Daily Cardiology Research Analysis
Three impactful cardiology studies stood out today: (1) a Nature Communications mechanistic study linking dysregulated N-terminal protein acetylation (NAA10 variant) to arrhythmia and cardiomyopathy; (2) a global prospective registry in European Heart Journal clarifying anticoagulation risks in pregnancy with prosthetic valves, especially with LMWH and mitral mechanical valves; and (3) a JAMA Cardiology multi-cohort study showing AI can predict heart failure risk from single-lead ECGs, outperfor
Summary
Three impactful cardiology studies stood out today: (1) a Nature Communications mechanistic study linking dysregulated N-terminal protein acetylation (NAA10 variant) to arrhythmia and cardiomyopathy; (2) a global prospective registry in European Heart Journal clarifying anticoagulation risks in pregnancy with prosthetic valves, especially with LMWH and mitral mechanical valves; and (3) a JAMA Cardiology multi-cohort study showing AI can predict heart failure risk from single-lead ECGs, outperforming standard risk scores.
Research Themes
- Mechanistic cardiogenetics: N-terminal acetylation and arrhythmia/cardiomyopathy
- Anticoagulation strategies in pregnancy with prosthetic heart valves
- AI-enabled cardiovascular risk stratification using wearable-scale ECG signals
Selected Articles
1. Dysregulation of N-terminal acetylation causes cardiac arrhythmia and cardiomyopathy.
This mechanistic study identifies a previously unreported NAA10 p.(Arg4Ser) variant that segregates with QT prolongation, cardiomyopathy, and developmental delay in a large kindred, implicating dysregulated N-terminal acetylation as a disease mechanism. The findings link protein acetylation biology to human cardiac electrophysiology and structure, highlighting a new pathogenic pathway.
Impact: It uncovers a novel, potentially targetable molecular mechanism (N-terminal acetylation) underlying arrhythmia and cardiomyopathy, bridging human genetics with cardiac pathophysiology.
Clinical Implications: While early-stage, these findings support considering NAA10 and N-terminal acetylation pathways in genetic evaluation of arrhythmia/cardiomyopathy and motivate development of modulators of protein acetylation as future therapeutics.
Key Findings
- A previously unidentified NAA10 p.(Arg4Ser) variant segregated with QT prolongation, cardiomyopathy, and developmental delay in a large kindred.
- Dysregulation of N-terminal acetylation was implicated as a causal mechanism for cardiac arrhythmia and cardiomyopathy.
- The study links protein N-terminal acetylation biology to human cardiac electrical and structural disease.
Methodological Strengths
- Human genetic evidence with variant segregation in a large kindred
- Mechanistic framing that connects post-translational modification to cardiac phenotype
Limitations
- Limited sample centered on a single kindred; broader generalizability requires additional cohorts
- Translational therapeutic implications remain to be tested in preclinical models
Future Directions: Validate NAA10-related acetylation defects across independent cohorts; delineate downstream substrates/pathways; assess therapeutic modulation of N-terminal acetylation in preclinical models.
N-terminal acetyltransferases including NAA10 catalyze N-terminal acetylation, an evolutionarily conserved co- and post-translational modification. However, little is known about the role of N-terminal acetylation in cardiac homeostasis. To gain insight into cardiac-dependent NAA10 function, we studied a previously unidentified NAA10 variant p.(Arg4Ser) segregating with QT-prolongation, cardiomyopathy, and developmental delay in a large kindred. Here, we show that the NAA10
2. Pregnancy with a prosthetic heart valve, thrombosis, and bleeding: the ESC EORP Registry of Pregnancy and Cardiac disease III.
In this global prospective registry of 613 pregnancies with prosthetic valves, biological valves had higher chances of uncomplicated live birth than mechanical valves (79% vs 54%). LMWH-based regimens were associated with more thromboembolic/hemorrhagic complications, valve thrombosis occurred in 6%, and mitral position strongly predicted thrombosis; anti-Xa monitoring benefits were inconclusive.
Impact: This is the most detailed contemporary prospective evidence to date informing anticoagulation choices and risk counseling in pregnant patients with prosthetic valves, with immediate implications for guideline updates.
