Circulation: Genomic and Precision Medicine

Background: Andersen-Tawil syndrome type 1 (ATS1) is caused by loss-of-function mutations in KCNJ2, which encodes the inward rectifier K+ channel Kir2.1, a key determinant of IK1. Impaired Kir2.1 destabilizes membrane excitability and predisposes to ventricular arrhythmias. Most ATS1 variants disrupt channel regulation by phosphatidylinositol 4,5-bisphosphate (PIP2), but whether specific mutations confer differential arrhythmic risk remains unclear. Objective: To determine whether ATS1 variants disrupting Kir2.1-PIP2 interactions define distinct arrhythmic risk profiles and establish a mechanistically informed framework for risk stratification. Methods: We performed a pooled patient-level analysis of 225 ATS1 patients carrying KCNJ2 variants impairing Kir2.1-PIP2 interaction. Inclusion of 22 clinical and electrocardiographic variables were used to identify mutation-specific risk profiles and predictors for arrhythmia risk. The approach was validated in a multicenter cohort of 20 ATS1 patients. Functional validation was performed using patient-derived iPSC-CMs, cardiac-targeted mouse models, and structural in silico analyses. Results: ATS1 variants segregated into three discrete clusters corresponding to high-, intermediate-, and low-risk arrhythmic phenotypes, establishing a mutation-dependent hierarchy of arrhythmic risk. Regression analyses identified six variables independently associated with severe arrhythmic outcomes. Patient-derived iPSC-CM demonstrated graded impairment of electrical propagation and arrhythmia susceptibility, with a hierarchy in conduction velocity, CV:Control > R82W > R218W > G215D). Cardiac-targeted ATS1 mouse models reproduced the clinical risk stratification. Structural modeling showed that high-risk variants localize near the channel pore and disrupt Kir2.1-PIP2 interactions through mutation-specific mechanisms. Conclusions: ATS1 caused by Kir2.1-PIP2-disrupting variants is not a uniform disorder but comprises biologically distinct subgroups with predictable differences in arrhythmic severity. Integrating genetics, functional phenotyping, and structural modeling provides a mechanistically grounded framework for ATS1 risk stratification and precision therapy development.

BackgroundMetabolic dysfunction-associated steatohepatitis (MASH) is emerging as a risk factor of cardiometabolic diseases, including the atrial fibrillation (AF) - the most common sustained arrhythmia. Given that the liver is a major source of inflammatory mediators, lipids, and hepatokines under metabolic stress, we hypothesized that hepatocyte-derived factors in MASH may accelerate atrial remodeling and arrhythmogenesis. MethodsAnalysis of the Atherosclerosis Risk in Communities (ARIC) visit 5 cohort was performed to determine the association between the FIB-4 index - a classic indicator of liver fibrosis, and AF risk, with multivariable adjustment for common comorbidities. A murine model of MASH was induced using the GAN (Gubra-Amylin NASH) diet. Programmed intracardiac stimulation and echocardiography were performed to assess AF susceptibility and cardiac function. Calcium imaging, histology, flow cytometry, plasma proteomics, and single-nucleus RNA sequencing (snRNA-seq) analyses were employed to elucidate the role of recruited inflammatory macrophages via hepatocyte-derived osteopontin (OPN) in MASH-induced atrial remodeling. ResultsAnalysis of the ARIC cohort confirmed a higher cumulative incidence of AF and an elevated adjusted hazard ratio (HR) in patients with intermediate and high FIB-4 indices compared to individuals with low FIB-4 scores. MASH mice exhibited increased susceptibility to pacing-induced AF, accompanied by enhanced proarrhythmic calcium release events, atrial enlargement, and fibrosis, independent of ventricular dysfunction. Proteomics and snRNA-seq revealed that the hepatocyte-secreted OPN under MASH conditions promoted the differentiation and recruitment of TGFBR1+ inflammatory macrophages to the atria, leading to gasdermin D (GSDMD) activation - an effector of inflammasome signaling and consequent proarrhythmic atrial remodeling. Activation of the monocyte-derived pro-inflammatory TGFBR1+ macrophages was dependent on the OPN receptor CD44. Furthermore, the MASH-induced atrial fibroinflammatory milieu and enhanced AF susceptibility were mitigated through several strategies, including hepatocyte-specific Spp1 (encoding OPN) deletion, neutralization of circulating OPN, ablation of CD44 or GSDMD. ConclusionsThese findings establish a pathogenic role of the hepatokine osteopontin in driving activation and recruitment of TGFBR1+ inflammatory macrophages into the atria, leading to proarrhythmic atrial remodeling under MASH. Osteopontin-targeted therapy or GSDMD inhibition prevents AF, indicating a novel therapeutic strategy for liver disease-related atrial arrhythmogenesis. Clinical PerspectiveO_ST_ABSWhat is new?C_ST_ABSO_LIIn the ARIC cohort, metabolic dysfunction-associated steatohepatitis (MASH) is associated with increased risk of atrial fibrillation (AF) after adjusting for common comorbidities. Elevated levels of circulating osteopontin (encoded by SPP1) predict an increased risk of AF in patients with MASH-induced liver fibrosis. C_LIO_LIMASH enhances hepatocyte secretion of osteopontin, leading to expansion of myeloid cells and recruitment of inflammatory macrophages into atria. This liver-to-atrial inflammatory circuit promotes the development of a substrate conducive to AF, which can be attenuated by hepatocyte-specific Spp1 deletion or neutralizing anti-anti-osteopontin antibody treatment to eliminate the mediator, or ablation of inflammasome effector gasdermin D to correct the atrial response. C_LI What are the clinical implications?O_LIOsteopontin may serve as a biomarker for AF in MASH cohorts. C_LIO_LIAnti-osteopontin therapy through neutralizing antibodies may serve as a novel therapeutic strategy for liver disease-related atrial arrhythmia. C_LI

