Inherited cardiomyopathies represent a major cause of ventricular arrhythmias (VA) and sudden cardiac death (SCD), frequently occurring in the absence of advanced systolic dysfunction. Traditional strategies for the primary prevention of SCD have relied predominantly on left ventricular ejection fraction (LVEF), an approach that fails to capture the substantial biological and clinical heterogeneity of non-ischemic cardiomyopathies. Over the past decade, advances in cardiac genetics and cardiac magnetic resonance imaging have identified specific genotypes associated with a disproportionate arrhythmic risk, which often precedes overt ventricular remodeling. The 2023 European Society of Cardiology (ESC) Guidelines on cardiomyopathies formalize this paradigm shift by integrating etiology, myocardial substrate, and electrical phenotype into contemporary risk stratification. In this narrative review, we focus on cardiomyopathy-associated genotypes consistently linked to high arrhythmic risk—LMNA, truncating variants in FLNC, RBM20, PLN p.Arg14del, and desmosomal genes—and examine their molecular mechanisms, phenotypic trajectories, and arrhythmogenic profiles. We discuss how genotype-specific patterns of myocardial fibrosis, conduction disease, and VA inform implantable cardioverter-defibrillator (ICD) decision-making beyond LVEF-based thresholds. By synthesizing genetic, imaging, and clinical evidence in light of ESC 2023 recommendations, this review highlights the evolving role of genotype-informed strategies in the personalized prevention of SCD and underscores remaining gaps in evidence and risk prediction.

Inherited cardiomyopathies associated with high-risk genotypes, are characterized by a disproportionate risk of malignant ventricular arrhythmias and sudden cardiac death, often independent of left ventricular systolic dysfunction or advanced structural remodeling. Traditional surveillance strategies based on intermittent electrocardiography and phenotype-driven risk assessment are insufficient to capture the dynamic and often silent progression of electrical instability in these populations. This narrative review evaluates the emerging role of artificial intelligence (AI)-enabled sensor technologies in remote arrhythmic monitoring of genetically defined cardiomyopathy cohorts. Wearable ECG devices, implantable cardiac monitors, multisensor cardiac implantable electronic device algorithms, pulmonary artery pressure sensors, and contact-free systems enable continuous acquisition of electrophysiological and hemodynamic data, generating digital biomarkers that may reflect early arrhythmic vulnerability and subclinical decompensation. AI-driven analytics enhance signal processing, automated event detection, and remote data triage, with the potential to reduce clinical workload while preserving diagnostic sensitivity. However, current evidence predominantly derives from heterogeneous heart failure or general arrhythmia populations, and prospective validation in genotype-specific cohorts remains limited. Key challenges include algorithm generalizability, signal quality in ambulatory environments, data governance, interpretability of AI models, and integration into structured remote-care pathways. The convergence of genotype-informed risk stratification and multimodal AI-enabled sensing represents a promising strategy to transition from reactive device-based protection to proactive, precision-guided arrhythmic prevention. Dedicated genotype-focused studies and standardized digital endpoints are required to support safe and effective implementation in inherited cardiomyopathies.

Regular endurance exercise is consistently associated with lower cardiovascular mortality, a favorable cardiometabolic profile, and superior cardiorespiratory fitness. However, coronary imaging studies in master endurance athletes have raised a clinically relevant paradox: despite a low burden of conventional risk factors, some athletes—particularly older men with high lifetime exercise exposure—show a greater prevalence of coronary artery calcium and subclinical coronary plaque than sedentary or less active controls. This observation has challenged the long-standing assumption that high-volume endurance exercise is uniformly protective against coronary artery disease. A binary interpretation of this literature is inadequate. Coronary flow reserve and ischemic threshold may remain adequate in some athletes, although this concept is supported by limited functional and outcome data. Based on experimental vascular biology and indirect human evidence, repetitive high-flow states during endurance exercise generate sustained laminar shear stress, cyclic wall strain, and marked increases in coronary blood flow, thereby activating endothelial mechanotransduction pathways and influencing vascular smooth muscle cell behavior, extracellular matrix remodeling, and calcification biology. These adaptations may culminate in positive arterial remodeling, luminal enlargement, and, in some individuals, a predominantly calcified plaque phenotype. Importantly, structural remodeling does not necessarily equate to functional impairment. In selected athletes, when outward remodeling and endothelial responsiveness are preserved, coronary flow reserve and ischemic threshold may remain adequate, although this concept remains supported by limited functional and outcome data. This narrative review integrates the clinical imaging literature with current concepts in vascular mechanobiology to propose that coronary remodeling in endurance athletes exists along an adaptive–maladaptive continuum shaped by cumulative exercise load, aging, sex, conventional risk factors, and biological susceptibility. This framework may help clinicians interpret CAC/CCTA findings in athletes more appropriately and avoid equating plaque burden with equivalent functional or prognostic significance.