The penetrance of rare variants in cardiomyopathy-associated genes: A cross-sectional approach to estimating penetrance for secondary findings.

Understanding the penetrance of pathogenic variants identified as secondary findings (SFs) is of paramount importance with the growing availability of genetic testing. We estimated penetrance through large-scale analyses of individuals referred for diagnostic sequencing for hypertrophic cardiomyopathy (HCM; 10,400 affected individuals, 1,332 variants) and dilated cardiomyopathy (DCM; 2,564 affected individuals, 663 variants), using a cross-sectional approach comparing allele frequencies against reference populations (293,226 participants from UK Biobank and gnomAD). We generated updated prevalence estimates for HCM (1:543) and DCM (1:220). In aggregate, the penetrance by late adulthood of rare, pathogenic variants (23% for HCM, 35% for DCM) and likely pathogenic variants (7% for HCM, 10% for DCM) was substantial for dominant cardiomyopathy (CM). Penetrance was significantly higher for variant subgroups annotated as loss of function or ultra-rare and for males compared to females for variants in HCM-associated genes. We estimated variant-specific penetrance for 316 recurrent variants most likely to be identified as SFs (found in 51% of HCM- and 17% of DCM-affected individuals). 49 variants were observed at least ten times (14% of affected individuals) in HCM-associated genes. Median penetrance was 14.6% (±14.4% SD). We explore estimates of penetrance by age, sex, and ancestry and simulate the impact of including future cohorts. This dataset reports penetrance of individual variants at scale and will inform the management of individuals undergoing genetic screening for SFs. While most variants had low penetrance and the costs and harms of screening are unclear, some individuals with highly penetrant variants may benefit from SFs.

Genetic and functional insights into the fractal structure of the heart.

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 development1,2. 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 arborization3,4. 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.

LAVASET: Latent Variable Stochastic Ensemble of Trees. An ensemble method for correlated datasets with spatial, spectral, and temporal dependencies.

MOTIVATION: Random forests (RFs) can deal with a large number of variables, achieve reasonable prediction scores, and yield highly interpretable feature importance values. As such, RFs are appropriate models for feature selection and further dimension reduction. However, RFs are often not appropriate for correlated datasets due to their mode of selecting individual features for splitting. Addressing correlation relationships in high-dimensional datasets is imperative for reducing the number of variables that are assigned high importance, hence making the dimension reduction most efficient. Here, we propose the LAtent VAriable Stochastic Ensemble of Trees (LAVASET) method that derives latent variables based on the distance characteristics of each feature and aims to incorporate the correlation factor in the splitting step. RESULTS: Without compromising on performance in the majority of examples, LAVASET outperforms RF by accurately determining feature importance across all correlated variables and ensuring proper distribution of importance values. LAVASET yields mostly non-inferior prediction accuracies to traditional RFs when tested in simulated and real 1D datasets, as well as more complex and high-dimensional 3D datatypes. Unlike traditional RFs, LAVASET is unaffected by single 'important' noisy features (false positives), as it considers the local neighbourhood. LAVASET, therefore, highlights neighbourhoods of features, reflecting real signals that collectively impact the model's predictive ability. AVAILABILITY AND IMPLEMENTATION: LAVASET is freely available as a standalone package from https://github.com/melkasapi/LAVASET.

A Human Hereditary Cardiomyopathy Shares a Genetic Substrate With Bicuspid Aortic Valve.

