Genetics of myocardial interstitial fibrosis in the human heart and association with disease.
Victor NauffalPaolo Di AchilleMarcus D R KlarqvistJonathan W CunninghamMatthew C HillJames Paul PirruccelloLu-Chen WangValerie N MorrillSeung Hoan ChoiShaan KhurshidSamuel Freesun FriedmanMahan NekouiCarolina RoselliKenney NgAnthony A PhilippakisPuneet BatraPatrick T EllinorSteven A LubitzPublished in: Nature genetics (2023)
Myocardial interstitial fibrosis is associated with cardiovascular disease and adverse prognosis. Here, to investigate the biological pathways that underlie fibrosis in the human heart, we developed a machine learning model to measure native myocardial T1 time, a marker of myocardial fibrosis, in 41,505 UK Biobank participants who underwent cardiac magnetic resonance imaging. Greater T1 time was associated with diabetes mellitus, renal disease, aortic stenosis, cardiomyopathy, heart failure, atrial fibrillation, conduction disease and rheumatoid arthritis. Genome-wide association analysis identified 11 independent loci associated with T1 time. The identified loci implicated genes involved in glucose transport (SLC2A12), iron homeostasis (HFE, TMPRSS6), tissue repair (ADAMTSL1, VEGFC), oxidative stress (SOD2), cardiac hypertrophy (MYH7B) and calcium signaling (CAMK2D). Using a transforming growth factor β1-mediated cardiac fibroblast activation assay, we found that 9 of the 11 loci consisted of genes that exhibited temporal changes in expression or open chromatin conformation supporting their biological relevance to myofibroblast cell state acquisition. By harnessing machine learning to perform large-scale quantification of myocardial interstitial fibrosis using cardiac imaging, we validate associations between cardiac fibrosis and disease, and identify new biologically relevant pathways underlying fibrosis.
Keyphrases
- left ventricular
- heart failure
- aortic stenosis
- machine learning
- hypertrophic cardiomyopathy
- transforming growth factor
- genome wide association
- genome wide
- atrial fibrillation
- magnetic resonance imaging
- cardiovascular disease
- oxidative stress
- left atrial
- endothelial cells
- rheumatoid arthritis
- cardiac resynchronization therapy
- mitral valve
- liver fibrosis
- epithelial mesenchymal transition
- computed tomography
- transcatheter aortic valve implantation
- deep learning
- dna damage
- aortic valve
- genome wide association study
- magnetic resonance
- signaling pathway
- oral anticoagulants
- coronary artery disease
- minimally invasive
- blood pressure
- stem cells
- percutaneous coronary intervention
- contrast enhanced
- bone marrow
- induced pluripotent stem cells
- skeletal muscle
- metabolic syndrome
- type diabetes
- dna methylation
- photodynamic therapy
- endoplasmic reticulum stress
- binding protein
- cardiovascular events