Clinical Implications: For women anticipating pregnancy, biological valves yield more favorable outcomes. In mechanical valves, particularly in the mitral position, LMWH-based regimens appear to carry higher thromboembolic/bleeding risks; careful regimen selection, shared decision-making, and close monitoring are warranted.
Key Findings
- Uncomplicated live birth was 54% with mechanical valves vs 79% with biological valves (P < .001).
- LMWH-based regimens had the highest rates of thromboembolic and hemorrhagic complications; overall valve thrombosis occurred in 6%.
- Mitral mechanical valve position predicted valve thrombosis (OR 3.3; 95% CI 1.9–8.0); anti-Xa monitoring benefits on events were inconclusive (P=0.060).
Methodological Strengths
- Prospective, global registry with detailed anticoagulation dosing and monitoring data
- Large sample capturing mechanical and biological valve pregnancies with clinically relevant endpoints
Limitations
- Observational design with potential confounding by indication; not randomized
- Anti-Xa monitoring analysis underpowered for definitive benefit assessment
Future Directions: Randomized or carefully controlled comparative studies of anticoagulation strategies in mechanical valve pregnancy, with standardized anti-Xa protocols and valve-position–specific risk stratification.
BACKGROUND AND AIMS: Pregnancy in women with a prosthetic heart valve is considered high risk, primarily due to the need for effective anticoagulation. However, data on the relationship between anticoagulation practices and pregnancy outcomes are very limited. METHODS: The Registry of Pregnancy and Cardiac disease is a global registry that prospectively enrolled pregnancies in women with a prosthetic heart valve between January 2018 and April 2023. Detailed data on anticoagulation, including dosage and monitoring, and cardiovascular, pregnancy, and perinatal outcomes were collected. RESULTS: In total, 613 pregnancies were included of which 411 pregnancies were in women with a mechanical valve and 202 were in women with a biological valve. The chance of an uncomplicated pregnancy with a live birth in women with a mechanical valve was 54%, compared with 79% in women with a biological valve (P < .001). Thromboembolic and haemorrhagic complications most frequently occurred when low-molecular weight heparin (LMWH)-based regimens were used. Valve thrombosis occurred in 24 (6%) women, and a prosthetic valve in mitral position was associated with valve thrombosis (odds ratio 3.3; 95% confidence interval 1.9-8.0). A thromboembolic event occurred in 12 (10%) women with anti-Xa monitoring and in 9 (21%) women without (P = .060). Foetal death occurred in 20% of all pregnancies. CONCLUSIONS: More favourable outcomes were found in women with a biological valve compared with a mechanical valve. In women with a mechanical valve, the use of LMWH was associated with an increased risk of thromboembolic complications. A mitral prosthetic valve was identified as a predictor for valve thrombosis. The benefit could not be confirmed nor refuted, in terms of reduced thromboembolic events, from using anti-Xa level monitoring in women on LMWH.
3. Artificial Intelligence-Enabled Prediction of Heart Failure Risk From Single-Lead Electrocardiograms.
A noise-adapted AI model using lead I ECGs predicted new-onset HF across three multinational cohorts (N≈248,000), with C-statistics 0.72–0.83 and substantial improvements over PCP-HF and PREVENT scores (C-statistic gains ~0.07–0.11; IDI 0.068–0.205; NRI up to ~47%). Each 0.1 increase in model probability conferred 27–65% higher hazard, independent of clinical covariates.
Impact: Demonstrates scalable HF risk prediction from single-lead ECG signals compatible with wearables, outperforming established risk scores and enabling community-level screening strategies.
Clinical Implications: AI-ECG could be integrated into primary care and wearable platforms to flag high-risk individuals for echocardiography/biomarker testing, earlier initiation of guideline-directed HF prevention (e.g., SGLT2 inhibitors), and targeted lifestyle interventions.
Key Findings
- Across YNHHS, UK Biobank, and ELSA-Brasil, AI-ECG discrimination for new-onset HF was 0.723, 0.736, and 0.828, respectively.
- Each 0.1 increase in model probability associated with a 27–65% higher hazard of incident HF independent of confounders.
