Hypertrophic Cardiomyopathy Research Articles (Page 1)

Background: Women with obstructive hypertrophic cardiomyopathy (oHCM) often present with a greater burden of disease and worse prognosis. Whether there are sex-related differences in response to aficamten is unknown. Methods: We performed a pre-specified subgroup analysis of sex differences in the doubleblind, randomized-controlled SEQUOIA-HCM trial of aficamten versus placebo in patients with oHCM. Baseline characteristics were compared using t-test for continuous variables and C2 test for categorical variables. Prespecified primary (change in peak oxygen uptake, pVO2) and secondary end points from baseline to end of treatment (week 24) were analyzed using linear regression models, adjusted for baseline values, beta-blocker use, and exercise mode. Results: Of the 282 participants, women (n=115) were older (64 years-old in women vs. 56 years-old in men), had lower Kansas City Cardiomyopathy Questionnaire Clinical Summary Score (KCCQ-CSS), higher NT-proBNP levels, and lower pVO2 at baseline. Women had smaller left ventricular (LV) chamber sizes, higher E/e' ratios, and higher LV outflow tract (LVOT) gradients. At 24 weeks, there was a significant treatment-related increase in pVO2 in women (+1.5, [+0.7 to +2.4] and men (+2.0 [+0.9 to +3.0])). Both women and men had significant treatment-related decreases in LVOT gradients at rest and with Valsalva, with no sex-bytreatment interaction at week 24 (p-interaction ³ 0.13). There was a significant improvement in KCCQ-CSS in women (11 [6 to 15]) and men (6 [2 to 9], p-interaction = 0.08). Women had a greater reduction in lateral E/e' ratio (p-interaction = 0.01). The geometric mean proportional reduction in NT-proBNP were similar in women and men (p-interaction = 0.10). Conclusions: Women enrolled in SEQUOIA-HCM were older with worse baseline health status, higher NT-proBNP, and higher LVOT gradients compared to men. Despite these differences, both men and women derived significant benefits in the primary and secondary end points following treatment with aficamten.

Exercise may lead to disease progression and higher risk of sudden death in individuals with genetic cardiomyopathies, but the effects of exercise among individuals carrying a cardiomyopathy-associated variant without clinical manifestations (G+P-) are unclear. To examine whether the effects of moderate to vigorous physical activity (MVPA) on cardiovascular (CV) outcomes, cardiac structure and function, and risk of developing overt cardiomyopathy and malignant ventricular arrhythmias (VAs) vary by G+P- status. UK Biobank participants with whole-genome sequencing providing 1 week of accelerometer-based physical activity data and without prevalent heart failure (HF), atrial fibrillation (AF), cardiomyopathy, VAs, or implantable cardioverter-defibrillators were included in this cohort study. The study was conducted at 22 assessment centers throughout the UK from February 2013 to December 2015 with a median follow-up of 8 years. Data were analyzed from March 2024 to June 2025. Accelerometer-measured MVPA (minutes/week). Associations were analyzed between MVPA volume and future incidence of adverse CV outcomes (AF, HF, myocardial infarction [MI], and stroke), cardiac magnetic resonance (CMR)-based measures of cardiac remodeling, and surrogates for clinical cardiomyopathy onset (cardiomyopathy and VA). Associations were compared between G+P- carriers and noncarriers. Among 84 699 individuals (mean [SD] age, 62 [8] years; 48 353 [57%] women; 3979 G+P- carriers), greater MVPA was associated with a lower risk of adverse CV outcomes over a median (IQR) 8.0 (7.5-8.5) years, irrespective of genotype. In multivariable models, higher MVPA was broadly associated with lower risk of incident CV disease in G+P- carriers (hazard ratio [HR] at optimal MVPA level vs zero [95% CI], AF: 0.68 [0.58-0.79]; HF: 0.58 [0.47-0.71]; MI: 0.49, [0.24-1.00]; stroke: 0.35 [0.12-0.99]). For G+P- carriers, MVPA in the range of 100 to 400 minutes per week was generally associated with lowest risk. Among individuals with CMR imaging, MVPA was associated with a similar pattern and extent of cardiac remodeling (eg, left ventricular dilation and left ventricular hypertrophy) in G+P- carriers vs noncarriers. Among G+P- carriers, higher MVPA was associated with lower risk of incident cardiomyopathy (HR at optimal MVPA vs 0, 0.03; 95% CI, 0.00-0.98) with no increase in risk of VA (eg, HR at 400 minutes of MVPA vs 0, 0.98; 95% CI, 0.83-1.14). Findings were generally consistent across variants associated with dilated cardiomyopathy, hypertrophic cardiomyopathy, or arrhythmogenic right ventricular cardiomyopathy, although precision of estimates for arrhythmogenic right ventricular cardiomyopathy were limited. In this cohort study, MVPA within the general range of guideline-based recommendations was associated with lower risk of adverse CV outcomes and similar degrees of cardiac remodeling for G+P- carriers compared to noncarriers. Findings support the appropriateness of guideline-based MVPA recommendations for G+P- carriers.

