Circulation: Genomic and Precision Medicine

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.

Abstract Background Spontaneous coronary artery dissection (SCAD) is a well-recognised cause of acute coronary syndrome particularly among women without conventional cardiovascular risk factors. Increasing evidence indicates a genetic contribution; however, the underlying genetic architecture of SCAD remains insufficiently understood. Objective The aim of this study was to assess the prevalence of rare variants in previously reported SCAD associated genes and to explore the potential presence of novel genetic alterations in well-characterised Swedish patients with SCAD. Methods The study comprised 201 patients enrolled in SweSCAD, a national project examining the clinical characteristics, aetiology, and outcomes of SCAD. All individuals had a confirmed diagnosis based on invasive coronary angiography. Comprehensive exome sequencing was performed to identify rare variants contributing to disease susceptibility. Results Genetic variants that have been associated with SCAD according to current clinical genetics practice for variant reporting were identified in approximately 4 % of patients. In addition, rare potentially relevant variants were detected in almost 60 % of patients in genes associated with vascular integrity and vascular remodelling. Conclusion This study supports SCAD as a genetically complex arteriopathy, driven by rare high?impact variants together with broader polygenic susceptibility. Variants in collagen, vascular extracellular matrix, and oestrogen?responsive pathways provide biologically plausible links to female?predominant disease. Although the diagnostic yield of clearly actionable variants is modest, these findings support broader genomic evaluation beyond overt syndromic presentations and highlight the need for larger integrative genomic and functional studies to refine risk stratification and management.

Background and Aims Despite treatment, patients with established atherosclerotic cardiovascular disease (ASCVD) are at high risk of recurrent events. Existing clinical risk scores for recurrence provide only moderate predictive performance and rely largely on the same conventional risk factors used to predict disease onset. Proteomics is a promising source of new biomarkers but the technologies need focused use cases in order to achieve utility and implementation. We aimed to determine whether plasma proteomics improves prediction of recurrent cardiovascular events beyond established clinical risk models in secondary prevention in a population-scale cohort. Methods Plasma proteomic profiles from ~9,300 participants in the UK Biobank with established ASCVD at baseline were analysed using machine learning methods to derive and evaluate proteomic predictors of recurrent cardiovascular events. The top performing model comprised proteins with non-zero weights (full protein score). Predictive performance of the proteomic predictors, an established clinical risk score (SMART2), and their combination was evaluated across six pre-defined testing datasets representing multiple ethnic and geographic groups. A parsimonious set of proteins with existing clinical-grade enzyme-linked immunosorbent assays (ELISAs) available was then derived. Results The full protein score achieved higher performance for recurrent ASCVD than the SMART2 risk score across all ethnic and geographic subgroups (mean C-index 0.743 vs 0.653). Adding the full protein score to SMART2 improved discrimination, with the largest increase in White Irish participants ({Delta}C-index, 0.140; 95% CI, 0.074-0.205; P<0.001). However, adding SMART2 to the protein score provided minimal additional value. The parsimonious score preserved most of the discrimination of the full protein model with C-indices of the recurrent ASCVD risk model comprising age, sex and the parsimonious protein score being nearly identical to the full protein model in the largest testing set (0.723 vs 0.728 for White British in England and Wales). The parsimonious protein score showed a marked gradient of risk with the top, middle and bottom quintiles showing 10-year recurrent ASCVD rates of ~27.4%, ~9.6% and ~2.4%, respectively. Conclusions In patients with established ASCVD, plasma protein measurements substantially improved prediction of recurrent events beyond conventional clinical risk factors, supporting their potential as a complementary tool to guide secondary prevention of cardiovascular disease.

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.

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.

