Daily Cardiology Research Analysis
Analyzed 198 papers and selected 3 impactful papers.
Summary
Three high-impact studies advance cardiology across prevention, genetics, and data science. A Nature Communications study links gut microbiome-derived metabolites to a kidney–heart axis and incident cardiovascular disease. An European Heart Journal registry builds and validates a risk score for severe heart failure events in laminopathies, while an external-validated machine learning model from the European Journal of Preventive Cardiology accurately identifies individuals at high risk of sudden cardiac death from electronic health records.
Research Themes
- Microbiome–cardiorenal axis and cardiovascular risk
- Genotype-informed risk prediction in inherited cardiomyopathy
- Artificial intelligence for sudden cardiac death risk stratification
Selected Articles
1. A gut microbiome-kidney-heart axis predictive of future cardiovascular diseases.
Using metabolomics in healthy Europeans and external validation in Canadians, the authors show that gut microbiome–derived aromatic amino acid metabolites correlate with pro-ANP and eGFR and predict incident CVD. Mendelian randomization supports a mediating role for these metabolites in a gut microbiome–kidney axis that influences cardiovascular risk.
Impact: This study connects microbiome-related metabolites to both subclinical cardiorenal physiology and incident CVD with genetic support, opening biomarker and therapeutic avenues. It advances understanding of interorgan crosstalk underlying cardiovascular risk.
Clinical Implications: Microbiome-derived metabolite panels could augment risk stratification before overt disease and identify candidates for microbiome-targeted interventions (diet, pre/probiotics). Integration with eGFR and natriuretic peptide testing may refine primary prevention strategies.
Key Findings
- Aromatic amino acid metabolism markers correlated with circulating pro-ANP and eGFR in a metabolically healthy European cohort.
- Mendelian randomization supported a mediating role of microbiome-related metabolites in a gut microbiome–kidney axis.
- Baseline levels of these metabolites were associated with incident cardiovascular disease in an external Canadian population.
Methodological Strengths
- Multi-cohort design with external validation across continents
- Use of genetic instruments (Mendelian randomization) to support causality
- Integration of metabolomics with cardiorenal phenotypes
Limitations
- Observational nature limits definitive causal inference despite MR support
- Generalizability to diverse ancestries and comorbidity profiles remains to be tested
- Lack of interventional data to show risk modification by altering the metabolites or microbiome
Future Directions: Prospective trials to test whether modifying gut microbiome or targeted metabolites reduces incident CVD; standardization of metabolite assays and integration into multivariable risk models; exploration across ancestries.
Cardiovascular diseases (CVD) remain a major global health challenge. Early markers of disease initiation and progression are urgently needed. We, and others, have previously shown changes in the gut microbiome in association with metabolic and CVD. Here, we demonstrate that gut microbiome-related changes can be detected in association with subclinical variations in heart and kidney function. Markers related to gut microbial metabolism of aromatic amino acids, phenylalanine and tyrosine, associate with circulating pro-atrial natriuretic peptide and estimated glomerular filtration rate in a metabolically healthy European population. Observational and genetic evidence further identify microbiome-related metabolites as mediators of this gut microbiome-kidney axis, with their baseline levels associating with incident CVD in an external Canadian population. Altogether, our work suggests that the gut microbiome interacts with the cardiorenal axis and participates in an interorgan crosstalk affecting host physiology and risk of CVD.
2. Laminopathies: natural history and risk prediction of heart failure.
In 470 derivation and 245 validation LMNA cohorts, a Fine-Gray model identified male sex, LVEF <50%, missense variants in head/rod domains, and complete LBBB as independent predictors of HF-MACE, with C-index ~0.75 in both cohorts. A simple risk count stratified 5-year HF-MACE from 1.5% to 22%.
Impact: This is the first validated HF-MACE risk model in laminopathies, enabling genotype- and phenotype-informed surveillance and timing of advanced therapies.
