Daily Cardiology Research Analysis
Three papers stand out today: a large multicenter registry shows left bundle branch area pacing (LBBAP) halves long-term mortality compared with right ventricular pacing in atrioventricular block. A meta-analysis across 15 trials demonstrates glucagon-like peptide-1 receptor agonists reduce heart failure hospitalization and cardiovascular death across cardiovascular-kidney-metabolic conditions. A UK Biobank machine-learning, multi-omics approach detects HFpEF years before symptoms with high accu
Summary
Three papers stand out today: a large multicenter registry shows left bundle branch area pacing (LBBAP) halves long-term mortality compared with right ventricular pacing in atrioventricular block. A meta-analysis across 15 trials demonstrates glucagon-like peptide-1 receptor agonists reduce heart failure hospitalization and cardiovascular death across cardiovascular-kidney-metabolic conditions. A UK Biobank machine-learning, multi-omics approach detects HFpEF years before symptoms with high accuracy and identifies biologically distinct risk clusters.
Research Themes
- Conduction system pacing improves survival in AV block
- Incretin therapies reduce HF hospitalization and CV death across CKM diseases
- Multi-omics and machine learning enable early HFpEF detection and risk stratification
Selected Articles
1. Left bundle branch area pacing vs right ventricular pacing for atrioventricular block: the MELOS RELOADED study.
In over 3,300 matched AV block patients, LBBAP reduced 4-year all-cause mortality versus right ventricular pacing (HR 0.53). Failure to confirm left bundle branch capture, lower ventricular pacing percentage, and older age predicted higher mortality within the LBBAP group.
Impact: This is the first large study demonstrating a survival benefit of conduction system pacing in routine AV block care, potentially shifting default pacing strategies away from right ventricular apical pacing.
Clinical Implications: For patients with AV block and preserved/mildly reduced LVEF requiring frequent pacing, LBBAP should be considered the preferred pacing strategy. Confirming true left bundle capture is advisable to optimize outcomes.
Key Findings
- After 1:1 propensity matching (n=3,382), LBBAP showed an 11.8% absolute 4-year survival advantage vs RVP (P<.001).
- LBBAP independently reduced mortality (HR 0.53; 95% CI 0.42–0.65).
- Within LBBAP, lack of confirmed left bundle capture (HR 1.85), lower pacing percentage, and older age increased mortality.
Methodological Strengths
- Large multicenter European registry with national death registry linkage
- Rigorous 1:1 propensity score matching and multivariable Cox modeling
Limitations
- Observational design with potential residual confounding despite matching
- Generalizability to lower pacing burdens or reduced LVEF populations remains uncertain
Future Directions: Randomized trials comparing LBBAP vs conventional pacing across LVEF strata and pacing burdens, and studies standardizing capture confirmation and implantation techniques.
BACKGROUND AND AIMS: Left bundle branch area pacing (LBBAP) promotes physiological synchronous activation of the left ventricle and may be particularly beneficial in patients with atrioventricular block (AVB), but its mortality benefit remains unclear. This study aims to compare long-term survival in AVB patients receiving either LBBAP or right ventricular pacing (RVP) and to analyse predictors of mortality during LBBAP. METHODS: MELOS RELOADED, a multicentre European collaboration, was a registry-based study of pacemaker patients with AVB, left ventricular ejection fraction (LVEF) >40% and ventricular pacing >20%. The primary outcome was all-cause mortality based on national registries. A 1:1 propensity score matching was performed between the RVP and LBBAP groups. Kaplan-Meier curves and multivariable Cox proportional hazards models were used to estimate survival. RESULTS: In total, 3382 patients receiving LBBAP or RVP were matched. At 4-year follow-up, the Kaplan-Meier curve showed an absolute difference in survival of 11.8% in favour of LBBAP (P < .001). LBBAP was a robust predictor of reduced mortality with a hazard ratio (HR) of 0.53 (95% confidence interval 0.42-0.65, P < .001). Within the LBBAP group, the following independent predictors of increased mortality were identified: lack of confirmed left bundle branch capture (HR 1.85, P < .001), lower percentage of ventricular pacing (HR 1.12), and age. CONCLUSIONS: This is the first large study demonstrating the long-term survival benefit of LBBAP. This strengthens the use of LBBAP in AVB patients with preserved/mildly reduced LVEF while awaiting the results of randomized trials. Confirmation of left bundle branch capture seems advisable to achieve optimal results with LBBAP.
