Whole-genome sequencing analysis of left ventricular structure and sphericity in 80,000 people.

Background: Sphericity is a measurement of how closely an object approximates a globe. The sphericity of the blood pool of the left ventricle (LV), is an emerging measure linked to myocardial dysfunction.

Methods: Video-based deep learning models were trained for semantic segmentation (pixel labeling) in cardiac magnetic resonance imaging in 84,327 UK Biobank participants.

Pathogenic Cardiomyopathy-Associated Gene Variants and Prognosis in Atrial Fibrillation: Results in 18,000 Clinical Trial Participants.

Background: Genetic variants in cardiomyopathy genes are associated with risk of atrial fibrillation (AF), although data on clinical outcomes for AF patients with such variants remain sparse.

Objectives: We aimed to study the prognostic implication of rare cardiomyopathy-associated pathogenic variants (CMP-PLP) in AF patients from large, well-phenotyped clinical trials.

Methods: CMP-PLP carriers were identified using exome sequencing in 5 multinational trials from the Thrombolysis in Myocardial Infarction study group (ENGAGE AF, FOURIER, SAVOR, PEGASUS, and DECLARE), with replication in the EAST-AFNET-4 trial.

Estimating ascending aortic diameter from the electrocardiogram.

In an analysis of 69,173 UK Biobank participants, we paired MRI-based measurements of the ascending aortic diameter with ECG signal. We trained a 1D convolutional neural network (ECGAI-TAA) to consume the 10-second 500Hz 12-lead signal and to emit an estimate of the ascending aortic diameter. We assessed model performance in an internal test set of 5,191 participants.

Electrocardiogram-Based Artificial Intelligence to Identify Coronary Artery Disease.

Background: Coronary artery disease (CAD) results in substantial morbidity and mortality.

Objectives: The purpose of this study was to develop a deep learning model to detect CAD defined using diagnostic codes ("ECG2CAD") and identify people at risk for adverse events using electrocardiograms (ECGs) in a primary care setting.

Methods: ECG2CAD was trained on 764,670 ECGs representing 137,199 individuals at Massachusetts General Hospital (MGH).

Predicting Thoracic Aortic Dissection in a Diverse Biobank Using a Polygenic Risk Score.

Background: Thoracic aortic dissection is a life-threatening condition that often occurs in the presence of aortic dilation. However, currently there are limited clinical risk factors beyond aortic diameter (AoD) used to determine individual-level dissection risk.

Objectives: The purpose of this study was to determine whether common variant genetics can be used to improve identification of individuals most at risk for dissection.

New Threshold for Defining Mild Aortic Stenosis Derived From Velocity-Encoded MRI in 60,000 Individuals.

Background: Mild aortic stenosis (AS) is associated with adverse outcomes but is incompletely defined.

Objectives: The purpose of this study was to examine the epidemiology of AV function measured without clinical indications.

Methods: We developed a deep learning model to measure aortic valve (AV) area, peak velocity, and mean gradient in velocity-encoded cardiac magnetic resonance imaging in 62,902 UK Biobank participants.

Sequencing in over 50,000 cases identifies coding and structural variation underlying atrial fibrillation risk.

Atrial fibrillation (AF) is a prevalent and morbid abnormality of the heart rhythm with a strong genetic component. Here, we meta-analyzed genome and exome sequencing data from 36 studies that included 52,416 AF cases and 277,762 controls. In burden tests of rare coding variation, we identified novel associations between AF and the genes MYBPC3, LMNA, PKP2, FAM189A2 and KDM5B.

Meta-analysis of genome-wide associations and polygenic risk prediction for atrial fibrillation in more than 180,000 cases.

Atrial fibrillation (AF) is the most common heart rhythm abnormality and is a leading cause of heart failure and stroke. This large-scale meta-analysis of genome-wide association studies increased the power to detect single-nucleotide variant associations and found more than 350 AF-associated genetic loci. We identified candidate genes related to muscle contractility, cardiac muscle development and cell-cell communication at 139 loci.

A deep learning digital biomarker to detect hypertension and stratify cardiovascular risk from the electrocardiogram.

Hypertension is a major risk factor for cardiovascular disease (CVD), yet blood pressure is measured intermittently and under suboptimal conditions. We developed a deep learning model to identify hypertension and stratify risk of CVD using 12-lead electrocardiogram waveforms. HTN-AI was trained to detect hypertension using 752,415 electrocardiograms from 103,405 adults at Massachusetts General Hospital.

"Weekend Warrior" Physical Activity and Adipose Tissue Deposition.

Background: Attaining guideline-recommended levels of physical activity is associated with substantially lower risk of cardiometabolic diseases.

