High prevalence of subtle systolic and diastolic dysfunction in genotype-positive phenotype-negative relatives of dilated cardiomyopathy patients

Differences in Cardiac Mechanics among Genetically At-Risk First-Degree Relatives: The DCM Precision Medicine Study

Aims

Among genetically at-risk first-degree relatives (FDRs) of probands with dilated cardiomyopathy (DCM), the ability to detect changes in left ventricular (LV) mechanics with normal LV size and ejection fraction (LVEF) remains incompletely explored. We sought to define a pre-DCM phenotype among at-risk FDRs, including those with variants of uncertain significance (VUSs), using echocardiographic measures of cardiac mechanics.

Methods and Results

LV structure and function, including speckle-tracking analysis for LV global longitudinal strain (GLS), were evaluated in 124 FDRs (65% female; median age 44.9 [IQR: 30.6-60.3] years) of 66 DCM probands of European ancestry sequenced for rare variants in 35 DCM genes. FDRs had normal LV size and LVEF. Negative FDRs of probands with pathogenic or likely pathogenic (P/LP) variants (n=28) were a reference group to which negative FDRs of probands without P/LP variants (n=30), FDRs with only VUSs (n=27), and FDRs with P/LP variants (n=39) were compared. In an analysis accounting for age-dependent penetrance, FDRs below the median age showed minimal differences in LV GLS across groups while those above it with P/LP variants or VUSs had lower absolute values than the reference group (-3.9 [95% CI: -5.7, -2.1] or -3.1 [-4.8, -1.4] %-units) and negative FDRs of probands without P/LP variants (-2.6 [-4.0, -1.2] or -1.8 [-3.1, -0.6]).

Conclusions

Older FDRs with normal LV size and LVEF who harbored P/LP variants or VUSs had lower absolute LV GLS values, indicating that some DCM-related VUSs are clinically relevant. LV GLS may have utility for defining a pre-DCM phenotype.

Clinical Trial Registration

clinicaltrials.gov, NCT03037632.

New perspectives in diagnosis and risk stratification of non-ischaemic dilated cardiomyopathy

Dilated cardiomyopathy is a primitive heart muscle condition, characterized by structural and functional abnormalities, in the absence of a specific cause sufficient to determine the disease. It is, though, an 'umbrella' term that describes the final common pathway of different pathogenic processes and gene-environment interactions. Performing an accurate diagnostic workup and appropriate characterization of the patient has a direct impact on the patient's outcome. The physician should adapt a multiparametric approach, including a careful anamnesis and physical examination and integrating imaging data and genetic testing. Aetiological characterization should be pursued, and appropriate arrhythmic risk stratification should be performed. Evaluations should be repeated thoroughly at follow-up, as the disease is dynamical over time and individual risk might evolve. The goal is an all-around characterization of the patient, a personalized medicine approach, in order to establish a diagnosis and therapy tailored for the individual patient.

Arrhythmic risk stratification in non-ischaemic dilated cardiomyopathy

Dilated cardiomyopathy is a primary disease of the heart muscle, which affects relatively young patients with a low comorbidity profile. It is characterized by structural and/or functional abnormalities leading to systolic dysfunction of the left ventricle or of both ventricles, often associated with dilatation, in the absence of an ischaemic, valvular, or pressure overload cause sufficient to explain the phenotype. Although the prognosis of the disease has greatly improved over the last few decades, prognostic stratification remains a fundamental objective, especially about the prediction of potentially life-threatening arrhythmic events. An accurate diagnostic work-up and an appropriate aetiopathogenetic characterization affect the patients' outcome and represent the essential basis of an adequate prognostic stratification. It is necessary to adopt a multiparametric approach, especially when the aim is the prediction of arrhythmic risk; it includes an integration of medical history and physical examination with cardiac imaging and genetic testing, in order to obtain a personalized diagnosis and therapeutic strategies. Furthermore, the evaluation should be repeated at every clinical check-up, considering the dynamic trend of the pathology and the arrhythmic risk changes over time. This article aims to illustrate how, starting from an exhaustive aetiological and clinical-instrumental characterization, including all diagnostic methods available at present time, it is possible to obtain a tailored diagnostic evaluation and stratification of the arrhythmic risk as accurate as possible.