Hypertrophic cardiomyopathy (HCM) is the most common myocardial genetic disease characterized by left ventricular hypertrophy (LVH) and diastolic dysfunction with preserved or elevated ejection fraction. Thirty-five years after the identification of the first genetic variant in myosin heavy chain 7, other variants have been discovered in numerous components of the sarcomere, pointing to a primary defect in cardiomyocyte contractility. Still, a large portion of HCM patients does not have a pathogenic variant and others present with LVH of another genetic origin. Research has uncovered a primary driver of hypercontractility at the sarcomere level and diverse molecular and cellular mechanisms contributing to HCM, including alterations of calcium handling and proteolysis, microtubule modifications, energy deficiency, and the impact of noncardiomyocyte cell types. These discoveries have fueled preclinical and translational research, leading to the development of myosin inhibitors, which are now on the market, and gene-based therapeutic products. This review summarizes current knowledge on the genetics, mechanisms, and targeted treatments of HCM.

Emerging evidence suggests a potential link between gut microbiota and cardiomyopathies, but the causal relationships and the mediating role of cytokines remain unclear. We performed a two-sample Mendelian randomization (MR) analysis using genome-wide association study (GWAS) summary data for 211 gut microbiota taxa, 41 cytokines, and cardiomyopathy subtypes. Instrumental variables were selected at a genome-wide significance threshold (P < 1 × 10-5). Primary causal estimates were derived via inverse-variance weighted (IVW) regression, supplemented by sensitivity analyses. Multivariable MR was utilized to reduce bias from confounders. Mediation analysis tested whether cytokines linked gut microbiota to cardiomyopathies. Genetic liability in gut microbiota showed significant associations with cardiomyopathy risk. For dilated cardiomyopathy (DCM), genus Ruminococcaceae (OR = 1.24, 95% CI: 1.00-1.54, P = .045) and genus Flavonifractor (OR = 1.66, 95% CI: 1.15-2.35, P = .0067) increased risk, while family Peptostreptococcaceae was protective (OR = 0.71, 95% CI: 0.51-0.99, P = .041). For hypertrophic cardiomyopathy (HCM), genus Enterorhabdus raised risk (OR = 1.53, 95% CI: 1.00-2.33, P = .049), whereas order Methanobacteria lowered it (OR = 0.69, 95% CI: 0.52-0.93, P = .013). For myocarditis, genus Faecalibacterium reduced risk (OR = 0.53, 95% CI: 0.33-0.85, P = .009), but genus Family XIII UCG001 increased it (OR = 2.06, 95% CI: 1.15-3.69, P = .015). Cytokines including IL-9 and MIP-1β showed protective effects for DCM, while pro-inflammatory cytokines TNF-α and VEGF elevated myocarditis risk. Mediation analysis revealed cytokines did not mediate gut microbiota-cardiomyopathy pathways. Specific gut microbiota and cytokines exert causal effects on cardiomyopathy subtypes, independent of cytokine mediation. These findings highlight potential therapeutic targets and underscore the need to explore alternative mechanistic pathways.

Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiomyopathy, affecting 1 in 500 people. With growing access to genetic testing and incorporation of genetics in diagnosis and management of HCM, it is important to identify phenotypic predictors of HCM genotype, to improve genetic targeting and counselling as well as cascade testing for first-degree relatives. This was a retrospective analysis of consecutive probands, aged over 18 years referred to a tertiary centre for HCM gene panel testing. Demographic information was obtained from clinic data. Left ventricular hypertrophy (LVH) pattern was classified based on trans-thoracic echocardiogram (TTE). Pathogenicity of variants was classified per the American College of Medical Genetics (ACMG) criteria. 166 patients were included for analysis. Mean age was 53 years (SD 14.28). 128 (77%) were male. 59 had a history of hypertension and 19 had a family history of sudden cardiac death (SCD). The most frequent HCM pattern at baseline was concentric HCM (31.9% n = 53). 48 patients had a likely pathogenic (LP) or pathogenic (P) variant, giving a genetic testing yield of 28.9%. The most common sarcomeric genes were MYBPC3 and MYH7 accounting for 57% of cases. Younger age, female sex, and reverse curve LVH pattern were predictors of a LP or P gene variant identification. Reverse curve morphology was found to be a significant predictor for a sarcomere variant (p < 0.001). Genetic testing was appropriately offered in this cohort. Younger age, female sex, family history of SCD, normal/well controlled blood pressure and reverse pattern LVH on TTE predicted a higher yield of pathogenic variant identification. Reverse curve morphology was found to be a significant predictor for a sarcomere variant. This study has implications for supporting better phenotype-based genetic counselling and resource usage for HCM patients.

