Residual risk in coronary artery disease: identification of cardio–reno–metabolic and psychosocial phenotypes in a Latinamerican cohort of the INTERASPIRE registry

Background Residual cardiovascular risk remains substantial in patients with established coronary artery disease despite contemporary secondary prevention strategies. Conventional approaches focused on isolated risk factors may fail to capture the multidimensional heterogeneity of this population. Purpose We aimed to identify distinct clinical phenotypes using an unsupervised analytical approach and to explore their association with recurrent cardiovascular events. Methods We conducted a multicenter observational study based on the INTERASPIRE registry, including 592 Latinamerican patients with prior acute coronary syndrome or coronary revascularization. Variables collected during a standardized face-to-face interview were grouped into demographic, clinical, metabolic and psychosocial domains. After systematic variable selection, clustering was performed using Partitioning Around Medoids with Gower distance. The change in mean silhouette width was evaluated after iterative exclusion of each variable to assess its contribution to cluster stability. Subsequently, factor analysis of mixed data (FAMD) was applied to identify variables with the greatest contribution to the principal latent dimensions of the data structure. Associations between derived clusters and 12-month cardiovascular events were assessed using Kaplan–Meier survival analysis and Cox proportional hazards models. Results Three clinically meaningful phenotypes were identified (Figure 1). Cluster 1 (42%) comprised predominantly younger patients with lower comorbidity burden and lower psychosocial vulnerability, representing a lower-risk profile. Cluster 2 (27%) was characterized by greater psychosocial vulnerability and an adverse lipid profile—including higher LDL cholesterol and apolipoprotein B—defining a psychosocial–atherogenic residual risk phenotype. Cluster 3 (31%) mainly included older patients with multiple comorbidities and greater renal dysfunction, defining an age-associated cardiorenal phenotype (Graphical abstract). During 12-month follow-up, 7% of patients experienced a cardiovascular event. Event rates differed across clusters (3%, 10%, and 9% in clusters 1, 2, and 3, respectively; log-rank p = 0.02). Compared with cluster 1, clusters 2 and 3 were associated with a higher risk of events (HR 3.03, 95% CI 1.22–7.54 and HR 3.11, 95% CI 1.27–7.63, respectively). Conclusions In this contemporary secondary prevention cohort, unsupervised phenotyping identified three multidimensional clinical profiles with distinct prognostic implications. Residual cardiovascular risk appears to arise from the interaction of lipid-related, renal and psychosocial domains beyond traditional risk factors. Integrating these dimensions into clinical assessment may support more personalized secondary prevention strategies.