Objectives: Although physical activity commonly follows a weekend warrior pattern, in which most moderate-to-vigorous physical activity is concentrated in 1 to 2 days rather than spread more evenly across the week (regular), the effects of activity pattern on imaging-based biomarkers of cardiometabolic health are unknown.

Methods: We analyzed 17,146 UK Biobank participants who wore accelerometers for 1 week, and later underwent cardiac magnetic resonance imaging.

Unsupervised deep learning of electrocardiograms enables scalable human disease profiling.

The 12-lead electrocardiogram (ECG) is inexpensive and widely available. Whether conditions across the human disease landscape can be detected using the ECG is unclear. We developed a deep learning denoising autoencoder and systematically evaluated associations between ECG encodings and ~1,600 Phecode-based diseases in three datasets separate from model development, and meta-analyzed the results.

Molecular convergence of risk variants for congenital heart defects leveraging a regulatory map of the human fetal heart.

Congenital heart defects (CHD) arise in part due to inherited genetic variants that alter genes and noncoding regulatory elements in the human genome. These variants are thought to act during fetal development to influence the formation of different heart structures. However, identifying the genes, pathways, and cell types that mediate these effects has been challenging due to the immense diversity of cell types involved in heart development as well as the superimposed complexities of interpreting noncoding sequences.

Genetics of Cardiac Aging Implicate Organ-Specific Variation.

Heart structure and function change with age, and the notion that the heart may age faster for some individuals than for others has driven interest in estimating cardiac age acceleration. However, current approaches have limited feature richness (heart measurements; radiomics) or capture extraneous data and therefore lack cardiac specificity (deep learning [DL] on unmasked chest MRI). These technical limitations have been a barrier to efforts to understand genetic contributions to age acceleration.

Noninvasive assessment of organ-specific and shared pathways in multi-organ fibrosis using T1 mapping.

Fibrotic diseases affect multiple organs and are associated with morbidity and mortality. To examine organ-specific and shared biologic mechanisms that underlie fibrosis in different organs, we developed machine learning models to quantify T1 time, a marker of interstitial fibrosis, in the liver, pancreas, heart and kidney among 43,881 UK Biobank participants who underwent magnetic resonance imaging. In phenome-wide association analyses, we demonstrate the association of increased organ-specific T1 time, reflecting increased interstitial fibrosis, with prevalent diseases across multiple organ systems.

Cardiovascular Significance and Genetics of Epicardial and Pericardial Adiposity.

Importance: Epicardial and pericardial adipose tissue (EPAT) has been associated with cardiovascular diseases such as atrial fibrillation or flutter (AF) and coronary artery disease (CAD), but studies have been limited in sample size or drawn from selected populations. It has been suggested that the association between EPAT and cardiovascular disease could be mediated by local or paracrine effects.

Objective: To evaluate the association of EPAT with prevalent and incident cardiovascular disease and to elucidate the genetic basis of EPAT in a large population cohort.

Using Genomics to Identify Novel Therapeutic Targets for Aortic Disease.

Aortic disease, including dissection, aneurysm, and rupture, carries significant morbidity and mortality and is a notable cause of sudden cardiac death. Much of our knowledge regarding the genetic basis of aortic disease has relied on the study of individuals with Mendelian aortopathies and, until recently, the genetic determinants of population-level variance in aortic phenotypes remained unclear. However, the application of machine learning methodologies to large imaging datasets has enabled researchers to rapidly define aortic traits and mine dozens of novel genetic associations for phenotypes such as aortic diameter and distensibility.

Selenoprotein deficiency disorder predisposes to aortic aneurysm formation.

Aortic aneurysms, which may dissect or rupture acutely and be lethal, can be a part of multisystem disorders that have a heritable basis. We report four patients with deficiency of selenocysteine-containing proteins due to selenocysteine Insertion Sequence Binding Protein 2 (SECISBP2) mutations who show early-onset, progressive, aneurysmal dilatation of the ascending aorta due to cystic medial necrosis. Zebrafish and male mice with global or vascular smooth muscle cell (VSMC)-targeted disruption of Secisbp2 respectively show similar aortopathy.

Sex differences in ascending aortic size reporting and growth on chest computed tomography and magnetic resonance imaging.

Purpose: Diameter-based guidelines for prophylactic repair of ascending aortic aneurysms have led to routine aortic evaluation in chest imaging. Despite sex differences in aneurysm outcomes, there is little understanding of sex-specific aortic growth rates. Our objective was to evaluate sex-specific temporal changes in radiologist-reported aortic size as well as sex differences in aortic reporting.