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Le A, Peng H, Golinsky D, Di Scipio M, Lali R, Paré G. What Causes Premature Coronary Artery Disease? Curr Atheroscler Rep 2024; 26:189-203. [PMID: 38573470 DOI: 10.1007/s11883-024-01200-y] [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] [Accepted: 03/22/2024] [Indexed: 04/05/2024]
Abstract
PURPOSE OF REVIEW This review provides an overview of genetic and non-genetic causes of premature coronary artery disease (pCAD). RECENT FINDINGS pCAD refers to coronary artery disease (CAD) occurring before the age of 65 years in women and 55 years in men. Both genetic and non-genetic risk factors may contribute to the onset of pCAD. Recent advances in the genetic epidemiology of pCAD have revealed the importance of both monogenic and polygenic contributions to pCAD. Familial hypercholesterolemia (FH) is the most common monogenic disorder associated with atherosclerotic pCAD. However, clinical overreliance on monogenic genes can result in overlooked genetic causes of pCAD, especially polygenic contributions. Non-genetic factors, notably smoking and drug use, are also important contributors to pCAD. Cigarette smoking has been observed in 25.5% of pCAD patients relative to 12.2% of non-pCAD patients. Finally, myocardial infarction (MI) associated with spontaneous coronary artery dissection (SCAD) may result in similar clinical presentations as atherosclerotic pCAD. Recognizing the genetic and non-genetic causes underlying pCAD is important for appropriate prevention and treatment. Despite recent progress, pCAD remains incompletely understood, highlighting the need for both awareness and research.
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Affiliation(s)
- Ann Le
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, 237 Barton Street East, Hamilton, ON, L8L 2X2, Canada
- Department of Medical Sciences, Faculty of Health Sciences, McMaster University, 1280 Main Street West, Hamilton, ON, L8S 4K1, Canada
| | - Helen Peng
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, 237 Barton Street East, Hamilton, ON, L8L 2X2, Canada
- Faculty of Health Sciences, McMaster University, 1280 Main Street West, Hamilton, ON, L8L 4K1, Canada
| | - Danielle Golinsky
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, 237 Barton Street East, Hamilton, ON, L8L 2X2, Canada
- School of Nursing, Faculty of Health Sciences, McMaster University, 1280 Main Street West, Hamilton, ON, L8L 4K1, Canada
| | - Matteo Di Scipio
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, 237 Barton Street East, Hamilton, ON, L8L 2X2, Canada
- Department of Medical Sciences, Faculty of Health Sciences, McMaster University, 1280 Main Street West, Hamilton, ON, L8S 4K1, Canada
- Department of Medicine, McMaster University, 1280 Main Street West, Hamilton, ON, L8L 4K1, Canada
| | - Ricky Lali
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, 237 Barton Street East, Hamilton, ON, L8L 2X2, Canada
- Department of Health Research Methods, Evidence, and Impact, McMaster University, 1280 Main Street West, Hamilton, ON, L8L 4K1, Canada
| | - Guillaume Paré
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, 237 Barton Street East, Hamilton, ON, L8L 2X2, Canada.
- Department of Medical Sciences, Faculty of Health Sciences, McMaster University, 1280 Main Street West, Hamilton, ON, L8S 4K1, Canada.
- Department of Biochemistry and Biomedical Sciences, Faculty of Health Sciences, McMaster University, 1280 Main Street West, Hamilton, ON, L8S 4K1, Canada.
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, 237 Barton Street East, Hamilton, ON, L8L 2X2, Canada.
- Department of Pathology and Molecular Medicine, Michael G. DeGroote School of Medicine, McMaster University, 1280 Main Street West, Hamilton, ON, L8S 4K1, Canada.
- Department of Health Research Methods, Evidence, and Impact, McMaster University, 1280 Main Street West, Hamilton, ON, L8L 4K1, Canada.
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Franks PW, Cefalu WT, Dennis J, Florez JC, Mathieu C, Morton RW, Ridderstråle M, Sillesen HH, Stehouwer CDA. Precision medicine for cardiometabolic disease: a framework for clinical translation. Lancet Diabetes Endocrinol 2023; 11:822-835. [PMID: 37804856 DOI: 10.1016/s2213-8587(23)00165-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 06/01/2023] [Accepted: 06/01/2023] [Indexed: 10/09/2023]
Abstract
Cardiometabolic disease is a major threat to global health. Precision medicine has great potential to help to reduce the burden of this common and complex disease cluster, and to enhance contemporary evidence-based medicine. Its key pillars are diagnostics; prediction (of the primary disease); prevention (of the primary disease); prognosis (prediction of complications of the primary disease); treatment (of the primary disease or its complications); and monitoring (of risk exposure, treatment response, and disease progression or remission). To contextualise precision medicine in both research and clinical settings, and to encourage the successful translation of discovery science into clinical practice, in this Series paper we outline a model (the EPPOS model) that builds on contemporary evidence-based approaches; includes precision medicine that improves disease-related predictions by stratifying a cohort into subgroups of similar characteristics, or using participants' characteristics to model treatment outcomes directly; includes personalised medicine with the use of a person's data to objectively gauge the efficacy, safety, and tolerability of therapeutics; and subjectively tailors medical decisions to the individual's preferences, circumstances, and capabilities. Precision medicine requires a well functioning system comprised of multiple stakeholders, including health-care recipients, health-care providers, scientists, health economists, funders, innovators of medicines and technologies, regulators, and policy makers. Powerful computing infrastructures supporting appropriate analysis of large-scale, well curated, and accessible health databases that contain high-quality, multidimensional, time-series data will be required; so too will prospective cohort studies in diverse populations designed to generate novel hypotheses, and clinical trials designed to test them. Here, we carefully consider these topics and describe a framework for the integration of precision medicine in cardiometabolic disease.
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Affiliation(s)
- Paul W Franks
- Department of Translational Medicine, Medical Science, Novo Nordisk Foundation, Hellerup, Denmark; Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmö, Sweden; Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK; Harvard T H Chan School of Public Health, Boston, MA, USA.
