Explore the vast range of titles available.

Purpose To characterize the genetic architecture of left ventricular noncompaction (LVNC) and investigate the extent to which it may represent a distinct pathology or a secondary phenotype associated with other cardiac diseases. Methods We performed rare variant association analysis with 840 LVNC cases and 125,748 gnomAD population controls, and compared results to similar analyses on dilated cardiomyopathy (DCM) and hypertrophic cardiomyopathy (HCM). Results We observed substantial genetic overlap indicating that LVNC often represents a phenotypic variation of DCM or HCM. In contrast, truncating variants in MYH7 , ACTN2, and PRDM16 were uniquely associated with LVNC and may reflect a distinct LVNC etiology. In particular, MYH7 truncating variants ( MYH7 tv), generally considered nonpathogenic for cardiomyopathies, were 20-fold enriched in LVNC cases over controls. MYH7 tv heterozygotes identified in the UK Biobank and healthy volunteer cohorts also displayed significantly greater noncompaction compared with matched controls. RYR2 exon deletions and HCN4 transmembrane variants were also enriched in LVNC, supporting prior reports of association with arrhythmogenic LVNC phenotypes. Conclusion LVNC is characterized by substantial genetic overlap with DCM/HCM but is also associated with distinct noncompaction and arrhythmia etiologies. These results will enable enhanced application of LVNC genetic testing and help to distinguish pathological from physiological noncompaction.

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimizing the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimized for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients, the predictive accuracy (quantified by Harrell’s C -index) was significantly higher ( P  = 0.0012) for our model C  = 0.75 (95% CI: 0.70–0.79) than the human benchmark of C  = 0.59 (95% CI: 0.53–0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival. A fully convolutional neural network is used to create time-resolved three-dimensional dense segmentations of heart images. This dense motion model forms the input to a supervised system called 4Dsurvival that can efficiently predict human survival.

Accurate discrimination of benign and pathogenic rare variation remains a priority for clinical genome interpretation. State-of-the-art machine learning variant prioritization tools are imprecise and ignore important parameters defining gene–disease relationships, e.g., distinct consequences of gain-of-function versus loss-of-function variants. We hypothesized that incorporating disease-specific information would improve tool performance. We developed a disease-specific variant classifier, CardioBoost, that estimates the probability of pathogenicity for rare missense variants in inherited cardiomyopathies and arrhythmias. We assessed CardioBoost’s ability to discriminate known pathogenic from benign variants, prioritize disease-associated variants, and stratify patient outcomes. CardioBoost has high global discrimination accuracy (precision recall area under the curve [AUC] 0.91 for cardiomyopathies; 0.96 for arrhythmias), outperforming existing tools (4–24% improvement). CardioBoost obtains excellent accuracy (cardiomyopathies 90.2%; arrhythmias 91.9%) for variants classified with >90% confidence, and increases the proportion of variants classified with high confidence more than twofold compared with existing tools. Variants classified as disease-causing are associated with both disease status and clinical severity, including a 21% increased risk (95% confidence interval [CI] 11–29%) of severe adverse outcomes by age 60 in patients with hypertrophic cardiomyopathy. A disease-specific variant classifier outperforms state-of-the-art genome-wide tools for rare missense variants in inherited cardiac conditions (https://www.cardiodb.org/cardioboost/), highlighting broad opportunities for improved pathogenicity prediction through disease specificity.

Cardiovascular diseases are the leading cause of death in schizophrenia. Patients with schizophrenia show evidence of concentric cardiac remodelling (CCR), defined as an increase in left-ventricular mass over end-diastolic volumes. CCR is a predictor of cardiac disease, but the molecular pathways leading to this in schizophrenia are unknown. We aimed to explore the relevance of hypertensive and non-hypertensive pathways to CCR and their potential molecular underpinnings in schizophrenia. In this multimodal case–control study, we collected cardiac and whole-body fat magnetic resonance imaging (MRI), clinical measures, and blood levels of several cardiometabolic biomarkers known to potentially cause CCR from individuals with schizophrenia, alongside healthy controls (HCs) matched for age, sex, ethnicity, and body surface area. Of the 50 participants, 34 (68%) were male. Participants with schizophrenia showed increases in cardiac concentricity (d = 0.71, 95% CI: 0.12, 1.30; p = 0.01), indicative of CCR, but showed no differences in overall content or regional distribution of adipose tissue compared to HCs. Despite the cardiac changes, participants with schizophrenia did not demonstrate activation of the hypertensive CCR pathway; however, they showed evidence of adipose dysfunction: adiponectin was reduced (d = −0.69, 95% CI: −1.28, −0.10; p = 0.02), with evidence of activation of downstream pathways, including hypertriglyceridemia, elevated C-reactive protein, fasting glucose, and alkaline phosphatase. In conclusion, people with schizophrenia showed adipose tissue dysfunction compared to body mass-matched HCs. The presence of non-hypertensive CCR and a dysmetabolic phenotype may contribute to excess cardiovascular risk in schizophrenia. If our results are confirmed, acting on this pathway could reduce cardiovascular risk and resultant life-years lost in people with schizophrenia.

