Daily Cardiology Research Analysis
Three advances span bench-to-bedside cardiology: (1) a mechanistic study identifies NEDD4-mediated ubiquitination and degradation of GSNOR as a driver of pressure-overload cardiac hypertrophy, showing pharmacologic NEDD4 inhibition as a potential therapy; (2) whole-heart histologic and CMR validation in an ovine infarct model establishes catheter-specific voltage thresholds that markedly improve electroanatomic scar detection; and (3) a dual-pathway AI system for echocardiography accurately stag
Summary
Three advances span bench-to-bedside cardiology: (1) a mechanistic study identifies NEDD4-mediated ubiquitination and degradation of GSNOR as a driver of pressure-overload cardiac hypertrophy, showing pharmacologic NEDD4 inhibition as a potential therapy; (2) whole-heart histologic and CMR validation in an ovine infarct model establishes catheter-specific voltage thresholds that markedly improve electroanatomic scar detection; and (3) a dual-pathway AI system for echocardiography accurately stages aortic stenosis and predicts outcomes across multiple cohorts.
Research Themes
- Ubiquitination pathways in cardiac hypertrophy
- Electroanatomic mapping validated by histology and CMR
- AI-enabled echocardiographic assessment and prognosis in aortic stenosis
Selected Articles
1. NEDD4-Mediated GSNOR Degradation Aggravates Cardiac Hypertrophy and Dysfunction.
This preclinical study shows that NEDD4 ubiquitinates and degrades GSNOR, driving pressure-overload cardiac hypertrophy. Genetic NEDD4 ablation or pharmacologic inhibition (including indole-3-carbinol) restored GSNOR, blunted hypertrophy, and improved function, highlighting a druggable pathway.
Impact: It uncovers a previously unappreciated ubiquitination axis controlling redox signaling in hypertrophy and provides immediate translational leverage through existing NEDD4 inhibitors.
Clinical Implications: While preclinical, targeting NEDD4-GSNOR could complement current neurohormonal therapies by directly modulating pathological remodeling. It suggests biomarker-driven trials of NEDD4 inhibition in hypertrophy/heart failure.
Key Findings
- GSNOR protein is reduced without mRNA change in hypertrophic human and TAC mouse myocardium, implicating post-translational regulation.
- NEDD4 acts as the E3 ubiquitin ligase for GSNOR, increasing its ubiquitination and degradation in hypertrophic hearts.
- Cardiomyocyte-specific NEDD4 deficiency or pharmacological NEDD4 inhibition suppresses GSNOR ubiquitination, reduces hypertrophy, and improves cardiac function.
- Indole-3-carbinol (a clinical NEDD4 inhibitor) demonstrated efficacy comparable to a selective NEDD4 inhibitor in mitigating hypertrophy.
Methodological Strengths
- Multi-level validation: human myocardial samples, mouse TAC models, genetic (cardiomyocyte-specific knockout) and pharmacologic inhibition.
- Clear mechanistic linkage using ubiquitination assays and mutant constructs (enzyme-dead NEDD4, nonubiquitylatable GSNOR).
Limitations
- Preclinical models; no human interventional data to confirm efficacy and safety of NEDD4 inhibitors in heart failure.
- Potential off-target effects and pleiotropy of NEDD4 and indole-3-carbinol require careful evaluation.
Future Directions: Biomarker-guided early-phase trials of NEDD4 inhibition in hypertrophy/heart failure; exploration of combination therapy with guideline-directed agents; refinement of cardiac-selective NEDD4 modulators.