Background and aimsCurrent FH VCEP specifications of ACMG/AMP guidelines for familial hypercholesterolemia (FH) variant interpretation assign a higher evidence weight to functional data obtained with flow cytometry than microscopy assays, due to lack of existing evidence. This restricts the use of microscopy-derived functional data for variant classification. We aimed to systematically compare functional data of LDLR variants obtained by high-content microscopy and flow cytometry to determine their concordance and assess whether microscopy-based assays could support a higher evidence level. MethodsFifty LDLR variants with available flow cytometry and high-content microscopy data were compared for LDL uptake activity, including 21 newly characterized variants by microscopy in this study. Variants were grouped by FH VCEP functional thresholds (<70% activity, abnormal function; >90% activity, normal function) and results were integrated with UK Biobank data to assess associations with lipid traits. ResultsFirst, we validated our scalable microscopy assay with FH VCEP-classified control variants. Then we compared functional activity measured by microscopy and flow cytometry assays for 50 variants, which showed significant correlation (r = 0.66, p<0.0001) and a close average agreement (Bland-Altman bias = -0.05). Applying FH VCEP functional classification thresholds yielded broadly consistent classification in both methods, with minor shifts among categories. Integration with UK Biobank data showed that carriers of variants with reduced LDLR activity (<70% and <50%) had higher LDL-C, total cholesterol and ApoB levels compared to those with normal activity (>90%) for both microscopy and flow cytometry assays, with more pronounced differences observed at the <50% LDLR activity threshold. ConclusionHigh-content microscopy provides reliable and scalable measurements of LDLR function, showing high concordance with flow cytometry and consistent associations with lipid phenotypes. These findings support reconsideration of the evidence weight assigned to validated microscopy assays within FH VCEP variant classification frameworks, namely to Strong (Level 1).

Background: Coronary artery calcium (CAC) scores inform subclinical atherosclerotic cardiovascular disease (ASCVD) burden, helping guide preventative treatments. However, prediction of cardiovascular (CV) events by CAC is largely limited to ASCVD outcomes. This study investigated whether a previously validated proteomic test for predicting a broad composite of four-year CV events could enhance the prognostic utility of CAC. Methods: We used a 27-protein CV risk score (Prot-CVR), derived from ~5,000 SomaScan? Assay plasma protein measurements, to predict four-year risk of a composite CV and mortality outcome (myocardial infarction, stroke/TIA, heart failure hospitalization, death) in 2,122 participants with ?1 CV risk factors from the Multi-Ethnic Study of Atherosclerosis (MESA) observational cohort at exam 5 and compared predictions to CAC Agatston scores. Discriminatory performance was assessed using C-Index and 4-year area under the curve (AUC). Cox Proportional Hazard (CoxPH) ratios were calculated for the composite outcome, ASCVD outcome (myocardial infarction, resuscitated cardiac arrest, stroke, coronary heart disease death), and individual events. Changes in Prot-CVR and CAC scores from baseline to MESA exam 5 (+10-years) in CV event versus event-free participants were assessed using 2-tailed paired t-tests. CoxPH regression models of CV event status distributed by Prot-CVR, CAC, and relevant co-variates were evaluated for performance relative to individual models. Results: Individual Prot-CVR and CAC models predicting the composite outcome had comparable 4-year AUCs, but Prot-CVR had a higher C-index (0.68 (0.65-0.70) versus 0.63 (0.60-0.65), p=0.001) and greater hazard ratios for the composite outcome (p<0.001), death (p<0.001), and heart failure (p=0.015). A combined CoxPH model of Prot-CVR + CAC + Age had a higher 4-year AUC (0.72, p<0.05) and C-Index (0.71, p<0.05) than Prot-CVR or CAC alone. Both Prot-CVR and CAC scores detected an increase in risk prior to an approaching CV event in ~10-year sensitivity-to-change analysis. For 49.6% of MESA population with CAC=0 at baseline, Prot-CVR was greater in composite event versus event free participants at 4 years (0.23 versus 0.15, p=0.006) and full follow-up (0.18 versus 0.13, p<0.001). Conclusion: Protein testing complements CAC for CV risk assessment although the improvement is modest. Prot-CVR may resolve which patients with CAC=0 are at heightened CV risk.