BACKGROUND: The complex genetics underlying human cardiac disease is evidenced by its heterogenous manifestation, multigenic basis, and sporadic occurrence. These features have hampered disease modeling and mechanistic understanding. Here, we show that 2 structural cardiac diseases, left ventricular noncompaction (LVNC) and bicuspid aortic valve, can be caused by a set of inherited heterozygous gene mutations affecting the NOTCH ligand regulator MIB1 (MINDBOMB1) and cosegregating genes. METHODS: We used CRISPR-Cas9 gene editing to generate mice harboring a nonsense or a missense MIB1 mutation that are both found in LVNC families. We also generated mice separately carrying these MIB1 mutations plus 5 additional cosegregating variants in the ASXL3, APCDD1, TMX3, CEP192, and BCL7A genes identified in these LVNC families by whole exome sequencing. Histological, developmental, and functional analyses of these mouse models were carried out by echocardiography and cardiac magnetic resonance imaging, together with gene expression profiling by RNA sequencing of both selected engineered mouse models and human induced pluripotent stem cell-derived cardiomyocytes. Potential biochemical interactions were assayed in vitro by coimmunoprecipitation and Western blot. RESULTS: Mice homozygous for the MIB1 nonsense mutation did not survive, and the mutation caused LVNC only in heteroallelic combination with a conditional allele inactivated in the myocardium. The heterozygous MIB1 missense allele leads to bicuspid aortic valve in a NOTCH-sensitized genetic background. These data suggest that development of LVNC is influenced by genetic modifiers present in affected families, whereas valve defects are highly sensitive to NOTCH haploinsufficiency. Whole exome sequencing of LVNC families revealed single-nucleotide gene variants of ASXL3, APCDD1, TMX3, CEP192, and BCL7A cosegregating with the MIB1 mutations and LVNC. In experiments with mice harboring the orthologous variants on the corresponding Mib1 backgrounds, triple heterozygous Mib1 Apcdd1 Asxl3 mice showed LVNC, whereas quadruple heterozygous Mib1 Cep192 Tmx3;Bcl7a mice developed bicuspid aortic valve and other valve-associated defects. Biochemical analysis suggested interactions between CEP192, BCL7A, and NOTCH. Gene expression profiling of mutant mouse hearts and human induced pluripotent stem cell-derived cardiomyocytes revealed increased cardiomyocyte proliferation and defective morphological and metabolic maturation. CONCLUSIONS: These findings reveal a shared genetic substrate underlying LVNC and bicuspid aortic valve in which MIB1-NOTCH variants plays a crucial role in heterozygous combination with cosegregating genetic modifiers.

Improving the efficiency and accuracy of CMR with AI - review of evidence and proposition of a roadmap to clinical translation.

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.

APOE ε4 carriage associates with improved myocardial performance from adolescence to older age.

BACKGROUND: Although APOE ε4 allele carriage confers a risk for coronary artery disease, its persistence in humans might be explained by certain survival advantages (antagonistic pleiotropy). METHODS: Combining data from ~ 37,000 persons from three older age British cohorts (1946 National Survey of Health and Development [NSHD], Southall and Brent Revised [SABRE], and UK Biobank) and one younger age cohort (Avon Longitudinal Study of Parents and Children [ALSPAC]), we explored whether APOE ε4 carriage associates with beneficial or unfavorable left ventricular (LV) structural and functional metrics by echocardiography and cardiovascular magnetic resonance (CMR). RESULTS: Compared to the non-APOE ε4 group, APOE ε4 carriers had similar cardiac phenotypes in terms of LV ejection fraction, E/e', posterior wall and interventricular septal thickness, and LV mass. However, they had improved myocardial performance resulting in greater LV stroke volume generation per 1 mL of myocardium (higher myocardial contraction fraction). In NSHD (n = 1467) and SABRE (n = 1187), ε4 carriers had a 4% higher MCF (95% CI 1-7%, p = 0.016) using echocardiography. Using CMR data, in UK Biobank (n = 32,972), ε4 carriers had a 1% higher MCF 95% (CI 0-1%, p = 0.020) with a dose-response relationship based on the number of ε4 alleles. In addition, UK Biobank ε4 carriers also had more favorable radial and longitudinal strain rates compared to non APOE ε4 carriers. In ALSPAC (n = 1397), APOE ε4 carriers aged < 24 years had a 2% higher MCF (95% CI 0-5%, p = 0.059). CONCLUSIONS: By triangulating results in four independent cohorts, across imaging modalities (echocardiography and CMR), and in ~ 37,000 individuals, our results point towards an association between ε4 carriage and improved cardiac performance in terms of LV MCF. This potentially favorable cardiac phenotype adds to the growing number of reported survival advantages attributed to the pleiotropic effects APOE ε4 carriage that might collectively explain its persistence in human populations.

