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Sun YV, Liu C, Hui Q, Zhou JJ, Gaziano JM, Wilson PWF, Joseph J, Phillips LS. Identification and correction for collider bias in a genome-wide association study of diabetes-related heart failure. Am J Hum Genet 2024; 111:1481-1493. [PMID: 38897203 PMCID: PMC11267521 DOI: 10.1016/j.ajhg.2024.05.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 05/20/2024] [Accepted: 05/21/2024] [Indexed: 06/21/2024] Open
Abstract
Type 2 diabetes (T2D) is a major risk factor for heart failure (HF) and has elevated incidence among individuals with HF. Since genetics and HF can independently influence T2D, collider bias may occur when T2D (i.e., collider) is controlled for by design or analysis. Thus, we conducted a genome-wide association study (GWAS) of diabetes-related HF with correction for collider bias. We first performed a GWAS of HF to identify genetic instrumental variables (GIVs) for HF and to enable bidirectional Mendelian randomization (MR) analysis between T2D and HF. We identified 61 genomic loci, significantly associated with all-cause HF in 114,275 individuals with HF and over 1.5 million controls of European ancestry. Using a two-sample bidirectional MR approach with 59 and 82 GIVs for HF and T2D, respectively, we estimated that T2D increased HF risk (odds ratio [OR] 1.07, 95% confidence interval [CI] 1.04-1.10), while HF also increased T2D risk (OR 1.60, 95% CI 1.36-1.88). Then we performed a GWAS of diabetes-related HF corrected for collider bias due to the study design of index cases. After removing the spurious association of TCF7L2 locus due to collider bias, we identified two genome-wide significant loci close to PITX2 (chromosome 4) and CDKN2B-AS1 (chromosome 9) associated with diabetes-related HF in the Million Veteran Program and replicated the associations in the UK Biobank. Our MR findings provide strong evidence that HF increases T2D risk. As a result, collider bias leads to spurious genetic associations of diabetes-related HF, which can be effectively corrected to identify true positive loci.
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Affiliation(s)
- Yan V Sun
- Atlanta VA Healthcare System, Decatur, GA, USA; Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA.
| | - Chang Liu
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
| | - Qin Hui
- Atlanta VA Healthcare System, Decatur, GA, USA; Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA
| | - Jin J Zhou
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA
| | - J Michael Gaziano
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA; Division of Aging, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Peter W F Wilson
- Atlanta VA Healthcare System, Decatur, GA, USA; Emory University School of Medicine, Atlanta, GA, USA
| | - Jacob Joseph
- VA Providence Healthcare System, Providence, RI, USA; The Warren Alpert Medical School of Brown University, Providence, RI, USA
| | - Lawrence S Phillips
- Atlanta VA Healthcare System, Decatur, GA, USA; Emory University School of Medicine, Atlanta, GA, USA
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Kirchhof P, Haas S, Amarenco P, Turpie AGG, Bach M, Lambelet M, Hess S, Camm AJ. Causes of death in patients with atrial fibrillation anticoagulated with rivaroxaban: a pooled analysis of XANTUS. Europace 2024; 26:euae183. [PMID: 38941511 PMCID: PMC11257075 DOI: 10.1093/europace/euae183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Accepted: 06/07/2024] [Indexed: 06/30/2024] Open
Abstract
AIMS Anticoagulation can prevent stroke and prolong lives in patients with atrial fibrillation (AF). However, anticoagulated patients with AF remain at risk of death. The aim of this study was to investigate the causes of death and factors associated with all-cause and cardiovascular death in the XANTUS population. METHODS AND RESULTS Causes of death occurring within a year after rivaroxaban initiation in patients in the XANTUS programme studies were adjudicated by a central adjudication committee and classified following international guidance. Baseline characteristics associated with all-cause or cardiovascular death were identified. Of 11 040 patients, 187 (1.7%) died. Almost half of these deaths were due to cardiovascular causes other than bleeding (n = 82, 43.9%), particularly heart failure (n = 38, 20.3%) and sudden or unwitnessed death (n = 24, 12.8%). Fatal stroke (n = 8, 4.3%), which was classified as a type of cardiovascular death, and fatal bleeding (n = 17, 9.1%) were less common causes of death. Independent factors associated with all-cause or cardiovascular death included age, AF type, body mass index, left ventricular ejection fraction, hospitalization at baseline, rivaroxaban dose, and anaemia. CONCLUSION The overall risk of death due to stroke or bleeding was low in XANTUS. Anticoagulated patients with AF remain at risk of death due to heart failure and sudden death. Potential interventions to reduce cardiovascular deaths in anticoagulated patients with AF require further investigation, e.g. early rhythm control therapy and AF ablation. TRIAL REGISTRATION NUMBERS NCT01606995, NCT01750788, NCT01800006.
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Affiliation(s)
- Paulus Kirchhof
- Department of Cardiology, University Heart and Vascular Center UKE Hamburg, Martinistraße 52, Gebäude Ost 70, 20246 Hamburg, Germany
- German Center for Cardiovascular Research (DZHK), partner site Hamburg/Kiel/Lübeck, Hamburg, Germany
- Institute of Cardiovascular Sciences, University of Birmingham, Birmingham, UK
| | - Sylvia Haas
- Formerly Technical University Munich, Munich, Germany
| | - Pierre Amarenco
- Department of Neurology and Stroke Centre, Paris-Diderot-Sorbonne University, Paris, France
| | | | | | | | | | - A John Camm
- Cardiovascular and Cell Sciences Research Institute, St George’s University of London, London, UK
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3
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Mukhopadhyay S, Dixit P, Khanom N, Sanghera G, McGurk KA. The Genetic Factors Influencing Cardiomyopathies and Heart Failure across the Allele Frequency Spectrum. J Cardiovasc Transl Res 2024:10.1007/s12265-024-10520-y. [PMID: 38771459 DOI: 10.1007/s12265-024-10520-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 05/03/2024] [Indexed: 05/22/2024]
Abstract
Heart failure (HF) remains a major cause of mortality and morbidity worldwide. Understanding the genetic basis of HF allows for the development of disease-modifying therapies, more appropriate risk stratification, and personalised management of patients. The advent of next-generation sequencing has enabled genome-wide association studies; moving beyond rare variants identified in a Mendelian fashion and detecting common DNA variants associated with disease. We summarise the latest GWAS and rare variant data on mixed and refined HF aetiologies, and cardiomyopathies. We describe the recent understanding of the functional impact of titin variants and highlight FHOD3 as a novel cardiomyopathy-associated gene. We describe future directions of research in this field and how genetic data can be leveraged to improve the care of patients with HF.
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Affiliation(s)
- Srinjay Mukhopadhyay
- National Heart and Lung Institute, Imperial College London, LMS Building, Hammersmith Campus, London, UK
- School of Medicine, Cardiff University, Wales, UK
| | - Prithvi Dixit
- National Heart and Lung Institute, Imperial College London, LMS Building, Hammersmith Campus, London, UK
| | - Najiyah Khanom
- National Heart and Lung Institute, Imperial College London, LMS Building, Hammersmith Campus, London, UK
| | - Gianluca Sanghera
- National Heart and Lung Institute, Imperial College London, LMS Building, Hammersmith Campus, London, UK
| | - Kathryn A McGurk
- National Heart and Lung Institute, Imperial College London, LMS Building, Hammersmith Campus, London, UK.
- MRC Laboratory of Medical Sciences, Imperial College London, London, UK.
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4
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Zhang Y, Liu B, Zhou Y. A novel disulfidptosis-related gene signature predicts overall survival of glioblastoma patients. Future Sci OA 2024; 10:FSO948. [PMID: 38817361 PMCID: PMC11137853 DOI: 10.2144/fsoa-2023-0136] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 11/30/2023] [Indexed: 06/01/2024] Open
Abstract
Aim: The aim of this study was to investigate the prognostic relevance of disulfidptosis-related genes in glioblastoma using bioinformatic analysis in The Cancer Genome Atlas Program-Glioblastoma (TCGA-GBM) database and develop a gene signature model for predicting patient prognosis. Methods: We conducted a bioinformatic analysis using the TCGA-GBM database and employed weighted co-expression network analysis to identify disulfidptosis-related genes. Subsequently, we developed a predictive gene signature model based on these genes to stratify glioblastoma patients into high and low-risk groups. Results: Patients categorized into the high-risk group based on the disulfidptosis-related gene signature exhibited a significantly reduced survival rate in comparison to those in the low-risk group. Functional analysis also revealed notable differences in the immune status between the two risk groups. Conclusion: In conclusion, a new disulfidptosis-related gene signature can be utilised to predict prognosis in GBM.
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Affiliation(s)
- Yuxia Zhang
- Intensive Care Unit, Shandong Dongying People's Hospital, Dongying, 257091, China
- Department of Oncology, Shandong Dongying People's Hospital, Dongying, 257091, China
| | - Bing Liu
- Department of Oncology, Shandong Dongying People's Hospital, Dongying, 257091, China
| | - Yuelian Zhou
- Department of Oncology, Shandong Dongying People's Hospital, Dongying, 257091, China
- Department of Social & Medical Work, Shandong Dongying People's Hospital, Dongying, 257091, China
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Chi K, Liu J, Li X, Wang H, Li Y, Liu Q, Zhou Y, Ge Y. Biomarkers of heart failure: advances in omics studies. Mol Omics 2024; 20:169-183. [PMID: 38224222 DOI: 10.1039/d3mo00173c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
Heart failure is a complex syndrome characterized by progressive circulatory dysfunction, manifesting clinically as pulmonary and systemic venous congestion, alongside inadequate tissue perfusion. The early identification of HF, particularly at the mild and moderate stages (stages B and C), presents a clinical challenge due to the overlap of signs, symptoms, and natriuretic peptide levels with other cardiorespiratory pathologies. Nonetheless, early detection coupled with timely pharmacological intervention is imperative for enhancing patient outcomes. Advances in high-throughput omics technologies have enabled researchers to analyze patient-derived biofluids and tissues, discovering biomarkers that are sensitive and specific for HF diagnosis. Due to the diversity of HF etiology, it is insufficient to study the diagnostic data of early HF using a single omics technology. This study reviewed the latest progress in genomics, transcriptomics, proteomics, and metabolomics for the identification of HF biomarkers, offering novel insights into the early clinical diagnosis of HF. However, the validity of biomarkers depends on the disease status, intervention time, genetic diversity and comorbidities of the subjects. Moreover, biomarkers lack generalizability in different clinical settings. Hence, it is imperative to conduct multi-center, large-scale and standardized clinical trials to enhance the diagnostic accuracy and utility of HF biomarkers.
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Affiliation(s)
- Kuo Chi
- Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China.
| | - Jing Liu
- Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China.
| | - Xinghua Li
- Changzhi People's Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi, China.
| | - He Wang
- Department of Cardiovascular Disease II, First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China.
| | - Yanliang Li
- Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China.
| | - Qingnan Liu
- Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China.
| | - Yabin Zhou
- Department of Cardiovascular Disease II, First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China.
| | - Yuan Ge
- Department of Cardiovascular Disease II, First Affiliated Hospital of Heilongjiang University of Chinese Medicine, Harbin, Heilongjiang, China.
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Cunningham JW, Singh P, Reeder C, Claggett B, Marti-Castellote PM, Lau ES, Khurshid S, Batra P, Lubitz SA, Maddah M, Philippakis A, Desai AS, Ellinor PT, Vardeny O, Solomon SD, Ho JE. Natural Language Processing for Adjudication of Heart Failure in a Multicenter Clinical Trial: A Secondary Analysis of a Randomized Clinical Trial. JAMA Cardiol 2024; 9:174-181. [PMID: 37950744 PMCID: PMC10640703 DOI: 10.1001/jamacardio.2023.4859] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 10/29/2023] [Indexed: 11/13/2023]
Abstract
Importance The gold standard for outcome adjudication in clinical trials is medical record review by a physician clinical events committee (CEC), which requires substantial time and expertise. Automated adjudication of medical records by natural language processing (NLP) may offer a more resource-efficient alternative but this approach has not been validated in a multicenter setting. Objective To externally validate the Community Care Cohort Project (C3PO) NLP model for heart failure (HF) hospitalization adjudication, which was previously developed and tested within one health care system, compared to gold-standard CEC adjudication in a multicenter clinical trial. Design, Setting, and Participants This was a retrospective analysis of the Influenza Vaccine to Effectively Stop Cardio Thoracic Events and Decompensated Heart Failure (INVESTED) trial, which compared 2 influenza vaccines in 5260 participants with cardiovascular disease at 157 sites in the US and Canada between September 2016 and January 2019. Analysis was performed from November 2022 to October 2023. Exposures Individual sites submitted medical records for each hospitalization. The central INVESTED CEC and the C3PO NLP model independently adjudicated whether the cause of hospitalization was HF using the prepared hospitalization dossier. The C3PO NLP model was fine-tuned (C3PO + INVESTED) and a de novo NLP model was trained using half the INVESTED hospitalizations. Main Outcomes and Measures Concordance between the C3PO NLP model HF adjudication and the gold-standard INVESTED CEC adjudication was measured by raw agreement, κ, sensitivity, and specificity. The fine-tuned and de novo INVESTED NLP models were evaluated in an internal validation cohort not used for training. Results Among 4060 hospitalizations in 1973 patients (mean [SD] age, 66.4 [13.2] years; 514 [27.4%] female and 1432 [72.6%] male]), 1074 hospitalizations (26%) were adjudicated as HF by the CEC. There was good agreement between the C3PO NLP and CEC HF adjudications (raw agreement, 87% [95% CI, 86-88]; κ, 0.69 [95% CI, 0.66-0.72]). C3PO NLP model sensitivity was 94% (95% CI, 92-95) and specificity was 84% (95% CI, 83-85). The fine-tuned C3PO and de novo NLP models demonstrated agreement of 93% (95% CI, 92-94) and κ of 0.82 (95% CI, 0.77-0.86) and 0.83 (95% CI, 0.79-0.87), respectively, vs the CEC. CEC reviewer interrater reproducibility was 94% (95% CI, 93-95; κ, 0.85 [95% CI, 0.80-0.89]). Conclusions and Relevance The C3PO NLP model developed within 1 health care system identified HF events with good agreement relative to the gold-standard CEC in an external multicenter clinical trial. Fine-tuning the model improved agreement and approximated human reproducibility. Further study is needed to determine whether NLP will improve the efficiency of future multicenter clinical trials by identifying clinical events at scale.
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Affiliation(s)
- Jonathan W. Cunningham
- Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
- Cardiovascular Disease Initiative, Broad Institute of Harvard University and the Massachusetts Institute of Technology, Cambridge
| | - Pulkit Singh
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge
| | - Christopher Reeder
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge
| | - Brian Claggett
- Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | | | - Emily S. Lau
- Cardiovascular Disease Initiative, Broad Institute of Harvard University and the Massachusetts Institute of Technology, Cambridge
- Division of Cardiology, Massachusetts General Hospital, Boston
| | - Shaan Khurshid
- Cardiovascular Disease Initiative, Broad Institute of Harvard University and the Massachusetts Institute of Technology, Cambridge
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston
| | - Puneet Batra
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge
| | - Steven A. Lubitz
- Cardiovascular Disease Initiative, Broad Institute of Harvard University and the Massachusetts Institute of Technology, Cambridge
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston
| | - Mahnaz Maddah
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge
| | - Anthony Philippakis
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge
| | - Akshay S. Desai
- Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Patrick T. Ellinor
- Cardiovascular Disease Initiative, Broad Institute of Harvard University and the Massachusetts Institute of Technology, Cambridge
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston
| | - Orly Vardeny
- Minneapolis VA Hospital, University of Minnesota, Minneapolis
| | - Scott D. Solomon
- Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Jennifer E. Ho
- Cardiovascular Disease Initiative, Broad Institute of Harvard University and the Massachusetts Institute of Technology, Cambridge
- CardioVascular Institute and Division of Cardiology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
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Qi B, Graff M, Eating Disorders Working Group of the Psychiatric Genomics Consortium, Bulik CM, North KE, Munn‐Chernoff MA. Shared genetic risk between anorexia nervosa and cardiovascular disease events: Evidence from genome-wide association studies. Brain Behav 2024; 14:e3294. [PMID: 38282367 PMCID: PMC10897497 DOI: 10.1002/brb3.3294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 10/04/2023] [Accepted: 10/12/2023] [Indexed: 01/30/2024] Open
Abstract
OBJECTIVE Cardiovascular complications occur in up to 80% of patients with anorexia nervosa (AN), yet the underlying mechanisms warrant further investigation. We assessed the genetic correlation (rg ) between AN and cardiovascular disease (CVD) events to inform whether elevated cardiovascular risk among individuals with AN is due to shared genetic effects. METHOD We used genome-wide association study summary statistics for AN (N = 72,517), AN with binge eating (N = 12,630), AN without binge eating (N = 12,516), and six CVD events (N = 390,142 to 977,323). We calculated the rg s via linkage disequilibrium score regression and corrected for multiple testing using false discovery rate. RESULTS Significant rg s emerged between AN with heart failure (rg = -0.11, SE = 0.05, q = .04) and myocardial infarction (rg = -0.10, SE = 0.03, q = .01). AN with binge eating had a significant rg with myocardial infarction (rg = -0.15, SE = 0.06, q = .02). No significant rg emerged between AN without binge eating and any CVD event. DISCUSSION Some loci affect the liability to AN and CVD in opposite directions and the shared genetic effects may not be consistent across all CVD events. Our results provide further evidence suggesting that the elevated cardiovascular risk in AN may not be due to shared genetic underpinnings, but more likely a downstream consequence of the disease.
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Affiliation(s)
- Baiyu Qi
- Department of EpidemiologyUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUS
| | - Mariaelisa Graff
- Department of EpidemiologyUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUS
| | | | - Cynthia M Bulik
- Department of PsychiatryUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUS
- Department of Medical Epidemiology and BiostatisticsKarolinska InstitutetStockholmSweden
- Department of NutritionUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUS
| | - Kari E North
- Department of EpidemiologyUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUS
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Agrawal V, Manouchehri A, Vaitinadin NS, Shi M, Bagheri M, Gupta DK, Kullo IJ, Luo Y, McNally EM, Puckelwartz MJ, Ferguson JF, Wells QS, Mosley JD. Identification of Clinical Drivers of Left Atrial Enlargement Through Genomics of Left Atrial Size. Circ Heart Fail 2024; 17:e010557. [PMID: 38126226 PMCID: PMC10842187 DOI: 10.1161/circheartfailure.123.010557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 10/24/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND Greater left atrial size is associated with a higher incidence of cardiovascular disease and mortality, but the full spectrum of diagnoses associated with left atrial enlargement in sex-stratified clinical populations is not well known. Our study sought to identify genetic risk mechanisms affecting left atrial diameter (LAD) in a clinical cohort. METHODS Using Vanderbilt deidentified electronic health record, we studied 6163 females and 5993 males of European ancestry who had at least 1 LAD measure and available genotyping. A sex-stratified polygenic score was constructed for LAD variation and tested for association against 1680 International Classification of Diseases code-based phenotypes. Two-sample univariable and multivariable Mendelian randomization approaches were used to assess etiologic relationships between candidate associations and LAD. RESULTS A phenome-wide association study identified 25 International Classification of Diseases code-based diagnoses in females and 11 in males associated with a polygenic score of LAD (false discovery rate q<0.01), 5 of which were further evaluated by Mendelian randomization (waist circumference [WC], atrial fibrillation, heart failure, systolic blood pressure, and coronary artery disease). Sex-stratified differences in the genetic associations between risk factors and a polygenic score for LAD were observed (WC for females; heart failure, systolic blood pressure, atrial fibrillation, and WC for males). By multivariable Mendelian randomization, higher WC remained significantly associated with larger LAD in females, whereas coronary artery disease, WC, and atrial fibrillation remained significantly associated with larger LAD in males. CONCLUSIONS In a clinical population, we identified, by genomic approaches, potential etiologic risk factors for larger LAD. Further studies are needed to confirm the extent to which these risk factors may be modified to prevent or reverse adverse left atrial remodeling and the extent to which sex modifies these risk factors.
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Affiliation(s)
- Vineet Agrawal
- Vanderbilt Translational and Clinical Cardiovascular Research Center and Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Veterans Affairs, Nashville, TN, USA
| | - Ali Manouchehri
- Vanderbilt Translational and Clinical Cardiovascular Research Center and Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nataraja Sarma Vaitinadin
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Mingjian Shi
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Minoo Bagheri
- Vanderbilt Translational and Clinical Cardiovascular Research Center and Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Deepak K. Gupta
- Vanderbilt Translational and Clinical Cardiovascular Research Center and Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Iftikhar J. Kullo
- Department of Cardiovascular Medicine, Mayo Clinic College of Medicine, Rochester, MN, USA
| | - Yuan Luo
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Elizabeth M. McNally
- Center for Genetic Medicine, Northwestern Feinberg School of Medicine, Chicago, IL, USA
| | - Megan J. Puckelwartz
- Center for Genetic Medicine, Northwestern Feinberg School of Medicine, Chicago, IL, USA
- Department of Pharmacology, Northwestern Feinberg School of Medicine, Chicago, IL, USA
| | - Jane F. Ferguson
- Vanderbilt Translational and Clinical Cardiovascular Research Center and Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Quinn S. Wells
- Vanderbilt Translational and Clinical Cardiovascular Research Center and Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jonathan D. Mosley
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
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9
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Marcoux E, Sosnowski D, Ninni S, Mackasey M, Cadrin-Tourigny J, Roberts JD, Olesen MS, Fatkin D, Nattel S. Genetic Atrial Cardiomyopathies: Common Features, Specific Differences, and Broader Relevance to Understanding Atrial Cardiomyopathy. Circ Arrhythm Electrophysiol 2023; 16:675-698. [PMID: 38018478 DOI: 10.1161/circep.123.003750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2023]
Abstract
Atrial cardiomyopathy is a condition that causes electrical and contractile dysfunction of the atria, often along with structural and functional changes. Atrial cardiomyopathy most commonly occurs in conjunction with ventricular dysfunction, in which case it is difficult to discern the atrial features that are secondary to ventricular dysfunction from those that arise as a result of primary atrial abnormalities. Isolated atrial cardiomyopathy (atrial-selective cardiomyopathy [ASCM], with minimal or no ventricular function disturbance) is relatively uncommon and has most frequently been reported in association with deleterious rare genetic variants. The genes involved can affect proteins responsible for various biological functions, not necessarily limited to the heart but also involving extracardiac tissues. Atrial enlargement and atrial fibrillation are common complications of ASCM and are often the predominant clinical features. Despite progress in identifying disease-causing rare variants, an overarching understanding and approach to the molecular pathogenesis, phenotypic spectrum, and treatment of genetic ASCM is still lacking. In this review, we aim to analyze the literature relevant to genetic ASCM to understand the key features of this rather rare condition, as well as to identify distinct characteristics of ASCM and its arrhythmic complications that are related to specific genotypes. We outline the insights that have been gained using basic research models of genetic ASCM in vitro and in vivo and correlate these with patient outcomes. Finally, we provide suggestions for the future investigation of patients with genetic ASCM and improvements to basic scientific models and systems. Overall, a better understanding of the genetic underpinnings of ASCM will not only provide a better understanding of this condition but also promises to clarify our appreciation of the more commonly occurring forms of atrial cardiomyopathy associated with ventricular dysfunction.
