Daily Cardiology Research Analysis
Analyzed 132 papers and selected 3 impactful papers.
Summary
Three impactful cardiology studies stood out today: a prespecified analysis from the AZALEA-TIMI 71 trial shows that the Factor XI inhibitor abelacimab substantially reduces bleeding vs rivaroxaban across ages, including ≥75 years, in atrial fibrillation. A large-scale proteomic meta-analysis identified and replicated dozens of novel circulating proteins associated with incident heart failure and its subtypes, with Mendelian randomization suggesting possible causal targets. Finally, a JACC cohort study of the PREVENT risk equations in young adults highlights algorithmic fairness and calibration challenges across racial/ethnic groups, with limited benefit from adding a social deprivation index.
Research Themes
- Safer anticoagulation strategies via Factor XI inhibition in atrial fibrillation
- Proteomics-driven precision cardiology and causal target discovery in heart failure
- Algorithmic fairness and calibration in cardiovascular risk prediction for young adults
Selected Articles
1. Abelacimab vs Rivaroxaban in Older Individuals With Atrial Fibrillation: A Prespecified Analysis of the Phase 2b AZALEA-TIMI 71 Trial.
In this prespecified subgroup analysis of 1287 AF patients, monthly abelacimab (90/150 mg) significantly reduced major/CRNM bleeding vs rivaroxaban in both <75 and ≥75-year groups, with larger absolute risk reductions in older adults. Bleeding risk increased with age on rivaroxaban but remained stable on abelacimab. FXI inhibition may offer a safer anticoagulant paradigm for older AF patients.
Impact: This RCT-based analysis supports FXI inhibition as a clinically safer anticoagulation strategy for older AF patients, addressing a major unmet need—bleeding risk—with consistent benefit across age strata.
Clinical Implications: For older AF patients at high bleeding risk, FXI inhibition with abelacimab may be preferred over factor Xa inhibitors pending phase 3 efficacy data, with potential to shift anticoagulation paradigms toward safer hemostasis-sparing strategies.
Key Findings
- Abelacimab (90/150 mg monthly) reduced major or CRNM bleeding vs rivaroxaban in ≥75-year patients (HR 0.32 and 0.40).
- Similar relative bleeding reduction was observed in <75-year patients (HR 0.28 and 0.35); no age interaction.
- Absolute bleeding risk reduction was greater in older patients (≈6–7 per 100 patient-years) than younger (≈4–5 per 100 patient-years).
- Bleeding risk increased with age on rivaroxaban but remained stable on abelacimab.
Methodological Strengths
- Randomized head-to-head comparison with prespecified age-stratified analysis
- Robust safety endpoint (major/CRNM bleeding) with consistent effects across age strata
Limitations
- Phase 2b analysis; phase 3 efficacy data are pending
- Open-label design may introduce performance bias, though bleeding adjudication is typically blinded
Future Directions: Confirm efficacy and net clinical benefit in phase 3 trials; evaluate stroke prevention efficacy and real-world implementation in high-bleeding-risk populations, frailty strata, and CKD.
IMPORTANCE: Older age is a strong risk factor for bleeding with currently available anticoagulants. Factor XI (FXI) inhibition may offer a safer anticoagulant strategy in this population. OBJECTIVE: To evaluate the safety of the novel FXI inhibitor abelacimab vs rivaroxaban by age in patients with atrial fibrillation (AF). DESIGN, SETTING, AND PARTICIPANTS: The randomized clinical trial AZALEA-TIMI 71 randomized patients with AF to receive 1 of 2 subcutaneous abelacimab doses (90 mg or 150 mg monthly) or oral rivaroxaban (20 mg daily, dose reduction to 15 mg). This prespecified analysis of the phase 2b AZALEA-TIMI 71 trial evaluated bleeding risk by age, analyzed continuously and categorically (<75 vs ≥75 years). The trial was conducted from March 2021 to September 2023; data analysis was performed from February to May 2025. INTERVENTIONS: Monthly subcutaneous abelacimab (90 or 150 mg) or daily oral rivaroxaban (20/15 mg). MAIN OUTCOMES AND MEASURES: The primary end point was the composite of major or clinically relevant nonmajor (CRNM) bleeding. RESULTS: Among 1287 patients randomized, 715 (55.6%) were male and 572 (44.4%) were female; there were 625 patients (49%) 75 years or older. Compared with younger patients, those 75 years or older had lower body mass index (28 vs 32), were less likely to be taking antiplatelet therapy at baseline (17% vs 32%), and were more likely to have creatinine clearance 50 mL/min or less (33% vs 8%). Both abelacimab doses were associated with significantly less major or CRNM bleeding compared with rivaroxaban in those 75 years or older (hazard ratio [HR], 0.32; 95% CI, 0.17-0.60; and HR, 0.40; 95% CI, 0.22-0.73; for abelacimab, 90 and 150 mg, vs rivaroxaban, respectively) and in those younger than 75 years (HR, 0.28; 95% CI, 0.12-0.61; and HR, 0.35; 95% CI, 0.17-0.70; P for interaction, .85 and .84, respectively). Patients 75 years or older tended to derive greater absolute risk reductions with abelacimab (7.1 and 6.2 per 100 patient-years for abelacimab, 90 and 150 mg, vs rivaroxaban, respectively) than those younger than 75 years (4.7 and 4.2 per 100 patient-years, respectively). When modeled continuously, bleeding risk tended to increase with age in the rivaroxaban group but remained stable in the abelacimab group (P for interaction, .33). CONCLUSIONS AND RELEVANCE: This study found that abelacimab consistently reduced bleeding compared with rivaroxaban regardless of age, with the potential for a greater absolute reduction in bleeding with older age. FXI inhibition with abelacimab may become a particularly attractive option in older patients with AF and higher bleeding risk. The results of ongoing phase 3 trials are necessary to establish the efficacy and benefit-to-risk ratio of abelacimab. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04755283.