BackgroundPathogenic desmin (DES) variants have been implicated in early-onset atrial disease, yet the mechanisms by which desmin dysfunction alters atrial structure and function remain unclear. Desmin anchors the cytoskeleton to the nuclear envelope (NE) through the linker of nucleoskeleton and cytoskeleton (LINC) complex, suggesting that defects in this network may drive atrial cardiomyopathy. MethodsHuman desmin wild-type (WT) and the pathogenic variants p.S13F, p.N342D, and p.R454W were stably expressed in HL-1 atrial cardiomyocytes. Desmin organization, nuclear morphology, LINC-complex integrity (nesprin-3, lamin A/C), and DNA leakage, assessed by cyclic GMP-AMP synthase (cGAS), were analyzed by confocal microscopy. Action potential duration (APD) and calcium transients (CaT) were measured optically. Human myocardium samples from DES variant carriers were analyzed for validation. Data-independent acquisition (DIA) mass spectrometry profiled atrial proteomes from desmin-network (DN) and titin variant carriers and controls. The heat-shock proteins (HSPs) inducer geranylgeranylacetone (GGA) was evaluated for rescue effects. Resultsp.N342D caused severe filament-assembly defects with prominent perinuclear aggregates, whereas p.S13F showed mixed phenotypes with frequent perinuclear aggregates, and p.R454W largely preserved filamentous networks. p.N342D and p.S13F induced nuclear deformation with disrupted nesprin-3 and lamin A/C distribution. In p.N342D and p.S13F, desmin aggregates drove focal lamin A/C accumulation, nuclear envelope (NE) rupture, DNA leakage, and increased cGAS activation. DES variants significantly shortened APD20/90 and reduced CaT amplitude, indicating pro-arrhythmic electrical remodeling. Atrial proteomics revealed a DN-specific signature enriched for cytoskeletal, NE, intermediate filament, and chaperone pathways, consistent with the structural injury observed in vitro. GGA prevented desmin aggregation and nuclear morphology changes, and mitigated APD shortening in p.N342D-expressing cardiomyocytes. Human myocardium from DES variant carriers showed concordant desmin aggregation and polarized lamin A/C distribution. ConclusionsDES variants induce a desmin-dependent atrial cardiomyopathy characterized by cytoskeletal disorganization, disruption of LINC-complex, NE rupture with DNA leakage, and pro-arrhythmic electrophysiological remodeling. These findings provide mechanistic insight into how DN variants promote atrial disease. HSPs induction by GGA partially restores structural and functional integrity, identifying a potential therapeutic approach for desmin-related atrial cardiomyopathy. Clinical perspectiveWhat is new? O_LIPathogenic DES variants induce a previously unrecognized atrial cardiomyopathy characterized by desmin aggregation, and desmin-network (DN) collapse, disruption of the linker of nucleoskeleton and cytoskeleton (LINC) complex, and nuclear envelope rupture with DNA leakage. C_LIO_LIVariants that lead to desmin aggregation (e.g., p.N342D) cause focal lamin A/C polarization, cyclic GMP-AMP synthase (cGAS) activation, and structural injury at the nuclear envelope. C_LIO_LIDES variants produce pro-arrhythmic electrical remodeling, including action potential duration shortening and impaired Ca{superscript 2} handling in HL-1 atrial cardiomyocytes. C_LIO_LIAtrial proteomics from DN variant carriers reveals enrichment of pathways related to cytoskeletal, nuclear envelope, intermediate filament, and chaperone, supporting a desmin-dependent remodeling program. C_LIO_LIThe heat-shock protein inducer geranylgeranylacetone (GGA) prevents desmin aggregation, restores nuclear morphology, and mitigates electrical and Ca{superscript 2} handling remodeling. C_LI What are the clinical implications? O_LIThese findings establish DN dysfunction as a distinct cause of atrial cardiomyopathy, providing a mechanistic basis for the association between pathogenic DES variants and atrial arrhythmias, including atrial fibrillation. C_LIO_LINuclear envelope rupture and cytosolic DNA leakage represent new mechanistic evidence which links cytoskeletal injury and atrial arrhythmogenesis. C_LIO_LIIdentifying structural vulnerability in DES variant carriers fosters awareness of genetic counseling for atrial disease, enabling early detection and risk stratification. C_LIO_LIThe protective effects of GGA suggest that restoring proteostasis may be a therapeutic strategy for desmin-related atrial cardiomyopathy and potentially other genetic atrial diseases. C_LI Novelty and significance statementO_ST_ABSNoveltyC_ST_ABSThis study identifies a desmin-dependent atrial cardiomyopathy driven by cytoskeletal aggregation, LINC-complex disruption, and nuclear envelope rupture with DNA leakage. We show that pathogenic DES variants are associated with pro-arrhythmic molecular remodeling and that human atrial proteomics confirm nuclear envelope and cytoskeletal injury as core features. Importantly, the heat-shock protein-inducer GGA rescues structural, molecular, and electrophysiological defects, revealing a modifiable pathway in desmin-mediated atrial disease. SignificanceThese findings provide the first integrated mechanistic explanation linking DN variants to atrial cardiomyopathy. By uncovering nuclear envelope rupture and cGAS activation as key drivers of atrial cardiomyopathy, this work expands the molecular framework for inherited atrial disease and highlights proteostasis enhancement as a potential therapeutic strategy for patients carrying DES and related cytoskeletal variants. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=166 HEIGHT=200 SRC="FIGDIR/small/26348559v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@1fb0bfborg.highwire.dtl.DTLVardef@cfc00borg.highwire.dtl.DTLVardef@1493578org.highwire.dtl.DTLVardef@1556b61_HPS_FORMAT_FIGEXP M_FIG C_FIG

