Ali Amr

Atlas of the clinical genetics of human dilated cardiomyopathy

AIM Numerous genes are known to cause dilated cardiomyopathy (DCM). However, until now technological limitations have hindered elucidation of the contribution of all clinically relevant disease genes to DCM phenotypes in larger cohorts. We now utilized next-generation sequencing to overcome these limitations and screened all DCM disease genes in a large cohort. METHODS AND RESULTS In this multi-centre, multi-national study, we have enrolled 639 patients with sporadic or familial DCM. To all samples, we applied a standardized protocol for ultra-high coverage next-generation sequencing of 84 genes, leading to 99.1% coverage of the target region with at least 50-fold and a mean read depth of 2415. In this well characterized cohort, we find the highest number of known cardiomyopathy mutations in plakophilin-2, myosin-binding protein C-3, and desmoplakin. When we include yet unknown but predicted disease variants, we find titin, plakophilin-2, myosin-binding protein-C 3, desmoplakin, ryanodine receptor 2, desmocollin-2, desmoglein-2, and SCN5A variants among the most commonly mutated genes. The overlap between DCM, hypertrophic cardiomyopathy (HCM), and channelopathy causing mutations is considerably high. Of note, we find that >38% of patients have compound or combined mutations and 12.8% have three or even more mutations. When comparing patients recruited in the eight participating European countries we find remarkably little differences in mutation frequencies and affected genes. CONCLUSION This is to our knowledge, the first study that comprehensively investigated the genetics of DCM in a large-scale cohort and across a broad gene panel of the known DCM genes. Our results underline the high analytical quality and feasibility of Next-Generation Sequencing in clinical genetic diagnostics and provide a sound database of the genetic causes of DCM.

Biomarker Changes after Strenuous Exercise Can Mimic Pulmonary Embolism and Cardiac Injury—A Metaanalysis of 45 Studies

BACKGROUND Biomarkers are well established for diagnosis of myocardial infarction [cardiac troponins, high-sensitivity cardiac troponins (hs-cTn)], exclusion of acute and chronic heart failure [B-type natriuretic peptide (BNP), N-terminal proBNP (NT-proBNP)] and venous thromboembolism (d-dimers). Several studies have demonstrated acute increases in cardiac biomarkers and altered cardiac function after strenuous sports that can pretend a cardiovascular emergency and interfere with state-of-the-art clinical assessment. METHODS We performed a systematic review and metaanalysis of biomarker and cardiovascular imaging changes after endurance exercise. We searched for observational studies published in the English language from 1997 to 2014 that assessed these biomarkers or cardiac function and morphology directly after endurance exercise. Of 1787 identified abstracts, 45 studies were included. RESULTS Across all studies cardiac troponin T (cTnT) exceeded the cutoff value (0.01 ng/mL) in 51% (95% CI, 37%-64%) of participants. The measured pooled changes from baseline for high-sensitivity cTnT (hs-cTnT) were +26 ng/L (95% CI, 5.2-46.0), for cTnI +40 ng/L (95% CI, 21.4; 58.0), for BNP +10 ng/L (95% CI, 4.3; 16.6), for NT-proBNP +67 ng/L (95% CI, 49.9; 84.7), and for d-dimer +262 ng/mL (95% CI, 165.9; 358.7). Right ventricular end diastolic diameter increased and right ventricular ejection fraction as well as the ratio of the early to late transmitral flow velocities decreased after exercise, while no significant changes were observed in left ventricular ejection fraction. CONCLUSIONS Current cardiovascular biomarkers (cTnT, hs-cTnT, BNP, NT-proBNP, and d-dimer) that are used in clinical diagnosis of pulmonary embolism, acute coronary syndrome, and heart failure are prone to alterations due to strenuous exercise. Hence, it is necessary to take previous physical exercise into account when a cardiac emergency is suspected.

Data-driven estimation of cardiac electrical diffusivity from 12-lead ECG signals

Diagnosis and treatment of dilated cardiomyopathy (DCM) is challenging due to a large variety of causes and disease stages. Computational models of cardiac electrophysiology (EP) can be used to improve the assessment and prognosis of DCM, plan therapies and predict their outcome, but require personalization. In this work, we present a data-driven approach to estimate the electrical diffusivity parameter of an EP model from standard 12-lead electrocardiograms (ECG). An efficient forward model based on a mono-domain, phenomenological Lattice-Boltzmann model of cardiac EP, and a boundary element-based mapping of potentials to the body surface is employed. The electrical diffusivity of myocardium, left ventricle and right ventricle endocardium is then estimated using polynomial regression which takes as input the QRS duration and electrical axis. After validating the forward model, we computed 9500 EP simulations on 19 different DCM patients in just under three seconds each to learn the regression model. Using this database, we quantify the intrinsic uncertainty of electrical diffusion for given ECG features and show in a leave-one-patient-out cross-validation that the regression method is able to predict myocardium diffusion within the uncertainty range. Finally, our approach is tested on the 19 cases using their clinical ECG. 84% of them could be personalized using our method, yielding mean prediction errors of 18.7ms for the QRS duration and 6.5° for the electrical axis, both values being within clinical acceptability. By providing an estimate of diffusion parameters from readily available clinical data, our data-driven approach could therefore constitute a first calibration step toward a more complete personalization of cardiac EP.

