European Heart Journal

Background. Hypertrophic cardiomyopathy (HCM) is a clinically variable disease in terms of onset and progression. Pathogenic MYBPC3 variants account for a substantial proportion of HCM diagnoses. This study sought to identify protein biomarkers associated with HCM severity. Methods. Olink-assayed plasma proteins of 144 MYBPC3 pathogenic variant carriers were tested for associations with HCM severity based on HCM diagnostic criteria (unaffected, mildly, or severely affected). The UK Biobank was used to replicate the identified proteins through considering time to onset of HCM (67 cases), cardiomyopathy (156 cases),and associations with cardiac MRI derived left ventricular maximum wall thickness (6,492 participants). Replicated proteins were further prioritised based on cardiac tissue expression and druggability, and annotated using pathway enrichment and association with onset of: heart failure (HF), dilated cardiomyopathy (DCM), sudden cardiac arrest (SCA), and ventricular arrhythmias (VA). Results. Among pathogenic MYBPC3 variant carriers, we identified 27 proteins associated with HCM severity. We independently replicated 21 proteins in the UK Biobank. Of the five prioritised proteins (NT-proBNP, GDF-15, FGF-23, ADM, and NCAM1), all but NT-proBNP were targeted by drugs with repurposing potential. The replicated proteins additionally associated with the incidence of HF (n=5), DCM (n=4), SCA (n=4), and VA (n=4). Conclusion. This study replicated 21 and prioritised five proteins associated with HCM severity in pathogenic MYBPC3 variant carriers. Replication in unselected HCM suggests the prioritised proteins are associated with HCM independent of genotype, providing important leads for plasma-based markers for diagnoses, disease monitoring, and drug targets.

Aims: Assessment of treatment response in HFrEF has largely relied on left ventricular (LV)-centric parameters, yet the left atrium (LA) plays a central role in modulating LV filling and reflects the cumulative hemodynamic burden. Whether discordant recovery between LV and LA function carries distinct prognostic implications in patients treated with ARNI-based therapy remains unknown. Methods and results: From the multicenter STRATS-HF-ARNI registry, 1,182 patients with HFrEF who underwent serial echocardiography at baseline and one-year follow-up were included. Patients were classified into four strain recovery phenotypes according to the direction of change in LVGLS and LASr at one year: Group A, concordant recovery (57.4%); Group B, discordant atrial non-recovery (11.2%); Group C, discordant ventricular non-recovery (15.6%); and Group D, concordant non-recovery (16.0%). Clinical outcomes included all-cause mortality, cardiovascular mortality, and HF hospitalization. Despite achieving LV functional improvement, Group B exhibited persistent LASr deterioration, accompanied by less favorable hemodynamic trajectories compared with Group A. On multivariable Cox regression, Group B was associated with significantly higher risks of all-cause mortality (adjusted hazard ratio [aHR] 3.53, 95% confidence interval [CI] 1.60-7.79) and cardiovascular mortality (aHR 5.68, 95% CI 1.91-16.92), comparable to Group D. Group C demonstrated higher HF hospitalization risk (aHR 2.25, 95% CI 1.31-3.86). The adverse prognostic impact of discordant atrial non-recovery was consistently observed across subgroups stratified by baseline LVGLS and LASr levels. Conclusion: In HFrEF patients treated with ARNI-based therapy, persistent LA dysfunction despite LV functional improvement identifies a high-risk phenotype comparable to concordant non-recovery. These findings suggest that concurrent assessment of LV and LA strain may provide incremental prognostic value beyond LV-centric metrics alone.

