Frontiers in Cardiovascular Medicine

BackgroundThe adult mammalian heart lacks the regenerative potential required to replenish depleted cardiomyocytes and restore cardiac function after injury. Ischemic cardiac injury contributes to heart failure, a leading cause of death worldwide. Neonatal mice possess the capacity to regenerate injured myocardium and macrophages contribute to this process. The mechanisms contributing to the regenerative crosstalk between macrophages and cardiomyocytes remain incompletely elucidated and offer potential to inform future therapeutic strategies. MethodsTo test the immune contribution during cardiac regeneration, we studied the response to myocardial ischemia in neonatal mice after silencing myeloid hypoxia inducible factor 1 (Hif1) and reconstituting HIF-dependent mitogens. In parallel, we examined epigenetic and transcriptional signatures of the cardiac macrophage response and focused on intercellular crosstalk with cardiomyocytes. ResultsIn myeloid Hif1 deficient mice, cardiac regenerative function was lost after coronary ligation. This manifested through loss of ventricular systolic function and elevated myocardial scarring. HIF1 was found to be activated in resident-type cardiac macrophages after ischemic insult. Hypoxia stimulated macrophages to secrete insulin-like growth factor 1 (IGF-1), and this required Hif1. Parallel multiomic analysis revealed epigenetic regenerative signatures. ConclusionsThe data reveal an age-restricted requirement for myeloid Hif1 in neonatal cardiac regeneration, likely through IGF-1 signaling.

Backgound: Accurate assessment of diastolic function and left ventricular (LV) filling pressure is central to heart failure diagnosis and risk stratification. Contemporary guideline algorithms rely on complex parameters that are not consistently available in routine clinical practice. Objective: To compare the diagnostic and prognostic performance of the 2016 American Society of Echocardiography/European Association of Cardiovascular Imaging (ASE/EACVI) and 2025 ASE guidelines with a deep learning model based on routinely acquired echocardiographic variables. Methods: This study evaluated the guideline-based algorithms and a deep learning model in participants from the Atherosclerosis Risk in Communities (ARIC) cohort (n=5450) for prognostication and two invasive hemodynamic validation cohorts from the United States (n=83) and Japan (n=130) for detection of elevated left ventricular filling pressure. Results: In the ARIC cohort, the deep learning model demonstrated superior prognostic performance compared with the 2016 and 2025 guidelines (C-index: 0.676 vs. 0.638 and 0.602, respectively; both p<0.001). Similar findings were observed among participants with preserved ejection fraction (C-index: 0.660 vs. 0.628 and 0.590; both p<0.001), with improved performance compared with the H2FPEF score (C-index: 0.660 vs. 0.607; p<0.001). In the US hemodynamic validation cohort, the deep learning model showed higher diagnostic performance than the 2025 guidelines (AUC: 0.879 vs. 0.822; p=0.041) and similar performance compared with the 2016 guidelines (AUC: 0.879 vs. 0.812; p=0.138). In the Japanese hemodynamic validation cohort, the deep learning model outperformed both guidelines (AUC: 0.816 vs. 0.634 and 0.694; both p<0.05). Conclusions: A deep learning model leveraging routinely available echocardiographic parameters demonstrated improved diagnostic and prognostic performance compared with contemporary guideline-based approaches, potentially offering a scalable alternative for assessing diastolic function and left ventricular filling pressures.

Purpose: Spatial distribution of coronary artery calcium (CAC) may provide additional prognostic value in patients undergoing SPECT and PET myocardial perfusion imaging (MPI). We aimed to automatically identify CAC in proximal segments from attenuation correction CT (CTAC) scans using artificial intelligence (AI) and to evaluate prognostic significance in two large international multicenter registries. Methods: From hybrid MPI/CT imaging (N=43,099) across 15 sites, we included 4,552 most relevant patients with 1) no prior coronary artery disease; 2) AI-derived mild CAC scores (1-99); and 3) normal perfusion (stress total perfusion deficit <5%). The independent associations between AI-identified proximal CAC and major adverse cardiovascular events (MACE) and all-cause mortality (ACM) were evaluated using multivariable Cox regression, likelihood ratio test (LRT), and continuous net reclassification index (NRI). Results: Among the patients with mild CAC and normal perfusion (mean age 65{+/-}12 years, 51% male), 1,730 (38%) had proximal CAC. Over 3.6 (inter-quartile interval 2.1, 5.2) years follow up, 599 (13%) and 444 (10%) patients had MACE or ACM, respectively. Proximal CAC was associated with an increased risk of MACE (adjusted hazard ratio [HR] 1.24, 95% CI 1.03-1.48, P=0.02) and ACM (adjusted HR 1.25, 95% CI 1.01-1.53, P=0.04) after the adjustment of CAC score and density, clinical risk factors, and perfusion deficit. Proximal CAC improved the risk stratification of MACE (LRT P=0.02; NRI 12%) and ACM (LRT P=0.04; NRI 12%). Conclusion: In patients with mild CAC and normal perfusion, AI detection of proximal CAC identified a higher-risk group for adverse outcomes, highlighting its prognostic utility.