Hypertrophic cardiomyopathy is a complex disease with variable clinical presentation and familial impact. Age at diagnosis may influence phenotypic expression, but it is unclear if age also affects clinical outcomes, genetic findings, and yield of family screening. This was a retrospective cohort study of families screened for hypertrophic cardiomyopathy in eastern Denmark (2006-2023). Probands were analyzed by age at diagnosis both continuously and in quartiles: 18 to 45, 46 to 56, 57 to 65, and >65 years. A total of 612 probands (62% men; median age, 56 years; median follow-up, 9 years) and 919 relatives (45% men; median age, 42 years) were studied. A higher proband age at diagnosis was associated with more left ventricular outflow tract obstruction (odds ratio [OR], 1.19/10 years), hypertension (OR, 1.57/10 years) and atrial fibrillation (OR, 1.24/10 years), but less left ventricular hypertrophy (wall thickness ≥30 mm; OR, 0.52/10 years), and ventricular arrhythmias (OR 0.81/10 years). Older age at diagnosis was associated with higher all-cause death, but similar cardiovascular death. Sarcomere variants were less common in the oldest versus youngest quartile of probands (13% versus 42%, P<0.001). The yield of family screening at baseline was higher in probands diagnosed at a younger age (OR, 1.34/10-year decrease). Long-term hypertrophic cardiomyopathy incidence in relatives was not associated with proband age at diagnosis, and overall yield of family screening was comparable across all proband ages at diagnosis. The proband's age at hypertrophic cardiomyopathy diagnosis was associated with clinical and genetic findings, but the cardiovascular death and the yield of family screening were similar across all ages at diagnosis, supporting the presently recommended follow-up and family screening irrespective of the proband's age at diagnosis.

Cardiomyopathies are rare diseases in children but are the primary indication for heart transplantation in this age group. Various causes of paediatric cardiomyopathies, ranging from gene-mediated to underlying infection or systemic disease, result in a wide spectrum of clinical presentations and imaging manifestations. Over the years, the classification and terminology of cardiomyopathy have evolved in children and are currently primarily based on the imaging phenotype (dilated, hypertrophic, and restrictive) and then subdivided based on pathogenesis, organ involvement, genetic or familial inheritance pattern, and aetiology. Dilated and hypertrophic cardiomyopathies are more common than non-compaction, restrictive, and arrhythmogenic cardiomyopathies. Echocardiography remains the first-line modality for functional and structural cardiac assessment. However, cardiac magnetic resonance imaging enhances diagnostic accuracy, provides serial cardiac functional evaluation and tissue characterization, and facilitates individual risk stratification and management in patients with heterogeneous phenotypes. This review provides an overview of paediatric cardiomyopathies with a focus on magnetic resonance imaging indications, technique, and key imaging findings that influence management decision-making.

Surgical management of midventricular and apical hypertrophic cardiomyopathy

Introduction: Diastolic dysfunction is a hallmark of heart failure with preserved ejection fraction, yet echocardiographic grading is challenging in patients with hypertrophic or amyloid cardiomyopathies due to variable structural and functional remodeling. Consequently, there is a need for additional diagnostic tools to identify patients at increased risk of diastolic dysfunction and enhanced filling pressure. Research Question: What is the accuracy of an artificial intelligence (AI)-enabled electrocardiogram (ECG) model to predict diastolic dysfunction in hypertrophic and amyloid cardiomyopathy? Methods: We included patients with diverse phenotypes of hypertrophic cardiomyopathy (HCM) and cardiac amyloidosis and validated Mayo Clinic's AI-enabled ECG model to grade diastolic function (normal: grade 0/1; abnormal: grade 2/3). Echocardiographic analysis of systolic (LVEF) and diastolic function was performed within 14 days of ECG recording. Diastolic function was graded based on E/e’ (&gt;14), left atrial volume index (&gt;34 mL/m2), pulmonary venous atrial reversal flow duration (≥30 ms), and peak tricuspid regurgitation velocity (&gt;2.8 m/s). Filling pressure was considered increased if ≥3 parameters were abnormal, inconclusive if 2 were abnomal, and normal if ≥3 were normal. Echocardiograms with severe mitral regurgitation were excluded. Results: We identified and matched 840 ECGs from non-obstructive HCM patients (n=125, age 58±19y, 70% male), 145 ECGs from obstructive HCM patients (n=29, age 56±18y, 66% male), 225 ECGs from apical HCM patients (n=33, age 66±14y, 61% male), and 382 ECGs from cardiac amyloidosis patients (n=49, age 70±13y, 57% male). The AI-enabled ECG model predicted diastolic dysfunction with an accuracy of 65% (non-obstructive HCM), 67% (obstructive HCM), 65% (apical HCM), and 75% (cardiac amyloidosis), with sensitivities of 94% and 97% for obstructive HCM and cardiac amyloidosis, respectively, and high positive and negative predictive values for cardiac amyloidosis. Systolic dysfunction (LVEF≤40%) was more common in cardiac amyloidosis (29%) than in all HCM patients (5%). Conclusion: The AI-enabled ECG model demonstrates moderate accuracy but high sensitivity for detecting diastolic dysfunction and increased filling pressure, especially in obstructive HCM and cardiac amyloidosis. The AI-enabled ECG is a useful, widely scalable, and low cost tool to identify patients at increased risk for diastolic dysfunction.