- AI-ECG improved risk prediction beyond PCP-HF and PREVENT (C-statistic gains 0.069–0.107; IDI 0.068–0.205; NRI 11.8–47.5%).
Methodological Strengths
- Large, multi-cohort external validation across healthcare and population cohorts
- Noise-adapted modeling reflecting wearable ECG quality; head-to-head comparison with standard HF risk equations
Limitations
- Retrospective design; prospective implementation studies are needed to assess real-world workflow and outcomes impact
- Outcome limited to first HF hospitalization; subclinical HF and outpatient events not captured
Future Directions: Prospective trials embedding AI-ECG screening in wearable and primary care workflows to test downstream testing, treatment initiation, patient-reported outcomes, and cost-effectiveness.
IMPORTANCE: Despite the availability of disease-modifying therapies, scalable strategies for heart failure (HF) risk stratification remain elusive. Portable devices capable of recording single-lead electrocardiograms (ECGs) may enable large-scale community-based risk assessment. OBJECTIVE: To evaluate whether an artificial intelligence (AI) algorithm can predict HF risk from noisy single-lead ECGs. DESIGN, SETTING, AND PARTICIPANTS: A retrospective cohort study of individuals without HF at baseline was conducted among individuals with conventionally obtained outpatient ECGs in the integrated Yale New Haven Health System (YNHHS) and prospective population-based cohorts of the UK Biobank (UKB) and the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Data analysis was performed from September 2023 to February 2025. EXPOSURE: AI-ECG-defined risk of left ventricular systolic dysfunction (LVSD). MAIN OUTCOMES AND MEASURES: Among individuals with ECGs, lead I ECGs were isolated and a noise-adapted AI-ECG model (to simulate ECG signals from wearable devices) trained to identify LVSD was deployed. The association of the model probability with new-onset HF, defined as the first HF hospitalization, was evaluated. The discrimination of AI-ECG was compared against 2 risk scores for new-onset HF (Pooled Cohort Equations to Prevent Heart Failure [PCP-HF] and Predicting Risk of Cardiovascular Disease Events [PREVENT] equations) using the Harrel C statistic, integrated discrimination improvement, and net reclassification improvement. RESULTS: There were 192 667 YNHHS patients (median [IQR] age, 56 [41-69] years; 111 181 women [57.7%]), 42 141 UKB participants (median [IQR] age, 65 [59-71] years; 21 795 women [51.7%]), and 13 454 ELSA-Brasil participants (median [IQR] age, 51 [45-58] years; 7348 women [54.6%]) with baseline ECGs. A total of 3697 (1.9%) developed HF in YNHHS over a median (IQR) of 4.6 (2.8-6.6) years, 46 (0.1%) in UKB over a median (IQR) of 3.1 (2.1-4.5) years, and 31 (0.2%) in ELSA-Brasil over a median (IQR) of 4.2 (3.7-4.5) years. A positive AI-ECG screening result for LVSD was associated with a 3- to 7-fold higher risk for HF, and each 0.1 increment in the model probability was associated with a 27% to 65% higher hazard across cohorts, independent of age, sex, comorbidities, and competing risk of death. AI-ECG's discrimination for new-onset HF was 0.723 (95% CI, 0.694-0.752) in YNHHS, 0.736 (95% CI, 0.606-0.867) in UKB, and 0.828 (95% CI, 0.692-0.964) in ELSA-Brasil. Across cohorts, incorporating AI-ECG predictions alongside PCP-HF and PREVENT equations was associated with a higher Harrel C statistic (difference in addition to PCP-HF, 0.080-0.107; difference in addition to PREVENT, 0.069-0.094). AI-ECG had an integrated discrimination improvement of 0.091 to 0.205 vs PCP-HF and 0.068 to 0.192 vs PREVENT; it had a net reclassification improvement of 18.2% to 47.2% vs PCP-HF and 11.8% to 47.5% vs PREVENT. CONCLUSIONS AND RELEVANCE: Across multinational cohorts, a noise-adapted AI-ECG model estimated HF risk using lead I ECGs, suggesting a potential HF risk-stratification strategy requiring prospective study using wearable and portable ECG devices.