Rationale: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome with substantial unmet diagnostic and therapeutic needs. Circulating lipid metabolism is increasingly implicated in HFpEF pathophysiology but has not been systematically leveraged for molecular stratification. Objective: To determine whether plasma lipidomics can identify molecular phenogroups of HFpEF associated with distinct clinical characteristics and outcomes. Methods and Results: Untargeted plasma lipidomics was performed in non-HF subjects and HFpEF patients from a primary Belgian cohort and an independent Canadian cohort (n=177 in each cohort). In the Belgian cohort, 235 unique lipids spanning 19 subclasses were annotated, including 96 significantly associated with HFpEF (q<0.02). Unsupervised analyses revealed marked lipidomic heterogeneity, with a distinct HFpEF subgroup separable from non-HF subjects. Hierarchical clustering identified three phenogroups with divergent lipid profiles and clinical features. One phenogroup exhibited severe atrial dysfunction, congestion-related biomarkers, elevated indices of cardiac and liver fibrosis, and markedly reduced survival, a second was characterized by prominent metabolic syndrome features, and a third by preserved renal function. Cross-cohort comparison using a supervised classifier trained on 158 shared lipids confirmed analogous lower-risk phenogroups in the Canadian cohort, while the high-risk phenotype was underrepresented. A signature of 10 lipids across six subclasses, including long-chain acylcarnitines, ether phosphatidylcholines, and oxidized sphingomyelins, discriminated the high-risk group and correlated with markers of disease severity. Conclusion: Our findings demonstrate that HFpEF comprises metabolically distinct patient subgroups across cohorts, revealing specific lipidomic dysfunctions that deepen our understanding of HFpEF heterogeneity and underlying pathophysiology.

Aims: Although metabolic dysregulation is implicated in DCM, the involvement of metabolic syndrome (MetS) remains unclear. This study aims to systematically examine MetS in DCM pathogenesis. Materials and methods: By leveraging 378,837 UK Biobank participants, instead of the conventional binary MetS, we calculated a continuous metabolic risk score (MRS) and evaluated its influence on DCM risk within a multi-model evidence framework. Bidirectional weighted quantile sum regression identified key MRS components, a nested case-control study assessed 14-year pre-diagnosis MRS trajectories, mediation analyses evaluated MRS mediating lifestyle-DCM links and inflammation mediating MRS-DCM relationships, and Mendelian randomization (MR) evaluated causality for genetically predicted MetS and components on DCM. Results: During a median follow-up period of 13.4 years (interquartile range 12.7~14.1 years), 820 (0.2%) participants developed DCM. Higher MRS (HR=1.26 [1.18~1.34]) was associated with increased DCM risk, and such an association persisted across all robustness assessments even among non-MetS individuals. Waist circumference (WC, HR=1.36 [1.28~1.45], weight=0.58) and glycated hemoglobin (HR=1.23 [1.16~1.30], weight=0.22) dominated MRS' risk contribution. The trajectories of MRS diverged in cases approximately 10 years pre-diagnosis. MRS mediated 5.1~26.2% of lifestyle-related DCM risk, while inflammation mediated 16.4% of the MRS-DCM association. MR analysis further confirmed causal effects of MetS (OR=1.65 [1.45~1.88]), WC (OR=1.79 [1.58~2.03]) on DCM risk. Conclusions: Metabolic dysfunction, which was dominated by central adiposity and hyperglycemia, plays a key role in the occurrence of DCM. Early intervention targeting metabolic factors may prevent DCM onset.

Background: Among cardiac measures, diastolic parameters demonstrate the earliest and most consistent age-related changes. This can be leveraged to develop a continuous left ventricular (LV) Diastolic Age from routine echocardiographic parameters. Analogous to how epigenetic clocks weight molecular markers against mortality risk, we calibrated Diastolic Age by weighting echocardiographic features against the validated PREVENT-Heart Failure (HF) risk score. Methods: We analyzed 1,952 participants from the Project Baseline Health Study (median age 50 [36-64] years, 54% female). The measure was derived using partial least-squares regression anchored on PREVENT-HF and calibrated within a healthy reference subgroup. External validation was performed in the WASE (n=1,708) and Stanford Cardiovascular Aging (n=313) cohorts. Associations with ASE-defined LV diastolic dysfunction (LVDD), epigenetic clocks, and major adverse cardiovascular events (MACE) were examined. Results: Diastolic Age correlated strongly with chronological age (r=0.78) with robust external validation (WASE r=0.76; Stanford r=0.82; calibration slopes {approx}1.0). It increased progressively across grades of diastolic dysfunction and discriminated LVDD with an AUC of 0.89 (95% CI 0.87-0.92), and was independently associated with hypertension, diabetes, and elevated C-reactive protein. While correlated with the Levine (r=0.76) and Horvath (r=0.41) epigenetic clocks, residual analyses indicated that Diastolic Age captures a distinct cardiac-specific dimension of biological aging. Over median follow-up of 4.2 years, it independently predicted MACE (HR 2.30, 95% CI 1.70-3.18), with accelerated diastolic aging across all age groups among those with events. Discrimination was comparable to ASE-defined LVDD (C-index 0.83 vs. 0.82). Conclusion: Diastolic Age provides a continuous, echocardiography-derived measure of cardiac biological aging that complements categorical diastolic grading and epigenetic aging clocks, and independently predicts cardiovascular outcomes.

