Transforming growth factor β (TGFβ) is a secreted factor sequestered in the extracellular matrix (ECM), but its role in developing mouse heart maturation is unknown. Here we show that cardiomyocyte-specific deletion of Tgfb1, Tgfb2 and Tgfb3 genes (TGFβ ligands) in the developing heart results in cardiac dysfunction by 6 weeks of age, altered fibroblast activity, abnormal ECM properties and endothelial and immune changes. Gene profiling reveals defective cardiomyocyte/fibroblast maturation with cardiomyocyte ectopic skeletal muscle gene expression beginning at embryonic day 17.5 (E17.5). Cardiomyocyte deletion of Tgfbr1/Tgfbr2 or Smad2/Smad3 does not phenocopy Tgfb1/2/3 deletion, suggesting that cardiomyocyte TGFβ acts through non-myocytes to regulate heart maturation. Similarly, Col1a2-/-;Col6a2-/- mice with reduced ECM stiffness, cardiomyocyte-specific Itgb1 deletion reducing ECM mechanosensing and Tcf21-/- embryos lacking cardiac fibroblasts fail to reproduce ectopic skeletal muscle gene expression. Thus, embryonic cardiomyocyte-derived TGFβ signals to non-myocytes to establish an environment that directs cardiomyocyte maturation and identity.

Cardiac fibrosis is a pathological process that arises as a compensatory response to injury or mechanical stress and is a common feature across diverse cardiovascular diseases. Here, we review recent advances in the translational biology of human cardiac fibrosis, highlighting insights from single-cell studies of human cardiac fibroblasts and genetic analyses of fibrotic phenotypes using cardiac imaging. We discuss the heterogeneity and origins of cardiac fibroblasts, the role of immune-fibroblast cross-talk in fibrogenesis, and the utility of imaging and circulating biomarkers for assessing fibrotic burden. Existing antihypertensive therapies have demonstrated direct effects on cardiac fibroblasts and tissue-level fibrosis, while emerging targeted anti-fibrotic therapies have shown promise in reducing fibrotic biomarkers, although their clinical importance remains to be established. Further research is needed to clarify the relationship between cardiac fibrosis and clinical outcomes, which will be essential for the development and implementation of novel anti-fibrotic interventions in targeted patient populations.

Right ventricular (RV) size and function inform prognosis in many cardiovascular diseases, but assessment by two-dimensional transthoracic echocardiography is challenging. Here we show that a deep learning model, Dimensional Reconstruction of Imaging Data-Right Ventricle (DROID-RV), can estimate RV end-diastolic diameter and identify RV systolic dysfunction from transthoracic echocardiography videos. We trained DROID-RV in 17,141 ambulatory cardiology patients (51,188 studies and 383,881 video clips) and validated model predictions in 9,112 primary care patients and 831 patients with RV ejection fraction assessed by three-dimensional transthoracic echocardiography. Across multiple test samples, DROID-RV accurately estimates RV end-diastolic diameter and identifies RV dysfunction, although model prediction error increases at extremes of RV size and function. We additionally show that RV size and function predictions are associated with incident mortality, atrial fibrillation and heart failure. Our work demonstrates how deep learning can automate RV assessment and risk stratification at scale using two-dimensional echocardiography.

Invasive coronary angiography is the reference standard for diagnosing coronary artery disease, yet assessment is subjective, and existing artificial-intelligence tools typically assess stenosis in individual frames or videos and do not integrate the full multiview angiographic study. Here we show that DeepCORO-CLIP, trained on 203,808 angiographic videos and reports from 28,117 patients, interprets whole multiple-video angiograms at once. It detects obstructive stenoses (≥70%), with an area under the receiver operating characteristic curve (AUROC) of 0.89 internally and externally, and identifies chronic total occlusion (AUROC = 0.90 to 0.95), intra-coronary thrombus and calcification (AUROC = 0.90 to 0.91). It also predicts 1-year cardiovascular events (AUROC = 0.79, 95% CI of 0.74 to 0.84, versus 0.65 for a model derived using clinical variables) and left ventricular ejection fraction (mean absolute error 7.3% versus 8.8% for a previous model), both achieved using coronary angiography alone. In a blinded review of 365 evaluations across 288 angiograms, its reports were clinically usable in 76% of evaluations versus 88% for cardiologists, with comparable false positives (17% versus 14%, P = 0.38); residual missed stenoses reflected an adjustable threshold rather than an absent signal.