| | - William T Cefalu
- Division of Diabetes, Endocrinology and Metabolic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - John Dennis
- Institute of Biomedical and Clinical Science, Royal Devon and Exeter Hospital, University of Exeter, Exeter, UK
| | - Jose C Florez
- Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Chantal Mathieu
- Clinical and Experimental Endocrinology, UZ Gasthuisberg, KU Leuven, Leuven, Belgium
| | - Robert W Morton
- Department of Translational Medicine, Medical Science, Novo Nordisk Foundation, Hellerup, Denmark
| | | | - Henrik H Sillesen
- Department of Clinical Medicine, Medical Science, Novo Nordisk Foundation, Hellerup, Denmark
| | - Coen D A Stehouwer
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands; Department of Internal Medicine, Maastricht University Medical Centre, Maastricht, Netherlands
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Kwak SH, Srinivasan S, Chen L, Todd J, Mercader J, Jensen E, Divers J, Mottl A, Pihoker C, Gandica R, Laffel L, Isganaitis E, Haymond M, Levitsky L, Pollin T, Florez J, Flannick J. Insights from rare variants into the genetic architecture and biology of youth-onset type 2 diabetes. RESEARCH SQUARE 2023:rs.3.rs-2886343. [PMID: 37292813 PMCID: PMC10246295 DOI: 10.21203/rs.3.rs-2886343/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Youth-onset type 2 diabetes (T2D) is a growing public health concern. Its genetic basis and relationship to other forms of diabetes are largely unknown. To gain insight into the genetic architecture and biology of youth-onset T2D, we analyzed exome sequences of 3,005 youth-onset T2D cases and 9,777 ancestry matched adult controls. We identified (a) monogenic diabetes variants in 2.1% of individuals; (b) two exome-wide significant (P < 4.3×10-7) common coding variant associations (in WFS1 and SLC30A8); (c) three exome-wide significant (P < 2.5×10-6) rare variant gene-level associations (HNF1A, MC4R, ATX2NL); and (d) rare variant association enrichments within 25 gene sets broadly related to obesity, monogenic diabetes, and β-cell function. Many association signals were shared between youth-onset and adult-onset T2D but had larger effects for youth-onset T2D risk (1.18-fold increase for common variants and 2.86-fold increase for rare variants). Both common and rare variant associations contributed more to youth-onset T2D liability variance than they did to adult-onset T2D, but the relative increase was larger for rare variant associations (5.0-fold) than for common variant associations (3.4-fold). Youth-onset T2D cases showed phenotypic differences depending on whether their genetic risk was driven by common variants (primarily related to insulin resistance) or rare variants (primarily related to β-cell dysfunction). These data paint a picture of youth-onset T2D as a disease genetically similar to both monogenic diabetes and adult-onset T2D, in which genetic heterogeneity might be used to sub-classify patients for different treatment strategies.
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Affiliation(s)
| | | | - Ling Chen
- Diabetes Research Center (Diabetes Unit), Department of Medicine, Massachusetts General Hospital
| | | | | | | | | | | | | | | | | | | | | | | | | | | | - Jason Flannick
- Broad Institute of MIT and Harvard/Boston Children's Hospital/Harvard Medical School
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Tsao NL, Judy R, Levin MG, Shakt G, Voight BF, Chen J, Damrauer SM. Evaluation of the Performance of the RECODe Equation with the Addition of Polygenic Risk Scores for Adverse Cardiovascular Outcomes in Individuals with Type II Diabetes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.03.23289457. [PMID: 37205500 PMCID: PMC10187440 DOI: 10.1101/2023.05.03.23289457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Aims/Hypothesis Individuals with T2D are at an increased risk of developing cardiovascular complications; early identification of individuals can lead to an alteration of the natural history of the disease. Current approaches to risk prediction tailored to individuals with T2D are exemplified by the RECODe algorithms which predict CVD outcomes among individuals with T2D. Recent efforts to improve CVD risk prediction among the general population have included the incorporation of polygenic risk scores (PRS). This paper aims to investigate the utility of the addition of a coronary artery disease (CAD), stroke and heart failure risk score to the current RECODe model for disease stratification. Methods We derived PRS using summary statistics for ischemic stroke (IS) from the coronary artery disease (CAD) and heart failure (HF) and tested prediction accuracy in the Penn Medicine Biobank (PMBB). A Cox proportional hazards model was used for time-to-event analyses within our cohort, and we compared model discrimination for the RECODe model with and without a PRS using AUC. Results The RECODe model alone demonstrated an AUC [95% CI] of 0.67 [0.62-0.72] for ASCVD; the addition of the three PRS to the model demonstrated an AUC [95% CI] of 0.66 [0.63-0.70]. A z-test to compare the AUCs of the two models did not demonstrate a detectable difference between the two models (p=0.97). Conclusions/Interpretation In the present study, we demonstrate that although PRS associate with CVD outcomes independent of traditional risk factors among individuals with T2D, the addition of PRS to contemporary clinical risk models does not specifically improve the predictive performance as compared to the baseline model.
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Affiliation(s)
- Noah L. Tsao
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Renae Judy
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael G. Levin
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Gabrielle Shakt
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - Benjamin F. Voight
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Institute for Translational Medicine and Therapeutics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Jinbo Chen
- Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Scott M. Damrauer
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, USA
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
- Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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eldesouky M, Abd-elazim A, Elhakim H, Fayed H. Impact of KIF6 Trp719Arg gene variant on Coronary Artery Disease Development.. [DOI: 10.21203/rs.3.rs-2705882/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
Abstract
Coronary artery disease (CAD) is a multifactorial disease resulting from the interaction of genetic varia-tion and environmental traditional risk factors (TRFs), including diabetes mellitus, smoking, dyslipidemia, and hypertension. KIF6 Trp719Arg (rs20455; A>G) is an interesting gene variant reported as one of the most important risk factors for CAD in different populations. The study enrolled 150 participants belong-ing to the National Heart Institute (NHI) catheterization unit in Egypt, who were grouped into three main study groups regarding the presence of different TRFs. Biochemical investigations and clinical data were assessed and recorded. Analysis for KIF6 Trp719Arg polymorphism (rs20455; A>G) was performed for all participants using the TaqMan genotyping real-time PCR assay (rs20455). The study demonstrated that diabetes mellitus, hypertension, dyslipidemia, and smoking were highly statistically significant among CAD with TRF and non-CAD with TRF patients with p-values of 0.009*, 0.003*, 0.046*, and 0.001**, re-spectively. The family history of premature CAD represents a high percentage of CAD without TRF pa-tients compared to the other groups with a statistical difference of p-value= 0.004*. A high prevalence of AG+GG genotypes among the different groups was obtained, representing 66.0% of CAD with TRF, 76.0% of CAD without TRF, and 60% of non-CAD with TRF patients. The present study elucidated the impact of KIF6 Trp719Arg as a dependent risk factor for CAD, as it could have a significant role in CAD develop-ment when it interacts with one or more of the other traditional risk factors.
