Genetic and functional insights into the fractal structure of the heart

3D Fractal Dimension Analysis: Prognostic Value of Right Ventricular Trabecular Complexity in Participants with Arrhythmogenic Cardiomyopathy

BACKGROUND

Arrhythmogenic cardiomyopathy (ACM) is characterized by progressive myocardial fibro-fatty infiltration accompanied by trabecular disarray. Traditionally, two-dimensional (2D) instead of 3D fractal dimension (FD) analysis has been used to evaluate trabecular disarray. However, the prognostic value of trabecular disorder assessed by 3D FD measurement remains unclear.

PURPOSE

To investigate the prognostic value of right ventricular trabecular complexity in ACM patients using 3D FD analysis based on cardiac MR cine images.

STUDY TYPE

Retrospective.

POPULATION

85 ACM patients (mean age: 45 ± 17 years, 52 male).

FIELD STRENGTH/SEQUENCE

3.0T/cine imaging, T2-short tau inversion recovery (T2-STIR), and late gadolinium enhancement (LGE).

ASSESSMENT

Using cine images, RV (right ventricular) volumetric and functional parameters were obtained. RV trabecular complexity was measured with 3D fractal analysis by box-counting method to calculate 3D-FD. Cox and logistic regression models were established to evaluate the prognostic value of 3D-FD for major adverse cardiac events (MACE).

STATISTICAL TESTS

Cox regression and logistic regression to explore the prognostic value of 3D-FD. C-index, time-dependent receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) to evaluate the incremental value of 3D-FD. Intraclass correlation coefficient for interobserver variability. P < 0.05 indicated statistical significance.

RESULTS

26 MACE were recorded during the 60 month follow-up (interquartile range: 48-67 months). RV 3D-FD significantly differed between ACM patients with MACE (2.67, interquartile range: 2.51 ~ 2.81) and without (2.52, interquartile range: 2.40 ~ 2.67) and was a significant independent risk factor for MACE (hazard ratio, 1.02; 95% confidence interval: 1.01, 1.04). In addition, prognostic model fitness was significantly improved after adding 3D-FD to RV global longitudinal strain, LV involvement, and 5-year risk score separately.

DATA CONCLUSION

The myocardial trabecular complexity assessed through 3D FD analysis was found associated with MACE and provided incremental prognostic value beyond conventional ACM risk factors.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 1.

Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study

BACKGROUND

Artificial intelligence (AI)-enabled electrocardiography (ECG) can be used to predict risk of future disease and mortality but has not yet been adopted into clinical practice. Existing model predictions do not have actionability at an individual patient level, explainability, or biological plausibi. We sought to address these limitations of previous AI-ECG approaches by developing the AI-ECG risk estimator (AIRE) platform.

METHODS

The AIRE platform was developed in a secondary care dataset (Beth Israel Deaconess Medical Center [BIDMC]) of 1 163 401 ECGs from 189 539 patients with deep learning and a discrete-time survival model to create a patient-specific survival curve with a single ECG. Therefore, AIRE predicts not only risk of mortality, but also time-to-mortality. AIRE was validated in five diverse, transnational cohorts from the USA, Brazil, and the UK (UK Biobank [UKB]), including volunteers, primary care patients, and secondary care patients.

FINDINGS

AIRE accurately predicts risk of all-cause mortality (BIDMC C-index 0·775, 95% CI 0·773-0·776; C-indices on external validation datasets 0·638-0·773), future ventricular arrhythmia (BIDMC C-index 0·760, 95% CI 0·756-0·763; UKB C-index 0·719, 95% CI 0·635-0·803), future atherosclerotic cardiovascular disease (0·696, 0·694-0·698; 0·643, 0·624-0·662), and future heart failure (0·787, 0·785-0·789; 0·768, 0·733-0·802). Through phenome-wide and genome-wide association studies, we identified candidate biological pathways for the prediction of increased risk, including changes in cardiac structure and function, and genes associated with cardiac structure, biological ageing, and metabolic syndrome.

INTERPRETATION

AIRE is an actionable, explainable, and biologically plausible AI-ECG risk estimation platform that has the potential for use worldwide across a wide range of clinical contexts for short-term and long-term risk estimation.

FUNDING

British Heart Foundation, National Institute for Health and Care Research, and Medical Research Council.

Retinal Microvasculature Causally Affects the Brain Cortical Structure: A Mendelian Randomization Study

Purpose

To reveal the causality between retinal vascular density (VD), fractal dimension (FD), and brain cortex structure using Mendelian randomization (MR).

Design

Cross-sectional study.

Participants

Genome-wide association studies of VD and FD involving 54 813 participants from the United Kingdom Biobank were used. The brain cortical features, including the cortical thickness (TH) and surface area (SA), were extracted from 51 665 patients across 60 cohorts. Surface area and TH were measured globally and in 34 functional regions using magnetic resonance imaging.

Methods

Bidirectional univariable MR (UVMR) was used to detect the causality between FD, VD, and brain cortex structure. Multivariable MR (MVMR) was used to adjust for confounding factors, including body mass index and blood pressure.

Main Outcome Measures

The global and regional measurements of brain cortical SA and TH.

Results

At the global level, higher VD is related to decreased TH (β = -0.0140 mm, 95% confidence interval: -0.0269 mm to -0.0011 mm, P = 0.0339). At the functional level, retinal FD is related to the TH of banks of the superior temporal sulcus and transverse temporal region without global weighted, as well as the SA of the posterior cingulate after adjustment. Vascular density is correlated with the SA of subregions of the frontal lobe and temporal lobe, in addition to the TH of the inferior temporal, entorhinal, and pars opercularis regions in both UVMR and MVMR. Bidirectional MR studies showed a causation between the SA of the parahippocampal and cauda middle frontal gyrus and retinal VD. No pleiotropy was detected.

Conclusions

Fractal dimension and VD causally influence the cortical structure and vice versa, indicating that the retinal microvasculature may serve as a biomarker for cortex structural changes. Our study provides insights into utilizing noninvasive fundus images to predict cortical structural deteriorations and neuropsychiatric disorders.

Financial Disclosures

The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Feasibility of fractal dimension analysis for left ventricular trabecular complexity using cardiac computed tomography

AIMS

To evaluate the consistency between fractal dimensions (FD) derived from cardiac computed tomography (CT-FD) and cardiac magnetic resonance (MR-FD) in assessing left ventricular trabecular complexity.

METHODS

This retrospective study included 170 patients who underwent CCT and CMR scans within two weeks. Five short-axis cine images were selected at end-diastole: one basal, three mid, and one apical slice. Short-axis CCT views were reconstructed and aligned with the cine images. CT-FD and MR-FD values were calculated for each slice, with mean values determined for each patient. Severe left ventricular hypertrophy (LVH) was defined as a maximum wall thickness > 15 mm in end-diastolic cine images.

RESULTS

The diastolic CT-FD and MR-FD values exhibited high consistency, with values of 1.253 ± 0.091 and 1.250 ± 0.102, respectively (n = 535, ICC = 0.882, 95 % CI: 0.861-0.899, P < 0.001). Similarly, the systolic CT-FD and MR-FD values demonstrated good consistency, with values of 1.268 ± 0.072 and 1.286 ± 0.093, respectively (n = 390, ICC = 0.720, 95 % CI: 0.669-0.765, P < 0.001). For subgroups of systolic NLVH and LVH, the ICCs were 0.773 (n = 305, CI: 0.723-0.814, P < 0.001) and 0.565 (n = 85, 95 % CI: 0.402-0.694, P < 0.001), respectively. The diagnostic efficacy of mean CT-FD aligned with that of mean MR-FD in distinguishing abnormal cardiac conditions from the CMR-negative group.

CONCLUSIONS

CCT is a feasible method for assessing left ventricular trabecular complexity, with good agreement with CMR, except in cases of severe left ventricular hypertrophy during systole.

