Circulation: Genomic and Precision Medicine

Williams syndrome is a developmental disorder caused by a microdeletion entailing the loss of a single copy of 25-27 genes on chromosome 7q11.23. Patients suffer from cardiovascular and neuropsychological symptoms. Structural abnormalities of the cardiovascular system in Williams syndrome have been attributed to the hemizygous loss of the elastin (ELN) gene. In contrast, the neuropsychological consequences of Williams syndrome, including sensorimotor deficits, hypersociability and cognitive impairments, have been mainly attributed to altered expression of transcription factors like LIMK1, GTF2I and GTF2IRD1, while the potential impact of altered cerebrovascular function has been largely overlooked. To study the relationship between Williams syndrome mutations and vascularization of both the heart and brain, we generated a mouse model carrying a relatively long microdeletion (LD) that includes the Ncf1 gene, thereby minimizing the confounding impact of hypertension. LD mice had elongated and tortuous aortas but, unlike Eln haploinsufficient mice, showed no signs of structural cardiac hypertrophy. Remarkably, LD mice also displayed structural abnormalities in coronary and brain vessels, including disorganized extracellular matrices. Importantly, LD mice faithfully replicated both cardiovascular and neuropsychological symptoms observed in patients. The phenotype was even more comprehensive than former models, with structure-function correlations evident in aberrant auditory and motor behaviors resembling those in patients with Williams syndrome. Together, our findings suggest that not only cardiovascular but also neuropsychological symptoms in Williams syndrome may be driven in part by vascular abnormalities affecting both heart and brain. Significance StatementWilliams syndrome is caused by microdeletion of 25-27 genes on chromosome 7q11.23, resulting in cardiovascular and neuropsychological symptoms. It remains unclear how the affected genes interact and whether cardiovascular deficits influence brain function. We developed and characterized a mouse model with the longest Williams syndrome microdeletion to date. This model reveals interactions between genes that can be compensatory or additive: haploinsufficiency of Ncf1 may counteract the cardiac hypertrophy caused by Eln deletion, while vascular defects that are potentially due to Eln haploinsufficiency extend to the brain and may worsen neuropsychological symptoms. Our findings support the hypothesis that structural vascular deficits putatively contribute to both cardiac and cognitive phenotypes in Williams syndrome, opening new avenues for understanding and treating this syndrome.

BackgroundAnnually 300,000 Americans experience sudden cardiac arrest (SCA). Studies in referral SCA cohorts have observed rare variants in genes associated with arrhythmia and cardiomyopathy. We sought to: (1) establish the population prevalence of rare disease-causing variants in a set of candidate genes and (2) confirm the association of disease-causing variants in these genes with SCA in two prospective population-based studies. MethodsSCA patients (n=3264) were accrued from the Oregon Sudden Unexpected Death Study and the PREdiction of Sudden death in mulTi-ethnic cOmmunities (PRESTO) study and compared to control patients (n=13713) from the Atherosclerosis Risk in Communities (ARIC) study. Whole genome sequencing was performed. Disease-causing (likely pathogenic or pathogenic) variants in candidate genes associated with arrhythmia/cardiomyopathy were identified using updated American College of Medical Genetics and Genomics criteria. Gene- collapsing case-control analysis was performed using the conditional logistic regression-sequence kernel association test. ResultsWe identified 300 disease-causing variants, the majority of which were in cardiomyopathy genes (71%). There were 136 patients (4.2%) in the SCA group and 351 patients (2.6%) in the control group with one or more disease-causing variants (OR 1.66, 95% confidence interval 1.33-2.07, p<0.001). We identified 13 genes associated with an increased risk of SCA, nine associated with cardiomyopathy (BAG3, DSC2, DSG2, FLNC, LMNA, MYBPC3, TNNI3, TNNT2, TTN) and four with arrhythmia (CACNA1C, CASQ2, KCNH2, KCNQ1). ConclusionsDisease-causing variants in cardiomyopathy genes were the predominant genetic cause of SCA. These findings inform which genes to include in genetic screening for SCA.

BackgroundVariant interpretation can change over time as new knowledge emerges. Our aim was to determine the frequency and causes of variant reinterpretation on systematic re-evaluation in pediatric patients with cardiomyopathy. MethodsOverall, 227 unrelated pediatric patients with cardiomyopathy enrolled in the Heart Centre Biobank harbored a pathogenic/likely pathogenic (P/LP) variant and/or a variant of uncertain significance (VUS) on clinical genetic testing (2005-2022). Variant pathogenicity was re-evaluated using the American College of Medical Genetics and Genomics (ACMG) guidelines. Additional extension cohorts (n=4547, cases) were analyzed to assess variant burden in cases versus controls (gnomAD 4.1.0). Results382 variants (110 P/LP, 272 VUS) in 227 patients were re-evaluated. Forty-nine variants in 49 patients (21.6%) changed classification. Twelve (10.9%) P/LP variants were downgraded to VUS in 14 patients. Leading criteria were high population allele frequency and variant not located in mutational hotspot or critical functional gene domain. Thirty-seven (13.6%) VUS were upgraded to P/LP in 35 patients. Leading criteria were variant location in mutational hotspot for gene, and deleteriousness on in silico prediction. Only 8 reclassified variants had been reported back by the clinical genetic testing laboratory at the time of the study. Ten of the 37 VUS upgraded to P/LP were significantly enriched in cardiomyopathy cases (n=4796) versus controls. ConclusionsOne in five patients with cardiomyopathy had a clinically relevant change in variant pathogenicity on systematic re-evaluation that would require modifying family clinical screening and cascade genetic testing. These findings underscore the clinical importance of regular variant re-interpretation on follow-up.

