Pub Date : 2023-12-01Epub Date: 2023-10-10DOI: 10.1161/CIRCGEN.123.004181
Maria C Costanzo, Carolina Roselli, MacKenzie Brandes, Marc Duby, Quy Hoang, Dongkeun Jang, Ryan Koesterer, Parul Kudtarkar, Annie Moriondo, Trang Nguyen, Oliver Ruebenacker, Patrick Smadbeck, Ying Sun, Adam S Butterworth, Krishna G Aragam, R Thomas Lumbers, Amit V Khera, Steven A Lubitz, Patrick T Ellinor, Kyle J Gaulton, Jason Flannick, Noël P Burtt
{"title":"Cardiovascular Disease Knowledge Portal: A Community Resource for Cardiovascular Disease Research.","authors":"Maria C Costanzo, Carolina Roselli, MacKenzie Brandes, Marc Duby, Quy Hoang, Dongkeun Jang, Ryan Koesterer, Parul Kudtarkar, Annie Moriondo, Trang Nguyen, Oliver Ruebenacker, Patrick Smadbeck, Ying Sun, Adam S Butterworth, Krishna G Aragam, R Thomas Lumbers, Amit V Khera, Steven A Lubitz, Patrick T Ellinor, Kyle J Gaulton, Jason Flannick, Noël P Burtt","doi":"10.1161/CIRCGEN.123.004181","DOIUrl":"10.1161/CIRCGEN.123.004181","url":null,"abstract":"","PeriodicalId":10326,"journal":{"name":"Circulation: Genomic and Precision Medicine","volume":" ","pages":"e004181"},"PeriodicalIF":6.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10843166/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41182178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01Epub Date: 2023-11-28DOI: 10.1161/CIRCGEN.123.004230
Kui Deng, Deepak K Gupta, Xiao-Ou Shu, Loren Lipworth, Wei Zheng, Victoria E Thomas, Hui Cai, Qiuyin Cai, Thomas J Wang, Danxia Yu
Background: Life's essential 8 (LE8) is a comprehensive construct of cardiovascular health. Yet, little is known about the LE8 score, its metabolic correlates, and their predictive implications among Black Americans and low-income individuals.
Methods: In a nested case-control study of coronary heart disease (CHD) among 299 pairs of Black and 298 pairs of White low-income Americans from the Southern Community Cohort Study, we estimated LE8 score and applied untargeted plasma metabolomics and elastic net with leave-one-out cross-validation to identify metabolite signature (MetaSig) of LE8. Associations of LE8 score and MetaSig with incident CHD were examined using conditional logistic regression. The mediation effect of MetaSig on the LE8-CHD association was also examined. The external validity of MetaSig was evaluated in another nested CHD case-control study among 299 pairs of Chinese adults.
Results: Higher LE8 score was associated with lower CHD risk (standardized odds ratio, 0.61 [95% CI, 0.53-0.69]). The MetaSig, consisting of 133 metabolites, showed significant correlation with LE8 score (r=0.61) and inverse association with CHD (odds ratio, 0.57 [0.49-0.65]), robust to adjustment for LE8 score and across participants with different sociodemographic and health status ([odds ratios, 0.42-0.69]; all P<0.05). MetaSig mediated a large portion of the LE8-CHD association: 53% (32%-80%). Significant associations of MetaSig with LE8 score and CHD risk were found in validation cohort (r=0.49; odds ratio, 0.57 [0.46-0.69]).
Conclusions: Higher LE8 score and its MetaSig were associated with lower CHD risk among low-income Black and White Americans. Metabolomics may offer an objective measure of LE8 and its metabolic phenotype relevant to CHD prevention among diverse populations.
