Daily Cardiology Research Analysis
Analyzed 304 papers and selected 3 impactful papers.
Summary
Analyzed 304 papers and selected 3 impactful articles.
Selected Articles
1. Neutrophil methylmalonic acid promotes microthrombus formation and adverse cardiac remodeling post-myocardial infarction through activating IL-6 signaling pathway-mediated NETosis.
In AMI, neutrophil-derived methylmalonic acid (MMA) was elevated and drove IL-6/JAK1/STAT3–mediated NETosis, promoting microthrombus formation and adverse cardiac remodeling. NETosis inhibitors, IL-6 neutralization, and colchicine attenuated NETs, microthrombi, and maladaptive remodeling, positioning MMA as an immunometabolic trigger and suggesting actionable therapeutic targets.
Impact: This study uncovers a novel immunometabolic axis (neutrophil MMA → IL-6–mediated NETosis) that mechanistically links inflammation, thrombosis, and remodeling after MI, and identifies readily translatable interventions (colchicine, NETosis blockade).
Clinical Implications: Neutrophil MMA could serve as a biomarker to identify post-MI patients at high risk of NET-driven microthrombosis and adverse remodeling. Targeting IL-6 signaling or NETosis (e.g., colchicine, PAD4 inhibition, DNase) may prevent HF progression, particularly in patients with elevated neutrophil MMA.
Key Findings
- Serum and neutrophil MMA levels were significantly higher in AMI than in angina, with the strongest elevation in neutrophils.
- Neutrophil-specific Mmut knockout increased NET formation, microthrombi, and worsened cardiac function 4 weeks post-MI.
- NETosis-targeted interventions (GSK484 or DNase I) reduced microthrombus burden and adverse remodeling.
- MMA-induced NETosis was mediated by activation of IL-6/JAK1/STAT3; IL-6 neutralization blunted NETosis.
- Colchicine suppressed neutrophilic IL-6 expression, NETosis, microthrombi, and attenuated adverse remodeling.
Methodological Strengths
- Translational design integrating human samples, genetic mouse models, and targeted pharmacologic interventions.
- Mechanistic mapping of the IL-6/JAK1/STAT3 axis with functional readouts (NETs, microthrombi, cardiac function).
Limitations
- Human cohort sizes and detailed enrollment characteristics are not specified in the abstract.
- Preclinical models may not fully capture heterogeneous human post-MI remodeling; clinical efficacy of targeted therapies requires trials.
Future Directions: Prospective trials testing MMA-guided colchicine or NETosis/IL-6–targeted therapies post-MI, validation of MMA as a risk biomarker, and exploration of combinatorial anti-thromboinflammatory strategies.
BACKGROUND: Neutrophils contribute critically to adverse cardiac remodeling following acute myocardial infarction (AMI), yet the precise regulatory mechanisms remain unclear. Our previous findings identified methylmalonic acid (MMA) as a novel cardiovascular prognostic biomarker. Thus, we aimed to investigate whether neutrophil-derived MMA mediates neutrophil extracellular trap (NET) formation and subsequent adverse cardiac remodeling post-MI, and to elucidate potential underlying mechanisms. METHODS: Serum and neutrophil MMA levels were measured in humans and mice with AMI. Neutrophil-specific Mmut knockout mice (S100a8 RESULTS: Compared with patients with angina, patients with AMI displayed significantly increased MMA levels in serum and neutrophils, particularly pronounced in neutrophils. Elevated NET markers were observed in thrombus tissue from patients with AMI with higher neutrophil MMA. Similarly, Mmut knockout mice exhibited increased NET formation, greater microthrombus burden, and worsened cardiac dysfunction 4 weeks after MI compared with S100a8Cre controls. NETosis-targeted interventions (GSK484 or DNase I) substantially reduced microthrombus formation and adverse cardiac remodeling, especially in Mmut knockout mice. Integrated transcriptomic and multifactorial analyses revealed that activation of the neutrophil IL-6/JAK1/STAT3 signaling pathway plays a key role in MMA-induced NETosis, which was largely compromised by the treatment with an IL-6 neutralizing antibody. Moreover, colchicine, an FDA-approved anti-inflammatory agent, significantly inhibited neutrophilic IL-6 expression, NETosis, and microthrombus formation, thereby attenuating post-MI cardiac remodeling against the hazards of neutrophil MMA elevation. CONCLUSIONS: Neutrophil-derived MMA promotes NETosis and microthrombus formation through IL-6 activation, contributing to maladaptive cardiac remodeling post-MI. These findings identify neutrophil MMA as a novel immunometabolic trigger driving NET-mediated adverse cardiac remodeling and suggest colchicine as a promising therapeutic strategy to prevent heart failure post-MI, particularly in patients with elevated neutrophil MMA contents.