2. Large-Scale Proteomic Profiling of Incident Heart Failure and Its Subtypes in Older Adults.
Across CHS and AGES-RS cohorts with ARIC replication, 119 proteins associated with incident HF (and subtype-specific sets), with 55 of 66 available markers replicating. Mendelian randomization implicated 7 proteins (eg, ADIPOQ, CD14, C9) as possibly causal. Findings refine HF pathobiology and highlight potential biomarkers and therapeutic targets for HFpEF and HFrEF.
Impact: This multi-cohort proteomic analysis with external replication and MR provides a robust atlas of HF-associated proteins, advancing mechanistic insights and prioritizing targets for precision therapies in HFpEF/HFrEF.
Clinical Implications: While not immediately practice-changing, the identified proteins can refine risk prediction panels, enable endotype-based stratification, and accelerate target validation for HFpEF/HFrEF therapeutics.
Key Findings
- Identified 119 HF-associated proteins, with subtype-specific associations (15 for HFpEF, 11 for HFrEF) at Bonferroni significance.
- External replication confirmed 55 of 66 available novel markers in ARIC.
- Mendelian randomization suggested 7 proteins with possible causal roles in HF or subtypes (e.g., ADIPOQ, CD14, C9), though colocalization was not demonstrated.
Methodological Strengths
- Two large population-based cohorts with standardized proteomics and external replication in ARIC
- Integration of meta-analysis and Mendelian randomization to support causal inference
Limitations
- Colocalization was not demonstrated, limiting causal certainty
- Generalizability primarily to older adults; platform-specific aptamer coverage
Future Directions: Prioritize experimental validation of the 7 putatively causal proteins, evaluate multi-marker panels for risk prediction, and test endotype-guided therapeutic strategies in HFpEF/HFrEF.
BACKGROUND: Heart failure (HF) and its main subtypes, heart failure with preserved ejection fraction (HFpEF) and heart failure with reduced ejection fraction (HFrEF), impose an enormous health burden on elders. Assessment of the circulating proteome to illuminate pathogenesis could open new opportunities for treatment. METHODS: We conducted a plasma proteomics screen of incident HF and its subtypes in 2 older population-based cohorts, the CHS (Cardiovascular Health Study) and the AGES-RS (Aging, Gene/Environment Susceptibility-Reykjavik Study). The 2 studies used SomaLogic platforms, with 4404 aptamers in common. Multivariable Cox models were fit to evaluate individual-protein associations with HF, HFpEF, and HFrEF separately in each cohort, and study-specific associations were combined by fixed-effects meta-analysis. Replication was performed in the ARIC (Atherosclerosis Risk in Communities) cohort. Two-sample Mendelian randomization of HF and its subtypes, along with colocalization analysis, was performed to support causal inference. RESULTS: Among 8599 participants, 1590 experienced incident HF (536 HFpEF, 471 HFrEF). There were 119 proteins associated with HF, 15 proteins with HFpEF, and 11 proteins with HFrEF, at Bonferroni-corrected significance. Among these, 9 have never previously been identified for cardiovascular diseases, and another 61 represent new associations with incident HF or its subtypes. Of these 70 proteins, 55 of the 66 available replicated externally. Mendelian randomization analysis revealed 7 proteins genetically associated with HF at nominal significance; 2 were separately associated with HFpEF, and another 2 with HFrEF. Seven of these 9 proteins (NCDP1, APOF, LMAN2 [lectin, mannose-binding 2], ADIPOQ [adiponectin], CD14 [cluster of differentiation 14], ARHGAP1 [Rho GTPase-activating protein 1], C9 [complement 9]) showed new, possibly causal associations, although we did not detect evidence for colocalization. CONCLUSIONS: In this large-scale proteomic study involving 3 longitudinal cohorts of older adults, we identified and replicated 55 novel protein markers of HF or its subtypes, and 7 new, possibly causal proteins. These proteins may enhance risk prediction, improve understanding of pathobiology, and help prioritize targets for therapeutic development of these foremost disorders in elders.