ObjectiveSex differences play a critical role in the presentation, progression and treatment outcomes of cardiac diseases. However, historical male predominance in clinical studies has led to disparities in evidence supporting care for both sexes. Clinical guidelines are essential for cardiovascular care, shaping practice and influencing patient outcomes. In this study, we reviewed 34 European Society of Cardiology (ESC) guidelines between 2002 and 2024 to evaluate the representation of women and the inclusion of female-specific recommendations.MethodsWe compiled 136 gender-related keywords, validated by six clinicians, and quantified their occurrence across guidelines. While our primary analysis focused on female-specific keywords, we also identified male-specific terms as a comparison point to help quantitatively interpret the representation of female-specific terminology in the guidelines. Each guideline underwent independent review by two auditors who used structured questions to assess its sensitivity to female-specific differences in disease presentation, diagnosis, management and treatment.ResultsThe most frequent terms were ‘pregnancy’, ‘women’ and ‘sex’, with 1768 (17.9%), 1573 (15.9%) and 676 (6.8%) overall repetitions, respectively, contrasted against ‘cardiac’ (6932 occurrences) as a baseline. Results showed inconsistency in addressing female-specific factors and health considerations in ESC guidelines. We were able to assess the relative frequency of female-specific language and highlight in contrast areas where female representation in cardiovascular guidelines may be insufficient. Most guidelines (24/34) mentioned pregnancy and provided related recommendations, with one of the guidelines entirely dedicated to cardiovascular disease (CVD) in pregnancy (2018) and a new one planned for 2025. Only 10/30 guidelines acknowledged menopause as a CVD risk factor and offered recommendations for clinical practice.ConclusionsThese findings highlight the need for systematic integration of female-specific considerations across all guidelines. In the wider context, there is also a need for improved representation of women in clinical trials and for making the available evidence on which the guidelines are based less biased toward men.

Stuart Cook and colleagues study the role of TTN (titin)-truncating variants using a combination of heart physiology experiments in rats and genomic analysis in humans. Their data show that TTN variants are associated with a range of cardiac phenotypes in healthy individuals and in patients with dilated cardiomyopathy. Titin-truncating variants (TTNtv) commonly cause dilated cardiomyopathy (DCM). TTNtv are also encountered in ∼1% of the general population, where they may be silent, perhaps reflecting allelic factors. To better understand TTNtv, we integrated TTN allelic series, cardiac imaging and genomic data in humans and studied rat models with disparate TTNtv. In patients with DCM, TTNtv throughout titin were significantly associated with DCM. Ribosomal profiling in rat showed the translational footprint of premature stop codons in Ttn , TTNtv-position-independent nonsense-mediated degradation of the mutant allele and a signature of perturbed cardiac metabolism. Heart physiology in rats with TTNtv was unremarkable at baseline but became impaired during cardiac stress. In healthy humans, machine-learning-based analysis of high-resolution cardiac imaging showed TTNtv to be associated with eccentric cardiac remodeling. These data show that TTNtv have molecular and physiological effects on the heart across species, with a continuum of expressivity in health and disease.

Cardiovascular ageing is a process that begins early in life and leads to a progressive change in structure and decline in function due to accumulated damage across diverse cell types, tissues and organs contributing to multi-morbidity. Damaging biophysical, metabolic and immunological factors exceed endogenous repair mechanisms resulting in a pro-fibrotic state, cellular senescence and end-organ damage, however the genetic architecture of cardiovascular ageing is not known. Here we use machine learning approaches to quantify cardiovascular age from image-derived traits of vascular function, cardiac motion and myocardial fibrosis, as well as conduction traits from electrocardiograms, in 39,559 participants of UK Biobank. Cardiovascular ageing is found to be significantly associated with common or rare variants in genes regulating sarcomere homeostasis, myocardial immunomodulation, and tissue responses to biophysical stress. Ageing is accelerated by cardiometabolic risk factors and we also identify prescribed medications that are potential modifiers of ageing. Through large-scale modelling of ageing across multiple traits our results reveal insights into the mechanisms driving premature cardiovascular ageing and reveal potential molecular targets to attenuate age-related processes. Cardiovascular ageing is characterised by a progressive decline in function, which contributes to multi-morbidity. Here, the authors use machine learning to predict biological age and identify key genetic risk factors.