Bicuspid aortic valve (BAV) is one of the most common congenital heart defects but its genetic basis remains incompletely defined. Extracellular matrix components play key roles in outflow tract (OFT) and valve development, but their contribution to BAV is not fully established. Following the analysis of a cohort of BAV patients, we identified a family harbouring a rare human ELASTIN (ELN) variant (p.Gln691X). To assess its pathogenicity, we generated a zebrafish elna/b double knockout (KO) using an RNAless CRISPR Cas9 strategy to avoid genetic compensation. This mutant exhibited cardiovascular defects including OFT anomalies, reduced stroke volume and dysmorphic aortic valves, highlighting Elastins critical role in cardiac development. We then used this model to test the ELN variant identified in the BAV family. We found that wild-type ELN mRNA was able to restore normal cardiac function and morphology, whereas the variant ELN mRNA failed to do so. This study establishes a robust in vivo model to assess ELN variant pathogenicity and provides evidence linking ELASTIN to BAV, opening new avenues for uncovering the genetic mechanisms underlying BAV.

Importance Dilated cardiomyopathy (DCM) is a major cause of heart failure that disproportionately affects individuals of African genetic ancestry (AFR), among whom familial clustering of disease is also more pronounced relative to those of European ancestry (EUR). However, established monogenic DCM genes, identified primarily in EUR populations, explain a smaller proportion of DCM cases in AFR populations. A recent study identified a common AFR-specific nonsense variant in CD36 that accounts for a substantial burden of DCM in AFR. How the risk and population impact of this variant compare with those of established genetic causes of DCM is unknown. Objective To compare the contribution of a CD36 nonsense variant to DCM risk with that of truncating variants in TTN and pathogenic or likely pathogenic (P/LP) variants in other established DCM genes. Design, Setting, and Participants Multicohort genetic association study including AFR and EUR participants with exome or genome sequence and DCM case status from four datasets: All of Us, Million Veteran Program, Penn Medicine Biobank, and the DCM Precision Medicine Study. Exposure Carrier status for TTN truncating variants, P/LP variants in 11 high confidence DCM genes, and the CD36 nonsense variant (Y325*; 0, 1, or 2 copies). Main Outcomes and Measures Odds of DCM; prevalence of risk-variant carriers among DCM cases; and population attributable fraction (PAF) for DCM. Results Among 82,623 AFR individuals across four studies, the mean age was 53.4 years and 1,625 had DCM. CD36 Y325* risk-allele homozygotes had 4.8-fold (95% CI, 3.1-7.3) increased odds of DCM, and CD36 Y325* heterozygotes had 1.4-fold (95% CI, 1.2-1.7) increased odds. TTN truncating variants also conferred elevated risk of DCM in AFR participants (OR, 8.46; 95% CI, 5.3-12.3). Among AFR DCM cases, 2.5% were CD36 homozygotes, second only to TTN truncating variants (4.3%) and exceeding all other high-confidence DCM genes combined (1.5%). In population-level analyses incorporating both heterozygous and homozygous CD36 Y325* carriers, the population-attributable fraction for CD36 (9.0%) surpassed that of TTN truncating variants (3.6%). Conclusions and Relevance An ancestry-specific CD36 variant contributes more to DCM burden in AFR ancestry than established DCM genes, including TTN truncating variants, typically considered the most common genetic cause of DCM. These findings reshape the known genetic architecture of DCM in individuals of African ancestry and highlight the importance of representation in genomic research.

Background: Polygenic risk scores (PRS) for coronary artery disease (CAD) are associated with cardiovascular events, but the relationship between inherited risk and routinely reported coronary computed tomography angiography (CTA) findings has not been studied. Objectives: To evaluate associations between a genome-wide PRS for angiographic coronary disease burden and coronary CTA-derived measures of atherosclerotic severity in a real-world clinical cohort. Methods: We studied Penn Medicine BioBank participants with available genotypes and clinically obtained coronary CTA reports. A previously published PRS for angiographic CAD burden was calculated using pgsc_calc. CAD-RADS scores and coronary artery calcium (CAC) values were extracted from radiology reports using the large language model Llama 3.1 8B. Associations between PRS and CAD-RADS severity were evaluated using Bayesian cumulative ordinal logit regression, while associations with log-transformed CAC burden were assessed using Bayesian linear regression. Results: Among 630 participants, median age was 59 years (IQR 49 - 68), 53% were female, 62% were genetically similar to a European reference population, and 34% to an African reference population. LLM-extracted CAD-RADS and CAC values demonstrated near-perfect agreement with manual abstraction. Higher PRS was associated with greater coronary atherosclerotic burden on CTA. Each 1-standard deviation (SD) increase in PRS was associated with a 20% higher odds of belonging to a more severe CAD-RADS category (cumulative OR 1.20, 95% credible interval 1.06-1.44). Higher PRS was also associated with greater CAC burden ({beta} 0.38, 95% credible interval 0.15 - 0.61). Conclusions: Polygenic risk for angiographic coronary disease burden is reflected in clinically reported coronary CTA severity measures, including CAD-RADS and CAC. These findings demonstrate that inherited susceptibility to CAD manifests as greater anatomic atherosclerotic burden at the time of clinical presentation and support further investigation of genetic risk integration into imaging-based cardiovascular risk assessment.

