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The inner surfaces of the human heart are covered by a complex network of muscular strands that is thought to be a remnant of embryonic development 1 , 2 . The function of these trabeculae in adults and their genetic architecture are unknown. Here we performed a genome-wide association study to investigate image-derived phenotypes of trabeculae using the fractal analysis of trabecular morphology in 18,096 participants of the UK Biobank. We identified 16 significant loci that contain genes associated with haemodynamic phenotypes and regulation of cytoskeletal arborization 3 , 4 . Using biomechanical simulations and observational data from human participants, we demonstrate that trabecular morphology is an important determinant of cardiac performance. Through genetic association studies with cardiac disease phenotypes and Mendelian randomization, we find a causal relationship between trabecular morphology and risk of cardiovascular disease. These findings suggest a previously unknown role for myocardial trabeculae in the function of the adult heart, identify conserved pathways that regulate structural complexity and reveal the influence of the myocardial trabeculae on susceptibility to cardiovascular disease. A genome-wide association study shows that myocardial trabeculae are an important determinant of cardiac performance in the adult heart, identifies conserved pathways that regulate structural complexity and reveals the influence of trabeculae on the susceptibility to cardiovascular disease.

Purpose Internationally adopted variant interpretation guidelines from the American College of Medical Genetics and Genomics (ACMG) are generic and require disease-specific refinement. Here we developed CardioClassifier ( http://www.cardioclassifier.org ), a semiautomated decision-support tool for inherited cardiac conditions (ICCs). Methods CardioClassifier integrates data retrieved from multiple sources with user-input case-specific information, through an interactive interface, to support variant interpretation. Combining disease- and gene-specific knowledge with variant observations in large cohorts of cases and controls, we refined 14 computational ACMG criteria and created three ICC-specific rules. Results We benchmarked CardioClassifier on 57 expertly curated variants and show full retrieval of all computational data, concordantly activating 87.3% of rules. A generic annotation tool identified fewer than half as many clinically actionable variants (64/219 vs. 156/219, Fisher’s P  = 1.1  ×  10 −18 ), with important false positives, illustrating the critical importance of disease and gene-specific annotations. CardioClassifier identified putatively disease-causing variants in 33.7% of 327 cardiomyopathy cases, comparable with leading ICC laboratories. Through addition of manually curated data, variants found in over 40% of cardiomyopathy cases are fully annotated, without requiring additional user-input data. Conclusion CardioClassifier is an ICC-specific decision-support tool that integrates expertly curated computational annotations with case-specific data to generate fast, reproducible, and interactive variant pathogenicity reports, according to best practice guidelines.

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.

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.

Immune-mediated inflammatory diseases (IMIDs) are a spectrum of disorders of overlapping immunopathogenesis, with a prevalence of up to 10% in Western populations and increasing incidence in developing countries. Although targeted treatments have revolutionized the management of rheumatic IMIDs, cardiovascular involvement confers an increased risk of mortality and remains clinically under-recognized. Cardiovascular pathology is diverse across rheumatic IMIDs, ranging from premature atherosclerotic cardiovascular disease (ASCVD) to inflammatory cardiomyopathy, which comprises myocardial microvascular dysfunction, vasculitis, myocarditis and pericarditis, and heart failure. Epidemiological and clinical data imply that rheumatic IMIDs and associated cardiovascular disease share common inflammatory mechanisms. This concept is strengthened by emergent trials that indicate improved cardiovascular outcomes with immune modulators in the general population with ASCVD. However, not all disease-modifying therapies that reduce inflammation in IMIDs such as rheumatoid arthritis demonstrate equally beneficial cardiovascular effects, and the evidence base for treatment of inflammatory cardiomyopathy in patients with rheumatic IMIDs is lacking. Specific diagnostic protocols for the early detection and monitoring of cardiovascular involvement in patients with IMIDs are emerging but are in need of ongoing development. This Review summarizes current concepts on the potentially targetable inflammatory mechanisms of cardiovascular pathology in rheumatic IMIDs and discusses how these concepts can be considered for the diagnosis and management of cardiovascular involvement across rheumatic IMIDs, with an emphasis on the potential of cardiovascular imaging for risk stratification, early detection and prognostication.Cardiovascular involvement is one of the many manifestations of rheumatic immune-mediated inflammatory diseases (IMIDs) that increase mortality. The pathogenesis of atherosclerosis and inflammatory cardiomyopathies involves inflammatory pathways common with those operating and targeted in rheumatic IMIDs. Here, Maya Buch and colleagues discuss implications of these shared pathways for the prevention, detection and management of cardiovascular involvement in patients with rheumatic IMIDs, while highlighting complexities and open questions.

To characterize effects of chronically elevated blood pressure on the brain, we tested for brain white matter microstructural differences associated with normotension, pre-hypertension and hypertension in recently available brain magnetic resonance imaging data from 4659 participants without known neurological or psychiatric disease (62.3±7.4 yrs, 47.0% male) in UK Biobank. For assessment of white matter microstructure, we used measures derived from neurite orientation dispersion and density imaging (NODDI) including the intracellular volume fraction (an estimate of neurite density) and isotropic volume fraction (an index of the relative extra-cellular water diffusion). To estimate differences associated specifically with blood pressure, we applied propensity score matching based on age, sex, educational level, body mass index, and history of smoking, diabetes mellitus and cardiovascular disease to perform separate contrasts of non-hypertensive (normotensive or pre-hypertensive, N = 2332) and hypertensive (N = 2337) individuals and of normotensive (N = 741) and pre-hypertensive (N = 1581) individuals (p<0.05 after Bonferroni correction). The brain white matter intracellular volume fraction was significantly lower, and isotropic volume fraction was higher in hypertensive relative to non-hypertensive individuals (N = 1559, each). The white matter isotropic volume fraction also was higher in pre-hypertensive than in normotensive individuals (N = 694, each) in the right superior longitudinal fasciculus and the right superior thalamic radiation, where the lower intracellular volume fraction was observed in the hypertensives relative to the non-hypertensive group. Pathological processes associated with chronically elevated blood pressure are associated with imaging differences suggesting chronic alterations of white matter axonal structure that may affect cognitive functions even with pre-hypertension.