Machine vision-assisted genomic prediction and genome-wide association of spleen-related traits in large yellow croaker infected with visceral white-nodules disease

High-resolution and high-throughput genotype-to-phenotype studies in fish are rapidly advancing, driven by innovative technologies that aim to address the challenges of modern breeding models. In recent years, machine vision and deep learning techniques, particularly convolutional neural networks (CNNs), have achieved significant success in image recognition and segmentation. Moreover, qualitative and quantitative analysis of disease resistance has always been a crucial field of research in genetics. This motivation has led us to investigate the potential of large yellow croaker visceral white-nodules disease (VWND) in encoding information on disease resistance for the task of accession classification. In this study, we proposed an image segmentation framework for the feature extraction of the spleen after VWND infection based on machine vision. We utilized deep CNNs and threshold segmentation for automatic feature learning and object segmentation. This approach eliminates subjectivity and enhances work efficiency compared to using hand-crafted features. Additionally, we employed spleen-related traits to conduct genome-wide association analysis (GWAS), which led to the identification of 24 significant SNPs and 10 major quantitative trait loci. The results of function enrichment analysis on candidate genes also indicated potential relationships with immune regulation mechanisms. Furthermore, we explored the use of genomic selection (GS) technology for phenotype prediction of extreme individuals, which further supports the predictability of spleen-related phenotypes for VWND resistance in large yellow croakers. Our findings demonstrate that artificial intelligence (AI)-based phenotyping approaches can deliver state-of-the-art performance for genetics research. We hope this work will provide a paradigm for applying deep learning and machine vision to phenotyping in aquaculture species.

Mathematical Models of Diffusion in Physiology

Diffusion is a mass transport phenomenon caused by chaotic thermal movements of molecules. Studying the transport in specific domain is simplified by using evolutionary differential equations for local concentration of the molecules instead of complete information on molecular paths [1]. Compounds in a fluid mixture tend to smooth out its spatial concentration inhomogeneities by diffusion. Rate of the transport is proportional to the concentration gradient and coefficient of diffusion of the compound in ordinary diffusion. The evolving concentration profile c(x,t) is then solution of evolutionary partial differential equation deltac/deltat=DDeltac where D is diffusion coefficient and Delta is Laplacian operator. Domain of the equation may be a region in space, plane or line, a manifold, such as surface embedded in space, or a graph. The Laplacian operates on smooth functions defined on given domain. We can use models of diffusion for such diverse tasks as: a) design of method for precise measurement of receptors mobility in plasmatic membrane by confocal microscopy [2], b) evaluation of complex geometry of trabeculae in developing heart [3] to show that the conduction pathway within the embryonic ventricle is determined by geometry of the trabeculae.

How the trabecular layer impacts on left ventricular function

The ventricular trabecular layer is crucial in embryonic life. In adults, the proportion of trabecular-to-compact myocardium varies substantially between individuals, within individuals over time, and yet exhibits almost no correlation to pump function since most individuals with excessive trabeculation are asymptomatic. The question of how functional is the myocardium of the trabecular layer, relative to the myocardium of the compact layer, has been difficult to answer but it is often assumed to be inferior. An answer is now emerging from recent advances and it can improve our understanding of how the trabecular layer impacts on pathogenicity. This narrative review concerns natural variation in trabeculation, tissue organization, transcriptomics, immunohistochemistry, vascularization, electrical propagation, diastolic function and compliance, systolic function, and ejection fraction. There are no overt transcriptional differences in the adult stage, and the myocardium is equally equipped with sarcomeric proteins, mitochondria, and vascular supply. The similar structural features are consistent with myocardium with a similar stroke work per gram tissue, along with a high ejection fraction of the trabecular layer. In conclusion, the myocardium of the trabecular and compact layers is highly similar and this offers a logical explanation for the reproducible observations that most individuals with excessive trabeculation are asymptomatic.

Prognostic value of right ventricular trabecular complexity in patients with arrhythmogenic cardiomyopathy

OBJECTIVES

The present study aimed to investigate the incremental prognostic value of the right ventricular fractal dimension (FD), a novel marker of myocardial trabecular complexity by cardiac magnetic resonance (CMR) in patients with arrhythmogenic cardiomyopathy (ACM).

METHODS

Consecutive patients with ACM undergoing CMR were followed up for major cardiac events, including sudden cardiac death, aborted cardiac arrest, and appropriate implantable cardioverter defibrillator intervention. Prognosis prediction was compared by Cox regression analysis. We established a multivariable model supplemented with RV FD and evaluated its discrimination by Harrell's C-statistic. We compared the category-free, continuous net reclassification improvement (cNRI) and integrated discrimination index (IDI) before and after the addition of FD.

RESULTS

A total of 105 patients were prospectively included from three centers and followed up for a median of 60 (48, 66) months; experienced 36 major cardiac events were recorded. Trabecular FD displayed a strong unadjusted association with major cardiac events (p < 0.05). In the multivariable Cox regression analysis, RV maximal apical FD maintained an independent association with major cardiac events (hazard ratio, 1.31 (1.11-1.55), p < 0.002). The Hosmer-Lemeshow goodness of fit test displayed good fit (X2 = 0.68, p = 0.99). Diagnostic performance was significantly improved after the addition of RV maximal apical FD to the multivariable baseline model, and the continuous net reclassification improvement increased 21% (p = 0.001), and the integrated discrimination index improved 16% (p = 0.045).

CONCLUSIONS

In patients with ACM, CMR-assessed myocardial trabecular complexity was independently correlated with adverse cardiovascular events and provided incremental prognostic value.

CLINICAL RELEVANCE STATEMENT

The application of FD values for assessing RV myocardial trabeculae may become an accessible and promising parameter in monitoring and early diagnosis of risk factors for adverse cardiovascular events in patients with ACM.

KEY POINTS

• Ventricular trabecular morphology, a novel quantitative marker by CMR, has been explored for the first time to determine the severity of ACM. • Patients with higher maximal apical fractal dimension of RV displayed significantly higher cumulative incidence of major cardiac events. • RV maximal apical FD was independently associated with major cardiac events and provided incremental prognostic value in patients with ACM.

Epistasis regulates genetic control of cardiac hypertrophy

The combinatorial effect of genetic variants is often assumed to be additive. Although genetic variation can clearly interact non-additively, methods to uncover epistatic relationships remain in their infancy. We develop low-signal signed iterative random forests to elucidate the complex genetic architecture of cardiac hypertrophy. We derive deep learning-based estimates of left ventricular mass from the cardiac MRI scans of 29,661 individuals enrolled in the UK Biobank. We report epistatic genetic variation including variants close to CCDC141 , IGF1R , TTN , and TNKS. Several loci where variants were deemed insignificant in univariate genome-wide association analyses are identified. Functional genomic and integrative enrichment analyses reveal a complex gene regulatory network in which genes mapped from these loci share biological processes and myogenic regulatory factors. Through a network analysis of transcriptomic data from 313 explanted human hearts, we found strong gene co-expression correlations between these statistical epistasis contributors in healthy hearts and a significant connectivity decrease in failing hearts. We assess causality of epistatic effects via RNA silencing of gene-gene interactions in human induced pluripotent stem cell-derived cardiomyocytes. Finally, single-cell morphology analysis using a novel high-throughput microfluidic system shows that cardiomyocyte hypertrophy is non-additively modifiable by specific pairwise interactions between CCDC141 and both TTN and IGF1R . Our results expand the scope of genetic regulation of cardiac structure to epistasis.

Predicting Peri-Operative Cardiorespiratory Adverse Events in Children with Idiopathic Pulmonary Arterial Hypertension Undergoing Cardiac Catheterization Using Echocardiography: A Cohort Study

General anesthesia in children with idiopathic pulmonary arterial hypertension (PAH) carries an increased risk of peri-operative cardiorespiratory complications though risk stratifying individual children pre-operatively remains difficult. We report the incidence and echocardiographic risk factors for adverse events in children with PAH undergoing general anesthesia for cardiac catheterization. Echocardiographic, hemodynamic, and adverse event data from consecutive PAH patients are reported. A multivariable predictive model was developed from echocardiographic variables identified by Bayesian univariable logistic regression. Model performance was reported by area under the curve for receiver operating characteristics (AUCroc) and precision/recall (AUCpr) and a pre-operative scoring system derived (0-100). Ninety-three children underwent 158 cardiac catheterizations with mean age 8.8 ± 4.6 years. Adverse events (n = 42) occurred in 15 patients (16%) during 16 catheterizations (10%) including cardiopulmonary resuscitation (n = 5, 3%), electrocardiographic changes (n = 3, 2%), significant hypotension (n = 2, 1%), stridor (n = 1, 1%), and death (n = 2, 1%). A multivariable model (age, right ventricular dysfunction, and dilatation, pulmonary and tricuspid regurgitation severity, and maximal velocity) was highly predictive of adverse events (AUCroc 0.86, 95% CI 0.75 to 1.00; AUCpr 0.68, 95% CI 0.50 to 0.91; baseline AUCpr 0.10). Pre-operative risk scores were higher in those who had a subsequent adverse event (median 47, IQR 43 to 53) than in those who did not (median 23, IQR 15 to 33). Pre-operative echocardiography informs the risk of peri-operative adverse events and may therefore be useful both for consent and multi-disciplinary care planning.