BackgroundPediatric dilated cardiomyopathy (DCM) is a rare, progressive heart disease with variable outcomes that range from recovery to heart transplantation. To date, there are no prognostic biomarkers for children with DCM. Identifying circulating biomarkers that are associated with clinical outcomes is critical for personalized management. MethodsmiRNAs were identified by RNA-seq, whereas proteins were identified by SomaScan(R). Machine learning methodologies were used to explore the predictive ability of circulating factors identified from serum samples collected at the time of presentation with acute heart failure. ResultsThirty patients experienced poor outcomes (cardiac transplantation, mechanical circulatory support, or death) and 19 patients recovered left ventricular function. Distinct miRNA and protein signatures differentiated outcomes groups. Top candidate proteins (COL2A1, CXCL12, and ADGRF5) and miRNAs (miR-874-3p, miR-335-3p, miR-323a-3p) demonstrated strong discriminatory performance within the study cohort (recovered vs poor outcomes; Area Under the Curve of 0.92). Ingenuity Pathway Analysis implicates cardiac remodeling, fibrosis, and inflammatory signaling as central pathways differentiating patient outcomes. ConclusionsCirculating miRNA and protein signatures at presentation identify a circulating molecular signature associated with divergent clinical trajectories in pediatric DCM. These findings support the potential utility of multi-omic biomarkers for early risk stratification and provide insight into mechanisms underlying divergent outcomes. CLINICAL PERSPECTIVEWhat Is New? O_LICirculating miRNA and protein profiles measured at presentation distinguish children with pediatric DCM who recover from those who progress to advanced heart failure. C_LIO_LIA combined multi-omic biomarker demonstrated strong discriminatory performance in this cohort (AUC 0.92). C_LIO_LIPathway analysis implicates extracellular matrix remodeling, fibrosis, and inflammatory signaling in children with adverse clinical trajectories. C_LI What Are the Clinical Implications? O_LISerum-based molecular biomarkers may enable earlier risk stratification in children presenting with dilated cardiomyopathy. C_LIO_LIMulti-omic integration may improve identification of pediatric patients at risk for transplantation, mechanical circulatory support, or death. C_LIO_LIThese findings support further validation of circulating biomarker panels to guide personalized management in this rare disease. C_LI RESEARCH PERSPECTIVEWhat New Question Does This Study Raise? O_LICan integrated circulating miRNA-protein signatures identify biologically distinct trajectories of recovery versus progression in children with dilated cardiomyopathy? C_LIO_LIDo circulating molecular profiles reflect underlying disease mechanisms that determine divergent clinical outcomes in pediatric DCM? C_LI What Question Should Be Addressed Next? O_LIDo the pathways identified by integrated miRNA-protein analysis (fibrosis, remodeling, and inflammation) play causal roles in determining recovery versus progression? C_LIO_LICan multi-omic biomarkers be incorporated into prospective studies to improve early risk stratification and guide clinical management? C_LI

BackgroundLipoprotein(a) (Lp(a)) levels are a largely genetically determined and often an unmeasured predictor of future Atherosclerotic Cardiovascular Disease (ASCVD). With the increased use of exome sequencing in the clinical setting, there is opportunity to identify patients who have a high chance of having elevated Lp(a) and are therefore at risk of ASCVD. However, accurate genetic predictors of Lp(a) are challenging to design. In addition to single nucleotide variants (SNVs), which are often summarized as a combined genetic risk score, Lp(a) levels are significantly impacted by copy number variation in repeats of the kringle IV subtype 2 domain (KIV-2), which are challenging to quantify. KIV-2 copy numbers are highly variable across populations, and understanding their impact on Lp(a) levels is important to creating an equitable and reliable genetic predictor of Lp(a)-driven cardiovascular risk for all individuals. MethodsWe develop a novel method to quantify individuals total number of KIV-2 repeats from exome data, validate this quantification against measured Lp(a) levels, and then use this method, combined with a SNV-based genetic risk score, to genotype an entire all-comers cohort of individuals from health systems across the United States (Helix Research Network; N = 76,147) for an estimated Lp(a) level. ResultsOur combined genotyping strategy improved prediction of those with clinically-elevated Lp(a) measurements across the genetically diverse cohort, especially for individuals not genetically similar to European reference populations, where GRS-based estimates fall short (r2= 0.04 for GRS, r2 = 0.34 KIV2+GRS in non-European). Importantly, high combined genetic risk of high Lp(a) genotypes are significantly associated with earlier onset and increased incidence in ASCVD, compared to average and low combined genetic risk genotypes in a retrospective analysis of atherosclerotic diagnoses derived from electronic health records (EHRs). This holds in the cohort at large (CAD HRs=1.29, 1.58), in the European subcohort (HRs=1.30,1.61) as well as at trending levels of significance in individuals not genetically similar to Europeans (HRs=1.22,1.31). In addition, high combined genetic risk for high Lp(a) genotypes are at least 2-fold enriched amongst individuals with ASCVD diagnosis despite a lack of EHR-based evidence of traditional risk factors for cardiovascular disease. ConclusionsOur study demonstrates that genetically predicted Lp(a) levels, incorporating both SNV and our novel KIV-2 repeat estimate, may be a practical method to predict clinically elevated Lp(a). Supporting this, individuals with high combined genetic risk for high Lp(a) have an increased risk for ASCVD, as evidenced across data from seven US-based health systems.