Aims: Dynamic left ventricular outflow tract obstruction (LVOTO) is a hemodynamically significant complication following transcatheter aortic valve replacement (TAVR) that remains difficult to predict with conventional transthoracic echocardiography (TTE). We examined whether a deep learning (DL) model developed for LVOTO detection in hypertrophic cardiomyopathy (HCM) could predict post-TAVR LVOTO from pre-TAVR TTE in patients with severe aortic stenosis (AS). Methods and Results: In this retrospective study of 302 consecutive patients undergoing TAVR for severe AS, a pre-trained DL model was applied to pre-TAVR TTE to generate a patient-level DL index of LVOTO (DLi-LVOTO; range 0-100). Post-TAVR LVOTO was defined as a peak pressure gradient [&ge;]30 mmHg on follow-up TTE. Logistic regression and receiver operating characteristic analyses assessed the association and discriminative performance of DLi-LVOTO. Pre-TAVR LVOTO was present in 32 patients (10.6%) and post-TAVR LVOTO in 35 (11.6%). Follow-up TTE was performed at a median of 47 days (IQR 37-63) after TAVR, with the majority of TTE (216 of 302, 71.5%) performed within 2 months. DLi-LVOTO was significantly higher in patients with LVOTO at both pre- and post-TAVR TTE (all p<0.001). In multivariable analysis, DLi-LVOTO remained independently associated with post-TAVR LVOTO even after adjusting for conventional TTE parameters and pre-TAVR LVOTO (adjusted OR 1.29, 95% CI 1.06-1.56 per 10-score increase, p=0.011), with an AUROC of 0.78 (95% CI 0.72-0.85). Among patients without pre-TAVR LVOTO, DLi-LVOTO retained independent predictive value (adjusted OR 1.56, 95% CI 1.19-2.06, p=0.001; AUROC 0.84, 95% CI 0.77-0.91). Conclusion: A DL model originally trained in HCM patients independently predicts post-TAVR LVOTO from pre-TAVR TTE, including in patients without pre-existing LVOTO, suggesting it captures hemodynamic features beyond conventional echocardiographic assessment.

BACKGROUNDExcessive trabeculations and myocardial crypts are recurrent features across cardiomyopathies, yet their developmental origins and clinical significance remain poorly defined. To reveal the link between cardiac morphogenesis and disease, we generated humanized mouse models carrying patient-derived MYBPC3 frameshift mutations associated with overlapping hypertrophic cardiomyopathy (HCM) and left ventricular non-compaction (LVNC). METHODSWe applied CRISPR-Cas9 to introduce distinct MYBPC3 frameshift alleles into the mouse genome and performed comprehensive phenotypic and transcriptomic profiling from fetal life through adulthood. RESULTSAdult homozygous Mybpc3 frameshift mutant mice like humans displayed hallmark HCM; however, without LVNC. Fetal and neonatal mutant hearts exhibited markedly enlarged ventricular trabeculae and crypts that progressed postnatally into the observed adult hypertrophy. Transcriptomic analysis revealed stage-specific dysregulation of oxidative metabolism, nonsense-mediated decay (NMD), and cell cycle pathways, peaking at postnatal days 1 and 7, indicating that these stages represent critical time points in disease onset. The persistent NMD signature, also observed in phenotype-negative heterozygotes, suggests a compensatory stress response. Enlarged trabeculae exhibited 2-fold increased trabecular cardiomyocyte proliferation, reversing the normal compact-trabecular proliferative gradient and leading to impaired ventricular compaction in neonates. Hey2CreERT2 lineage tracing demonstrated invasion of Hey2+ compact cardiomyocytes into the trabeculae and ectopic trabecular expression of the Prdm16 transcription factor, indicating defective ventricular wall patterning and maturation. Postnatally, Hey2+-derived cardiomyocytes became restricted to the outer/compact myocardium in mutants, while the inner/trabecular myocardium underwent accelerated hypertrophy concurrent with Prdm16 downregulation. Mice with a Mybpc3 missense variant also exhibited Hey2+ myocardial lineage expansion into trabeculae but no increased proliferation, implicating additional mechanisms beyond Hey2 regulation. Postnatal Prdm16 restoration, via transgenic expression in Mybpc3-null mice effectively attenuated hypertrophy, establishing a causal link between Mybpc3 loss, Prdm16 decline, and pathological remodeling. CONCLUSIONSMybpc3 governs ventricular wall maturation by regulating cardiomyocyte proliferation, patterning, and maturation, partly via Prdm16. Disruption of these developmental programs precedes and drives adult HCM, highlighting a developmental role for sarcomeric proteins, and revealing postnatal Prdm16 modulation as an antihypertrophic therapeutic strategy.