Abstract Background: Hypertrophic (HCM) and dilated (DCM) cardiomyopathy constitute the principal phenotypes of primary cardiomyopathy, yet both lack sufficient therapeutic options. Integrating genetic insights with detailed cardiac phenotyping offers a promising strategy to prioritize targets and elucidate their mechanisms of action. Methods: We conducted an three-stage analysis. First, drug-target Mendelian randomization (MR) was performed using cis-acting protein (pQTL) and expression (eQTL) quantitative trait loci as genetic instruments for potential drug targets. Second, we examined causal associations between 82 cardiac magnetic resonance (CMR)-derived imaging traits and HCM/DCM risk in a CMR-based MR analysis. Third, mediation MR was employed to quantify the proportion of the genetic effect of prioritized drug targets on cardiomyopathy risk that was mediated through specific CMR phenotypes. Results: Our analyses identified 19 and 13 potential therapeutic targets for HCM and DCM, respectively. CMR-based MR revealed that HCM risk was causally associated with increased right ventricular ejection fraction (RVEF) and greater left ventricular wall thickness, whereas DCM risk was linked to ventricular dilation, impaired myocardial strain, and altered aortic dimensions. Critically, mediation analysis established that these CMR traits served as significant intermediate pathways. The protective effect of ALPK3 on HCM risk was mediated through a reduction in myocardial wall thickness. Conversely, the effects of PDLIM5, HSPA4, and FBXO32 on DCM risk were exerted in part via alterations in aortic dimensions. Conclusion: This integrative genetic and imaging study systematically identify candidate therapeutic targets for HCM and DCM and delineates the specific CMR phenotypes through which they likely exert their causal effects. Our findings advance the understanding of disease pathogenesis and highlight new possibilities for improving the diagnosis and management of cardiomyopathy.

Background: Late gadolinium enhancement (LGE) quantification by cardiovascular magnetic resonance is central to risk stratification in hypertrophic cardiomyopathy (HCM), yet conventional techniques require contour tracing and region-of-interest (ROI) placement, which may reduce reproducibility and increase analysis time. We developed a novel visual standardized approach, the Visual Standardized Quantification of LGE (VISTAQ), that does not require myocardial contouring, arbitrary ROI positioning, or dedicated post-processing software. Methods: In this multicenter, multivendor retrospective study, LGE images from 400 patients (100 prior myocardial infarction, 250 HCM, 50 other non-ischemic heart diseases) were analyzed. VISTAQ subdivides each myocardial segment into transmural mini-segments and classifies LGE visually using predefined criteria, expressing global LGE burden as the percentage of positive mini-segments. Reproducibility was assessed in 250 patients across different observer expertise levels using intraclass correlation coefficients (ICC) and Bland?Altman analysis. In 100 HCM patients, VISTAQ was compared with conventional methods (mean+2SD, +5SD, +6SD, FWHM, visual thresholding). Prognostic performance was evaluated in 250 HCM patients over a median 5-year follow-up. Results: VISTAQ demonstrated excellent intra- and inter-observer reproducibility (ICC up to 0.98 and 0.97, respectively), consistent across disease subtypes. Compared with conventional techniques, VISTAQ showed similar ICC to FWHM but significantly lower net and absolute inter-observer differences (median absolute difference 1.3%). Mean+2SD markedly overestimated LGE, whereas mean+6SD slightly underestimated LGE compared with VISTAQ, mean+5SD, FWHM, and visual thresholding. Analysis time was substantially shorter with VISTAQ (median 105 vs. 375 seconds, p<0.0001). During follow-up, 21 hard cardiac events occurred in HCM population. An LGE threshold >10% predicted events with higher accuracy using VISTAQ (AUC 0.90; sensitivity 85%; specificity 94%) compared with mean+6SD (AUC 0.75; sensitivity 57%; specificity 93%). Conclusions: VISTAQ provides highly reproducible, time-efficient LGE quantification without dedicated software and demonstrates non-inferior prognostic discrimination in HCM compared with conventional threshold-based techniques.