BackgroundMonogenic cardiovascular diseases (MCVD) remain substantially under-diagnosed, and thus interest is shifting from a phenotype-forward approach towards a genotype-first strategy that applies to population-wide genomic screening. We evaluated the prevalence of pathogenic and predicted pathogenic MCVD variants and assessed disease expression in the population-based UK Biobank (UKB) cohort to determine whether yield is greater in phenotype-versus genotype-forward approach. MethodsPathogenic and likely pathogenic (P/LP) variants, and variants of uncertain significance (VUS) predicted to be pathogenic (pp-VUS) in 47 MCVD genes were assessed. In the genotype-forward approach, UKB participants with P/LPs or pp-VUS were assessed for symptoms of disease using electronic health record (EHR) interrogation, emulating a clinical approach to population-based screening. In the phenotype-forward approach, participants with stricter EHR-based evidence of disease expression were identified (emulating current clinical care) and the presence of P/LPs and pp-VUS was determined. ResultsFollowing QC, 467,850 participants were included. Overall, 1 in 125 participants in UKB carry an MCVD P/LP. In the genotype-forward approach, 3,709 (0.79%) participants carried an MCVD P/LP and 42.1% of these had symptoms of the associated MCVD; another 29,269 (6.3%) carried a pp-VUS with 21.5% of these having symptoms of disease. In the phenotype-forward approach, 62,488 (13.4%) expressed an MCVD using strict evidence of disease expression and of these individuals 1% carried an associated P/LP variant and 2.4% carried an associated pp-VUS. ConclusionWe show here that a genotype-forward approach leads to a 2.5 times higher diagnostic yield (3.6 times higher when considering pp-VUS) compared with the phenotype-forward approach, which missed 936 P/LP carriers with disease symptoms. While cost, access and ethics need to be considered, these results support expanded population-based genetic screening to improve diagnosis of potentially treatable MCVD.

The role of race in medical decision-making has been a contentious issue. Insights from history and population genetics suggest considering race as a differentiating marker for medical practices can be influenced by systemic bias, leading to serious errors. This may negatively impact treatment of complex diseases such as cardiovascular disease (CVD). We seek to identify instrumental variables and independently verifiable epidemiological tests of whether diagnoses and treatments impacting severe cardiovascular conditions are racially linked. Using data from the UK Biobank (UKB), we found minimal, non-significant racial differences in log odds ratio (OR) between a range of cardiovascular outcomes such as atrial fibrillation, coronary artery disease, coronary thrombosis, heart failure and cardiac fatality. Genetics classification with respect to principal components vs. racial identification of Black British showed no significant differences in diagnoses or therapeutics for CVD related diseases and their associated comorbidities. However, Black British had significant risk of association with genetically predisposed risk of CVD as captured by polygenic risk scores (PRS) of CVD (OR=1.12; 95%CI:1.034-1.223; p < 0.006) as well as in 14 related traits. We used a sub-population based feature selection method to find Townsend Deprivation Index, smoking history, hypertension, PRS for ischemic stroke, low density lipoprotein cholesterol, and type II diabetes as the top features predicting the ethnographic category of Black British with an AUC of 79.5%. Therefore, PRS can be used to understand racial disparities in disease outcome which is otherwise not reflected in clinical factors such as diagnoses outcome status or therapeutics in large observational cohorts such as UKB. PRS yield better predictive power with underrepresented minorities and can improve clinical decision-making.

BackgroundThe routine genetic testing of cardiomyopathy patients has significantly accelerated the identification of causative cardiomyopathy variants. However, translating these genetic insights into effective patient management poses significant challenges, since the impact of gene variants on physiological function and clinical outcomes is not yet fully understood. Therefore, there is an urgent need for large-scale methods to assess the effects of genetic variants on cardiomyocyte physiology and to establish correlations between functional phenotypes and clinical severity. MethodsWe developed a high throughput imaging platform to measure force generation and calcium handling throughout the cardiac cycle of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). By expressing variants of a sarcomeric protein [cardiac Troponin-I (TNNI3)] in a healthy genetic background, we were able to assess sarcomeric calcium sensitivity as well as systolic and diastolic function. Analysis of these parameters distinguished subgroups of variants, and permitted the correlation of in vitro physiological effects with a measure of disease severity in a single-center cardiomyopathy cohort. ResultsCombining contractile force and calcium cycling measurements accurately distinguished known pathogenic from non-pathogenic TNNI3 variants and also revealed pathogenicity of two variants of unknown significance (VUS) that occurred in two families, suggesting the ability to prospectively discern pathogenicity. Clustering of TNNI3 variants based on quantitative physiological phenotypes identified subgroups that correlated with age of disease onset across a well-characterized cardiomyopathy patient cohort, showing clinical relevance of the in vitro phenotypes. Interestingly, normalized measures of in vitro diastolic function correlated with age of onset (R2 = 0.6), but calcium sensitivity, which accurately predicted pathogenicity, did not translate into disease severity. ConclusionsA high throughput in vitro platform that measures multidimensional cardiomyocyte function can link subgroups of human genetic variants in TNNI3 with differential patient outcomes. Comprehensive determination of variant effects on disease-relevant cardiomyocyte function will help classify variants into different pathogenic mechanisms leading to variable disease severity, and potentially lead to class-targeted ameliorative strategies.

