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Daily Cardiology Research Analysis

3 papers

Three impactful cardiology papers stood out: a phase 3 randomized trial showing aficamten outperforms metoprolol across multiple clinically meaningful domains in obstructive hypertrophic cardiomyopathy; a large-scale, semi-supervised deep learning pipeline enabling reliable, vendor-agnostic pediatric echocardiographic strain analysis with strong predictive utility; and a mechanistic study linking KLHL24 gain-of-function to intermediate filament degradation, mitochondrial dysfunction, and fibrosi

Summary

Three impactful cardiology papers stood out: a phase 3 randomized trial showing aficamten outperforms metoprolol across multiple clinically meaningful domains in obstructive hypertrophic cardiomyopathy; a large-scale, semi-supervised deep learning pipeline enabling reliable, vendor-agnostic pediatric echocardiographic strain analysis with strong predictive utility; and a mechanistic study linking KLHL24 gain-of-function to intermediate filament degradation, mitochondrial dysfunction, and fibrosis in cardiomyopathy.

Research Themes

  • Targeted myosin inhibition redefining first-line therapy in obstructive HCM
  • AI-enabled, vendor-agnostic echocardiographic strain for early pediatric cardiac dysfunction detection
  • Ubiquitin–proteasome-driven cytoskeletal and mitochondrial mechanisms in cardiomyopathy

Selected Articles

1. Aficamten in Obstructive Hypertrophic Cardiomyopathy: A Multidomain, Patient-Level Analysis of the MAPLE-HCM Trial.

88.5Level IRCTJournal of the American College of Cardiology · 2025PMID: 41348072

In a randomized, active-comparator phase 3 trial, aficamten monotherapy achieved superior, rapid improvements versus metoprolol across multiple clinically relevant endpoints in obstructive HCM, including LVOT gradients, symptoms (NYHA), quality of life (KCCQ-CSS), NT-proBNP, and peak VO2. These patient-level, multidomain gains support aficamten as a potential monotherapy standard for symptomatic oHCM.

Impact: This head-to-head trial challenges decades of beta-blocker first-line practice by demonstrating multidomain superiority of a targeted myosin inhibitor. It directly informs therapeutic guidelines and everyday management of oHCM.

Clinical Implications: Aficamten may be considered as frontline monotherapy for symptomatic oHCM, with titration guided by vitals and echocardiography to reduce LVOT obstruction and improve functional status and quality of life.

Key Findings

  • Aficamten achieved greater rates of target LVOT gradient reduction at rest and with Valsalva than metoprolol.
  • Patient-centric outcomes improved more with aficamten, including ≥1 NYHA class improvement and ≥10-point KCCQ-CSS gains.
  • Biomarker and exercise capacity gains favored aficamten, including ≥50% NT-proBNP reduction and ≥1.0 mL/kg/min increase in peak VO2.
  • Benefits were rapid and observed across baseline subgroups.

Methodological Strengths

  • Randomized, active-comparator phase 3 design with prespecified multidomain endpoints
  • Dose titration guided by objective echocardiographic and vital sign criteria; trial registered (NCT05767346)

Limitations

  • Follow-up duration and detailed safety outcomes are not specified in the abstract; long-term durability is unknown
  • Comparison limited to metoprolol; broader comparisons to other standard therapies are needed

Future Directions: Longer-term outcomes, safety profiling, and head-to-head comparisons with other pharmacologic options (e.g., mavacamten, disopyramide) and device or surgical strategies will clarify positioning in oHCM care.

2. Digital profile of children's hearts: automated echocardiogram strain analysis facilitates earlier detection of cardiac dysfunction.

82Level IIIObservational (model development/validation)European heart journal · 2025PMID: 41347951

A semi-supervised, vendor-agnostic deep learning pipeline delivered accurate and generalizable pediatric echocardiographic strain estimates with MAEs near 2% and strong correlations. Automated strain and motion features enabled high AUCs for predicting cardio-oncology dysfunction, late gadolinium enhancement, LVEF decline (outperforming manual strain), and myocardial infarction detection.