Background: Hypertrophic cardiomyopathy (HCM) risk is incompletely explained by pathogenic variants and polygenic background. Mendelian randomization studies have implicated body mass index (BMI) as a modifiable risk factor for HCM in individuals with a pathogenic HCM variant (‘genotype-positive’; G+) and in those without (‘genotype-negative’; G-), and diastolic blood pressure (DBP) as a modifiable risk factor for genotype-negative HCM only. Research Questions: How do BMI, DBP, and HCM polygenic risk influence HCM diagnoses and echocardiographic endophenotypes by genotype status? Methods: Penn Medicine BioBank participants with and without HCM were identified using electronic health records. G+ participants carried pathogenic variants in definitive HCM genes. To avoid confounding, BMI and DBP were represented by polygenic scores (PGSs). PGSs for BMI, DBP, and common variant HCM risk were identified from the PGS Catalog (IDs PGS004150, PGS004604, PGS004910). Stratifying by pathogenic variant status (G+/G-), we tested PGS associations with HCM status with logistic regression. We evaluated their associations with interventricular septal thickness and left ventricular ejection fraction (EF) on echocardiogram using linear regression. Models included age and sex as covariates. Formal interaction testing between each PGS and monogenic variant status was performed. Results: Among 32,615 unrelated participants, 363 (1.1%) were diagnosed with HCM. G+ status conferred a 58-fold increased HCM risk (p=2.6x10 -135 ). Estimated associations between BMI and HCM PGSs and HCM were positive regardless of pathogenic variant status (p&lt;0.05 for HCM PGS only); DBP was associated with HCM only among G- individuals (OR 1.63; 95% CI 1.09 to 1.39; p=7.0x10 -4 ; Figure 1A). All PGSs were positively associated with septal thickness, a defining feature of HCM (Figure 1B). A significant (p&lt;0.05) interaction term was observed between monogenic and polygenic HCM risk. DBP and BMI PGSs were negatively associated with EF among G- individuals, discordant with the typical HCM phenotype (Figure 1B). Conclusions: Increased polygenic BMI, DBP, and HCM risk may promote septal thickening. The interaction between HCM PGS and variant carrier status suggests a synergistic role of common- and rare-variation directly related to HCM. In contrast, DBP appears to modify the risk of genotype-negative disease only. These findings suggest distinct roles of modifiable risk factors in G- and G+ HCM.

Introduction: Hypertrophic cardiomyopathy (HCM) is the most common genetic cardiac disease. It can be challenging to distinguish HCM from other cardiomyopathies with left ventricular hypertrophy (LVH), including hypertensive LVH, transthyretin amyloid cardiomyopathy (ATTR-CM), and aortic stenosis (AS). Hypothesis: A small set of plasma microRNAs discriminate between HCM and other cardiomyopathies that cause LVH. Methods: In this multicenter case-control study, plasma transcriptomics profiling was performed in cases with HCM and controls with hypertensive LVH, ATTR-CM, and AS. Half of the cases enrolled earlier in each disease group were categorized as the training set and the remaining half as the test set. We specified microRNAs that were significantly (univariable P&lt;0.05) upregulated or downregulated in HCM compared to hypertensive LVH in both the training and test sets (comparison #1). We performed the same comparison between HCM and ATTR-CM (comparison #2) as well as between HCM and AS (comparison #3). We identified microRNAs that were consistently upregulated or downregulated in HCM throughout all 3 comparisons. Then, we identified microRNAs independently associated with HCM (multivariable P&lt;0.05) after adjusting for clinical parameters that were significantly different between HCM and controls. Using the selected microRNAs, a logistic regression model to distinguish HCM from controls was developed in the training set. We calculated an area under the receiver-operating-characteristics curve (AUROC) in the test set. We performed subgroup analyses comparing HCM with each of the controls. Results: We analyzed 2,656 microRNAs in patients with HCM (n=294), hypertensive LVH (n=321), ATTR-CM (n=167), and AS (n=38). After adjusting for 12 parameters that were significantly different between HCM and controls, 3 microRNAs were selected ( Figure 1 ). The logistic regression model using the 3 microRNAs had an AUROC of 0.95 (95% confidence interval [CI] 0.93-0.97) with a sensitivity of 0.95 and a specificity of 0.90. ( Figure 2 ). In the subgroup analysis, the model had AUROCs of 0.98 (95% CI, 0.96-0.99) for HCM vs. hypertensive LVH, 0.94 (95% CI, 0.90-0.98) for HCM vs. ATTR-CM, and 0.82 (95% CI, 0.71-0.92) for HCM vs. AS ( Figure 3 ). Conclusions: Our comprehensive plasma transcriptomics profiling identified a small set of circulating microRNAs that distinguish HCM from other cardiomyopathies with LVH independently of potential confounders.

