Cardiac Magnetic Resonance Guidance for the Pathogenetic Definition of Cardiomyopathies

Abstract Purpose of Review Pathogenetics is the study of genetics in disease pathogenesis. Many abnormal gene alleles have been identified in cardiomyopathies, but their clinical utility remains limited. This review aims to examine the integration of cardiac MRI (CMR) with genetic data to enhance early detection, prognostication, and treatment strategies for cardiomyopathies. Recent Findings CMR is the gold standard imaging modality for cardiomyopathy evaluation, capable of detecting subtle structural and functional changes throughout the disease course. When applied to patients with genetic mutations, with or without phenotypic expression, CMR aids in early diagnosis and risk stratification. Cardiomyopathies can be categorized into at least seven clinical groups based on morphology, function, and genetic associations: (1) Dilated cardiomyopathy (DCM), (2) Hypertrophic cardiomyopathy (HCM), (3) Restrictive cardiomyopathy, including transthyretin amyloidosis (ATTR-CM), iron overload, and Anderson-Fabry disease, (4) Arrhythmogenic cardiomyopathy (ACM), (5) Non-dilated left ventricular cardiomyopathy (NDLVC), (6) Peripartum cardiomyopathy, and (7) Muscular dystrophy-related cardiomyopathy. We have described left ventricular noncompaction (LVNC) as a morphological trait rather than a distinct cardiomyopathy. Emerging CMR and genetic data suggest an inflammatory component in DCM and ACM, with potential therapeutic implications for immunotherapy. Advanced CMR techniques, such as quantitative perfusion, can distinguish cardiomyopathies from ischemic heart disease and detect early microvascular dysfunction, particularly in ATTR-CM and HCM. Late gadolinium enhancement (LGE) and parametric mapping (T1 and extracellular volume [ECV]) further enhance early diagnosis, prognostication and treatment response by assessing fibrosis and myocardial composition. Summary The integration of CMR and genetic insights improves our understanding of cardiomyopathy pathogenesis, aiding in early diagnosis and prognostic assessment. Future research should leverage artificial intelligence (AI) to analyze genetic and radiomic CMR features, including perfusion data, to establish a comprehensive pathogenetic framework. This approach could refine disease classification, identify novel therapeutic targets, and advance precision medicine in cardiomyopathy management.