Lara Curran, Antonio de Marvao, Paolo Inglese, Kathryn A McGurk, Pierre-Raphaël Schiratti, Adam Clement, Sean L Zheng, Surui Li, Chee Jian Pua, Mit Shah, Mina Jafari, Pantazis Theotokis, Rachel J Buchan, Sean J Jurgens, Claire E Raphael, Arun John Baksi, Antonis Pantazis, Brian P Halliday, Dudley J Pennell, Wenjia Bai, Calvin W L Chin, Rafik Tadros, Connie R Bezzina, Hugh Watkins, Stuart A Cook, Sanjay K Prasad, James S Ware, Declan P O'Regan
{"title":"肥厚性心肌病的基因型-表型分类。","authors":"Lara Curran, Antonio de Marvao, Paolo Inglese, Kathryn A McGurk, Pierre-Raphaël Schiratti, Adam Clement, Sean L Zheng, Surui Li, Chee Jian Pua, Mit Shah, Mina Jafari, Pantazis Theotokis, Rachel J Buchan, Sean J Jurgens, Claire E Raphael, Arun John Baksi, Antonis Pantazis, Brian P Halliday, Dudley J Pennell, Wenjia Bai, Calvin W L Chin, Rafik Tadros, Connie R Bezzina, Hugh Watkins, Stuart A Cook, Sanjay K Prasad, James S Ware, Declan P O'Regan","doi":"10.1161/CIRCGEN.123.004200","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Hypertrophic cardiomyopathy (HCM) is an important cause of sudden cardiac death associated with heterogeneous phenotypes, but there is no systematic framework for classifying morphology or assessing associated risks. Here, we quantitatively survey genotype-phenotype associations in HCM to derive a data-driven taxonomy of disease expression.</p><p><strong>Methods: </strong>We enrolled 436 patients with HCM (median age, 60 years; 28.8% women) with clinical, genetic, and imaging data. An independent cohort of 60 patients with HCM from Singapore (median age, 59 years; 11% women) and a reference population from the UK Biobank (n=16 691; mean age, 55 years; 52.5% women) were also recruited. We used machine learning to analyze the 3-dimensional structure of the left ventricle from cardiac magnetic resonance imaging and build a tree-based classification of HCM phenotypes. Genotype and mortality risk distributions were projected on the tree.</p><p><strong>Results: </strong>Carriers of pathogenic or likely pathogenic variants for HCM had lower left ventricular mass, but greater basal septal hypertrophy, with reduced life span (mean follow-up, 9.9 years) compared with genotype negative individuals (hazard ratio, 2.66 [95% CI, 1.42-4.96]; <i>P</i><0.002). Four main phenotypic branches were identified using unsupervised learning of 3-dimensional shape: (1) nonsarcomeric hypertrophy with coexisting hypertension; (2) diffuse and basal asymmetrical hypertrophy associated with outflow tract obstruction; (3) isolated basal hypertrophy; and (4) milder nonobstructive hypertrophy enriched for familial sarcomeric HCM (odds ratio for pathogenic or likely pathogenic variants, 2.18 [95% CI, 1.93-2.28]; <i>P</i>=0.0001). Polygenic risk for HCM was also associated with different patterns and degrees of disease expression. The model was generalizable to an independent cohort (trustworthiness, M<sub>1</sub>: 0.86-0.88).</p><p><strong>Conclusions: </strong>We report a data-driven taxonomy of HCM for identifying groups of patients with similar morphology while preserving a continuum of disease severity, genetic risk, and outcomes. This approach will be of value in understanding the causes and consequences of disease diversity.</p>","PeriodicalId":10326,"journal":{"name":"Circulation: Genomic and Precision Medicine","volume":null,"pages":null},"PeriodicalIF":6.0000,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10729901/pdf/","citationCount":"0","resultStr":"{\"title\":\"Genotype-Phenotype Taxonomy of Hypertrophic Cardiomyopathy.\",\"authors\":\"Lara Curran, Antonio de Marvao, Paolo Inglese, Kathryn A McGurk, Pierre-Raphaël Schiratti, Adam Clement, Sean L Zheng, Surui Li, Chee Jian Pua, Mit Shah, Mina Jafari, Pantazis Theotokis, Rachel J Buchan, Sean J Jurgens, Claire E Raphael, Arun John Baksi, Antonis Pantazis, Brian P Halliday, Dudley J Pennell, Wenjia Bai, Calvin W L Chin, Rafik Tadros, Connie R Bezzina, Hugh Watkins, Stuart A Cook, Sanjay K Prasad, James S Ware, Declan P O'Regan\",\"doi\":\"10.1161/CIRCGEN.123.004200\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Hypertrophic cardiomyopathy (HCM) is an important cause of sudden cardiac death associated with heterogeneous phenotypes, but there is no systematic framework for classifying morphology or assessing associated risks. Here, we quantitatively survey genotype-phenotype associations in HCM to derive a data-driven taxonomy of disease expression.