Impact: This study establishes a scalable, generalizable framework for pediatric strain analysis that can standardize measurements across vendors and image qualities while demonstrating strong clinical predictive utility.

Clinical Implications: Automated, robust pediatric strain analysis can enable earlier detection of subclinical dysfunction (e.g., cardio-oncology surveillance), support risk stratification, and reduce inter-operator variability across centers.

Key Findings

  • Motion-Echo achieved MAEs of 2.099% (GLS) and 2.665% (GCS) with correlations of 0.799 and 0.781, respectively.
  • Automated strain predicted cancer therapy-related cardiac dysfunction with AUC 0.906 and detected LGE with AUC 0.782.
  • Forecasting LVEF decline outperformed manual strain (DeLong P < .001); motion flows improved MI detection to AUC 0.952.
  • Framework trained on >22,000 echocardiograms across vendors and image qualities using minimal manual annotations.

Methodological Strengths

  • Large, heterogeneous dataset with semi-supervised learning minimizing annotation burden
  • Multiple downstream clinical validations demonstrating external utility across tasks

Limitations

  • Retrospective design; prospective impact on clinical decision-making not yet demonstrated
  • Generalizability to additional institutions and imaging protocols requires further validation

Future Directions: Prospective, multicenter trials to assess clinical workflow integration, patient outcomes, fairness across subpopulations, and regulatory-grade validation.

3. KLHL24 mutation drives intermediate filament degradation, mitochondrial dysfunction and fibrosis in heart failure patients.

78Level IVBasic/mechanistic research (integrated human tissue and hiPSC models)Cardiovascular research · 2025PMID: 41348940

Across patient heart tissue and patient-specific hiPSC-derived cardiomyocytes, KLHL24 gain-of-function drives proteasome-dependent degradation of multiple intermediate filament proteins, mitochondrial mislocalization with enhanced mitophagy, reduced PKA activity, sarcomere shortening, and an early fibrotic signature. These convergent data establish a unifying mechanism for cardiomyopathy in KLHL24 mutation carriers.

Impact: This work links a ubiquitin–proteasome adaptor to multi-lineage cytoskeletal degradation and mitochondrial pathology in human cardiomyopathy, expanding therapeutic hypotheses beyond desmin to broader intermediate filaments and organelle quality control.

Clinical Implications: Genetic screening for KLHL24 variants in epidermolysis bullosa and unexplained cardiomyopathy may be warranted; therapeutic exploration could include proteasome modulation, stabilization of intermediate filaments, or mitochondrial protection strategies.

Key Findings

  • Proteomics in patient LV tissue and patient-derived hiPSC-CMs showed reductions in multiple intermediate filament proteins (desmin, synemin, vimentin) and early fibrotic signatures.
  • KLHL24 gain-of-function increased proteasomal activity, caused mitochondrial mislocalization with elevated mitophagy, reduced PKA activity, and induced sarcomere shortening in cardiomyocytes.
  • Phenotypes were reproduced across cardiac cell types, including cardiomyocytes and fibroblasts, and aligned between in vitro models and end-stage explants.

Methodological Strengths

  • Integrated multi-omic and multi-model approach combining human tissue proteomics with patient-specific hiPSC-derived cardiac models
  • Convergent validation using mass spectrometry, flow cytometry, immunofluorescence, and Western blot across cell types

Limitations

  • Human cohort limited to two patients; lack of in vivo rescue experiments or therapeutic modulation
  • hiPSC models may not capture hemodynamic and multicellular tissue architecture present in vivo

Future Directions: Define the KLHL24 substrate landscape, test proteostasis and mitochondrial-targeted therapies in preclinical models, and expand clinical cohorts to link genotype, proteomic signatures, and phenotype severity.