Fractal analysis of left ventricular trabeculae in post-STEMI: from acute to chronic phase

PURPOSE

The temporal evolution of ventricular trabecular complexity and its correlation with major adverse cardiovascular events (MACE) remain indeterminate in patients presenting with acute ST elevation myocardial infarction (STEMI).

METHODS

This retrospective analysis enrolled patients undergoing primary percutaneous coronary intervention (pPCI) for acute STEMI, possessing cardiac magnetic resonance (CMR) data in the acute (within 7 days), subacute (1 month after pPCI), and chronic phases (6 months after pPCI) from January 2015 to January 2020 at the three participating sites. Fractal dimensions (FD) were measured for the global, infarct, and remote regions of left ventricular trabeculae during each phase. The potential association of FD with MACE was analyzed using multivariate Cox regression.

RESULTS

Among the 200 analyzed patients (182 men; median age, 61 years; age range, 50-66 years), 37 (18.5%) encountered MACE during a median follow-up of 31.2 months. FD exhibited a gradual decrement (global FD at acute, subacute, and chronic phases: 1.253 ± 0.049, 1.239 ± 0.046, 1.230 ± 0.045, p < 0.0001), with a more pronounced decrease observed in patients subsequently experiencing MACE (p < 0.001). The global FD at the subacute phase correlated with MACE (hazard ratio 0.89 (0.82, 0.97), p = 0.01), and a global FD value below 1.26 was associated with a heightened risk.

CONCLUSION

In patients post-STEMI, the global FD, serving as an indicator of left ventricular trabeculae complexity, independently demonstrated an association with subsequent major adverse cardiovascular events, beyond factors encompassing left ventricular ejection fraction, indexed left ventricular end-diastolic volume, infarct size, heart rate, NYHA class, and post-pPCI TIMI flow.

CRITICAL RELEVANCE STATEMENT

In patients who have had an ST-segment elevation myocardial infarction, global fractal dimension, as a measure of left ventricular trabeculae complexity, provided independent association with subsequent major adverse cardiovascular event.

KEY POINTS

• Global and regional FD decreased after STEMI, and more so in patients with subsequent MACE. • Lower global FD at the subacute phase and Δglobal FD from acute to subacute phase were associated with subsequent MACE besides clinical and CMR factors. • Global FD at the subacute phase independently correlated with MACE and global FD value below 1.26 was associated with higher risk.

Unsupervised ensemble-based phenotyping enhances discoverability of genes related to left-ventricular morphology

Recent genome-wide association studies have successfully identified associations between genetic variants and simple cardiac morphological parameters derived from cardiac magnetic resonance images. However, the emergence of large databases, including genetic data linked to cardiac magnetic resonance facilitates the investigation of more nuanced patterns of cardiac shape variability than those studied so far. Here we propose a framework for gene discovery coined unsupervised phenotype ensembles. The unsupervised phenotype ensemble builds a redundant yet highly expressive representation by pooling a set of phenotypes learnt in an unsupervised manner, using deep learning models trained with different hyperparameters. These phenotypes are then analysed via genome-wide association studies, retaining only highly confident and stable associations across the ensemble. We applied our approach to the UK Biobank database to extract geometric features of the left ventricle from image-derived three-dimensional meshes. We demonstrate that our approach greatly improves the discoverability of genes that influence left ventricle shape, identifying 49 loci with study-wide significance and 25 with suggestive significance. We argue that our approach would enable more extensive discovery of gene associations with image-derived phenotypes for other organs or image modalities.

Multivariate canonical correlation analysis identifies additional genetic variants for chronic kidney disease

Chronic kidney diseases (CKD) have genetic associations with kidney function. Univariate genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with estimated glomerular filtration rate (eGFR) and blood urea nitrogen (BUN), two complementary kidney function markers. However, it is unknown whether additional SNPs for kidney function can be identified by multivariate statistical analysis. To address this, we applied canonical correlation analysis (CCA), a multivariate method, to two individual-level CKD genotype datasets, and metaCCA to two published GWAS summary statistics datasets. We identified SNPs previously associated with kidney function by published univariate GWASs with high replication rates, validating the metaCCA method. We then extended discovery and identified previously unreported lead SNPs for both kidney function markers, jointly. These showed expression quantitative trait loci (eQTL) colocalisation with genes having significant differential expression between CKD and healthy individuals. Several of these identified lead missense SNPs were predicted to have a functional impact, including in SLC14A2. We also identified previously unreported lead SNPs that showed significant correlation with both kidney function markers, jointly, in the European ancestry CKDGen, National Unified Renal Translational Research Enterprise (NURTuRE)-CKD and Salford Kidney Study (SKS) datasets. Of these, rs3094060 colocalised with FLOT1 gene expression and was significantly more common in CKD cases in both NURTURE-CKD and SKS, than in the general population. Overall, by using multivariate analysis by CCA, we identified additional SNPs and genes for both kidney function and CKD, that can be prioritised for further CKD analyses.

Sex-Specific Effect of MTSS1 Downregulation on Dilated Cardiomyopathy

MTSS1 (metastasis suppressor 1) is an I-BAR protein that regulates cytoskeleton dynamics through interactions with actin, Rac, and actin-associated proteins. In a prior study, we identified genetic variants in a cardiac-specific enhancer upstream of MTSS1 that reduce human left ventricular (LV) MTSS1 expression and associate with protection against dilated cardiomyopathy (DCM). We sought to probe these effects further using population genomics and in vivo murine models. We crossed Mtss1 -/- mice with a transgenic ( Tg ) murine model of human DCM caused by the D230N pathogenic mutation in Tpm1 (tropomyosin 1). In females, Tg/Mtss1 +/- mice had significantly increased LV ejection fraction and reduced LV volumes relative to their Tg/Mtss1 +/+ counterparts, signifying partial rescue of DCM due to Mtss1 haploinsufficiency. No differences were observed in males. To study effects in humans, we fine-mapped the MTSS1 locus with 82 cardiac magnetic resonance (CMR) traits in 22,381 UK Biobank participants. MTSS1 enhancer variants showed interaction with biological sex in their associations with several CMR traits. After stratification by biological sex, associations with CMR traits and colocalization with MTSS1 expression in the Genotype-Tissue Expression (GTEx) Project were observed principally in women and were substantially weaker in men. These findings suggest sex dimorphism in the effects of MTSS1-lowering alleles, and parallel the increased LV ejection fraction and reduced LV volumes observed female Tg/Mtss1 +/- mice. Together, our findings at the MTSS1 locus suggest a genetic basis for sex dimorphism in cardiac remodeling and motivate sex-specific study of common variants associated with cardiac traits and disease.

Sex-stratified genome-wide association and transcriptome-wide Mendelian randomization studies reveal drug targets of heart failure

The prevalence of heart failure (HF) subtypes, which are classified by left ventricular ejection fraction (LVEF), demonstrate significant sex differences. Here, we perform sex-stratified genome-wide association studies (GWASs) on LVEF and transcriptome-wide Mendelian randomization (MR) on LVEF, all-cause HF, HF with reduced ejection fraction (HFrEF), and HF with preserved ejection fraction (HFpEF). The sex-stratified GWASs of LVEF identified three sex-specific loci that were exclusively detected in the sex-stratified GWASs. Three drug target genes show sex-differential effects on HF/HFrEF via influencing LVEF, with NPR2 as the target gene for the HF drug Cenderitide under phase 2 clinical trial. Our study highlights the importance of considering sex-differential genetic effects in sex-balanced diseases such as HF and emphasizes the value of sex-stratified GWASs and MR in identifying putative genetic variants, causal genes, and candidate drug targets for HF, which is not identifiable using a sex-combined strategy.

Left Ventricular Non-Compaction in Children: Aetiology and Diagnostic Criteria

Left ventricular non-compaction (LVNC) is a heterogeneous myocardial disorder characterized by prominent trabeculae protruding into the left ventricular lumen and deep intertrabecular recesses. LVNC can manifest in isolation or alongside other heart muscle diseases. Its occurrence among children is rising due to advancements in imaging techniques. The origins of LVNC are diverse, involving both genetic and acquired forms. The clinical manifestation varies greatly, with some cases presenting no symptoms, while others typically manifesting with heart failure, systemic embolism, and arrhythmias. Diagnosis mainly relies on assessing heart structure using imaging tools like echocardiography and cardiac magnetic resonance. However, the absence of a universally agreed-upon standard and limitations in diagnostic criteria have led to ongoing debates in the scientific community regarding the most reliable methods. Further research is crucial to enhance the diagnosis of LVNC, particularly in early life stages.