Introduction: Spontaneous coronary artery dissection (SCAD) is frequently accompanied by persistent symptoms of unknown pathogenesis after the index event. Autonomic dysfunction is a plausible mechanism for these but has not been systematically characterized. We quantified antecedent and contemporary autonomic symptoms in survivors of SCAD and examined their associations with cardiac and extra-cardiac symptoms and health-related quality of life. Methods: This cross-sectional study recruited 227 volunteers from multiple countries with a self-reported history of SCAD. Participants completed validated patient-reported measures, including the Composite Autonomic Symptom Score-31 (COMPASS-31), Anxiety Sensitivity Index-3 (ASI-3), and EuroQol-5 Dimension-5L (EQ-5D-5L). They also completed an internally derived retrospective autonomic predisposition score assessing symptoms during adolescence and early adulthood. Results: Participants were predominantly female (97.8%), median age 53 (47-58) years, and were surveyed a median of 3 (1-5) years after their index SCAD event. 21.6% reported SCAD recurrence. Moderate autonomic symptom burden (COMPASS-31 20) was present in 56.4% and severe burden (40) in 16.3%. History of antecedent autonomic symptoms was the strongest independent predictor of contemporary autonomic symptom burden after adjustment for demographic and clinical covariates (=0.514; P <0.001). Greater autonomic symptom burden independently predicted lower EQ-5D health utility (=0.150; P=0.029) and was associated with the ASI-3 physical concerns (=0.232; P <0.001), but not social concerns domain. Autonomic symptoms were not associated with SCAD recurrence. Conclusion: Symptoms of autonomic dysregulation are common in survivors of SCAD and are associated with reduced quality of life. Their association with antecedent dysautonomic features during adolescence and early adulthood suggests a longstanding predisposition, the significance of which warrants further evaluation.

BackgroundDespite improved cancer outcomes with kinase inhibitors (KIs), their cardiotoxicity remains a significant clinical challenge. Current approaches to predict and prevent KI-induced cardiac adverse events (CAEs) are limited by an incomplete understanding of underlying mechanisms, including the contribution of off-target kinase engagement. ObjectivesTo establish links between kinase inhibition profiles and cardiotoxic phenotypes using empirical proteomic data, and to leverage these profiles in machine learning (ML) models capable of predicting KI cardiotoxicity. MethodsWe curated a database connecting kinome-wide target binding profiles of FDA-approved KIs (n=44) with documented incidence rates of six distinct CAEs. Binding profiles were derived from unbiased chemoproteomics and used to assess associations between KI selectivity, specific kinase targets, and CAEs. Profiles were further used to develop ML models to predict CAE risk, with SHAP-based model interpretation applied to identify cardiotoxicity-associated kinases. ResultsKI promiscuity was not a significant predictor of cardiotoxicity across all six CAEs. Frequency analysis revealed that kinases including RET, PDGFRB, and DDR1 are recurrently inhibited across CAE-linked compounds, with nearly all identified as off-targets not annotated by the FDA. Network and pathway enrichment analyses supported a systems-level model in which cardiotoxicity arises from coordinated disruption of cardiac-relevant signaling networks. ML models achieved 66-84% cross-validated accuracy (ROC-AUC 0.75-0.8) across CAE endpoints, with SHAP analysis identifying PDGFRB, EGFR, and MEK1/2 among the most predictive kinases. ConclusionsProteomic kinome profiling combined with machine learning provides a mechanistically grounded framework for predicting KI cardiotoxicity and supports off-target-aware drug design to minimize cardiovascular risk.