BACKGROUND: Guidelines recommend aortic valve replacement (AVR) in patients with severe aortic regurgitation (AR) based on progressive changes in left ventricular (LV) function or size. We aimed to reassess the clinical relevance of current guideline recommendations pertaining to traditional echocardiographic measurements in routine practice. METHODS: Retrospective analysis of patients with severe AR who underwent serial echocardiographic follow-up over at least 18 months. The composite outcome was symptom-driven AVR, acute heart failure hospitalization, or death. We used a joint modelling approach to handle within-subject correlation and censoring. RESULTS: The cohort consisted of 140 patients, with a median follow?up of 93 months (interquartile range 58?130). LV end-systolic (LVESD) and fractional shortening (FS) showed a small but statistically significant longitudinal trend, while LVEDD did not. Changes in all three parameters in parallel joint models adjusted for age and gender were consistently associated with increased risk of the composite event. Each 1?mm increase in LVESD and LVEDD was associated with a 6% and 5% increase in risk, respectively; each 1% decrease in FS corresponded to a 12% increase in risk. Only 8 (5.7%) of patients were predicted to exceed the guideline-recommended LVEDD threshold of 65 mm over 10 years. Age at onset was also a significant risk factor, with each decade increasing risk by 65% for each of the three parallel joint models. CONCLUSIONS: LV parameters show modest changes over time, despite holding strong prognostic value in patients with severe AR. LVEDD, while associated with overall risk, does not predictably or significantly dilate over time in most patients. AVR decisions should be based on comprehensive clinical and volumetric assessment rather than waiting for simple linear progression to guideline cutoffs.

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.

Sickle cell disease (SCD) is associated with substantial cardiovascular morbidity, but echocardiographic data from Latin American populations remain scarce. We aimed to characterise the structural, functional, and haemodynamic echocardiographic profile of adults with SCD attending a tertiary referral centre in Cali, Colombia. We conducted an observational, cross-sectional study based on systematic review of medical records and transthoracic echocardiography reports of consecutive adult patients ([&ge;]18 years) with confirmed SCD evaluated between January 2022 and December 2024. Patients with complex congenital heart disease, severe valvular disease of unrelated aetiology, pregnancy, or echocardiograms of insufficient quality were excluded. Of 669 patients screened, 57 met inclusion criteria. Reporting followed STROBE recommendations. The median age was 24 years (interquartile range [IQR] 21-32) and 59.6% were female; the SS genotype was the most frequent (76.4%) and 71.4% were on hydroxyurea. Median haemoglobin was 10.2 g/dL (IQR 9.3-11.4) and median NT-proBNP 491 pg/mL (IQR 98-1290). Most patients had preserved left ventricular dimensions and systolic function (median ejection fraction 63%, IQR 57-66.5; mean global longitudinal strain -18.9% {+/-} 2.9). Right ventricular function was preserved (mean tricuspid annular plane systolic excursion 25.4 {+/-} 4.6 mm). Left ventricular geometry was normal in 42.1%, with concentric remodelling in 24.6%, concentric hypertrophy in 21.1%, and eccentric hypertrophy in 12.3%. Diastolic function was normal in 71.4%. Valvular disease, when present, was predominantly mild. Tricuspid regurgitation velocity exceeded 2.5 m/s in 29.8% of patients and exceeded 3.0 m/s in 10.5%, identifying a substantial subgroup at intermediate-to-high probability of pulmonary hypertension. In this Colombian cohort of relatively young adults with SCD, cardiac structure and biventricular function were largely preserved, but nearly one-third of patients had echocardiographic findings suggestive of pulmonary hypertension. These findings support the routine use of transthoracic echocardiography as an accessible tool for early cardiovascular risk stratification in adults with SCD in low- and middle-income settings.