BackgroundA coronary artery calcium (CAC) score of 0 is widely considered to indicate low short- to intermediate-term risk for coronary artery disease (CAD) and is frequently used to defer lipid-lowering therapy. However, a subset of individuals with CAC=0 still experience events, highlighting residual risk not captured by imaging alone. Polygenic risk scores (PRS) quantify lifelong inherited susceptibility, but conventional approaches rely on predefined ancestry labels despite human genetic diversity existing along a continuum. To address this limitation, we developed 8 Billion, a novel, label-free framework that models genetic similarity without pre-labeling individuals by ancestry. We evaluated whether a CAD PRS derived using this approach identifies clinically meaningful residual risk among individuals with baseline CAC=0. MethodsWe analyzed participants from the Multi-Ethnic Study of Atherosclerosis (MESA) with baseline CAC=0. The 8 Billion framework estimates individualized PRS by anchoring each participant to a genetically similar reference neighborhood rather than discrete ancestry groups. Multivariable Cox proportional hazards models assessed associations between PRS-defined risk groups and incident CAD, adjusting for principal components of genetic variation (PC1-PC4), age, sex, smoking status, systolic blood pressure, total and high-density lipoprotein cholesterol, diabetes, and antihypertensive medication use. Two classifications were evaluated: (1) a Top 5% group defined by the highest 5% of PRS-derived odds ratios in the cohort; and (2) an individualized high-risk group defined using a personalized threshold derived from the 8 Billion framework. Ten-year absolute risk estimates were derived from adjusted models. ResultsDespite CAC=0 at baseline, polygenic burden was independently associated with incident CAD. Individuals in the Top 5% PRS group had higher risk of CAD events compared with the remainder (hazard ratio [HR], 3.12; 95% CI, 1.05-9.31; P=0.041). The individualized high-risk group defined through 8 Billion was similarly associated with increased CAD risk (HR, 2.52; 95% CI, 1.12-5.66; P=0.025). Estimated 10-year ASCVD risk among high-PRS individuals exceeded the 7.5% threshold commonly used to guide initiation of lipid-lowering therapy, despite CAC=0. In fully adjusted models, conventional risk factors were not statistically significant within this subset. ConclusionsAmong individuals with CAC=0 in a multi-ethnic cohort, a label-free, ancestry-continuum PRS approach identified subgroups at significantly increased risk of incident CAD and at guideline-relevant 10-year treatment thresholds. Integration of polygenic risk with CAC imaging refines preventive decision-making beyond imaging alone. Clinical PerspectiveO_ST_ABSWhat is new?C_ST_ABSO_LIAmong individuals with baseline CAC=0, the Allelica Multi-ancestry CAD PRS calculated with the 8 Billion framework identified subgroups at significantly increased risk of incident CAD. C_LIO_LIIn this CAC=0 population, high polygenic risk was associated with 10-year risk estimates above the 7.5% treatment threshold, whereas conventional risk factors were not statistically significant in adjusted models. C_LI What are the clinical implications?O_LIA CAC score of 0 should not be interpreted as uniformly protective, because genetically high-risk individuals may still experience clinically meaningful coronary events. C_LIO_LIIntegrating PRS with CAC assessment may improve preventive decision-making by identifying patients with residual risk despite reassuring baseline imaging. C_LIO_LIIn selected patients with CAC=0, high polygenic risk may support closer follow-up and earlier consideration of lipid-lowering therapy or other preventive strategies and imaging modalities. C_LI

BackgroundTruncating variants in TTN (TTNtvs) represent the largest known genetic cause of dilated cardiomyopathies (DCM). At the population level, even when limited to TTNtvs in cardiac-specific exons (hiPSI TTNtvs) penetrance estimates for DCM are low. Recent work shows that individuals harboring TTNtvs have a high prevalence of other cardiac conditions aside from heart failure, in particular, atrial fibrillation (Afib). ObjectivesPinpoint the genetic footprint TTN-related diagnoses aside from DCM, such as Afib, and determine if vetting additional significantly-associated phenotypes better stratifies cardiomyopathy risk across TTN carriers. MethodsWe leverage longitudinal EHR and exome sequencing data from two cohorts to determine the penetrance of TTNtvs using multiple gene expression models against Afib, CM, and other cardiac diagnoses. ResultsControlling for CM and Afib, related cardio phenotypes retain only nominal association with TTNtvs. An unbiased sliding window analysis of TTNtvs across the locus confirms the association is specific to hiPSI exons for both CM and Afib, with no meaningful associations in lowPSI exons nor improvements from LOFTEE designations. We find 34% of hiPSI TTNtv carriers with early Afib have a CM diagnosis - a 5-fold increase in risk over non-carriers with early Afib and 47-fold increase over population controls. ConclusionCM and Afib are often coincident in hiPSI TTNtv carriers, which represent varying and progressive manifestations of structurally-based heart failure. We provide statistical support for a hiPSI variant interpretation model for TTNtvs and evidence for the first population-level screening method with clinical utility for cardiomyopathies, especially in relation to an Afib finding.