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Affiliation(s)
- Edouard Marcoux
- Research Center, Montreal Heart Institute, Université de Montréal. (E.M., D.S., S. Ninni, M.M., S. Nattel)
- Faculty of Pharmacy, Université de Montréal. (E.M.)
| | - Deanna Sosnowski
- Research Center, Montreal Heart Institute, Université de Montréal. (E.M., D.S., S. Ninni, M.M., S. Nattel)
- Department of Pharmacology and Therapeutics, McGill University, Montreal, Canada (D.S., M.M., S. Nattel)
| | - Sandro Ninni
- Research Center, Montreal Heart Institute, Université de Montréal. (E.M., D.S., S. Ninni, M.M., S. Nattel)
- Université de Lille, Inserm, CHU Lille, Institut Pasteur de Lille, France (S. Ninni)
| | - Martin Mackasey
- Research Center, Montreal Heart Institute, Université de Montréal. (E.M., D.S., S. Ninni, M.M., S. Nattel)
- Department of Pharmacology and Therapeutics, McGill University, Montreal, Canada (D.S., M.M., S. Nattel)
| | - Julia Cadrin-Tourigny
- Cardiovascular Genetics Center, Montreal Heart Institute, Faculty of Medicine, Université de Montréal. (J.C.-T.)
| | - Jason D Roberts
- Population Health Research Institute, McMaster University and Hamilton Health Sciences, Canada (J.D.R.)
| | - Morten Salling Olesen
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark (M.S.O.)
| | - Diane Fatkin
- Victor Chang Cardiac Research Institute, Darlinghurst (D.F.)
- School of Clinical Medicine, Faculty of Medicine and Health, UNSW Sydney, Kensington (D.F.)
- Department of Cardiology, St Vincent's Hospital, Darlinghurst, NSW, Australia (D.F.)
| | - Stanley Nattel
- Research Center, Montreal Heart Institute, Université de Montréal. (E.M., D.S., S. Ninni, M.M., S. Nattel)
- Department of Pharmacology and Physiology, Faculty of Medicine, Université de Montréal. (S. Nattel.)
- Department of Pharmacology and Therapeutics, McGill University, Montreal, Canada (D.S., M.M., S. Nattel)
- Institute of Pharmacology. West German Heart and Vascular Center, University Duisburg-Essen, Germany (S. Nattel)
- IHU LYRIC & Fondation Bordeaux Université de Bordeaux, France (S. Nattel)
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10
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Tan K, Foo R, Loh M. Cardiomyopathy in Asian Cohorts: Genetic and Epigenetic Insights. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2023; 16:496-506. [PMID: 37589150 DOI: 10.1161/circgen.123.004079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/18/2023]
Abstract
Previous studies on cardiomyopathies have been particularly valuable for clarifying pathological mechanisms in heart failure, an etiologically heterogeneous disease. In this review, we specifically focus on cardiomyopathies in Asia, where heart failure is particularly pertinent. There has been an increase in prevalence of cardiomyopathies in Asia, in sharp contrast with the decline observed in Western countries. Indeed, important disparities in cardiomyopathy incidence, clinical characteristics, and prognosis have been reported in Asian versus White cohorts. These have been accompanied by emerging descriptions of a distinct rare and common genetic basis for disease among Asian cardiomyopathy patients marked by an increased burden of variants with uncertain significance, reclassification of variants deemed pathogenic based on evidence from predominantly White cohorts, and the discovery of Asian-specific cardiomyopathy-associated loci with underappreciated pathogenicity under conventional classification criteria. Findings from epigenetic studies of heart failure, particularly DNA methylation studies, have complemented genetic findings in accounting for the phenotypic variability in cardiomyopathy. Though extremely limited, findings from Asian ancestry-focused DNA methylation studies of cardiomyopathy have shown potential to contribute to general understanding of cardiomyopathy pathophysiology by proposing disease and cause-relevant pathophysiological mechanisms. We discuss the value of multiomics study designs incorporating genetic, methylation, and transcriptomic information for future DNA methylation studies in Asian cardiomyopathy cohorts to yield Asian ancestry-specific insights that will improve risk stratification in the Asian population.
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Affiliation(s)
- Konstanze Tan
- Lee Kong Chian School of Medicine, Nanyang Technological University, Clinical Sciences Building, Singapore (K.T., M.L.)
| | - Roger Foo
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore (R.F.)
- Department of Cardiology, National University Heart Centre, National University Health System, Singapore (R.F.)
| | - Marie Loh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Clinical Sciences Building, Singapore (K.T., M.L.)
- Genome Institute of Singapore, Singapore (GIS), Agency for Science, Technology and Research (A*STAR) (M.L.)
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, United Kingdom (M.L.)
- National Skin Centre, Singapore (M.L.)
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11
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Rauseo E, Abdulkareem M, Khan A, Cooper J, Lee AM, Aung N, Slabaugh GG, Petersen SE. Phenotyping left ventricular systolic dysfunction in asymptomatic individuals for improved risk stratification. Eur Heart J Cardiovasc Imaging 2023; 24:1363-1373. [PMID: 37699069 PMCID: PMC10531121 DOI: 10.1093/ehjci/jead218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 08/23/2023] [Indexed: 09/14/2023] Open
Abstract
AIMS Left ventricular systolic dysfunction (LSVD) is a heterogeneous condition with several factors influencing prognosis. Better phenotyping of asymptomatic individuals can inform preventative strategies. This study aims to explore the clinical phenotypes of LVSD in initially asymptomatic subjects and their association with clinical outcomes and cardiovascular abnormalities through multi-dimensional data clustering. METHODS AND RESULTS Clustering analysis was performed on 60 clinically available variables from 1563 UK Biobank participants without pre-existing heart failure (HF) and with left ventricular ejection fraction (LVEF) < 50% on cardiovascular magnetic resonance (CMR) assessment. Risks of developing HF, other cardiovascular events, death, and a composite of major adverse cardiovascular events (MACE) associated with clusters were investigated. Cardiovascular imaging characteristics, not included in the clustering analysis, were also evaluated. Three distinct clusters were identified, differing considerably in lifestyle habits, cardiovascular risk factors, electrocardiographic parameters, and cardiometabolic profiles. A stepwise increase in risk profile was observed from Cluster 1 to Cluster 3, independent of traditional risk factors and LVEF. Compared with Cluster 1, the lowest risk subset, the risk of MACE ranged from 1.42 [95% confidence interval (CI): 1.03-1.96; P < 0.05] for Cluster 2 to 1.72 (95% CI: 1.36-2.35; P < 0.001) for Cluster 3. Cluster 3, the highest risk profile, had features of adverse cardiovascular imaging with the greatest LV re-modelling, myocardial dysfunction, and decrease in arterial compliance. CONCLUSIONS Clustering of clinical variables identified three distinct risk profiles and clinical trajectories of LVSD amongst initially asymptomatic subjects. Improved characterization may facilitate tailored interventions based on the LVSD sub-type and improve clinical outcomes.
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Affiliation(s)
- Elisa Rauseo
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
| | - Musa Abdulkareem
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
- Health Data Research UK, 215 Euston Rd, London NW1 2BE, UK
| | - Abbas Khan
- School of Electronic Engineering and Computer Science, Queen Mary University of London, UK
- Digital Environment Research Institute, Queen Mary University of London, UK
| | - Jackie Cooper
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London EC1M 6BQ, UK
| | - Aaron M Lee
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London EC1M 6BQ, UK
| | - Nay Aung
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
| | - Gregory G Slabaugh
- School of Electronic Engineering and Computer Science, Queen Mary University of London, UK
- Digital Environment Research Institute, Queen Mary University of London, UK
- Alan Turing Institute, British Library, 96 Euston Rd, London NW1 2DB, UK
| | - Steffen E Petersen
- William Harvey Research Institute, NIHR Barts Biomedical Research Centre, Queen Mary University London, Charterhouse Square, London EC1M 6BQ, UK
- Barts Heart Centre, St Bartholomew’s Hospital, Barts Health NHS Trust, West Smithfield, London EC1A 7BE, UK
- Health Data Research UK, 215 Euston Rd, London NW1 2BE, UK
- Alan Turing Institute, British Library, 96 Euston Rd, London NW1 2DB, UK
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12
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Niskanen JE, Ohlsson Å, Ljungvall I, Drögemüller M, Ernst RF, Dooijes D, van Deutekom HWM, van Tintelen JP, Snijders Blok CJB, van Vugt M, van Setten J, Asselbergs FW, Petrič AD, Salonen M, Hundi S, Hörtenhuber M, Kere J, Pyle WG, Donner J, Postma AV, Leeb T, Andersson G, Hytönen MK, Häggström J, Wiberg M, Friederich J, Eberhard J, Harakalova M, van Steenbeek FG, Wess G, Lohi H. Identification of novel genetic risk factors of dilated cardiomyopathy: from canine to human. Genome Med 2023; 15:73. [PMID: 37723491 PMCID: PMC10506233 DOI: 10.1186/s13073-023-01221-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 08/17/2023] [Indexed: 09/20/2023] Open
Abstract
BACKGROUND Dilated cardiomyopathy (DCM) is a life-threatening heart disease and a common cause of heart failure due to systolic dysfunction and subsequent left or biventricular dilatation. A significant number of cases have a genetic etiology; however, as a complex disease, the exact genetic risk factors are largely unknown, and many patients remain without a molecular diagnosis. METHODS We performed GWAS followed by whole-genome, transcriptome, and immunohistochemical analyses in a spontaneously occurring canine model of DCM. Canine gene discovery was followed up in three human DCM cohorts. RESULTS Our results revealed two independent additive loci associated with the typical DCM phenotype comprising left ventricular systolic dysfunction and dilatation. We highlight two novel candidate genes, RNF207 and PRKAA2, known for their involvement in cardiac action potentials, energy homeostasis, and morphology. We further illustrate the distinct genetic etiologies underlying the typical DCM phenotype and ventricular premature contractions. Finally, we followed up on the canine discoveries in human DCM patients and discovered candidate variants in our two novel genes. CONCLUSIONS Collectively, our study yields insight into the molecular pathophysiology of DCM and provides a large animal model for preclinical studies.
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Affiliation(s)
- Julia E Niskanen
- Department of Medical and Clinical Genetics, University of Helsinki, Haartmaninkatu 8, 00290, Helsinki, Finland
- Department of Veterinary Biosciences, University of Helsinki, Agnes Sjöbergin katu 2, 00790, Helsinki, Finland
- Folkhälsan Research Center, Haartmaninkatu 8, P.O.Box 63, 00290, Helsinki, Finland
| | - Åsa Ohlsson
- Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Ingrid Ljungvall
- Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Michaela Drögemüller
- Institute of Genetics, Vetsuisse Faculty, University of Bern, Bern, 3001, Switzerland
| | - Robert F Ernst
- Department of Genetics, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Dennis Dooijes
- Department of Genetics, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Hanneke W M van Deutekom
- Department of Genetics, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - J Peter van Tintelen
- Department of Genetics, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Christian J B Snijders Blok
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht (UMCU), Utrecht, The Netherlands
- Regenerative Medicine Centre Utrecht, University of Utrecht, Utrecht, The Netherlands
| | - Marion van Vugt
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht (UMCU), Utrecht, The Netherlands
| | - Jessica van Setten
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht (UMCU), Utrecht, The Netherlands
| | - Folkert W Asselbergs
- Amsterdam University Medical Centers, Department of Cardiology, University of Amsterdam, Amsterdam, The Netherlands
- Health Data Research UK and Institute of Health Informatics, University College London, London, UK
| | | | - Milla Salonen
- Department of Medical and Clinical Genetics, University of Helsinki, Haartmaninkatu 8, 00290, Helsinki, Finland
- Department of Veterinary Biosciences, University of Helsinki, Agnes Sjöbergin katu 2, 00790, Helsinki, Finland
- Folkhälsan Research Center, Haartmaninkatu 8, P.O.Box 63, 00290, Helsinki, Finland
| | - Sruthi Hundi
- Department of Medical and Clinical Genetics, University of Helsinki, Haartmaninkatu 8, 00290, Helsinki, Finland
- Department of Veterinary Biosciences, University of Helsinki, Agnes Sjöbergin katu 2, 00790, Helsinki, Finland
- Folkhälsan Research Center, Haartmaninkatu 8, P.O.Box 63, 00290, Helsinki, Finland
| | - Matthias Hörtenhuber
- Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden
| | - Juha Kere
- Folkhälsan Research Center, Haartmaninkatu 8, P.O.Box 63, 00290, Helsinki, Finland
- Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden
- Research Programs Unit, Stem Cells and Metabolism Research Program, University of Helsinki, Helsinki, Finland
| | - W Glen Pyle
- Department of Biomedical Sciences, University of Guelph, Guelph, ON, Canada
- IMPART Investigator Team Canada, Dalhousie Medicine, Saint John, NB, Canada
| | - Jonas Donner
- Wisdom Panel Research Team, Wisdom Panel, Kinship, Helsinki, Finland
| | - Alex V Postma
- Department of Human Genetics, Amsterdam University Medical Center, Amsterdam, The Netherlands
- Department of Medical Biology, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Tosso Leeb
- Institute of Genetics, Vetsuisse Faculty, University of Bern, Bern, 3001, Switzerland
| | - Göran Andersson
- Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Marjo K Hytönen
- Department of Medical and Clinical Genetics, University of Helsinki, Haartmaninkatu 8, 00290, Helsinki, Finland
- Department of Veterinary Biosciences, University of Helsinki, Agnes Sjöbergin katu 2, 00790, Helsinki, Finland
- Folkhälsan Research Center, Haartmaninkatu 8, P.O.Box 63, 00290, Helsinki, Finland
| | - Jens Häggström
- Department of Clinical Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | - Maria Wiberg
- Department of Equine and Small Animal Medicine, University of Helsinki, Helsinki, Finland
| | - Jana Friederich
- LMU Small Animal Clinic, Ludwig Maximilians University of Munich, Munich, Germany
| | - Jenny Eberhard
- LMU Small Animal Clinic, Ludwig Maximilians University of Munich, Munich, Germany
| | - Magdalena Harakalova
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht (UMCU), Utrecht, The Netherlands
- Regenerative Medicine Centre Utrecht, University of Utrecht, Utrecht, The Netherlands
| | - Frank G van Steenbeek
- Department of Cardiology, Division Heart & Lungs, University Medical Center Utrecht (UMCU), Utrecht, The Netherlands
- Regenerative Medicine Centre Utrecht, University of Utrecht, Utrecht, The Netherlands
- Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University, Yalelaan 108, Utrecht, 3584 CM, The Netherlands
| | - Gerhard Wess
- LMU Small Animal Clinic, Ludwig Maximilians University of Munich, Munich, Germany
| | - Hannes Lohi
- Department of Medical and Clinical Genetics, University of Helsinki, Haartmaninkatu 8, 00290, Helsinki, Finland.
- Department of Veterinary Biosciences, University of Helsinki, Agnes Sjöbergin katu 2, 00790, Helsinki, Finland.
- Folkhälsan Research Center, Haartmaninkatu 8, P.O.Box 63, 00290, Helsinki, Finland.
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13
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Cunningham JW, Singh P, Reeder C, Claggett B, Marti-Castellote PM, Lau ES, Khurshid S, Batra P, Lubitz SA, Maddah M, Philippakis A, Desai AS, Ellinor PT, Vardeny O, Solomon SD, Ho JE. Natural Language Processing for Adjudication of Heart Failure Hospitalizations in a Multi-Center Clinical Trial. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.17.23294234. [PMID: 37662283 PMCID: PMC10473787 DOI: 10.1101/2023.08.17.23294234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Background The gold standard for outcome adjudication in clinical trials is chart review by a physician clinical events committee (CEC), which requires substantial time and expertise. Automated adjudication by natural language processing (NLP) may offer a more resource-efficient alternative. We previously showed that the Community Care Cohort Project (C3PO) NLP model adjudicates heart failure (HF) hospitalizations accurately within one healthcare system. Methods This study externally validated the C3PO NLP model against CEC adjudication in the INVESTED trial. INVESTED compared influenza vaccination formulations in 5260 patients with cardiovascular disease at 157 North American sites. A central CEC adjudicated the cause of hospitalizations from medical records. We applied the C3PO NLP model to medical records from 4060 INVESTED hospitalizations and evaluated agreement between the NLP and final consensus CEC HF adjudications. We then fine-tuned the C3PO NLP model (C3PO+INVESTED) and trained a de novo model using half the INVESTED hospitalizations, and evaluated these models in the other half. NLP performance was benchmarked to CEC reviewer inter-rater reproducibility. Results 1074 hospitalizations (26%) were adjudicated as HF by the CEC. There was high agreement between the C3PO NLP and CEC HF adjudications (agreement 87%, kappa statistic 0.69). C3PO NLP model sensitivity was 94% and specificity was 84%. The fine-tuned C3PO and de novo NLP models demonstrated agreement of 93% and kappa of 0.82 and 0.83, respectively. CEC reviewer inter-rater reproducibility was 94% (kappa 0.85). Conclusion Our NLP model developed within a single healthcare system accurately identified HF events relative to the gold-standard CEC in an external multi-center clinical trial. Fine-tuning the model improved agreement and approximated human reproducibility. NLP may improve the efficiency of future multi-center clinical trials by accurately identifying clinical events at scale.
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Affiliation(s)
- Jonathan W. Cunningham
- Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
- Cardiovascular Disease Initiative, Broad Institute of Harvard University and the Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Pulkit Singh
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Christopher Reeder
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Brian Claggett
- Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | | | - Emily S. Lau
- Cardiovascular Disease Initiative, Broad Institute of Harvard University and the Massachusetts Institute of Technology, Cambridge, Massachusetts
- Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Shaan Khurshid
- Cardiovascular Disease Initiative, Broad Institute of Harvard University and the Massachusetts Institute of Technology, Cambridge, Massachusetts
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, Massachusetts
| | - Puneet Batra
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Steven A. Lubitz
- Cardiovascular Disease Initiative, Broad Institute of Harvard University and the Massachusetts Institute of Technology, Cambridge, Massachusetts
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, Massachusetts
| | - Mahnaz Maddah
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Anthony Philippakis
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Akshay S. Desai
- Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Patrick T. Ellinor
- Cardiovascular Disease Initiative, Broad Institute of Harvard University and the Massachusetts Institute of Technology, Cambridge, Massachusetts
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, Massachusetts
| | - Orly Vardeny
- Minneapolis VA Hospital, University of Minnesota, Minneapolis, Minnesota
| | - Scott D. Solomon
- Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Jennifer E. Ho
- Cardiovascular Disease Initiative, Broad Institute of Harvard University and the Massachusetts Institute of Technology, Cambridge, Massachusetts
- CardioVascular Institute and Division of Cardiology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
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14
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Gregg JT, Himes BE, Asselbergs FW, Moore JH. Improving Genetic Association Studies with a Novel Methodology that Unveils the Hidden Complexity of All-Cause Heart Failure. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.02.23293567. [PMID: 37577697 PMCID: PMC10418568 DOI: 10.1101/2023.08.02.23293567] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
Motivation Genome-Wide Association Studies (GWAS) commonly assume phenotypic and genetic homogeneity that is not present in complex conditions. We designed Transformative Regression Analysis of Combined Effects (TRACE), a GWAS methodology that better accounts for clinical phenotype heterogeneity and identifies gene-by-environment (GxE) interactions. We demonstrated with UK Biobank (UKB) data that TRACE increased the variance explained in All-Cause Heart Failure (AHF) via the discovery of novel single nucleotide polymorphism (SNP) and SNP-by-environment (i.e. GxE) interaction associations. First, we transformed 312 AHF-related ICD10 codes (including AHF) into continuous low-dimensional features (i.e., latent phenotypes) for a more nuanced disease representation. Then, we ran a standard GWAS on our latent phenotypes to discover main effects and identified GxE interactions with target encoding. Genes near associated SNPs subsequently underwent enrichment analysis to explore potential functional mechanisms underlying associations. Latent phenotypes were regressed against their SNP hits and the estimated latent phenotype values were used to measure the amount of AHF variance explained. Results Our method identified over 100 main GWAS effects that were consistent with prior studies and hundreds of novel gene-by-smoking interactions, which collectively accounted for approximately 10% of AHF variance. This represents an improvement over traditional GWAS whose results account for a negligible proportion of AHF variance. Enrichment analyses suggested that hundreds of miRNAs mediated the SNP effect on various AHF-related biological pathways. The TRACE framework can be applied to decode the genetics of other complex diseases. Availability All code is available at https://github.com/EpistasisLab/latent_phenotype_project.
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Affiliation(s)
- John T. Gregg
- Department of Biostatistics Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Blanca E. Himes
- Department of Biostatistics Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Jason H. Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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15
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Barat A, Chen CW, Patel-Murray N, McMurray JJV, Packer M, Solomon SD, Desai AS, Rouleau JL, Zile MR, Attari Z, Zhang C, Xu H, Hartman N, Hon C, Healey M, Chutkow W, O'Donnell CJ, Jacob J, Lefkowitz M, Mendelson MM, Wandel S, Yates D, Gimpelewicz C. Clinical characteristics of heart failure with reduced ejection fraction patients with rare pathogenic variants in dilated cardiomyopathy-associated genes: A subgroup analysis of the PARADIGM-HF trial. Eur J Heart Fail 2023; 25:1256-1266. [PMID: 37191081 DOI: 10.1002/ejhf.2886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 04/18/2023] [Accepted: 05/08/2023] [Indexed: 05/17/2023] Open
Abstract
AIMS To evaluate the prevalence of pathogenic variants in genes associated with dilated cardiomyopathy (DCM) in a clinical trial population with heart failure and reduced ejection fraction (HFrEF) and describe the baseline characteristics by variant carrier status. METHODS AND RESULTS This was a post hoc analysis of the Phase 3 PARADIGM-HF trial. Forty-four genes, divided into three tiers, based on definitive, moderate or limited evidence of association with DCM, were assessed for rare predicted loss-of-function (pLoF) variants, which were prioritized using ClinVar annotations, measures of gene transcriptional output and evolutionary constraint, and pLoF confidence predictions. Prevalence was reported for pLoF variant carriers based on DCM-associated gene tiers. Clinical features were compared between carriers and non-carriers. Of the 1412 HFrEF participants with whole-exome sequence data, 68 (4.8%) had at least one pLoF variant in the 8 tier-1 genes (definitive/strong association with DCM), with Titin being most commonly affected. The prevalence increased to 7.5% when considering all 44 genes. Among patients with idiopathic aetiology, 10.0% (23/229) had tier-1 variants only and 12.6% (29/229) had tier-1, -2 or -3 variants. Compared to non-carriers, tier-1 carriers were younger (4 years; adjusted p-value [padj ] = 4 × 10-3 ), leaner (27.8 kg/m2 vs. 29.4 kg/m2 ; padj = 3.2 × 10-3 ), had lower ejection fraction (27.3% vs. 29.8%; padj = 5.8 × 10-3 ), and less likely to have ischaemic aetiology (37.3% vs. 67.4%; padj = 4 × 10-4 ). CONCLUSION Deleterious pLoF variants in genes with definitive/strong association with DCM were identified in ∼5% of HFrEF patients from a PARADIGM-HF trial subset, who were younger, had lower ejection fraction and were less likely to have had an ischaemic aetiology.