Background and Aims: Acute myocarditis (AM) is a T cell-mediated myocardial disease with clinical manifestations ranging from mild chest pain to cardiogenic shock. Reliable biomarkers to stratify patients and guide therapy are currently lacking. In particular, the extent of the dysregulation of inflammatory pathways, and the impact on myocardial dysfunction, remain elusive. Methods: Serum analyses were performed in prospectively recruited AM patients (n = 103) from two independent cohorts. Multimodal data integration combining profiling of cytokine and chemokine dysregulation with clinical biomarkers was used to define clinical phenotypes with distinct inflammatory signatures. Machine-learning and regression models were applied to determine biomarkers that indicate clinical severity. Results: Immuno-proteomic profiling revealed conserved inflammatory patterns across AM cohorts, dominated by T cell-related cytokines and chemokines. In addition, AM patients showed dysregulation of fibroblast-derived cytokines, including hepatocyte growth factor (HGF), bone morphogenic protein 4 (BMP4) and the BMP4 inhibitors Gremlin-1 (GREM1) and Gremlin-2 (GREM2). Data integration and unsupervised clustering revealed two immuno-clinical phenotypes, linking T cell activation and fibroblast dysregulation to disease severity. Machine learning-based analysis identified CXCL10, GREM2 and LVEF as critical parameters for stratifying disease severity. Conclusions: These findings highlight a systemic T cell activation signature as diagnostic hallmark of AM. In addition, dysregulation of fibroblast-derived tissue cytokines serves as an indicator for distinct immuno-clinical phenotypes in myocardial inflammatory disease. Thus, the clinically relevant link between T cell-driven immune activation, myocardial inflammation and fibroblast-driven remodelling provides a versatile set of parameters to identify severe manifestations of AM.

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 Functional mitral regurgitation results from interacting mechanisms whose relative contributions vary between atrial and ventricular subtypes and shift dynamically within each heartbeat, producing temporal patterns that static analyses cannot capture. Objectives To identify which structural determinants predict mitral regurgitation variability beat to beat using Granger causality within vector autoregression, focusing on papillary muscle dynamics across subtypes. Methods Frame-level echocardiographic time series from 41 patients (21 atrial, 20 ventricular; 1,959 frames) were z-score standardised within patient. Individual (lag 3) and pooled (lag 2) vector autoregression models tested whether left ventricular volume, left atrial volume, papillary muscle length, and annulus diameter Granger-predict mitral regurgitation area. Results Individual models revealed marked heterogeneity. In pooled analysis, left ventricular volume was the strongest Granger predictor at short lags (atrial p=0.011; ventricular p=0.006), while left atrial volume emerged at longer lags (lag 7: atrial p=0.043; ventricular p=0.011). Systolic papillary muscle length was not predictive. Full-cycle analysis revealed a subtype-specific dissociation: papillary muscle length Granger-predicted regurgitation only in the ventricular subtype (p=0.001), while regurgitation predicted papillary muscle displacement only in the atrial subtype (p<0.001). Left ventricular volume dominated within-beat prediction but lost cross-beat relevance in the ventricular subtype, while left atrial volume gained cross-beat predictive relevance in the atrial subtype. No structural determinant correlated with severity cross-sectionally. Conclusions Beat-to-beat vector autoregression and Granger modelling reveals heterogeneous, subtype-specific temporal patterns with distinct temporal windows of predictability for ventricular loading and papillary geometry. This framework may support patient-specific temporal phenotyping of functional mitral regurgitation.