Background: Mutations in the β-MYH7 gene are a leading cause of unexplained left ventricular hypertrophy with abnormal loading conditions, which progressively increase the risk of heart failure (HF) and sudden cardiac death (SCD) across all age groups. Despite the high morbidity and mortality rates in South Asia, this area remains relatively unexplored, with limited research studies. India, with its large ethnic diversity, presents a unique and intriguing opportunity for a deeper understanding of population-specific genetic variations. Methods: In our case-control study, we conducted targeted direct sequencing of the β-MYH7 gene to detect the causative variations in 160 patients diagnosed with hypertrophic cardiomyopathy (HCM) through clinical examination, ECG, and echocardiography, along with 178 ethnically matched healthy controls. We also constructed homology protein models for causative mutations to evaluate their impact on the β-MYH7 protein structure in Indian HCMs. Results: Our study identified a total of 33 variations in the β-myosin heavy chain gene ( β-MYH7 ). We defined six missense mutations (M362L, K367N, E525K, M684R, L725P, and D896N) as causative because they co-segregated with the disease in family pedigrees, altered conserved amino acids, and were absent in the 178 healthy controls. By comparing the homology protein models of each of the six mutants (p.M362L, p.K367N, p.E525K, p.M684R, p.L725P, and p.D896N) to the native protein, we found that each mutant protein uniquely disrupts the structure, and potentially affecting the protein function and the phenotypic expression of the disease. Additionally, several rare compound variations in the β-MYH7 gene were observed in 4 HCM patients, specifically: HCM1, (G354G, M362L&amp;G389K); HCM2, (splice site (SS8)&amp;F244F); HCM3, (E525K&amp;D896N); and HCM4, (A423T, V431M&amp;N602K-fs). Notably, one patient with hypertrophic obstructive cardiomyopathy (HOCM) exhibited rare compound digenic variations in the Myosin ( β-MYH7_ G354G, A423T, A917A) and Troponin T2 ( TNNT2_ A28V) genes. Principal component analysis (PCA) and HapMap data indicated that both the cases and controls derived from the same ethnic background. Conclusion: We identified Novel Mutations in the β-MYH7 Gene that lead to HCM among Indians. We provided valuable insights about causative mutations and their impact on the β-MYH7 protein structure, which may facilitate the development of therapeutic strategies for personalised medicine.

Background: Left ventricular hypertrophy (LVH) is a common feature of both hypertrophic cardiomyopathy (HCM) and hypertensive heart disease (HHD), making differentiation through macrostructural imaging challenging. Given the distinct tissue characteristics of these two conditions, the emerging cardiac diffusion tensor imaging (cDTI) allows in vivo characterization of myocardial microstructure, offering insights for differential diagnosis. Purpose To validate the feasibility of cDTI for non-invasive myocardial microstructural characterization in HCM and HHD and to explore its potential value in distinguishing them. Materials and methods: In this prospective study, 40 participants diagnosed with HCM, 20 diagnosed with HHD and 20 controls were enrolled. All consecutively underwent a 3.0-T CMR protocol including DTI, cine imaging, T1 mapping and late gadolinium enhancement (LGE) imaging. LGE was performed in HCM and HHD participants. Fractional anisotropy (FA) and secondary eigenvector angle (E2A) were assessed after adjusting for hypertrophy and fibrosis. Results: FA was reduced in HCM and HHD participants compared with healthy controls (FA HCM = 0.32 ± 0.05, FA HHD = 0.4 ± 0.07, FA Control = 0.46 ± 0.06, p&lt;0.001). Meanwhile, HCM participants had significantly higher E2A (E2A HCM = 49.2 ± 7.1°, E2A HHD = 38.7 ± 5.3°, E2A Control = 38.1 ± 5.3°, p&lt;0.001). FA (p=0.005) and E2A (p=0.002) remained significant even after adjusting for left ventricular wall thickness (LVWT), native T1 and extracellular volume (ECV). FA showed the best diagnostic performance in identifying HCM (area under the curve [AUC]=0.97), HHD (AUC=0.75) from healthy controls and differentiating HCM from HHD (AUC=0.84). E2A also exhibited excellent precision for identifying HCM (AUC=0.89) and discriminating HCM from HHD (AUC=0.87). Conclusions: In our study, there were significant differences in FA and E2A between HCM and HHD even after adjusting for hypertrophy and fibrosis. cDTI demonstrates potential as a useful tool for discriminating HCM, HHD and healthy controls with great diagnostic efficiency.