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Affiliation(s)
- Ana Barat
- Novartis Ireland Ltd, Dublin, Ireland
| | - Chien-Wei Chen
- Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA
| | | | - John J V McMurray
- University of Glasgow, BHF Cardiovascular Research Centre, Glasgow, UK
| | - Milton Packer
- Baylor University Medical Center, Baylor Heart and Vascular Institute, Dallas, TX, USA
| | - Scott D Solomon
- Cardiovascular Division, Brigham and Women's Hospital, Boston, MA, USA
| | - Akshay S Desai
- Cardiovascular Division, Brigham and Women's Hospital, Boston, MA, USA
| | - Jean L Rouleau
- Institut de Cardiologie de Montréal, Université de Montréal, Montreal, Quebec, Canada
| | - Michael R Zile
- Medical University of South Carolina, Charleston, SC, USA
- Ralph H. Johnson Department of Veterans Affairs Medical Center, Charleston, SC, USA
| | - Zenab Attari
- Global Development Operations, Novartis, Hyderabad, India
| | - Cong Zhang
- Novartis Institutes for Biomedical Research, Shanghai, China
| | - Huilei Xu
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | | | - Claudia Hon
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | - Margaret Healey
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | - William Chutkow
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | | | - Jaison Jacob
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | | | | | | | - Denise Yates
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
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16
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Shuey MM, Lee KM, Keaton J, Khankari NK, Breeyear JH, Walker VM, Miller DR, Heberer KR, Reaven PD, Clarke SL, Lee J, Lynch JA, Vujkovic M, Edwards TL. A genetically supported drug repurposing pipeline for diabetes treatment using electronic health records. EBioMedicine 2023; 94:104674. [PMID: 37399599 PMCID: PMC10328805 DOI: 10.1016/j.ebiom.2023.104674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 06/06/2023] [Accepted: 06/07/2023] [Indexed: 07/05/2023] Open
Abstract
BACKGROUND The identification of new uses for existing drug therapies has the potential to identify treatments for comorbid conditions that have the added benefit of glycemic control while also providing a rapid, low-cost approach to drug (re)discovery. METHODS We developed and tested a genetically-informed drug-repurposing pipeline for diabetes management. This approach mapped genetically-predicted gene expression signals from the largest genome-wide association study for type 2 diabetes mellitus to drug targets using publicly available databases to identify drug-gene pairs. These drug-gene pairs were then validated using a two-step approach: 1) a self-controlled case-series (SCCS) using electronic health records from a discovery and replication population, and 2) Mendelian randomization (MR). FINDINGS After filtering on sample size, 20 candidate drug-gene pairs were validated and various medications demonstrated evidence of glycemic regulation including two anti-hypertensive classes: angiotensin-converting enzyme inhibitors as well as calcium channel blockers (CCBs). The CCBs demonstrated the strongest evidence of glycemic reduction in both validation approaches (SCCS HbA1c and glucose reduction: -0.11%, p = 0.01 and -0.85 mg/dL, p = 0.02, respectively; MR: OR = 0.84, 95% CI = 0.81, 0.87, p = 5.0 x 10-25). INTERPRETATION Our results support CCBs as a strong candidate medication for blood glucose reduction in addition to cardiovascular disease reduction. Further, these results support the adaptation of this approach for use in future drug-repurposing efforts for other conditions. FUNDING National Institutes of Health, Medical Research Council Integrative Epidemiology Unit at the University of Bristol, UK Medical Research Council, American Heart Association, and Department of Veterans Affairs (VA) Informatics and Computing Infrastructure and VA Cooperative Studies Program.
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Affiliation(s)
- Megan M Shuey
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Kyung Min Lee
- VA Informatics and Computer Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA
| | - Jacob Keaton
- Medical Genomics and Metabolic Genetics Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA; Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nikhil K Khankari
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Joseph H Breeyear
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA; Nashville VA Medical Center, Nashville, TN, USA
| | - Venexia M Walker
- Medical Research Council, Integrative Epidemiology Unit, University of Bristol, Bristol, UK; Bristol Medical School, UK; Population Health Sciences, University of Bristol, Bristol, UK; Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Donald R Miller
- Center for Healthcare Organization and Implementation Research, Bedford VA Healthcare System, Bedford, MA, USA; Center for Population Health, Department of Biomedical and Nutritional Sciences, University of Massachusetts, Lowell, MA, USA
| | - Kent R Heberer
- VA Palo Alto Health Care System, Palo Alto, CA, USA; Departments of Medicine and Endocrinology, Stanford University School of Medicine, Stanford, CA, USA
| | - Peter D Reaven
- Phoenix VA Health Care System, Phoenix, AZ, USA; College of Medicine, University of Arizona, Phoenix, AZ, USA
| | - Shoa L Clarke
- Departments of Medicine and Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Jennifer Lee
- VA Palo Alto Health Care System, Palo Alto, CA, USA
| | - Julie A Lynch
- VA Informatics and Computer Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA; School of Medicine, University of Utah, Salt Lake City, UT, USA
| | - Marijana Vujkovic
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA; Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
| | - Todd L Edwards
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA; Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA; Nashville VA Medical Center, Nashville, TN, USA.
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17
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Wang X, Musunuru K. A common coding variant in BAG3 protects from heart failure. NATURE CARDIOVASCULAR RESEARCH 2023; 2:609-610. [PMID: 39195921 DOI: 10.1038/s44161-023-00297-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/29/2024]
Affiliation(s)
- Xiao Wang
- Cardiovascular Institute, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Division of Cardiovascular Medicine, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Kiran Musunuru
- Cardiovascular Institute, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
- Division of Cardiovascular Medicine, Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
- Department of Genetics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
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18
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Perez-Bermejo JA, Judge LM, Jensen CL, Wu K, Watry HL, Truong A, Ho JJ, Carter M, Runyon WV, Kaake RM, Pulido EH, Mandegar MA, Swaney DL, So PL, Krogan NJ, Conklin BR. Functional analysis of a common BAG3 allele associated with protection from heart failure. NATURE CARDIOVASCULAR RESEARCH 2023; 2:615-628. [PMID: 39195919 DOI: 10.1038/s44161-023-00288-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 05/18/2023] [Indexed: 08/29/2024]
Abstract
Multiple genetic association studies have correlated a common allelic block linked to the BAG3 gene with a decreased incidence of heart failure, but the molecular mechanism remains elusive. In this study, we used induced pluripotent stem cells to test if the only coding variant in this allele block, BAG3C151R, alters protein and cellular function in human cardiomyocytes. Quantitative protein interaction analysis identified changes in BAG3C151R protein partners specific to cardiomyocytes. Knockdown of genes encoding for BAG3-interacting factors in cardiomyocytes followed by myofibrillar analysis revealed that BAG3C151R associates more strongly with proteins involved in the maintenance of myofibrillar integrity. Finally, we demonstrate that cardiomyocytes expressing the BAG3C151R variant have improved response to proteotoxic stress in a dose-dependent manner. This study suggests that BAG3C151R could be responsible for the cardioprotective effect of the haplotype block, by increasing cardiomyocyte protection from stress. Preferential binding partners of BAG3C151R may reveal potential targets for cardioprotective therapies.
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Affiliation(s)
| | - Luke M Judge
- Gladstone Institutes, San Francisco, CA, USA
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA
| | | | - Kenneth Wu
- Gladstone Institutes, San Francisco, CA, USA
| | | | | | - Jaclyn J Ho
- Tenaya Therapeutics, South San Francisco, CA, USA
| | | | | | - Robyn M Kaake
- Gladstone Institutes, San Francisco, CA, USA
- Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
| | | | | | - Danielle L Swaney
- Gladstone Institutes, San Francisco, CA, USA
- Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
| | - Po-Lin So
- Gladstone Institutes, San Francisco, CA, USA
| | - Nevan J Krogan
- Gladstone Institutes, San Francisco, CA, USA
- Quantitative Biosciences Institute (QBI), University of California, San Francisco, San Francisco, CA, USA
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA
| | - Bruce R Conklin
- Gladstone Institutes, San Francisco, CA, USA.
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, CA, USA.
- Department of Medicine, University of California, San Francisco, San Francisco, CA, USA.
- Innovative Genomics Institute, Berkeley, CA, USA.
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19
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Kandola MS, Kulm S, Kim LK, Markowitz SM, Liu CF, Thomas G, Ip JE, Lerman BB, Elemento O, Cheung JW. Population-Level Prevalence of Rare Variants Associated With Atrial Fibrillation and its Impact on Patient Outcomes. JACC Clin Electrophysiol 2023; 9:1137-1146. [PMID: 36669898 DOI: 10.1016/j.jacep.2022.11.022] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 11/21/2022] [Accepted: 11/22/2022] [Indexed: 01/20/2023]
Abstract
BACKGROUND Whole exome sequencing may identify rare pathogenic/likely pathogenic variants (LPVs) that are linked to atrial fibrillation (AF). The impact of LPVs associated with AF on a population level on outcomes is unclear. OBJECTIVES This study sought to examine the association of LPVs with AF and their impact on clinical outcomes using the UK Biobank, a national repository of participants with available whole exome sequencing data. METHODS A total of 200,631 individuals in the UK Biobank were studied. Incident and prevalent AF, comorbidities, and outcomes were identified using self-reported assessments and hospital stay operative, and death registry records. LPVs were determined using arrhythmia and cardiomyopathy gene panels with LOFTEE and ClinVar to predict variants of functional significance. RESULTS Compared with control subjects, there was a modestly increased prevalence of LPVs among 9,585 patients with AF (2.0% vs 1.7%, respectively; P = 0.01). Among those with prevalent AF at <45 years of age, 4.2% were LPV carriers. LPVs in TTN and PKP2 were associated with AF with adjusted odds ratios of 2.69 (95% CI: 1.57-4.61) and 2.69 (95% CI: 1.54-4.68), respectively. There was no significant difference in combined ischemic stroke, heart failure hospitalization, and mortality among patients who have AF with and without LPVs (25.1% vs 23.8%; P = 0.49). Among participants with AF and available cardiac magnetic resonance imaging data, LPV carriers had lower left ventricular ejection fractions than non-LPV carriers (42% vs 52%; P = 0.027). CONCLUSIONS Patients with AF had a modestly increased prevalence of LPVs. Among reference arrhythmia and cardiomyopathy genes, the contribution of rare variants to AF risk at a population level is modest and its impact on outcomes appears to be limited, despite an association of LPVs with reduced left ventricular ejection fraction among patients with AF.
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Affiliation(s)
- Manjinder S Kandola
- Weill Cornell Cardiovascular Outcomes Research Group, Department of Medicine, Division of Cardiology, Weill Cornell Medicine-New York Presbyterian Hospital, New York, New York, USA
| | - Scott Kulm
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York, USA
| | - Luke K Kim
- Weill Cornell Cardiovascular Outcomes Research Group, Department of Medicine, Division of Cardiology, Weill Cornell Medicine-New York Presbyterian Hospital, New York, New York, USA
| | - Steven M Markowitz
- Weill Cornell Cardiovascular Outcomes Research Group, Department of Medicine, Division of Cardiology, Weill Cornell Medicine-New York Presbyterian Hospital, New York, New York, USA
| | - Christopher F Liu
- Weill Cornell Cardiovascular Outcomes Research Group, Department of Medicine, Division of Cardiology, Weill Cornell Medicine-New York Presbyterian Hospital, New York, New York, USA
| | - George Thomas
- Weill Cornell Cardiovascular Outcomes Research Group, Department of Medicine, Division of Cardiology, Weill Cornell Medicine-New York Presbyterian Hospital, New York, New York, USA
| | - James E Ip
- Weill Cornell Cardiovascular Outcomes Research Group, Department of Medicine, Division of Cardiology, Weill Cornell Medicine-New York Presbyterian Hospital, New York, New York, USA
| | - Bruce B Lerman
- Weill Cornell Cardiovascular Outcomes Research Group, Department of Medicine, Division of Cardiology, Weill Cornell Medicine-New York Presbyterian Hospital, New York, New York, USA
| | - Olivier Elemento
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York, USA
| | - Jim W Cheung
- Weill Cornell Cardiovascular Outcomes Research Group, Department of Medicine, Division of Cardiology, Weill Cornell Medicine-New York Presbyterian Hospital, New York, New York, USA.
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20
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Patel KK, Venkatesan C, Abdelhalim H, Zeeshan S, Arima Y, Linna-Kuosmanen S, Ahmed Z. Genomic approaches to identify and investigate genes associated with atrial fibrillation and heart failure susceptibility. Hum Genomics 2023; 17:47. [PMID: 37270590 DOI: 10.1186/s40246-023-00498-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 05/31/2023] [Indexed: 06/05/2023] Open
Abstract
Atrial fibrillation (AF) and heart failure (HF) contribute to about 45% of all cardiovascular disease (CVD) deaths in the USA and around the globe. Due to the complex nature, progression, inherent genetic makeup, and heterogeneity of CVDs, personalized treatments are believed to be critical. To improve the deciphering of CVD mechanisms, we need to deeply investigate well-known and identify novel genes that are responsible for CVD development. With the advancements in sequencing technologies, genomic data have been generated at an unprecedented pace to foster translational research. Correct application of bioinformatics using genomic data holds the potential to reveal the genetic underpinnings of various health conditions. It can help in the identification of causal variants for AF, HF, and other CVDs by moving beyond the one-gene one-disease model through the integration of common and rare variant association, the expressed genome, and characterization of comorbidities and phenotypic traits derived from the clinical information. In this study, we examined and discussed variable genomic approaches investigating genes associated with AF, HF, and other CVDs. We collected, reviewed, and compared high-quality scientific literature published between 2009 and 2022 and accessible through PubMed/NCBI. While selecting relevant literature, we mainly focused on identifying genomic approaches involving the integration of genomic data; analysis of common and rare genetic variants; metadata and phenotypic details; and multi-ethnic studies including individuals from ethnic minorities, and European, Asian, and American ancestries. We found 190 genes associated with AF and 26 genes linked to HF. Seven genes had implications in both AF and HF, which are SYNPO2L, TTN, MTSS1, SCN5A, PITX2, KLHL3, and AGAP5. We listed our conclusion, which include detailed information about genes and SNPs associated with AF and HF.
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Affiliation(s)
- Kush Ketan Patel
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA
| | - Cynthia Venkatesan
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA
| | - Habiba Abdelhalim
- Rutgers Institute for Health, Health Care Policy and Aging Research, Rutgers University, 112 Paterson St, New Brunswick, NJ, USA
| | - Saman Zeeshan
- Rutgers Cancer Institute of New Jersey, Rutgers University, 195 Little Albany St, New Brunswick, NJ, USA
| | - Yuichiro Arima
- Developmental Cardiology Laboratory, International Research Center for Medical Sciences, Kumamoto University, 2-2-1 Honjo, Kumamoto City, Kumamoto, Japan
| | - Suvi Linna-Kuosmanen
- A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70211, Kuopio, Finland
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Zeeshan Ahmed
- Department of Genetics and Genome Sciences, UConn Health, 400 Farmington Ave, Farmington, CT, USA.
- Department of Medicine, Robert Wood Johnson Medical School, Rutgers Biomedical and Health Sciences, 125 Paterson St, New Brunswick, NJ, USA.
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21
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Lin X, Song W, Zhang C, Zhou M, Li J. Reappraising the role of chronic inflammatory burden in heart failure. J Gene Med 2023:e3519. [PMID: 37211702 DOI: 10.1002/jgm.3519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 04/04/2023] [Accepted: 04/10/2023] [Indexed: 05/23/2023] Open
Abstract
BACKGROUND Heart failure (HF) is a clinical syndrome associated with poor quality of life, substantial utilization of health care resources, and premature mortality. It is now considered to be the most urgent unmet medical need in the field of cardiovascular disease. Accumulated evidence suggested that comorbidity-driven inflammation has emerged as a critical component of HF pathogenesis. Although anti-inflammatory therapies have increased in popularity, very few effective treatments are still available. A comprehensive understanding of the interplay between chronic inflammation and its impact on HF will facilitate the identification of future therapeutic targets. METHODS A two-sample Mendelian randomization study was conducted to assess the association between genetic liability for chronic inflammation and HF. By analyzing functional annotations and enrichment data, we were able to identify common pathophysiological mechanisms. RESULTS The present study did not provide evidence for chronic inflammation as the cause of HF and the reliability of the results was enhanced by the other three Mendelian randomization analysis methods. Functional annotations of genes and pathway enrichment analyses have indicated that chronic inflammation and HF share a common pathophysiology. CONCLUSIONS The associations between chronic inflammation and cardiovascular disease from observational studies may be explained by shared risk factors and comorbidities rather than direct effects.
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Affiliation(s)
- Xueqi Lin
- Department of Cardiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Wei Song
- Department of Cardiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
- Jinyang Community Health Service Center in Pudong District, Shanghai, China
| | - Chunsheng Zhang
- Department of Cardiology, Shanghai East Hospital of Clinical Medical College, Nanjing Medical University, Nanjing, China
| | - Miaomiao Zhou
- Department of Cardiology, Shanghai East Hospital of Clinical Medical College, Dalian Medical University, Dalian, China
| | - Jiming Li
- Department of Cardiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China
- Department of Cardiology, Shanghai East Hospital of Clinical Medical College, Nanjing Medical University, Nanjing, China
- Department of Cardiology, Shanghai East Hospital of Clinical Medical College, Dalian Medical University, Dalian, China
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22
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Vukadinovic M, Kwan AC, Yuan V, Salerno M, Lee DC, Albert CM, Cheng S, Li D, Ouyang D, Clarke SL. Deep learning-enabled analysis of medical images identifies cardiac sphericity as an early marker of cardiomyopathy and related outcomes. MED 2023; 4:252-262.e3. [PMID: 36996817 PMCID: PMC10106428 DOI: 10.1016/j.medj.2023.02.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 01/02/2023] [Accepted: 02/15/2023] [Indexed: 03/31/2023]
Abstract
BACKGROUND Quantification of chamber size and systolic function is a fundamental component of cardiac imaging. However, the human heart is a complex structure with significant uncharacterized phenotypic variation beyond traditional metrics of size and function. Examining variation in cardiac shape can add to our ability to understand cardiovascular risk and pathophysiology. METHODS We measured the left ventricle (LV) sphericity index (short axis length/long axis length) using deep learning-enabled image segmentation of cardiac magnetic resonance imaging data from the UK Biobank. Subjects with abnormal LV size or systolic function were excluded. The relationship between LV sphericity and cardiomyopathy was assessed using Cox analyses, genome-wide association studies, and two-sample Mendelian randomization. FINDINGS In a cohort of 38,897 subjects, we show that a one standard deviation increase in sphericity index is associated with a 47% increased incidence of cardiomyopathy (hazard ratio [HR]: 1.47, 95% confidence interval [CI]: 1.10-1.98, p = 0.01) and a 20% increased incidence of atrial fibrillation (HR: 1.20, 95% CI: 1.11-1.28, p < 0.001), independent of clinical factors and traditional magnetic resonance imaging (MRI) measurements. We identify four loci associated with sphericity at genome-wide significance, and Mendelian randomization supports non-ischemic cardiomyopathy as causal for LV sphericity. CONCLUSIONS Variation in LV sphericity in otherwise normal hearts predicts risk for cardiomyopathy and related outcomes and is caused by non-ischemic cardiomyopathy. FUNDING This study was supported by grants K99-HL157421 (D.O.) and KL2TR003143 (S.L.C.) from the National Institutes of Health.
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Affiliation(s)
- Milos Vukadinovic
- Department of Bioengineering, University of California Los Angeles, Los Angeles, CA 90095, USA; Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Alan C Kwan
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Victoria Yuan
- School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Michael Salerno
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA 94306, USA
| | - Daniel C Lee
- Department of Medicine and Radiology, Northwestern Medicine, Chicago, IL 60611, USA
| | - Christine M Albert
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Susan Cheng
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Debiao Li
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - David Ouyang
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA.
| | - Shoa L Clarke
- Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA 94306, USA.
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23
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Tadros R, Zheng SL, Grace C, Jordà P, Francis C, Jurgens SJ, Thomson KL, Harper AR, Ormondroyd E, West DM, Xu X, Theotokis PI, Buchan RJ, McGurk KA, Mazzarotto F, Boschi B, Pelo E, Lee M, Noseda M, Varnava A, Vermeer AM, Walsh R, Amin AS, van Slegtenhorst MA, Roslin N, Strug LJ, Salvi E, Lanzani C, de Marvao A, Roberts JD, Tremblay-Gravel M, Giraldeau G, Cadrin-Tourigny J, L'Allier PL, Garceau P, Talajic M, Pinto YM, Rakowski H, Pantazis A, Baksi J, Halliday BP, Prasad SK, Barton PJ, O'Regan DP, Cook SA, de Boer RA, Christiaans I, Michels M, Kramer CM, Ho CY, Neubauer S, Matthews PM, Wilde AA, Tardif JC, Olivotto I, Adler A, Goel A, Ware JS, Bezzina CR, Watkins H. Large scale genome-wide association analyses identify novel genetic loci and mechanisms in hypertrophic cardiomyopathy. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.01.28.23285147. [PMID: 36778260 PMCID: PMC9915807 DOI: 10.1101/2023.01.28.23285147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Hypertrophic cardiomyopathy (HCM) is an important cause of morbidity and mortality with both monogenic and polygenic components. We here report results from the largest HCM genome-wide association study (GWAS) and multi-trait analysis (MTAG) including 5,900 HCM cases, 68,359 controls, and 36,083 UK Biobank (UKB) participants with cardiac magnetic resonance (CMR) imaging. We identified a total of 70 loci (50 novel) associated with HCM, and 62 loci (32 novel) associated with relevant left ventricular (LV) structural or functional traits. Amongst the common variant HCM loci, we identify a novel HCM disease gene, SVIL, which encodes the actin-binding protein supervillin, showing that rare truncating SVIL variants cause HCM. Mendelian randomization analyses support a causal role of increased LV contractility in both obstructive and non-obstructive forms of HCM, suggesting common disease mechanisms and anticipating shared response to therapy. Taken together, the findings significantly increase our understanding of the genetic basis and molecular mechanisms of HCM, with potential implications for disease management.
