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Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiovascular disease with a prevalence of 1 in 500 people and varying clinical presentations. Although there is much research on HCM, underlying molecular mechanisms are poorly understood, and research on the molecular mechanisms of its specific clinical presentations is scarce. Our aim was to explore the molecular mechanisms shared by HCM and its clinical presentations through the automated extraction of molecular mechanisms. Molecular mechanisms were congregated by a query of the INDRA database, which aggregates knowledge from pathway databases and combines it with molecular mechanisms extracted from abstracts and open-access full articles by multiple machine-reading systems. The molecular mechanisms were extracted from 230,072 articles on HCM and 19 HCM clinical presentations, and their intersections were found. Shared molecular mechanisms of HCM and its clinical presentations were represented as networks; the most important elements in the intersections’ networks were found, centrality scores for each element of each network calculated, networks with reduced level of noise generated, and cooperatively working elements detected in each intersection network. The identified shared molecular mechanisms represent possible mechanisms underlying different HCM clinical presentations. Applied methodology produced results consistent with the information in the scientific literature.

Background and Objectives: Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac disease that affects approximately 1 in 500 people. Due to an incomplete disease penetrance associated with numerous factors, HCM is not manifested in all carriers of genetic mutation. Although about two-thirds of patients are male, it seems that female gender is associated with more severe disease phenotype and worse prognosis. The objective of this study was to evaluate the gender related differences in HCM presentation. Materials and Methods: This study was conducted as a part of the international multidisciplinary SILICOFCM project. Clinical information, laboratory analyses, electrocardiography, echocardiography, and genetic testing data were collected for 362 HCM patients from four clinical centers (Florence, Newcastle, Novi Sad, and Regensburg). There were 33% female patients, and 67% male patients. Results: Female patients were older than males (64.5 vs. 53.5 years, p < 0.0005). The male predominance was present across all age groups until the age of 70, when gender distribution became comparable. Females had higher number of symptomatic individuals then males (69% vs. 52%, p = 0.003), most frequently complaining of dyspnea (50% vs. 30%), followed by chest pain (30% vs. 17%), fatigue (26% vs. 13%), palpitations (22% vs. 13%), and syncope (13% vs. 8%). The most common rhythm disorder was atrial fibrillation which was present in a similar number of females and males (19% vs. 13%, p = 0.218). Levels of N-terminal pro-brain natriuretic peptide were comparable between the genders (571 vs. 794 ng/L, p = 0.244). Echocardiography showed similar thickness of interventricular septum (18 vs. 16 mm, p = 0.121) and posterolateral wall (13 vs. 12 mm, p = 0.656), however, females had a lower number of systolic anterior motion (8% vs. 16%, p = 0.020) and other mitral valve abnormalities. Conclusions: Female patients are underrepresented but seem to have a more pronounced clinical presentation of HCM. Therefore, establishing gender specific diagnostic criteria for HCM should be considered.

Hypertrophic cardiomyopathy (HCM) is among the most common forms of cardiomyopathies, with a prevalence of 1:200 to 1:500 people. HCM is caused by variants in genes encoding cardiac sarcomeric proteins, of which a majority reside in MYH7, MYBPC3, and TNNT2. Up to 40% of the HCM cases do not have any known HCM variant. Genotype–phenotype associations in HCM remain incompletely understood. This study involved two visits of 46 adult patients with a confirmed diagnosis of HCM. In total, 174 genes were analyzed on the Next-Generation Sequencing platform, and transthoracic echocardiography was performed. Gene-specific discriminative echocardiogram findings were identified using the computer vision library Fast AI. This was accomplished with the generation of deep learning models for the classification of ultrasonic images based on the underlying genotype and a later analysis of the most decisive image regions. Gene-specific echocardiogram findings were identified: for variants in the MYH7 gene (vs. variant not detected), the most discriminative structures were the septum, left ventricular outflow tract (LVOT) segment, anterior wall, apex, right ventricle, and mitral apparatus; for variants in MYBPC3 gene (vs. variant not detected) these were the septum, left ventricle, and left ventricle/chamber; while for variants in the TNNT2 gene (vs. variant not detected), the most discriminative structures were the septum and right ventricle.

