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