Genetically Based Atrial Fibrillation: Current Considerations for Diagnosis and Management

Innovative approaches to atrial fibrillation prediction: should polygenic scores and machine learning be implemented in clinical practice?

Atrial fibrillation (AF) prediction and screening are of important clinical interest because of the potential to prevent serious adverse events. Devices capable of detecting short episodes of arrhythmia are now widely available. Although it has recently been suggested that some high-risk patients with AF detected on implantable devices may benefit from anticoagulation, long-term management remains challenging in lower-risk patients and in those with AF detected on monitors or wearable devices as the development of clinically meaningful arrhythmia burden in this group remains unknown. Identification and prediction of clinically relevant AF is therefore of unprecedented importance to the cardiologic community. Family history and underlying genetic markers are important risk factors for AF. Recent studies suggest a good predictive ability of polygenic risk scores, with a possible additive value to clinical AF prediction scores. Artificial intelligence, enabled by the exponentially increasing computing power and digital data sets, has gained traction in the past decade and is of increasing interest in AF prediction using a single or multiple lead sinus rhythm electrocardiogram. Integrating these novel approaches could help predict AF substrate severity, thereby potentially improving the effectiveness of AF screening and personalizing the management of patients presenting with conditions such as embolic stroke of undetermined source or subclinical AF. This review presents current evidence surrounding deep learning and polygenic risk scores in the prediction of incident AF and provides a futuristic outlook on possible ways of implementing these modalities into clinical practice, while considering current limitations and required areas of improvement.

Heart failure with improved ejection fraction: Beyond diagnosis to trajectory analysis

Left ventricular (LV) systolic dysfunction represents a highly treatable cause of heart failure (HF). A substantial proportion of patients with HF with reduced ejection fraction (EF;HFrEF) demonstrate improvement in LV systolic function (termed HF with improved EF [HFimpEF]), either spontaneously or when treated with guideline-directed medical therapy (GDMT). Although it is a relatively new HF classification, HFimpEF has emerged in recent years as an important and distinct clinical entity. Improvement in LVEF leads to decreased rates of mortality and adverse HF-related outcomes compared to patients with sustained LV systolic dysfunction (HFrEF). While numerous clinical and imaging factors have been associated with HFimpEF, identification of which patients do and do not improve requires further investigation. In addition, patients improve at different rates, and what determines the trajectory of HFimpEF patients after improvement is incompletely characterized. A proportion of patients maintain improvement in LV systolic function, while others experience a recrudescence of systolic dysfunction, especially with GDMT discontinuation. In this review we discuss the contemporary guideline-recommended classification definition of HFimpEF, the epidemiology of improvement in LV systolic function, and the clinical course of this unique patient population. We also offer evidence-based recommendations for the clinical management of HFimpEF and provide a roadmap for future directions in understanding and improving outcomes in the care of patients with HFimpEF.

Myosin-binding protein H-like regulates myosin-binding protein distribution and function in atrial cardiomyocytes

Mutations in atrial-enriched genes can cause a primary atrial myopathy that can contribute to overall cardiovascular dysfunction. MYBPHL encodes myosin-binding protein H-like (MyBP-HL), an atrial sarcomere protein that shares domain homology with the carboxy-terminus of cardiac myosin-binding protein-C (cMyBP-C). The function of MyBP-HL and the relationship between MyBP-HL and cMyBP-C is unknown. To decipher the roles of MyBP-HL, we used structured illumination microscopy, immuno-electron microscopy, and mass spectrometry to establish the localization and stoichiometry of MyBP-HL. We found levels of cMyBP-C, a major regulator of myosin function, were half as abundant compared to levels in the ventricle. In genetic mouse models, loss of MyBP-HL doubled cMyBP-C abundance in the atria, and loss of cMyBP-C doubled MyBP-HL abundance in the atria. Structured illumination microscopy showed that both proteins colocalize in the C-zone of the A-band, with MyBP-HL enriched closer to the M-line. Immuno-electron microscopy of mouse atria showed MyBP-HL strongly localized 161 nm from the M-line, consistent with localization to the third 43 nm repeat of myosin heads. Both cMyBP-C and MyBP-HL had less-defined sarcomere localization in the atria compared to ventricle, yet areas with the expected 43 nm repeat distance were observed for both proteins. Isometric force measurements taken from control and Mybphl null single atrial myofibrils revealed that loss of Mybphl accelerated the linear phase of relaxation. These findings support a mechanism where MyBP-HL regulates cMyBP-C abundance to alter the kinetics of sarcomere relaxation in atrial sarcomeres.

Mechanisms of Sarcomere Protein Mutation-Induced Cardiomyopathies

PURPOSE OF REVIEW

The pace of identifying cardiomyopathy-associated mutations and advances in our understanding of sarcomere function that underlies many cardiomyopathies has been remarkable. Here, we aim to synthesize how these advances have led to the promising new treatments that are being developed to treat cardiomyopathies.

RECENT FINDINGS

The genomics era has identified and validated many genetic causes of hypertrophic and dilated cardiomyopathies. Recent advances in our mechanistic understanding of sarcomere pathophysiology include high-resolution molecular models of sarcomere components and the identification of the myosin super-relaxed state. The advances in our understanding of sarcomere function have yielded several therapeutic agents that are now in development and clinical use to correct contractile dysfunction-mediated cardiomyopathy. New genes linked to cardiomyopathy include targets with limited clinical evidence and require additional investigation. Large portions of cardiomyopathy with family history remain genetically undiagnosed and may be due to polygenic disease.