Echocardiographic Deformation Imaging for Early Detection of Genetic Cardiomyopathies: JACC Review Topic of the Week

Clinical screening of the relatives of patients with genetic cardiomyopathies is challenging, as they often lack detectable cardiac abnormalities at presentation. Life-threatening adverse events can already occur in these early stages of disease, so sensitive tools to reveal the earliest signs of disease are needed. The utility of echocardiographic deformation imaging for early detection has been explored for this population in multiple studies but has not been broadly implemented in clinical practice. The authors discuss contemporary evidence on the utility of deformation imaging in relatives of patients with genetic cardiomyopathies. The available body of data shows that deformation imaging reveals early disease-specific abnormalities in dilated cardiomyopathy, hypertrophic cardiomyopathy, and arrhythmogenic cardiomyopathy. Deformation imaging seems promising to enhance the screening and follow-up protocols in relatives, and the authors propose measures to accelerate its implementation in clinical care.

Contemporary etiology and prognosis of dilated non-ischemic cardiomyopathy

INTRODUCTION

Non-ischemic dilated cardiomyopathy (NI-DCM) represents a specific etiology of systolic heart failure that usually affect young individuals with a genetic background in up to 40% of cases. Behind the term NI-DCM there is a spectrum of different diseases, and an accurate etiological classification appears pivotal for the clinical management and prognostic stratification of these patients.

EVIDENCE ACQUISITION

In the last years the prognosis of NI-DCM patients dramatically improved thanks to the progresses in medical treatment/ device therapy and earlier diagnosis especially in familial context. In this review we summarize the actual state of art in the management of these patients.

EVIDENCE SYNTHESIS

In the era of precision medicine, a lot of progresses have been made to expand our knowledge on the management of NI-DCM patients. A complex interaction between genotype and external triggers is the main determinant of the clinical phenotype in NI-DCM, and a lot of efforts must be done by clinicians to systematically rule out all the possible causes involved in the pathogenesis. Progresses in cardiac imaging and familial screening led us to detect subtle abnormalities in the initial phase of the disease and also helped us to furtherly stratify the prognosis and arrhythmic risk of these patients. It is plausible that a more precise etiological classification will be needed in the near future.

CONCLUSIONS

NI-DCM contains a spectrum of different diseases. Proper etiological classification, early diagnosis and strict follow-up are essential to tailor care of these patients.

Contemporary and Future Approaches to Precision Medicine in Inherited Cardiomyopathies: JACC Focus Seminar 3/5

Inherited cardiomyopathies are commonly occurring myocardial disorders that are associated with substantial morbidity and mortality. Clinical management strategies have focused on treatment of heart failure and arrhythmic complications in symptomatic patients according to standardized guidelines. Clinicians are now being urged to implement precision medicine, but what does this involve? Advances in understanding of the genetic underpinnings of inherited cardiomyopathies have brought new possibilities for interventions that are tailored to genes, specific variants, or downstream mechanisms. However, the phenotypic variability that can occur with any given pathogenic variant suggests that factors other than single driver gene mutations are often involved. This is propelling a new imperative to elucidate the nuanced ways in which individual combinations of genetic variation, comorbidities, and lifestyle may influence cardiomyopathy phenotypes. Here, Part 3 of a 5-part precision medicine Focus Seminar series reviews the current status and future opportunities for precision medicine in the inherited cardiomyopathies.

Diagnosis and Risk Prediction of Dilated Cardiomyopathy in the Era of Big Data and Genomics

Dilated cardiomyopathy (DCM) is a leading cause of heart failure and life-threatening ventricular arrhythmias (LTVA). Work-up and risk stratification of DCM is clinically challenging, as there is great heterogeneity in phenotype and genotype. Throughout the last decade, improved genetic testing of patients has identified genotype-phenotype associations and enhanced evaluation of at-risk relatives leading to better patient prognosis. The field is now ripe to explore opportunities to improve personalised risk assessments. Multivariable risk models presented as "risk calculators" can incorporate a multitude of clinical variables and predict outcome (such as heart failure hospitalisations or LTVA). In addition, genetic risk scores derived from genome/exome-wide association studies can estimate an individual's lifetime genetic risk of developing DCM. The use of clinically granular investigations, such as late gadolinium enhancement on cardiac magnetic resonance imaging, is warranted in order to increase predictive performance. To this end, constructing big data infrastructures improves accessibility of data by using electronic health records, existing research databases, and disease registries. By applying methods such as machine and deep learning, we can model complex interactions, identify new phenotype clusters, and perform prognostic modelling. This review aims to provide an overview of the evolution of DCM definitions as well as its clinical work-up and considerations in the era of genomics. In addition, we present exciting examples in the field of big data infrastructures, personalised prognostic assessment, and artificial intelligence.