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Affiliation(s)
- Rafik Tadros
- Cardiovascular Genetics Centre, Montreal Heart Institute, Montreal, QC, Canada
- Faculty of Medicine, Université de Montréal, Montreal, QC, Canada
- Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Sean L Zheng
- National Heart & Lung Institute, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
- Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Christopher Grace
- Radcliffe Department of Medicine, University of Oxford, Division of Cardiovascular Medicine, John Radcliffe Hospital, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Paloma Jordà
- Cardiovascular Genetics Centre, Montreal Heart Institute, Montreal, QC, Canada
- Faculty of Medicine, Université de Montréal, Montreal, QC, Canada
| | - Catherine Francis
- National Heart & Lung Institute, Imperial College London, London, UK
- Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Sean J Jurgens
- Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kate L Thomson
- Radcliffe Department of Medicine, University of Oxford, Division of Cardiovascular Medicine, John Radcliffe Hospital, Oxford, UK
- Oxford Genetics Laboratories, Churchill Hospital, Oxford, UK
| | - Andrew R Harper
- Radcliffe Department of Medicine, University of Oxford, Division of Cardiovascular Medicine, John Radcliffe Hospital, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Elizabeth Ormondroyd
- Radcliffe Department of Medicine, University of Oxford, Division of Cardiovascular Medicine, John Radcliffe Hospital, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Dominique M West
- Radcliffe Department of Medicine, University of Oxford, Division of Cardiovascular Medicine, John Radcliffe Hospital, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Xiao Xu
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Pantazis I Theotokis
- National Heart & Lung Institute, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
- Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Rachel J Buchan
- National Heart & Lung Institute, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
- Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Kathryn A McGurk
- National Heart & Lung Institute, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Francesco Mazzarotto
- National Heart & Lung Institute, Imperial College London, London, UK
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | | | | | - Michael Lee
- National Heart & Lung Institute, Imperial College London, London, UK
| | - Michela Noseda
- National Heart & Lung Institute, Imperial College London, London, UK
| | - Amanda Varnava
- National Heart & Lung Institute, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, Imperial College London, London, UK
| | - Alexa Mc Vermeer
- Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- Department of Clinical Genetics, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart, (ERN GUARD-HEART; https://guardheart.ern-net.eu)
| | - Roddy Walsh
- Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Ahmad S Amin
- Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart, (ERN GUARD-HEART; https://guardheart.ern-net.eu)
- Department of Clinical Cardiology, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | - Marjon A van Slegtenhorst
- Department of Clinical Genetics, Erasmus Medical Center, University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Nicole Roslin
- The Centre for Applied Genomics, Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
| | - Lisa J Strug
- Departments of Statistical Sciences and Computer Science, Data Sciences Institute, University of Toronto, Toronto, ON, Canada
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, Canada
- Ontario Regional Centre, Canadian Statistical Sciences Institute, University of Toronto, Toronto, ON, Canada
| | - Erika Salvi
- Neuroalgology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Chiara Lanzani
- Genomics of Renal Diseases and Hypertension Unit, Nephrology Operative Unit, IRCCS San Raffaele Hospital, Milan, Italy
- Chair of Nephrology, Vita-Salute San Raffaele University, Milan, Italy
| | - Antonio de Marvao
- National Heart & Lung Institute, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Jason D Roberts
- Section of Cardiac Electrophysiology, Division of Cardiology, Department of Medicine, Western University, London, ON, Canada
| | - Maxime Tremblay-Gravel
- Cardiovascular Genetics Centre, Montreal Heart Institute, Montreal, QC, Canada
- Faculty of Medicine, Université de Montréal, Montreal, QC, Canada
| | - Genevieve Giraldeau
- Cardiovascular Genetics Centre, Montreal Heart Institute, Montreal, QC, Canada
- Faculty of Medicine, Université de Montréal, Montreal, QC, Canada
| | - Julia Cadrin-Tourigny
- Cardiovascular Genetics Centre, Montreal Heart Institute, Montreal, QC, Canada
- Faculty of Medicine, Université de Montréal, Montreal, QC, Canada
| | - Philippe L L'Allier
- Cardiovascular Genetics Centre, Montreal Heart Institute, Montreal, QC, Canada
- Faculty of Medicine, Université de Montréal, Montreal, QC, Canada
| | - Patrick Garceau
- Cardiovascular Genetics Centre, Montreal Heart Institute, Montreal, QC, Canada
- Faculty of Medicine, Université de Montréal, Montreal, QC, Canada
| | - Mario Talajic
- Cardiovascular Genetics Centre, Montreal Heart Institute, Montreal, QC, Canada
- Faculty of Medicine, Université de Montréal, Montreal, QC, Canada
| | - Yigal M Pinto
- Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart, (ERN GUARD-HEART; https://guardheart.ern-net.eu)
- Department of Clinical Cardiology, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
| | | | - Antonis Pantazis
- Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - John Baksi
- National Heart & Lung Institute, Imperial College London, London, UK
- Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Brian P Halliday
- National Heart & Lung Institute, Imperial College London, London, UK
- Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Sanjay K Prasad
- National Heart & Lung Institute, Imperial College London, London, UK
- Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Paul Jr Barton
- National Heart & Lung Institute, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
- Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
| | - Declan P O'Regan
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
| | - Stuart A Cook
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
- National Heart Centre Singapore, Singapore
- Duke-National University of Singapore Medical School, Singapore
| | - Rudolf A de Boer
- Department of Cardiology, Thoraxcenter, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Imke Christiaans
- Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Michelle Michels
- European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart, (ERN GUARD-HEART; https://guardheart.ern-net.eu)
- Department of Cardiology, Thoraxcenter, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Christopher M Kramer
- Department of Medicine, Cardiovascular Division, University of Virginia Health, Charlottesville, VA, USA
| | - Carolyn Y Ho
- Cardiovascular Division, Brigham and Women's Hospital, Boston, MA, USA
| | - Stefan Neubauer
- Division of Cardiovascular Medicine, Radcliffe Department of Medicine, NIHR Oxford Health Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Paul M Matthews
- Department of Brain Sciences and UK Dementia Research Institute, Imperial College London, London, UK
| | - Arthur A Wilde
- Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart, (ERN GUARD-HEART; https://guardheart.ern-net.eu)
- Department of Clinical Cardiology, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- ECGen, Cardiogenetics Focus Group of EHRA, France
| | - Jean-Claude Tardif
- Cardiovascular Genetics Centre, Montreal Heart Institute, Montreal, QC, Canada
- Faculty of Medicine, Université de Montréal, Montreal, QC, Canada
| | - Iacopo Olivotto
- Department of Experimental and Clinical Medicine, Meyer Children Hospital, University of Florence, Florence, Italy
| | - Arnon Adler
- Division of Cardiology, Peter Munk Cardiac Centre, University Health Network, Toronto, ON, Canada
- Department of Medicine, University of Toronto, Toronto, ON, Canada
| | - Anuj Goel
- Radcliffe Department of Medicine, University of Oxford, Division of Cardiovascular Medicine, John Radcliffe Hospital, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - James S Ware
- National Heart & Lung Institute, Imperial College London, London, UK
- MRC London Institute of Medical Sciences, Imperial College London, London, UK
- Royal Brompton & Harefield Hospitals, Guy's and St. Thomas' NHS Foundation Trust, London, UK
- Program in Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Connie R Bezzina
- Department of Experimental Cardiology, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
- European Reference Network for Rare and Low Prevalence Complex Diseases of the Heart, (ERN GUARD-HEART; https://guardheart.ern-net.eu)
| | - Hugh Watkins
- Radcliffe Department of Medicine, University of Oxford, Division of Cardiovascular Medicine, John Radcliffe Hospital, Oxford, UK
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
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24
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Schmidt A, Finan C, Bourfiss M, Velthuis B, Puyol-Antón E, Alasiri A, Ruijsink B, Asselbergs F, Ter Riele A, van Setten J. Cardiac MRI to guide heart failure and atrial fibrillation drug discovery: a Mendelian randomization analysis. RESEARCH SQUARE 2023:rs.3.rs-2449265. [PMID: 36778476 PMCID: PMC9915782 DOI: 10.21203/rs.3.rs-2449265/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Background drug development and disease prevention of heart failure (HF) and atrial fibrillation (AF) are impeded by a lack of robust early-stage surrogates. We determined to what extent cardiac magnetic resonance (CMR) measurements act as surrogates for the development of HF or AF in healthy individuals. Methods Genetic data was sourced on the association with 22 atrial and ventricular CMR measurements. Mendelian randomization was used to determine CMR associations with atrial fibrillation (AF), heart failure (HF), non-ischemic cardiomyopathy (CMP), and dilated cardiomyopathy (DCM). Additionally, for the CMR surrogates of AF and HF, we explored their association with non-cardiac traits. Results In total we found that 9 CMR measures were associated with the development of HF, 7 with development of non-ischemic CMR, 6 with DCM, and 12 with AF. biventricular ejection fraction (EF), biventricular or end-systolic volumes (ESV) and left-ventricular (LV) end diastolic volume (EDV) were associated with all 4 cardiac outcomes. Increased LV-MVR (mass to volume ratio) affected HF (odds ratio (OR) 0.83, 95%CI 0.79; 0.88), and DCM (OR 0.26, 95%CI 0.20; 0.34. We were able to identify 9 CMR surrogates for HF and/or AF (including LV-MVR, biventricular EDV, ESV, and right-ventricular EF) which associated with non-cardiac traits such as blood pressure, lung function traits, BMI, cardioembolic stroke, and late-onset Alzheimer's disease. Conclusion CMR measurements may act as surrogate endpoints for the development of HF (including non-ischemic CMP and DCM) or AF. Additionally, we show that changes in cardiac function and structure measured through CMR, may affect diseases of other organs leading to lung disease or late-onset Alzheimer's disease.
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25
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Cunningham JW, Di Achille P, Morrill VN, Weng LC, Hoan Choi S, Khurshid S, Nauffal V, Pirruccello JP, Solomon SD, Batra P, Ho JE, Philippakis AA, Ellinor PT, Lubitz SA. Machine Learning to Understand Genetic and Clinical Factors Associated With the Pulse Waveform Dicrotic Notch. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2023; 16:e003676. [PMID: 36580284 PMCID: PMC9975074 DOI: 10.1161/circgen.121.003676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Accepted: 09/30/2022] [Indexed: 12/30/2022]
Abstract
BACKGROUND Absence of a dicrotic notch on finger photoplethysmography is an easily ascertainable and inexpensive trait that has been associated with age and prevalent cardiovascular disease. However, the trait exists along a continuum, and little is known about its genetic underpinnings or prognostic value for incident cardiovascular disease. METHODS In 169 787 participants in the UK Biobank, we identified absent dicrotic notch on photoplethysmography and created a novel continuous trait reflecting notch smoothness using machine learning. Next, we determined the heritability, genetic basis, polygenic risk, and clinical relations for the binary absent notch trait and the newly derived continuous notch smoothness trait. RESULTS Heritability of the continuous notch smoothness trait was 7.5%, compared with 5.6% for the binary absent notch trait. A genome-wide association study of notch smoothness identified 15 significant loci, implicating genes including NT5C2 (P=1.2×10-26), IGFBP3 (P=4.8×10-18), and PHACTR1 (P=1.4×10-13), compared with 6 loci for the binary absent notch trait. Notch smoothness stratified risk of incident myocardial infarction or coronary artery disease, stroke, heart failure, and aortic stenosis. A polygenic risk score for notch smoothness was associated with incident cardiovascular disease and all-cause death in UK Biobank participants without available photoplethysmography data. CONCLUSIONS We found that a machine learning derived continuous trait reflecting dicrotic notch smoothness on photoplethysmography was heritable and associated with genes involved in vascular stiffness. Greater notch smoothness was associated with greater risk of incident cardiovascular disease. Raw digital phenotyping may identify individuals at risk for disease via specific genetic pathways.
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Affiliation(s)
- Jonathan W. Cunningham
- Cardiovascular Division, Brigham & Women’s Hospital, Boston
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
| | - Paolo Di Achille
- Data Sciences Platform, The Broad Institute of MIT & Harvard, Cambridge
| | - Valerie N. Morrill
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
| | - Lu-Chen Weng
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- Cardiovascular Research Center, Massachusetts General Hospital
| | - Seung Hoan Choi
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
| | - Shaan Khurshid
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital
| | - Victor Nauffal
- Cardiovascular Division, Brigham & Women’s Hospital, Boston
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
| | - James P Pirruccello
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- Division of Cardiology, Massachusetts General Hospital
| | | | - Puneet Batra
- Data Sciences Platform, The Broad Institute of MIT & Harvard, Cambridge
| | - Jennifer E. Ho
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- CardioVascular Institute and Division of Cardiology, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA
| | | | - Patrick T. Ellinor
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- Cardiovascular Research Center, Massachusetts General Hospital
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital
| | - Steven A. Lubitz
- Cardiovascular Disease Initiative, The Broad Institute of MIT & Harvard, Cambridge
- Cardiovascular Research Center, Massachusetts General Hospital
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital
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26
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Joseph J, Liu C, Hui Q, Aragam K, Wang Z, Charest B, Huffman JE, Keaton JM, Edwards TL, Demissie S, Djousse L, Casas JP, Gaziano JM, Cho K, Wilson PWF, Phillips LS, O’Donnell CJ, Sun YV. Genetic architecture of heart failure with preserved versus reduced ejection fraction. Nat Commun 2022; 13:7753. [PMID: 36517512 PMCID: PMC9751124 DOI: 10.1038/s41467-022-35323-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 11/28/2022] [Indexed: 12/15/2022] Open
Abstract
Pharmacologic clinical trials for heart failure with preserved ejection fraction have been largely unsuccessful as compared to those for heart failure with reduced ejection fraction. Whether differences in the genetic underpinnings of these major heart failure subtypes may provide insights into the disparate outcomes of clinical trials remains unknown. We utilize a large, uniformly phenotyped, single cohort of heart failure sub-classified into heart failure with reduced and with preserved ejection fractions based on current clinical definitions, to conduct detailed genetic analyses of the two heart failure sub-types. We find different genetic architectures and distinct genetic association profiles between heart failure with reduced and with preserved ejection fraction suggesting differences in underlying pathobiology. The modest genetic discovery for heart failure with preserved ejection fraction (one locus) compared to heart failure with reduced ejection fraction (13 loci) despite comparable sample sizes indicates that clinically defined heart failure with preserved ejection fraction likely represents the amalgamation of several, distinct pathobiological entities. Development of consensus sub-phenotyping of heart failure with preserved ejection fraction is paramount to better dissect the underlying genetic signals and contributors to this highly prevalent condition.
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Affiliation(s)
- Jacob Joseph
- grid.410370.10000 0004 4657 1992Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA USA ,grid.38142.3c000000041936754XDepartment of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA ,Cardiology Section (111A), VA Providence Healthcare System, 830 Chalkstone Avenue, Providence, RI 02908 USA
| | - Chang Liu
- grid.189967.80000 0001 0941 6502Emory University Rollins School of Public Health, Atlanta, GA USA
| | - Qin Hui
- grid.189967.80000 0001 0941 6502Emory University Rollins School of Public Health, Atlanta, GA USA ,grid.484294.7Atlanta VA Health Care System, Decatur, GA USA
| | - Krishna Aragam
- grid.410370.10000 0004 4657 1992Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA USA ,grid.32224.350000 0004 0386 9924Massachusetts General Hospital, Boston, MA USA ,grid.66859.340000 0004 0546 1623Broad Institute of Harvard and MIT, Cambridge, MA USA
| | - Zeyuan Wang
- grid.189967.80000 0001 0941 6502Emory University Rollins School of Public Health, Atlanta, GA USA ,grid.484294.7Atlanta VA Health Care System, Decatur, GA USA
| | - Brian Charest
- grid.410370.10000 0004 4657 1992Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA USA
| | - Jennifer E. Huffman
- grid.410370.10000 0004 4657 1992Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA USA
| | - Jacob M. Keaton
- grid.94365.3d0000 0001 2297 5165Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD USA ,grid.412807.80000 0004 1936 9916Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN USA
| | - Todd L. Edwards
- grid.412807.80000 0004 1936 9916Division of Epidemiology, Department of Medicine, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN USA
| | - Serkalem Demissie
- grid.410370.10000 0004 4657 1992Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA USA ,grid.189504.10000 0004 1936 7558Boston University School of Medicine, Boston, MA USA
| | - Luc Djousse
- grid.410370.10000 0004 4657 1992Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA USA ,grid.38142.3c000000041936754XDepartment of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA
| | - Juan P. Casas
- grid.410370.10000 0004 4657 1992Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA USA ,grid.38142.3c000000041936754XDepartment of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA
| | - J. Michael Gaziano
- grid.410370.10000 0004 4657 1992Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA USA ,grid.38142.3c000000041936754XDepartment of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA
| | - Kelly Cho
- grid.410370.10000 0004 4657 1992Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA USA ,grid.38142.3c000000041936754XDepartment of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA
| | - Peter W. F. Wilson
- grid.484294.7Atlanta VA Health Care System, Decatur, GA USA ,grid.189967.80000 0001 0941 6502Emory University School of Medicine, Atlanta, GA USA
| | - Lawrence S. Phillips
- grid.484294.7Atlanta VA Health Care System, Decatur, GA USA ,grid.189967.80000 0001 0941 6502Emory University School of Medicine, Atlanta, GA USA
| | | | - Christopher J. O’Donnell
- grid.410370.10000 0004 4657 1992Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA USA ,grid.38142.3c000000041936754XDepartment of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA
| | - Yan V. Sun
- grid.189967.80000 0001 0941 6502Emory University Rollins School of Public Health, Atlanta, GA USA ,grid.484294.7Atlanta VA Health Care System, Decatur, GA USA
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27
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Levin MG, Tsao NL, Singhal P, Liu C, Vy HMT, Paranjpe I, Backman JD, Bellomo TR, Bone WP, Biddinger KJ, Hui Q, Dikilitas O, Satterfield BA, Yang Y, Morley MP, Bradford Y, Burke M, Reza N, Charest B, Judy RL, Puckelwartz MJ, Hakonarson H, Khan A, Kottyan LC, Kullo I, Luo Y, McNally EM, Rasmussen-Torvik LJ, Day SM, Do R, Phillips LS, Ellinor PT, Nadkarni GN, Ritchie MD, Arany Z, Cappola TP, Margulies KB, Aragam KG, Haggerty CM, Joseph J, Sun YV, Voight BF, Damrauer SM. Genome-wide association and multi-trait analyses characterize the common genetic architecture of heart failure. Nat Commun 2022; 13:6914. [PMID: 36376295 PMCID: PMC9663424 DOI: 10.1038/s41467-022-34216-6] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 10/17/2022] [Indexed: 11/16/2022] Open
Abstract
Heart failure is a leading cause of cardiovascular morbidity and mortality. However, the contribution of common genetic variation to heart failure risk has not been fully elucidated, particularly in comparison to other common cardiometabolic traits. We report a multi-ancestry genome-wide association study meta-analysis of all-cause heart failure including up to 115,150 cases and 1,550,331 controls of diverse genetic ancestry, identifying 47 risk loci. We also perform multivariate genome-wide association studies that integrate heart failure with related cardiac magnetic resonance imaging endophenotypes, identifying 61 risk loci. Gene-prioritization analyses including colocalization and transcriptome-wide association studies identify known and previously unreported candidate cardiomyopathy genes and cellular processes, which we validate in gene-expression profiling of failing and healthy human hearts. Colocalization, gene expression profiling, and Mendelian randomization provide convergent evidence for the roles of BCKDHA and circulating branch-chain amino acids in heart failure and cardiac structure. Finally, proteome-wide Mendelian randomization identifies 9 circulating proteins associated with heart failure or quantitative imaging traits. These analyses highlight similarities and differences among heart failure and associated cardiovascular imaging endophenotypes, implicate common genetic variation in the pathogenesis of heart failure, and identify circulating proteins that may represent cardiomyopathy treatment targets.
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Affiliation(s)
- Michael G Levin
- Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA
| | - Noah L Tsao
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Pankhuri Singhal
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Chang Liu
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Ha My T Vy
- The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ishan Paranjpe
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | | | - Tiffany R Bellomo
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - William P Bone
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kiran J Biddinger
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Qin Hui
- Emory University School of Public Health, Atlanta, GA, USA
- Atlanta VA Health Care System, Decatur, GA, USA
| | - Ozan Dikilitas
- Departments of Internal Medicine and Cardiovascular Medicine, and Mayo Clinician-Investigator Training Program, Mayo Clinic, Rochester, MN, USA
| | | | - Yifan Yang
- Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael P Morley
- Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yuki Bradford
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Megan Burke
- Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Nosheen Reza
- Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Brian Charest
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, USA
| | - Renae L Judy
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Megan J Puckelwartz
- Department of Pharmacology, Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Hakon Hakonarson
- Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Atlas Khan
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Leah C Kottyan
- Department of Pediatrics, Division of Human Genetics and Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Iftikhar Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Yuan Luo
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Elizabeth M McNally
- Center for Genetic Medicine, Bluhm Cardiovascular Institute, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Laura J Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Sharlene M Day
- Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ron Do
- The Charles Bronfman Institute for Personalized Medicine, BioMe Phenomics Center, and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lawrence S Phillips
- Atlanta VA Health Care System, Decatur, GA, USA
- Division of Endocrinology, Emory University School of Medicine, Atlanta, GA, USA
| | - Patrick T Ellinor
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center and Cardiac Arrhythmia Service, Massachusetts General Hospital, Boston, MA, USA
| | - Girish N Nadkarni
- Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Zoltan Arany
- Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Thomas P Cappola
- Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kenneth B Margulies
- Division of Cardiovascular Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Krishna G Aragam
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Christopher M Haggerty
- Department of Translational Data Science and Informatics and Heart Institute, Geisinger, Danville, PA, USA
| | - Jacob Joseph
- Massachusetts Veterans Epidemiology Research and Information Center, VA Boston Healthcare System, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yan V Sun
- Emory University School of Public Health, Atlanta, GA, USA
- Atlanta VA Health Care System, Decatur, GA, USA
| | - Benjamin F Voight
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute of Translational Medicine and Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Scott M Damrauer
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, PA, USA.
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
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28
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Koskinen M, Salmi JK, Loukola A, Mäkelä MJ, Sinisalo J, Carpén O, Renkonen R. Data-driven comorbidity analysis of 100 common disorders reveals patient subgroups with differing mortality risks and laboratory correlates. Sci Rep 2022; 12:18492. [PMID: 36323789 PMCID: PMC9630271 DOI: 10.1038/s41598-022-23090-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 10/25/2022] [Indexed: 11/07/2022] Open
Abstract
The populational heterogeneity of a disease, in part due to comorbidity, poses several complexities. Individual comorbidity profiles, on the other hand, contain useful information to refine phenotyping, prognostication, and risk assessment, and they provide clues to underlying biology. Nevertheless, the spectrum and the implications of the diagnosis profiles remain largely uncharted. Here we mapped comorbidity patterns in 100 common diseases using 4-year retrospective data from 526,779 patients and developed an online tool to visualize the results. Our analysis exposed disease-specific patient subgroups with distinctive diagnosis patterns, survival functions, and laboratory correlates. Computational modeling and real-world data shed light on the structure, variation, and relevance of populational comorbidity patterns, paving the way for improved diagnostics, risk assessment, and individualization of care. Variation in outcomes and biological correlates of a disease emphasizes the importance of evaluating the generalizability of current treatment strategies, as well as considering the limitations that selective inclusion criteria pose on clinical trials.