Introduction: Accurate stratification of hard atherosclerotic cardiovascular disease (ASCVD) risk remains challenging despite advances in prevention. Liver function biomarkers (LFBs), particularly gamma - glutamyl transferase (GGT), have been linked to cardiovascular outcomes, yet their contribution to hard ASCVD risk prediction is not well defined. Methods: This study analyzed data from the National Health and Nutrition Examination Survey (NHANES, 2005 - 2018) to assess cross - sectional associations between LFBs and 10 - year hard ASCVD risk estimated by the ACC/AHA Pooled Cohort Equations. Multivariable regression, restricted cubic splines, and mediation analyses were applied to examine independent and dose - response relationships. External validation was performed in the China Health and Retirement Longitudinal Study (CHARLS) and NHANES using machine learning models (CoxBoost, Naive Bayes and Random Forest). Results: Among 5,731 NHANES participants, GGT showed an independent linear association with hard ASCVD risk (P - trend = 0.003), partly mediated by systolic blood pressure (44.8%), HbA1c (19.0%), and high density lipoprotein cholesterol (13.4%). Machine learning (ML) models incorporating GGT, alkaline phosphatase (ALP), and globulin alongside traditional risk factors improved predictive accuracy, with Naive Bayes achieving an AUC of 0.751 in NHANES validation. Conclusions: GGT is an independent and biologically plausible biomarker of hard ASCVD risk, acting through cardiometabolic pathways. Incorporating LFBs into risk prediction models, particularly with machine learning, enhances risk stratification and may facilitate early identification of high - risk individuals.

BACKGROUNDMono-ADP ribosylation is a post-translational modification that regulates various cellular physiological processes, including cell cycle progression, genomic stability, transcription, and cellular protein turnover. PARP16 is an endoplasmic reticulum (ER)-localized mono-ADP-ribosyltransferase that has been shown to regulate the unfolded protein response and maintain ER homeostasis under stress conditions. Despite its established role in ER stress signaling, the functional significance of PARP16 in cardiac pathophysiology, particularly in cardiac hypertrophy and heart failure, remains poorly understood. In this study, we aim to investigate the role of PARP16 in cardiac hypertrophy and heart failure using in vitro and mouse model systems. METHODSWe analysed PARP16 expression in human heart failure samples as well as in heart failure-based mouse models. We evaluated gene expression by RT-PCR, immunoblotting, and confocal microscopy to understand the role of PARP16 in heart failure under phenylephrine- or isoproterenol-treated conditions. We also investigated the role of PARP16 in regulating cardiac function in genetically engineered mouse models, including whole-body PARP16 knockout, cardiac-specific PARP16 knockout, inducible cardiac-specific PARP16 knockout, and cardiac-specific PARP16 Transgenic mice. We performed echocardiography to assess cardiac function. We also used an in vitro primary cardiomyocyte system to knock down and overexpress PARP16. We performed RNA sequencing and mass spectrometry, followed by molecular docking, molecular dynamics simulation, immunoprecipitation, and luciferase assay to characterise the molecular mechanism by which PARP16 regulates cardiac function. RESULTSHuman heart failure samples showed reduced PARP16 expression. PARP16 expression was also significantly reduced in models of heart failure, including the hearts of isoproterenol-treated C57B/L6 mice and phenylephrine-treated primary cardiomyocytes. PARP16-deficient NRCMs showed signs of pathological remodelling. Whole-body, cardiac-specific, and inducible cardiac-specific PARP16 KO mice exhibited cardiac remodelling and dysfunction. In contrast, cardiac-specific PARP16-overexpressing mice were protected from iso-induced cardiac hypertrophy. Mechanistically, several hypertrophic signalling pathway genes are dysregulated in PARP16 knockout mouse hearts concomitant with upregulated NFAT1 transcriptional activity and nuclear translocation. PARP16 binds to and catalytically downregulates NFAT activity, thereby maintaining cardiac function. Mass spectrometry analysis showed that PARP16 is involved in ADP-ribosylation of NFAT1 at E398 and T533. Pharmacological inhibition of NFAT activation attenuates structural and functional abnormalities associated with PARP16 deficiency. CONCLUSIONSPARP16 binds to and inhibits NFAT1 activity to regulate cardiac function in mice, and its downregulation may activate NFAT1 signalling, leading to hypertrophy. In this manner, PARP16 plays a critical role in cardiac hypertrophy and failure and may serve as a potential therapeutic target for the treatment of heart failure.

