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Background: Sudden cardiac death is a natural and unexpected death that occurs within 1 h of the first symptom. Most sudden cardiac deaths occur during exercise, mostly as a result of myocardial infarction. After autopsy, some cases, especially in the young, are diagnosed as cardiomyopathies or remain without a conclusive cause of death. In both situations, genetic alterations may explain the arrhythmia. Objective: Our aim was to identify a genetic predisposition to sudden cardiac death in a cohort of post-mortem cases of individuals who died during exercise, with a structurally normal heart, and were classified as arrhythmogenic death. Methods: We analyzed a cohort of 52 post-mortem samples from individuals <50 years old who had a negative autopsy. Next-generation sequencing technology was used to screen genes associated with sudden cardiac death. Results: Our cohort showed a male prevalence (12:1). Half of the deaths occurred in individuals 41-50 years of age. Running was the most common exercise activity during the fatal event, accounting for 46.15% of cases. Genetic analysis identified 83 rare variants in 37 samples (71.15% of all samples). Of all rare variants, 36.14% were classified as deleterious, being present in 53.84% of all cases. Conclusions: A comprehensive analysis of sudden cardiac death-related genes in individuals who died suddenly while exercising enabled the identification of potentially causative variants. However, many genetic variants remain of indeterminate significance, thus further work is needed before clinical translation. Nonetheless, comprehensive genetic analysis of individuals who died during exercise enables the detection of potentially causative variants and helps to identify at-risk relatives. [ABSTRACT FROM AUTHOR]

This study sought to determine the usefulness of genetic testing to predict evolution in hypertrophic cardiomyopathy (HCM) and to assess the role of genetic testing in clinical practice. Genetic results of 100 HCM patients tested for mutations in ≥10 HCM-causing genes were evaluated. Patients were classified as with poor (group A) or favourable (group B) clinical course. Forty-five pathogenic mutations (PM) were identified in 28 patients (56 %) from group A and in 23 (46 %) from group B ( p = 0.317). Only 40 patients (40 %) exhibited PM that had been previously reported and only 15 (15 %) had PM reported in ≥10 individuals. PM associated with poor prognosis were identified in just five patients from group A (10 %). Genetic findings are not useful to predict prognosis in most HCM patients. By contrast, real-world data reinforce the usefulness of genetic testing to provide genetic counselling and to enable cascade genetic screening. [ABSTRACT FROM AUTHOR]

The heritable cardiovascular disorder long QT syndrome (LQTS), characterized by prolongation of the QT interval on electrocardiogram, carries a high risk of sudden cardiac death. We sought to add new data to the existing knowledge of genetic mutations contributing to LQTS to both expand our understanding of its genetic basis and assess the value of genetic testing in clinical decision-making. Direct sequencing of the five major contributing genes, KCNQ1, KCNH2, SCN5A, KCNE1, and KCNE2, was performed in a cohort of 115 non-related LQTS patients. Pathogenicity of the variants was analyzed using family segregation, allele frequency from public databases, conservation analysis, and Condel and Provean in silico predictors. Phenotype-genotype correlations were analyzed statistically. Sequencing identified 36 previously described and 18 novel mutations. In 51.3% of the index cases, mutations were found, mostly in KCNQ1, KCNH2, and SCN5A; 5.2% of cases had multiple mutations. Pathogenicity analysis revealed 39 mutations as likely pathogenic, 12 as VUS, and 3 as non-pathogenic. Clinical analysis revealed that 75.6% of patients with QTc≥500 ms were genetically confirmed. Our results support the use of genetic testing of KCNQ1, KCNH2, and SCN5A as part of the diagnosis of LQTS and to help identify relatives at risk of SCD. Further, the genetic tools appear more valuable as disease severity increases. However, the identification of genetic variations in the clinical investigation of single patients using bioinformatic tools can produce erroneous conclusions regarding pathogenicity. Therefore segregation studies are key to determining causality. [ABSTRACT FROM AUTHOR]

The name of author M. Alejandra Restrepo-Cordoba was parsed incorrectly (in such a way as to suggest that her surname is "Alejandra Restrepo-Cordoba") in this article as published. [ABSTRACT FROM AUTHOR]