BACKGROUND: The decrease in S-nitrosoglutathione reductase (GSNOR) leads to an elevation of S-nitrosylation, thereby exacerbating the progression of cardiomyopathy in response to hemodynamic stress. However, the mechanisms under GSNOR decrease remain unclear. Here, we identify NEDD4 (neuronal precursor cell expressed developmentally downregulated 4) as a novel molecule that plays a crucial role in the pathogenesis of pressure overload-induced cardiac hypertrophy, by modulating GSNOR levels, thereby demonstrating significant therapeutic potential. METHODS: Protein synthesis and degradation inhibitors were used to verify the reasons for the decrease in GSNOR. Mass spectrometry and database filtering were used to uncover NEDD4, the E3 Ub (ubiquitin) ligase, involved in GSNOR decrease. NEDD4 cardiomyocyte-specific deficiency mice were used to evaluate the role of NEDD4 and NEDD4-induced ubiquitination of GSNOR in cardiac hypertrophy in vivo. Both IBM (indolebutenate methyl ester derivatives), a highly specific NEDD4 inhibitor, and indole-3-carbinol, a NEDD4 inhibitor currently undergoing phase 2 clinical trial, were used to effectively suppress the NEDD4/GSNOR axis. RESULTS: GSNOR protein levels were reduced, while mRNA levels remained unchanged in myocardium samples from hypertrophic patients and transverse aortic constriction-induced mice, indicating GSNOR is regulated by ubiquitination. NEDD4, an E3 Ub ligase, was associated with GSNOR ubiquitination, which exhibited significantly higher expression levels in hypertrophic myocardial samples. Moreover, either the NEDD4 enzyme-dead mutant or GSNOR nonubiquitylated mutant decreased GSNOR ubiquitination and inhibited cardiac hypertrophic growth. Cardiomyocyte-specific NEDD4 deficiency inhibited cardiac hypertrophy in vitro and in vivo. NEDD4 inhibitor IBM effectively suppressed GSNOR ubiquitination and cardiac hypertrophy. Clinically, indole-3-carbinol, a NEDD4 inhibitor in phase II clinical trials used as an antitumor drug, demonstrated comparable efficacy. CONCLUSIONS: Our findings showed that upregulated NEDD4 leads to GSNOR ubiquitination and subsequent degradation, thereby facilitating the progression of cardiac hypertrophy. NEDD4 inhibitors may serve as a potential therapeutic strategy for the treatment of cardiac hypertrophy and heart failure.
2. Whole-Heart Histological and CMR Validation of Electroanatomic Mapping by Multielectrode Catheters in an Ovine Model.
In an ovine infarct model co-registered with whole-heart histology and CMR, the authors derived catheter-specific bipolar and unipolar voltage thresholds that substantially increased scar detection accuracy versus traditional criteria. Improvements reached 1.8%-15.6% for endo-mid layers and 25.3%-81.1% for mid-epicardial layers.
Impact: It provides a histology/CMR-grounded calibration of electroanatomic mapping across widely used multielectrode catheters, enabling more accurate substrate definition for VT ablation.
Clinical Implications: Adopting catheter-specific voltage thresholds may improve scar delineation and procedural planning in VT ablation, potentially reducing arrhythmia recurrence.
Key Findings
- Derived catheter-specific bipolar/unipolar voltage thresholds for normal myocardium across five mapping catheters (e.g., HD Grid >2.78 mV bipolar; >6.19 mV unipolar).
- Catheter-specific thresholds improved CMR-correlated scar detection by 1.8%-15.6% (endo-mid) and 25.3%-81.1% (mid-epicardial) over traditional criteria.
- Minimal differences in voltages, scar areas, and abnormal electrograms were observed between catheters and mapping rhythms.
Methodological Strengths
- Whole-heart co-registration of electroanatomic maps with CMR and transmural histology.
- Extensive sampling (315,487 analyzed points) across multiple catheter designs and rhythms.
Limitations
- Preclinical ovine model with small number of animals (n=10); clinical generalizability needs confirmation.
- Manual review of points and potential differences in human myocardial anisotropy may affect translation.
Future Directions: Prospective clinical validation of catheter-specific thresholds in human VT ablation; integration into mapping systems as device-aware threshold presets; outcome studies on recurrence reduction.
BACKGROUND: Accurate electroanatomic mapping is critical for identifying scar and the long-term success of ventricular tachycardia ablation. OBJECTIVES: This study sought to determine the accuracy of multielectrode mapping (MEM) catheters to identify scar on cardiac magnetic resonance (CMR) and histopathology. METHODS: In an ovine model of myocardial infarction, we examined the effect of electrode size, spacing, and mapping rhythm on scar identification compared to CMR and histopathology using 5 multielectrode mapping catheters. We co-registered electroanatomic mapping, CMR, and histopathology for comparison. Catheter-specific voltage thresholds were identified based on underlying amounts of normal myocardium on transmural histology biopsies. RESULTS: Ten animals were included: 6 with anteroseptal myocardial infarction and 4 control animals. A total of 419,597 points were manually reviewed across the catheters, with 315,487 points used in the analysis. There were minimal differences in bipolar and unipolar voltages, scar areas, and abnormal electrograms between catheters and between rhythms. Catheter-specific bipolar and unipolar voltage thresholds for normal myocardium were High-Density Grid >2.78 mV and >6.19 mV, DuoDecapolar >2.22 mV and >6.05 mV, PentaRay >1.66 mV and >5.35 mV, Decanav >1.36 mV and >4.75 mV, Orion >1.21 mV and >6.05 mV, respectively. Catheter-specific bipolar thresholds improved the accuracy for detecting endo-mid myocardial scar on CMR by 1.8%-15.6% and catheter-specific unipolar thresholds improved the accuracy in the mid-epicardial layers by 25.3%-81.1%. CONCLUSIONS: Minimal differences were observed in scar detection and electrogram markers between commercially available multielectrode mapping catheters and differing wave fronts. Compared to traditional voltage criteria for bipolar and unipolar scar, catheter-specific thresholds markedly improved accuracy for delineating scar on CMR.