Fractal analysis in cardiovascular magnetic resonance: prognostic value of biventricular trabecular complexity in hypertrophic cardiomyopathy

Background

Trabecular complexity can be quantified by fractal analysis based on cine images of cardiovascular magnetic resonance (CMR), yielding fractal dimension (FD) index. We aimed to investigate the prognostic value of biventricular FD in patients with hypertrophic cardiomyopathy (HCM).

Methods

This retrospective study included 284 (192 men, median age 53 years) patients with HCM who underwent CMR, with median follow-up of 24 months. Biventricular trabeculae complexity was quantified as FD using short-axis cine images. The primary end point included sudden cardiac death (SCD) events. The secondary end point included both SCD events and rehospitalization due to heart failure. Cox regressions were performed. Prediction models were established by adding ventricular FDs to ESC predictors and late gadolinium enhancement (LGE) percentage and the C indices were calculated.

Results

Cox regressions revealed that left ventricular (LV) maximal apical FD (HR range 1.114-1.133; all P<0.05) and right ventricular (RV) global FD (HR range 1.135-1.150; all P<0.05) were significant prognostic factors of both end points after adjustment for the European Society of Cardiology (ESC) predictors (age, maximum LV wall thickness, LV atrial size, peak left ventricular outflow tract (LVOT) gradient, family history of SCD, unexplained syncope, non-sustained ventricular tachycardia), and LGE percentage. The prediction model with the addition of biventricular FDs (C-index: 0.864-0.877) had the best performance.

Conclusions

LV maximal apical FD and RV global FD were independent predictors of SCD events and rehospitalization due to heart failure in patients with HCM. The addition of biventricular FDs to the conventional prediction model contributed incremental prognosis value in HCM.

Symmetry and asymmetry in atherosclerosis

Atherosclerosis remains the main cause of death worldwide. Most important issues concerning atherosclerosis are hemodynamics and how it affects plaque prevalence and distribution, as well as the symmetry and asymmetry of vasculature and plaques. To present the symmetry in the vascular system an analysis of PubMed and MEDLINE databases was performed. As of February 21, 2023, the results were as follows: for "symmetry" AND "atherosclerosis" there were 47 results; for "symmetry" AND "atherosclerotic lesions" - 20 results; for "symmetry" AND "artery stenosis" - 82 results; for "asymmetry" AND "atherosclerosis" - 87 results. Not without meaning are preventive measures. In the light of the Fourth Industrial Revolution artificial intelligence (AI) solutions help to develop new tools outperforming already existing cardiovascular risk scales. The aim of this paper is to present a current view on symmetry within vasculature and atherosclerosis as well as present a new approach to assess individuals' cardiovascular risk in accordance with precision medicine assumptions. Symmetry and asymmetry within the human vascular system play a crucial role in understanding of arterial diseases, including atherosclerosis. Moreover, it is unavoidable to use AI in cardiovascular risk stratification. Int J Occup Med Environ Health. 2023;36(6):693-703.

The Trouble with Trabeculation: How Genetics Can Help to Unravel a Complex and Controversial Phenotype

Excessive trabeculation of the cardiac left ventricular wall is a complex phenotypic substrate associated with various physiological and pathological processes. There has been considerable conjecture as to whether hypertrabeculation contributes to disease and whether left ventricular non-compaction (LVNC) cardiomyopathy is a distinct pathology. Building on recent insights into the genetic basis of LVNC cardiomyopathy, in particular three meta-analysis studies exploring genotype-phenotype associations using different methodologies, this review examines how genetic research can advance our understanding of trabeculation. Three groups of genes implicated in LVNC are described-those associated with other cardiomyopathies, other cardiac/syndromic conditions and putatively with isolated LVNC cardiomyopathy-demonstrating how these findings can inform the underlying pathologies in LVNC patients and aid differential diagnosis and management in clinical practice despite the limited utility suggested for LVNC genetic testing in recent guidelines. The outstanding questions and future research priorities for exploring the genetics of hypertrabeculation are discussed.

Genotype-Phenotype Taxonomy of Hypertrophic Cardiomyopathy

BACKGROUND

Hypertrophic cardiomyopathy (HCM) is an important cause of sudden cardiac death associated with heterogeneous phenotypes, but there is no systematic framework for classifying morphology or assessing associated risks. Here, we quantitatively survey genotype-phenotype associations in HCM to derive a data-driven taxonomy of disease expression.

METHODS

We enrolled 436 patients with HCM (median age, 60 years; 28.8% women) with clinical, genetic, and imaging data. An independent cohort of 60 patients with HCM from Singapore (median age, 59 years; 11% women) and a reference population from the UK Biobank (n=16 691; mean age, 55 years; 52.5% women) were also recruited. We used machine learning to analyze the 3-dimensional structure of the left ventricle from cardiac magnetic resonance imaging and build a tree-based classification of HCM phenotypes. Genotype and mortality risk distributions were projected on the tree.

RESULTS

Carriers of pathogenic or likely pathogenic variants for HCM had lower left ventricular mass, but greater basal septal hypertrophy, with reduced life span (mean follow-up, 9.9 years) compared with genotype negative individuals (hazard ratio, 2.66 [95% CI, 1.42-4.96]; P<0.002). Four main phenotypic branches were identified using unsupervised learning of 3-dimensional shape: (1) nonsarcomeric hypertrophy with coexisting hypertension; (2) diffuse and basal asymmetrical hypertrophy associated with outflow tract obstruction; (3) isolated basal hypertrophy; and (4) milder nonobstructive hypertrophy enriched for familial sarcomeric HCM (odds ratio for pathogenic or likely pathogenic variants, 2.18 [95% CI, 1.93-2.28]; P=0.0001). Polygenic risk for HCM was also associated with different patterns and degrees of disease expression. The model was generalizable to an independent cohort (trustworthiness, M1: 0.86-0.88).

CONCLUSIONS

We report a data-driven taxonomy of HCM for identifying groups of patients with similar morphology while preserving a continuum of disease severity, genetic risk, and outcomes. This approach will be of value in understanding the causes and consequences of disease diversity.

Fractal analysis: Left ventricular trabecular complexity cardiac MRI adds independent risks for heart failure with preserved ejection fraction in participants with end-stage renal disease

PURPOSE

To measure left ventricular (LV) trabecular complexity by fractal dimension (FD) in patients with end-stage renal disease (ESRD), and assess whether FD was an independent risk factor for heart failure with preserved ejection fraction (HFpEF), or a significant predictor for adverse outcome in this population.

METHODS

The study retrospectively enrolled 104 participants with ESRD who underwent 3.0 T cardiac magnetic resonance imaging (MRI) from June 2018 to November 2020. LV trabeculation was quantified with fractal analysis of short-axis cine slices to estimate the FD. Logistic regression analyses were used to evaluate FD and cardiac MRI parameters and to find independent risk predictors. Cox proportional hazard regression was used to investigate the association between FD and MACE.

RESULTS

LV FD was higher in in the HFpEF group than those in the non-HFpEF group, with the greatest difference near the base of the ventricle. Age, minimum left atrial volume index, and LV mean basal FD were independent predictors for HFpEF in patients with ESRD. Combining the mean basal FD with typical predictive factors resulted in a C-index (0.902 vs 0.921), which was not significantly higher. Same improvements were found for net reclassification improvement [0.642; 95% confidence interval (CI), 0.254-1.029] and integrated discrimination index (0.026; 95% CI, 0.008-0.061). Participants with a LV global FD above the cutoff value (1.278) had higher risks of MACE in ESRD patients.

CONCLUSIONS

LV trabecular complexity measured by FD was an independent risk factor for HFpEF, and a significant predictor for MACE among patients with ESRD.

Environmental and genetic predictors of human cardiovascular ageing

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.