BackgroundThe genetic underpinnings of cardiovascular disease remain elusive. Contrastive learning algorithms have recently shown cutting-edge performance in extracting representations from electrocardiogram (ECG) signals that characterize cross-temporal cardiovascular state. However, there is currently no connection between these representations and genetics. MethodsWe designed a new metric, denoted as Delta ECG, which measures temporal shifts in patients cardiovascular state, and inherently adjusts for inter-patient differences at baseline. We extracted this measure for 4,782 patients in the Human Phenotype Project using a novel self-supervised learning model, and quantified the associated genetic signals with Genome-Wide-Association Studies (GWAS). We predicted the expression of thousands of genes extracted from Peripheral Blood Mononuclear Cells (PBMCs). Downstream, we ran enrichment and overrepresentation analysis of genes we identified as significantly predicted from ECG. FindingsIn a Genome-Wide Association Study (GWAS) of Delta ECG, we identified five associations that achieved genome-wide significance. From baseline embeddings, our models significantly predict the expression of 57 genes in men and 9 in women. Enrichment analysis showed that these genes were predominantly associated with the electron transport chain and the same immune pathways as identified in our GWAS. ConclusionsWe validate a novel method integrating self-supervised learning in the medical domain and simple linear models in genetics. Our results indicate that the processes underlying temporal changes in cardiovascular health share a genetic basis with CVD, its major risk factors, and its known correlates. Moreover, our functional analysis confirms the importance of leukocytes, specifically eosinophils and mast cells with respect to cardiac structure and function.

BackgroundMeasures of genetic predisposition can improve prediction of risk of cardiometabolic diseases but more data is needed in groups under-represented in genomics research. In this study, we investigated the impact of genetic risk factors for coronary heart disease (CHD) - polygenic risk, monogenic risk [in the form of familial hypercholesterolemia (FH)], and family history (FamHx) - on CHD risk estimates, across the age spectrum, in two diverse cohorts of US adults - eMERGE IV (eIV) and All of Us (AoU). MethodsCHD was defined as myocardial infarction, unstable angina, and coronary revascularization. Self-identified race/ethnicity (SIRE) was used as a population descriptor. We calculated a polygenic risk score for CHD (PRSCHD, PGS004698), ascertained FH as presence of pathogenic/likely pathogenic variants in FH genes, and defined FamHx as early-onset CHD in a first-degree family member. We employed Pooled Cohort Equations (PCE) to estimate the 10-year risk of CHD for adults [&ge;]40 y and modeled the association of conventional risk factors with CHD in adults <40 y. We analyzed the impact of PRSCHD and FamHx on CHD risk estimates by a) using multivariable logistic regression and Cox proportional hazard models, assessing discrimination and the extent of risk reclassification; and b) net benefit analysis and decision curves to assess the performance of prediction models across actionable thresholds. ResultsWe analyzed data for 19,348 participants from eIV (age 50.6{+/-}15.0, 68% female, 40.5% non-White) and 239,645 participants from AoU (age 55.4{+/-}17.0, 60.6% female, 48% non-White). The effects of PRSCHD and FamHx on CHD were independent and additive in the two cohorts and incorporating both into PCE for eIV participants significantly improved discrimination (C-statistic increased from 0.719 to 0.753; P-diff=9.1x10-3) and reclassified risk in 18.7% and 20.2% of participants at the 7.5% and 10% 10-y CHD risk thresholds, respectively. Between the 7.5% and 10% 10-y CHD risk thresholds, incorporating PRSCHD and FamHx into the PCE improved the net benefit of the risk prediction models across all SIRE groups. ConclusionPRSCHD and FamHx were independently and additively associated with CHD across major SIRE groups in two diverse cohorts in the US. Incorporating PRSCHD and FamHx into PCE improved risk discrimination, reclassified risk in a significant portion of participants at actionable 10-y CHD risk thresholds, and improved net benefit of the PCE, motivating the addition of these factors to clinical risk algorithms.

BackgroundAortic stenosis (AS) and hypertrophic cardiomyopathy (HCM) are highly distinct disorders leading to left ventricular hypertrophy (LVH), but whether cardiac metabolism substantially differs between these in humans remains to be elucidated.\n\nMethodWe undertook a detailed invasive (aortic root and coronary sinus) metabolic profiling in patients with severe AS and HCM in comparison to non-LVH controls, to investigate cardiac fuel selection and metabolic remodelling. These patients were assessed under different physiological states (at rest and during stress induced by pacing). The identified changes in the metabolome were further validated by metabolomic and orthogonal transcriptomic analysis, in separately recruited patient cohorts.\n\nResultsWe identified a highly discriminant metabolomic signature in severe AS characterised by striking accumulation of long-chain acylcarnitines, intermediates of long-chain transport and fatty acid metabolism, and validated this in a separate cohort. Mechanistically, we identify a down-regulation in the PPAR- transcriptional network, including expression of genes regulating FAO.\n\nConclusionsWe present a comprehensive analysis of changes in the metabolic pathways (transcriptome to metabolome) in severe AS, and its comparison to HCM. Our results demonstrate fundamental distinctions in substrate preference between AS and HCM, highlighting insufficient long-chain FAO, and the PPAR- signalling network as a specific metabolic therapeutic target in AS.