2. Effect of glucagon-like peptide-1 receptor agonists on heart failure outcomes and cardiovascular death across varying cardiovascular-kidney-metabolic comorbidity.
Across 87,549 participants in 15 trials, GLP-1 receptor agonists reduced the composite of HF hospitalization or CV death in HF, T2DM, and obesity, and lowered CV death across these groups. In HFrEF, CV death was reduced but HF hospitalization showed a nonsignificant numeric increase, indicating a need for definitive outcomes trials.
Impact: Synthesizes contemporary outcome data showing class-wide benefits of GLP-1RAs on HF and CV outcomes across CKM phenotypes, informing guideline updates and treatment sequencing.
Clinical Implications: GLP-1RAs should be considered for patients with T2DM and/or obesity to reduce HF events and CV death, and may be reasonable adjuncts in HF, with caution in established HFrEF pending dedicated trials.
Key Findings
- GLP-1RAs reduced composite HF hospitalization/CV death in HF (HR 0.81), T2DM (HR 0.85), and obesity (HR 0.70).
- CV death was significantly reduced in HF (HR 0.88), T2DM (HR 0.85), and obesity (HR 0.83).
- In HFrEF, CV death decreased (HR 0.67) but HF hospitalization showed a nonsignificant increase (HR 1.17).
- No increase in serious adverse events (RR 0.94).
Methodological Strengths
- Meta-analysis of 15 randomized clinical outcome trials with subgroup analyses across CKM phenotypes
- Use of random-effects models and reporting of HRs/RRs with 95% CIs
Limitations
- Heterogeneity in populations and endpoints; limited trial data focused specifically on HFrEF
- Aggregate data meta-analysis without individual patient-level data
Future Directions: Dedicated randomized trials in established HFrEF populations to clarify effects on HF hospitalization, and mechanistic studies to understand CV death reduction pathways.
AIMS: Effects of glucagon-like peptide-1 receptor agonists (GLP-1RAs) on heart failure hospitalization (HFH) and cardiovascular (CV) death among patients with varying overlap of cardiovascular-kidney-metabolic (CKM) comorbidity are not well characterized. This study aimed to assess effects GLP-1RAs on HFH and CV death across populations with varying type and number of CKM comorbidity. METHODS AND RESULTS: Online databases were queried through November 2024 for primary and secondary analyses of clinical outcome trials of GLP-1RAs in patients with heart failure (HF), type 2 diabetes mellitus (T2DM), chronic kidney disease (CKD), obesity, and combinations of these diseases. Primary outcome was a composite HFH or CV death. Secondary outcomes were first HFH and CV death. Hazard ratios (HRs), risk ratios (RRs), and their 95% confidence intervals (CI) were derived using random-effects models. Fifteen trials (n = 87 549) were included. Compared with placebo, GLP-1RAs reduced the relative risk of composite HFH/CV death in HF (HR 0.81, 95% CI 0.69-0.96), T2DM (HR 0.85, 95% CI 0.78-0.93), and obesity (HR 0.70, 95% CI 0.58-0.86), with a non-significant risk reduction in CKD (HR 0.79, 95% CI 0.61-1.01). GLP-1RAs reduced the risk of HFH in T2DM (HR 0.89, 95% CI 0.80-0.99) and obesity (HR 0.63, 95% CI 0.45-0.87), with a non-significant risk reduction among patients with HF (HR 0.85, 95% CI 0.69-1.04) and CKD (HR 0.82, 95% CI 0.64-1.06). GLP-1RAs also significantly reduced CV death in HF (HR 0.88, 95% CI 0.77-0.99), T2DM (HR 0.85, 95% CI 0.78-0.93), and in obesity (HR 0.83, 95% CI 0.73-0.93), with a non-significant risk reduction in CKD (HR 0.80, 95% CI 0.60-1.08). Effects were consistent across subgroups, except for HF with reduced ejection fraction (HFrEF), where GLP-1RAs showed a non-significant risk increase in HFH (HR 1.17, 95% CI 0.93-1.47) but significantly reduced CV death (HR 0.67, 95% CI 0.50-0.90). GLP-1RAs were not associated with increased risk for serious adverse events (RR 0.94, 95% CI 0.89-1.00). CONCLUSIONS: Glucagon-like peptide-1 receptor agonists reduce HFH and CV death across CKM conditions, with generally consistent effects in varying combinations of these diseases. The potential exception is among patients with HFrEF, where a reduction in risk of CV death, but a numeric increase in HFH, was observed. Definitive CV outcome trials are needed to definitively determine effects of GLP-1RAs in patients with established HFrEF.
