Echocardiographic Phenomapping in HFpEF: From Imaging Biomarkers to Precision Cardiovascular Medicine

Heart failure with preserved ejection fraction (HFpEF) is a highly prevalent and clinically heterogeneous syndrome characterized by substantial variability in pathophysiology, disease progression, therapeutic response, and clinical outcomes. Although conventional echocardiography remains central to diagnosis, traditional imaging parameters incompletely capture the biological complexity of HFpEF. Growing recognition of distinct disease phenotypes has stimulated interest in echocardiographic phenomapping as a strategy to improve patient characterization and support future precision medicine approaches.

To critically review the contemporary role of echocardiography in HFpEF phenomapping, evaluate the strengths and limitations of emerging imaging biomarkers, and propose a conceptual echocardiographic framework for phenotype-oriented classification.

A comprehensive narrative review was conducted using PubMed/MEDLINE, Scopus, and Web of Science. Contemporary evidence addressing myocardial mechanics, left atrial and right ventricular function, stress echocardiography, artificial intelligence, machine learning, and multimodal phenotyping was narratively reviewed, critically appraised, and synthesized, with emphasis on clinical applicability, methodological limitations, and future research directions.

Contemporary echocardiography enables multidimensional assessment of myocardial structure, ventricular mechanics, atrial function, right ventricular performance, pulmonary vascular interactions, and exercise physiology, providing information beyond conventional diagnostic parameters. Advanced imaging biomarkers, including global longitudinal strain, myocardial work indices, left atrial strain, right ventricular strain, and right ventricular-pulmonary arterial coupling, demonstrate incremental diagnostic and prognostic value in selected patient populations, although their routine clinical implementation remains limited by incomplete standardization, inter-vendor variability, and the need for external validation. Exercise echocardiography further uncovers latent hemodynamic abnormalities and impaired cardiovascular reserve. Artificial intelligence and machine learning have identified candidate HFpEF phenogroups in research settings and may facilitate integration of multidimensional imaging data, although prospective validation and demonstration of clinical utility remain necessary before widespread adoption. Based on the current evidence, we propose a conceptual echocardiographic phenomapping framework encompassing myocardial-dominant, atrial-myopathy, pulmonary vascular-right ventricular, and obesity-metabolic phenotypes.

HFpEF should be regarded as a heterogeneous spectrum of overlapping biological phenotypes rather than a single clinical entity. Modern echocardiography provides a comprehensive platform for multidimensional phenotyping and risk stratification that extends beyond conventional diagnostic assessment. However, important challenges - including biomarker standardization, external validation of proposed phenotypes, and demonstration of incremental value over existing clinical frameworks - must be addressed before phenotype-guided management can be routinely incorporated into clinical practice. Future integration of advanced imaging biomarkers with artificial intelligence, exercise physiology, and biological profiling may further refine individualized approaches to HFpEF management.