2. Non-antiarrhythmic pharmacotherapy in cardio-renal-metabolic disease and incident atrial fibrillation: a trial meta-analysis.
Across 249 RCTs (745,041 participants), several cardio-renal-metabolic drug classes were associated with reduced incident AF in specific conditions: ACEi/ARB, MRA, and SGLT2 inhibitors in HFrEF; SGLT2 inhibitors in CKD; and GLP-1RA in obesity. However, AF was rarely a prespecified endpoint and event counts were low, highlighting the need for dedicated AF prevention trials.
Impact: This synthesis challenges the view that AF prevention requires antiarrhythmic drugs alone by implicating cardio-renal-metabolic therapies in reducing incident AF and prioritizes future AF-focused endpoints in trials.
Clinical Implications: When selecting cardio-renal-metabolic therapies for HFrEF, CKD, or obesity, clinicians may consider potential AF risk reduction as a secondary benefit, while recognizing that definitive AF-preventive indications require dedicated RCTs.
Key Findings
- 249 RCTs (n=745,041) were synthesized; 161 were placebo-controlled and only 15 prespecified AF endpoints.
- In HFrEF, ACEi/ARB (RR 0.69), MRA (RR 0.62), and SGLT2 inhibitors (RR 0.62) were associated with reduced incident AF.
- In CKD, SGLT2 inhibitors reduced incident AF (RR 0.53); in obesity, GLP-1RA reduced incident AF (RR 0.79).
- Low AF event counts and lack of AF-powered designs limit causal inference for AF prevention.
Methodological Strengths
- Large-scale meta-analysis across multiple disease domains with random-effects modeling
- Focus on randomized trial data increases internal validity versus observational syntheses
Limitations
- AF often captured via adverse event reporting; few trials prespecified AF endpoints
- Low AF event rates and heterogeneity across indications limit precision
Future Directions: Prospective, adequately powered RCTs with incident AF as a prespecified endpoint across HFrEF, CKD, obesity, and multimorbidity; exploration of mechanisms linking metabolic therapies to atrial remodeling.
BACKGROUND AND AIMS: Atrial fibrillation (AF) disease burden is increasing. Pharmacotherapy of cardio-renal-metabolic diseases may prevent incident AF. This meta-analysis estimates the effect of different pharmacotherapies on risk of incident AF across cardio-renal-metabolic diseases. METHODS: The Medline, Embase, and Cochrane Central databases were searched to 7 October 2025 for randomized clinical trials (RCTs) comparing the effect of a non-antiarrhythmic cardio-renal-metabolic medication with control or another agent for incident AF. Random-effects meta-analysis using the Mantel-Haenszel method, with between-study variance estimated using the DerSimonian-Laird method, was performed to synthesize risk ratios (RR) with 95% confidence intervals (CI). RESULTS: Two hundred and forty-nine RCTs involving 745 041 patients were included, of which 207 identified AF through adverse event reports, 161 were placebo-controlled, and 15 had AF as a pre-specified endpoint. In placebo-controlled trials, significant differences in incident AF were observed with treatment of heart failure with reduced ejection fraction with angiotensin-converting enzyme inhibitors and angiotensin receptor blockers (RR 0.69, 95% CI 0.60-0.80), mineralocorticoid receptor antagonists (RR 0.62, 95% CI 0.43-0.90), and sodium-glucose co-transporter 2 (SGLT2) inhibitors (RR 0.62, 95% CI 0.44-0.87); treatment of chronic kidney disease with SGLT2 inhibitors (RR 0.53, 95% CI 0.33-0.85); and treatment of obesity with glucagon-like peptide-1 receptor agonists (RR 0.79, 95% CI 0.63-0.99). However, the number of AF events per trial was low and none were adequately powered for incident AF. CONCLUSIONS: Prospective RCTs with AF as a pre-specified outcome should be integrated into the design of future trials of cardio-renal-metabolic medications to determine whether they reduce incident AF.