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Affiliation(s)
- Miika Koskinen
- grid.7737.40000 0004 0410 2071Faculty of Medicine, University of Helsinki, Helsinki, Finland ,grid.15485.3d0000 0000 9950 5666Helsinki Biobank, Helsinki University Hospital, Helsinki, Finland ,grid.15485.3d0000 0000 9950 5666Analytics and AI Development Services, Helsinki University Hospital, Helsinki, Finland
| | - Jani K. Salmi
- grid.15485.3d0000 0000 9950 5666Analytics and AI Development Services, Helsinki University Hospital, Helsinki, Finland
| | - Anu Loukola
- grid.15485.3d0000 0000 9950 5666Helsinki Biobank, Helsinki University Hospital, Helsinki, Finland
| | - Mika J. Mäkelä
- grid.15485.3d0000 0000 9950 5666Division of Allergology, Skin and Allergy Hospital, Helsinki University Hospital and Helsinki University, Helsinki, Finland
| | - Juha Sinisalo
- grid.7737.40000 0004 0410 2071Faculty of Medicine, University of Helsinki, Helsinki, Finland ,grid.7737.40000 0004 0410 2071Heart and Lung Center, Helsinki University Hospital, and Helsinki University, Helsinki, Finland
| | - Olli Carpén
- grid.7737.40000 0004 0410 2071Faculty of Medicine, University of Helsinki, Helsinki, Finland ,grid.15485.3d0000 0000 9950 5666Helsinki Biobank, Helsinki University Hospital, Helsinki, Finland ,grid.15485.3d0000 0000 9950 5666HUS Diagnostics, Helsinki University Hospital, Helsinki, Finland
| | - Risto Renkonen
- grid.7737.40000 0004 0410 2071Faculty of Medicine, University of Helsinki, Helsinki, Finland ,grid.15485.3d0000 0000 9950 5666HUS Diagnostics, Helsinki University Hospital, Helsinki, Finland
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29
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Forrest IS, Rocheleau G, Bafna S, Argulian E, Narula J, Natarajan P, Do R. Genetic and phenotypic profiling of supranormal ejection fraction reveals decreased survival and underdiagnosed heart failure. Eur J Heart Fail 2022; 24:2118-2127. [PMID: 35278270 PMCID: PMC9464795 DOI: 10.1002/ejhf.2482] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 03/04/2022] [Accepted: 03/10/2022] [Indexed: 01/18/2023] Open
Abstract
AIMS Individuals with supranormal left ventricular ejection fraction (snLVEF; LVEF >70%) have increased mortality. However, the genetic and phenotypic profile of snLVEF remains unknown. This study aimed to determine the relationship of both snLVEF genetic risk and phenotype with survival and underdiagnosed heart failure (HF). METHODS AND RESULTS A snLVEF genetic risk score (GRS) was applied and cases of snLVEF were identified in 486 754 individuals across two population-based cohorts (BioMe Biobank and UK Biobank). The snLVEF GRS and phenotype were evaluated for association with survival, as well as HF diagnosis, markers, symptoms, and medications. Of 486 754 participants, the median age was 58 years, 20 069 (4.1%) died, and 10 088 (2.1%) had diagnosed HF. Both snLVEF GRS (hazard ratio [HR] 1.1 for top 10% vs. bottom 10% GRS; p = 0.002) and phenotype (HR 1.4; p = 0.003) were associated with increased all-cause mortality. Both snLVEF GRS and phenotype were associated with reduced HF diagnosis (odds ratio [OR] 0.97 and OR 0.63, respectively; both p ≤0.002). However, the snLVEF GRS and phenotype were both associated with elevated brain natriuretic peptide (BNP) levels (146 and 185 pg/ml increase, respectively; p <0.001), including 268 out of 455 (59%) individuals with snLVEF phenotype who had BNP >100 pg/ml. Among 476 666 participants without HF diagnoses, snLVEF GRS and phenotype were associated with increased HF symptoms (e.g. exertional dyspnoea OR 1.4 and OR 1.3; p <0.003) and HF medications (e.g. loop diuretic OR 1.2 and OR 1.03; p <0.02). Associations were consistent in hypertensive individuals without cardiac comorbidities. CONCLUSIONS Genetic predisposition to and presence of snLVEF are associated with decreased survival and underdiagnosed HF.
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Affiliation(s)
- Iain S Forrest
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ghislain Rocheleau
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Shantanu Bafna
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Edgar Argulian
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jagat Narula
- Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Pradeep Natarajan
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ron Do
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The BioMe Phenomics Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Jia P, Hu R, Yan F, Dai Y, Zhao Z. scGWAS: landscape of trait-cell type associations by integrating single-cell transcriptomics-wide and genome-wide association studies. Genome Biol 2022; 23:220. [PMID: 36253801 PMCID: PMC9575201 DOI: 10.1186/s13059-022-02785-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 10/05/2022] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND The rapid accumulation of single-cell RNA sequencing (scRNA-seq) data presents unique opportunities to decode the genetically mediated cell-type specificity in complex diseases. Here, we develop a new method, scGWAS, which effectively leverages scRNA-seq data to achieve two goals: (1) to infer the cell types in which the disease-associated genes manifest and (2) to construct cellular modules which imply disease-specific activation of different processes. RESULTS scGWAS only utilizes the average gene expression for each cell type followed by virtual search processes to construct the null distributions of module scores, making it scalable to large scRNA-seq datasets. We demonstrated scGWAS in 40 genome-wide association studies (GWAS) datasets (average sample size N ≈ 154,000) using 18 scRNA-seq datasets from nine major human/mouse tissues (totaling 1.08 million cells) and identified 2533 trait and cell-type associations, each with significant modules for further investigation. The module genes were validated using disease or clinically annotated references from ClinVar, OMIM, and pLI variants. CONCLUSIONS We showed that the trait-cell type associations identified by scGWAS, while generally constrained to trait-tissue associations, could recapitulate many well-studied relationships and also reveal novel relationships, providing insights into the unsolved trait-tissue associations. Moreover, in each specific cell type, the associations with different traits were often mediated by different sets of risk genes, implying disease-specific activation of driving processes. In summary, scGWAS is a powerful tool for exploring the genetic basis of complex diseases at the cell type level using single-cell expression data.
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Affiliation(s)
- Peilin Jia
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030 USA
| | - Ruifeng Hu
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030 USA
| | - Fangfang Yan
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030 USA
| | - Yulin Dai
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030 USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030 USA
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030 USA
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030 USA
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31
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Juan AM, Foong YH, Thorvaldsen JL, Lan Y, Leu NA, Rurik JG, Li L, Krapp C, Rosier CL, Epstein JA, Bartolomei MS. Tissue-specific Grb10/Ddc insulator drives allelic architecture for cardiac development. Mol Cell 2022; 82:3613-3631.e7. [PMID: 36108632 PMCID: PMC9547965 DOI: 10.1016/j.molcel.2022.08.021] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 07/12/2022] [Accepted: 08/18/2022] [Indexed: 11/24/2022]
Abstract
Allele-specific expression of imprinted gene clusters is governed by gametic DNA methylation at master regulators called imprinting control regions (ICRs). Non-gametic or secondary differentially methylated regions (DMRs) at promoters and exonic regions reinforce monoallelic expression but do not control an entire cluster. Here, we unveil an unconventional secondary DMR that is indispensable for tissue-specific imprinting of two previously unlinked genes, Grb10 and Ddc. Using polymorphic mice, we mapped an intronic secondary DMR at Grb10 with paternal-specific CTCF binding (CBR2.3) that forms contacts with Ddc. Deletion of paternal CBR2.3 removed a critical insulator, resulting in substantial shifting of chromatin looping and ectopic enhancer-promoter contacts. Destabilized gene architecture precipitated abnormal Grb10-Ddc expression with developmental consequences in the heart and muscle. Thus, we redefine the Grb10-Ddc imprinting domain by uncovering an unconventional intronic secondary DMR that functions as an insulator to instruct the tissue-specific, monoallelic expression of multiple genes-a feature previously ICR exclusive.
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Affiliation(s)
- Aimee M Juan
- Epigenetics Institute, Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yee Hoon Foong
- Epigenetics Institute, Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Joanne L Thorvaldsen
- Epigenetics Institute, Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yemin Lan
- Epigenetics Institute, Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Nicolae A Leu
- Department of Biomedical Sciences, Center for Animal Transgenesis and Germ Cell Research, University of Pennsylvania School of Veterinary Medicine, Philadelphia, PA 19104, USA
| | - Joel G Rurik
- Penn Cardiovascular Institute, Department of Medicine, Department Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Li Li
- Penn Cardiovascular Institute, Department of Medicine, Department Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Christopher Krapp
- Epigenetics Institute, Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Casey L Rosier
- Epigenetics Institute, Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Jonathan A Epstein
- Epigenetics Institute, Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Cardiovascular Institute, Department of Medicine, Department Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Marisa S Bartolomei
- Epigenetics Institute, Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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Zhu Z, Li FR, Jia Y, Li Y, Guo D, Chen J, Tian H, Yang J, Yang HH, Chen LH, Zhang K, Yang P, Sun L, Shi M, Zhang Y, Qin LQ, Chen GC. Association of Lifestyle With Incidence of Heart Failure According to Metabolic and Genetic Risk Status: A Population-Based Prospective Study. Circ Heart Fail 2022; 15:e009592. [PMID: 35975661 DOI: 10.1161/circheartfailure.122.009592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Whether lifestyle factors are similarly associated with risk of heart failure (HF) for individuals with different metabolic or genetic risk status remains unclear. METHODS We included 464 483 participants from UK Biobank who were free of major cardiovascular disease or HF during baseline recruitment. Healthy lifestyle factors included avoidance of smoking, no obesity, regular physical activity, and healthy diet. Lifestyle was categorized as favorable (3 or 4 healthy lifestyle factors), intermediate (2 healthy lifestyle factors), and unfavorable (0 or 1 healthy lifestyle factor) lifestyles. Metabolic status was defined by the presence of hypertension, high total cholesterol, or diabetes at baseline. A weighted genetic risk score was created based on 12 single-nucleotide polymorphisms associated with HF. RESULTS Compared with favorable lifestyle, the multivariable-adjusted hazard ratios of HF were 1.79 (95% CI, 1.68-1.90) and 2.90 (95% CI, 2.70-3.11) for intermediate lifestyle and unfavorable lifestyle, respectively (Ptrend <0.0001). This association was largely consistent regardless of the presence of any single metabolic risk factor or the number of metabolic risk factors (Pinteraction ≥0.21). The association was also similar across different genetic risk categories (Pinteraction=0.92). In a joint analysis, the hazard ratio of HF was 4.05 (95% CI, 3.43-4.77) comparing participants who had both higher genetic risk and an unfavorable lifestyle with those having lower genetic risk and a favorable lifestyle. CONCLUSIONS Combined lifestyle was associated with incident HF regardless of metabolic or genetic risk status, supporting the recommendation of healthy lifestyles for HF prevention across the entire population.
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Affiliation(s)
- Zhengbao Zhu
- Department of Epidemiology (Z.Z., Y.J., D.G., K.Z., P.Y., L.S., M.S., Y.Z.), Suzhou Medical College of Soochow University, China
| | - Fu-Rong Li
- Shenzhen Key Laboratory of Cardiovascular Health and Precision Medicine (F.-R.L.), Southern University of Science and Technology, China
- School of Public Health and Emergency Management (F.-R.L.), Southern University of Science and Technology, China
- Department of Epidemiology, School of Public Health, Southern Medical University, Guangzhou, China (F.-R.L.)
| | - Yiming Jia
- Department of Epidemiology (Z.Z., Y.J., D.G., K.Z., P.Y., L.S., M.S., Y.Z.), Suzhou Medical College of Soochow University, China
| | - Yang Li
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY (Y.L.)
| | - Daoxia Guo
- Department of Epidemiology (Z.Z., Y.J., D.G., K.Z., P.Y., L.S., M.S., Y.Z.), Suzhou Medical College of Soochow University, China
- School of Public Health and Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases and School of Nursing (D.G.), Suzhou Medical College of Soochow University, China
| | - Jingsi Chen
- Department of Nutrition and Food Hygiene (J.C., J.Y., L.-Q.Q., G.-C.C.), Suzhou Medical College of Soochow University, China
| | - Haili Tian
- School of Kinesiology, Shanghai University of Sport, China (H.T.)
| | - Jing Yang
- Department of Nutrition and Food Hygiene (J.C., J.Y., L.-Q.Q., G.-C.C.), Suzhou Medical College of Soochow University, China
- Department of Clinical Nutrition, First Affiliated Hospital of Soochow University, Suzhou, China (J.Y.)
| | - Huan-Huan Yang
- Vanke School of Public Health, Tsinghua University, Beijing, China (H.-H.Y.)
| | - Li-Hua Chen
- Department of Nutrition and Food Hygiene, School of Public Health, Nantong University, China (L.-H.C.)
| | - Kaixin Zhang
- Department of Epidemiology (Z.Z., Y.J., D.G., K.Z., P.Y., L.S., M.S., Y.Z.), Suzhou Medical College of Soochow University, China
| | - Pinni Yang
- Department of Epidemiology (Z.Z., Y.J., D.G., K.Z., P.Y., L.S., M.S., Y.Z.), Suzhou Medical College of Soochow University, China
| | - Lulu Sun
- Department of Epidemiology (Z.Z., Y.J., D.G., K.Z., P.Y., L.S., M.S., Y.Z.), Suzhou Medical College of Soochow University, China
| | - Mengyao Shi
- Department of Epidemiology (Z.Z., Y.J., D.G., K.Z., P.Y., L.S., M.S., Y.Z.), Suzhou Medical College of Soochow University, China
| | - Yonghong Zhang
- Department of Epidemiology (Z.Z., Y.J., D.G., K.Z., P.Y., L.S., M.S., Y.Z.), Suzhou Medical College of Soochow University, China
| | - Li-Qiang Qin
- Department of Nutrition and Food Hygiene (J.C., J.Y., L.-Q.Q., G.-C.C.), Suzhou Medical College of Soochow University, China
| | - Guo-Chong Chen
- Department of Nutrition and Food Hygiene (J.C., J.Y., L.-Q.Q., G.-C.C.), Suzhou Medical College of Soochow University, China
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Tan VY, Timpson NJ. The UK Biobank: A Shining Example of Genome-Wide Association Study Science with the Power to Detect the Murky Complications of Real-World Epidemiology. Annu Rev Genomics Hum Genet 2022; 23:569-589. [PMID: 35508184 DOI: 10.1146/annurev-genom-121321-093606] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Genome-wide association studies (GWASs) have successfully identified thousands of genetic variants that are reliably associated with human traits. Although GWASs are restricted to certain variant frequencies, they have improved our understanding of the genetic architecture of complex traits and diseases. The UK Biobank (UKBB) has brought substantial analytical opportunity and performance to association studies. The dramatic expansion of many GWAS sample sizes afforded by the inclusion of UKBB data has improved the power of estimation of effect sizes but, critically, has done so in a context where phenotypic depth and precision enable outcome dissection and the application of epidemiological approaches. However, at the same time, the availability of such a large, well-curated, and deeply measured population-based collection has the capacity to increase our exposure to the many complications and inferential complexities associated with GWASs and other analyses. In this review, we discuss the impact that UKBB has had in the GWAS era, some of the opportunities that it brings, and exemplar challenges that illustrate the reality of using data from this world-leading resource.
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Affiliation(s)
- Vanessa Y Tan
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom;
- Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Nicholas J Timpson
- Medical Research Council (MRC) Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom;
- Bristol Medical School, University of Bristol, Bristol, United Kingdom
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34
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Gene networks under circadian control exhibit diurnal organization in primate organs. Commun Biol 2022; 5:764. [PMID: 35906476 PMCID: PMC9334736 DOI: 10.1038/s42003-022-03722-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 07/14/2022] [Indexed: 12/11/2022] Open
Abstract
Mammalian organs are individually controlled by autonomous circadian clocks. At the molecular level, this process is defined by the cyclical co-expression of both core transcription factors and their downstream targets across time. While interactions between these molecular clocks are necessary for proper homeostasis, these features remain undefined. Here, we utilize integrative analysis of a baboon diurnal transcriptome atlas to characterize the properties of gene networks under circadian control. We found that 53.4% (8120) of baboon genes are oscillating body-wide. Additionally, two basic network modes were observed at the systems level: daytime and nighttime mode. Daytime networks were enriched for genes involved in metabolism, while nighttime networks were enriched for genes associated with growth and cellular signaling. A substantial number of diseases only form significant disease modules at either daytime or nighttime. In addition, a majority of SARS-CoV-2-related genes and modules are rhythmically expressed, which have significant network proximities with circadian regulators. Our data suggest that synchronization amongst circadian gene networks is necessary for proper homeostatic functions and circadian regulators have close interactions with SARS-CoV-2 infection. Integrative analysis of the high-resolution baboon diurnal transcriptome, provides insights into the effect of circadian rhythm on the whole-body primate gene network.
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35
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Shah RA, Asatryan B, Sharaf Dabbagh G, Aung N, Khanji MY, Lopes LR, van Duijvenboden S, Holmes A, Muser D, Landstrom AP, Lee AM, Arora P, Semsarian C, Somers VK, Owens AT, Munroe PB, Petersen SE, Chahal CAA. Frequency, Penetrance, and Variable Expressivity of Dilated Cardiomyopathy-Associated Putative Pathogenic Gene Variants in UK Biobank Participants. Circulation 2022; 146:110-124. [PMID: 35708014 PMCID: PMC9375305 DOI: 10.1161/circulationaha.121.058143] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
BACKGROUND There is a paucity of data regarding the phenotype of dilated cardiomyopathy (DCM) gene variants in the general population. We aimed to determine the frequency and penetrance of DCM-associated putative pathogenic gene variants in a general adult population, with a focus on the expression of clinical and subclinical phenotype, including structural, functional, and arrhythmic disease features. METHODS UK Biobank participants who had undergone whole exome sequencing, ECG, and cardiovascular magnetic resonance imaging were selected for study. Three variant-calling strategies (1 primary and 2 secondary) were used to identify participants with putative pathogenic variants in 44 DCM genes. The observed phenotype was graded DCM (clinical or cardiovascular magnetic resonance diagnosis); early DCM features, including arrhythmia or conduction disease, isolated ventricular dilation, and hypokinetic nondilated cardiomyopathy; or phenotype-negative. RESULTS Among 18 665 individuals included in the study, 1463 (7.8%) possessed ≥1 putative pathogenic variant in 44 DCM genes by the main variant calling strategy. A clinical diagnosis of DCM was present in 0.34% and early DCM features in 5.7% of individuals with putative pathogenic variants. ECG and cardiovascular magnetic resonance analysis revealed evidence of subclinical DCM in an additional 1.6% and early DCM features in an additional 15.9% of individuals with putative pathogenic variants. Arrhythmias or conduction disease (15.2%) were the most common early DCM features, followed by hypokinetic nondilated cardiomyopathy (4%). The combined clinical/subclinical penetrance was ≤30% with all 3 variant filtering strategies. Clinical DCM was slightly more prevalent among participants with putative pathogenic variants in definitive/strong evidence genes as compared with those with variants in moderate/limited evidence genes. CONCLUSIONS In the UK Biobank, ≈1 of 6 of adults with putative pathogenic variants in DCM genes exhibited early DCM features potentially associated with DCM genotype, most commonly manifesting with arrhythmias in the absence of substantial ventricular dilation or dysfunction.
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Affiliation(s)
- Ravi A Shah
- Imperial College Healthcare NHS Trust, London, United Kingdom (R.A.S.)
| | - Babken Asatryan
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Switzerland (B.A.)
| | - Ghaith Sharaf Dabbagh
- Center for Inherited Cardiovascular Diseases, WellSpan Health, Lancaster, PA (G.S.D., C.A.A.C.).,University of Michigan, Division of Cardiovascular Medicine, Ann Arbor (G.S.D.)
| | - Nay Aung
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom (N.A., M.Y.K., L.R.L., A.M.L., S.E.P., C.A.A.C.).,NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, United Kingdom (N.A., M.Y.K., S.v.D., A.M.L., P.B.M., S.E.P.)
| | - Mohammed Y Khanji
- Barts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, United Kingdom (N.A., M.Y.K., L.R.L., A.M.L., S.E.P., C.A.A.C.).,NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, United Kingdom (N.A., M.Y.K., S.v.D., A.M.L., P.B.M., S.E.P.)
| | - Luis R Lopes
- Centre for Heart Muscle Disease, Institute of Cardiovascular Science, University College London, United Kingdom (L.R.L.)
| | - Stefan van Duijvenboden
- NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, United Kingdom (N.A., M.Y.K., S.v.D., A.M.L., P.B.M., S.E.P.)
| | | | - Daniele Muser
- Cardiac Electrophysiology, Cardiovascular Division, Hospital of the University of Pennsylvania, Philadelphia (D.M., C.A.A.C.)
| | - Andrew P Landstrom
- Departments of Pediatrics, Division of Cardiology, and Cell Biology, Duke University School of Medicine, Durham, NC (A.P.L.)
| | - Aaron Mark Lee
- NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, United Kingdom (N.A., M.Y.K., S.v.D., A.M.L., P.B.M., S.E.P.)
| | - Pankaj Arora
- Division of Cardiovascular Disease, University of Alabama at Birmingham (P.A.)
| | - Christopher Semsarian
- Agnes Ginges Centre for Molecular Cardiology at Centenary Institute (C.S.), The University of Sydney, New South Wales, Australia.,Sydney Medical School Faculty of Medicine and Health (C.S.), The University of Sydney, New South Wales, Australia.,Department of Cardiology, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia (C.S.)
| | - Virend K Somers
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN (V.K.S., C.A.A.C.)
| | - Anjali T Owens
- Center for Inherited Cardiovascular Disease, Cardiovascular Division, University of Pennsylvania Perelman School of Medicine, Philadelphia (A.T.O.)
| | - Patricia B Munroe
- NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, United Kingdom (N.A., M.Y.K., S.v.D., A.M.L., P.B.M., S.E.P.)
| | - Steffen E Petersen
- NIHR Barts Biomedical Research Centre, William Harvey Research Institute, Queen Mary University of London, United Kingdom (N.A., M.Y.K., S.v.D., A.M.L., P.B.M., S.E.P.)
| | - C Anwar A Chahal
- Center for Inherited Cardiovascular Diseases, WellSpan Health, Lancaster, PA (G.S.D., C.A.A.C.).,Cardiac Electrophysiology, Cardiovascular Division, Hospital of the University of Pennsylvania, Philadelphia (D.M., C.A.A.C.).,Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN (V.K.S., C.A.A.C.)
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O’Nunain K, Park C, Urquijo H, Leyden GM, Hughes AD, Davey Smith G, Richardson TG. A lifecourse mendelian randomization study highlights the long-term influence of childhood body size on later life heart structure. PLoS Biol 2022; 20:e3001656. [PMID: 35679339 PMCID: PMC9182693 DOI: 10.1371/journal.pbio.3001656] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 05/03/2022] [Indexed: 12/12/2022] Open
Abstract
Children with obesity typically have larger left ventricular heart dimensions during adulthood. However, whether this is due to a persistent effect of adiposity extending into adulthood is challenging to disentangle due to confounding factors throughout the lifecourse. We conducted a multivariable mendelian randomization (MR) study to separate the independent effects of childhood and adult body size on 4 magnetic resonance imaging (MRI) measures of heart structure and function in the UK Biobank (UKB) study. Strong evidence of a genetically predicted effect of childhood body size on all measures of adulthood heart structure was identified, which remained robust upon accounting for adult body size using a multivariable MR framework (e.g., left ventricular end-diastolic volume (LVEDV), Beta = 0.33, 95% confidence interval (CI) = 0.23 to 0.43, P = 4.6 × 10-10). Sensitivity analyses did not suggest that other lifecourse measures of body composition were responsible for these effects. Conversely, evidence of a genetically predicted effect of childhood body size on various other MRI-based measures, such as fat percentage in the liver (Beta = 0.14, 95% CI = 0.05 to 0.23, P = 0.002) and pancreas (Beta = 0.21, 95% CI = 0.10 to 0.33, P = 3.9 × 10-4), attenuated upon accounting for adult body size. Our findings suggest that childhood body size has a long-term (and potentially immutable) influence on heart structure in later life. In contrast, effects of childhood body size on other measures of adulthood organ size and fat percentage evaluated in this study are likely explained by the long-term consequence of remaining overweight throughout the lifecourse.