BackgroundThe interventricular septum (IVS) plays a primary role in cardiovascular physiology and a large proportion of genetic risk remains unexplained for structural heart disease involving the IVS such as hypertrophic cardiomyopathy (HCM) and ventricular septal defects (VSD). ObjectivesWe sought to develop a reproducible proxy of IVS structure from standard medical imaging, discover novel genetic determinants of IVS structure, and relate these loci to two rare diseases of the IVS. MethodsWe performed machine learning to estimate the cross-sectional area of the interventricular septum (IVS.csad) obtained from the 4-chamber view of cardiac MRI in 32,219 individuals from the UK Biobank. Using these extracted measurement of IVS.csad we performed phenome-wide association to relate this proxy measure to relevant clinical phenotypes, followed by genome-wide association studies and Mendelian Randomization. ResultsAutomated measures of IVS.csad were highly accurate, and strongly correlated with anthropometric measures, blood pressure, and diagnostic codes related to cardiovascular physiology. A Single nucleotide polymorphism in the intron of CDKN1A was associated with IVS.csad (rs2376620, Beta 8.4 mm2, 95% confidence intervals (CI) 5.8 to 11.0, p=2.0e-10), and a common inversion incorporating KANSL1 predicted to disrupt local chromatin structure was associated with an increase in IVS.csad (Beta 8.6 mm2, 95% CI 6.3-10.9, p=1.3e-13). Mendelian Randomization suggested that inheritance of a larger IVS.csad was causal for HCM (Beta 2.45 log odds ratio (OR) HCM per increase in SD of IVS.csad, standard error (SE) 0.48, pIVW = 2.8e-7) while inheritance of a smaller IVS.csad was causal for VSD (Beta -2.06 log odds ratio (OR) VSD per decrease in SD of IVS.csad, SE 0.75, pIVW = 0.006) ConclusionAutomated derivation of the cross sectional area of the IVS from the 4-chamber view allowed discovery of loci mapping to genes related to cardiac development and Mendelian disease. Inheritance of a genetic liability for either large or small interventricular septum, appears to confer risk for HCM or VSD respectively, which suggests that a considerable proportion of risk for structural and congenital heart disease may be localized to the common genetic determinants of cardiovascular anatomy.

2.TEXT ABSTRACT AND KEYWORDSO_ST_ABSBackground and AimsC_ST_ABSPeople with monogenic familial hypercholesterolaemia (FH) are at an increased risk of premature coronary heart disease and death. Currently there is no population screening strategy for FH, and most carriers are identified late in life, delaying timely and cost-effective interventions. The aim was to derive an algorithm to improve detection of people with monogenic FH. MethodsA penalised (LASSO) logistic regression model was used to identify predictors that most accurately identified people with a higher probability of FH in 139,779 unrelated participants of the UK Biobank, including 488 FH carriers. Candidate predictors included information on medical and family history, anthropometric measures, blood biomarkers, and an LDL-C polygenic score (PGS). Model derivation and evaluation was performed using a random split of 80% training and 20% testing data. ResultsA 14-variable algorithm for FH was derived, where the top five variables included triglyceride, LDL-C, and apolipoprotein A1 concentrations, self-reported statin use, and an LDL-C PGS. Model evaluation in the test data resulted in an area under the curve (AUC) of 0.77 (95% CI: 0.71; 0.83), and appropriate calibration (calibration-in-the-large: -0.07 (95% CI: -0.28; 0.13); calibration slope: 1.02 (95% CI: 0.85; 1.19)). Employing this model to prioritise people with suspected monogenic FH is anticipated to reduce the number of people requiring sequencing by 88% compared to a population-wide sequencing screen, and by 18% compared to prioritisation based on LDL-C and statin use. ConclusionsThe detection of individuals with monogenic FH can be improved with the inclusion of additional non-genetic variables and a PGS for LDL-C.