Background: Hypertrophic cardiomyopathy (HCM) is the most common heritable cardiac disease, yet it remains profoundly underdiagnosed; it is estimated that 6 out of 7 (86%) individuals with HCM have not been identified and are unaware of their diagnosis. HCM is typically diagnosed using routine echocardiography. However, the cost, time to perform each study, need for patient privacy, and inherent issues with transporting large echocardiogram machines, has limited the use of echocardiography for wide-scale community-based screening. Hypothesis: Given that the parasternal long axis (PLAx) view can visualize many of the characteristic findings seen in HCM, we hypothesized that a streamlined, cost-effective approach to the diagnosis of HCM would utilize a point-of-care echo probe for obtaining a single PLAx sweep of the heart (SPLASH) + color Doppler to screen for HCM. Here we report the sensitivity and specificity of our SPLASH screening. Methods: We studied 108 consecutive patients presenting to our echocardiography laboratory for exercise stress testing on days that HCM patients and patients with chronic kidney disease (CKD) and end stage renal disease are routinely screened. SPLASH echocardiograms were performed by an experienced sonographer. Each was read independently in random order by 2 echo attendings and 2 independent sonographers, all blinded to patient diagnosis. Results were categorized as definite HCM, probable HCM, possible HCM, probably not HCM, and no evidence of HCM. Readers also indicated whether obtaining additional views for further assessment was indicated. Results were compared to the gold standard of full echocardiographic result combined with clinical diagnosis. Results: The mean age of the cohort was 57.3 years (53% women; 22% self-reported as Black). 63% had hypertension and 35% had CKD. Time to perform one SPLASH study was, on average, less than 5 minutes. Specificity of the SPLASH was 91.0% for attendings and 83.7% for sonographers. Sensitivity was 84.5% for attendings and 85% for sonographers. When the studies that the reader indicated needed additional views for further assessment were included, the sensitivity increased to 92.5%. Conclusions: We report a novel and streamlined method for performing echocardiographic screening for HCM with high sensitivity and specificity. If validated in community settings, this method has the potential to change medical practice and improve access to rapid yet accurate diagnostic testing for HCM.

Background: Myocardial bridging (MB) occurs when an epicardial coronary artery traverses into the myocardium. There are limited data comparing plaque characteristics of patients who have left anterior descending artery (LAD) MB and hypertrophic cardiomyopathy (HCM) with those who have a normal left ventricular phenotype and no HCM utilizing coronary computed tomography angiography (CCTA). Aims: To explore the differences between patients with and without HCM who have MB on CCTA. Methods: We retrospectively analyzed all adult patients with HCM at our specialized HCM center from January 2014 to January 2025 who had an available CCTA. Patients who had a percutaneous coronary intervention or coronary artery bypass graft surgery before the CCTA were excluded. The cohort was then propensity-score matched to a control group of individuals who did not have HCM and had an available CCTA. Only patients with LAD MB on CCTA were included. Results: A total of 244 patients with HCM were identified and propensity-score matched 1 to 1 with non-HCM patients. There were 68 HCM patients (28%) with MB and 18 non-HCM patients (7%) with MB who were the subjects of this study. The HCM-MB group had a mean age of 51 ± 15 years (37% female), and the non-HCM-MB group had a mean age of 55 ± 15 years (39% female). In the HCM-MB group, the HCM phenotypes observed were basal septal (54%), reverse curvature (24%), apical (13%), midventricular (6%), and concentric (3%). Sixty-two patients had genetic testing: 29 patients (47%) were likely pathogenic, 19 patients (31%) had a variance of uncertain significance, and 14 patients (23%) were gene-negative. The average coronary artery calcium score in the HCM-MB group was 79 Agatston units, which is comparable to the average score of 101 Agatston units in the non-HCM-MB group (P=0.32). In the HCM-MB group, 39 patients (57.3%) were diagnosed with CAD compared to 13 patients (72%) in the non-HCM-MB group (P=0.41). There were no statistically significant differences in plaque prevalence or characteristics between the two cohorts for any of the coronary artery vessels (Table 1). Conclusions: Myocardial bridging was more common in patients with HCM, yet these patients had lower coronary artery calcium scores and fewer CAD diagnoses.