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Affiliation(s)
- Katie O’Nunain
- Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
| | - Chloe Park
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science and Experimental Medicine, Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Helena Urquijo
- Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
| | - Genevieve M. Leyden
- Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
- Bristol Medical School: Translational Health Sciences, Dorothy Hodgkin Building, University of Bristol, Bristol, United Kingdom
| | - Alun D. Hughes
- MRC Unit for Lifelong Health and Ageing at UCL, Department of Population Science and Experimental Medicine, Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - George Davey Smith
- Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
| | - Tom G. Richardson
- Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, United Kingdom
- Novo Nordisk Research Centre, Headington, Oxford, United Kingdom
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37
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Manduchi E, Le TT, Fu W, Moore JH. Genetic Analysis of Coronary Artery Disease Using Tree-Based Automated Machine Learning Informed By Biology-Based Feature Selection. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1379-1386. [PMID: 34310318 PMCID: PMC9291719 DOI: 10.1109/tcbb.2021.3099068] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Machine Learning (ML) approaches are increasingly being used in biomedical applications. Important challenges of ML include choosing the right algorithm and tuning the parameters for optimal performance. Automated ML (AutoML) methods, such as Tree-based Pipeline Optimization Tool (TPOT), have been developed to take some of the guesswork out of ML thus making this technology available to users from more diverse backgrounds. The goals of this study were to assess applicability of TPOT to genomics and to identify combinations of single nucleotide polymorphisms (SNPs) associated with coronary artery disease (CAD), with a focus on genes with high likelihood of being good CAD drug targets. We leveraged public functional genomic resources to group SNPs into biologically meaningful sets to be selected by TPOT. We applied this strategy to data from the U.K. Biobank, detecting a strikingly recurrent signal stemming from a group of 28 SNPs. Importance analysis of these SNPs uncovered functional relevance of the top SNPs to genes whose association with CAD is supported in the literature and other resources. Furthermore, we employed game-theory based metrics to study SNP contributions to individual-level TPOT predictions and discover distinct clusters of well-predicted CAD cases. The latter indicates a promising approach towards precision medicine.
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38
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Roselli C, Yu M, Nauffal V, Georges A, Yang Q, Love K, Weng LC, Delling FN, Maurya SR, Schrölkamp M, Tfelt-Hansen J, Hagège A, Jeunemaitre X, Debette S, Amouyel P, Guan W, Muehlschlegel JD, Body SC, Shah S, Samad Z, Kyryachenko S, Haynes C, Rienstra M, Le Tourneau T, Probst V, Roussel R, Wijdh-Den Hamer IJ, Siland JE, Knowlton KU, Jacques Schott J, Levine RA, Benjamin EJ, Vasan RS, Horne BD, Muhlestein JB, Benfari G, Enriquez-Sarano M, Natale A, Mohanty S, Trivedi C, Shoemaker MB, Yoneda ZT, Wells QS, Baker MT, Farber-Eger E, Michelena HI, Lundby A, Norris RA, Slaugenhaupt SA, Dina C, Lubitz SA, Bouatia-Naji N, Ellinor PT, Milan DJ. Genome-wide association study reveals novel genetic loci: a new polygenic risk score for mitral valve prolapse. Eur Heart J 2022; 43:1668-1680. [PMID: 35245370 PMCID: PMC9649914 DOI: 10.1093/eurheartj/ehac049] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 08/18/2021] [Accepted: 02/01/2022] [Indexed: 11/12/2022] Open
Abstract
AIMS Mitral valve prolapse (MVP) is a common valvular heart disease with a prevalence of >2% in the general adult population. Despite this high incidence, there is a limited understanding of the molecular mechanism of this disease, and no medical therapy is available for this disease. We aimed to elucidate the genetic basis of MVP in order to better understand this complex disorder. METHODS AND RESULTS We performed a meta-analysis of six genome-wide association studies that included 4884 cases and 434 649 controls. We identified 14 loci associated with MVP in our primary analysis and 2 additional loci associated with a subset of the samples that additionally underwent mitral valve surgery. Integration of epigenetic, transcriptional, and proteomic data identified candidate MVP genes including LMCD1, SPTBN1, LTBP2, TGFB2, NMB, and ALPK3. We created a polygenic risk score (PRS) for MVP and showed an improved MVP risk prediction beyond age, sex, and clinical risk factors. CONCLUSION We identified 14 genetic loci that are associated with MVP. Multiple analyses identified candidate genes including two transforming growth factor-β signalling molecules and spectrin β. We present the first PRS for MVP that could eventually aid risk stratification of patients for MVP screening in a clinical setting. These findings advance our understanding of this common valvular heart disease and may reveal novel therapeutic targets for intervention.
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Affiliation(s)
- Carolina Roselli
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Mengyao Yu
- Université de Paris, PARCC, Inserm, F-75015 Paris, France
| | - Victor Nauffal
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Adrien Georges
- Université de Paris, PARCC, Inserm, F-75015 Paris, France
| | - Qiong Yang
- School of Public Health, Boston University, Boston, MA, USA
| | - Katie Love
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Lu Chen Weng
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Francesca N Delling
- Division of Cardiology, University of California San Francisco, San Francisco, CA, USA
| | - Svetlana R Maurya
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, København 2200, Denmark
| | - Maren Schrölkamp
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, København 2200, Denmark
| | - Jacob Tfelt-Hansen
- Department of Cardiology, The Heart Centre, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Forensic Medicine, Faculty of Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Albert Hagège
- Université de Paris, PARCC, Inserm, F-75015 Paris, France
- Assistance Publique–Hôpitaux de Paris, Departments of Cardiology and Genetics, Hôpital Européen Georges Pompidou, 75015 Paris, France
| | - Xavier Jeunemaitre
- Université de Paris, PARCC, Inserm, F-75015 Paris, France
- Assistance Publique–Hôpitaux de Paris, Departments of Cardiology and Genetics, Hôpital Européen Georges Pompidou, 75015 Paris, France
| | - Stéphanie Debette
- Bordeaux Population Health Research Center, Inserm Center U1219, University of Bordeaux, Bordeaux, France
- Department of Neurology, Bordeaux University Hospital, Inserm U1219, Bordeaux, France
| | - Philippe Amouyel
- Univ. Lille, Inserm, Centre Hosp. Univ Lille, Institut Pasteur de Lille, UMR1167 – RID-AGE- Risk factors and molecular determinants of aging-related diseases, F-59000 Lille, France
| | - Wyliena Guan
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA
| | - Jochen D Muehlschlegel
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Simon C Body
- Department of Anesthesiology, Boston University School of Medicine, Boston, MA, USA
| | - Svati Shah
- Duke Molecular Physiology Institute, Duke University, Durham, NC, USA
- Division of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Zainab Samad
- Division of Cardiology, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
- Department of Medicine, Aga Khan University, Karachi, Pakistan
| | | | - Carol Haynes
- Duke Molecular Physiology Institute, Duke University, Durham, NC, USA
| | - Michiel Rienstra
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Thierry Le Tourneau
- l’institut du thorax, INSERM, CNRS, Univ Nantes, CHU Nantes, Nantes, France
- l’institut du thorax, CHU Nantes, Nantes, France
| | - Vincent Probst
- l’institut du thorax, INSERM, CNRS, Univ Nantes, CHU Nantes, Nantes, France
| | - Ronan Roussel
- Cordeliers Research Centre, ImMeDiab Team, INSERM, Université de Paris, Paris, France
- Hôpital Bichat-Claude-Bernard, APHP, Department of Diabetology, Paris, France
| | - Inez J Wijdh-Den Hamer
- Department of Cardiothoracic Surgery, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Joylene E Siland
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Kirk U Knowlton
- Intermountain Medical Center Heart Institute, Salt Lake City, UT, USA
- Division of Cardiovascular Medicine, Department of Medicine, University of California San Diego, San Diego, CA, USA
| | | | - Robert A Levine
- Cardiac Ultrasound Laboratory, Harvard Medical School, Massachusetts General Hospital, Boston, MA, USA
| | - Emelia J Benjamin
- National Heart, Lung, and Blood Institute’s and Boston University’s, The Framingham Heart Study, Framingham, MA, USA
- Section of Cardiovascular Medicine, Boston University School of Medicine, Boston, MA, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA, USA
| | - Ramachandran S Vasan
- School of Public Health, Boston University, Boston, MA, USA
- National Heart, Lung, and Blood Institute’s and Boston University’s, The Framingham Heart Study, Framingham, MA, USA
- School of Medicine, Boston University, Boston, MA, USA
| | - Benjamin D Horne
- Intermountain Medical Center Heart Institute, Salt Lake City, UT, USA
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USA
| | - Joseph B Muhlestein
- Intermountain Medical Center Heart Institute, Salt Lake City, UT, USA
- Cardiology Division, Department of Internal Medicine, University of Utah, Salt Lake City, UT, USA
| | - Giovanni Benfari
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | | | - Andrea Natale
- Texas Cardiac Arrhythmia Institute, St David’s Medical Center, Austin, TX, USA
| | - Sanghamitra Mohanty
- Texas Cardiac Arrhythmia Institute, St David’s Medical Center, Austin, TX, USA
| | - Chintan Trivedi
- Texas Cardiac Arrhythmia Institute, St David’s Medical Center, Austin, TX, USA
| | - Moore B Shoemaker
- Department of Medicine, Division of Cardiovascular Diseases, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Zachary T Yoneda
- Department of Medicine, Division of Cardiovascular Diseases, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Quinn S Wells
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Pharmacology, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Michael T Baker
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Eric Farber-Eger
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Alicia Lundby
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, København 2200, Denmark
- The Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, København 2200, Denmark
| | - Russell A Norris
- Cardiovascular Developmental Biology Center, Department of Regenerative Medicine and Cell Biology, Medical University of South Carolina, Charleston, SC, USA
| | - Susan A Slaugenhaupt
- Center for Genomic Medicine and Department of Neurology, Massachusetts General Hospital Research Institute and Harvard Medical School, Boston, MA, USA
| | - Christian Dina
- l’institut du thorax, INSERM, CNRS, Univ Nantes, CHU Nantes, Nantes, France
| | - Steven A Lubitz
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA
| | | | - Patrick T Ellinor
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA
| | - David J Milan
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Leducq Foundation, Boston, MA 02110, USA
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Jurgens SJ, Choi SH, Morrill VN, Chaffin M, Pirruccello JP, Halford JL, Weng LC, Nauffal V, Roselli C, Hall AW, Oetjens MT, Lagerman B, vanMaanen DP, Aragam KG, Lunetta KL, Haggerty CM, Lubitz SA, Ellinor PT. Analysis of rare genetic variation underlying cardiometabolic diseases and traits among 200,000 individuals in the UK Biobank. Nat Genet 2022; 54:240-250. [PMID: 35177841 PMCID: PMC8930703 DOI: 10.1038/s41588-021-01011-w] [Citation(s) in RCA: 59] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 12/22/2021] [Indexed: 12/30/2022]
Abstract
Cardiometabolic diseases are the leading cause of death worldwide. Despite a known genetic component, our understanding of these diseases remains incomplete. Here, we analyzed the contribution of rare variants to 57 diseases and 26 cardiometabolic traits, using data from 200,337 UK Biobank participants with whole-exome sequencing. We identified 57 gene-based associations, with broad replication of novel signals in Geisinger MyCode. There was a striking risk associated with mutations in known Mendelian disease genes, including MYBPC3, LDLR, GCK, PKD1 and TTN. Many genes showed independent convergence of rare and common variant evidence, including an association between GIGYF1 and type 2 diabetes. We identified several large effect associations for height and 18 unique genes associated with blood lipid or glucose levels. Finally, we found that between 1.0% and 2.4% of participants carried rare potentially pathogenic variants for cardiometabolic disorders. These findings may facilitate studies aimed at therapeutics and screening of these common disorders.
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Affiliation(s)
- Sean J. Jurgens
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Department of Experimental Cardiology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Seung Hoan Choi
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Valerie N. Morrill
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Mark Chaffin
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - James P. Pirruccello
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Jennifer L. Halford
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Lu-Chen Weng
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Victor Nauffal
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Carolina Roselli
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Amelia W. Hall
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | | | - Braxton Lagerman
- Department of Translational Data Science and Informatics, Geisinger, Danville, PA, USA
| | - David P. vanMaanen
- Department of Translational Data Science and Informatics, Geisinger, Danville, PA, USA
| | | | - Krishna G. Aragam
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Kathryn L. Lunetta
- NHLBI and Boston University’s Framingham Heart Study, Framingham, MA, USA.,Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Christopher M. Haggerty
- Department of Translational Data Science and Informatics, Geisinger, Danville, PA, USA.,Heart Institute, Geisinger, Danville, PA, USA
| | - Steven A. Lubitz
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.,Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA
| | - Patrick T. Ellinor
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.,Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA.,
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40
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Bang ML, Bogomolovas J, Chen J. Understanding the molecular basis of cardiomyopathy. Am J Physiol Heart Circ Physiol 2022; 322:H181-H233. [PMID: 34797172 PMCID: PMC8759964 DOI: 10.1152/ajpheart.00562.2021] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/16/2021] [Accepted: 11/16/2021] [Indexed: 02/03/2023]
Abstract
Inherited cardiomyopathies are a major cause of mortality and morbidity worldwide and can be caused by mutations in a wide range of proteins located in different cellular compartments. The present review is based on Dr. Ju Chen's 2021 Robert M. Berne Distinguished Lectureship of the American Physiological Society Cardiovascular Section, in which he provided an overview of the current knowledge on the cardiomyopathy-associated proteins that have been studied in his laboratory. The review provides a general summary of the proteins in different compartments of cardiomyocytes associated with cardiomyopathies, with specific focus on the proteins that have been studied in Dr. Chen's laboratory.
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Affiliation(s)
- Marie-Louise Bang
- Institute of Genetic and Biomedical Research (IRGB), National Research Council (CNR), Milan Unit, Milan, Italy
- IRCCS Humanitas Research Hospital, Rozzano (Milan), Italy
| | - Julius Bogomolovas
- Division of Cardiovascular Medicine, Department of Medicine Cardiology, University of California, San Diego, La Jolla, California
| | - Ju Chen
- Division of Cardiovascular Medicine, Department of Medicine Cardiology, University of California, San Diego, La Jolla, California
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41
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Zhou Y, Liang ZS, Jin Y, Ding J, Huang T, Moore JH, Zheng ZJ, Huang J. Shared Genetic Architecture and Causal Relationship Between Asthma and Cardiovascular Diseases: A Large-Scale Cross-Trait Analysis. Front Genet 2022; 12:775591. [PMID: 35126453 PMCID: PMC8811262 DOI: 10.3389/fgene.2021.775591] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 12/08/2021] [Indexed: 12/11/2022] Open
Abstract
Background: Accumulating evidence has suggested that there is a positive association between asthma and cardiovascular diseases (CVDs), implying a common architecture between them. However, the shared genetic architecture and causality of asthma and CVDs remain unclear. Methods: Based on the genome-wide association study (GWAS) summary statistics of recently published studies, our study examined the genetic correlation, shared genetic variants, and causal relationship between asthma (N = 127,669) and CVDs (N = 86,995–521,612). Statistical methods included high-definition likelihood (HDL), cross-trait meta-analyses of large-scale GWAS, transcriptome-wide association studies (TWAS), and Mendelian randomization (MR). Results: First, we observed a significant genetic correlation between asthma and heart failure (HF) (Rg = 0.278, P = 5 × 10−4). Through cross-trait analyses, we identified a total of 145 shared loci between asthma and HF. Fifteen novel loci were not previously reported for association with either asthma or HF. Second, we mapped these 145 loci to a total of 99 genes whose expressions are enriched in a broad spectrum of tissues, including the seminal vesicle, tonsil, appendix, spleen, skin, lymph nodes, breast, cervix and uterus, skeletal muscle, small intestine, lung, prostate, cardiac muscle, and liver. TWAS analysis identified five significant genes shared between asthma and HF in tissues from the hemic and immune system, digestive system, integumentary system, and nervous system. GSDMA, GSDMB, and ORMDL3 are statistically independent genetic effects from all shared TWAS genes between asthma and HF. Third, through MR analysis, genetic liability to asthma was significantly associated with heart failure at the Bonferroni-corrected significance level. The odds ratio (OR) is 1.07 [95% confidence interval (CI): 1.03–1.12; p = 1.31 × 10−3] per one-unit increase in loge odds of asthma. Conclusion: These findings provide strong evidence of genetic correlations and causal relationship between asthma and HF, suggesting a shared genetic architecture for these two diseases.
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Affiliation(s)
- Yi Zhou
- Department of Global Health, School of Public Health, Peking University, Beijing, China
| | - Zhi-Sheng Liang
- Department of Global Health, School of Public Health, Peking University, Beijing, China
| | - Yinzi Jin
- Department of Global Health, School of Public Health, Peking University, Beijing, China
| | - Jiayuan Ding
- College of Arts and Sciences, Boston University, Boston, MA, United States
| | - Tao Huang
- Department of Global Health, School of Public Health, Peking University, Beijing, China
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Jason H. Moore
- Department of Biostatistics, Epidemiology and Informatics, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, United States
| | - Zhi-Jie Zheng
- Department of Global Health, School of Public Health, Peking University, Beijing, China
- Institute for Global Health and Development, Peking University, Beijing, China
| | - Jie Huang
- Department of Global Health, School of Public Health, Peking University, Beijing, China
- Institute for Global Health and Development, Peking University, Beijing, China
- *Correspondence: Jie Huang,
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42
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Paranjpe I, Tsao NL, De Freitas JK, Judy R, Chaudhary K, Forrest IS, Jaladanki SK, Paranjpe M, Sharma P, Glicksberg BS, Narula J, Do R, Damrauer SM, Nadkarni GN. Derivation and Validation of Genome-Wide Polygenic Score for Ischemic Heart Failure. J Am Heart Assoc 2021; 10:e021916. [PMID: 34713709 PMCID: PMC8751935 DOI: 10.1161/jaha.121.021916] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 08/03/2021] [Indexed: 11/16/2022]
Abstract
Background Despite advances in cardiovascular disease and risk factor management, mortality from ischemic heart failure (HF) in patients with coronary artery disease (CAD) remains high. Given the partial role of genetics in HF and lack of reliable risk stratification tools, we developed and validated a polygenic risk score for HF in patients with CAD, which we term HF-PRS. Methods and Results Using summary statistics from a recent genome-wide association study for HF, we developed candidate PRSs in the Mount Sinai BioMe CAD patient cohort (N=6274) by using the pruning and thresholding method and LDPred. We validated the best score in the Penn Medicine BioBank (N=7250) and performed a subgroup analysis in a high-risk cohort who had undergone coronary catheterization. We observed a significant association between HF-PRS score and ischemic HF even after adjusting for evidence of obstructive CAD in patients of European ancestry in both BioMe (odds ratio [OR], 1.14 per SD; 95% CI, 1.05-1.24; P=0.003) and Penn Medicine BioBank (OR, 1.07 per SD; 95% CI, 1.01-1.13; P=0.016). In European patients with CAD in Penn Medicine BioBank who had undergone coronary catheterization, individuals in the top 10th percentile of PRS had a 2-fold increased odds of ischemic HF (OR, 2.0; 95% CI, 1.1-3.7; P=0.02) compared with the bottom 10th percentile. Conclusions A PRS for HF enables risk stratification in patients with CAD. Future prospective studies aimed at demonstrating clinical utility are warranted for adoption in the patient setting.
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Affiliation(s)
- Ishan Paranjpe
- The Charles Bronfman Institute for Personalized MedicineIcahn School of Medicine at Mount SinaiNew YorkNY
- Mount Sinai Clinical Intelligence Center (MSCIC)Icahn School of Medicine at Mount SinaiNew YorkNY
- The Hasso Plattner Institute for Digital Health at Mount SinaiIcahn School of Medicine at Mount SinaiNew YorkNY
| | - Noah L. Tsao
- Department of SurgeryPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPA
| | - Jessica K. De Freitas
- Mount Sinai Clinical Intelligence Center (MSCIC)Icahn School of Medicine at Mount SinaiNew YorkNY
- The Hasso Plattner Institute for Digital Health at Mount SinaiIcahn School of Medicine at Mount SinaiNew YorkNY
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNY
| | - Renae Judy
- Department of SurgeryPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPA
| | - Kumardeep Chaudhary
- The Charles Bronfman Institute for Personalized MedicineIcahn School of Medicine at Mount SinaiNew YorkNY
- Mount Sinai Clinical Intelligence Center (MSCIC)Icahn School of Medicine at Mount SinaiNew YorkNY
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNY
| | - Iain S. Forrest
- The Charles Bronfman Institute for Personalized MedicineIcahn School of Medicine at Mount SinaiNew YorkNY
- Mount Sinai Clinical Intelligence Center (MSCIC)Icahn School of Medicine at Mount SinaiNew YorkNY
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNY
| | - Suraj K. Jaladanki
- The Charles Bronfman Institute for Personalized MedicineIcahn School of Medicine at Mount SinaiNew YorkNY
- Mount Sinai Clinical Intelligence Center (MSCIC)Icahn School of Medicine at Mount SinaiNew YorkNY
- The Hasso Plattner Institute for Digital Health at Mount SinaiIcahn School of Medicine at Mount SinaiNew YorkNY
| | - Manish Paranjpe
- Division of Health Science and TechnologyHarvard Medical SchoolBostonMA
| | | | - CBIPM Genomics Team
- The Charles Bronfman Institute for Personalized MedicineIcahn School of Medicine at Mount SinaiNew YorkNY
| | | | - Benjamin S. Glicksberg
- Mount Sinai Clinical Intelligence Center (MSCIC)Icahn School of Medicine at Mount SinaiNew YorkNY
- The Hasso Plattner Institute for Digital Health at Mount SinaiIcahn School of Medicine at Mount SinaiNew YorkNY
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNY
| | - Jagat Narula
- Zena and Michael A. Wiener Cardiovascular InstituteIcahn School of Medicine at Mount SinaiNew YorkNY
| | - Ron Do
- The Charles Bronfman Institute for Personalized MedicineIcahn School of Medicine at Mount SinaiNew YorkNY
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNY
| | - Scott M. Damrauer
- Department of SurgeryPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPA
| | - Girish N. Nadkarni
- The Charles Bronfman Institute for Personalized MedicineIcahn School of Medicine at Mount SinaiNew YorkNY
- Mount Sinai Clinical Intelligence Center (MSCIC)Icahn School of Medicine at Mount SinaiNew YorkNY
- The Hasso Plattner Institute for Digital Health at Mount SinaiIcahn School of Medicine at Mount SinaiNew YorkNY
- Division of NephrologyDepartment of MedicineIcahn School of Medicine at Mount SinaiNew YorkNY
- Renal ProgramJames J. Peters VA Medical Center at BronxNew YorkNY
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43
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D'Antonio M, Nguyen JP, Arthur TD, Matsui H, D'Antonio-Chronowska A, Frazer KA. SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues. Cell Rep 2021; 37:110020. [PMID: 34762851 PMCID: PMC8563343 DOI: 10.1016/j.celrep.2021.110020] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 10/06/2021] [Accepted: 10/28/2021] [Indexed: 01/08/2023] Open
Abstract
Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types.