3. Artificial intelligence-enhanced comprehensive assessment of the aortic valve stenosis continuum in echocardiography.
A dual-pathway AI system using limited 2D TTE videos and automated conventional measurements accurately stages the AS continuum and predicts outcomes. Across internal and external cohorts, discrimination was excellent (AUC up to 0.99) and prognostic hazard increased per 10-point DLi-ASc.
Impact: This work lowers operator dependency and extends AS evaluation to resource-limited settings while adding prognostic stratification, aligning with scalable, equitable cardiovascular care.
Clinical Implications: AI-assisted TTE could streamline AS screening, triage, and follow-up, with automated staging and risk prediction aiding timely referral for AVR and resource allocation.
Key Findings
- The deep learning index (DLi-ASc) showed excellent discrimination for any, significant, and severe AS (AUC 0.91–0.99, 0.95–0.98, 0.97–0.99).
- DLi-ASc independently predicted a composite of cardiovascular death, heart failure, and AVR with adjusted HR per 10-point increase of 2.19 (ITDS), 1.64 (DHDS), and 1.61 (TDDS).
- Automated conventional metrics achieved high staging accuracy (98.2% ITDS; 82.1% DHDS; 96.8% TDDS) and prognostic performance comparable to manual measurements.
Methodological Strengths
- Large nationwide development cohort with internal and two external validations (site and temporal).
- Dual-pathway design combining video-based DL with automation of conventional parameters; outcome-linked prognostic validation.
Limitations
- Generalizability beyond the originating health system and across vendors requires further multi-national validation.
- Potential dataset shift and black-box interpretability issues typical of DL models.
Future Directions: Prospective multicenter trials assessing clinical workflow impact, equity, and outcomes; integration with handheld/POCUS devices; calibration across vendors and acquisition settings.
BACKGROUND: Transthoracic echocardiography (TTE) is the primary modality for diagnosing aortic stenosis (AS), yet it requires skilled operators and can be resource-intensive. We developed and validated an artificial intelligence (AI)-based system for evaluating AS that is effective in both resource-limited and advanced settings. METHODS: We created a dual-pathway AI system for AS evaluation using a nationwide echocardiographic dataset (developmental dataset, n = 8427): 1) a deep learning (DL)-based AS continuum assessment algorithm using limited 2D TTE videos, and 2) automating conventional AS evaluation. We performed internal (internal test dataset [ITDS], n = 841) and external validation (distinct hospital dataset [DHDS], n = 1696; temporally distinct dataset [TDDS], n = 772) for diagnostic value across various stages of AS and prognostic value for composite endpoints (cardiovascular death, heart failure, and aortic valve replacement). FINDINGS: The DL index for the AS continuum (DLi-ASc, range 0-100) increased with worsening AS severity and demonstrated excellent discrimination for any AS (AUC 0.91-0.99), significant AS (0.95-0.98), and severe AS (0.97-0.99). DLi-ASc was independent predictor for composite endpoint (adjusted hazard ratios 2.19, 1.64, and 1.61 per 10-point increase in ITDS, DHDS, and TDDS, respectively). Automatic measurement of conventional AS parameters demonstrated excellent correlation with manual measurement, resulting in high accuracy for AS staging (98.2% for ITDS, 82.1% for DHDS, and 96.8% for TDDS) and comparable prognostic value to manually-derived parameters. INTERPRETATION: The AI-based system provides accurate and prognostically valuable AS assessment, suitable for various clinical settings. Further validation studies are planned to confirm its effectiveness across diverse environments. FUNDING: This work was supported by a grant from the Institute of Information & Communications Technology Planning & Evaluation (IITP) funded by the Korea government (Ministry of Science and ICT; MSIT, Republic of Korea) (No. 2022000972, Development of a Flexible Mobile Healthcare Software Platform Using 5G MEC); and the Medical AI Clinic Program through the National IT Industry Promotion Agency (NIPA) funded by the MSIT, Republic of Korea (Grant No.: H0904-24-1002).