CRISPR-based knockout and base editing confirm the role of MYRF in heart development and congenital heart disease

High-throughput DNA sequencing studies increasingly associate DNA variants with congenital heart disease (CHD). However, functional modeling is a crucial prerequisite for translating genomic data into clinical care. We used CRISPR-Cas9-mediated targeting of 12 candidate genes in the vertebrate model medaka (Oryzias latipes), five of which displayed a novel cardiovascular phenotype spectrum in F0 (crispants): mapre2, smg7, cdc42bpab, ankrd11 and myrf, encoding a transcription factor recently linked to cardiac-urogenital syndrome. Our myrf mutant line showed particularly prominent embryonic cardiac defects recapitulating phenotypes of pediatric patients, including hypoplastic ventricle. Mimicking human mutations, we edited three sites to generate specific myrf single-nucleotide variants via cytosine and adenine base editors. The Glu749Lys missense mutation in the conserved intramolecular chaperon autocleavage domain fully recapitulated the characteristic myrf mutant phenotype with high penetrance, underlining the crucial function of this protein domain. The efficiency and scalability of base editing to model specific point mutations accelerate gene validation studies and the generation of human-relevant disease models.

Multi-modality cardiac image computing: A survey

Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases. It allows a combination of complementary anatomical, morphological and functional information, increases diagnosis accuracy, and improves the efficacy of cardiovascular interventions and clinical outcomes. Fully-automated processing and quantitative analysis of multi-modality cardiac images could have a direct impact on clinical research and evidence-based patient management. However, these require overcoming significant challenges including inter-modality misalignment and finding optimal methods to integrate information from different modalities. This paper aims to provide a comprehensive review of multi-modality imaging in cardiology, the computing methods, the validation strategies, the related clinical workflows and future perspectives. For the computing methodologies, we have a favored focus on the three tasks, i.e., registration, fusion and segmentation, which generally involve multi-modality imaging data, either combining information from different modalities or transferring information across modalities. The review highlights that multi-modality cardiac imaging data has the potential of wide applicability in the clinic, such as trans-aortic valve implantation guidance, myocardial viability assessment, and catheter ablation therapy and its patient selection. Nevertheless, many challenges remain unsolved, such as missing modality, modality selection, combination of imaging and non-imaging data, and uniform analysis and representation of different modalities. There is also work to do in defining how the well-developed techniques fit in clinical workflows and how much additional and relevant information they introduce. These problems are likely to continue to be an active field of research and the questions to be answered in the future.

Harnessing developmental cues for cardiomyocyte production

Developmental research has attempted to untangle the exact signals that control heart growth and size, with knockout studies in mice identifying pivotal roles for Wnt and Hippo signaling during embryonic and fetal heart growth. Despite this improved understanding, no clinically relevant therapies are yet available to compensate for the loss of functional adult myocardium and the absence of mature cardiomyocyte renewal that underlies cardiomyopathies of multiple origins. It remains of great interest to understand which mechanisms are responsible for the decline in proliferation in adult hearts and to elucidate new strategies for the stimulation of cardiac regeneration. Multiple signaling pathways have been identified that regulate the proliferation of cardiomyocytes in the embryonic heart and appear to be upregulated in postnatal injured hearts. In this Review, we highlight the interaction of signaling pathways in heart development and discuss how this knowledge has been translated into current technologies for cardiomyocyte production.

Multi-response Mendelian randomization: Identification of shared and distinct exposures for multimorbidity and multiple related disease outcomes

The existing framework of Mendelian randomization (MR) infers the causal effect of one or multiple exposures on one single outcome. It is not designed to jointly model multiple outcomes, as would be necessary to detect causes of more than one outcome and would be relevant to model multimorbidity or other related disease outcomes. Here, we introduce multi-response Mendelian randomization (MR2), an MR method specifically designed for multiple outcomes to identify exposures that cause more than one outcome or, conversely, exposures that exert their effect on distinct responses. MR2 uses a sparse Bayesian Gaussian copula regression framework to detect causal effects while estimating the residual correlation between summary-level outcomes, i.e., the correlation that cannot be explained by the exposures, and vice versa. We show both theoretically and in a comprehensive simulation study how unmeasured shared pleiotropy induces residual correlation between outcomes irrespective of sample overlap. We also reveal how non-genetic factors that affect more than one outcome contribute to their correlation. We demonstrate that by accounting for residual correlation, MR2 has higher power to detect shared exposures causing more than one outcome. It also provides more accurate causal effect estimates than existing methods that ignore the dependence between related responses. Finally, we illustrate how MR2 detects shared and distinct causal exposures for five cardiovascular diseases in two applications considering cardiometabolic and lipidomic exposures and uncovers residual correlation between summary-level outcomes reflecting known relationships between cardiovascular diseases.

Heart-brain connections: Phenotypic and genetic insights from magnetic resonance images

Cardiovascular health interacts with cognitive and mental health in complex ways, yet little is known about the phenotypic and genetic links of heart-brain systems. We quantified heart-brain connections using multiorgan magnetic resonance imaging (MRI) data from more than 40,000 subjects. Heart MRI traits displayed numerous association patterns with brain gray matter morphometry, white matter microstructure, and functional networks. We identified 80 associated genomic loci (P < 6.09 × 10-10) for heart MRI traits, which shared genetic influences with cardiovascular and brain diseases. Genetic correlations were observed between heart MRI traits and brain-related traits and disorders. Mendelian randomization suggests that heart conditions may causally contribute to brain disorders. Our results advance a multiorgan perspective on human health by revealing heart-brain connections and shared genetic influences.

Genetic Determinants of the Interventricular Septum Are Linked to Ventricular Septal Defects and Hypertrophic Cardiomyopathy

BACKGROUND

A large proportion of genetic risk remains unexplained for structural heart disease involving the interventricular septum (IVS) including hypertrophic cardiomyopathy and ventricular septal defects. This study 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 diseases of the IVS, hypertrophic cardiomyopathy, and ventricular septal defect.

METHODS

We estimated the cross-sectional area of the IVS from the 4-chamber view of cardiac magnetic resonance imaging in 32 219 individuals from the UK Biobank which was used as the basis of genome wide association studies and Mendelian randomization.

RESULTS

Measures of IVS cross-sectional area at diastole were a strong proxy for the 3-dimensional volume of the IVS (Pearson r=0.814, P=0.004), and correlated with anthropometric measures, blood pressure, and diagnostic codes related to cardiovascular physiology. Seven loci with clear genomic consequence and relevance to cardiovascular biology were uncovered by genome wide association studies, most notably a single nucleotide polymorphism in an intron of CDKN1A (rs2376620; β, 7.7 mm2 [95% CI, 5.8-11.0]; P=6.0×10-10), and a common inversion incorporating KANSL1 predicted to disrupt local chromatin structure (β, 8.4 mm2 [95% CI, 6.3-10.9]; P=4.2×10-14). Mendelian randomization suggested that inheritance of larger IVS cross-sectional area at diastole was strongly associated with hypertrophic cardiomyopathy risk (pIVW=4.6×10-10) while inheritance of smaller IVS cross-sectional area at diastole was associated with risk for ventricular septal defect (pIVW=0.007).

CONCLUSIONS

Automated estimates of cross-sectional area of the IVS supports discovery of novel loci related to cardiac development and Mendelian disease. Inheritance of genetic liability for either small or large IVS, appears to confer risk for ventricular septal defect or hypertrophic cardiomyopathy, respectively. These data suggest that a proportion of risk for structural and congenital heart disease can be localized to the common genetic determinants of size and shape of cardiovascular anatomy.

Deep learning-enabled analysis of medical images identifies cardiac sphericity as an early marker of cardiomyopathy and related outcomes

BACKGROUND

Quantification of chamber size and systolic function is a fundamental component of cardiac imaging. However, the human heart is a complex structure with significant uncharacterized phenotypic variation beyond traditional metrics of size and function. Examining variation in cardiac shape can add to our ability to understand cardiovascular risk and pathophysiology.

METHODS

We measured the left ventricle (LV) sphericity index (short axis length/long axis length) using deep learning-enabled image segmentation of cardiac magnetic resonance imaging data from the UK Biobank. Subjects with abnormal LV size or systolic function were excluded. The relationship between LV sphericity and cardiomyopathy was assessed using Cox analyses, genome-wide association studies, and two-sample Mendelian randomization.

FINDINGS

In a cohort of 38,897 subjects, we show that a one standard deviation increase in sphericity index is associated with a 47% increased incidence of cardiomyopathy (hazard ratio [HR]: 1.47, 95% confidence interval [CI]: 1.10-1.98, p = 0.01) and a 20% increased incidence of atrial fibrillation (HR: 1.20, 95% CI: 1.11-1.28, p < 0.001), independent of clinical factors and traditional magnetic resonance imaging (MRI) measurements. We identify four loci associated with sphericity at genome-wide significance, and Mendelian randomization supports non-ischemic cardiomyopathy as causal for LV sphericity.

