Effective Diagnosis of Cardiomyopathies

Recent advances in research on cardiomyopathies have substantially deepened our understanding of these complex myocardial disorders, leading to more refined and effective treatment approaches. The new 2023 European Society of Cardiology (ESC) Guidelines represent a significant step forward, offering an updated and comprehensive framework that emphasises the importance of a patient-centred, multidisciplinary approach. This approach integrates the latest developments in genetic testing and multimodality imaging, providing clinicians with powerful tools to more effectively diagnose, stratify, and manage patients with cardiomyopathy.

Genetic screening has become increasingly effective, playing a critical role in identifying individuals at risk and enabling the development of personalised treatment plans, particularly for those with hypertrophic and dilated cardiomyopathy. Furthermore, the use of cardiac magnetic resonance imaging (CMR) has emerged as a cornerstone in the evaluation of myocardial scarring and tissue characterisation, offering vital insights that are essential for both prognosis and the tailoring of therapeutic strategies.

This month’s newsletter curates a selection of innovative studies that exemplify the application of these guidelines in clinical practice, highlighting the transformative potential of genetic testing in familial cardiomyopathies and the indispensable role of CMR in risk stratification, patient management, and outcome prediction. These articles provide essential insights and knowledge for clinicians and researchers and offer valuable perspectives on how these innovations can be integrated into clinical practice to significantly enhance patient care and outcomes. For a comprehensive understanding of these important topics, we strongly recommend reading the full articles linked below.

Recent key publications

1) Artificial Intelligence in the Differential Diagnosis of Cardiomyopathy Phenotypes. Diagnostics (Basel) 2024; 14. Cau R, Pisu F, Suri JS et al. Link to full article

Hypertrophic cardiomyopathy (HCM) and dilated cardiomyopathy (DCM) are complex conditions that often present diagnostic challenges due to their overlapping clinical features and heterogeneous presentations. This article delves into the innovative application of AI-driven models for the differential diagnosis of cardiomyopathies, with a particular focus on HCM and DCM. By integrating multimodal data, from genetic and clinical parameters to advanced imaging techniques, AI has demonstrated its capability to discover hidden relationships in complex data. The potential of these technologies not only promises to revolutionise clinical practice but also to significantly improve patient outcomes in the management of cardiomyopathies.

2) Familial Dilated Cardiomyopathy: A Novel MED9 Short Isoform Identification. Int J Mol Sci 2024; 25. Franzese M, Zanfardino M, Soricelli A et al. Link to full article

Dilated cardiomyopathy (DCM) is associated with high morbidity and mortality rates. Familial DCM, which often stems from genetic mutations, presents unique challenges in both diagnosis and treatment. This article presents a detailed exploration of a novel MED9 short isoform identified in patients with familial DCM. The study leverages advanced RNA sequencing and bioinformatics to reveal crucial alterations in the mediator complex subunits that may drive the pathogenesis of DCM. By offering insights into the molecular mechanisms underlying this condition, these new insights can catalyse the development of new targeted therapeutic strategies.

3) Lamin A/C deficiency-mediated ROS elevation contributes to pathogenic phenotypes of dilated cardiomyopathy in iPSC model. Nat Commun 2024; 15:7000. Qiu H, Sun Y, Wang X et al. Link to full article

Lamin A/C deficiency is a critical factor in the pathogenesis of dilated cardiomyopathy (DCM), characterised by mitochondrial dysfunction and elevated reactive oxygen species (ROS). This study presents novel insights into how these molecular disruptions contribute to arrhythmogenesis and nuclear envelope deformations in DCM. Linking Lamin A/C deficiency to increased ROS and its downstream effects, this research highlights potential therapeutic targets, including the modulation of SIRT1 activity, to mitigate the severe clinical manifestations of Lamin A/C-associated DCM.

4) Prognostic implications of genotype findings in non-ischaemic dilated cardiomyopathy: A network meta-analysis. Eur J Heart Fail 2024; Anastasiou V, Papazoglou AS, Gossios T et al. Link to full article

Genetic factors play a critical role in the prognosis of patients with non-ischemic dilated cardiomyopathy (DCM), yet the relative impact of specific genotypes remains an area of active investigation. This study presents a comprehensive network meta-analysis that synthesises data from multiple studies to evaluate the long-term outcomes associated with various genetic mutations, including pathogenic/likely pathogenic (P/LP) variants, truncating titin variants (TTNtv), lamin A/C variants (LMNA), and desmosomal proteins. The findings underscore the importance of incorporating genetic testing into routine clinical practice to refine risk stratification and improve therapeutic strategies.

5) Molecular autopsy in Chinese sudden cardiac death in the young. Am J Med Genet A 2024:e63797. Kwok SY, Ho S, Shih FY et al. Link to full article

Sudden cardiac death in the young (SCDY) remains a significant public health concern, with inherited cardiovascular conditions often implicated as the underlying cause. This study examines the role of molecular autopsy in uncovering genetic variants responsible for SCDY in a Chinese cohort, providing critical insights into the genetic landscape of these tragic events. By identifying likely pathogenic variants in key genes associated with cardiomyopathies and channelopathies, the findings emphasise the importance of integrating genetic testing into routine forensic investigations and familial screening programmes.

6) A personalized mRNA signature for predicting hypertrophic cardiomyopathy applying machine-learning methods. Sci Rep 2024; 14:17023. Gu J, Zhao Y, Ben Y et al. Link to full article

Hypertrophic cardiomyopathy (HCM) represents a significant cause of sudden cardiac death, particularly among young individuals. Traditional diagnostic approaches often fall short in predicting the risk and progression of HCM due to the genetic heterogeneity and complex pathophysiology associated with the condition. This article presents a novel approach utilising personalised mRNA signatures and machine-learning methods to enhance the predictive accuracy for HCM. By integrating advanced bioinformatics and leveraging key molecular markers, this study offers critical insights that could revolutionise the early diagnosis and personalised treatment of HCM.