3. Development and validation of a neural network survival prediction model for ischemic heart disease.
PMHnet, a discrete-time neural network survival model using 584 EHR features, achieved tdAUCs of 0.88 at 6–12 months and 0.82 at 5 years, outperforming GRACE2.0 and less feature-rich models. Performance generalized to an external Icelandic cohort, supporting clinical utility for post-angiography risk stratification.
Impact: Demonstrates that comprehensive, feature-rich ML survival modeling substantially improves prognostication beyond established scores and generalizes across health systems, enabling precision risk communication and management.
Clinical Implications: Enhanced mortality prediction after angiography can refine intensity of secondary prevention, follow-up, and shared decision-making; integration into EHR could enable automated risk dashboards to guide therapy.
Key Findings
- In the internal test set (n=5,000), PMHnet achieved tdAUC 0.88 at 6 months and 1 year, 0.84 at 3 years, and 0.82 at 5 years.
- External validation in Iceland showed similar discrimination, supporting generalizability.
- PMHnet outperformed GRACE2.0 and reduced-feature or single-modality neural models at all timepoints.
- SHAP analysis provided model explainability and feature importance across 584 inputs.
Methodological Strengths
- Very large, real-world EHR cohort with external validation and appropriate time-to-event modeling for censoring.
- Comprehensive feature integration (clinical, labs, codes) and benchmarking to established risk scores.
Limitations
- Retrospective design susceptible to residual confounding and coding biases in EHR data.
- Clinical impact on management and outcomes not yet demonstrated in prospective implementation studies.
Future Directions: Prospective impact and implementation trials, calibration/transportability assessments across diverse systems, and integration into clinical workflows with decision support.
BACKGROUND: Current risk prediction models for ischemic heart disease in clinical use are relatively simple and use a limited collection of well-known risk factors. Using machine learning to integrate a broader panel of features from electronic health records (EHRs) may improve post-angiography prognostication. METHODS: This retrospective model development and validation study was based on Danish EHR data. Icelandic EHR data were used for external test. Patients with a coronary angiography-confirmed diagnosis of coronary atherosclerosis between 2006 and 2016 were included for model development (n = 39,746). Time to all-cause mortality, the prediction target, was tracked until 2019, or up to 5 years, whichever came first. To model time-to-event data and deal with censoring, neural network-based discrete-time survival models were used. The model, PMHnet, uses 584 different features including clinical characteristics, laboratory tests, and diagnosis and procedure codes. Model performance was evaluated using time-dependent AUC (tdAUC) and the Brier score. PMHnet was benchmarked against the updated GRACE2.0 risk score and less feature-rich neural network models. Models were evaluated using hold-out data (n = 5000) and external validation data from Iceland. Feature importance and model explainability were assessed using SHAP analysis. RESULTS: On the test set (n = 5000), the tdAUC of PMHnet was 0.88 [ 0.86-0.90] (case count = 196) at six months, 0.88 [0.86-0.90] (cc = 261) at one year, 0.84 [0.82-0.86] (cc = 395) at three years, and 0.82 [0.80-0.84] (cc = 763) at five years. PMHnet showed similar performance in the Icelandic data. Compared to the GRACE2.0 score and intermediate models limited to GRACE2.0 features or single data modalities, PMHnet had significantly better model discrimination across all evaluated prediction timepoints. CONCLUSIONS: More complex and feature-rich machine learning models can better predict all-cause mortality in ischemic heart disease and may be used by clinicians and patients to inform and guide treatment and management.