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Affiliation(s)
- Matteo D'Antonio
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA.
| | - Jennifer P Nguyen
- Department of Biomedical Informatics, University of California, San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA
| | - Timothy D Arthur
- Department of Biomedical Informatics, University of California, San Diego, La Jolla, CA 92093, USA; Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA
| | - Hiroko Matsui
- Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093, USA
| | | | | | - Kelly A Frazer
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Institute of Genomic Medicine, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093, USA.
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44
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Yang R, Lv J, Yu C, Guo Y, Pei P, Huang N, Yang L, Millwood IY, Walters RG, Chen Y, Du H, Tao R, Chen J, Chen Z, Clarke R, Huang T, Li L. Modification effect of ideal cardiovascular health metrics on genetic association with incident heart failure in the China Kadoorie Biobank and the UK Biobank. BMC Med 2021; 19:259. [PMID: 34674714 PMCID: PMC8532287 DOI: 10.1186/s12916-021-02122-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Accepted: 09/09/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Both genetic and cardiovascular factors contribute to the risk of developing heart failure (HF), but whether idea cardiovascular health metrics (ICVHMs) offset the genetic association with incident HF remains unclear. OBJECTIVES To investigate the genetic association with incident HF as well as the modification effect of ICVHMs on such genetic association in Chinese and British populations. METHODS An ICVHMs based on smoking, drinking, physical activity, diets, body mass index, waist circumference, blood pressure, blood glucose, and blood lipids, and a polygenic risk score (PRS) for HF were constructed in the China Kadoorie Biobank (CKB) of 96,014 participants and UK Biobank (UKB) of 335,782 participants which were free from HF and severe chronic diseases at baseline. RESULTS During the median follow-up of 11.38 and 8.73 years, 1451 and 3169 incident HF events were documented in CKB and UKB, respectively. HF risk increased monotonically with the increase of PRS per standard deviation (CKB: hazard ratio [HR], 1.19; 95% confidence interval [CI], 1.07, 1.32; UKB: 1.07; 1.03, 1.11; P for trend < 0.001). Each point increase in ICVHMs was associated with 15% and 20% lower risk of incident HF in CKB (0.85; 0.81, 0.90) and UKB (0.80; 0.77, 0.82), respectively. Compared with unfavorable ICVHMs, favorable ICVHMs was associated with a lower HF risk, with 0.71 (0.44, 1.15), 0.41 (0.22, 0.77), and 0.48 (0.30, 0.77) in the low, intermediate, and high genetic risk in CKB and 0.34 (0.26, 0.44), 0.32 (0.25, 0.41), and 0.37 (0.28, 0.47) in UKB (P for multiplicative interaction > 0.05). Participants with low genetic risk and favorable ICVHMs, as compared with high genetic risk and unfavorable ICVHMs, had 56~72% lower risk of HF (CKB 0.44; 0.28, 0.70; UKB 0.28; 0.22, 0.37). No additive interaction between PRS and ICVHMs was observed (relative excess risk due to interaction was 0.05 [-0.22, 0.33] in CKB and 0.04 [-0.14, 0.22] in UKB). CONCLUSIONS In CKB and UKB, genetic risk and ICVHMs were independently associated with the risk of incident HF, which suggested that adherence to favorable cardiovascular health status was associated with a lower HF risk among participants with all gradients of genetic risk.
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Affiliation(s)
- Ruotong Yang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University Health Science Center, Peking University, 38 Xueyuan Road, Beijing, 100191, China
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University Health Science Center, Peking University, 38 Xueyuan Road, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
- Key Laboratory of Molecular Cardiovascular Sciences (Peking University), Ministry of Education, Beijing, China
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University Health Science Center, Peking University, 38 Xueyuan Road, Beijing, 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China
| | - Yu Guo
- Chinese Academy of Medical Sciences, Beijing, China
| | - Pei Pei
- Chinese Academy of Medical Sciences, Beijing, China
| | - Ninghao Huang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University Health Science Center, Peking University, 38 Xueyuan Road, Beijing, 100191, China
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Iona Y Millwood
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Robin G Walters
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, United Kingdom
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Ran Tao
- Institute of Chronic Disease, Jiangsu Provincial Center for Disease Control and Prevention, Jiangsu, China
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Robert Clarke
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Tao Huang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University Health Science Center, Peking University, 38 Xueyuan Road, Beijing, 100191, China.
- Key Laboratory of Molecular Cardiovascular Sciences (Peking University), Ministry of Education, Beijing, China.
- Center for Intelligent Public Health, Academy for Artificial Intelligence, Peking University, Beijing, 100191, China.
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University Health Science Center, Peking University, 38 Xueyuan Road, Beijing, 100191, China.
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, 100191, China.
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45
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Lumbers RT, Shah S, Lin H, Czuba T, Henry A, Swerdlow DI, Mälarstig A, Andersson C, Verweij N, Holmes MV, Ärnlöv J, Svensson P, Hemingway H, Sallah N, Almgren P, Aragam KG, Asselin G, Backman JD, Biggs ML, Bloom HL, Boersma E, Brandimarto J, Brown MR, Brunner-La Rocca HP, Carey DJ, Chaffin MD, Chasman DI, Chazara O, Chen X, Chen X, Chung JH, Chutkow W, Cleland JGF, Cook JP, de Denus S, Dehghan A, Delgado GE, Denaxas S, Doney AS, Dörr M, Dudley SC, Engström G, Esko T, Fatemifar G, Felix SB, Finan C, Ford I, Fougerousse F, Fouodjio R, Ghanbari M, Ghasemi S, Giedraitis V, Giulianini F, Gottdiener JS, Gross S, Guðbjartsson DF, Gui H, Gutmann R, Haggerty CM, van der Harst P, Hedman ÅK, Helgadottir A, Hillege H, Hyde CL, Jacob J, Jukema JW, Kamanu F, Kardys I, Kavousi M, Khaw KT, Kleber ME, Køber L, Koekemoer A, Kraus B, Kuchenbaecker K, Langenberg C, Lind L, Lindgren CM, London B, Lotta LA, Lovering RC, Luan J, Magnusson P, Mahajan A, Mann D, Margulies KB, Marston NA, März W, McMurray JJV, Melander O, Melloni G, Mordi IR, Morley MP, Morris AD, Morris AP, Morrison AC, Nagle MW, Nelson CP, Newton-Cheh C, Niessner A, Niiranen T, Nowak C, O'Donoghue ML, Owens AT, Palmer CNA, Paré G, Perola M, Perreault LPL, Portilla-Fernandez E, Psaty BM, Rice KM, Ridker PM, Romaine SPR, Roselli C, Rotter JI, Ruff CT, Sabatine MS, Salo P, Salomaa V, van Setten J, Shalaby AA, Smelser DT, Smith NL, Stefansson K, Stender S, Stott DJ, Sveinbjörnsson G, Tammesoo ML, Tardif JC, Taylor KD, Teder-Laving M, Teumer A, Thorgeirsson G, Thorsteinsdottir U, Torp-Pedersen C, Trompet S, Tuckwell D, Tyl B, Uitterlinden AG, Vaura F, Veluchamy A, Visscher PM, Völker U, Voors AA, Wang X, Wareham NJ, Weeke PE, Weiss R, White HD, Wiggins KL, Xing H, Yang J, Yang Y, Yerges-Armstrong LM, Yu B, Zannad F, Zhao F, Wilk JB, Holm H, Sattar N, Lubitz SA, Lanfear DE, Shah S, Dunn ME, Wells QS, Asselbergs FW, Hingorani AD, Dubé MP, Samani NJ, Lang CC, Cappola TP, Ellinor PT, Vasan RS, Smith JG. The genomics of heart failure: design and rationale of the HERMES consortium. ESC Heart Fail 2021; 8:5531-5541. [PMID: 34480422 PMCID: PMC8712846 DOI: 10.1002/ehf2.13517] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 06/09/2021] [Accepted: 07/05/2021] [Indexed: 12/28/2022] Open
Abstract
Aims The HERMES (HEart failure Molecular Epidemiology for Therapeutic targetS) consortium aims to identify the genomic and molecular basis of heart failure. Methods and results The consortium currently includes 51 studies from 11 countries, including 68 157 heart failure cases and 949 888 controls, with data on heart failure events and prognosis. All studies collected biological samples and performed genome‐wide genotyping of common genetic variants. The enrolment of subjects into participating studies ranged from 1948 to the present day, and the median follow‐up following heart failure diagnosis ranged from 2 to 116 months. Forty‐nine of 51 individual studies enrolled participants of both sexes; in these studies, participants with heart failure were predominantly male (34–90%). The mean age at diagnosis or ascertainment across all studies ranged from 54 to 84 years. Based on the aggregate sample, we estimated 80% power to genetic variant associations with risk of heart failure with an odds ratio of ≥1.10 for common variants (allele frequency ≥ 0.05) and ≥1.20 for low‐frequency variants (allele frequency 0.01–0.05) at P < 5 × 10−8 under an additive genetic model. Conclusions HERMES is a global collaboration aiming to (i) identify the genetic determinants of heart failure; (ii) generate insights into the causal pathways leading to heart failure and enable genetic approaches to target prioritization; and (iii) develop genomic tools for disease stratification and risk prediction.
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Affiliation(s)
- R Thomas Lumbers
- Institute of Health Informatics, University College London, Gower St, London, WC1E 7HB, UK.,Health Data Research UK London, University College London, London, UK.,BHF Research Accelerator, University College London, London, UK
| | - Sonia Shah
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.,Institute of Cardiovascular Science, University College London, London, UK
| | - Honghuang Lin
- Section of Computational Biomedicine, Department of Medicine, Boston University School of Medicine, Boston, MA, USA.,National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA, USA
| | - Tomasz Czuba
- Department of Cardiology, Clinical Sciences, Lund University and Skåne University Hospital, Lund, Sweden
| | - Albert Henry
- Institute of Health Informatics, University College London, Gower St, London, WC1E 7HB, UK.,Institute of Cardiovascular Science, University College London, London, UK
| | - Daniel I Swerdlow
- Institute of Cardiovascular Science, University College London, London, UK.,Department of Medicine, Imperial College London, London, UK
| | - Anders Mälarstig
- Pfizer Worldwide Research & Development, Cambridge, MA, USA.,Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institute, Stockholm, Sweden
| | - Charlotte Andersson
- National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA, USA.,Department of Cardiology, Herlev Gentofte Hospital, Herlev, Denmark
| | - Niek Verweij
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Michael V Holmes
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK.,Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, Big Data Institute, University of Oxford, Oxford, UK.,National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospital, Oxford, UK
| | - Johan Ärnlöv
- Department of Neurobiology, Care Sciences and Society/Section of Family Medicine and Primary Care, Karolinska Institutet, Stockholm, Sweden.,School of Health and Social Sciences, Dalarna University, Falun, Sweden
| | - Per Svensson
- Department of Clinical Science and Education, Karolinska Institutet, Södersjukhuset, Stockholm, Sweden.,Department of Cardiology, Södersjukhuset, Stockholm, Sweden
| | - Harry Hemingway
- Institute of Health Informatics, University College London, Gower St, London, WC1E 7HB, UK.,Health Data Research UK London, University College London, London, UK.,The National Institute for Health Research, University College London Hospitals Biomedical Research Centre, University College London, London, UK
| | - Neneh Sallah
- Institute of Health Informatics, University College London, Gower St, London, WC1E 7HB, UK.,Health Data Research UK London, University College London, London, UK.,UCL Genetics Institute, University College London, London, UK
| | - Peter Almgren
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Krishna G Aragam
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.,Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | | | | | - Mary L Biggs
- Department of Biostatistics, University of Washington, Seattle, WA, USA.,Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology and Health Services, University of Washington, Seattle, WA, USA
| | - Heather L Bloom
- Division of Cardiology, Department of Medicine, Emory University Medical Center, Atlanta, GA, USA
| | - Eric Boersma
- Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Jeffrey Brandimarto
- Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael R Brown
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | | | - David J Carey
- Department of Molecular and Functional Genomics, Geisinger, Danville, PA, USA
| | - Mark D Chaffin
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Daniel I Chasman
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Olympe Chazara
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Xing Chen
- Pfizer Worldwide Research & Development, Cambridge, MA, USA
| | - Xu Chen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | | | - William Chutkow
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | - John G F Cleland
- Robertson Centre for Biostatistics & Glasgow Clinical Trials Unit, Institute of Health and Wellbeing, University of Glasgow, Glasgow Royal Infirmary, Glasgow, UK.,National Heart and Lung Institute, Imperial College, London, UK
| | - James P Cook
- Department of Biostatistics, University of Liverpool, Liverpool, UK
| | - Simon de Denus
- Montreal Heart Institute, Montreal, Quebec, Canada.,Faculty of Pharmacy, Université de Montréal, Montreal, Quebec, Canada
| | - Abbas Dehghan
- Department of Epidemiology and Biostatistics, Imperial College London, St Mary's Campus, London, UK.,MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, St Mary's Campus, London, UK
| | - Graciela E Delgado
- Vth Department of Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology), Medical Faculty of Mannheim, University of Heidelberg, Heidelberg, Germany
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, Gower St, London, WC1E 7HB, UK.,Health Data Research UK London, University College London, London, UK.,The National Institute for Health Research, University College London Hospitals Biomedical Research Centre, University College London, London, UK.,The Alan Turing Institute, British Library, London, UK
| | - Alexander S Doney
- Division of Molecular and Clinical Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Marcus Dörr
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany.,DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
| | - Samuel C Dudley
- Cardiovascular Division, Department of Medicine, University of Minnesota, Minneapolis, MN, USA
| | - Gunnar Engström
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Tõnu Esko
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Ghazaleh Fatemifar
- Institute of Health Informatics, University College London, Gower St, London, WC1E 7HB, UK.,Health Data Research UK London, University College London, London, UK
| | - Stephan B Felix
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany.,DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
| | - Chris Finan
- Institute of Cardiovascular Science, University College London, London, UK
| | - Ian Ford
- Robertson Centre for Biostatistics & Glasgow Clinical Trials Unit, Institute of Health and Wellbeing, University of Glasgow, Glasgow Royal Infirmary, Glasgow, UK
| | - Francoise Fougerousse
- Translational and Clinical Research, Servier Cardiovascular Center for Therapeutic Innovation, Suresnes, France
| | | | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Sahar Ghasemi
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany.,Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Vilmantas Giedraitis
- Department of Public Health and Caring Sciences, Geriatrics, Uppsala University, Uppsala, Sweden
| | - Franco Giulianini
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - John S Gottdiener
- Department of Medicine, Division of Cardiology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Stefan Gross
- Department of Internal Medicine B, University Medicine Greifswald, Greifswald, Germany.,DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany
| | - Daníel F Guðbjartsson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland.,School of Engineering and Natural Sciences, University of Iceland, Reykjavik, Iceland
| | - Hongsheng Gui
- Center for Individualized and Genomic Medicine Research, Department of Internal Medicine, Henry Ford Hospital, Detroit, MI, USA
| | - Rebecca Gutmann
- Division of Cardiovascular Medicine, University of Iowa Carver College of Medicine, Iowa City, IA, USA
| | | | - Pim van der Harst
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Durrer Center for Cardiogenetic Research, ICIN-Netherlands Heart Institute, Utrecht, The Netherlands
| | - Åsa K Hedman
- Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institute, Stockholm, Sweden
| | | | - Hans Hillege
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Craig L Hyde
- Pfizer Worldwide Research & Development, Cambridge, MA, USA
| | - Jaison Jacob
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | - J Wouter Jukema
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands.,Netherlands Heart Institute, Utrecht, The Netherlands
| | - Frederick Kamanu
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.,TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Isabella Kardys
- Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Kay-Tee Khaw
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Marcus E Kleber
- Vth Department of Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology), Medical Faculty of Mannheim, University of Heidelberg, Heidelberg, Germany
| | - Lars Køber
- Department of Cardiology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Andrea Koekemoer
- Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Bill Kraus
- Duke Molecular Physiology Institute, Durham, NC, USA
| | - Karoline Kuchenbaecker
- UCL Genetics Institute, University College London, London, UK.,Division of Psychiatry, University College of London, London, UK
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Cecilia M Lindgren
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK.,Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Barry London
- Division of Cardiovascular Medicine and Abboud Cardiovascular Research Center, University of Iowa, Iowa City, IA, USA
| | - Luca A Lotta
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Ruth C Lovering
- Institute of Cardiovascular Science, University College London, London, UK
| | - Jian'an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Patrik Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | | | - Douglas Mann
- Center for Cardiovascular Research, Division of Cardiology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Kenneth B Margulies
- Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Nicholas A Marston
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Winfried März
- Vth Department of Medicine (Nephrology, Hypertensiology, Endocrinology, Diabetology, Rheumatology), Medical Faculty of Mannheim, University of Heidelberg, Heidelberg, Germany.,Synlab Academy, Synlab Holding Deutschland GmbH, Mannheim, Germany.,Clinical Institute of Medical and Chemical Laboratory Diagnostics, Medical University of Graz, Graz, Austria
| | - John J V McMurray
- BHF Cardiovascular Research Centre, University of Glasgow, Glasgow, UK
| | - Olle Melander
- Department of Internal Medicine, Clinical Sciences, Lund University and Skåne University Hospital, Malmö, Sweden
| | - Giorgio Melloni
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.,TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ify R Mordi
- Division of Molecular and Clinical Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Michael P Morley
- Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Andrew D Morris
- Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
| | - Andrew P Morris
- Department of Biostatistics, University of Liverpool, Liverpool, UK.,Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | | | - Christopher P Nelson
- Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Christopher Newton-Cheh
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.,Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
| | - Alexander Niessner
- Department of Internal Medicine II, Division of Cardiology, Medical University of Vienna, Vienna, Austria
| | - Teemu Niiranen
- Finnish Institute for Health and Welfare, Helsinki, Finland.,Department of Medicine, Turku University Hospital and University of Turku, Turku, Finland
| | - Christoph Nowak
- Department of Neurobiology, Care Sciences and Society/Section of Family Medicine and Primary Care, Karolinska Institutet, Stockholm, Sweden
| | - Michelle L O'Donoghue
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Anjali T Owens
- Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Colin N A Palmer
- Division of Molecular and Clinical Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Guillaume Paré
- Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - Markus Perola
- National Institute for Health and Welfare, Helsinki, Finland
| | | | - Eliana Portilla-Fernandez
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands.,Division of Vascular Medicine and Pharmacology, Department of Internal Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Departments of Medicine, Epidemiology and Health Services, University of Washington, Seattle, WA, USA.,Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA
| | - Kenneth M Rice
- Department of Biostatistics, University of Washington, Seattle, WA, USA
| | - Paul M Ridker
- Division of Preventive Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Simon P R Romaine
- Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Carolina Roselli
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Christian T Ruff
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Marc S Sabatine
- TIMI Study Group, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Perttu Salo
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Veikko Salomaa
- Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Jessica van Setten
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands
| | - Alaa A Shalaby
- Division of Cardiology, Department of Medicine, University of Pittsburgh Medical Center and VA Pittsburgh HCS, Pittsburgh, PA, USA
| | - Diane T Smelser
- Department of Molecular and Functional Genomics, Geisinger, Danville, PA, USA
| | - Nicholas L Smith
- Kaiser Permanente Washington Health Research Institute, Kaiser Permanente Washington, Seattle, WA, USA.,Department of Epidemiology, University of Washington, Seattle, WA, USA.,Department of Veterans Affairs Office of Research and Development, Seattle Epidemiologic Research and Information Center, Seattle, WA, USA
| | - Kari Stefansson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland.,Faculty of Medicine, Department of Medicine, University of Iceland, Reykjavik, Iceland
| | - Steen Stender
- Department of Clinical Biochemistry, Copenhagen University Hospital, Herlev and Gentofte, Denmark
| | - David J Stott
- Institute of Cardiovascular and Medical Sciences, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | | | - Mari-Liis Tammesoo
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Jean-Claude Tardif
- Montreal Heart Institute, Montreal, Quebec, Canada.,Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Maris Teder-Laving
- Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Alexander Teumer
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany.,Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Guðmundur Thorgeirsson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland.,Faculty of Medicine, Department of Medicine, University of Iceland, Reykjavik, Iceland
| | - Unnur Thorsteinsdottir
- deCODE genetics/Amgen Inc., Reykjavik, Iceland.,Faculty of Medicine, Department of Medicine, University of Iceland, Reykjavik, Iceland
| | - Christian Torp-Pedersen
- Department of Epidemiology and Biostatistics, Aalborg University Hospital, Aalborg, Denmark.,Department of Health, Science and Technology, Aalborg University Hospital, Aalborg, Denmark.,Department of Cardiology, Aalborg University Hospital, Aalborg, Denmark
| | - Stella Trompet
- Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands.,Section of Gerontology and Geriatrics, Department of Internal Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - Danny Tuckwell
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | - Benoit Tyl
- Translational and Clinical Research, Servier Cardiovascular Center for Therapeutic Innovation, Suresnes, France
| | - Andre G Uitterlinden
- Department of Epidemiology, Erasmus University Medical Center, Rotterdam, The Netherlands.,Department of Internal Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Felix Vaura
- Finnish Institute for Health and Welfare, Helsinki, Finland.,Department of Clinical Medicine, University of Turku, Turku, Finland
| | - Abirami Veluchamy
- Division of Molecular and Clinical Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Uwe Völker
- DZHK (German Center for Cardiovascular Research), partner site Greifswald, Greifswald, Germany.,Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Adriaan A Voors
- Department of Cardiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Xiaosong Wang
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | - Nicholas J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Peter E Weeke
- Department of Cardiology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Raul Weiss
- Division of Cardiovascular Medicine, Department of Internal Medicine, The Ohio State University Medical Center, Columbus, OH, USA
| | - Harvey D White
- Green Lane Cardiovascular Service, Auckland City Hospital, Auckland, New Zealand
| | - Kerri L Wiggins
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Heming Xing
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Yifan Yang
- Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Bing Yu
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Faiez Zannad
- CHU de Nancy, Inserm and INI-CRCT (F-CRIN), Institut Lorrain du Coeur et des Vaisseaux, Université de Lorraine, Nancy, France
| | - Faye Zhao
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | -
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Jemma B Wilk
- Pfizer Worldwide Research & Development, Cambridge, MA, USA
| | - Hilma Holm
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
| | - Naveed Sattar
- BHF Cardiovascular Research Centre, University of Glasgow, Glasgow, UK
| | - Steven A Lubitz
- Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Cardiac Arrhythmia Service and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - David E Lanfear
- Center for Individualized and Genomic Medicine Research, Department of Internal Medicine, Henry Ford Hospital, Detroit, MI, USA.,Heart and Vascular Institute, Henry Ford Hospital, Detroit, MI, USA
| | - Svati Shah
- Duke Molecular Physiology Institute, Durham, NC, USA.,Division of Cardiology, Department of Medicine, Duke University Medical Center, Durham, NC, USA.,Duke Clinical Research Institute, Durham, NC, USA
| | - Michael E Dunn
- Regeneron Pharmaceuticals, Cardiovascular Research, Tarrytown, NY, USA
| | - Quinn S Wells
- Division of Cardiovascular Medicine and the Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University, Nashville, TN, USA
| | - Folkert W Asselbergs
- Health Data Research UK London, University College London, London, UK.,BHF Research Accelerator, University College London, London, UK.,Institute of Cardiovascular Science, University College London, London, UK.,Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, University of Utrecht, Utrecht, The Netherlands
| | - Aroon D Hingorani
- BHF Research Accelerator, University College London, London, UK.,Institute of Cardiovascular Science, University College London, London, UK
| | - Marie-Pierre Dubé
- Montreal Heart Institute, Montreal, Quebec, Canada.,Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada
| | - Nilesh J Samani
- Department of Cardiovascular Sciences, University of Leicester and NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Chim C Lang
- Division of Molecular and Clinical Medicine, University of Dundee, Ninewells Hospital and Medical School, Dundee, UK
| | - Thomas P Cappola
- Penn Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Patrick T Ellinor
- Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Cardiac Arrhythmia Service and Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Ramachandran S Vasan
- National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study, Framingham, MA, USA.,Sections of Cardiology, Preventive Medicine and Epidemiology, Department of Medicine, Boston University Schools of Medicine and Public Health, Boston, MA, USA
| | - J Gustav Smith
- Department of Cardiology, Clinical Sciences, Lund University and Skåne University Hospital, Lund, Sweden.,Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.,Wallenberg Center for Molecular Medicine and Lund University Diabetes Center, Lund University, Lund, Sweden.,The Wallenberg Laboratory/Department of Molecular and Clinical Medicine, Institute of Medicine, Gothenburg University and the Department of Cardiology, Sahlgrenska University Hospital, Gothenburg, Sweden
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46
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Abstract
Endotyping is an emerging concept in which diseases are classified into distinct subtypes based on underlying molecular mechanisms. Heart failure (HF) is a complex clinical syndrome that encompasses multiple endotypes with differential risks of adverse events, and varying responses to treatment. Identifying these distinct endotypes requires molecular-level investigation involving multi-"omics" approaches, including genomics, transcriptomics, proteomics, and metabolomics. The derivation of these HF endotypes has important implications in promoting individualized treatment and facilitating more targeted selection of patients for clinical trials, as well as in potentially revealing new pathways of disease that may serve as therapeutic targets. One challenge in the integrated analysis of high-throughput omics and detailed clinical data is that it requires the ability to handle "big data", a task for which machine learning is well suited. In particular, unsupervised machine learning has the ability to uncover novel endotypes of disease in an unbiased approach. In this review, we will discuss recent efforts to identify HF endotypes and cover approaches involving proteomics, transcriptomics, and genomics, with a focus on machine-learning methods.