Background information: Hypertrophic cardiomyopathy (HCM) is the commonest inherited cardiovascular disease. Whilst important for family screening, the utility of genetic testing is limited by a persistent low yield. This may be partly explained by only exonic and canonical splice site variants considered clinically significant, while intronic variation is overlooked. Mutations in MYBPC3 are a common cause of HCM, but studies into the pathogenesis of MYBPC3 -HCM are hindered by the need for myocardial tissue. Our previous work has shown that intronic MYBPC3 variants disrupt splicing in patient derived iPSC-CMs, suggesting a role for intronic MYBPC3 variation in HCM pathogenesis. Aim: Herein we sought to characterise iPSC-CMs generated from patients harbouring deep intronic MYBPC3 variants (c.1224-52G&gt;A (-52)&amp;c.1898-23A&gt;G (-23)) and establish any morphological and physiological abnormalities arising from splicing alterations consistent with the HCM phenotype. Methods: Patient-derived iPSC-CMs were characterised for cellular features suggestive of HCM by analysis of contractility, calcium handing ability, western blot of MYBPC3 protein levels, and immunocytochemistry to examine cellular morphology and sarcomeric arrangement. Results: Upon differentiation to cardiomyocytes, iPSC-CMs from both -52 and -23 variants exhibited splicing abnormalities, albeit through different mechanisms. The -52 variant produced mutant mRNA that was degraded by regulatory mechanisms. The -23 also produced aberrant mRNA transcripts but were shown to escape such degradation. Both variants showed abnormalities in contractile ability and calcium handling compared to a wildtype (WT). Cells of both variants also showed morphological features consistent with HCM of sarcomeric disarray, large cell size, cell roundness, and multinucleation. Western blot analysis showed lower overall MYBPC3 protein levels for both variants compared to a WT, agreeing well with current literature that haploinsufficiency underpins the pathogenicity of MYBPC3 mutations in HCM. Conclusion: Functional analysis of iPSC-CMs harbouring deep intronic MYBPC3 variants exhibit splicing abnormalities alongside structural and physiological impairments consistent with the HCM phenotype. These findings add to growing evidence that intronic variation may play a role in HCM pathogenesis and highlights the importance of expanding genetic testing beyond traditional regions in gene-elusive individuals.

Objective: Dilated Cardiomyopathy (DCM) and Hypertrophic Cardiomyopathy (HCM) are two major forms of heart diseases characterized by different pathological mechanisms. DCM is often marked by ventricular dilation and impaired contractility, while HCM involves abnormal thickening of the heart muscle, leading to compromised cardiac function. Despite the significant differences in these two conditions, their underlying molecular mechanisms are not fully understood. This study aims to use Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses to identify and compare the key biological processes and signaling pathways involved in DCM and HCM. Methods: We performed GO and KEGG pathway analysis on gene expression data from DCM and HCM patient samples in two published RNA-seq datasets (GSE116250 and GSE89714). Differential gene expression analysis was performed using DESeq2 with a statistical cutoff of FDR (False discovery rate) &lt; 0.05 and fold change &gt; 1.5. The enrichment analysis was performed using R package “clusterProfiler” and “org.Hs.eg.db”. Results: The analysis identified 3599 and 711 differentially expressed genes (DEGs) between DCM versus HCM. Interestingly, both DCM and HCM shared several overlapping genes and pathways, exhibiting a significantly common enrichment in cytoskeleton in muscle cells, suggesting common pathophysiological mechanisms despite the distinct phenotypes of these diseases (A). The GO analysis for DCM revealed significant alterations in processes related to sarcomere/myofibril organization. However, GO analysis for HCM suggests a crucial role of mitochondrial function alternations. The KEGG analysis also demonstrated that AMPK signaling pathway, citrate cycle, focal adhesion and insulin signaling pathway were highly enriched in DCM patients (B, C). While the same analysis for HCM patients demonstrated that chemical carcinogenesis − reactive oxygen species, diabetic cardiomyopathy, non−alcoholic fatty liver disease and oxidative phosphorylation were dramatically affected in HCM (D, E). Conclusion: This study provides a profound understanding of DCM and HCM, helping provide new insights into the pathogenesis of two diseases. DCM exhibited a more pronounced structural disorders of cardiomyocytes whereas HCM was predominantly affected in mitochondria and metabolism disorders. These findings may give novel insights into the discovery of potential therapeutic targets for treating various types of heart failure.

Background: Several studies have described anteroapical displacement of the anterolateral papillary muscle (PM) in patients with hypertrophic cardiomyopathy (HCM), implying a role in left ventricular outflow tract obstruction. Some surgeons address PM position during myectomy, but no quantitative imaging measures of abnormal versus normal PM anatomy exist. Objective: To quantify PM anatomy in HCM using cardiac magnetic resonance imaging (CMR) and determine if differences in PM anatomy alter myectomy outcomes. Methods: 143 patients with HCM (median age 58 (IQR 48-65) years, 64% male) who had undergone CMR were compared to 50 healthy controls (median age 32 (IQR 28-50) years, 40% male). The HCM cohort included 50 patients with non-obstructive HCM, 50 with obstructive HCM requiring transaortic myectomy, and 43 with obstructive HCM requiring transapical myectomy. Six independent measurements of PM anatomy were performed (Figure), comprising both established and novel measurements. Intergroup comparisons were made, and correlative analysis of PM measurements was performed using echocardiographic data and surgical outcomes. Results: There was significant variability in PM anatomy in all patients. The SXAd measurement was smaller (26.76 +/- 6.10 mm vs 32.10 +/- 4.85 mm, p &lt; 0.001) in HCM patients versus controls. The 4Cd measurement was smaller in patients with apical HCM than in all other groups (p &lt; 0.001). D_PM, d_apex, and the ratio between these measurements were consistent among all groups. No PM quantification in HCM patients correlated to pre-, intra-, or postoperative LVOT obstruction, NYHA class, or postoperative length of stay. Conclusions: Although there were some statistically significant differences between healthy patients and those with HCM, analysis of six different PM measurements yielded no associations with LVOT obstruction or outcomes of septal myectomy.