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Viigimaa M, Jürisson M, Pisarev H, Kalda R, Alavere H, Irs A, Saar A, Fischer K, Läll K, Kruuv-Käo K, Mars N, Widen E, Ripatti S, Metspalu A. Effectiveness and feasibility of cardiovascular disease personalized prevention on high polygenic risk score subjects: a randomized controlled pilot study. EUROPEAN HEART JOURNAL OPEN 2022; 2:oeac079. [PMID: 36600884 PMCID: PMC9803971 DOI: 10.1093/ehjopen/oeac079] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 11/23/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022]
Abstract
Aims The aim of this study was to evaluate the effect of the intervention by proactively sharing a patient's high polygenic risk score (PRS) for coronary artery disease (CAD). Outcomes included: (i) reduction in cardiovascular disease (CVD) risk factors over 12 months; (ii) difference in purchased prescriptions of lipid-lowering and anti-hypertensive drugs between intervention group and control group subjects; and (iii) opinion of the participating physicians and subjects on PRS usefulness. Methods and results This randomized controlled trial was conducted among middle-aged subjects with a top 20% CAD PRS in a family medicine setting. Participants were selected from 26 953 Estonian Biobank cohort participants. Subjects were informed and counselled about their PRS score and CAD risk using the visual tool at baseline (Visit I), counselling session (Visit II), and on the final Visit III at 12 months. The primary endpoint was not significantly different. However, the intervention group participants had a significantly higher probability of initiating statin treatment compared with the controls. Their levels of LDL-cholesterol (LDL-C) were significantly decreased compared with baseline on Visit III and significantly lower than in the control group. The vast majority of participating family physicians believe that finding out about genetic risks will affect the subject's lifestyle and medication compliance. Conclusion Most of our outcome measures were in favour of this intervention. Participants achieved larger changes in cholesterol and blood pressure values. The vast majority (98.4%) of family physicians are interested in continuing to use genetic risk assessment in practice.
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Affiliation(s)
| | | | - Heti Pisarev
- Institute of Family Medicine and Public Health, University of Tartu, Ravila 19, 50411 Tartu, Estonia
| | - Ruth Kalda
- Institute of Family Medicine and Public Health, University of Tartu, Ravila 19, 50411 Tartu, Estonia
| | - Helene Alavere
- Institute of Genomics, University of Tartu, Riia 23b, 51010 Tartu, Estonia
| | - Alar Irs
- Heart Clinic, Tartu University Hospital, L. Puusepa 8, 50406 Tartu, Estonia
| | - Aet Saar
- Centre of Cardiology, North Estonia Medical Centre, Sütiste St. 19, 13419 Tallinn, Estonia,Heart Clinic, University of Tartu, L. Puusepa 8, 50406 Tartu, Estonia
| | - Krista Fischer
- Institute of Genomics, University of Tartu, Riia 23b, 51010 Tartu, Estonia,Institute of Mathematics and Statistics, University of Tartu, Narva mnt 18, 51009 Tartu, Estonia
| | - Kristi Läll
- Institute of Genomics, University of Tartu, Riia 23b, 51010 Tartu, Estonia
| | - Krista Kruuv-Käo
- Institute of Genomics, University of Tartu, Riia 23b, 51010 Tartu, Estonia
| | - Nina Mars
- Institute for Molecular Medicine Finland, University of Helsinki, Tukholmankatu 8, 00014 Helsinki, Finland
| | - Elisabeth Widen
- Institute for Molecular Medicine Finland, University of Helsinki, Tukholmankatu 8, 00014 Helsinki, Finland
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, University of Helsinki, Tukholmankatu 8, 00014 Helsinki, Finland
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King A, Wu L, Deng HW, Shen H, Wu C. Polygenic risk score improves the accuracy of a clinical risk score for coronary artery disease. BMC Med 2022; 20:385. [PMID: 36336692 PMCID: PMC9639312 DOI: 10.1186/s12916-022-02583-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 09/26/2022] [Indexed: 11/09/2022] Open
Abstract
BACKGROUND The value of polygenic risk scores (PRSs) towards improving guideline-recommended clinical risk models for coronary artery disease (CAD) prediction is controversial. Here we examine whether an integrated polygenic risk score improves the prediction of CAD beyond pooled cohort equations. METHODS: An observation study of 291,305 unrelated White British UK Biobank participants enrolled from 2006 to 2010 was conducted. A case-control sample of 9499 prevalent CAD cases and an equal number of randomly selected controls was used for tuning and integrating of the polygenic risk scores. A separate cohort of 272,307 individuals (with follow-up to 2020) was used to examine the risk prediction performance of pooled cohort equations, integrated polygenic risk score, and PRS-enhanced pooled cohort equation for incident CAD cases. The performance of each model was analyzed by discrimination and risk reclassification using a 7.5% threshold. RESULTS In the cohort of 272,307 individuals (mean age, 56.7 years) used to analyze predictive accuracy, there were 7036 incident CAD cases over a 12-year follow-up period. Model discrimination was tested for integrated polygenic risk score, pooled cohort equation, and PRS-enhanced pooled cohort equation with reported C-statistics of 0.640 (95% CI, 0.634-0.646), 0.718 (95% CI, 0.713-0.723), and 0.753 (95% CI, 0.748-0.758), respectively. Risk reclassification for the addition of the integrated polygenic risk score to the pooled cohort equation at a 7.5% risk threshold resulted in a net reclassification improvement of 0.117 (95% CI, 0.102 to 0.129) for cases and - 0.023 (95% CI, - 0.025 to - 0.022) for noncases [overall: 0.093 (95% CI, 0.08 to 0.104)]. For incident CAD cases, this represented 14.2% correctly reclassified to the higher-risk category and 2.6% incorrectly reclassified to the lower-risk category. CONCLUSIONS Addition of the integrated polygenic risk score for CAD to the pooled cohort questions improves the predictive accuracy for incident CAD and clinical risk classification in the White British from the UK Biobank. These findings suggest that an integrated polygenic risk score may enhance CAD risk prediction and screening in the White British population.
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Affiliation(s)
- Austin King
- Department of Statistics, Florida State University, Tallahassee, FL, USA
| | - Lang Wu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Hong-Wen Deng
- Center of Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, USA
| | - Hui Shen
- Center of Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, USA
| | - Chong Wu
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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Tada H, Yeo KK, Li JJ, Tan K, Ako J, Krittayaphong R, San Tan R, Aylward PE, Lam CS, Baek SH, Dalal J, Fong A, Li YH, O’Brien RC, Natalie Koh SY, Scherer DJ, Kang V, Nelson AJ, Butters J, Nicholls SJ. Polygenic Risk Scores for Atherosclerotic Cardiovascular Disease in the Asia-Pacific Region. JACC: ASIA 2021; 1:294-302. [PMID: 36341217 PMCID: PMC9627888 DOI: 10.1016/j.jacasi.2021.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Revised: 08/04/2021] [Accepted: 08/09/2021] [Indexed: 11/29/2022]
Abstract
Approximately one-half of the phenotypic susceptibility to atherosclerotic cardiovascular disease (ASCVD) has a genetic basis. Although individual allelic variants generally impart a small effect on risk for ASCVD, an emerging body of data has shown that the aggregation and weighting of many of these genetic variations into “scores” can further discriminate an individual’s risk beyond traditional risk factors alone. Consistent with the theory of population genetics, such polygenic risk scores (PRS) appear to be ethnicity specific because their elements comprise single-nucleotide variants that are always ethnicity specific. The currently available PRS are derived predominantly from European ancestry and thus predictably perform less well among non-European participants, a fact that has implications for their use in the Asia-Pacific region. This paper describes the current state of knowledge of PRS, the available data that support their use in this region, and highlights the needs moving forward to safely and effectively implement them in clinical care in the Asia-Pacific region. Genetic factors should be fully accounted for in the clinical care of atherosclerotic cardiovascular disease. A health inequity exists regarding polygenic risk score for atherosclerotic cardiovascular disease in the world. We propose a call to action to address this issue in the Asia-Pacific region.