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 recur-rently 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.

Diastolic heart failure (HF) in primary cardiomyopathy is under-recognized and often diagnosed late, particularly in children. While recent studies have advanced understanding of HF with preserved ejection fraction in older adults, the prevalence, outcomes and molecular drivers of diastolic HF in pediatric and young adult cardiomyopathy remain poorly defined, where disease is typically driven by primary myocardial disease rather than acquired co-morbidities. The Canadian Cardiomyopathy Collaborative (C3) was assembled to leverage three of Canadas leading pediatric and adult cardiomyopathy biobank registries. Its flagship initiative, Artificial Intelligence to Model Diastolic Heart Failure (AID-HF), aims to integrate deep phenotyping - including comprehensive diastolic function assessment - with genomics, lipidomics and proteomics and apply machine learning to identify biological and clinical signatures that drive cardiac function and outcomes in cardiomyopathy. Harmonized phenotyping and multiomics protocols across registries will create a uniquely integrated national data resource and enable the goals of AID-HF i.e., earlier diagnosis and new therapeutic targets for diastolic HF in cardiomyopathy.

Background Atrial fibrillation (AF) is a leading cause of stroke, cardiovascular morbidity, and mortality. Atrial myopathy, characterized by progressive metabolic, electrical, and structural changes, creates the arrhythmogenic substrate that drives AF. Defining the key drivers of atrial myopathic processes is essential for targeted therapies that can mitigate AF progression. Here we explore how reduced ERBB4 expression contributes to the development of left atrial myopathy. Methods We analyzed the Cleveland Clinic Biobank to compare left atrial ERBB4 levels in patients grouped by AF diagnosis. To investigate the impact of reduced ERBB4 levels on atrial tissue substrate, we created mouse models of cardiac-specific Erbb4 deficiency using Mlc2a (myosin light chain 2a)-Cre. Comprehensive physiological assessments were performed. Transcriptomic analyses of the left atrium were performed in an Erbb4 haploinsufficient mouse model and compared with human atrial datasets. Molecular validation of key dysregulated pathways was performed. Results We found that left atrial ERBB4 levels are reduced in patients with AF. Adult cardiomyocyte-specific Erbb4 heterozygous (Erbb4fl/+;Mlc2a-Cre) mice exhibited prolonged P-wave duration in the absence of ventricular dysfunction. Left atrial transcriptomic analysis in Erbb4 haploinsufficient mice showed upregulation of pathways related to fibrosis, apoptosis, and coagulation, and downregulation of pathways related to fatty acid metabolism and mitochondrial function, mirroring changes observed in pressure overload mouse models. A cross-species transcriptomic comparison revealed significant overlap between ERBB4-correlated gene expression and functional pathways in adult human atria and mice with Erbb4 haploinsufficiency. Validating the transcriptomic data, protein and functional assays demonstrated increased fibrosis, apoptosis, and oxidative stress in the mutant left atrial tissue. Conclusion Left atrial ERBB4 levels are reduced in AF patients. A mouse model of Erbb4 deficiency and human atrial transcriptomic analyses highlight a role for ERBB4 in supporting normal atrial metabolism while protecting against inflammation, apoptosis, and fibrosis.

Background: Traditional heart rate (HR) adjusted QT correction (QTc) formulae often fail to eliminate the inverse HR-QT interval relationship, particularly in pediatric patients. In this study, we optimized our previously published adaptive QTc (QTcAd) formula by including additional demographic variables and broadening the pediatric age range. We tested the hypothesis that QTcAd improves congenital long QT syndrome (congenital LQTS) detection performance and reduces erroneous classifications across pediatric cohorts. Methods: We retrospectively analyzed 8,306 ECGs from 4,556 cardiovascular disease (CVD)-free pediatric patients. For neonatal patients (1-30 days old), we derived daily QTcAd parameter values. For older patients, we developed regression models to estimate QTcAd parameters (mean Heart Rate (HR) = -15.9ln(days) + 219; |m| = 0.0001(days) + 1, where |m|=absolute HR-QT regression slope). To support LQTS screening, we constructed dynamic QTcAd thresholds by estimating age-specific reference limits. Diagnostic performance was tested in a clinically confirmed LQTS cohort (n=137), and further evaluated in the Pediatric Heart Network (PHN; n=2,394) and Emergency Department (ED; n=2,002) cohorts. Results: Using the confirmed LQTS cohort as the event population and the CVD-free cohort as the non-event population, QTcAd demonstrated higher sensitivity than QTcB (92% vs 46.7%). QTcAd maintained high specificity (96.9% vs 98.9%), which resulted in a higher Youden index (0.889 vs 0.456). In the PHN healthy cohort, both QTc formulae classified the majority of individuals as normal (QTcAd 95%; QTcB 98.2%) indicating few false-positives. In the ED cohort, QTcAd reduced borderline/prolonged QTc classifications requiring follow-up, yielding 270 fewer repeat-testing triggers than QTcB. We developed a publicly accessible calculator to compute QTcAd and classify congenital LQTS risk. Conclusion: We developed and validated an enhanced QTcAd formula for pediatric patients. QTcAd-based-age-adjusted dynamic thresholding improved performance for congenital LQTS screening, while maintaining high specificity. This reduces false-positive LQTS classifications and repeat ECGs, thereby decreasing unnecessary downstream clinical evaluation.