Objective: To investigate the association of the modified cardiometabolic index (MCMI) with cardiovascular-kidney-metabolic (CKM) syndrome staging, all-cause and cardiovascular mortality, and compare its predictive performance with traditional indices. Methods: This prospective cohort study included 5,189 adults with CKM syndrome (stages 0-4) from NHANES 1999-2018 (median follow-up 10.4 years). Associations were assessed using polynomial/ordinal logistic regression, Cox models, and restricted cubic splines. Mediation analysis explored diabetes' role. Competing risks (Fine-Gray), E-values, and sensitivity analyses ensured robustness. Predictive performance was compared using C-index and AUC. Results: MCMI showed a "decelerating increase" nonlinear association with CKM staging (adjusted OR=3.90, 95%CI: 3.38-4.50). For all-cause mortality, MCMI>3.5 exhibited a threshold effect (Q4 vs Q1: HR=1.412, 1.046-1.907); RCS curves identified MCMI<3.5 as a safety interval. For cardiovascular mortality, MCMI showed a fluctuating nonlinear pattern with low-risk (3.0-3.5) and high-risk (<2.5 or >4.0) intervals. Diabetes mediated 45.5% of MCMI-cardiovascular mortality risk (total HR=1.374, indirect HR=1.141). Competing risks revealed substantial underestimation of true effects (Q4 vs Q1 sHR=3.25, trend P<0.001). MCMI remained independently associated with all-cause mortality after extensive adjustments (HR=1.22, 1.05-1.40); E-values (1.73/1.29) indicated robustness. MCMI demonstrated superior predictive performance over CMI and TyG (mean AUC difference 0.0243). Conclusions: MCMI is an independent predictor of CKM progression and mortality. Its cardiovascular mortality risk is predominantly mediated by diabetes. MCMI>3.5 may serve as a clinical cut-off, outperforming traditional metabolic indices for CKM risk stratification. Keywords: modified cardiometabolic index, cardiovascular-kidney-metabolic syndrome, all-cause mortality, cardiovascular mortality, diabetes mellitus, competing risks model, cohort study, risk prediction

BackgroundAccumulating evidence indicates that moderate exercise may reduce the incidence of Stanford type A aortic dissection (TAAD), but the specific mechanisms remain unclear. This study aims to identify exercise-related biomarkers in TAAD patients and to investigate their underlying mechanisms. MethodsTranscriptome data related to TAAD and exercise-related genes were obtained from publicly available databases. Candidate biomarkers for TAAD were identified through an integrative approach incorporating differential expression analysis, machine learning, and expression level assessment, leading to the construction of a diagnostic model. Subsequently, functional enrichment, immune infiltration, regulatory network analysis, and computational drug prediction were conducted to systematically investigate the pathological mechanisms and translational potential of the indentified biomarkers. ResultsABCA3 and SCN4B were identified as exercise-related biomarkers in TAAD progression. A nomogram incorporating these two biomarkers exhibited strong diagnostic performance for identifying the disease. Functional enrichment analysis revealed potential involvement of these biomarkers in disease progression through pathways including circadian rhythm regulation and ribosome biosynthesis. Additionally, immune cells like M1 macrophages and naive B cells, as well as regulatory factors including hsa-miR-1343-3p and XIST, were found to be involved in this process. Finally, zonisamide and MRS1097 were identified through computation prediction as potential therapeutic drugs. ConclusionABCA3 and SCN4B were identified as exercise-related biomarkers associatied with TAAD and represent potential valuable targets for both diagnosis and treatment strategies.