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Affiliation(s)
- Hayato Tada
- Department of Cardiovascular Medicine, Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan
| | - Khung Keong Yeo
- National Heart Centre and SingHealth Duke-NUS Cardiovascular Sciences, Singapore
| | - Jian-Jun Li
- State Key Laboratory of Cardiovascular Disease, FuWai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Kathryn Tan
- Department of Medicine, University of Hong Kong, Hong Kong
| | - Junya Ako
- Kitasato University, Sagamihara, Japan
| | - Rungroj Krittayaphong
- Division of Cardiology, Department of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Ru San Tan
- National Heart Centre and SingHealth Duke-NUS Cardiovascular Sciences, Singapore
| | - Philip E. Aylward
- South Australian Health and Medical Research Institute and Flinders University, Adelaide, South Australia, Australia
| | - Carolyn S.P. Lam
- National Heart Centre and SingHealth Duke-NUS Cardiovascular Sciences, Singapore
| | - Sang Hong Baek
- Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | | | - Alan Fong
- Department of Cardiology, Sarawak Heart Centre, and Clinical Research Centre, Sarawak General Hospital, Kuching, Malaysia
| | - Yi-Heng Li
- National Cheng Kung University Hospital, Tainan, Taiwan
| | - Richard C. O’Brien
- University of Melbourne and Austin Health, Melbourne, Victoria, Australia
| | - Si Ya Natalie Koh
- National Heart Centre and SingHealth Duke-NUS Cardiovascular Sciences, Singapore
| | - Daniel J. Scherer
- South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
| | | | - Adam J. Nelson
- Victorian Heart Institute, Monash University, Melbourne, Victoria, Australia
| | - Julie Butters
- Victorian Heart Institute, Monash University, Melbourne, Victoria, Australia
| | - Stephen J. Nicholls
- Victorian Heart Institute, Monash University, Melbourne, Victoria, Australia
- Address for correspondence: Dr Stephen J. Nicholls, Monash Cardiovascular Research Centre, 246 Clayton Road, Clayton, Victoria 3168, Australia. Hayato_Tada_KU
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Manikpurage HD, Eslami A, Perrot N, Li Z, Couture C, Mathieu P, Bossé Y, Arsenault BJ, Thériault S. Polygenic Risk Score for Coronary Artery Disease Improves the Prediction of Early-Onset Myocardial Infarction and Mortality in Men. CIRCULATION-GENOMIC AND PRECISION MEDICINE 2021; 14:e003452. [PMID: 34670400 DOI: 10.1161/circgen.121.003452] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND Several risk factors for coronary artery disease (CAD) have been described, some of which are genetically determined. The use of a polygenic risk score (PRS) could improve CAD risk assessment, but predictive accuracy according to age and sex is not well established. METHODS A PRSCAD including the weighted effects of >1.14 million SNPs associated with CAD was calculated in UK Biobank (n=408 422), using LDpred. Cox regressions were performed, stratified by age quartiles and sex, for incident myocardial infarction (MI) and mortality, with a median follow-up of 11.0 years. Improvement in risk prediction of MI was assessed by comparing PRSCAD to the pooled cohort equation with categorical net reclassification index using a 2% threshold (NRI0.02) and continuous NRI (NRI>0). RESULTS From 7746 incident MI cases and 393 725 controls, hazard ratio for MI reached 1.53 (95% CI, 1.49-1.56; P=2.69×10-296) per SD increase of PRSCAD. PRSCAD was significantly associated with MI in both sexes, with a stronger association in men (interaction P=0.002), particularly in those aged between 40 and 51 years (hazard ratio, 2.00 [95% CI, 1.86-2.16], P=1.93×10-72). This group showed the highest reclassification improvement, mainly driven by the up-classification of cases (NRI0.02, 0.199 [95% CI, 0.157-0.248] and NRI>0, 0.602 [95% CI, 0.525-0.683]). From 23 982 deaths, hazard ratio for mortality was 1.08 (95% CI, 1.06-1.09; P=5.46×10-30) per SD increase of PRSCAD, with a stronger association in men (interaction P=1.60×10-6). CONCLUSIONS Our PRSCAD predicts MI incidence and all-cause mortality, especially in men aged between 40 and 51 years. PRS could optimize the identification and management of individuals at risk for CAD.
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Affiliation(s)
- Hasanga D Manikpurage
- Centre de Recherche de l'Institut Universitaire de Cardiologie et de Pneumologie de Québec, Canada (H.D.M., A.E., N.P., Z.L., C.C., P.M., Y.B., B.J.A., S.T.)
| | - Aida Eslami
- Centre de Recherche de l'Institut Universitaire de Cardiologie et de Pneumologie de Québec, Canada (H.D.M., A.E., N.P., Z.L., C.C., P.M., Y.B., B.J.A., S.T.).,Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Québec, Canada. (A.E.)
| | - Nicolas Perrot
- Centre de Recherche de l'Institut Universitaire de Cardiologie et de Pneumologie de Québec, Canada (H.D.M., A.E., N.P., Z.L., C.C., P.M., Y.B., B.J.A., S.T.)
| | - Zhonglin Li
- Centre de Recherche de l'Institut Universitaire de Cardiologie et de Pneumologie de Québec, Canada (H.D.M., A.E., N.P., Z.L., C.C., P.M., Y.B., B.J.A., S.T.)
| | - Christian Couture
- Centre de Recherche de l'Institut Universitaire de Cardiologie et de Pneumologie de Québec, Canada (H.D.M., A.E., N.P., Z.L., C.C., P.M., Y.B., B.J.A., S.T.)
| | - Patrick Mathieu
- Centre de Recherche de l'Institut Universitaire de Cardiologie et de Pneumologie de Québec, Canada (H.D.M., A.E., N.P., Z.L., C.C., P.M., Y.B., B.J.A., S.T.).,Department of Surgery, Faculty of Medicine, Université Laval, Québec, Canada. (P.M.)
| | - Yohan Bossé
- Centre de Recherche de l'Institut Universitaire de Cardiologie et de Pneumologie de Québec, Canada (H.D.M., A.E., N.P., Z.L., C.C., P.M., Y.B., B.J.A., S.T.).,Department of Molecular Medicine, Faculty of Medicine, Université Laval, Québec, Canada. (Y.B.)
| | - Benoit J Arsenault
- Centre de Recherche de l'Institut Universitaire de Cardiologie et de Pneumologie de Québec, Canada (H.D.M., A.E., N.P., Z.L., C.C., P.M., Y.B., B.J.A., S.T.).,Department of Medicine, Faculty of Medicine, Université Laval, Québec, Canada. (B.J.A.)
| | - Sébastien Thériault
- Centre de Recherche de l'Institut Universitaire de Cardiologie et de Pneumologie de Québec, Canada (H.D.M., A.E., N.P., Z.L., C.C., P.M., Y.B., B.J.A., S.T.).,Department of Molecular Biology, Medical Biochemistry and Pathology, Faculty of Medicine, Université Laval, Québec, Canada. (S.T.)