{"title":"Metabolite Signature of Life's Essential 8 and Risk of Coronary Heart Disease Among Low-Income Black and White Americans.","authors":"Kui Deng, Deepak K Gupta, Xiao-Ou Shu, Loren Lipworth, Wei Zheng, Victoria E Thomas, Hui Cai, Qiuyin Cai, Thomas J Wang, Danxia Yu","doi":"10.1161/CIRCGEN.123.004230","DOIUrl":"10.1161/CIRCGEN.123.004230","url":null,"abstract":"<p><strong>Background: </strong>Life's essential 8 (LE8) is a comprehensive construct of cardiovascular health. Yet, little is known about the LE8 score, its metabolic correlates, and their predictive implications among Black Americans and low-income individuals.</p><p><strong>Methods: </strong>In a nested case-control study of coronary heart disease (CHD) among 299 pairs of Black and 298 pairs of White low-income Americans from the Southern Community Cohort Study, we estimated LE8 score and applied untargeted plasma metabolomics and elastic net with leave-one-out cross-validation to identify metabolite signature (MetaSig) of LE8. Associations of LE8 score and MetaSig with incident CHD were examined using conditional logistic regression. The mediation effect of MetaSig on the LE8-CHD association was also examined. The external validity of MetaSig was evaluated in another nested CHD case-control study among 299 pairs of Chinese adults.</p><p><strong>Results: </strong>Higher LE8 score was associated with lower CHD risk (standardized odds ratio, 0.61 [95% CI, 0.53-0.69]). The MetaSig, consisting of 133 metabolites, showed significant correlation with LE8 score (<i>r</i>=0.61) and inverse association with CHD (odds ratio, 0.57 [0.49-0.65]), robust to adjustment for LE8 score and across participants with different sociodemographic and health status ([odds ratios, 0.42-0.69]; all <i>P</i><0.05). MetaSig mediated a large portion of the LE8-CHD association: 53% (32%-80%). Significant associations of MetaSig with LE8 score and CHD risk were found in validation cohort (<i>r</i>=0.49; odds ratio, 0.57 [0.46-0.69]).</p><p><strong>Conclusions: </strong>Higher LE8 score and its MetaSig were associated with lower CHD risk among low-income Black and White Americans. Metabolomics may offer an objective measure of LE8 and its metabolic phenotype relevant to CHD prevention among diverse populations.</p>","PeriodicalId":10326,"journal":{"name":"Circulation: Genomic and Precision Medicine","volume":" ","pages":"e004230"},"PeriodicalIF":6.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10843634/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138444117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01Epub Date: 2023-10-05DOI: 10.1161/CIRCGEN.122.004251
Carlos Bueno-Beti, David C Johnson, Chris Miles, Joseph Westaby, Mary N Sheppard, Elijah R Behr, Angeliki Asimaki
{"title":"Potential Diagnostic Role for a Combined Postmortem DNA and RNA Sequencing for Brugada Syndrome.","authors":"Carlos Bueno-Beti, David C Johnson, Chris Miles, Joseph Westaby, Mary N Sheppard, Elijah R Behr, Angeliki Asimaki","doi":"10.1161/CIRCGEN.122.004251","DOIUrl":"10.1161/CIRCGEN.122.004251","url":null,"abstract":"","PeriodicalId":10326,"journal":{"name":"Circulation: Genomic and Precision Medicine","volume":" ","pages":"e004251"},"PeriodicalIF":6.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10729895/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41112761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01Epub Date: 2023-11-28DOI: 10.1161/CIRCGEN.123.004200
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
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.
{"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":"10.1161/CIRCGEN.123.004200","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":" ","pages":"e004200"},"PeriodicalIF":6.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10729901/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138444116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-01Epub Date: 2023-10-31DOI: 10.1161/CIRCGEN.123.004262
Dominic E Fullenkamp, Ryan M Jorgensen, Desiree F Leach, Arjun Sinha, Isabella M Salamone, Jamie R Johnston, Lisa M Dellefave-Castillo, Lubna Choudhury, Elizabeth M McNally, Lisa D Wilsbacher
{"title":"Hypertrophic Cardiomyopathy Secondary to <i>RAF1</i> Cysteine-Rich Domain Variants.","authors":"Dominic E Fullenkamp, Ryan M Jorgensen, Desiree F Leach, Arjun Sinha, Isabella M Salamone, Jamie R Johnston, Lisa M Dellefave-Castillo, Lubna Choudhury, Elizabeth M McNally, Lisa D Wilsbacher","doi":"10.1161/CIRCGEN.123.004262","DOIUrl":"10.1161/CIRCGEN.123.004262","url":null,"abstract":"","PeriodicalId":10326,"journal":{"name":"Circulation: Genomic and Precision Medicine","volume":" ","pages":"e004262"},"PeriodicalIF":6.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10841507/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71410959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01Epub Date: 2023-09-27DOI: 10.1161/CIRCGEN.123.004053
Peter Loof Møller, Palle Duun Rohde, Jonathan Nørtoft Dahl, Laust Dupont Rasmussen, Samuel Emil Schmidt, Louise Nissen, Victoria McGilligan, Jacob F Bentzon, Daniel F Gudbjartsson, Kari Stefansson, Hilma Holm, Simon Winther, Morten Bøttcher, Mette Nyegaard
Background: Patients with de novo chest pain, referred for evaluation of possible coronary artery disease (CAD), frequently have an absence of CAD resulting in millions of tests not having any clinical impact. The objective of this study was to investigate whether polygenic risk scores and targeted proteomics improve the prediction of absence of CAD in patients with suspected CAD, when added to the PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) minimal risk score (PMRS).
Methods: Genotyping and targeted plasma proteomics (N=368 proteins) were performed in 1440 patients with symptoms suspected to be caused by CAD undergoing coronary computed tomography angiography. Based on individual genotypes, a polygenic risk score for CAD (PRSCAD) was calculated. The prediction was performed using combinations of PRSCAD, proteins, and PMRS as features in models using stability selection and machine learning.