Clinical Implications: Use the four predictors to stratify surveillance intensity, optimize GDMT, and plan early referral for advanced HF therapies in high-risk LMNA patients; exclude LVEF <30% patients who already face very high near-term risk.
Key Findings
- Independent predictors of HF-MACE: male sex (aHR 1.86), LVEF <50% (aHR 2.18), LMNA missense variants in head/rod domains (aHR 2.91), and complete LBBB (aHR 2.99).
- Model discrimination was strong and consistent: C-index 0.750 in derivation and 0.758 in validation cohorts.
- Five-year HF-MACE incidence was 1.5%, 5.0%, and 22.0% with 0, 1, and ≥2 risk factors, respectively; patients with baseline LVEF <30% had 50% 1-year HF-MACE and were excluded from scoring.
Methodological Strengths
- Large national registry with independent international validation
- Competing risk modeling (Fine-Gray) and robust discrimination (C-index ~0.75)
- Clear, clinically actionable predictors integrating genotype and electrophysiology
Limitations
- Observational registry; residual confounding and selection bias possible
- Applicability to pediatric-onset laminopathies or other ancestries uncertain
- Risk factors limited to baseline variables; dynamic changes not modeled
Future Directions: Prospective validation across healthcare systems, incorporation of longitudinal biomarkers and imaging, and evaluation of score-guided care pathways on HF-MACE reduction.
BACKGROUND AND AIMS: Patients with LMNA gene variants are at high risk for dilated cardiomyopathy and heart failure (HF), but no prediction model for severe HF events exists. This study aimed to describe the incidence of severe HF events and develop a prediction model in a large cohort of patients with adult-onset laminopathies. METHODS: From a population of 660 patients enrolled in the French LMNA nationwide registry, 470 adults were included in the derivation cohort. An independent international validation cohort included 245 additional patients. Baseline characteristics at genetic testing were assessed and the cumulative incidence of the primary endpoint HF-major adverse cardiac events (HF-MACE) was calculated, defined as HF hospitalization, HF-related death, mechanical circulatory support, or heart transplantation. Predictors of HF-MACE were studied after excluding patients with left ventricular ejection fraction (LVEF) <30% at baseline using a Fine-Gray competing risk model, adjusted hazard ratio (aHR) with 95% confidence interval (CI), and Harrell's concordance (C-) index. A secondary composite endpoint, without hospitalization, was also studied. RESULTS: Among 470 patients of the derivation cohort, HF-MACE occurred in 65 over a median follow-up of 7.1 years (interquartile range: 3.4-12.1). Four independent predictors of HF-MACE were identified: male sex (aHR 1.86; 95% CI 1.060-3.290), LVEF <50% (aHR 2.18; 95% CI 1.080-4.400), missense variants in head and rod domains (aHR 2.91; 95% CI 1.110-7.630), and complete left bundle branch block (aHR 2.99; 95% CI 1.400-6.400). The C-index of the model was 0.750 (95% CI 0.720-0.780) in the derivation cohort and 0.758 (95% CI 0.720-0.800) in the validation cohort. The 5-year cumulative incidence of HF-MACE was 1.5% (95% CI 0.6-3.6), 5.0% (95% CI 1.8-8.2), and 22.0% (95% CI 15.6-28.4) among patients with 0, 1, and ≥2 risk factors, respectively. In patients with LVEF <30% at baseline, the 1-year incidence of HF-MACE was 50%, and those patients were excluded from the risk score. CONCLUSIONS: The first prediction model for severe HF events in adult laminopathies was developed, which may facilitate early and optimal preventive management. CLINICAL TRIAL REGISTRATION: URL: https://www.clinicaltrials.gov Unique identifier: NCT03058185.
3. Machine learning-based prediction of sudden cardiac death in the general population using electronic health record data.