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10
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Lali R, Chong M, Omidi A, Mohammadi-Shemirani P, Le A, Cui E, Paré G. Calibrated rare variant genetic risk scores for complex disease prediction using large exome sequence repositories. Nat Commun 2021; 12:5852. [PMID: 34615865 PMCID: PMC8494733 DOI: 10.1038/s41467-021-26114-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 09/06/2021] [Indexed: 11/24/2022] Open
Abstract
Rare variants are collectively numerous and may underlie a considerable proportion of complex disease risk. However, identifying genuine rare variant associations is challenging due to small effect sizes, presence of technical artefacts, and heterogeneity in population structure. We hypothesize that rare variant burden over a large number of genes can be combined into a predictive rare variant genetic risk score (RVGRS). We propose a method (RV-EXCALIBER) that leverages summary-level data from a large public exome sequencing database (gnomAD) as controls and robustly calibrates rare variant burden to account for the aforementioned biases. A calibrated RVGRS strongly associates with coronary artery disease (CAD) in European and South Asian populations by capturing the aggregate effect of rare variants through a polygenic model of inheritance. The RVGRS identifies 1.5% of the population with substantial risk of early CAD and confers risk even when adjusting for known Mendelian CAD genes, clinical risk factors, and a common variant genetic risk score.
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Affiliation(s)
- Ricky Lali
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, 237 Barton Street East, Hamilton, ON, L8L 2X2, Canada
- Department of Health Research Methodology, Evidence, and Impact, McMaster University, Faculty of Health Sciences, 1280 Main Street West, Hamilton, ON, L8S 4K1, Canada
| | - Michael Chong
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, 237 Barton Street East, Hamilton, ON, L8L 2X2, Canada
- Department of Biochemistry and Biomedical Sciences, McMaster University, Faculty of Health Sciences, 1280 Main Street West, Hamilton, ON, L8S 4K1, Canada
| | - Arghavan Omidi
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, 237 Barton Street East, Hamilton, ON, L8L 2X2, Canada
| | - Pedrum Mohammadi-Shemirani
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, 237 Barton Street East, Hamilton, ON, L8L 2X2, Canada
- Department of Medical Sciences, McMaster University, Faculty of Health Sciences, 1280 Main Street West, Hamilton, ON, L8S 4K1, Canada
| | - Ann Le
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, 237 Barton Street East, Hamilton, ON, L8L 2X2, Canada
- Department of Medical Sciences, McMaster University, Faculty of Health Sciences, 1280 Main Street West, Hamilton, ON, L8S 4K1, Canada
| | - Edward Cui
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, 237 Barton Street East, Hamilton, ON, L8L 2X2, Canada
| | - Guillaume Paré
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, 237 Barton Street East, Hamilton, ON, L8L 2X2, Canada.
- Department of Health Research Methodology, Evidence, and Impact, McMaster University, Faculty of Health Sciences, 1280 Main Street West, Hamilton, ON, L8S 4K1, Canada.
- Department of Biochemistry and Biomedical Sciences, McMaster University, Faculty of Health Sciences, 1280 Main Street West, Hamilton, ON, L8S 4K1, Canada.
- Department of Medical Sciences, McMaster University, Faculty of Health Sciences, 1280 Main Street West, Hamilton, ON, L8S 4K1, Canada.
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, 237 Barton Street East, Hamilton, ON, L8L 2×2, Canada.
- Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, 1280 Main Street West, Hamilton, ON, L8S 4K1, Canada.
- Department of Clinical Epidemiology & Biostatistics, McMaster University, 1280 Main Street West, Hamilton, ON, L8S 4K1, Canada.
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11
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Wu H, Luan J, Forgetta V, Engert JC, Thanassoulis G, Mooser V, Wareham NJ, Langenberg C, Richards JB. Utility of Genetically Predicted Lp(a) (Lipoprotein [a]) and ApoB Levels for Cardiovascular Risk Assessment. CIRCULATION-GENOMIC AND PRECISION MEDICINE 2021; 14:e003312. [PMID: 34461734 DOI: 10.1161/circgen.121.003312] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
BACKGROUND Current lipid guidelines suggest measurement of Lp(a) (lipoprotein[a]) and ApoB (apolipoprotein B) for atherosclerotic cardiovascular disease risk assessment. Polygenic risk scores (PRSs) for Lp(a) and ApoB may identify individuals unlikely to have elevated Lp(a) or ApoB and thus reduce such suggested testing. METHODS PRSs were developed using least absolute shrinkage and selection operator regression among 273 222 and 356 958 UK Biobank participants of white British ancestry for Lp(a) and ApoB, respectively, and validated in separate sets of 60 771 UK Biobank and 15 050 European Prospective Investigation into Cancer and Nutrition-Norfolk participants. We then assessed the proportion of participants who, based on these PRSs, were unlikely to benefit from Lp(a) or ApoB measurements, according to current lipid guidelines. RESULTS In the UK Biobank and European Prospective Investigation into Cancer and Nutrition-Norfolk cohorts, the area under the receiver operating curve for the PRS-predicted Lp(a) and ApoB to identify individuals with elevated Lp(a) and ApoB was at least 0.91 (95% CI, 0.90-0.92) and 0.74 (95% CI, 0.73-0.75), respectively. The Lp(a) PRS and measured Lp(a) showed comparable association with atherosclerotic cardiovascular disease incidence, whereas the ApoB PRS was in general less predictive of atherosclerotic cardiovascular disease risk than measured ApoB. In the context of the European Society of Cardiology/European Atherosclerosis Society lipid guidelines, at a 95% sensitivity to identify individuals with elevated Lp(a) and ApoB levels, at least 54% of Lp(a) and 24% of ApoB testing could be reduced by prescreening with a PRS while maintaining a low false-negative rate. CONCLUSIONS A substantial proportion of suggested testing for elevated Lp(a) and a modest proportion of testing for elevated ApoB could potentially be reduced by prescreening individuals with PRSs.