STRUCTERED ABSTRACTO_ST_ABSBACKGROUNDC_ST_ABSCoronary artery bypass graft (CABG) is a widely performed procedure for coronary artery disease (CAD), yet its association with Impaired Cognition (IC), i.e., mild-cognitive impairment or all-cause dementia, while accounting for APO ({varepsilon}) genotype, remains unclear. METHODSWe analyzed AllofUS participants with CAD (Age[&ge;]60 yrs) from 2017-2023. We defined CAD as a history of angina/myocardial infarction/chronic ischemic heart disease or having percutaneous coronary intervention/CABG, and IC as mild cognitive impairment or all-cause dementia using ICD/SNOMED codes. We performed logistic regression analyses to assess the association between CABG and IC, adjusting for clinical factors (age, sex, hypertension, diabetes, hyperlipidemia, depression, stroke, smoking, alcohol use, statin/antihypertensive/antidiabetic use), social determinants (self-reported race/ethnicity, income, employment), and APO ({varepsilon}) genotypes. We further performed stratified analyses across APO ({varepsilon}) genotypes ({varepsilon}2/{varepsilon}2, {varepsilon}2/{varepsilon}3 {varepsilon}3/{varepsilon}3, {varepsilon}2/{varepsilon}4, {varepsilon}3/{varepsilon}4, {varepsilon}4/{varepsilon}4). We defined significance at p [&le;] 0.05. RESULTSWe included 22,349 with CAD and identified 908 with IC after CAD till 2023. 40% were females, 70% were White, 12% were Black, and 9% were Hispanic. The proportion of IC was higher (5.1% vs 3.5%, p=1e-08) in CABG (n=8,135) vs non-CABG (n=14,214). After adjusting for clinical factors, social determinants, and APO ({varepsilon}) genotypes, CABG (1.23;1.06-1.41, p = 0.005) was associated with IC. In APO ({varepsilon}) stratified analysis, the association of CABG with IC was strongest in the APO {varepsilon}2/{varepsilon}3 group (1.91;1.21-3.02, p = 0.005). CONCLUSIONIn the AllofUS cohort, we observed an association between CABG and IC in CAD participants, with the strongest association in the APO {varepsilon}2/{varepsilon}3 group. Key MessageO_ST_ABSWhat is already known on this topicC_ST_ABSCoronary artery disease (CAD) and Impaired Cognitive (IC) disease, i.e., mild cognitive impairment and all-cause dementia, share genetic, sociodemographic, and clinical factors, including cardiovascular conditions like coronary artery bypass grafting (CABG) procedure. What this study addsWe observed an association between CABG and IC in CAD participants after adjusting for sociodemographic, clinical factors, and APO ({varepsilon}) effects. Further, when CAD participants were stratified across APO ({varepsilon}) groups, CABG was significantly associated with IC in the APO {varepsilon}2/{varepsilon}3 group. How this study might affect research, practice or policyOur observations highlight the role of APO ({varepsilon}) genotype evaluation in CAD patients for IC risk assessment.

Background: Transthyretin cardiomyopathy (ATTR-CM) is a progressive, potentially fatal disease requiring accurate risk stratification. Echocardiography is the first-line imaging modality, with AI-based tools increasingly applied for automated analysis, yet their prognostic value remains unknown. Objectives: To examine the prognostic value of AI-derived echocardiographic measurements and their incremental value beyond biomarker staging in ATTR-CM. Methods: This retrospective study included patients from two ATTR-CM registries. Baseline echocardiograms were analyzed using the fully automated AI-based software Us2.ai. Prognostic performance was assessed by Kaplan-Meier analysis, Cox regression, and ROC curves. A two-parameter echocardiographic staging system combining left ventricular (LV) global longitudinal strain (GLS) and right ventricular (RV) fractional area change (FAC) stratified patients into low (both normal), intermediate (one abnormal), and high risk (both abnormal). Results: Among 347 patients (91% male, median age 78 years), 141 experienced all-cause death or heart failure hospitalization over a median follow-up of 2.4 years. In multivariable analysis, AI-derived LV-GLS (HR 1.13 [1.03-1.25], p=0.011) and RV FAC (HR 0.96 [0.93-0.99], p=0.014) were independent outcome predictors. Echo staging stratified risk into groups with 3-fold (95% CI 1.70-5.91) and 6-fold (95% CI 3.22-10.30) increased hazard compared to low risk (p<0.001), with incremental prognostic value beyond National Amyloidosis Centre (NAC) staging and age (chi-square from 53 to 80; p<0.001). AI and human measurements showed comparable 1-year predictive performance (all p>0.05). Conclusion: AI-derived echocardiographic measurements demonstrate independent and incremental prognostic value beyond biomarker-based NAC staging in ATTR-CM, comparable to human measurements, supporting their integration into clinical risk stratification.

Background Microvascular dysfunction is a key component of many cardiovascular (CV) diseases. Assessing the retinal microvasculature through retinal imaging may therefore provide a window into evaluating a range of CV diseases. This study sought to generate hypotheses regarding relationships between different retinal microvascular features (RVF) and measures of subclinical CV dysfunction derived from cardiovascular magnetic resonance (CMR) imaging. Methods 182 participants with type 2 diabetes enrolled in the UK Imaging Diabetes Study (UKIDS) with CMR image data were considered for inclusion in this cross-sectional study. Fifteen CMR measures of cardiac structure, function, tissue characterisation, adiposity, and aortic distensibility were derived. One-hundred-twenty-eight participants (70%) were found to have eligible retinal images. An artificial intelligence (AI)-enabled retinal imaging analysis tool (QUARTZ) quantified eight RVFs from each participant's retinal image: arteriolar and venular diameter, area, calibre uniformity, and tortuosity. Correlation analysis shortlisted RVF-CMR variable pairs for multivariable regression. Regression coefficients represent change per 1 standard deviation (SD) increase in RVF. Results Sixteen RVF-CMR regression pairs were shortlisted for regression, and five remained associated after adjustment for potential confounders. Per 1-SD increase in venular tortuosity was associated with a 0.5ms greater left ventricular (LV) T2 mean, 0.6% worsening in LV global longitudinal strain, and a 2 mL greater left atrial max volume. Per 1-SD increase in arteriolar calibre uniformity and retinal venular area were associated with 9ms lower LV T1 mean and 0.2x10-3mmHg-1 greater proximal descending aortic distensibility respectively. No significant associations were found between RVF and LV volumetric or functional measures, or adiposity. Conclusions In a diabetic cohort, we identified novel and biologically plausible associations between RVF and CMR measures of subclinical CV dysfunction. This provides new insight into the relationship between the retinal and systemic vascular beds and supports the potential role of retinal imaging in evaluating CV dysfunction prior to onset of overt disease.