</p><p><strong>Methods: </strong>We enrolled 436 patients with HCM (median age, 60 years; 28.8% women) with clinical, genetic, and imaging data. An independent cohort of 60 patients with HCM from Singapore (median age, 59 years; 11% women) and a reference population from the UK Biobank (n=16 691; mean age, 55 years; 52.5% women) were also recruited. We used machine learning to analyze the 3-dimensional structure of the left ventricle from cardiac magnetic resonance imaging and build a tree-based classification of HCM phenotypes. Genotype and mortality risk distributions were projected on the tree.</p><p><strong>Results: </strong>Carriers of pathogenic or likely pathogenic variants for HCM had lower left ventricular mass, but greater basal septal hypertrophy, with reduced life span (mean follow-up, 9.9 years) compared with genotype negative individuals (hazard ratio, 2.66 [95% CI, 1.42-4.96]; <i>P</i><0.002). Four main phenotypic branches were identified using unsupervised learning of 3-dimensional shape: (1) nonsarcomeric hypertrophy with coexisting hypertension; (2) diffuse and basal asymmetrical hypertrophy associated with outflow tract obstruction; (3) isolated basal hypertrophy; and (4) milder nonobstructive hypertrophy enriched for familial sarcomeric HCM (odds ratio for pathogenic or likely pathogenic variants, 2.18 [95% CI, 1.93-2.28]; <i>P</i>=0.0001). Polygenic risk for HCM was also associated with different patterns and degrees of disease expression. The model was generalizable to an independent cohort (trustworthiness, M<sub>1</sub>: 0.86-0.88).</p><p><strong>Conclusions: </strong>We report a data-driven taxonomy of HCM for identifying groups of patients with similar morphology while preserving a continuum of disease severity, genetic risk, and outcomes. 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Genotype-Phenotype Taxonomy of Hypertrophic Cardiomyopathy.
Background: Hypertrophic cardiomyopathy (HCM) is an important cause of sudden cardiac death associated with heterogeneous phenotypes, but there is no systematic framework for classifying morphology or assessing associated risks. Here, we quantitatively survey genotype-phenotype associations in HCM to derive a data-driven taxonomy of disease expression.
Methods: We enrolled 436 patients with HCM (median age, 60 years; 28.8% women) with clinical, genetic, and imaging data. An independent cohort of 60 patients with HCM from Singapore (median age, 59 years; 11% women) and a reference population from the UK Biobank (n=16 691; mean age, 55 years; 52.5% women) were also recruited. We used machine learning to analyze the 3-dimensional structure of the left ventricle from cardiac magnetic resonance imaging and build a tree-based classification of HCM phenotypes. Genotype and mortality risk distributions were projected on the tree.
Results: Carriers of pathogenic or likely pathogenic variants for HCM had lower left ventricular mass, but greater basal septal hypertrophy, with reduced life span (mean follow-up, 9.9 years) compared with genotype negative individuals (hazard ratio, 2.66 [95% CI, 1.42-4.96]; P<0.002). Four main phenotypic branches were identified using unsupervised learning of 3-dimensional shape: (1) nonsarcomeric hypertrophy with coexisting hypertension; (2) diffuse and basal asymmetrical hypertrophy associated with outflow tract obstruction; (3) isolated basal hypertrophy; and (4) milder nonobstructive hypertrophy enriched for familial sarcomeric HCM (odds ratio for pathogenic or likely pathogenic variants, 2.18 [95% CI, 1.93-2.28]; P=0.0001). Polygenic risk for HCM was also associated with different patterns and degrees of disease expression. The model was generalizable to an independent cohort (trustworthiness, M1: 0.86-0.88).
Conclusions: We report a data-driven taxonomy of HCM for identifying groups of patients with similar morphology while preserving a continuum of disease severity, genetic risk, and outcomes. This approach will be of value in understanding the causes and consequences of disease diversity.
期刊介绍:
Circulation: Genomic and Precision Medicine is a distinguished journal dedicated to advancing the frontiers of cardiovascular genomics and precision medicine. It publishes a diverse array of original research articles that delve into the genetic and molecular underpinnings of cardiovascular diseases. The journal's scope is broad, encompassing studies from human subjects to laboratory models, and from in vitro experiments to computational simulations.
Circulation: Genomic and Precision Medicine is committed to publishing studies that have direct relevance to human cardiovascular biology and disease, with the ultimate goal of improving patient care and outcomes. The journal serves as a platform for researchers to share their groundbreaking work, fostering collaboration and innovation in the field of cardiovascular genomics and precision medicine.