BackgroundProteins linked to heritable coronary heart disease (CHD) could uncover new pathophysiological mechanisms of atherosclerosis. We report on the protein profile associated with a family history of early-onset CHD and whether the relation between proteins and coronary atherosclerotic burden differs according to family history status, as well as inferences from mendelian randomization. MethodsData on coronary atherosclerotic burden from computed tomography angiography and Olink proteomics were retrieved for 4,521 subjects, free of known CHD, from the Swedish CArdioPulmonary bioImage Study (SCAPIS). Records of myocardial infarction and coronary revascularization therapies in any parent of subjects were retrieved from national registers. Linear associations between family history and proteins were adjusted for age, sex and study site. Statistical interactions between proteins and family history for the association between proteins and the coronary atherosclerotic burden were also studied. Mendelian randomization for causal associations between proteins and CHD was performed with GWAS summary data from UKB-PPP, CARDIoGRAMplusC4D and FinnGen. ResultsOf 4,251 subjects, family history of early-onset CHD was present in 9.5%. 38 proteins, with biological features of inflammation, lipid metabolism and vascular function, were associated with family history using a false discovery rate of 0.05. The strongest associations were observed for follistatin and cathepsin D, neither of which were attenuated by adjusting for cardiovascular risk factors.18 proteins were statistical interactors with family history in the association between each protein and the coronary atherosclerotic burden, most notably the LDL-receptor, transferrin receptor protein 1 and platelet endothelial cell adhesion molecule 1 (PECAM1). In two-sample mendelian randomization, a novel association was found for follistatin and myocardial infarction, and previous associations for PCSK9 and PECAM1 were repeated. ConclusionsThese findings highlight new potential mechanisms for heritable and general atherosclerosis. Clinical perspectives Whats new?O_LIProteins involved in inflammation and tissue remodeling, such as follistatin and cathepsin D, are strongly and independently associated with family history of early-onset CHD, indicating novel pathophysiological mechanisms of these proteins in familial disease. C_LIO_LIIn subjects with a family history of early-onset CHD, the LDL-receptor, transferrin receptor protein 1 and PECAM1 were more strongly associated with coronary atherosclerotic burden, as compared to in subjects without family history. C_LIO_LIA novel, potentially causal association between follistatin and myocardial infarction was reported from Mendelian randomization using summary data from several GWAS projects. C_LI What are the clinical implications?O_LIThis work brings evidence of the plasma protein profile in familial coronary heart disease, guiding further research of pathophysiological mechanisms in familial and general atherosclerotic disease. C_LIO_LINew target treatments for familial and general atherosclerotic disease may evolve from identified plasma proteins of importance in familial disease. C_LI

BackgroundIndividuals with familial hypercholesterolemia (FH) require intensive lipid-lowering therapy, starting with high-intensity statins and adding ezetimibe and PCSK9 inhibitors (PCSK9i) as needed to reach target LDL-C levels. There are limited data on disparities in the use of these therapies among individuals with FH in the US. MethodsWe queried a large US healthcare claims repository consisting of 324 million individuals, focusing on prescriptions for high-intensity statins, ezetimibe, and PCSK9i in two patient groups: those diagnosed with FH (ICD-10 E.78.01) and those not diagnosed with FH but identified as having probable FH (PFH) via the FIND-FH(R) machine learning algorithm. We used multivariable regression models to examine correlations with demographic/socioeconomic variables. ResultsIn the FH cohort (n = 85,457), 45.9% were female, 79.4% identified as White, 12.2% Black, and 8.4% as Hispanic. In the PFH cohort (n = 287,580), 42.2% were female, 78.2% White, 13.7% as Black, and 8.1% as Hispanic. Males were more likely to be prescribed high-intensity statins than females: odds ratio (OR) [95% confidence interval (CI)] = 2.05 [1.97, 2.13] and 1.60 [1.56,1.63] in the FH and the PFH cohorts, respectively. In both cohorts, White individuals were more likely to get ezetimibe, PCSK9i, or combination therapy compared to Black individuals (ORs: 1.12-1.40). Higher income was associated with increased odds of receiving these treatments (OR: 1.17-1.59 for incomes >$50,000). Higher education was linked to a higher likelihood of combination therapy (ORs [95% CI] = 1.49 [1.33, 1.68] and 1.18 [1.10, 1.27] in the FH and PFH cohorts, respectively). ConclusionsReal-world data indicate that more aggressive lipid-lowering therapy (ezetimibe and PCSK9i) is more often prescribed to White individuals, individuals with higher income, or those with advanced education, highlighting the need to improve equity in cardiovascular risk reduction for all individuals with FH. Clinical PerspectiveO_ST_ABSWhat is new?C_ST_ABSO_LIHealth disparities exist for individuals with FH, regardless of whether theyve been diagnosed. C_LIO_LIThe use of ezetimibe and PCSK9i is more likely to occur in individuals who are White, higher income, and advanced education. C_LI What are the clinical ImplicationsO_LIAddressing health disparities in FH requires a multifaceted approach, including systemic changes to reduce bias in healthcare systems. C_LIO_LIEnsuring equitable access to expensive lipid medications, such as PCSK9 inhibitors, is crucial for individuals from lower socioeconomic groups. C_LIO_LIProviding access to case managers and geneticists can enhance health education and support, ultimately improving treatment outcomes for individuals with FH. C_LI