Background: Disease expressivity in hypertrophic cardiomyopathy (HCM) varies widely, ranging from unaffected genetically predisposed individuals to life-threatening complications. Polygenic scores (PGS) were shown to predict disease penetrance of HCM-causing rare genetic variants (HCMrv) and HCM-related outcomes in large biobanks. However, the utility of PGS in clinical cohorts remains unclear. Research Questions: Can PGS predict disease penetrance in carriers of HCMrv in the clinical setting? Is PGS associated with disease severity and complications in individuals with HCM? Methods: We studied a well-characterized clinical HCM cohort from Canada, Italy, the Netherlands and Spain, comprising 6,111 individuals affected by HCM and/or carrying a HCMrv. We used SBayesRC to derive a novel PGS for HCM from the largest published genome-wide association study. Standardized ancestry-adjusted PGS were calculated for all individuals and tested for association with HCM penetrance, maximal left ventricular wall thickness (MLVWT) and major adverse clinical events (MACE) using logistic, linear and Cox regression models, respectively, with adjustment for sex, rare variant status, site, and other covariates as relevant. MACE were defined as major ventricular arrhythmic or heart failure event, stroke, septal reduction therapy or all-cause mortality. Results: PGS was tested for association with HCM in the subset of 1,667 relatives carrying a HCMrv (age at last follow-up 47 ± 19, 49% female), of which 57% meet diagnostic criteria for HCM. PGS was associated with a diagnosis of HCM (Odds ratio 1.6 per standard deviation [SD] increase in PGS; 95% CI: 1.4-1.8). Male sex and hypertension also independently increased penetrance by 3-fold and 2-fold, respectively. HCM-penetrance increased with increasing PGS, in the entire set as well as in carriers of MYH7 pathogenic variants, MYBPC3 truncating variants, or intermediate effect variants (Figure). In 4,949 affected individuals (age at diagnosis 48 ± 17, 33% female, 49% carrying HCMrv), PGS was associated with disease severity. Each SD increase in PGS was associated with a 0.5 mm increase in MLVWT (95% CI: 0.3-0.6), and a 12% increase in lifetime risk of MACE (Hazard ratio 1.12, 95% CI: 1.06-1.18). Conclusions: PGS assessment may enhance risk stratification and personalize monitoring strategies—guiding the timing, frequency, and scope of clinical evaluations in both genetically predisposed individuals and patients with manifest HCM.

Background: Hypertrophic cardiomyopathy (HCM) often presents later in the disease course, with frequent delays in diagnoses, high rates of misdiagnoses, and underdiagnosis on a population level. Diagnosis often requires access to specialty care, meaning that underserved patients based on race and socioeconomic status may have even more marked delays in diagnosis. Objective: To retrospectively test the hypothesis that artificial intelligence applied to ECG analysis (AI-ECG) could have afforded the opportunity for earlier diagnosis of hypertrophic cardiomyopathy in one health system. Methods: We collected all available ECGs from all patients referred for possible HCM in an HCM Center of Excellence over a period of 15 years, both before and after their HCM clinical diagnosis. An AI-ECG algorithm was applied to each ECG in blinded fashion to predict the probability of a diagnosis of HCM. Lead time, the time between the first AI-ECG diagnosis and the clinical diagnosis, was calculated for each patient. The sensitivity and specificity of the AI-ECG tool was examined for all patients with HCM. These metrics, along with lead time, were evaluated by subgroups including sex, race, obstruction, genetic test result, and septal subtype as seen on cardiac MRI. Results: 3,499 ECGs were analyzed in 404 patients (age 56 ± 18 years, 52% female) between 2010 and 2024. Of these patients, 230 have an HCM diagnosis. AI-ECG correctly identified HCM in 155 patients with a sensitivity of 67%, a specificity of 95%, a positive predictive value of 94%, and a negative predictive value of 69%. Accuracy was highest for apical and reverse curvature septal compared with the basal septal morphology (p=0.003) (Table 1). HCM was diagnosed at least a year prior to the clinical diagnosis in 27 patients with the longest lead time being 16.3 years for a single patient. Black patients were more likely than white patients to have AI-ECG diagnosis before clinical diagnosis (p=0.005) (Table 2), with significantly greater overall lead time (p=0.005) (Figure 1). Accuracy was higher for obstructive patients (p=0.03), while lead time for AI-ECG diagnosis was greater for non-obstructive patients (p=0.02). Conclusions: AI-ECG offers the potential for advanced diagnosis of HCM before disease progression. Differences in identification timing between subgroups highlight inequities in current care and show the potential of AI diagnosis for greatest benefit in underserved racial groups.