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Affiliation(s)
- Haoyu Wu
- Department of Epidemiology, Biostatistics and Occupational Health (H.W., J.B.R.), McGill University, Montréal, Québec, Canada.,Center for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital (H.W., V.F., J.B.R.), McGill University, Montréal, Québec, Canada
| | - Jian'an Luan
- MRC Epidemiology Unit, University of Cambridge, United Kingdom (J.L., N.J.W., C.L.)
| | - Vincenzo Forgetta
- Center for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital (H.W., V.F., J.B.R.), McGill University, Montréal, Québec, Canada
| | - James C Engert
- Department of Medicine (J.C.E., G.T.), McGill University, Montréal, Québec, Canada.,Department of Human Genetics (J.C.E., G.T., V.M., J.B.R.), McGill University, Montréal, Québec, Canada.,McGill University Health Centre Research Institute, Montreal, Québec, Canada (J.C.E., G.T.)
| | - George Thanassoulis
- Department of Medicine (J.C.E., G.T.), McGill University, Montréal, Québec, Canada.,Department of Human Genetics (J.C.E., G.T., V.M., J.B.R.), McGill University, Montréal, Québec, Canada.,McGill University Health Centre Research Institute, Montreal, Québec, Canada (J.C.E., G.T.)
| | - Vincent Mooser
- Department of Human Genetics (J.C.E., G.T., V.M., J.B.R.), McGill University, Montréal, Québec, Canada
| | - Nicholas J Wareham
- MRC Epidemiology Unit, University of Cambridge, United Kingdom (J.L., N.J.W., C.L.)
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, United Kingdom (J.L., N.J.W., C.L.)
| | - J Brent Richards
- Department of Epidemiology, Biostatistics and Occupational Health (H.W., J.B.R.), McGill University, Montréal, Québec, Canada.,Center for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital (H.W., V.F., J.B.R.), McGill University, Montréal, Québec, Canada.,Department of Human Genetics (J.C.E., G.T., V.M., J.B.R.), McGill University, Montréal, Québec, Canada.,Department of Twin Research, King's College London, United Kingdom (J.B.R.)
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12
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Risk Factors for Premature Myocardial Infarction: A Systematic Review and Meta-analysis of 77 Studies. Mayo Clin Proc Innov Qual Outcomes 2021; 5:783-794. [PMID: 34401655 PMCID: PMC8358212 DOI: 10.1016/j.mayocpiqo.2021.03.009] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
Objective To evaluate the magnitude of the association between risk factors and premature myocardial infarction (MI) (men aged 18-55 years; women aged 18-65 years). Patients and Methods We searched MEDLINE and other databases from inception through April 30, 2020, as well as bibliography of articles selected for data extraction. We selected observational studies reporting the magnitude of the association of at least 1 risk factor (demographic characteristics, lifestyle factors, clinical risk factors, or biomarkers) with premature MI and a control group. Pooled risk estimates (random effects) from all studies unadjusted and adjusted for risk factors were reported as summary odds ratios (ORs) with 95% CIs. Results From 35,320 articles of 12.7 million participants, we extracted data on 19 risk factors from 77 studies across 58 countries. Men had a higher risk of premature MI (OR, 2.39; 95% CI, 1.71 to 3.35) than did women. Family history of cardiac disease was associated with a higher risk of premature MI (OR, 2.67; 95% CI, 2.29 to 3.27). Major modifiable risk factors associated with higher risk were current smoking (OR, 4.34; 95% CI, 3.68 to 5.12 vs no/former), diabetes mellitus (OR, 3.54; 95% CI, 2.69 to 4.65), dyslipidemia (OR, 2.94; 95% CI, 1.76 to 4.91), and hypertension (OR, 2.85; 95% CI, 2.48 to 3.27). Higher body mass index carried higher risk (OR, 1.46; 95% CI, 1.24 to 1.71 for ≥25 kg/m2 vs <25 kg/m2). Biomarkers associated with 2- to 3-fold higher risk were total cholesterol levels greater than 200 mg/dL, triglyceride levels higher than 150 mg/dL, and high-density lipoprotein cholesterol levels less than 60 mg/dL (to convert to mmol/L, multiply by 0.0259). Conclusion Major risk factors for premature MI are mostly amenable to patient, population, and policy level interventions. Mild elevations in body mass index and triglyceride levels were associated with higher risk, which has implications for the growing worldwide epidemic of cardiometabolic diseases.
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13
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Abstract
Atherosclerotic cardiovascular disease (ASCVD) proceeds through a series of stages: initiation, progression (or regression), and complications. By integrating known biology regarding molecular signatures of each stage with recent advances in high-dimensional molecular data acquisition platforms (to assay the genome, epigenome, transcriptome, proteome, metabolome, and gut microbiome), snapshots of each phase of atherosclerotic cardiovascular disease development can be captured. In this review, we will summarize emerging approaches for assessment of atherosclerotic cardiovascular disease risk in humans using peripheral blood molecular signatures and molecular imaging approaches. We will then discuss the potential (and challenges) for these snapshots to be integrated into a personalized movie providing dynamic readouts of an individual's atherosclerotic cardiovascular disease risk status throughout the life course.
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Affiliation(s)
- Matthew Nayor
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Kemar J. Brown
- Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Ramachandran S. Vasan
- Sections of Preventive Medicine & Epidemiology, and Cardiology, Department of Medicine, Boston University School of Medicine, Boston, MA; Department of Epidemiology, Boston University School of Public Health; Boston University Center for Computing and Data Sciences
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14
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Zeitouni M, Clare RM, Chiswell K, Abdulrahim J, Shah N, Pagidipati NP, Shah SH, Roe MT, Patel MR, Jones WS. Risk Factor Burden and Long-Term Prognosis of Patients With Premature Coronary Artery Disease. J Am Heart Assoc 2020; 9:e017712. [PMID: 33287625 PMCID: PMC7955368 DOI: 10.1161/jaha.120.017712] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Background Coronary artery disease (CAD) is increasing among young adults. We aimed to describe the cardiovascular risk factors and long-term prognosis of premature CAD. Methods and Results Using the Duke Databank for Cardiovascular Disease, we evaluated 3655 patients admitted between 1995 and 2013 with a first diagnosis of obstructive CAD before the age of 50 years. Major adverse cardiovascular events (MACEs), defined as the composite of death, myocardial infarction, stroke, or revascularization, were ascertained for up to 10 years. Cox proportional hazard regression models were used to assess associations with the rate of first recurrent event, and negative binomial log-linear regression was used for rate of multiple event recurrences. Past or current smoking was the most frequent cardiovascular factor (60.8%), followed by hypertension (52.8%) and family history of CAD (39.8%). Within a 10-year follow-up, 52.9% of patients had at least 1 MACE, 18.6% had at least 2 recurrent MACEs, and 7.9% had at least 3 recurrent MACEs, with death occurring in 20.9% of patients. Across follow-up, 31.7% to 37.2% of patients continued smoking, 81.7% to 89.3% had low-density lipoprotein cholesterol levels beyond the goal of 70 mg/dL, and 16% had new-onset diabetes mellitus. Female sex, diabetes mellitus, chronic kidney disease, multivessel disease, and chronic inflammatory disease were factors associated with recurrent MACEs. Conclusions Premature CAD is an aggressive disease with frequent ischemic recurrences and premature death. Individuals with premature CAD have a high proportion of modifiable cardiovascular risk factors, but failure to control them is frequently observed.