Towards Personalized Cardiology: Multi-Scale Modeling of the Failing Heart

Background Despite modern pharmacotherapy and advanced implantable cardiac devices, overall prognosis and quality of life of HF patients remain poor. This is in part due to insufficient patient stratification and lack of individualized therapy planning, resulting in less effective treatments and a significant number of non-responders. Methods and Results State-of-the-art clinical phenotyping was acquired, including magnetic resonance imaging (MRI) and biomarker assessment. An individualized, multi-scale model of heart function covering cardiac anatomy, electrophysiology, biomechanics and hemodynamics was estimated using a robust framework. The model was computed on n=46 HF patients, showing for the first time that advanced multi-scale models can be fitted consistently on large cohorts. Novel multi-scale parameters derived from the model of all cases were analyzed and compared against clinical parameters, cardiac imaging, lab tests and survival scores to evaluate the explicative power of the model and its potential for better patient stratification. Model validation was pursued by comparing clinical parameters that were not used in the fitting process against model parameters. Conclusion This paper illustrates how advanced multi-scale models can complement cardiovascular imaging and how they could be applied in patient care. Based on obtained results, it becomes conceivable that, after thorough validation, such heart failure models could be applied for patient management and therapy planning in the future, as we illustrate in one patient of our cohort who received CRT-D implantation.

Epigenome-Wide Association Study Identifies Cardiac Gene Patterning and a Novel Class of Biomarkers for Heart Failure

Background: Biochemical DNA modification resembles a crucial regulatory layer among genetic information, environmental factors, and the transcriptome. To identify epigenetic susceptibility regions and novel biomarkers linked to myocardial dysfunction and heart failure, we performed the first multi-omics study in myocardial tissue and blood of patients with dilated cardiomyopathy and controls. Methods: Infinium human methylation 450 was used for high-density epigenome-wide mapping of DNA methylation in left-ventricular biopsies and whole peripheral blood of living probands. RNA deep sequencing was performed on the same samples in parallel. Whole-genome sequencing of all patients allowed exclusion of promiscuous genotype-induced methylation calls. Results: In the screening stage, we detected 59 epigenetic loci that are significantly associated with dilated cardiomyopathy (false discovery corrected P⩽0.05), with 3 of them reaching epigenome-wide significance at P⩽5×10−8. Twenty-seven (46%) of these loci could be replicated in independent cohorts, underlining the role of epigenetic regulation of key cardiac transcription regulators. Using a staged multi-omics study design, we link a subset of 517 epigenetic loci with dilated cardiomyopathy and cardiac gene expression. Furthermore, we identified distinct epigenetic methylation patterns that are conserved across tissues, rendering these CpGs novel epigenetic biomarkers for heart failure. Conclusions: The present study provides to our knowledge the first epigenome-wide association study in living patients with heart failure using a multi-omics approach.

Clinical genetics and outcome of left ventricular non-compaction cardiomyopathy

Aims In this study, we aimed to clinically and genetically characterize LVNC patients and investigate the prevalence of variants in known and novel LVNC disease genes. Introduction Left ventricular non-compaction cardiomyopathy (LVNC) is an increasingly recognized cause of heart failure, arrhythmia, thromboembolism, and sudden cardiac death. We sought here to dissect its genetic causes, phenotypic presentation and outcome. Methods and results In our registry with follow-up of in the median 61 months, we analysed 95 LVNC patients (68 unrelated index patients and 27 affected relatives; definite familial LVNC = 23.5%) by cardiac phenotyping, molecular biomarkers and exome sequencing. Cardiovascular events were significantly more frequent in LVNC patients compared with an age-matched group of patients with non-ischaemic dilated cardiomyopathy (hazard ratio = 2.481, P = 0.002). Stringent genetic classification according to ACMG guidelines revealed that TTN, LMNA, and MYBPC3 are the most prevalent disease genes (13 patients are carrying a pathogenic truncating TTN variant, odds ratio = 40.7, Confidence interval = 21.6-76.6, P < 0.0001, percent spliced in 76-100%). We also identified novel candidate genes for LVNC. For RBM20, we were able to perform detailed familial, molecular and functional studies. We show that the novel variant p.R634L in the RS domain of RBM20 co-segregates with LVNC, leading to titin mis-splicing as revealed by RNA sequencing of heart tissue in mutation carriers, protein analysis, and functional splice-reporter assays. Conclusion Our data demonstrate that the clinical course of symptomatic LVNC can be severe. The identified pathogenic variants and distribution of disease genes-a titin-related pathomechanism is found in every fourth patient-should be considered in genetic counselling of patients. Pathogenic variants in the nuclear proteins Lamin A/C and RBM20 were associated with worse outcome.