IntroductionHeart failure (HF) is understudied among women; especially, genomic evidence implicating shared or unique mechanisms of HF with respect to reduced or preserved ejection fraction (HFrEF, HFpEF) is lacking across ethnic populations of women. Prior genome-wide association studies (GWAS) have identified approximately 30 suggestive genetic variants for HF, although none have been specifically linked to HFrEF or HFpEF.\n\nObjectivesWe aimed to define, replicate, and annotate genetic variants to HFrEF, HFpEF, or both, as well as to investigate potential biological mechanisms underlying HFrEF and HFpEF among African American (AA) and European American (EA) women in three well-characterized, high-quality prospective cohorts, the Womens Health Initiative (WHI) study, the Jackson Heart Study (JHS), and the Framingham Heart Study (FHS).\n\nMethodsGWAS analysis on HFrEF and HFpEF were first performed among 7,982 AA and 4,133 EA in the WHI, followed by pathway analysis employing two independent methodological platforms (GSA-SNP and Mergeomics) curating KEGG, Reactome, and BioCarta pathway databases. GWAS signals and biological pathways identified using the WHI were replicated in the JHS and FHS. For all replicated pathways, we performed cross-phenotype and cross-ethnicity validation analyses to examine shared pathways between HFrEF and HFpEF, and phenotype-specific pathways, across ethnicities. We further prioritized key driver genes for HF according to specific pathways identified.\n\nResultsWe validated one previously reported genetic locus and identified six new ones, among which one locus was allocated to HFrEF and five to HFpEF. Additionally, we defined five biological pathways shared between HFrEF and HFpEF and discovered six HFpEF-specific pathways. These pathways overlapped in two main domains for molecular signaling: 1) inflammation and 2) vascular remodeling (including angiogenesis and vascular patterning), involving key driver genes from collagen and HLA gene families.\n\nConclusionsOur network analysis of three large prospective cohorts of women in the United States defined several novel loci for HF and its subtypes. In particular, several key driver genes reinforce the mechanistic role of inflammation and vascular remodeling in the development of HF, especially HFpEF. Given that therapeutic strategies developed for left ventricular dysfunction have had limited success for HFpEF, several new targets and pathways identified and validated in this study should be further assessed in risk stratification as well as the design of potential new HF interventions.

BackgroundAtrial Fibrillation (AF) is a common and clinically heterogeneous arrythmia. Machine learning (ML) algorithms can define data-driven disease subtypes in an unbiased fashion, but whether the AF subgroups defined in this way align with underlying mechanisms, such as high polygenic liability to AF or inflammation, and associate with clinical outcomes is unclear. MethodsWe identified individuals with AF in a large biobank linked to electronic health records (EHR) and genome-wide genotyping. The phenotypic architecture in the AF cohort was defined using principal component analysis of 35 expertly curated and uncorrelated clinical features. We applied an unsupervised co-clustering machine learning algorithm to the 35 features to identify distinct phenotypic AF clusters. The clinical inflammatory status of the clusters was defined using measured biomarkers (CRP, ESR, WBC, Neutrophil %, Platelet count, RDW) within 6 months of first AF mention in the EHR. Polygenic risk scores (PRS) for AF and cytokine levels were used to assess genetic liability of clusters to AF and inflammation, respectively. Clinical outcomes were collected from EHR up to the last medical contact. ResultsThe analysis included 23,271 subjects with AF, of which 6,023 had available genome-wide genotyping. The machine learning algorithm identified 3 phenotypic clusters that were distinguished by increasing prevalence of comorbidities, particularly renal dysfunction, and coronary artery disease. Polygenic liability to AF across clusters was highest in the low comorbidity cluster. Clinically measured inflammatory biomarkers were highest in the high comorbid cluster, while there was no difference between groups in genetically predicted levels of inflammatory biomarkers. Subgroup assignment was associated with multiple clinical outcomes including mortality, stroke, bleeding, and use of cardiac implantable electronic devices after AF diagnosis. ConclusionPatient subgroups identified by unsupervised clustering were distinguished by comorbidity burden and associated with risk of clinically important outcomes. Polygenic liability to AF across clusters was greatest in the low comorbidity subgroup. Clinical inflammation, as reflected by measured biomarkers, was lowest in the subgroup with lowest comorbidities. However, there were no differences in genetically predicted levels of inflammatory biomarkers, suggesting associations between AF and inflammation is driven by acquired comorbidities rather than genetic predisposition.

RationaleThe coronary microvasculature is crucial for proper cardiac function, and coronary microvascular disease (CMVD) has emerged as an underdiagnosed and undertreated cause of ischemic heart disease. Friend of GATA 2 (FOG2) is a transcriptional co-regulator crucial for coronary development and the maintenance of the coronary microvasculature in adult mice.Little is known about the role of FOG2 in humans or its role in CMVD. ObjectiveHere, we report a genotype-first approach to determine the role of FOG2 in human coronary microvascular disease. FindingsWe performed Phenome-Wide association studies and deep cardiac phenotyping through the Electronic Health record in patients with FOG2 coding variants and identified an association between rs28374544 (A1969G, S657G) and CMVD. Patients with S657G had increased chest pain, smaller burden of obstructive coronary artery disease, and altered coronary blood flow. Differential gene and pathway analysis using several genomic datasets showed that carriers of S657G have increased expression of genes involved in angiogenesis, glycolysis, and the hypoxia-inducible factor (HIF) pathway. In vitro functional studies show that FOG2 S657G promotes angiogenic gene expression and angiogenesis while decreasing oxygen consumption rate. FOG2 also regulates HIF1a occupancy of angiogenic genes. ConclusionsWe identified a human missense variant which is associated with CMVD and established a potential mechanism by which the variant may altered angiogenic gene expression.