Cardiovascular magnetic resonance (CMR) is an important imaging modality for the assessment of heart disease; however, limitations of CMR include long exam times and high complexity compared to other cardiac imaging modalities. Recently advancements in artificial intelligence (AI) technology have shown great potential to address many CMR limitations. While the developments are remarkable, translation of AI-based methods into real-world CMR clinical practice remains at a nascent stage and much work lies ahead to realize the full potential of AI for CMR. Herein we review recent cutting-edge and representative examples demonstrating how AI can advance CMR in areas such as exam planning, accelerated image reconstruction, post-processing, quality control, classification and diagnosis. These advances can be applied to speed up and simplify essentially every application including cine, strain, late gadolinium enhancement, parametric mapping, 3D whole heart, flow, perfusion and others. AI is a unique technology based on training models using data. Beyond reviewing the literature, this paper discusses important AI-specific issues in the context of CMR, including (1) properties and characteristics of datasets for training and validation, (2) previously published guidelines for reporting CMR AI research, (3) considerations around clinical deployment, (4) responsibilities of clinicians and the need for multi-disciplinary teams in the development and deployment of AI in CMR, (5) industry considerations, and (6) regulatory perspectives. Understanding and consideration of all these factors will contribute to the effective and ethical deployment of AI to improve clinical CMR. [Display omitted]

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.

Glutamatergic excitotoxicity is hypothesised to underlie synaptic loss in schizophrenia pathogenesis, but it is unknown whether synaptic markers are related to glutamatergic function in vivo. Additionally, it has been proposed that N-acetyl aspartate (NAA) levels reflect neuronal integrity. Here, we investigated whether synaptic vesicle glycoprotein 2 A (SV2A) levels are related to glutamatergic markers and NAA in healthy volunteers (HV) and schizophrenia patients (SCZ). Forty volunteers (SCZ n = 18, HV n = 22) underwent [11C]UCB-J positron emission tomography and proton magnetic resonance spectroscopy (1H-MRS) imaging in the left hippocampus and anterior cingulate cortex (ACC) to index [11C]UCB-J distribution volume ratio (DVR), and creatine-scaled glutamate (Glu/Cr), glutamate and glutamine (Glx/Cr) and NAA (NAA/Cr). In healthy volunteers, but not patients, [11C]UCB-J DVR was significantly positively correlated with Glu/Cr, in both the hippocampus and ACC. Furthermore, in healthy volunteers, but not patients, [11C]UCB-J DVR was significantly positively correlated with Glx/Cr, in both the hippocampus and ACC. There were no significant relationships between [11C]UCB-J DVR and NAA/Cr in the hippocampus or ACC in healthy volunteers or patients. Therefore, an appreciable proportion of the brain 1H-MRS glutamatergic signal is related to synaptic density in healthy volunteers. This relationship is not seen in schizophrenia, which, taken with lower synaptic marker levels, is consistent with lower levels of glutamatergic terminals and/or a lower proportion of glutamatergic relative to GABAergic terminals in the ACC in schizophrenia.

ObjectivesWe investigated the feasibility and reproducibility of free-breathing motion-corrected multiple inversion time (multi-TI) pulsed renal arterial spin labelling (PASL), with general kinetic model parametric mapping, to simultaneously quantify renal perfusion (RBF), bolus arrival time (BAT) and tissue T1.MethodsIn a study approved by the Health Research Authority, 12 healthy volunteers (mean age, 27.6 ± 18.5 years; 5 male) gave informed consent for renal imaging at 3 T using multi-TI ASL and conventional single-TI ASL. Glyceryl trinitrate (GTN) was used as a vasodilator challenge in six subjects. Flow-sensitive alternating inversion recovery (FAIR) preparation was used with background suppression and 3D-GRASE (gradient and spin echo) read-out, and images were motion-corrected. Parametric maps of RBF, BAT and T1 were derived for both kidneys. Agreement was assessed using Pearson correlation and Bland-Altman plots.ResultsInter-study correlation of whole-kidney RBF was good for both single-TI (r2 = 0.90), and multi-TI ASL (r2 = 0.92). Single-TI ASL gave a higher estimate of whole-kidney RBF compared to multi-TI ASL (mean bias, 29.3 ml/min/100 g; p <0.001). Using multi-TI ASL, the median T1 of renal cortex was shorter than that of medulla (799.6 ms vs 807.1 ms, p = 0.01), and mean whole-kidney BAT was 269.7 ± 56.5 ms. GTN had an effect on systolic blood pressure (p < 0.05) but the change in RBF was not significant.ConclusionsFree-breathing multi-TI renal ASL is feasible and reproducible at 3 T, providing simultaneous measurement of renal perfusion, haemodynamic parameters and tissue characteristics at baseline and during pharmacological challenge.Key points• Multiple inversion time arterial spin labelling (ASL) of the kidneys is feasible and reproducible at 3 T.• This approach allows simultaneous mapping of renal perfusion, bolus arrival time and tissue T1during free breathing.• This technique enables repeated measures of renal haemodynamic characteristics during pharmacological challenge.