CONCLUSIONS

Variation in LV sphericity in otherwise normal hearts predicts risk for cardiomyopathy and related outcomes and is caused by non-ischemic cardiomyopathy.

FUNDING

This study was supported by grants K99-HL157421 (D.O.) and KL2TR003143 (S.L.C.) from the National Institutes of Health.

Microvascular and macrovascular complications of type 2 diabetes mellitus: Exome wide association analyses

Background

Type 2 diabetes mellitus (T2DM) is a chronic, metabolic disorder in which concomitant insulin resistance and β-cell impairment lead to hyperglycemia, influenced by genetic and environmental factors. T2DM is associated with long-term complications that have contributed to the burden of morbidity and mortality worldwide. The objective of this manuscript is to conduct an Exome-Wide Association Study (EWAS) on T2DM Emirati individuals to improve our understanding on diabetes-related complications to improve early diagnostic methods and treatment strategies.

Methods

This cross-sectional study recruited 310 Emirati participants that were stratified according to their medically diagnosed diabetes-related complications: diabetic retinopathy, diabetic neuropathy, diabetic nephropathy, and cardiovascular complications. The Illumina's Infinium Exome-24 array was used and 39,840 SNPs remained for analysis after quality control.

Findings

The analysis revealed the associations of various genes with each complication category: 1) diabetic retinopathy was associated to SHANK3 gene in locus 22q13.33 (SNP rs9616915; p=5.18 x10-4), ZSCAN5A gene in locus 19q13.43 (SNP rs7252603; p=7.55 x10-4), and DCP1B gene in locus 12p13.33 (SNPs rs715146, rs1044950, rs113147414, rs34730825; p=7.62 x10-4); 2) diabetic neuropathy was associated to ADH4 gene in locus 4q23 (SNP rs4148883; p=1.23 x10-4), SLC11A1 gene in locus 2q35 (SNP rs17235409; p=1.85 x10-4), and MATN4 gene in locus 20q13.12 (SNP rs2072788; p=2.68 x10-4); 3) diabetic nephropathy was associated to PPP1R3A gene in locus 7q31.1 (SNP rs1799999; p=1.91 x10-4), ZNF136 gene in locus 19p13.2 (SNP rs140861589; p=2.80 x10-4), and HSPA12B gene in locus 20p13 (SNP rs6076550; p=2.86 x10-4); and 4) cardiovascular complications was associated to PCNT gene in locus 21q22.3 (SNPs rs7279204, rs6518289, rs2839227, rs2839223; p=2.18 x10-4,3.04 x10-4,4.51 x10-4,5.22 x10-4 respectively), SEPT14 gene in locus 7p11.2 (SNP rs146350220; p=2.77 x10-4), and WDR73 gene in locus 15q25.2 (SNP rs72750868; p=4.47 x10-4).

Interpretation

We have identified susceptibility loci associated with each category of T2DM-related complications in the Emirati population. Given that only 16% of the markers from the Illumina's Infinium Exome chip passed quality control assessment, this demonstrates that multiple variants were, either, monomorphic in the Arab population or were not genotyped due to the use of a Euro-centric EWAS array that limits the possibility of including targeted ethnic-specific SNPs. Our results suggest the alarming possibility that lack of representation in reference panels could inhibit discovery of functionally important loci associated to T2DM complications. Further effort must be conducted to improve the representation of diverse populations in genotyping and sequencing studies.

Excessive Trabeculation of the Left Ventricle: JACC: Cardiovascular Imaging Expert Panel Paper

Excessive trabeculation, often referred to as "noncompacted" myocardium, has been described at all ages, from the fetus to the adult. Current evidence for myocardial development, however, does not support the formation of compact myocardium from noncompacted myocardium, nor the arrest of this process to result in so-called noncompaction. Excessive trabeculation is frequently observed by imaging studies in healthy individuals, as well as in association with pregnancy, athletic activity, and with cardiac diseases of inherited, acquired, developmental, or congenital origins. Adults with incidentally noted excessive trabeculation frequently require no further follow-up based on trabecular pattern alone. Patients with cardiomyopathy and excessive trabeculation are managed by cardiovascular symptoms rather than the trabecular pattern. To date, the prognostic role of excessive trabeculation in adults has not been shown to be independent of other myocardial disease. In neonates and children with excessive trabeculation and normal or abnormal function, clinical caution seems warranted because of the reported association with genetic and neuromuscular disorders. This report summarizes the evidence concerning the etiology, pathophysiology, and clinical relevance of excessive trabeculation. Gaps in current knowledge of the clinical relevance of excessive trabeculation are indicated, with priorities suggested for future research and improved diagnosis in adults and children.

Impact of Measurement Imprecision on Genetic Association Studies of Cardiac Function

Background

Recent studies have leveraged quantitative traits from imaging to amplify the power of genome-wide association studies (GWAS) to gain further insights into the biology of diseases and traits. However, measurement imprecision is intrinsic to phenotyping and can impact downstream genetic analyses.

Methods

Left ventricular ejection fraction (LVEF), an important but imprecise quantitative imaging measurement, was examined to assess the impact of precision of phenotype measurement on genetic studies. Multiple approaches to obtain LVEF, as well as simulated measurement noise, were evaluated with their impact on downstream genetic analyses.

Results

Even within the same population, small changes in the measurement of LVEF drastically impacted downstream genetic analyses. Introducing measurement noise as little as 7.9% can eliminate all significant genetic associations in an GWAS with almost forty thousand individuals. An increase of 1% in mean absolute error (MAE) in LVEF had an equivalent impact on GWAS power as a decrease of 10% in the cohort sample size, suggesting optimizing phenotyping precision is a cost-effective way to improve power of genetic studies.

Conclusions

Improving the precision of phenotyping is important for maximizing the yield of genome-wide association studies.

Cardiac MRI to guide heart failure and atrial fibrillation drug discovery: a Mendelian randomization analysis

Background

drug development and disease prevention of heart failure (HF) and atrial fibrillation (AF) are impeded by a lack of robust early-stage surrogates. We determined to what extent cardiac magnetic resonance (CMR) measurements act as surrogates for the development of HF or AF in healthy individuals.

Methods

Genetic data was sourced on the association with 22 atrial and ventricular CMR measurements. Mendelian randomization was used to determine CMR associations with atrial fibrillation (AF), heart failure (HF), non-ischemic cardiomyopathy (CMP), and dilated cardiomyopathy (DCM). Additionally, for the CMR surrogates of AF and HF, we explored their association with non-cardiac traits.

Results

In total we found that 9 CMR measures were associated with the development of HF, 7 with development of non-ischemic CMR, 6 with DCM, and 12 with AF. biventricular ejection fraction (EF), biventricular or end-systolic volumes (ESV) and left-ventricular (LV) end diastolic volume (EDV) were associated with all 4 cardiac outcomes. Increased LV-MVR (mass to volume ratio) affected HF (odds ratio (OR) 0.83, 95%CI 0.79; 0.88), and DCM (OR 0.26, 95%CI 0.20; 0.34. We were able to identify 9 CMR surrogates for HF and/or AF (including LV-MVR, biventricular EDV, ESV, and right-ventricular EF) which associated with non-cardiac traits such as blood pressure, lung function traits, BMI, cardioembolic stroke, and late-onset Alzheimer's disease.

Conclusion

CMR measurements may act as surrogate endpoints for the development of HF (including non-ischemic CMP and DCM) or AF. Additionally, we show that changes in cardiac function and structure measured through CMR, may affect diseases of other organs leading to lung disease or late-onset Alzheimer's disease.

BMI-adjusted adipose tissue volumes exhibit depot-specific and divergent associations with cardiometabolic diseases

For any given body mass index (BMI), individuals vary substantially in fat distribution, and this variation may have important implications for cardiometabolic risk. Here, we study disease associations with BMI-independent variation in visceral (VAT), abdominal subcutaneous (ASAT), and gluteofemoral (GFAT) fat depots in 40,032 individuals of the UK Biobank with body MRI. We apply deep learning models based on two-dimensional body MRI projections to enable near-perfect estimation of fat depot volumes (R2 in heldout dataset = 0.978-0.991 for VAT, ASAT, and GFAT). Next, we derive BMI-adjusted metrics for each fat depot (e.g. VAT adjusted for BMI, VATadjBMI) to quantify local adiposity burden. VATadjBMI is associated with increased risk of type 2 diabetes and coronary artery disease, ASATadjBMI is largely neutral, and GFATadjBMI is associated with reduced risk. These results - describing three metabolically distinct fat depots at scale - clarify the cardiometabolic impact of BMI-independent differences in body fat distribution.