BackgroundRisk stratification in hypertension remains challenging. The prognostic value of plasma renin in guiding therapy for hypertension is not well established. MethodsIn this multicenter retrospective cohort of 16,600 people with hypertension, we evaluated the association between plasma renin activity and major adverse cardiovascular events (MACE) defined as stroke, myocardial infarction, and all-cause death. Plasma renin was analyzed as a continuous variable using restricted cubic splines. A 6-month landmark analysis assessed treatment effects of mineralocorticoid receptor antagonists (MRA) as opposed to baseline renin-angiotensin system inhibitors. ResultsContinuous renin level showed a U-shaped association with MACE, with the lowest risk at 1.17ng/mL/h. In categorical analyses, low renin (<0.3 ng/mL/h; adjusted hazard ratio [HR]=1.29, 95% CI 1.15-1.45) and high renin (>3.0ng/mL/h; HR=1.19, 95% CI 1.06-1.33) were both associated with higher MACE risk. Initiation of MRA therapy after renin measurement was associated with a graded reduction in MACE risk where patients with low renin had the lowest risk (HR=0.75, 95%CI 0.60-0.92), and patients with high-renin had the highest risk (HR=1.41, 95%CI 1.03-1.94). In contrast, baseline use of renin-angiotensin system inhibitors was associated with a graded reduction in MACE risk where patients with high-renin had the lowest risk (HR=0.76, 95%CI 0.63-0.92) but those with low renin did not benefit (HR=0.87, 95%CI 0.72-1.04). ConclusionsPlasma renin is a prognostic biomarker for MACE and may serve as a guide for treatment selection. A renin-guided strategy that favors MRAs in patients with low renin may reduce MACE and support individualized hypertension care. Clinical PerspectiveO_ST_ABSWhat is News?C_ST_ABSO_LIIn this large multicenter cohort of 16,600 patients with hypertension, plasma renin activity demonstrated a U-shaped association with major adverse cardiovascular events, with increased risk observed at both suppressed and elevated renin levels. C_LIO_LIRenin-defined hypertensive phenotypes were associated with differential treatment responses that mineralocorticoid receptor antagonist initiation was associated with lower cardiovascular risk in patients with low renin, whereas baseline renin-angiotensin system inhibitor use was associated with lower risk in patients with higher renin. C_LIO_LIThese findings extend the clinical role of renin beyond screening for primary aldosteronism, suggesting that renin may serve as an accessible marker that links hypertension pathophysiology, cardiovascular risk, and treatment responsiveness. C_LI What Are the Clinical Implications?O_LIPlasma renin may help clinicians move beyond blood pressure levels alone and recognize biologically distinct forms of hypertension that may require different therapeutic strategies. C_LIO_LISuppressed renin may identify a broader phenotype of renin-independent aldosteronism or mineralocorticoid receptor activation, in which earlier consideration of mineralocorticoid receptor antagonist therapy may be appropriate even without a formal diagnosis of primary aldosteronism. C_LIO_LIA renin-guided treatment strategy may provide a practical framework for mechanism-based hypertension care, while prospective studies are needed to determine whether this approach improves long-term cardiovascular outcomes. C_LI

Background. Adults with congenital heart disease (CHD) are a growing population and face unique challenges as they age. Unlike acquired diseases that disrupt a previously healthy baseline, CHD is developmentally embedded. Allostatic load, the multi-system biological "wear and tear" exacted by the continuous cost of coping, offers a framework for indexing this lifelong psychophysiological stress. Methods. We analyzed 14,469 adults from the All of Us Research Program: non-syndromic CHD (n = 6,810), acquired heart disease (AHD; n = 2,264), non-cardiac chronic illness (n = 4,331), and a general population comparison cohort (GP; n = 1,064). Using a standardized operationalization, allostatic load was scored across five biomarker domains (AL5, range 0-5). A pre-specified primary test compared adjusted AL5 between CHD and GP. Exploratory analyses examined clinical predictor of this gap and whether baseline subjective health predicted prospective AL5 change, utilizing strictly matched biomarkers across timepoints to prevent substitution artifacts. Results. Adults with CHD carried significantly higher allostatic load than the general population comparison cohort (adjusted difference +0.30 AL5 units, 95% CI 0.24-0.37, p < .001). Cumulative comorbidity and cardiac medication burden explained most of this gap. Congenital anatomical complexity did not independently predict this burden. In a prospective subsample (n = 8,031, mean follow-up 2.7 years), worse baseline mental health predicted increases in allostatic load over time in CHD. Baseline physical health showed no such prospective association. The general population and acquired heart disease cohorts demonstrated the inverse dissociation: subjective physical health predicted these longitudinal physiological changes. Conclusions. Adults with CHD carry an elevated allostatic burden dictated by the cumulative cost of acquired medical and treatment intensity. The original congenital anatomy does not predict this accumulation. Furthermore, subjective mental health prospectively tracks future increases in allostatic load in CHD. This dissociation is absent in adult-onset acquired heart disease, suggesting that the mental aspects of coping with CHD may impact outcomes above and beyond those with acquired heart disease. These findings position psychological care as a potentially physiologically consequential intervention.