Robust Image-Based Estimation of Cardiac Tissue Parameters and Their Uncertainty from Noisy Data

Clinical applications of computational cardiac models require precise personalization, i.e. fitting model parameters to capture patients physiology. However, due to parameter non-identifiability, limited data, uncertainty in the clinical measurements, and modeling assumptions, various combinations of parameter values may exist that yield the same quality of fit. Hence, there is a need for quantifying the uncertainty in estimated parameters and to ascertain the uniqueness of the found solution. This paper presents a stochastic method to estimate the parameters of an image-based electromechanical model of the heart and their uncertainty due to noise in measurements. First, Bayesian inference is applied to fully estimate the posterior probability density function (PDF) of the model. To that end, Markov Chain Monte Carlo sampling is used, which is made computationally tractable by employing a fast surrogate model based on Polynomial Chaos Expansion, instead of the true forward model. Then, we use the mean-shift algorithm to automatically find the modes of the PDF and select the most likely one while being robust to noise. The approach is used to estimate global active stress and passive stiffness from invasive pressure and image-based volume quantification. Experiments on eight patients showed that not only our approach yielded goodness of fits equivalent to a well-established deterministic method, but we could also demonstrate the non-uniqueness of the problem and report uncertainty estimates, crucial information for subsequent clinical assessments of the personalized models.

Fast Data-Driven Calibration of a Cardiac Electrophysiology Model from Images and ECG

Recent advances in computational electrophysiology (EP) models make them attractive for clinical use. We propose a novel data-driven approach to calibrate an EP model from standard 12-lead electrocardiograms (ECG), which are in contrast to invasive or dense body surface measurements widely available in clinical routine. With focus on cardiac depolarization, we first propose an efficient forward model of ECG by coupling a mono-domain, Lattice-Boltzmann model of cardiac EP to a boundary element formulation of body surface potentials. We then estimate a polynomial regression to predict myocardium, left ventricle and right ventricle endocardium electrical diffusion from QRS duration and ECG electrical axis. Training was performed on 4,200 ECG simulations, calculated in aproximately 3 s each, using different diffusion parameters on 13 patient geometries. This allowed quantifying diffusion uncertainty for given ECG parameters due to the ill-posed nature of the ECG problem. We show that our method is able to predict myocardium diffusion within the uncertainty range, yielding a prediction error of less than 5 ms for QRS duration and 2 degree for electrical axis. Prediction results compared favorably with those obtained with a standard optimization procedure, while being 60 times faster. Our data-driven model can thus constitute an efficient preliminary step prior to more refined EP personalization.

From medical images to fast computational models of heart electromechanics: an integrated framework towards clinical use

With the recent advances in computational power, realistic modeling of heart function within a clinical environment has come into reach. Yet, current modeling frameworks either lack overall completeness or computational performance, and their integration with clinical imaging and data is still tedious. In this paper, we propose an integrated framework to model heart electromechanics from clinical and imaging data, which is fast enough to be embedded in a clinical setting. More precisely, we introduce data-driven techniques for cardiac anatomy estimation and couple them with an efficient GPU (graphics processing unit) implementation of the orthotropic Holzapfel-Ogden model of myocardium tissue, a GPU implementation of a mono-domain electrophysiology model based on the Lattice-Boltzmann method, and a novel method to correctly capture motion during isovolumetric phases. Benchmark experiments conducted on patient data showed that the computation of a whole heart cycle including electrophysiology and biomechanics with mesh resolutions of around 70k elements takes on average 1min 10s on a standard desktop machine (Intel Xeon 2.4GHz, NVIDIA GeForce GTX 580). We were able to compute electrophysiology up to 40.5× faster and biomechanics up to 15.2× faster than with prior CPU-based approaches, which breaks ground towards model-based therapy planning.

Automatic image-to-model framework for patient-specific electromechanical modeling of the heart

A key requirement for recent advances in computational modeling to be clinically applicable is the ability to fit models to patient data. Various personalization techniques have been proposed for isolated sub-components of complex models of heart physiology. However, no work has been presented that focuses on personalizing full electromechanical (EM) models in a streamlined, consistent and automatic fashion, which has been evaluated on a large population. We present an integrated system for full EM personalization from routinely acquired clinical data. The importance of mechanical parameters is analyzed in a comprehensive sensitivity study, revealing that myocyte contraction and Youngs modulus are the main determinants of model output variation, what lead to the proposed personalization strategy. On a large, physiologically diverse set of 15 patients, we demonstrate the effectiveness of our framework by comparing measured and calculated parameters, yielding left ventricular ejection fraction and stroke volume errors of 6.6% and 9.2 mL, respectively.