Background International guidelines for variant interpretation in Mendelian disease set stringent criteria to report a variant as (likely) pathogenic, prioritising control of false-positive rate over test sensitivity and diagnostic yield. Genetic testing is also more likely informative in individuals with well-characterised variants from extensively studied European-ancestry populations. Inherited cardiomyopathies are relatively common Mendelian diseases that allow empirical calibration and assessment of this framework. Methods We compared rare variants in large hypertrophic cardiomyopathy (HCM) cohorts (up to 6179 cases) to reference populations to identify variant classes with high prior likelihoods of pathogenicity, as defined by etiological fraction (EF). We analysed the distribution of variants using a bespoke unsupervised clustering algorithm to identify gene regions in which variants are significantly clustered in cases. Results Analysis of variant distribution identified regions in which variants are significantly enriched in cases and variant location was a better discriminator of pathogenicity than generic computational functional prediction algorithms. Non-truncating variant classes with an EF ≥ 0.95 were identified in five established HCM genes. Applying this approach leads to an estimated 14–20% increase in cases with actionable HCM variants, i.e. variants classified as pathogenic/likely pathogenic that might be used for predictive testing in probands’ relatives. Conclusions When found in a patient confirmed to have disease, novel variants in some genes and regions are empirically shown to have a sufficiently high probability of pathogenicity to support a “likely pathogenic” classification, even without additional segregation or functional data. This could increase the yield of high confidence actionable variants, consistent with the framework and recommendations of current guidelines. The techniques outlined offer a consistent and unbiased approach to variant interpretation for Mendelian disease genetic testing. We propose adaptations to ACMG/AMP guidelines to incorporate such evidence in a quantitative and transparent manner.

Heart disease is the leading cause of death in schizophrenia. However, there has been little research directly examining cardiac function in schizophrenia. To investigate cardiac structure and function in individuals with schizophrenia using cardiac magnetic resonance imaging (CMR) after excluding medical and metabolic comorbidity. In total, 80 participants underwent CMR to determine biventricular volumes and function and measures of blood pressure, physical activity and glycated haemoglobin levels. Individuals with schizophrenia ('patients') and controls were matched for age, gender, ethnicity and body surface area. Patients had significantly smaller indexed left ventricular (LV) end-diastolic volume (effect size d = -0.82, P = 0.001), LV end-systolic volume (d = -0.58, P = 0.02), LV stroke volume (d = -0.85, P = 0.001), right ventricular (RV) end-diastolic volume (d = -0.79, P = 0.002), RV end-systolic volume (d = -0.58, P = 0.02), and RV stroke volume (d = -0.87, P = 0.001) but unaltered ejection fractions relative to controls. LV concentricity (d = 0.73, P = 0.003) and septal thickness (d = 1.13, P < 0.001) were significantly larger in the patients. Mean concentricity in patients was above the reference range. The findings were largely unchanged after adjusting for smoking and/or exercise levels and were independent of medication dose and duration. Individuals with schizophrenia show evidence of concentric cardiac remodelling compared with healthy controls of a similar age, gender, ethnicity, body surface area and blood pressure, and independent of smoking and activity levels. This could be contributing to the excess cardiovascular mortality observed in schizophrenia. Future studies should investigate the contribution of antipsychotic medication to these changes.

IntroductionImprovements in cardiac magnetic resonance (CMR) have enabled better phenotyping of the left atrium (LA). However, little is known of the incremental prognostic value of the novel LA measurements (phasic LA strain, LA ejection fraction [LAEF], and LA minimum volume [LAVImin]) compared to LA maximum volume [LAVImax] in dilated cardiomyopathy (DCM). Thus, we decided to evaluate the prognostic value of each LA measure in DCM.Materials and MethodsCMR studies of 580 DCM patients, in sinus rhythm, prospectively enrolled into a biobank between 2009 and 2017 were used. The primary endpoint was a composite of cardiovascular (CV) mortality and non-fatal major heart failure (HF) events, which included HF hospitalisations, heart transplantation or Left Ventricular (LV) assist device implantation. Event rates were compared between patients in sinus rhythm and those with persistent atrial fibrillation (AF).ResultsOver a median follow-up duration of 7.4 years (IQR 4.7–9.3), 103 patients (18%) met the primary endpoint. On univariable Cox regression analysis, all LA metrics were significantly associated with the primary endpoint (all, p<0.05). All indices, apart from LA conduit strain, remained associated with the endpoint on multivariate analyses adjusted for age, sex, NYHA, LV ejection fraction and the presence of fibrosis (all, p<0.05). The addition of the LA metrics to a baseline model containing conventional risk predictors improved model discrimination, with LAVImin providing the greatest improvement (C-statistic 0.702 to 0.738), similar to that of LAVImax (C-Statistic: 0.702 to 0.732) and LAEF (C-Statistic: 0.702 to 0.734). LA strain variables did not improve baseline model discrimination over LA volumes. Patients in the highest tercile of LAVImin had similar event rates to those with persistent atrial fibrillation.DiscussionIn line with previous studies, LA structure and function was independently associated with CV death and HF events. LA volumes and LAEF provided better prognostication than LA strain. Amongst the volumes, LAVImin improved baseline model discrimination better than LAVImax, perhaps because it reflects both LA structure and function. This is important as LAVImin can easily be added to CMR reporting protocols.ConclusionLA metrics provide incremental prognostic information in DCM patients. LA strain did not provide any additional prognostic information over LA volumes. AcknowledgementsWe would like to thank the Royal Brompton and Harefield Cardiovascular Research Centre nurses and support staff.