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: Insulin resistance (IR) and obesity are key drivers of atrial fibrillation (AF). However, the comparative predictive value of the Triglyceride-Glucose (TyG) index versus composite indices combining IR and anthropometric measures such as TyG-BMI, TyG-Waist Circumference (TyG-WC), and Waist-to-Height Ratio (WHtR) remains undefined. We aimed to evaluate these associations and the modifying effect of genetic susceptibility. Methods: We analyzed 293,318 UK Biobank participants free of AF at baseline. Hazard ratios (HRs) were estimated using Cox proportional hazards models, and non-linearity was assessed using restricted cubic splines. Incremental predictive value was evaluated via Net Reclassification Improvement (NRI). Interactions with AF Polygenic Risk Scores (PRS) were examined. Results: During follow-up, 22,707 incident AF cases occurred. While the TyG index was associated with AF in unadjusted models, this association was nullified after full adjustment. In contrast, composite indices (TyG-BMI, TyG-WC) and WHtR showed robust, positive associations (WHtR HR per SD: 1.30, 95% CI 1.28-1.32). Spline analysis identified non-linear threshold effects (e.g., WHtR inflection at 0.556). Adding WHtR or TyG-BMI to baseline models significantly improved risk reclassification (NRI ~10.3-11.8%, P<0.001), whereas TyG alone did not (P=0.73). Elevated metabolic risk increased AF incidence across all genetic categories, with significant interactions suggesting greater relative impact in low-genetic risk groups. Conclusions: Composite indices integrating central obesity and insulin resistance are superior to the TyG index alone in predicting incident AF. The identification of specific risk thresholds and genetic interactions highlights "metabolic health" as a crucial, modifiable target for AF prevention.

Background: Progression of transthyretin (TTR) amyloid cardiomyopathy (ATTR-CM) can lead to worsening congestion requiring diuretic intensification (DI), heart failure (HF)-related hospitalizations (HFH), and death. Tafamidis was the only approved ATTR-CM therapy in the US from 2019 until the 2024 approval of acoramidis, which achieves near-complete ([&ge;]90%) TTR stabilization. As head-to-head trials are lacking, real-world comparative effectiveness (CE) data are needed to guide treatment selection. Objective: To evaluate real-world CE of acoramidis versus tafamidis in newly treated patients with ATTR-CM. Methods: Retrospective study using Komodo Healthcare Map (R) US claims data tokenized to Claritas. Patients newly initiating acoramidis or tafamidis between 12/11/2024 and 04/30/2025 with [&ge;]1 prescription claim (first defined as index date) and [&ge;]6 months of continuous enrollment preindex date were included and followed until disenrollment, death, treatment switch, or study end date (07/31/2025). Outcomes included DI (initiation or dose-equivalent escalation of oral loop diuretics, parenteral loop diuretic use, or addition of thiazide-like diuretic) and a composite of DI, HFH (inpatient admission with a HF-related ICD-10-CM diagnosis code in any position), and mortality. Propensity score weighting balanced baseline characteristics, disease severity, comorbidity burden, and baseline medication use. Time-to-event outcomes were assessed using weighted Cox proportional hazards models. Results: After weighting, acoramidis (n=170) and tafamidis (weighted sample size=448) patients were comparable at baseline (mean age, 78.6 vs 78.7 years; male, 80.0% vs 80.2%) with mean follow-up of 139 and 143 days, respectively. DI cumulative incidence curves separated early and remained divergent, with acoramidis significantly reducing the hazard of DI events by 43% compared with tafamidis (11.8% vs 20.5%; HR, 0.57; 95% CI, 0.35-0.92; P=0.021). Acoramidis also had a significantly lower risk of composite events, with a 34% reduction in hazard compared with tafamidis (17.6% vs 26.4%; HR, 0.66; 95% CI, 0.44-0.99; P=0.046). Conclusions: In this first real-world CE study of newly treated patients, acoramidis had significantly lower risk of DI events and composite events of DI, HFH, and mortality than tafamidis, potentially supporting improved clinical stability with acoramidis initiation. Additional evaluation with longer follow-up, larger cohorts, and/or prospective clinical outcomes is warranted.

Abstract Aims: While traditional anthropometric indices are established cardiovascular predictors, their prognostic value for incident infective endocarditis (IE) remains undefined. Methods: We included 386,859 participants (mean age 57.0 years; 52.9% female) from the UK Biobank between 2006 and 2010 with standardized baseline data on BMI, waist circumference (WC), waist-to-height ratio (WhtR), and the triglyceride-glucose (TyG) index.Multivariable Cox proportional hazard models with restricted cubic splines were used to estimate the hazard ratio (HR) of these indices, adjusting for demographic and clinical risk factors. Results: Over 16.87 median years (25th, 16.02; 75th, 17.60 percentile) of follow-up, there were a total of 1,124 incident IE events. During the follow-up period, 38,342 total deaths were recorded, of which 8,524 were cardiovascular disease (CVD)-related.Overall, compared to individuals with normal weight and baseline metabolic indices, those in the fourth quartile of WC, WHtR, and TyG index exhibited the highest risk of incident IE. Compared to other metabolic indices, WC (HR = 1.53, 95% CI 1.23?1.90,P < 0.001) and WHtR (HR = 1.46, 95% CI 1.20?1.78,P < 0.001) demonstrated higher relative increases in risk associated with IE. Furthermore, the risk of IE was significantly elevated among the younger population with abdominal obesity and concomitant diabetes. However, no significant increase in IE risk was observed among participants with pre-existing valvular heart disease (P = 0.796). Conclusion: Compared with BMI, higher WC and WHtR were robustly associated with increased risk of IE, even after adjusting for traditional risk factors. Furthermore, the risk of IE was markedly elevated among younger individuals with abdominal obesity and diabetes.

