Genetics of myocardial interstitial fibrosis in the human heart and association with disease

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...

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Published inNature genetics Vol. 55; no. 5; pp. 777 - 786
Main Authors Nauffal, Victor, Di Achille, Paolo, Klarqvist, Marcus D. R., Cunningham, Jonathan W., Hill, Matthew C., Pirruccello, James P., Weng, Lu-Chen, Morrill, Valerie N., Choi, Seung Hoan, Khurshid, Shaan, Friedman, Samuel F., Nekoui, Mahan, Roselli, Carolina, Ng, Kenney, Philippakis, Anthony A., Batra, Puneet, Ellinor, Patrick T., Lubitz, Steven A.
Format Journal Article
LanguageEnglish
Published New York Nature Publishing Group US 01.05.2023
Nature Publishing Group
Subjects
38
38/91
45
45/43
631/208/205/2138
631/208/457
692/699/75/230
692/699/75/29/1309
692/699/75/74
Agriculture
Animal Genetics and Genomics
Aorta
Aortic stenosis
Association analysis
Biobanks
Biomarkers
Biomedical and Life Sciences
Biomedicine
Body mass index
Calcium signalling
Cancer Research
Cardiac arrhythmia
Cardiomyopathies - genetics
Cardiomyopathies - pathology
Cardiomyopathy
Cardiovascular disease
Cardiovascular diseases
Chromatin
Clinical outcomes
Conformation
Congestive heart failure
Coronary artery disease
Diabetes
Diabetes mellitus
Ejection fraction
Electrocardiography
Failure analysis
Fibrosis
Gene Function
Genes
Genetics
Genome-Wide Association Study
Genomes
Glucose transport
Growth factors
Heart
Heart diseases
Heart failure
Homeostasis
Human Genetics
Humans
Hypertension
Hypertrophy
Inflammatory diseases
Learning algorithms
Machine learning
Magnetic resonance imaging
Medical imaging
Metabolic disorders
Myocardium - pathology
Oxidative stress
Rheumatoid arthritis
United Kingdom > UK
Online AccessGet full text
ISSN1061-4036
1546-1718
1546-1718
DOI10.1038/s41588-023-01371-5

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Abstract 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. Genome-wide association analyses identify 11 loci associated with native myocardial T1 time, a marker of interstitial fibrosis, providing insights into the pathways involved in myocardial fibrosis and myofibroblast cell state acquisition.
AbstractList 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. Genome-wide association analyses identify 11 loci associated with native myocardial T1 time, a marker of interstitial fibrosis, providing insights into the pathways involved in myocardial fibrosis and myofibroblast cell state acquisition.
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.
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.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.
Myocardial interstitial fibrosis is associated with cardiovascular disease and adverse prognosis. 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 TGFβ1-mediated cardiac fibroblast activation assay, we found that 9 out of the 11 loci comprised genes that exhibited temporal changes in expression and/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 novel biologically relevant pathways underlying fibrosis.
Author Hill, Matthew C.
Pirruccello, James P.
Weng, Lu-Chen
Friedman, Samuel F.
Klarqvist, Marcus D. R.
Morrill, Valerie N.
Choi, Seung Hoan
Di Achille, Paolo
Cunningham, Jonathan W.
Khurshid, Shaan
Nekoui, Mahan
Roselli, Carolina
Lubitz, Steven A.
Nauffal, Victor
Ng, Kenney
Ellinor, Patrick T.
Philippakis, Anthony A.
Batra, Puneet
AuthorAffiliation 11 These authors contributed equally
2 Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
12 These authors jointly supervised this work
6 Division of Cardiology, University of California San Francisco, San Francisco, CA, USA
3 Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
7 Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
4 Cardiology Division, Massachusetts General Hospital, Boston, MA, USA
5 Demoulas Center for Cardiac Arrhythmias and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
8 Medical Center Groningen, University of Groningen, Groningen, the Netherlands
1 Cardiovascular Division, Brigham and Women’s Hospital, Boston, MA, USA
9 Center for Computational Health, IBM Research, Cambridge, MA, USA
10 Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA, USA
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/37081215$$D View this record in MEDLINE/PubMed
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V.N., J.W.C., P.T.E. and S.A.L. conceived the study. M.D.R.K. and P.D.A. ingested and prepared cMRI data. V.N., M.D.R.K., P.D.A., and J.W.C. performed quality control. M.D.R.K. trained machine learning models. V.N., M.D.R.K., and P.D.A. performed the main analyses. M.C.H. performed in vitro experiments. V.N., M.D.R.K., P.D.A., J.W.C., M.C.H., P.T.E. and S.A.L. wrote the paper. J.P.P., L.-C.W., V.N.M., S.H.C., S.K., S.F.F., M.N., C.R., K.N., A.A.P. and P.B. contributed to the analysis plan or provided critical revisions.
Author Contributions
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0000-0001-7630-2708
0000-0003-1475-4930
0000-0002-2067-0533
0000-0001-7199-299X
0000-0001-5267-6756
0000-0001-6088-4037
0000-0002-2797-3190
0000-0001-6953-3794
0000-0002-9599-4866
0000-0002-0688-2169
0000-0003-4481-7867
0000-0001-6822-0593
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Snippet Myocardial interstitial fibrosis is associated with cardiovascular disease and adverse prognosis. Here, to investigate the biological pathways that underlie...
Myocardial interstitial fibrosis is associated with cardiovascular disease and adverse prognosis. To investigate the biological pathways that underlie fibrosis...
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Agriculture
Animal Genetics and Genomics
Aorta
Aortic stenosis
Association analysis
Biobanks
Biomarkers
Biomedical and Life Sciences
Biomedicine
Body mass index
Calcium signalling
Cancer Research
Cardiac arrhythmia
Cardiomyopathies - genetics
Cardiomyopathies - pathology
Cardiomyopathy
Cardiovascular disease
Cardiovascular diseases
Chromatin
Clinical outcomes
Conformation
Congestive heart failure
Coronary artery disease
Diabetes
Diabetes mellitus
Ejection fraction
Electrocardiography
Failure analysis
Fibrosis
Gene Function
Genes
Genetics
Genome-Wide Association Study
Genomes
Glucose transport
Growth factors
Heart
Heart diseases
Heart failure
Homeostasis
Human Genetics
Humans
Hypertension
Hypertrophy
Inflammatory diseases
Learning algorithms
Machine learning
Magnetic resonance imaging
Medical imaging
Metabolic disorders
Myocardium - pathology
Oxidative stress
Rheumatoid arthritis
Title Genetics of myocardial interstitial fibrosis in the human heart and association with disease
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