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.

Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) are widely used to model cardiovascular disease, yet numerous differentiation protocols generate cardiomyocytes with heterogeneous molecular and functional properties, complicating experimental design. Here we systematically compare sixteen commonly used cardiomyocyte differentiation protocols and characterize their resulting cell states using single-nucleus RNA sequencing, functional phenotyping and computational integration with human genetic data. Despite similar cardiomyocyte yields, protocols produced distinct transcriptional programs, subtype compositions and physiological properties. By integrating protocol-specific gene expression signatures with genome-wide association studies of cardiovascular traits, we identify cardiomyocyte states enriched for genetic architectures underlying specific diseases. These analyses accurately predict protocols most suitable for modelling particular disease contexts, including electrophysiological defects associated with Brugada syndrome and metabolic vulnerability relevant to myocardial infarction. Our results demonstrate that differentiation protocols encode biologically distinct cardiomyocyte states with differential disease relevance and establish a framework for aligning stem-cell differentiation strategies with human complex trait genetics to guide model selection. This approach enables rational design of iPSC-based disease models and highlights how population-scale genetic data can inform experimental systems in stem cell biology.

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.

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.

BackgroundGenetic studies using cardiac magnetic resonance (CMR) imaging have identified loci related to cardiac shape, but most focus on static morphology. The value of a dynamic cardiac shape atlas capturing both shape and function remains unknown. MethodsA dynamic shape atlas comprising CMR-derived shape models at end-diastole and end-systole was combined with genetic and outcome data in 36,992 UK Biobank participants. Dynamic shape principal components (PCs) describing >1% of variance were characterized, and tested for associations with prevalent and incident cardiometabolic diseases, including ischemic heart disease (IHD), heart failure (HF), significant atrioventricular block (AVB), and atrial fibrillation (AF), and independent predictive power alongside standard CMR measures. Genome-wide association studies (GWAS) were performed to identify candidate genes and biological pathways, and polygenic risk scores (PRS) were assessed for disease associations. Mendelian randomization (MR) was performed to test causality of observed disease associations. ResultsWe identified 14 dynamic cardiac shape PCs capturing 83.3% of total dynamic cardiac shape variance. These PCs captured distinct functional remodeling patterns such as variation in annular plane systolic excursion, while remaining only modestly correlated with standard CMR measures. All 14 PCs were associated with at least one incident cardiometabolic disease, with the strongest associations observed for incident IHD, HF, and AVB. Notably, incorporating dynamic shape PCs improved the prediction of incident IHD beyond standard CMR measures. GWAS identified 75 genetic loci associated with dynamic shape, including 14 variants previously unreported for cardiac traits, and candidate genes demonstrated enrichment in pathways related to cardiac development and contractile function. PRS derived from dynamic shape loci were significantly associated with multiple outcomes, most prominently HF. MR identified significant causal relationships between several PCs and cardiometabolic disease. ConclusionsDynamic cardiac shape features capture aspects of cardiac structure and function not fully represented by standard CMR measures. These features are strongly associated with incident cardiometabolic disease and provide new insights into the genetic architecture of cardiac remodeling. Clinical perspectiveO_ST_ABSWhat is new?C_ST_ABSO_LIGenetic and outcome relationships with a dynamic statistical shape model capturing both left and right ventricles at end-diastole and end-systole. C_LIO_LIDemonstration of incremental value over existing cardiac shape models, through capture of functional remodeling not represented by standard imaging measures. C_LIO_LIIdentification of genetic susceptibility loci for dynamic cardiac shape, including 14 variants not previously reported for cardiac traits. C_LI What are the clinical implications?O_LIThe results enhance our understanding of the genetic architecture of dynamic cardiac shape and function in the general population and clarify their relationships with other cardiovascular endophenotypes and incident cardiometabolic diseases. C_LIO_LINewly identified candidate genes expand the biological pathways implicated in cardiac remodeling and provide targets for future functional and mechanistic studies. C_LIO_LIThe improved prediction of incident cardiometabolic disease, particularly ischemic heart disease, achieved by adding dynamic shape PCs to traditional CMR measures suggests potential value for their inclusion in evaluation of patients. C_LI