Background Disparities between sexes in mortality and morbidity after coronary artery bypass grafting remain incompletely understood. Multi-arterial grafting demonstrates superior outcome compared to single arterial grafting, although the optimal type of a second arterial graft and possible sex-dependent differences in grafting strategy have not been elucidated. We aim to determine whether the right internal thoracic artery or the radial artery is the optimal second arterial graft. Methods We analyzed data from 14,196 patients undergoing primary isolated coronary artery bypass grafting with the left internal thoracic artery and either right internal thoracic artery or radial artery between 2013 and 2022 from the Netherlands Heart Registration. Patients were stratified by sex and type of second arterial graft. Inverse probability treatment weighting was used to balance baseline characteristics. The primary outcome was long-term mortality. Secondary outcomes included short-term complications and repeat revascularization. Results In both sexes, the choice of second arterial graft did not significantly impact long-term survival. Postoperative arrhythmias were more prevalent in both sexes following right internal thoracic artery use (p<0.001). The radial artery was associated with higher rate of repeat revascularization in men (p=0.044 at 5 years follow-up) and more cerebrovascular accidents in women (0.9% vs 0.2%, p=0.028). Conclusion The choice of second arterial graft did not affect long-term survival in either sex. The radial artery was associated with an increased risk of repeat revascularization in men and more cerebrovascular accidents in women. These results underscore the need for further research in the field of sex-specific considerations in operative strategy.

BackgroundAccurate assessment of left ventricular outflow tract (LVOT) gradients is critical for hypertrophic cardiomyopathy (HCM) management, yet Doppler-based measurements are technically demanding and require expertise. ObjectiveTo develop a multi-view deep learning model capable of classifying LVOT obstruction (> 20mmHg) using routine 2D echocardiographic windows without reliance on Doppler imaging. MethodsWe trained and externally validated a cross-attention-based video-to-video fusion framework that integrated EchoPrime-derived video representations from three standard transthoracic echocardiographic views to classify LVOT gradients. ResultsTraining was performed on a derivation cohort (N = 1833) from a tertiary care system in the United States, with model performance evaluated on an internal held-out test set (N = 275) and a Korean external validation cohort (N = 46). Single-view baselines showed limited discrimination (external AUROCs 0.47-0.70). Conversely, domain-specific foundational model (EchoPrime) achieved superior single-view performance (AUROCs 0.75-0.80 internal; 0.79-0.83 external), highlighting the importance of echo-specific pretraining and temporal modeling. The proposed multi-view fusion further enhanced predictive performance, with the late fusion model reaching an AUROC of 0.84 on the external cohort with significant population-shift. ConclusionsThese results suggest LVOT physiology is encoded in routine 2D imaging and can be leveraged for clinically relevant gradient classification without Doppler input- proposed AI-guided strategy demonstrates substantial cost savings compared with the screen-all approach. By integrating complementary spatial-temporal information across multiple views, our approach generalizes robustly across populations and may enable real-time decision support, extend LVOT assessment to portable or resource-limited settings, and complement Doppler-based evaluation for longitudinal HCM management.