Deep learning-derived cardiovascular age shares a genetic basis with other cardiac phenotypes

Artificial intelligence (AI)-based approaches can now use electrocardiograms (ECGs) to provide expert-level performance in detecting heart abnormalities and diagnosing disease. Additionally, patient age predicted from ECGs by AI models has shown great potential as a biomarker for cardiovascular age, where recent work has found its deviation from chronological age ("delta age") to be associated with mortality and co-morbidities. However, despite being crucial for understanding underlying individual risk, the genetic underpinning of delta age is unknown. In this work we performed a genome-wide association study using UK Biobank data (n=34,432) and identified eight loci associated with delta age ([Formula: see text]), including genes linked to cardiovascular disease (CVD) (e.g. SCN5A) and (heart) muscle development (e.g. TTN). Our results indicate that the genetic basis of cardiovascular ageing is predominantly determined by genes directly involved with the cardiovascular system rather than those connected to more general mechanisms of ageing. Our insights inform the epidemiology of CVD, with implications for preventative and precision medicine.

Functional screening of congenital heart disease risk loci identifies 5 genes essential for heart development in zebrafish

Congenital heart disease (CHD) is the most common birth defect worldwide and a main cause of perinatal and infant mortality. Our previous genome-wide association study identified 53 SNPs that associated with CHD in the Han Chinese population. Here, we performed functional screening of 27 orthologous genes in zebrafish using injection of antisense morpholino oligos. From this screen, 5 genes were identified as essential for heart development, including iqgap2, ptprt, ptpn22, tbck and maml3. Presumptive roles of the novel CHD-related genes include heart chamber formation (iqgap2 and ptprt) and atrioventricular canal formation (ptpn22 and tbck). While deficiency of maml3 led to defective cardiac trabeculation and consequent heart failure in zebrafish embryos. Furthermore, we found that maml3 mutants showed decreased cardiomyocyte proliferation which caused a reduction in cardiac trabeculae due to inhibition of Notch signaling. Together, our study identifies 5 novel CHD-related genes that are essential for heart development in zebrafish and first demonstrates that maml3 is required for Notch signaling in vivo.

Myocardial perfusion in excessively trabeculated hearts: Insights from imaging and histological studies

In gestation, the coronary circulation develops initially in the compact layer and it expands only in fetal development to the trabeculations. Conflicting data have been published as to whether the trabecular layer is hypoperfused relative to the compact wall after birth. If so, this could explain the poor pump function in patients with left ventricular excessive trabeculation, or so-called noncompaction. Here, we review direct and indirect assessments of myocardial perfusion in normal and excessively trabeculated hearts by in vivo imaging by magnetic resonance imaging (MRI), positron emission tomography (PET)/single photon emission computed tomography (SPECT), and echocardiography in addition to histology, injections of labelled microspheres in animals, and electrocardiography. In MRI, PET/SPECT, and echocardiography, flow of blood or myocardial uptake of blood-borne tracer molecules are measured. The imaged trabecular layer comprises trabeculations and blood-filled intertrabecular spaces whereas the compact layer comprises tissue only, and spatio-temporal resolution likely affects measurements of myocardial perfusion differently in the two layers. Overall, studies measuring myocardial uptake of tracers (PET/SPECT) suggest trabecular hypoperfusion. Studies measuring the quantity of blood (echocardiography and MRI) suggest trabecular hyperperfusion. These conflicting results are reconciled if the low uptake from intertrabecular spaces in PET/SPECT and the high signal from intertrabecular spaces in MRI and echocardiography are considered opposite biases. Histology on human hearts reveal a similar capillary density of trabecular and compact myocardium. Injections of labelled microspheres in animals reveal a similar perfusion of trabecular and compact myocardium. In conclusion, trabecular and compact muscle are likely equally perfused in normal hearts and most cases of excessive trabeculation.

A large genome-wide association study of QT interval length utilizing electronic health records

QT interval length is an important risk factor for adverse cardiovascular outcomes; however, the genetic architecture of QT interval remains incompletely understood. We conducted a genome-wide association study of 76,995 ancestrally diverse Kaiser Permanente Northern California members enrolled in the Genetic Epidemiology Research on Adult Health and Aging cohort using 448,517 longitudinal QT interval measurements, uncovering 9 novel variants, most replicating in 40,537 individuals in the UK Biobank and Population Architecture using Genomics and Epidemiology studies. A meta-analysis of all 3 cohorts (n = 117,532) uncovered an additional 19 novel variants. Conditional analysis identified 15 additional variants, 3 of which were novel. Little, if any, difference was seen when adjusting for putative QT interval lengthening medications genome-wide. Using multiple measurements in Genetic Epidemiology Research on Adult Health and Aging increased variance explained by 163%, and we show that the ≈6 measurements in Genetic Epidemiology Research on Adult Health and Aging was equivalent to a 2.4× increase in sample size of a design with a single measurement. The array heritability was estimated at ≈17%, approximately half of our estimate of 36% from family correlations. Heritability enrichment was estimated highest and most significant in cardiovascular tissue (enrichment 7.2, 95% CI = 5.7-8.7, P = 2.1e-10), and many of the novel variants included expression quantitative trait loci in heart and other relevant tissues. Comparing our results to other cardiac function traits, it appears that QT interval has a multifactorial genetic etiology.

Equal force generation potential of trabecular and compact wall ventricular cardiomyocytes

Trabecular myocardium makes up most of the ventricular wall of the human embryo. A process of compaction in the fetal period presumably changes ventricular wall morphology by converting ostensibly weaker trabecular myocardium into stronger compact myocardium. Using developmental series of embryonic and fetal humans, mice and chickens, we show ventricular morphogenesis is driven by differential rates of growth of trabecular and compact layers rather than a process of compaction. In mouse, fetal cardiomyocytes are relatively weak but adult cardiomyocytes from the trabecular and compact layer show an equally large force generating capacity. In fetal and adult humans, trabecular and compact myocardium are not different in abundance of immunohistochemically detected vascular, mitochondrial and sarcomeric proteins. Similar findings are made in human excessive trabeculation, a congenital malformation. In conclusion, trabecular and compact myocardium is equally equipped for force production and their proportions are determined by differential growth rates rather than by compaction.

Pathogenic variants damage cell composition and single cell transcription in cardiomyopathies

Pathogenic variants in genes that cause dilated cardiomyopathy (DCM) and arrhythmogenic cardiomyopathy (ACM) convey high risks for the development of heart failure through unknown mechanisms. Using single-nucleus RNA sequencing, we characterized the transcriptome of 880,000 nuclei from 18 control and 61 failing, nonischemic human hearts with pathogenic variants in DCM and ACM genes or idiopathic disease. We performed genotype-stratified analyses of the ventricular cell lineages and transcriptional states. The resultant DCM and ACM ventricular cell atlas demonstrated distinct right and left ventricular responses, highlighting genotype-associated pathways, intercellular interactions, and differential gene expression at single-cell resolution. Together, these data illuminate both shared and distinct cellular and molecular architectures of human heart failure and suggest candidate therapeutic targets.

Spatially Distinct Genetic Determinants of Aortic Dimensions Influence Risks of Aneurysm and Stenosis

BACKGROUND

The left ventricular outflow tract (LVOT) and ascending aorta are spatially complex, with distinct pathologies and embryologic origins. Prior work examined the genetics of thoracic aortic diameter in a single plane.

OBJECTIVES

We sought to elucidate the genetic basis for the diameter of the LVOT, aortic root, and ascending aorta.

METHODS

Using deep learning, we analyzed 2.3 million cardiac magnetic resonance images from 43,317 UK Biobank participants. We computed the diameters of the LVOT, the aortic root, and at 6 locations of ascending aorta. For each diameter, we conducted a genome-wide association study and generated a polygenic score. Finally, we investigated associations between these scores and disease incidence.

RESULTS

A total of 79 loci were significantly associated with at least 1 diameter. Of these, 35 were novel, and most were associated with 1 or 2 diameters. A polygenic score of aortic diameter approximately 13 mm from the sinotubular junction most strongly predicted thoracic aortic aneurysm (n = 427,016; mean HR: 1.42 per SD; 95% CI: 1.34-1.50; P = 6.67 × 10-21). A polygenic score predicting a smaller aortic root was predictive of aortic stenosis (n = 426,502; mean HR: 1.08 per SD; 95% CI: 1.03-1.12; P = 5 × 10-6).