Background: Detection of disease progression is key to personalize treatment strategies in transthyretin cardiomyopathy (ATTR-CM), particularly with emerging therapies. Echocardiography can detect subtle longitudinal changes but is limited by operator dependence. This study evaluates agreement and reproducibility of fully automated, AI-assisted echocardiographic measurements under real-world conditions. Methods: This retrospective study included 62 patients with ATTR-CM undergoing 178 serial annual echocardiograms assessed by a reference cardiologist, a second cardiologist, a novice reader, and a fully automated AI algorithm (Us2.ai). Interrater agreement was assessed using Bland-Altman analysis and intraclass correlation coefficients (ICCs). Intrarater variability for human readers was derived from repeated blinded measurements, with limits of agreement (LoA = mean difference +/- 1.96 x SD) defining the smallest detectable change. AI repeatability was assessed using within-study pairwise differences. Results: AI showed moderate agreement with the reference cardiologist for IVSd and LVEDV (ICC 0.65 and 0.51), with biases of -1.9 mm and -39 mL, respectively. Interrater agreement between cardiologists was good (ICC 0.79 and 0.84) with minimal bias (-0.2 mm and +3 mL). Intrarater variability was moderate to excellent for both cardiologists (LoA 3.0 mm and 43 mL for the reference cardiologist; 2.7 mm and 31 mL for the second cardiologist). AI demonstrated comparable repeatability (LoA 3.6 mm and 37 mL), while the novice showed higher variability (5.1 mm and 61 mL). Conclusion: AI-based measurements demonstrated repeatability comparable to experienced cardiologists. Despite moderate agreement and systematic differences in volumetric assessments, their reproducibility supports automated analysis for longitudinal echocardiographic monitoring.

Background: Heart failure (HF) is a major and growing public health problem, and prior studies support a meaningful genetic contribution to HF susceptibility. Clinically, HF is commonly categorized into the major clinical sub-types of HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF), which differ in pathophysiology and clinical profiles. However, previous genome-wide association studies have focused on autosomal variation and have routinely excluded the X chromosome, leaving X-linked genetic contributions to HF and its subtypes under-characterized. Methods: We performed X-chromosome wide association study (XWAS) utilizing directly genotyped data from 590,568 Million Veteran Program participants, including 90,694 HF cases across European, African, Hispanic, and Asian Americans. Sex- and ancestry-stratified logistic regression was used with XWAS quality control measures, adjusting for age and population structure, followed by fixed-effects multi-ancestry meta-analysis. Functional annotation, gene-based testing, fine-mapping, and colocalization were performed. We replicated genetic associations with all-cause HF in the UK Biobank. Results: In the multi-ancestry meta-analysis, we identified five X-chromosome-wide significant loci for all-cause HF, five for HFrEF, and one locus for HFpEF in males. No loci reached significance in female-specific analyses. In sex-combined analyses, we identified six loci for all-cause HF and four for HFrEF. The strongest and most emphasized signals mapped to genes were BRWD3, FHL1, and CHRDL1. Ancestry-specific analyses revealed additional loci, including NDP and WDR44 in African ancestry and PHF8 in Hispanic ancestry. One locus, BRWD3, was replicated in UK Biobank HF cohort. Integrated post-GWAS analyses (fine-mapping, colocalization and pleiotropy trait association studies) reinforced the biological plausibility of the X-linked signals. Conclusions: This multi-ancestry, sex-stratified XWAS identifies X-linked genetic contributions to HF and its subtypes and highlights the role of X-chromosome in heart failure pathogenesis.

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.