Results: Prediction of absence of CAD yielded an area under the curve of PRSCAD-model, 0.64±0.03; proteomic-model, 0.58±0.03; and PMRS model, 0.76±0.02. No significant correlation was found between the genetic and proteomic risk scores (Pearson correlation coefficient, -0.04; P=0.13). Optimal predictive ability was achieved by the full model (PRSCAD+protein+PMRS) yielding an area under the curve of 0.80±0.02 for absence of CAD, significantly better than the PMRS model alone (P<0.001). For reclassification purpose, the full model enabled down-classification of 49% (324 of 661) of the 5% to 15% pretest probability patients and 18% (113 of 611) of >15% pretest probability patients.
Conclusions: For patients with chest pain and low-intermediate CAD risk, incorporating targeted proteomics and polygenic risk scores into the risk assessment substantially improved the ability to predict the absence of CAD. Genetics and proteomics seem to add complementary information to the clinical risk factors and improve risk stratification in this large patient group.
{"title":"Combining Polygenic and Proteomic Risk Scores With Clinical Risk Factors to Improve Performance for Diagnosing Absence of Coronary Artery Disease in Patients With de novo Chest Pain.","authors":"Peter Loof Møller, Palle Duun Rohde, Jonathan Nørtoft Dahl, Laust Dupont Rasmussen, Samuel Emil Schmidt, Louise Nissen, Victoria McGilligan, Jacob F Bentzon, Daniel F Gudbjartsson, Kari Stefansson, Hilma Holm, Simon Winther, Morten Bøttcher, Mette Nyegaard","doi":"10.1161/CIRCGEN.123.004053","DOIUrl":"10.1161/CIRCGEN.123.004053","url":null,"abstract":"<p><strong>Background: </strong>Patients with de novo chest pain, referred for evaluation of possible coronary artery disease (CAD), frequently have an absence of CAD resulting in millions of tests not having any clinical impact. The objective of this study was to investigate whether polygenic risk scores and targeted proteomics improve the prediction of absence of CAD in patients with suspected CAD, when added to the PROMISE (Prospective Multicenter Imaging Study for Evaluation of Chest Pain) minimal risk score (PMRS).</p><p><strong>Methods: </strong>Genotyping and targeted plasma proteomics (N=368 proteins) were performed in 1440 patients with symptoms suspected to be caused by CAD undergoing coronary computed tomography angiography. Based on individual genotypes, a polygenic risk score for CAD (PRS<sub>CAD</sub>) was calculated. The prediction was performed using combinations of PRS<sub>CAD</sub>, proteins, and PMRS as features in models using stability selection and machine learning.</p><p><strong>Results: </strong>Prediction of absence of CAD yielded an area under the curve of PRS<sub>CAD</sub>-model, 0.64±0.03; proteomic-model, 0.58±0.03; and PMRS model, 0.76±0.02. No significant correlation was found between the genetic and proteomic risk scores (Pearson correlation coefficient, -0.04; <i>P</i>=0.13). Optimal predictive ability was achieved by the full model (PRS<sub>CAD</sub>+protein+PMRS) yielding an area under the curve of 0.80±0.02 for absence of CAD, significantly better than the PMRS model alone (<i>P</i><0.001). For reclassification purpose, the full model enabled down-classification of 49% (324 of 661) of the 5% to 15% pretest probability patients and 18% (113 of 611) of >15% pretest probability patients.</p><p><strong>Conclusions: </strong>For patients with chest pain and low-intermediate CAD risk, incorporating targeted proteomics and polygenic risk scores into the risk assessment substantially improved the ability to predict the absence of CAD. Genetics and proteomics seem to add complementary information to the clinical risk factors and improve risk stratification in this large patient group.</p><p><strong>Registration: </strong>URL: https://www.</p><p><strong>Clinicaltrials: </strong>gov; Unique identifier: NCT02264717.</p>","PeriodicalId":10326,"journal":{"name":"Circulation: Genomic and Precision Medicine","volume":" ","pages":"442-451"},"PeriodicalIF":7.4,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41117441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-01Epub Date: 2023-07-04DOI: 10.1161/CIRCGEN.123.004099
Matteo Castrichini, Kolade M Agboola, Hridyanshu Vyas, Omar F Abou Ezzeddine, Konstantinos C Siontis, John R Giudicessi, Andrew N Rosenbaum, Naveen L Pereira
{"title":"Cardiac Sarcoidosis Mimickers: Genetic Testing in Undifferentiated Inflammatory Cardiomyopathies.","authors":"Matteo Castrichini, Kolade M Agboola, Hridyanshu Vyas, Omar F Abou Ezzeddine, Konstantinos C Siontis, John R Giudicessi, Andrew N Rosenbaum, Naveen L Pereira","doi":"10.1161/CIRCGEN.123.004099","DOIUrl":"10.1161/CIRCGEN.123.004099","url":null,"abstract":"","PeriodicalId":10326,"journal":{"name":"Circulation: Genomic and Precision Medicine","volume":" ","pages":"478-479"},"PeriodicalIF":7.4,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9746813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}