Abstract Introduction: Cardiovascular disease is a leading cause of maternal and neonatal morbidity and mortality in the UK. Its prevalence in pregnancy continues to rise, driven by both improved survival of women with congenital and inherited heart disease into reproductive age and an increasing burden of acquired cardiovascular risk factors. However, its natural history and optimal management remain poorly defined. Current research is limited by small sample sizes, drawn from highly selected patient cohorts from individual units. The aim of the PREGnancy, HEART Health, and Cardiovascular Disease (PREG-HEART) study is to develop a patient driven, clinically relevant, digital platform to understand the epidemiology of cardiovascular disease in pregnancy and support clinical trials of management strategies. This paper provides the protocol for PREG-HEART, which will start with a 6-month pilot study. Methods and analysis: PREG-HEART will utilise an online, direct-to-patient platform to enrol patients with cardiovascular disease in pregnancy alongside healthy pregnant controls. Enrolled women will be invited to provide self-reported demographic and clinical data and consent to linkage with national health records for long-term follow up. We will also seek consent for storage and analysis of leftover clinical biosamples and to re-contact participants, enabling recruitment into sub-studies and clinical trials. Planned analysis for the pilot study at 6 months will assess feasibility, including recruitment rates, case-mix of cardiovascular diagnoses, and participant geographical, socio-economic, and ethnic background compared to the UK general pregnant population. Findings from the pilot study will inform subsequent phases of PREG-HEART, which will explore associations between different cardiovascular diagnoses and adverse cardiovascular, obstetric, and neonatal events. We will work closely with patients and clinicians to define priority research questions and use the PREG-HEART platform to support a range of observational and interventional studies to address these. Ethics: This study was approved by the West Midlands Solihull Research Ethics Committee. Registration details: PREG-HEART has been registered prospectively on the ISRCTN registry (ISRCTN11700499)

BackgroundSympathetic modulation via the stellate ganglia is increasingly recognized as a contributor to ventricular arrhythmogenesis after myocardial infarction. However, the mechanisms by which autonomic remodeling interacts with chronic infarct substrates to shape arrhythmic vulnerability remain incompletely understood. ObjectivesTo test the hypothesis that left- and right-sided stellate ganglion-mediated SNS modulation differentially reshapes ventricular arrhythmic vulnerability in chronic post-infarcted substrates, and that the RVI detects changes in vulnerability beyond conventional stimulation-based inducibility. MethodsFourteen patient-specific ventricular models with chronic post-infarcted remodeling were reconstructed from imaging data. A total of 336 simulations were performed under different combinations of stellate ganglion modulation, border zone remodeling, and fibroblast density. Arrhythmic vulnerability was quantified using 3D RVI mapping during paced rhythms and compared with conventional stimulation-based inducibility outcomes. ResultsStellate ganglion modulation induced marked, regionally heterogeneous changes in repolarization timing, resulting in lower and more negative RVI values in vulnerable regions. More negative RVI values reflect increased propensity for wavefront-waveback interaction and reentry initiation. Across the cohort, stellate modulation consistently decreased RVImin, even when inducibility outcomes remained unchanged. These findings indicate that SNS modulation can create a substrate more permissive to reentry independently of whether ventricular arrhythmia is triggered during programmed stimulation. ConclusionsStellate ganglion-mediated sympathetic modulation dynamically reshapes ventricular arrhythmic vulnerability in chronic post-infarcted substrates. RVI provides a spatially resolved, vulnerability-based metric that complements inducibility testing by revealing autonomic-substrate interactions underlying arrhythmogenesis Condensed AbstractSympathetic modulation via the stellate ganglia can alter ventricular repolarization and promote arrhythmogenesis after myocardial infarction, yet clinical responses remain heterogeneous. Using 14 patient-specific post-infarction ventricular models, we simulated left- and right-sided stellate modulation across combinations of border zone remodeling and fibrosis (336 simulations). Stellate modulation induced regionally heterogeneous repolarization shortening and reduced RVI values, even when programmed stimulation inducibility remained unchanged. These findings suggest that RVI captures substrate-level vulnerability beyond binary induction testing and may improve mechanistic assessment of autonomic-substrate interactions in chronic infarct substrates.