Peripheral artery disease (PAD) is a complex vascular disorder characterized by heterogeneous molecular mechanisms and incomplete functional annotation, limiting systematic biomarker discovery. Network-based learning approaches provide a powerful framework for disease gene prioritization; however, most existing methods produce overconfident predictions without explicitly accounting for model uncertainty or structural novelty. Here, we present an uncertainty-aware framework for PAD biomarker discovery that integrates unsupervised graph representation learning, positive-unlabeled (PU) classification, ensemble prediction, and mechanistic explainability. Node embeddings were learned using multiple unsupervised graph neural network (GNN) objectives and combined with heterogeneous classifiers to generate ensemble-averaged probability estimates and epistemic uncertainty. By jointly modeling predictive confidence and embedding-space novelty, we stratified candidates into high-confidence rediscoveries and structurally novel hypotheses under explicit uncertainty control. Across eight embedding objectives and five classifiers, ensemble aggregation produced stable, well-calibrated predictions and enabled prioritization of 100 candidate PAD-associated proteins. Probability-heavy candidates clustered tightly with known PAD proteins and were enriched for established vascular and hemostatic pathways, including extracellular matrix organization, integrin signaling, coagulation, and fibrinolysis. In contrast, novelty-heavy candidates occupied distinct embedding-space regions and partitioned into multiple coherent clusters enriched for upstream regulatory and signaling processes, including G protein-coupled receptor, ephrin receptor, kinase-driven, and NF-{kappa}B-associated pathways. Five-fold cross-validated comparison with established PU learning baselines demonstrated consistent improvement across all evaluation metrics (AUC 0.916 {+/-} 0.019 vs. 0.821 {+/-} 0.030 for the best baseline), and external validity was confirmed by significant enrichment of top candidates for related cardiovascular disease annotations (5.7x above background). Together, these results demonstrate that integrating uncertainty, novelty, and explainability enables calibrated and biologically grounded biomarker prioritization, with broad applicability to PAD and other complex diseases. Author summaryPeripheral artery disease affects millions of people worldwide but remains underdiagnosed, partly because we lack reliable molecular markers to detect it early. In this study, we developed a computational framework that uses protein interaction network data to predict which proteins may be involved in PAD, even when we only know a small number of confirmed disease-associated proteins. Our approach combines graph neural network embeddings with a machine learning technique called positive-unlabeled learning, which is specifically designed for situations where you have confirmed positives but no confirmed negatives. We also quantify how confident the model is in each prediction and identify candidates that are genuinely novel compared to what is already known. Tested against established methods, our framework consistently found more known disease proteins in cross-validated evaluation. The candidates we identified map to biologically coherent pathways relevant to vascular disease, and our top predictions are enriched for proteins associated with related cardiovascular conditions, providing external validation. This work provides a principled and transparent approach to biomarker discovery that could be applied to other complex diseases with limited molecular annotations.

Congenital heart defects (CHD) are a common congenital anomaly and a leading cause of neonatal mortality. Even in ostensibly isolated cases, genetic testing can reveal monogenic causes of isolated CHD or identify syndromic conditions before additional features become clinically apparent. A timely and accurate genetic diagnosis can inform medical management and surveillance, reduce the need for unnecessary investigations, and offer families valuable information about prognosis, recurrence risk, and anticipatory guidance. In September of 2023, Primary Childrens Hospital introduced a universal genetic testing protocol that implemented whole genome sequencing for all neonates admitted to the cardiac intensive care unit (CICU) undergoing cardiac surgery before 30 days of life, with the goal of increasing the number of patients who receive a timely genetic diagnosis and improving clinical care. This is a retrospective chart review of patients who underwent whole genome sequencing (WGS) under the new universal genetic testing protocol at Primary Childrens Hospital from its initiation in September 2023 to February 2026. Over the study period, 217 neonates with CHD participated in the universal WGS protocol. Of these patients, 23 (10.6%) received a genetic diagnosis that was causative of their CHD, of which 11 patients (48%) had no major extracardiac features at the time testing was ordered. Twenty patients were diagnosed with a syndromic condition, and three patients were diagnosed with a non-syndromic condition. All of these patients received additional referrals to specialists following their new diagnosis, and six families used results to inform decisions regarding continuation of care. An additional 19 patients (8.8%) received WGS results that were clinically relevant but non-diagnostic for their CHD, including partial diagnoses, secondary findings, and carrier status. In total, 19.4% of patients (n=42) had clinically relevant variants identified on their WGS.