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Affiliation(s)
- Lusha W Liang
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center
| | - Yuichi J Shimada
- Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center
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47
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RBM20 is a candidate gene for hypertrophic cardiomyopathy. Can J Cardiol 2021; 37:1751-1759. [PMID: 34333030 DOI: 10.1016/j.cjca.2021.07.014] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 07/22/2021] [Accepted: 07/23/2021] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND The genetic basis of a considerable fraction of hypertrophic cardiomyopathy (HCM) cases remains unknown. Whether the gene encoding RNA Binding Motif Protein 20 (RBM20) is implicated in HCM and the correlation of clinical characteristics of RBM20 heterozygotes with HCM remain unresolved. We aimed to investigate the association between RBM20 variants and HCM. METHODS We compared rare variants in the RBM20 gene by exome sequencing in 793 HCM patients and 414 healthy controls. Based on a case-control approach, we used SKAT-O to explore whether RBM20 is associated with HCM. The genetic distribution of RBM20 rare variants was then compared between HCM heterozygotes and dilated cardiomyopathy (DCM) heterozygotes. Clinical features and prognosis of RBM20 heterozygotes were compared with non-heterozygotes. RESULTS Gene-based association analysis implicated RBM20 as a susceptibility gene for developing HCM. Patients with RBM20 variants displayed a higher prevalence of sudden cardiac arrest (SCA) (6.7% vs. 0.9%, p = 0.001), increased sudden cardiac death (SCD) risk factor counts and impaired left ventricle systolic function. Further survival analysis revealed that RBM20 heterozygotes had higher incidences of resuscitated cardiac arrest, recurrent non-sustained ventricular tachycardia and malignant arrhythmias. Mendelian randomization suggested that RBM20 expression in left ventricle was causally associated with HCM and DCM with opposite effects. CONCLUSIONS This study identified RBM20 as a potential causal gene of HCM. RBM20 variants are associated with increased risk for SCA in HCM.
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48
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Choi SH, Jurgens SJ, Haggerty CM, Hall AW, Halford JL, Morrill VN, Weng LC, Lagerman B, Mirshahi T, Pettinger M, Guo X, Lin HJ, Alonso A, Soliman EZ, Kornej J, Lin H, Moscati A, Nadkarni GN, Brody JA, Wiggins KL, Cade BE, Lee J, Austin-Tse C, Blackwell T, Chaffin MD, Lee CJY, Rehm HL, Roselli C, Redline S, Mitchell BD, Sotoodehnia N, Psaty BM, Heckbert SR, Loos RJ, Vasan RS, Benjamin EJ, Correa A, Boerwinkle E, Arking DE, Rotter JI, Rich SS, Whitsel EA, Perez M, Kooperberg C, Fornwalt BK, Lunetta KL, Ellinor PT, Lubitz SA, Lubitz SA. Rare Coding Variants Associated With Electrocardiographic Intervals Identify Monogenic Arrhythmia Susceptibility Genes: A Multi-Ancestry Analysis. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2021; 14:e003300. [PMID: 34319147 PMCID: PMC8373440 DOI: 10.1161/circgen.120.003300] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND Alterations in electrocardiographic (ECG) intervals are well-known markers for arrhythmia and sudden cardiac death (SCD) risk. While the genetics of arrhythmia syndromes have been studied, relations between electrocardiographic intervals and rare genetic variation at a population level are poorly understood. METHODS Using a discovery sample of 29 000 individuals with whole-genome sequencing from Trans-Omics in Precision Medicine and replication in nearly 100 000 with whole-exome sequencing from the UK Biobank and MyCode, we examined associations between low-frequency and rare coding variants with 5 routinely measured electrocardiographic traits (RR, P-wave, PR, and QRS intervals and corrected QT interval). RESULTS We found that rare variants associated with population-based electrocardiographic intervals identify established monogenic SCD genes (KCNQ1, KCNH2, and SCN5A), a controversial monogenic SCD gene (KCNE1), and novel genes (PAM and MFGE8) involved in cardiac conduction. Loss-of-function and pathogenic SCN5A variants, carried by 0.1% of individuals, were associated with a nearly 6-fold increased odds of the first-degree atrioventricular block (P=8.4×10-5). Similar variants in KCNQ1 and KCNH2 (0.2% of individuals) were associated with a 23-fold increased odds of marked corrected QT interval prolongation (P=4×10-25), a marker of SCD risk. Incomplete penetrance of such deleterious variation was common as over 70% of carriers had normal electrocardiographic intervals. CONCLUSIONS Our findings indicate that large-scale high-depth sequence data and electrocardiographic analysis identifies monogenic arrhythmia susceptibility genes and rare variants with large effects. Known pathogenic variation in conventional arrhythmia and SCD genes exhibited incomplete penetrance and accounted for only a small fraction of marked electrocardiographic interval prolongation.
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Affiliation(s)
- Seung Hoan Choi
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (S.H.C., S.J.J., A.W.H., J.L.H., V.N.M., L.-C.W., M.D.C., C.J.-Y.L., H.L.R., C.R., P.T.E., S.A.L.)
| | - Sean J. Jurgens
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (S.H.C., S.J.J., A.W.H., J.L.H., V.N.M., L.-C.W., M.D.C., C.J.-Y.L., H.L.R., C.R., P.T.E., S.A.L.)
| | - Christopher M. Haggerty
- Department of Translational Data Science and Informatics (C.M.H., B.K.F.), Geisinger, Danville, PA.,Heart Institute (C.M.H., B.K.F.), Geisinger, Danville, PA
| | - Amelia W. Hall
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (S.H.C., S.J.J., A.W.H., J.L.H., V.N.M., L.-C.W., M.D.C., C.J.-Y.L., H.L.R., C.R., P.T.E., S.A.L.).,Cardiovascular Research Center (A.W.H., V.N.M., L.-C.W., P.T.E., S.A.L.), Boston, MA
| | - Jennifer L. Halford
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (S.H.C., S.J.J., A.W.H., J.L.H., V.N.M., L.-C.W., M.D.C., C.J.-Y.L., H.L.R., C.R., P.T.E., S.A.L.).,Harvard Medical School (J.L.H., C.A.-T., H.L.R.), Boston, MA
| | - Valerie N. Morrill
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (S.H.C., S.J.J., A.W.H., J.L.H., V.N.M., L.-C.W., M.D.C., C.J.-Y.L., H.L.R., C.R., P.T.E., S.A.L.).,Cardiovascular Research Center (A.W.H., V.N.M., L.-C.W., P.T.E., S.A.L.), Boston, MA
| | - Lu-Chen Weng
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (S.H.C., S.J.J., A.W.H., J.L.H., V.N.M., L.-C.W., M.D.C., C.J.-Y.L., H.L.R., C.R., P.T.E., S.A.L.).,Cardiovascular Research Center (A.W.H., V.N.M., L.-C.W., P.T.E., S.A.L.), Boston, MA
| | - Braxton Lagerman
- Phenomic Analytics and Clinical Data Core (B.L.), Geisinger, Danville, PA
| | - Tooraj Mirshahi
- Department of Molecular and Functional Genomics (T.M.), Geisinger, Danville, PA
| | - Mary Pettinger
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA (M.P., C.K.)
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Insti for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA (X.G., H.J.L., J.I.R.)
| | - Henry J. Lin
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Insti for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA (X.G., H.J.L., J.I.R.)
| | - Alvaro Alonso
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA (A.A.)
| | - Elsayed Z. Soliman
- Epidemiological Cardiology Research Center, Wake Forest School of Medicine, Winston Salem, NC (E.Z.S.)
| | - Jelena Kornej
- NHLBI and Boston University’s Framingham Heart Study (J.K., E.J.B., R.S.V).,Sections of Cardiovascular Medicine and Preventive Medicine, Boston Medical Center (J.K., R.S.V), Boston University School of Medicine, MA
| | - Honghuang Lin
- Section of Computational Biomedicine, Department of Medicine (H.L.), Boston University School of Medicine, MA
| | - Arden Moscati
- The Charles Bronfman Institute for Personalized Medicine (A.M., G.N., R.J.F.L.), Icahn School of Medicine, Mount Sinai, New York, NY
| | - Girish N. Nadkarni
- The Charles Bronfman Institute for Personalized Medicine (A.M., G.N., R.J.F.L.), Icahn School of Medicine, Mount Sinai, New York, NY.,Division of Nephrology, Department of Medicine (G.N.), Icahn School of Medicine, Mount Sinai, New York, NY
| | - Jennifer A. Brody
- Cardiovascular Health Research Unit, Department of Medicine (J.A.B., K.L.W., N.S., B.M.P., S.R.H.), University of Washington, Seattle
| | - Kerri L. Wiggins
- Cardiovascular Health Research Unit, Department of Medicine (J.A.B., K.L.W., N.S., B.M.P., S.R.H.), University of Washington, Seattle
| | - Brian E. Cade
- Massachusetts General Hospital. Division of Sleep Medicine, Department of Medicine (B.E.C.), Boston, MA.,Division of Sleep and Circadian Disorders, Departments of Medicine and Neurology (B.E.C.), Harvard Medical School, Brigham and Women’s Hospital, Boston
| | - Jiwon Lee
- Division of Sleep and Circadian Disorders (J.L.), Harvard Medical School, Brigham and Women’s Hospital, Boston
| | - Christina Austin-Tse
- Center for Genomic Medicine (C.A.-T., H.L.R.), Boston, MA.,Harvard Medical School (J.L.H., C.A.-T., H.L.R.), Boston, MA.,Laboratory for Molecular Medicine, Partners Healthcare Personalized Medicine, Cambridge, MA (C.A.-T.)
| | - Tom Blackwell
- Department of Biostatistics, University of Michigan, Ann Arbor (T.B.)
| | - Mark D. Chaffin
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (S.H.C., S.J.J., A.W.H., J.L.H., V.N.M., L.-C.W., M.D.C., C.J.-Y.L., H.L.R., C.R., P.T.E., S.A.L.)
| | - Christina J.-Y. Lee
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (S.H.C., S.J.J., A.W.H., J.L.H., V.N.M., L.-C.W., M.D.C., C.J.-Y.L., H.L.R., C.R., P.T.E., S.A.L.)
| | - Heidi L. Rehm
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (S.H.C., S.J.J., A.W.H., J.L.H., V.N.M., L.-C.W., M.D.C., C.J.-Y.L., H.L.R., C.R., P.T.E., S.A.L.).,Center for Genomic Medicine (C.A.-T., H.L.R.), Boston, MA.,Harvard Medical School (J.L.H., C.A.-T., H.L.R.), Boston, MA
| | - Carolina Roselli
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (S.H.C., S.J.J., A.W.H., J.L.H., V.N.M., L.-C.W., M.D.C., C.J.-Y.L., H.L.R., C.R., P.T.E., S.A.L.)
| | - Susan Redline
- Regeneron Genetics Center, Tarrytown, NY. Departments of Medicine, Brigham and Women’s Hospital, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA (S.R.)
| | - Braxton D. Mitchell
- University of Maryland School of Medicine (B.D.M.).,Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, MD (B.D.M.)
| | - Nona Sotoodehnia
- Cardiovascular Health Research Unit, Department of Medicine (J.A.B., K.L.W., N.S., B.M.P., S.R.H.), University of Washington, Seattle.,Division of Cardiology, Department of Epidemiology (N.S.), University of Washington, Seattle
| | - Bruce M. Psaty
- Cardiovascular Health Research Unit, Department of Medicine (J.A.B., K.L.W., N.S., B.M.P., S.R.H.), University of Washington, Seattle.,Department of Epidemiology (B.M.P., S.R.H.), University of Washington, Seattle.,Department of Health Services (B.M.P.), University of Washington, Seattle.,Kaiser Permanente Washington Health Research Institute, Seattle (B.M.P.)
| | - Susan R. Heckbert
- Cardiovascular Health Research Unit, Department of Medicine (J.A.B., K.L.W., N.S., B.M.P., S.R.H.), University of Washington, Seattle.,Department of Epidemiology (B.M.P., S.R.H.), University of Washington, Seattle
| | - Ruth J.F. Loos
- The Charles Bronfman Institute for Personalized Medicine (A.M., G.N., R.J.F.L.), Icahn School of Medicine, Mount Sinai, New York, NY.,The Mindich Child Health and Development Institute (R.J.F.L.), Icahn School of Medicine, Mount Sinai, New York, NY
| | - Ramachandran S. Vasan
- NHLBI and Boston University’s Framingham Heart Study (J.K., E.J.B., R.S.V).,Sections of Cardiovascular Medicine and Preventive Medicine, Boston Medical Center (J.K., R.S.V), Boston University School of Medicine, MA.,Department of Medicine (E.J.B., R.S.V), Boston University School of Medicine, MA
| | - Emelia J. Benjamin
- NHLBI and Boston University’s Framingham Heart Study (J.K., E.J.B., R.S.V).,Department of Medicine (E.J.B., R.S.V), Boston University School of Medicine, MA.,Department of Epidemiology (E.J.B.), Boston University School of Public Health, MA
| | - Adolfo Correa
- Departments of Medicine, Pediatrics, and Population Health Science, University of Mississippi Medical Center, Jackson (A.C.)
| | - Eric Boerwinkle
- Human Genetics Center, University of Texas Health Science Center, Houston (E.B.)
| | - Dan E. Arking
- McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (D.E.A.)
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Insti for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA (X.G., H.J.L., J.I.R.)
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia School of Medicine, Charlottesville (S.S.R.)
| | - Eric A. Whitsel
- Department of Epidemiology, Gillings School of Global Public Health (E.A.W.), School of Medicine, University of North Carolina, Chapel Hill.,Department of Medicine (E.A.W.), School of Medicine, University of North Carolina, Chapel Hill
| | - Marco Perez
- Division of Cardiovascular Medicine, Stanford University, CA (M.P.). Dr Sotoodehnia is supported by NIH grant R01HL141989, by AHA grant 19SFRN34830063, and by the Laughlin Family
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA (M.P., C.K.)
| | - Brandon K. Fornwalt
- Department of Translational Data Science and Informatics (C.M.H., B.K.F.), Geisinger, Danville, PA.,Heart Institute (C.M.H., B.K.F.), Geisinger, Danville, PA.,Department of Radiology (B.K.F.), Geisinger, Danville, PA
| | - Kathryn L. Lunetta
- Department of Biostatistics (K.L.L.), Boston University School of Public Health, MA
| | - Patrick T. Ellinor
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (S.H.C., S.J.J., A.W.H., J.L.H., V.N.M., L.-C.W., M.D.C., C.J.-Y.L., H.L.R., C.R., P.T.E., S.A.L.).,Cardiovascular Research Center (A.W.H., V.N.M., L.-C.W., P.T.E., S.A.L.), Boston, MA.,Cardiac Arrhythmia Service (P.T.E., S.A.L.), Boston, MA
| | - Steven A. Lubitz
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (S.H.C., S.J.J., A.W.H., J.L.H., V.N.M., L.-C.W., M.D.C., C.J.-Y.L., H.L.R., C.R., P.T.E., S.A.L.).,Cardiovascular Research Center (A.W.H., V.N.M., L.-C.W., P.T.E., S.A.L.), Boston, MA.,Cardiac Arrhythmia Service (P.T.E., S.A.L.), Boston, MA
| | - Steven A Lubitz
- Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (S.H.C., S.J.J., A.W.H., J.L.H., V.N.M., L.-C.W., M.D.C., C.J.-Y.L., H.L.R., C.R., P.T.E., S.A.L.).,Cardiovascular Research Center (A.W.H., V.N.M., L.-C.W., P.T.E., S.A.L.), Boston, MA.,Cardiac Arrhythmia Service (P.T.E., S.A.L.), Boston, MA
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49
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Ding C, O'Neill D, Bell S, Stamatakis E, Britton A. Association of alcohol consumption with morbidity and mortality in patients with cardiovascular disease: original data and meta-analysis of 48,423 men and women. BMC Med 2021; 19:167. [PMID: 34311738 PMCID: PMC8314518 DOI: 10.1186/s12916-021-02040-2] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 06/17/2021] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Light-to-moderate alcohol consumption has been reported to be cardio-protective among apparently healthy individuals; however, it is unclear whether this association is also present in those with disease. To examine the association between alcohol consumption and prognosis in individuals with pre-existing cardiovascular disease (CVD), we conducted a series of meta-analyses of new findings from three large-scale cohorts and existing published studies. METHODS We assessed alcohol consumption in relation to all-cause mortality, cardiovascular mortality, and subsequent cardiovascular events via de novo analyses of 14,386 patients with a previous myocardial infarction, angina, or stroke in the UK Biobank Study (median follow-up 8.7 years, interquartile range [IQR] 8.0-9.5), involving 1640 deaths and 2950 subsequent events, and 2802 patients and 1257 deaths in 15 waves of the Health Survey for England 1994-2008 and three waves of the Scottish Health Survey 1995, 1998, and 2003 (median follow-up 9.5 years, IQR 5.7-13.0). This was augmented with findings from 12 published studies identified through a systematic review, providing data on 31,235 patients, 5095 deaths, and 1414 subsequent events. To determine the best-fitting dose-response association between alcohol and each outcome in the combined sample of 48,423 patients, models were constructed using fractional polynomial regression, adjusting at least for age, sex, and smoking status. RESULTS Alcohol consumption was associated with all assessed outcomes in a J-shaped manner relative to current non-drinkers, with a risk reduction that peaked at 7 g/day (relative risk 0.79, 95% confidence interval 0.73-0.85) for all-cause mortality, 8 g/day (0.73, 0.64-0.83) for cardiovascular mortality and 6 g/day (0.50, 0.26-0.96) for cardiovascular events, and remained significant up to 62, 50, and 15 g/day, respectively. No statistically significant elevated risks were found at higher levels of drinking. In the few studies that excluded former drinkers from the non-drinking reference group, reductions in risk among light-to-moderate drinkers were attenuated. CONCLUSIONS For secondary prevention of CVD, current drinkers may not need to stop drinking. However, they should be informed that the lowest risk of mortality and having another cardiovascular event is likely to be associated with lower levels of drinking, that is up to approximately 105g (or equivalent to 13 UK units, with one unit equal to half a pint of beer/lager/cider, half a glass of wine, or one measure of spirits) a week.
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Affiliation(s)
- Chengyi Ding
- Research Department of Epidemiology and Public Health, University College London, London, UK.
| | - Dara O'Neill
- CLOSER, Department of Social Science, Institute of Education, University College London, London, UK
| | - Steven Bell
- The National Institute for Health Research Blood and Transplant Unit in Donor Health and Genomics, University of Cambridge, Cambridge, UK.,British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.,Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, Cambridge Biomedical Campus, Cambridge, UK
| | - Emmanuel Stamatakis
- Charles Perkins Centre, Sydney School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | - Annie Britton
- Research Department of Epidemiology and Public Health, University College London, London, UK
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50
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Caleyachetty R, Littlejohns T, Lacey B, Bešević J, Conroy M, Collins R, Allen N. United Kingdom Biobank (UK Biobank): JACC Focus Seminar 6/8. J Am Coll Cardiol 2021; 78:56-65. [PMID: 34210415 DOI: 10.1016/j.jacc.2021.03.342] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Accepted: 03/21/2021] [Indexed: 11/19/2022]
Abstract
An increasing number of people are now living with cardiovascular disease (CVD), with concomitant CVD-related hospitalizations, operations, and prescriptions. To ultimately deliver optimal cardiovascular care, access to population-based biobanks with data on multiomics, phenotypes, and lifestyle risk factors are crucial. UK Biobank is a cohort study that incorporated data between 2006 and 2010 from over half a million individuals (40 to 69 years of age) at recruitment from across the United Kingdom. As one of the most accessible, largest, and in-depth cohort studies in the world, UK Biobank continues to enhance the resource with the addition of data from various omics platforms (eg, genomics, metabolomics, proteomics), multimodal imaging, self-reported risk factors and health outcomes, and linkage to electronic health records. The vision of UK Biobank is to allow as many researchers as possible to apply their expertise and imagination to undertake research to prevent, diagnose, and treat a wide range of chronic conditions, including CVD.
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Affiliation(s)
- Rishi Caleyachetty
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Thomas Littlejohns
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Ben Lacey
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
| | - Jelena Bešević
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Megan Conroy
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Rory Collins
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
| | - Naomi Allen
- Big Data Institute, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
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