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.

Validation of Artificial Intelligence Cardiac MRI Measurements: Relationship to Heart Catheterization and Mortality Prediction.

Background Cardiac MRI measurements have diagnostic and prognostic value in the evaluation of cardiopulmonary disease. Artificial intelligence approaches to automate cardiac MRI segmentation are emerging but require clinical testing. Purpose To develop and evaluate a deep learning tool for quantitative evaluation of cardiac MRI functional studies and assess its use for prognosis in patients suspected of having pulmonary hypertension. Materials and Methods A retrospective multicenter and multivendor data set was used to develop a deep learning-based cardiac MRI contouring model using a cohort of patients suspected of having cardiopulmonary disease from multiple pathologic causes. Correlation with same-day right heart catheterization (RHC) and scan-rescan repeatability was assessed in prospectively recruited participants. Prognostic impact was assessed using Cox proportional hazard regression analysis of 3487 patients from the ASPIRE (Assessing the Severity of Pulmonary Hypertension In a Pulmonary Hypertension Referral Centre) registry, including a subset of 920 patients with pulmonary arterial hypertension. The generalizability of the automatic assessment was evaluated in 40 multivendor studies from 32 centers. Results The training data set included 539 patients (mean age, 54 years ± 20 [SD]; 315 women). Automatic cardiac MRI measurements were better correlated with RHC parameters than were manual measurements, including left ventricular stroke volume (r = 0.72 vs 0.68; P = .03). Interstudy repeatability of cardiac MRI measurements was high for all automatic measurements (intraclass correlation coefficient range, 0.79-0.99) and similarly repeatable to manual measurements (all paired t test P > .05). Automated right ventricle and left ventricle cardiac MRI measurements were associated with mortality in patients suspected of having pulmonary hypertension. Conclusion An automatic cardiac MRI measurement approach was developed and tested in a large cohort of patients, including a broad spectrum of right ventricular and left ventricular conditions, with internal and external testing. Fully automatic cardiac MRI assessment correlated strongly with invasive hemodynamics, had prognostic value, were highly repeatable, and showed excellent generalizability. Clinical trial registration no. NCT03841344 Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Ambale-Venkatesh and Lima in this issue. An earlier incorrect version appeared online. This article was corrected on June 27, 2022.

Genome-wide association analysis in dilated cardiomyopathy reveals two new players in systolic heart failure on chromosomes 3p25.1 and 22q11.23.

AIMS: Our objective was to better understand the genetic bases of dilated cardiomyopathy (DCM), a leading cause of systolic heart failure. METHODS AND RESULTS: We conducted the largest genome-wide association study performed so far in DCM, with 2719 cases and 4440 controls in the discovery population. We identified and replicated two new DCM-associated loci on chromosome 3p25.1 [lead single-nucleotide polymorphism (SNP) rs62232870, P = 8.7 × 10-11 and 7.7 × 10-4 in the discovery and replication steps, respectively] and chromosome 22q11.23 (lead SNP rs7284877, P = 3.3 × 10-8 and 1.4 × 10-3 in the discovery and replication steps, respectively), while confirming two previously identified DCM loci on chromosomes 10 and 1, BAG3 and HSPB7. A genetic risk score constructed from the number of risk alleles at these four DCM loci revealed a 3-fold increased risk of DCM for individuals with 8 risk alleles compared to individuals with 5 risk alleles (median of the referral population). In silico annotation and functional 4C-sequencing analyses on iPSC-derived cardiomyocytes identify SLC6A6 as the most likely DCM gene at the 3p25.1 locus. This gene encodes a taurine transporter whose involvement in myocardial dysfunction and DCM is supported by numerous observations in humans and animals. At the 22q11.23 locus, in silico and data mining annotations, and to a lesser extent functional analysis, strongly suggest SMARCB1 as the candidate culprit gene. CONCLUSION: This study provides a better understanding of the genetic architecture of DCM and sheds light on novel biological pathways underlying heart failure.