Background: Chronic low-grade inflammation drives cardiovascular-kidney-metabolic (CKM) syndrome. Clonal hematopoiesis of indeterminate potential (CHIP), an age-related driver of systemic inflammation, is linked to several cardiometabolic disorders. However, whether CHIP modifies CKM progression and contributes to heterogeneity in cardiovascular disease (CVD) risk within the CKM framework remains uninvestigated. Methods: This cohort study included 307,025 UK Biobank participants at CKM stages 0-3 free of baseline CVD. CHIP status was identified via whole-exome sequencing (WES). The association between CHIP and baseline CKM severity was examined, along with the independent and joint effects of CHIP and CKM stages on incident CVD risk. The joint effects of CHIP and polygenic risk scores (PRS) were further assessed, and the incremental predictive value of incorporating CHIP into the AHA PREVENT equations was evaluated. Results: CHIP carriers were more likely to present with advanced CKM stages [OR 1.14 (1.09-1.20), P < 0.001] and exhibited higher incident CVD risk during follow-up [HR 1.13 (1.08-1.18), P < 0.001]. Significant joint effects between CHIP and CKM stages were observed, with the highest risk among CHIP carriers at CKM stage 3 [HR 1.63 (1.50-1.78), P < 0.001]. Large or multiple CHIP mutations conferred greater hazards, with distinct gene-specific effects observed. Moreover, CHIP and high genetic risk also jointly amplified CVD susceptibility. Most importantly, incorporating CHIP into AHA PREVENT significantly improved risk discrimination. Conclusions: CHIP is a significant risk factor associated with more advanced CKM stages and amplifies incident CVD risk. Integrating CHIP into existing prevention strategies may refine CVD risk stratification.

Background: Resting electrocardiogram (ECG) are associated with heart failure (HF) events, even though it is not currently recommended in risk assessment. Objective: To examine the association between ECG abnormalities and incident HF events according to the 2023 PREVENT HF equation. And identify a subgroup of individuals who are misclassified as being at low risk. Design: Secondary data analysis from the REasons for Geographic And Racial Differences in Stroke (REGARDS) prospective cohort, including study participants without baseline HF. Exposure: ECG abnormalities were classified by Minnesota Code (MC) as normal, only minor, or any major abnormality at baseline (2003-2007). Outcome: Participants were followed for expert adjudicated incident HF hospitalizations/ deaths through December 2021. Results: Among 20,923 participants (mean age at baseline 63.6 years, 53.7% female), 26.0% of the sample was classified as low risk (<3%), 17.5% as borderline risk (3-<5%), 27.5% as intermediate risk (5%-<10%), and 29.0% as high risk (10%). Compared to those without ECG abnormality, the adjusted HR for incident HF was 1.56 (95% CI 1.35-1.80) for any minor abnormality and 2.56 (2.18-3.00) for any major abnormality. 43.5% of the population were in the less than 5% risk by PREVENT among whom 45.8% had any ECG abnormalities. The fully adjusted HR for only minor ECG abnormalities in the <3% was 1.47 (95% CI 0.72-3.01), and the fully adjusted HR for any major ECG abnormality was 5.22 (95% CI 2.42-11.30). In the borderline risk group, the fully adjusted HR for only minor ECG abnormalities was 1.37 (95% CI 0.89 - 2.11), and the fully adjusted HR for any major ECG abnormality was stronger than the HR in the intermediate and high-risk groups; 3.05 (95% CI 1.85 - 5.03). Conclusion: ECG abnormalities were common and associated with HF events across all PREVENT risk groups, especially in the low/borderline risk groups with major ECG abnormalities.

Background: Concomitant arrhythmogenic right ventricular cardiomyopathy (ARVC) and mitral valve prolapse (MVP) has only been described in case reports. Little is known about genetic and phenotypic characteristics of these patients. Objective: To describe the prevalence, genetics, and imaging characteristics of MVP in ARVC patients. Methods: We identified 111 definite ARVC cases through medical record review, arrhythmia/cardiomyopathy targeted gene panels, and contrast cardiac magnetic resonance data. MVP was diagnosed on echocardiography as mitral leaflet displacement greater than 2 mm above the annular plane in systole, with borderline MVP defined as less than or equal to 2 mm. Results: We found MVP/borderline MVP in 14% of ARVC patients. Cardiac arrest occurred in 20% of those with MVP/borderline MVP compared to 16% without valve abnormalities. Among 69 ARVC patients with identified genetic variants, PKP2 mutations were highly prevalent (64%), particularly in those with MVP (83%). Most MVPs had posterior prolapse (73%) and trace/mild mitral regurgitation (87%). None had mitral annular disjunction. ARVCs with MVP had higher LV mass (93 vs. 75 g/m2, p = 0.02) and a higher prevalence of LV wall motion abnormalities (27% vs. 5%, p = 0.02) compared to ARVCs without valve abnormalities. Conclusions: MVP is prevalent in ARVC and characterized by PKP2 variants in most cases. Typical features of arrhythmic MVP like bileaflet involvement and annular disjunction are rare in ARVC with MVP; features of arrhythmogenic left-sided cardiomyopathy (increased LV mass index and wall motion abnormalities) are more common. Further studies are needed to understand the role of MVP in arrhythmic risk stratification of ARVC.