Background: Pretest probability (PTP) models using clinical risk factors guide decision-making for coronary artery disease (CAD). Existing models (Updated Diamond-Forrester [UDF] and CAD Consortium [CAD2]) exhibit suboptimal predictive efficacy in Asian populations due to ethnic differences in atherosclerosis and risk profiles. We developed an advanced CAD-specific PTP model using ridge-penalized logistic regression and validated its reliability. Methods: Utilizing data from 4,696 Korean patients (3 trials and 2 cohorts), we employed ridge regression to develop an advanced PTP model (K-CAD) for identifying patients with CAD with >=50% diameter stenosis, determined using coronary computed tomography or invasive coronary angiography. External validation used datasets from another tertiary center (External Validation Cohort 1, n=428) and a nationwide health checkup cohort (External Validation Cohort 2, n=117,294). We compared K-CAD with existing models using continuous receiver operating characteristic (ROC) and ternary net reclassification improvement (NRI) analyses. Findings: Continuous ROC analysis in External Validation Cohort 1 revealed areas under the curves (AUCs) for UDF, 0.68 (95% confidence interval [CI] 0.63-0.73); CAD2, 0.71 (95%CI 0.67-0.76), and K-CAD, 0.76 (95%CI 0.71-0.80). K-CAD significantly outperformed UDF (p <0.001) and CAD2 (p <0.05). NRI analysis demonstrated that K-CAD improved reclassification of non-obstructive patients into low-risk categories. External validation using the nationwide dataset (surrogate endpoint: ICD-10 I20) yielded AUCs for UDF, 0.61 (95% CI 0.58-0.64); CAD2, 0.66 (95%CI 0.63-0.69); and K-CAD, 0.67 (95%CI 0.64-0.70). Interpretation: The study demonstrated K-CAD's utility employing extensive high-quality datasets, highlighting its potential for predicting CAD risk in the Korean population.

Background: Children with congenital heart disease (CHD) require specialized care and may face worse outcomes if they experience food insecurity (FI). FI is associated with poor nutrition, hospitalizations, and developmental delays, compounding cardiac risks. Limited research evaluated impact of FI on health status among children with CHD. This study examines socioeconomic factors and the relationship between FI and health status in children with CHD. Methods: 2023 National Survey of Children?s Health (NSCH) data were used to compare rates of FI between children ages < 17 years with and without CHD and to assess overall health status of those with CHD. Descriptive, univariate, and multivariable logistic regression were utilized. Results: Among 53,477 children, 1,233(2%) had CHD. FI was reported in 35% of children with CHD vs. 27% without CHD(p=0.005). After adjustment, children with CHD had higher odds of FI (OR 1.49; 95% CI: 1.05?2.12). Hispanic ethnicity, residence in Midwest or South, lower household education, and lower poverty index were significantly associated with FI. Households receiving food assistance had higher FI. Living in grandparent household was associated with lower odds of FI. Within the CHD subgroup, 5% reported fair or poor health. Children with CHD experiencing FI had greater odds of fair or poor health than those without FI (OR 3.91, 95% CI 1.70?9.02; p=0.001). Conclusions: Children with CHD face higher odds of FI, which is strongly associated with worse reported health. Addressing socioeconomic vulnerability and FI may improve outcomes and reduce disparities in this high-risk population through targeted screening and intervention strategies nationwide. Keywords: Congenital Heart Disease, Food Insecurity Screening, National Survey of Children?s Health (NSCH), Health Disparities

Background | Angina with nonobstructive coronary arteries (ANOCA) is a heterogeneous condition encompassing distinct endotypes representing different underlying pathophysiological mechanisms. Endothelial dysfunction is considered a central hallmark of ANOCA. However, studying patient-derived endothelial cells (ECs) remains challenging due to the limited availability of disease-specific endothelial samples. We therefore aimed to assess the feasibility of isolating and culturing ECs from catheterization material obtained during routine coronary function testing in ANOCA patients. Methods | Catheterization material was collected from 79 ANOCA patients (84% female, age 58{+/-}10 years) undergoing coronary function testing. ECs were isolated, expanded and characterized using immunostaining, flow cytometry, gene expression profiling and functional assays. Results | EC isolation was successful in 43% of cases and resulted in 34 primary EC cultures that were expanded up to passage 10. Isolation success was independent of clinical or procedural characteristics. Isolated cells exhibited typical EC morphology and expressed EC markers confirmed by immunostaining, flow cytometry and gene expression analyses. EC marker gene expression remained largely stable over passages. However, stress- and defense-related gene expression programs increased over time, while proliferation-related processes decreased. Functional assays demonstrated that the coronary catheterization-derived ECs showed typical properties of wound healing, angiogenesis, activation responses upon stimuli and monocyte adhesion. Conclusions | This study demonstrates the feasibility of isolating and expanding ECs directly from catheterization material collected during routine coronary function testing in ANOCA patients. These patient-derived ECs retain characteristic endothelial features and functionality. This approach offers primary EC cultures to study the mechanisms underlying endothelial dysfunction in ANOCA.