A large EHR-based ML model trained in Paris and externally validated temporally and geographically achieved AUC 0.81 and 0.66, respectively, and identified 26–33% of SCD cases in the top decile. The model captured risk even when 25.7% of cases had no prior cardiovascular diagnoses and was specific to SCD (not MI).
Impact: Demonstrates externally validated SCD risk prediction from routinely collected EHRs, highlighting feasibility of population-level screening tools beyond traditional risk factors.
Clinical Implications: Can inform targeted preventive strategies (e.g., ambulatory monitoring, imaging, genetic testing) for top-decile risk groups in healthcare systems; requires prospective impact and fairness evaluation before clinical deployment.
Key Findings
- Model trained on 12,338 SCD cases and 12,338 controls (Paris) achieved AUC 0.81 in temporal validation and 0.66 in geographic validation (Seattle).
- Top decile captured 26% (Paris) and 33% (Seattle) of all SCD cases, enabling focused prevention.
- 25.7% of SCD subjects had no prior cardiovascular diagnoses; model remained specific to SCD and did not predict myocardial infarction.
Methodological Strengths
- Massive-scale EHR features with rigor in temporal and geographic external validation
- Demonstrated calibration in high-risk strata and specificity to SCD vs MI
- Use of up to 5 years of longitudinal prescriptions and diagnoses
Limitations
- Performance attenuation on geographic validation (AUC 0.66) indicates domain shift
- Black-box ML may limit interpretability; reliance on coding/recording quality
- Case-control sampling; prospective utility and clinical workflow integration untested
Future Directions: Prospective impact trials to assess clinical utility and harms/benefits; fairness audits across demographics; integration with wearable data and imaging; trigger-based care pathways.
AIMS: The vast majority of sudden cardiac death (SCD) cases occur in the general population with few known risk factors instead of just patients already identified to be at high risk, making the prediction of SCD very difficult. Therefore, a better screening tool should be developed to facilitate early identification. METHODS AND RESULTS: To estimate the risk of SCD, we trained and validated a machine learning model on electronic health record (EHR) data covering 17 172 359 drug prescriptions and 1 639 057 hospital diagnoses up to 5 years for cases and controls. Training was done on data obtained from a cohort of 12 338 SCD cases in Greater Paris and 12 338 controls from 2011 to 2015. We then validated the results on two external cohorts: a temporal cohort in the same area from 2016 and 2020 with 11 620 SCD cases and 11 620 controls and a geographical cohort from the University of Washington (Seattle, USA) with 892 SCD cases and 892 controls from 2013 to 2021. In the 5 years preceding the SCD, cardiovascular diagnoses were prevalent in only a few patients, scarce in many patients, and totally nonexistent for 25.7% of subjects. Our model achieved an area under the curve of 0.81 [95% confidence interval (CI), 0.80-0.82] and 0.66 (95% CI, 0.58-0.73) in the validation and geographical cohort, respectively. The prediction model discriminated SCD from the general population, especially in the highest decile, where the model detected 26% and 33% of all SCD in the Paris and Seattle datasets, respectively. Our prediction model was specific to SCD and was not predictive of myocardial infarction. In addition to classical cardiovascular risk factors, various non-cardiovascular drugs and diagnoses contributed to the prediction model. CONCLUSION: We propose a prediction model designed to identify individuals at high risk of SCD among the general population. When combined with EHR, this artificial intelligence model has the potential to assist in risk stratification and may help inform the prioritization of preventive strategies, contributing to more targeted use of cardiovascular health resources. This study developed a machine learning model that uses medical records to help identify people in the general population who may be at high risk of sudden cardiac death, even if they have no known heart disease.Key finding 1: The model accurately predicted sudden cardiac death using electronic health records up to 5 years before the event, even when traditional heart disease warning signs were absent in over 25% of cases.Key finding 2: When tested on populations in Paris and Seattle, the model successfully identified 26–33% of all sudden cardiac death cases within the highest-risk group, showing promise as a future screening tool.