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Affiliation(s)
- Michel Zeitouni
- Duke Clinical Research Institute Duke University School of Medicine Durham NC
| | - Robert M Clare
- Duke Clinical Research Institute Duke University School of Medicine Durham NC
| | - Karen Chiswell
- Duke Clinical Research Institute Duke University School of Medicine Durham NC
| | | | - Nishant Shah
- Duke Clinical Research Institute Duke University School of Medicine Durham NC.,Duke University Medical Center Durham NC
| | - Neha P Pagidipati
- Duke Clinical Research Institute Duke University School of Medicine Durham NC.,Duke University Medical Center Durham NC
| | - Svati H Shah
- Duke Clinical Research Institute Duke University School of Medicine Durham NC.,Duke Molecular Physiology Institute Durham NC
| | - Matthew T Roe
- Duke Clinical Research Institute Duke University School of Medicine Durham NC.,Duke University Medical Center Durham NC
| | - Manesh R Patel
- Duke Clinical Research Institute Duke University School of Medicine Durham NC.,Duke University Medical Center Durham NC
| | - W Schuyler Jones
- Duke Clinical Research Institute Duke University School of Medicine Durham NC.,Duke University Medical Center Durham NC
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15
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Rostami S, Hoff M, Dalen H, Hveem K, Videm V. Genetic risk score associations for myocardial infarction are comparable in persons with and without rheumatoid arthritis: the population-based HUNT study. Sci Rep 2020; 10:20416. [PMID: 33235261 PMCID: PMC7686351 DOI: 10.1038/s41598-020-77432-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 11/11/2020] [Indexed: 11/09/2022] Open
Abstract
Persons with rheumatoid arthritis (RA) have increased risk of myocardial infarction (MI). Overlapping associations with MI of weighted genetic risk scores (wGRS) for coronary artery disease (CAD) and RA is unknown in a population-based setting. Data from the prospective Nord-Trøndelag Health Study (HUNT2: 1995–1997 and HUNT3: 2006–2008) were used. wGRS added each participant’s carriage of all risk variants weighted by the coefficient from published association studies. Published wGRS for CAD and RA were analysed in Cox regression with MI as outcome, age as analysis time, and censoring at the first MI, death, or 31.12.2017. 2609 of 61,465 participants developed MI during follow-up (mean 17.7 years). The best-fitting wGRS for CAD and RA included 157 and 27 single-nucleotide polymorphisms, respectively. In multivariable analysis including traditional CAD risk factors, the CAD wGRS was associated with MI [hazard ratio = 1.23 (95% CI 1.18–1.27) for each SD increase, p < 0.0001] in RA patients (n = 433) and controls. The RA wGRS was not significant (p = 0.06). Independently from traditional risk factors, a CAD wGRS was significantly associated with the risk for MI in RA patients and controls, whereas an RA wGRS was not. The captured genetic risk for RA contributed little to the risk of MI.
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Affiliation(s)
- S Rostami
- Department of Clinical and Molecular Medicine, St. Olavs Hospital, NTNU - Norwegian University of Science and Technology, Lab Center 3 East, 7006, Trondheim, Norway
| | - M Hoff
- Department of Neuromedicine and Movement Science, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.,Department of Public Health and Nursing, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.,Department of Rheumatology, St. Olavs University Hospital, Trondheim, Norway
| | - H Dalen
- Department of Circulation and Medical Imaging, NTNU - Norwegian University of Science and Technology, Trondheim, Norway.,Clinic of Cardiology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway.,Levanger Hospital, Nord-Trøndelag Hospital Trust, Levanger, Norway
| | - K Hveem
- KG Jebsen Center for Genetic Epidemiology, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - V Videm
- Department of Clinical and Molecular Medicine, St. Olavs Hospital, NTNU - Norwegian University of Science and Technology, Lab Center 3 East, 7006, Trondheim, Norway. .,Department of Immunology and Transfusion Medicine, St. Olavs University Hospital, Trondheim, Norway.
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16
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Wang H, Liu Z, Shao J, Jiang M, Lu X, Lin L, Wang L, Xu Q, Zhang H, Li X, Zhou J, Chen Y, Zhang R. Pathogenesis of premature coronary artery disease: Focus on risk factors and genetic variants. Genes Dis 2020; 9:370-380. [PMID: 35224153 PMCID: PMC8843894 DOI: 10.1016/j.gendis.2020.11.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2020] [Revised: 10/17/2020] [Accepted: 11/04/2020] [Indexed: 11/24/2022] Open
Abstract
The development of premature coronary artery disease (PCAD) is dependent on both genetic predisposition and traditional risk factors. Strategies for unraveling the genetic basis of PCAD have evolved with the advent of modern technologies. Genome-wide association studies (GWASs) have identified a considerable number of common genetic variants that are associated with PCAD. Most of these genetic variants are attributable to lipid and blood pressure-related single-nucleotide polymorphisms (SNPs). The genetic variants that predispose individuals to developing PCAD may depend on race and ethnicity. Some characteristic genetic variants have been identified in Chinese populations. Although translating this genetic knowledge into clinical applications is still challenging, these genetic variants can be used for CAD phenotype identification, genetic prediction and therapy. In this article we will provide a comprehensive review of genetic variants detected by GWASs that are predicted to contribute to the development of PCAD. We will highlight recent findings regarding CAD-related genetic variants in Chinese populations and discuss the potential clinical utility of genetic variants for preventing and managing PCAD.
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17
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Gooding HC, Gidding SS, Moran AE, Redmond N, Allen NB, Bacha F, Burns TL, Catov JM, Grandner MA, Harris KM, Johnson HM, Kiernan M, Lewis TT, Matthews KA, Monaghan M, Robinson JG, Tate D, Bibbins-Domingo K, Spring B. Challenges and Opportunities for the Prevention and Treatment of Cardiovascular Disease Among Young Adults: Report From a National Heart, Lung, and Blood Institute Working Group. J Am Heart Assoc 2020; 9:e016115. [PMID: 32993438 PMCID: PMC7792379 DOI: 10.1161/jaha.120.016115] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Improvements in cardiovascular disease (CVD) rates among young adults in the past 2 decades have been offset by increasing racial/ethnic and gender disparities, persistence of unhealthy lifestyle habits, overweight and obesity, and other CVD risk factors. To enhance the promotion of cardiovascular health among young adults 18 to 39 years old, the medical and broader public health community must understand the biological, interpersonal, and behavioral features of this life stage. Therefore, the National Heart, Lung, and Blood Institute, with support from the Office of Behavioral and Social Science Research, convened a 2-day workshop in Bethesda, Maryland, in September 2017 to identify research challenges and opportunities related to the cardiovascular health of young adults. The current generation of young adults live in an environment undergoing substantial economic, social, and technological transformations, differentiating them from prior research cohorts of young adults. Although the accumulation of clinical and behavioral risk factors for CVD begins early in life, and research suggests early risk is an important determinant of future events, few trials have studied prevention and treatment of CVD in participants <40 years old. Building an evidence base for CVD prevention in this population will require the engagement of young adults, who are often disconnected from the healthcare system and may not prioritize long-term health. These changes demand a repositioning of existing evidence-based treatments to accommodate new sociotechnical contexts. In this article, the authors review the recent literature and current research opportunities to advance the cardiovascular health of today's young adults.