Cardiovascular disease (CVD) remains the leading cause of mortality in the United States, despite being largely preventable through effective management of risk factors. This study evaluates the impact of Phase II cardiac rehabilitation (CR) on functional capacity and quality of life, using data from the Montana Outcomes Project Cardiac Rehabilitation Registry. Functional capacity improvements were assessed via the six-minute walk test (6MWT) and Dartmouth COOP questionnaire, with statistical analyses exploring the influence of CR session attendance, demographic factors, and referring diagnoses. Results demonstrated significant gains in 6MWT, with a mean improvement of 330.73 feet (p < .0001), and quality of life scores across all subgroups. A dose-response relationship was observed, indicating greater improvements with increased CR sessions (p < .0001), though diminishing returns were observed beyond 24-35 visits. Demographic factors and complex conditions influenced outcomes, underscoring the need for tailored strategies to enhance CR access and effectiveness. These findings highlight the critical role of CR in improving patient outcomes and emphasize the importance of addressing barriers to participation in underserved populations.

BackgroundIndividuals with JAK2V617F-mutant myeloproliferative neoplasms or clonal hematopoiesis of indeterminate potential have a markedly increased risk of cardiovascular disease, yet the mechanisms by which mutant blood cells drive vascular and cardiac dysfunction remain incompletely understood. Although the thrombopoietin (TPO) receptor MPL is central to hematopoiesis and is expressed in vascular endothelial cells (ECs), its role in JAK2V617F-associated cardiovascular complications is unknown. Methods and ResultsWe generated chimeric mice with JAK2V617F-mutant blood cells and wild-type endothelium by bone marrow transplantation and challenged them with a high-fat/high-cholesterol diet to model cardiometabolic stress. These mice developed a distinct cardiovascular phenotype characterized by microvascular disease, increased left ventricular mass, and relatively preserved left ventricular ejection fraction. Histological analysis revealed coronary arteriole stenosis, perivascular fibrosis, reduced microvascular density, and endocardial injury, without evidence of epicardial coronary stenosis or myocardium infarction. Single-cell RNA sequencing revealed activation of inflammatory, stress-response, and endothelial-to-mesenchymal transition gene signatures in ECs, most prominently within the endocardial ECs. Immunohistochemistry identified MPL expression predominantly in endocardial ECs. TPO/MPL signaling was upregulated in endocardial ECs in mice with JAK2V617F-mutant hematopoiesis, and treatment with an anti-MPL neutralizing antibody markedly improved cardiac pathology, restored endocardial integrity, and increased coronary microvascular density despite persistent systemic inflammation. ConclusionsJAK2V617F-mutant hematopoiesis induces coronary microvascular dysfunction. Endocardial ECs represent a key cellular target under cardiometabolic stress, and endocardial MPL signaling constitutes a potential targetable pathway in JAK2V617F-associated cardiovascular disease. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=122 SRC="FIGDIR/small/715884v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@1b0c2d7org.highwire.dtl.DTLVardef@1c7da20org.highwire.dtl.DTLVardef@1c19af9org.highwire.dtl.DTLVardef@1a588b3_HPS_FORMAT_FIGEXP M_FIG C_FIG Key PointsO_LIJAK2V617F-mutant hematopoiesis induces cardiac microvascular disease C_LIO_LIMPL is expressed in endocardial ECs and MPL inhibition restores endocardial integrity and improves cardiac microvascular function C_LI

Background: Increasing evidence suggests that blood pressure variability (BPV) may offer prognostic value beyond average blood pressure levels. However, data on the association between BPV of ambulatory blood pressure monitoring and mortality in patients aged 80 and older are limited. This study aimed to investigate the relationship between BPV and all-cause mortality in this population. Methods: A total of 5,838 ABPM records from the Geriatrics Department of Beijing Friendship Hospital, collected between October 12, 2018, and June 9, 2025, were analyzed. Patients were divided into death and non-death groups. Subgroup analyses were performed based on the number of completed ABPM sessions. Cox proportional hazards models assessed the associations between BPV and mortality. Kaplan?Meier analysis and log-rank tests were used to compare survival across groups. Results: A median follow-up of 32.0 months included 727 hypertensive patients aged ?80 years. Multivariable cox regression and kaplan?meier analyses showed that the reverse-dipper blood pressure pattern was significantly associated with increased mortality. While short-term BPV was not linked to mortality, greater long-term variability in nighttime SBP and daytime DBP was significantly associated with higher mortality. Conclusion: Among individuals aged 80 and older, those with a reverse-dipper pattern and higher long-term BPV had a significantly higher mortality risk, despite achieving recommended blood pressure targets. Key words: blood pressure variability, ABPM, reverse-dipper pattern, elderly hypertension, mortality