Reevaluating the Genetic Contribution of Monogenic Dilated Cardiomyopathy.

BACKGROUND: Dilated cardiomyopathy (DCM) is genetically heterogeneous, with >100 purported disease genes tested in clinical laboratories. However, many genes were originally identified based on candidate-gene studies that did not adequately account for background population variation. Here we define the frequency of rare variation in 2538 patients with DCM across protein-coding regions of 56 commonly tested genes and compare this to both 912 confirmed healthy controls and a reference population of 60 706 individuals to identify clinically interpretable genes robustly associated with dominant monogenic DCM. METHODS: We used the TruSight Cardio sequencing panel to evaluate the burden of rare variants in 56 putative DCM genes in 1040 patients with DCM and 912 healthy volunteers processed with identical sequencing and bioinformatics pipelines. We further aggregated data from 1498 patients with DCM sequenced in diagnostic laboratories and the Exome Aggregation Consortium database for replication and meta-analysis. RESULTS: Truncating variants in TTN and DSP were associated with DCM in all comparisons. Variants in MYH7, LMNA, BAG3, TNNT2, TNNC1, PLN, ACTC1, NEXN, TPM1, and VCL were significantly enriched in specific patient subsets, with the last 2 genes potentially contributing primarily to early-onset forms of DCM. Overall, rare variants in these 12 genes potentially explained 17% of cases in the outpatient clinic cohort representing a broad range of adult patients with DCM and 26% of cases in the diagnostic referral cohort enriched in familial and early-onset DCM. Although the absence of a significant excess in other genes cannot preclude a limited role in disease, such genes have limited diagnostic value because novel variants will be uninterpretable and their diagnostic yield is minimal. CONCLUSIONS: In the largest sequenced DCM cohort yet described, we observe robust disease association with 12 genes, highlighting their importance in DCM and translating into high interpretability in diagnostic testing. The other genes analyzed here will need to be rigorously evaluated in ongoing curation efforts to determine their validity as Mendelian DCM genes but have limited value in diagnostic testing in DCM at present. This data will contribute to community gene curation efforts and will reduce erroneous and inconclusive findings in diagnostic testing.

Systematic large-scale assessment of the genetic architecture of left ventricular noncompaction reveals diverse etiologies.

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 (MYH7tv), generally considered nonpathogenic for cardiomyopathies, were 20-fold enriched in LVNC cases over controls. MYH7tv 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.

Disease-specific variant pathogenicity prediction significantly improves variant interpretation in inherited cardiac conditions.

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

Genetic Variants Associated With Cancer Therapy-Induced Cardiomyopathy.

BACKGROUND: Cancer therapy-induced cardiomyopathy (CCM) is associated with cumulative drug exposures and preexisting cardiovascular disorders. These parameters incompletely account for substantial interindividual susceptibility to CCM. We hypothesized that rare variants in cardiomyopathy genes contribute to CCM. METHODS: We studied 213 patients with CCM from 3 cohorts: retrospectively recruited adults with diverse cancers (n=99), prospectively phenotyped adults with breast cancer (n=73), and prospectively phenotyped children with acute myeloid leukemia (n=41). Cardiomyopathy genes, including 9 prespecified genes, were sequenced. The prevalence of rare variants was compared between CCM cohorts and The Cancer Genome Atlas participants (n=2053), healthy volunteers (n=445), and an ancestry-matched reference population. Clinical characteristics and outcomes were assessed and stratified by genotypes. A prevalent CCM genotype was modeled in anthracycline-treated mice. RESULTS: CCM was diagnosed 0.4 to 9 years after chemotherapy; 90% of these patients received anthracyclines. Adult patients with CCM had cardiovascular risk factors similar to the US population. Among 9 prioritized genes, patients with CCM had more rare protein-altering variants than comparative cohorts ( P≤1.98e-04). Titin-truncating variants (TTNtvs) predominated, occurring in 7.5% of patients with CCM versus 1.1% of The Cancer Genome Atlas participants ( P=7.36e-08), 0.7% of healthy volunteers ( P=3.42e-06), and 0.6% of the reference population ( P=5.87e-14). Adult patients who had CCM with TTNtvs experienced more heart failure and atrial fibrillation ( P=0.003) and impaired myocardial recovery ( P=0.03) than those without. Consistent with human data, anthracycline-treated TTNtv mice and isolated TTNtv cardiomyocytes showed sustained contractile dysfunction unlike wild-type ( P=0.0004 and P<0.002, respectively). CONCLUSIONS: Unrecognized rare variants in cardiomyopathy-associated genes, particularly TTNtvs, increased the risk for CCM in children and adults, and adverse cardiac events in adults. Genotype, along with cumulative chemotherapy dosage and traditional cardiovascular risk factors, improves the identification of patients who have cancer at highest risk for CCM. CLINICAL TRIAL REGISTRATION: URL: https://www.clinicaltrials.gov . Unique identifiers: NCT01173341; AAML1031; NCT01371981.