3. PREVENT Equations in Young Adults: Fairness, Calibration, and Performance Across Racial and Ethnic Groups.
In 161,202 young adults (30–39 years), PREVENT equations showed fair discrimination but underpredicted risk in non-Hispanic Black individuals; calibration errors varied across racial/ethnic groups and were not improved by adding SDI. Findings were consistent in 20–29-year exploratory analyses, underscoring real-world fairness and calibration challenges.
Impact: This study rigorously evaluates model performance and fairness in a critical but understudied group—young adults—highlighting calibration gaps that can exacerbate disparities and informing equitable deployment of preventive strategies.
Clinical Implications: Clinicians should be cautious applying PREVENT in young adults, particularly non-Hispanic Black patients, and consider local recalibration or complementary markers when initiating preventive therapies.
Key Findings
- Base PREVENT equations had fair discrimination (Harrell's C ~0.68–0.72) but underpredicted risk in non-Hispanic Black individuals (mean calibration 0.54).
- Calibration errors differed across racial/ethnic groups for CVD, ASCVD, and HF endpoints.
- Adding a zip code–based social deprivation index did not improve overall performance or fairness.
- Exploratory analyses in 20–29-year-olds yielded similar performance and fairness patterns.
Methodological Strengths
- Large, diverse integrated health system cohort with 10-year follow-up
- Comprehensive fairness metrics (concordance imparity, fair calibration) alongside discrimination and calibration
Limitations
- Single health system may limit generalizability to other care settings
- SDI at zip code level may be too coarse to capture individual social risk
Future Directions: Pursue local recalibration, incorporate individual-level social determinants, and evaluate hybrid models (e.g., biomarkers, imaging) to improve fairness and calibration in young adults.
BACKGROUND: Cardiovascular disease (CVD) is increasing among young adults. The American Heart Association's PREVENT (Predicting Risk of Cardiovascular Disease Events) equations estimate risk of CVD, atherosclerotic cardiovascular disease (ASCVD), and heart failure (HF) for primary prevention. Augmented equations additionally include zip code-based social deprivation index (SDI) to address adverse social exposures. OBJECTIVES: We assessed performance and algorithmic fairness of base and SDI-augmented PREVENT equations in young adults aged 30 to 39 years, defining fairness as similar performance across racial and ethnic groups. An exploratory analysis was conducted among young adults aged 20 to 29 years. METHODS: We included Kaiser Permanente Southern California members aged 20 to 39 years without prior CVD between 2008 and 2009, followed through 2019. We compared 10-year predicted and observed CVD, ASCVD, and HF events for base and SDI-augmented PREVENT models. Performance (Harell's C, calibration slopes, mean calibration) and fairness (concordance imparity, fair calibration) were estimated by race and ethnicity and age group (30-39 years [primary analysis], 20-29 years [exploratory analysis]). RESULTS: Among 161,202 young adults aged 30 to 39 years (60.0% women; 51.7% Hispanic, 26.9% non-Hispanic White, 12.5% Asian/Pacific Islander, 8.9% non-Hispanic Black), 10-year CVD incidence was 0.7%. Race-specific Harrell's C-statistics for the base PREVENT CVD model ranged from 0.68 to 0.72, yielding low concordance imparity (0.04; 95% CI: 0.02-0.22) which implies fair discrimination. Mean calibration showed underprediction in non-Hispanic Black participants (0.54; 95% CI: 0.48-0.65) vs other groups (range: 0.96-1.07). In fair calibration testing, prediction errors differed across racial and ethnic groups. Results were similar for ASCVD and HF. Adding SDI did not improve performance or fairness despite disparities across groups. In exploratory analyses among 80,978 individuals aged 20 to 29 years, performance and fairness results were similar. CONCLUSIONS: This large, diverse cohort of young adults demonstrates how the PREVENT equations may perform when applied in real-world clinical settings, reflecting the true operational environment faced by large health systems. Applications of PREVENT in clinical patient care, eg, early initiation of preventive strategies, should consider variations in model performance across age, race, and ethnicity.