3. Deep phenotyping of heart failure with preserved ejection fraction through multi-omics integration.
A supervised multi-omics classifier trained and validated in UK Biobank predicted HFpEF on average 6.3 years before symptom onset with AUC 0.931 and identified a high-risk inflammatory pathway-driven cluster (AUC 0.988). This approach supports earlier detection and biologically grounded HFpEF subtyping.
Impact: Provides a high-accuracy, scalable framework for pre-symptomatic HFpEF detection and mechanistic subtyping, addressing a major diagnostic gap and enabling prevention-oriented strategies.
Clinical Implications: If externally validated and operationalized, risk-enriched screening and targeted prevention (e.g., weight, BP, metabolic control, anti-inflammatory trials) could be deployed years before symptomatic HFpEF.
Key Findings
- Classifier achieved ROC AUC 0.931 (sensitivity 0.857; specificity 0.847) in a 100,446-participant validation set.
- Predicted incident HFpEF a mean of 6.3±3.9 years before symptom onset in asymptomatic individuals.
- Similarity network fusion revealed a high-risk cluster with excess mortality and dysregulated inflammatory pathways (AUC 0.988).
Methodological Strengths
- Very large training (n=401,917) and independent hold-out validation (n=100,446) cohorts
- Integration of clinical and multi-omics data with similarity network fusion for subtyping
Limitations
- Generalizability beyond UK Biobank population requires external validation in diverse health systems
- Translational pathways (screening infrastructure, cost-effectiveness) not yet established
Future Directions: Prospective external validation, health-economic analyses, and interventional trials targeting high-risk molecular clusters to test prevention strategies.
AIMS: Heart failure with preserved ejection fraction (HFpEF) has become the predominant form of heart failure and a leading cause of global cardiovascular morbidity and mortality. Due to its heterogeneous nature, HFpEF presents substantial challenges in diagnosis and management. Given the limited treatment options and lifestyle-associated comorbidities, early identification is crucial for establishing effective preventive strategies. Here, we introduce and validate a machine learning-based multi-omics approach that integrates clinical and molecular data to detect and characterize HFpEF. METHODS AND RESULTS: A supervised classifier was trained on a stratified subset of UK Biobank participants (n = 401 917) to identify phenotypic profiles associated with subsequent symptom-defined HFpEF during longitudinal follow-up. Model performance was validated in a non-overlapping hold-out subset from all 22 UK Biobank assessment centres (n = 100 446; 6726 HFpEF cases; 7394 with multi-omics data). The classifier demonstrated robust discriminatory performance, with a receiver operating characteristic area under the curve (ROC AUC) of 0.931 (95% confidence interval [CI] 0.930-0.931), a sensitivity of 0.857 (95% CI 0.855-0.860) and a specificity of 0.847 (95% CI 0.846-0.847). It identified individuals who subsequently developed HFpEF an average of 6.3 ± 3.9 years before symptom onset in asymptomatic individuals. Similarity network fusion (SNF) identified distinct subgroups, including a high-risk cluster characterized by elevated mortality and dysregulated inflammatory pathways, which was distinguishable with high accuracy (ROC AUC 0.988; 95% CI 0.985-0.990). CONCLUSIONS: We identified HFpEF phenotypes at an early stage, often several years before the onset of clinical symptoms, when the disease trajectory may still be amenable to modification. The molecular characterization provides novel insights into the underlying disease complexity and enables more refined risk stratification.