Cardiac structure and function in schizophrenia: cardiac magnetic resonance imaging study.

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

Sex-Dependent QRS Guidelines for Cardiac Resynchronization Therapy Using Computer Model Predictions.

Cardiac resynchronization therapy (CRT) is an important treatment for heart failure. Low female enrollment in clinical trials means that current CRT guidelines may be biased toward males. However, females have higher response rates at lower QRS duration (QRSd) thresholds. Sex differences in the left ventricle (LV) size could provide an explanation for the improved female response at lower QRSd. We aimed to test if sex differences in CRT response at lower QRSd thresholds are explained by differences in LV size and hence predict sex-specific guidelines for CRT. We investigated the effect that LV size sex difference has on QRSd between male and females in 1093 healthy individuals and 50 CRT patients using electrophysiological computer models of the heart. Simulations on the healthy mean shape models show that LV size sex difference can account for 50-100% of the sex difference in baseline QRSd in healthy individuals. In the CRT patient cohort, model simulations predicted female-specific guidelines for CRT, which were 9-13 ms lower than current guidelines. Sex differences in the LV size are able to account for a significant proportion of the sex difference in QRSd and provide a mechanistic explanation for the sex difference in CRT response. Simulations accounting for the smaller LV size in female CRT patients predict 9-13 ms lower QRSd thresholds for female CRT guidelines.

Three-dimensional cardiovascular imaging-genetics: a mass univariate framework.

Motivation: Left ventricular (LV) hypertrophy is a strong predictor of cardiovascular outcomes, but its genetic regulation remains largely unexplained. Conventional phenotyping relies on manual calculation of LV mass and wall thickness, but advanced cardiac image analysis presents an opportunity for high-throughput mapping of genotype-phenotype associations in three dimensions (3D). Results: High-resolution cardiac magnetic resonance images were automatically segmented in 1124 healthy volunteers to create a 3D shape model of the heart. Mass univariate regression was used to plot a 3D effect-size map for the association between wall thickness and a set of predictors at each vertex in the mesh. The vertices where a significant effect exists were determined by applying threshold-free cluster enhancement to boost areas of signal with spatial contiguity. Experiments on simulated phenotypic signals and SNP replication show that this approach offers a substantial gain in statistical power for cardiac genotype-phenotype associations while providing good control of the false discovery rate. This framework models the effects of genetic variation throughout the heart and can be automatically applied to large population cohorts. Availability and implementation: The proposed approach has been coded in an R package freely available at https://doi.org/10.5281/zenodo.834610 together with the clinical data used in this work. Contact: declan.oregan@imperial.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.

Motion-corrected multiparametric renal arterial spin labelling at 3 T: reproducibility and effect of vasodilator challenge.

OBJECTIVES: We 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. METHODS: In 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. RESULTS: Inter-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. CONCLUSIONS: Free-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 T 1 during free breathing. • This technique enables repeated measures of renal haemodynamic characteristics during pharmacological challenge.

Exercise cardiac MRI unmasks right ventricular dysfunction in acute hypoxia and chronic pulmonary arterial hypertension.