CONCLUSIONS

We detected distinct genetic loci underpinning the diameters of the LVOT, aortic root, and at several segments of ascending aorta. We spatially defined a region of aorta whose genetics may be most relevant to predicting thoracic aortic aneurysm. We further described a genetic signature that may predispose to aortic stenosis. Understanding genetic contributions to proximal aortic diameter may enable identification of individuals at risk for aortic disease and facilitate prioritization of therapeutic targets.

Left Ventricular Noncompaction in Children: The Role of Genetics, Morphology, and Function for Outcome

Left ventricular noncompaction (LVNC) is a ventricular wall anomaly morphologically characterized by numerous, excessively prominent trabeculations and deep intertrabecular recesses. Accumulating data now suggest that LVNC is a distinct phenotype but must not constitute a pathological phenotype. Some individuals fulfill the morphologic criteria of LVNC and are without clinical manifestations. Most importantly, morphologic criteria for LVNC are insufficient to diagnose patients with an associated cardiomyopathy (CMP). Genetic testing has become relevant to establish a diagnosis associated with CMP, congenital heart disease, neuromuscular disease, inborn error of metabolism, or syndromic disorder. Genetic factors play a more decisive role in children than in adults and severe courses of LVNC tend to occur in childhood. We reviewed the current literature and highlight the difficulties in establishing the correct diagnosis for children with LVNC. Novel insights show that the interplay of genetics, morphology, and function determine the outcome in pediatric LVNC.

Genome-wide association analysis reveals insights into the genetic architecture of right ventricular structure and function

Right ventricular (RV) structure and function influence the morbidity and mortality from coronary artery disease (CAD), dilated cardiomyopathy (DCM), pulmonary hypertension and heart failure. Little is known about the genetic basis of RV measurements. Here we perform genome-wide association analyses of four clinically relevant RV phenotypes (RV end-diastolic volume, RV end-systolic volume, RV stroke volume, RV ejection fraction) from cardiovascular magnetic resonance images, using a state-of-the-art deep learning algorithm in 29,506 UK Biobank participants. We identify 25 unique loci associated with at least one RV phenotype at P < 2.27 ×10^-8, 17 of which are validated in a combined meta-analysis (n = 41,830). Several candidate genes overlap with Mendelian cardiomyopathy genes and are involved in cardiac muscle contraction and cellular adhesion. The RV polygenic risk scores (PRSs) are associated with DCM and CAD. The findings substantially advance our understanding of the genetic underpinning of RV measurements.

Monogenic and Polygenic Contributions to QTc Prolongation in the Population

BACKGROUND

Rare sequence variation in genes underlying cardiac repolarization and common polygenic variation influence QT interval duration. However, current clinical genetic testing of individuals with unexplained QT prolongation is restricted to examination of monogenic rare variants. The recent emergence of large-scale biorepositories with sequence data enables examination of the joint contribution of rare and common variations to the QT interval in the population.

METHODS

We performed a genome-wide association study of the QTc in 84 630 UK Biobank participants and created a polygenic risk score (PRS). Among 26 976 participants with whole-genome sequencing and ECG data in the TOPMed (Trans-Omics for Precision Medicine) program, we identified 160 carriers of putative pathogenic rare variants in 10 genes known to be associated with the QT interval. We examined QTc associations with the PRS and with rare variants in TOPMed.

RESULTS

Fifty-four independent loci were identified by genome-wide association study in the UK Biobank. Twenty-one loci were novel, of which 12 were replicated in TOPMed. The PRS composed of 1 110 494 common variants was significantly associated with the QTc in TOPMed (ΔQTc/decile of PRS=1.4 ms [95% CI, 1.3 to 1.5]; P=1.1×10-196). Carriers of putative pathogenic rare variants had longer QTc than noncarriers (ΔQTc=10.9 ms [95% CI, 7.4 to 14.4]). Of individuals with QTc>480 ms, 23.7% carried either a monogenic rare variant or had a PRS in the top decile (3.4% monogenic, 21% top decile of PRS).

CONCLUSIONS

QTc duration in the population is influenced by both rare variants in genes underlying cardiac repolarization and polygenic risk, with a sizeable contribution from polygenic risk. Comprehensive assessment of the genetic determinants of QTc prolongation includes incorporation of both polygenic and monogenic risk.

Precise in vivo functional analysis of DNA variants with base editing using ACEofBASEs target prediction

Single nucleotide variants (SNVs) are prevalent genetic factors shaping individual trait profiles and disease susceptibility. The recent development and optimizations of base editors, rubber and pencil genome editing tools now promise to enable direct functional assessment of SNVs in model organisms. However, the lack of bioinformatic tools aiding target prediction limits the application of base editing in vivo. Here, we provide a framework for adenine and cytosine base editing in medaka (Oryzias latipes) and zebrafish (Danio rerio), ideal for scalable validation studies. We developed an online base editing tool ACEofBASEs (a careful evaluation of base-edits), to facilitate decision-making by streamlining sgRNA design and performing off-target evaluation. We used state-of-the-art adenine (ABE) and cytosine base editors (CBE) in medaka and zebrafish to edit eye pigmentation genes and transgenic GFP function with high efficiencies. Base editing in the genes encoding troponin T and the potassium channel ERG faithfully recreated known cardiac phenotypes. Deep-sequencing of alleles revealed the abundance of intended edits in comparison to low levels of insertion or deletion (indel) events for ABE8e and evoBE4max. We finally validated missense mutations in novel candidate genes of congenital heart disease (CHD) dapk3, ube2b, usp44, and ptpn11 in F0 and F1 for a subset of these target genes with genotype-phenotype correlation. This base editing framework applies to a wide range of SNV-susceptible traits accessible in fish, facilitating straight-forward candidate validation and prioritization for detailed mechanistic downstream studies.

Genetic and environmental determinants of diastolic heart function

Diastole is the sequence of physiological events that occur in the heart during ventricular filling and principally depends on myocardial relaxation and chamber stiffness. Abnormal diastolic function is related to many cardiovascular disease processes and is predictive of health outcomes, but its genetic architecture is largely unknown. Here, we use machine learning cardiac motion analysis to measure diastolic functional traits in 39,559 participants of the UK Biobank and perform a genome-wide association study. We identified 9 significant, independent loci near genes that are associated with maintaining sarcomeric function under biomechanical stress and genes implicated in the development of cardiomyopathy. Age, sex and diabetes were independent predictors of diastolic function and we found a causal relationship between genetically-determined ventricular stiffness and incident heart failure. Our results provide insights into the genetic and environmental factors influencing diastolic function that are relevant for identifying causal relationships and potential tractable targets.

Deep Learning of the Retina Enables Phenome- and Genome-Wide Analyses of the Microvasculature

BACKGROUND

The microvasculature, the smallest blood vessels in the body, has key roles in maintenance of organ health and tumorigenesis. The retinal fundus is a window for human in vivo noninvasive assessment of the microvasculature. Large-scale complementary machine learning-based assessment of the retinal vasculature with phenome-wide and genome-wide analyses may yield new insights into human health and disease.

METHODS

We used 97 895 retinal fundus images from 54 813 UK Biobank participants. Using convolutional neural networks to segment the retinal microvasculature, we calculated vascular density and fractal dimension as a measure of vascular branching complexity. We associated these indices with 1866 incident International Classification of Diseases-based conditions (median 10-year follow-up) and 88 quantitative traits, adjusting for age, sex, smoking status, and ethnicity.

RESULTS

Low retinal vascular fractal dimension and density were significantly associated with higher risks for incident mortality, hypertension, congestive heart failure, renal failure, type 2 diabetes, sleep apnea, anemia, and multiple ocular conditions, as well as corresponding quantitative traits. Genome-wide association of vascular fractal dimension and density identified 7 and 13 novel loci, respectively, that were enriched for pathways linked to angiogenesis (eg, vascular endothelial growth factor, platelet-derived growth factor receptor, angiopoietin, and WNT signaling pathways) and inflammation (eg, interleukin, cytokine signaling).

CONCLUSIONS

Our results indicate that the retinal vasculature may serve as a biomarker for future cardiometabolic and ocular disease and provide insights into genes and biological pathways influencing microvascular indices. Moreover, such a framework highlights how deep learning of images can quantify an interpretable phenotype for integration with electronic health record, biomarker, and genetic data to inform risk prediction and risk modification.