Cardiovascular diseases (CVDs) are highly heritable, but pathogenesis at the organ and physiological level is still poorly defined. Polygenic risk scores (PRSs), which estimate individual genetic susceptibility to a disease, may allow for the identification of associated abnormal organ structures. Ultimately, identifying where cardiovascular polygenic risk manifests can guide early interventions, shape mechanistic hypotheses, and motivate prevention trials for cardiac remodelling. This study investigated the association between PRSs for five common CVDs [heart failure (HF), coronary artery disease (CAD), atrial fibrillation (AF), abdominal aortic aneurysm (AAA) and ischaemic stroke (IS)] and 28 imaging-derived phenotypes (IDPs) from cardiac magnetic resonance imaging of ~62,000 participants in UK Biobank. To investigate the cardiac features associated with elevated polygenic risk of CVDs, we tested CVD PRSs against cardiac IDPs and identified 97 significant associations (FDR [&le;] 0.05). We further identified 32 significant putative mediators between CVD PRSs and incident disease events, revealing that across CVDs, polygenic risk manifested as distinct patterns in cardiac structures. HF implicated all cardiac chambers, including left ventricular and left atrial dysfunction alongside enlarged aorta. AF was characterised by biatrial enlargement and reduced ejection fractions, most prominently in the left atrium but also involving left ventricular wall thickness. IS exhibited left ventricular hypertrophy and left atrial dysfunction, while CAD predominantly involved left ventricular hypertrophy. AAA was primarily characterised by enlarged descending aorta. Overall, cardiac IDPs mediated a substantial proportion of polygenic risk for CVDs, in particular for HF. Taken together, our results show that cardiac structure and function lie on the pathway between polygenic risk and cardiovascular events.

Background World Symposium on Pulmonary Hypertension (WSPH) Group 2 pulmonary hypertension (PH) is a clinically integrated phenotype attributed to left heart disease, whereas pre- versus post-capillary classification is operationalized primarily by pulmonary capillary wedge pressure (PCWP). Although current recommendations emphasize contextual interpretation and provocative testing for intermediate PCWP values, the relationship between PCWP-based classification and underlying phenotype has not been systematically evaluated. We aim to quantify phenotype-hemodynamic discordance across the PCWP spectrum and evaluate a staged physiology-guided framework incorporating inhaled nitric oxide (iNO), ventricular geometry, and provocative testing. Methods We studied 1,032 participants from the NHLBI-sponsored PVDOMICS cohort with multidisciplinary adjudicated phenotypes integrating clinical, imaging, physiologic, and hemodynamic data. Stage-specific PCWP thresholds classified pre- versus post-capillary physiology at rest, during iNO, and during provocation (fluid challenge or invasive cardiopulmonary exercise testing [iCPET]). Echocardiographic right ventricular-to-left ventricular (RV/LV) ratio was evaluated as a marker of ventricular interdependence. Restricted cubic spline and staged concordance analyses defined certainty-based PCWP ranges and incremental diagnostic yield. Results Adjudicated Group 2 phenotype was present in 37.0% of participants. Resting PCWP demonstrated good discrimination (AUC 0.86), but substantial bidirectional phenotype-hemodynamic discordance persisted across intermediate PCWP ranges. At a resting PCWP of 12 mmHg, 25% of participants classified as pre-capillary had adjudicated Group 2 PH, whereas at 18 mmHg, 35% classified as post-capillary remained discordant non-Group 2. Concordance did not approach 90% until PCWP values were <9 mmHg or >24 mmHg. Dynamic testing incrementally improved concordance within these overlap zones. Nearly half of adjudicated Group 2 PH participants (46.5%) were not identified by resting PCWP alone; incorporation of iNO and provocative testing increased cumulative Group 2 identification by 63.4% and improved sensitivity from 79.9% to 83.7%. Model discrimination improved from an AUC of 0.863 to 0.908 (likelihood-ratio P<0.001). iNO increased PCWP in discordant Pre/G2 participants, unmasking latent left-sided limitation, while lowering PCWP in discordant Post/NonG2 participants, consistent with ventricular interdependence. RV/LV ratio [&ge;]0.94 reduced discordant Post/NonG2 classification by 70.5%, and incorporation of PCWP/cardiac output slope improved physiologic specificity during exercise. Conclusions Group 2 PH is a dynamic, load-dependent phenotype inadequately characterized by resting PCWP alone. Intermediate PCWP values represent continuous probabilities of bidirectional discordance rather than discrete diagnostic states. A staged physiology-guided approach integrating iNO, ventricular geometry, and provocative testing improves concordance between hemodynamic classification and clinically integrated phenotype assignment.