Coupling of right ventricular (RV) contractility to afterload is maintained at rest in the early stages of pulmonary arterial hypertension (PAH), but exercise may unmask depleted contractile reserves. We assessed whether elevated afterload reduces RV contractile reserve despite compensated resting function using noninvasive exercise imaging. Fourteen patients with PAH (mean age: 39.1 yr, 10 women and 4 men) and 34 healthy control subjects (mean ageL 35.6 yr, 17 women and 17 men) completed real-time cardiac magnetic resonance imaging during submaximal exercise breathing room air. Control subjects were then also exercised during acute normobaric hypoxia (fraction of inspired O2: 12%). RV contractile reserve was assessed by the effect of exercise on ejection fraction. In control subjects, the increase in RV ejection fraction on exercise was less during hypoxia ( P = 0.017), but the response of left ventricular ejection fraction to exercise did not change. Patients with PAH had an impaired RV reserve, with half demonstrating a fall in RV ejection fraction on exercise despite comparable resting function to controls (PAH: rest 53.6 ± 4.3% vs. exercise 51.4 ± 10.7%; controls: rest 57.1 ± 5.2% vs. exercise 69.6 ± 6.1%, P < 0.0001). In control subjects, the increase in stroke volume index on exercise was driven by reduced RV end-systolic volume, whereas patients with PAH did not augment the stroke volume index, with increases in both end-diastolic and end-systolic volumes. From baseline hemodynamic and exercise capacity variables, only the minute ventilation-to-CO2 output ratio was an independent predictor of RV functional reserve ( P = 0.021). In conclusion, noninvasive cardiac imaging during exercise unmasks depleted RV contractile reserves in healthy adults under hypoxic conditions and patients with PAH under normoxic conditions despite preserved ejection fraction at rest. NEW & NOTEWORTHY Right ventricular (RV) reserve was assessed using real-time cardiac magnetic resonance imaging in patients with pulmonary arterial hypertension and in healthy control subjects under normobaric hypoxia, which has been previously associated with acute pulmonary hypertension. Hypoxia caused a mild reduction in RV reserve, whereas chronic pulmonary arterial hypertension was associated with a marked reduction in RV reserve.

Fractal Analysis of Right Ventricular Trabeculae in Pulmonary Hypertension.

Purpose To measure right ventricular (RV) trabecular complexity by its fractal dimension (FD) in healthy subjects and patients with pulmonary hypertension (PH) and to assess its relationship with hemodynamic and functional parameters and future cardiovascular events. Materials and Methods This retrospective study used data acquired from May 2004 to October 2013 in 256 patients with newly diagnosed PH who underwent cardiac MRI, right-sided heart catheterization, and 6-minute walk distance testing, with median follow-up of 4.0 years. A total of 256 healthy control subjects underwent cardiac MRI only. Biventricular FD, volumes, and function were assessed on short-axis cine images. Reproducibility was assessed with the intraclass correlation coefficient, correlation between variables was assessed with the Pearson correlation test, and mortality prediction was compared by using uni- and multivariable Cox regression analyses. Results RV FD reproducibility had an intraclass correlation coefficient of 0.97 (95% confidence interval [CI]: 0.96, 0.98). RV FD was higher in patients with PH (median, 1.310; interquartile range [IQR], 1.281-1.341) than in healthy subjects (median, 1.264; IQR, 1.242-1.295; P < .001), with the greatest difference near the apex. RV FD was associated with pulmonary vascular resistance (r = 0.30, P < .001). At univariable Cox regression analysis, RV FD was a significant predictor of death (hazard ratio [HR], 1.256; 95% CI: 1.011, 1.560; P = .04); however, at multivariable analysis, RV FD did not enable prediction of survival independently of conventional parameters of RV remodeling (HR, 1.179; 95% CI: 0.871, 1.596; P = .29). Conclusion Fractal analysis of RV trabecular complexity is a highly reproducible measure of remodeling in patients with PH that is associated with afterload, although the gain in survival prediction over traditional markers is not significant. Published under a CC BY 4.0 license. Online supplemental material is available for this article.

CardioClassifier: disease- and gene-specific computational decision support for clinical genome interpretation.

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.