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.

Mitral valve prolapse (MVP) is the most common cause of primary mitral regurgitation and is associated with the development of malignant arrhythmias, often in the context of myocardial fibrosis. The genetic architecture of MVP, and whether there are genetic factors explaining why only some individuals with MVP have adverse outcomes, remains poorly understood. We performed a meta-analysis of genome-wide association studies (GWAS) for MVP encompassing 21,517 cases among a total sample size of over 2.2 million individuals. We discovered 89 genomic risk loci for MVP, of which 72 were novel findings. Prioritization of causal genes and pathways using epigenetic and transcriptomic data from mitral valve and extra-valvular tissues replicated known gene associations to MVP including those involved in TGF-{beta} signaling and extracellular matrix biology, but additionally emphasized a role in MVP for biological pathways relevant to cardiomyocyte biology. Accordingly, we identified several MVP risk loci with pleiotropy to cardiomyopathies, especially hypertrophic cardiomyopathy, and demonstrated a significant genetic correlation between MVP and hypertrophic cardiomyopathy. Finally, we interrogated snRNA-seq data in human papillary muscle tissue from two individuals with severe MVP, characterizing genes associated with both risk of papillary muscle fibrosis and MVP.

Background: Clinical genetic evaluation for patients with dilated cardiomyopathy (DCM) is minimally implemented and models of care are not defined. To understand current genetics care for DCM, a systematic needs assessment was conducted. Methods: Principal Investigators (PIs) of the DCM Consortium convened at the Summer Scientific Symposium in July 2025. An electronic needs assessment was collected from the 24 PIs in advance to define current care models by evaluating which Heart Failure Society of America-recommended genetic evaluation components are conducted, by whom, and time required. Descriptive statistics were generated to characterize model features. Focus group discussions explored barriers and facilitators to implementing genetic services. Results: Four care models emerged from the PI responses: 1 -- Traditional-Synchronous (25%, n=6, requiring the most time per patient), 2 -- Traditional-Asynchronous (33%, n=8), 3 -- Externally Sourced (17%, n=4), and 4 -- Physician/Advanced Practice Provider Conducted (25%, n=6, requiring the least time per patient). All models used genetic testing, whereas other components were implemented variably or not at all. Models 1 (15.7{+/-}4.1) and 2 (15.4{+/-}3.0) were rated more acceptable than Model 4 (9.8{+/-}2.9, 1 vs 4: p=0.027; 2 vs 4, p=0.023). Notably, 88% of PIs used genetic information for treatment decisions, including ICD placement (83%; n=20) or cardiac transplant (63%; n=15). Major facilitator themes from focus group discussions included having a genetic counselor on the HF team and developing authoritative standards directing provision of DCM genetic services. Barrier themes included operational challenges, limited personnel, clinician under-recognition, need for new service delivery models, and billing/reimbursement. Conclusions: DCM genetic care models and components were highly variable across the 24 sites of the DCM Consortium, even though all sites discussed similar factors that enable or hinder implementing genetic services for DCM. Understanding the basis of practice model variability may provide insight to yield more scalable care approaches.

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: 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.