Abstract Background Biomedical knowledge is dispersed in scientific literature and is growing constantly. Curation is the extraction of knowledge from unstructured data into a computable form and could be done manually or automatically. Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac disease, with genotype–phenotype associations still incompletely understood. We compared human- and machine-curated HCM molecular mechanisms’ models and examined the performance of different machine approaches for that task. Results We created six models representing HCM molecular mechanisms using different approaches and made them publicly available, analyzed them as networks, and tried to explain the models’ differences by the analysis of factors that affect the quality of machine-curated models (query constraints and reading systems’ performance). A result of this work is also the Interactive HCM map, the only publicly available knowledge resource dedicated to HCM. Sizes and topological parameters of the networks differed notably, and a low consensus was found in terms of centrality measures between networks. Consensus about the most important nodes was achieved only with respect to one element (calcium). Models with a reduced level of noise were generated and cooperatively working elements were detected. REACH and TRIPS reading systems showed much higher accuracy than Sparser, but at the cost of extraction performance. TRIPS proved to be the best single reading system for text segments about HCM, in terms of the compromise between accuracy and extraction performance. Conclusions Different approaches in curation can produce models of the same disease with diverse characteristics, and they give rise to utterly different conclusions in subsequent analysis. The final purpose of the model should direct the choice of curation techniques. Manual curation represents the gold standard for information extraction in biomedical research and is most suitable when only high-quality elements for models are required. Automated curation provides more substance, but high level of noise is expected. Different curation strategies can reduce the level of human input needed. Biomedical knowledge would benefit overwhelmingly, especially as to its rapid growth, if computers were to be able to assist in analysis on a larger scale.

Hypertrophic cardiomyopathy (HCM) is the most prevalent heritable cardiomyopathy. HCM is considered to be caused by mutations in cardiac sarcomeric protein genes. Recent research suggests that the genetic foundation of HCM is much more complex than originally postulated. The clinical presentations of HCM are very variable. Some mutation carriers remain asymptomatic, while others develop severe HCM, terminal heart failure, or sudden cardiac death. Heterogeneity regarding both genetic mutations and the clinical course of HCM hinders the establishment of universal genotype–phenotype correlations. However, some trends have been identified. The presence of a mutation in some genes encoding sarcomeric proteins is associated with earlier HCM onset, more severe left ventricular hypertrophy, and worse clinical outcomes. There is a diversity in the mechanisms implicated in the pathogenesis of HCM. They may be classified into groups, but they are interrelated. The lack of known supplementary elements that control the progression of HCM indicates that molecular mechanisms that exist between genotype and clinical presentations may be crucial. Secondary molecular changes in pathways implicated in HCM pathogenesis, post-translational protein modifications, and epigenetic factors affect HCM phenotypes. Cardiac loading conditions, exercise, hypertension, diet, alcohol consumption, microbial infection, obstructive sleep apnea, obesity, and environmental factors are non-molecular aspects that change the HCM phenotype. Many mechanisms are implicated in the course of HCM. They are mostly interconnected and contribute to some extent to final outcomes.

Hypertrophic cardiomyopathy (HCM) is a relatively common inherited cardiac disease that results in left ventricular hypertrophy. Machine learning uses algorithms to study patterns in data and develop models able to make predictions. The aim of this study is to identify HCM subtypes and examine the mechanisms of HCM using machine learning algorithms. Clinical and laboratory findings of 143 adult patients with a confirmed diagnosis of nonobstructive HCM are analyzed; HCM subtypes are determined by clustering, while the presence of different HCM features is predicted in classification machine learning tasks. Four clusters are determined as the optimal number of clusters for this dataset. Models that can predict the presence of particular HCM features from other genotypic and phenotypic information are generated, and subsets of features sufficient to predict the presence of other features of HCM are determined. This research proposes four subtypes of HCM assessed by machine learning algorithms and based on the overall phenotypic expression of the participants of the study. The identified subsets of features sufficient to determine the presence of particular HCM aspects could provide deeper insights into the mechanisms of HCM.