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Affiliation(s)
- Holly C Gooding
- Division of General Pediatrics and Adolescent Medicine Emory UniversityChildren's Healthcare of Atlanta Atlanta GA
| | | | - Andrew E Moran
- Division of General Medicine Columbia University New York NY
| | | | - Norrina B Allen
- Department of Preventive Medicine Northwestern University Feinberg School of Medicine Chicago IL
| | - Fida Bacha
- Division of Pediatric Endocrinology and Diabetes Texas Children's HospitalBaylor College of Medicine Houston TX
| | - Trudy L Burns
- Department of Epidemiology University of Iowa Iowa City IA
| | - Janet M Catov
- Department of Obstetrics, Gynecology & Reproductive Sciences Department of Epidemiology University of Pittsburgh Pittsburgh PA
| | | | | | - Heather M Johnson
- Blechman Center for Specialty Care and Preventive Cardiology Boca Raton Regional Hospital/Baptist Health South Florida Boca Raton FL
| | - Michaela Kiernan
- Department of Medicine Stanford University School of Medicine Stanford CA
| | - Tené T Lewis
- Department of Epidemiology Emory University, Children's Healthcare of Atlanta Atlanta GA
| | | | - Maureen Monaghan
- Department of Psychiatry and Behavioral Sciences Department of Pediatrics Children's National Health System George Washington University School of Medicine Washington DC
| | | | - Deborah Tate
- Department of Sociology University of North Carolina at Chapel Hill Chapel Hill NC
| | - Kirsten Bibbins-Domingo
- Department of Epidemiology and Biostatistics University of California San Francisco San Francisco CA
| | - Bonnie Spring
- Department of Preventive Medicine Northwestern University Feinberg School of Medicine Chicago IL
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18
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Ouyang H, Han F, Zhou ZC, Zhang J. Differences in clinical and genetic characteristics between early- and late-onset narcolepsy in a Han Chinese cohort. Neural Regen Res 2020; 15:1887-1893. [PMID: 32246636 PMCID: PMC7513989 DOI: 10.4103/1673-5374.280322] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Revised: 11/05/2019] [Accepted: 12/31/2019] [Indexed: 01/16/2023] Open
Abstract
Early- and late-onset narcolepsy constitutes two distinct diagnostic subgroups. However, it is not clear whether symptomology and genetic risk factors differ between early- and late-onset narcoleptics. This study compared clinical data and single-nucleotide polymorphisms (SNPs) between early- and late-onset patients in a large cohort of 899 Han Chinese narcolepsy patients. Blood, cerebrospinal fluid, and clinical data were prospectively collected from patients, and patients were genotyped for 40 previously reported narcolepsy risk-conferring SNPs. Genetic risk scores (GRSs), associations of five different sets of SNPs (GRS1-GRS5) with early- and late-onset narcolepsy, were evaluated using logistic regression and receiver operating characteristic curves. Mean sleep latency was significantly shorter in early-onset cases than in late-onset cases. Symptom severity was greater among late-onset patients, with higher rates of sleep paralysis, hypnagogic hallucinations, health-related quality of life impairment, and concurrent presentation with four or more symptoms. Hypocretin levels did not differ significantly between early- and late-onset cases. Only rs3181077 (CCR1/CCR3) and rs9274477 (HLA-DQB1) were more prevalent among early-onset cases. Only GRS1 (26 SNPs; OR = 1.513, 95% CI: 0.893-2.585; P < 0.05) and GRS5 (6 SNPs; OR = 1.893, 95% CI: 1.204-2.993; P < 0.05) were associated with early-onset narcolepsy, with areas under the receiver operating characteristic curves of 0.731 and 0.732, respectively. Neither GRS1 nor GRS5 included SNPs in HLA regions. Our results indicate that symptomology and genetic risk factors differ between early- and late-onset narcolepsy. This protocol was approved by the Institutional Review Board (IRB) Panels on Medical Human Subjects at Peking University People's Hospital, China (approval No. Yuanlunshenlinyi 86) in October 2011.
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Affiliation(s)
- Hui Ouyang
- Department of Clinical Neurology, Peking University People's Hospital, Beijing, China
| | - Fang Han
- Department of Clinical Pulmonology, Peking University People's Hospital, Beijing, China
| | - Ze-Chen Zhou
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, Beijing, China
| | - Jun Zhang
- Department of Clinical Neurology, Peking University People's Hospital, Beijing, China
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19
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Manousaki D, Forgetta V, Keller-Baruch J, Zhao K, Greenwood CM, Mooser V, Bassett JD, Leslie WD, Richards JB. A Polygenic Risk Score as a Risk Factor for Medication-Associated Fractures. J Bone Miner Res 2020; 35:1935-1941. [PMID: 32511779 DOI: 10.1002/jbmr.4104] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 03/17/2020] [Accepted: 04/13/2020] [Indexed: 12/11/2022]
Abstract
Some commonly prescribed drugs are associated with increased risk of osteoporotic fractures. However, fracture risk stratification using skeletal measures is not often performed to identify those at risk before these medications are prescribed. We tested whether a genomically predicted skeletal measure, speed of sound (gSOS) from heel ultrasound, which was developed in 341,449 individuals from UK Biobank and tested in a separate subset consisting of 80,027 individuals, is an independent risk factor for fracture in users of fracture-related drugs (FRDs). To do this, we first assessed 80,014 UK Biobank participants (including 12,678 FRD users) for incident major osteoporotic fracture (MOF, n = 1189) and incident hip fracture (n = 209). Effects of gSOS on incident fracture were adjusted for baseline clinical fracture risk factors. We found that each standard deviation decrease in gSOS increased the adjusted odds of MOF by 42% (95% confidence interval [CI] 1.34-1.51, p < 2 × 10-16 ) and of hip fracture by 31% (95% CI 1.15-1.50, p = 9 × 10-5 ). gSOS below versus above the mean increased the adjusted odds of MOF by 79% (95% CI 1.58-2.01, p < 2 × 10-16 ) and of hip fracture by 42% (95% CI 1.08-1.88, p = 1.3 × 10-2 ). Among FRD users, each standard deviation decrease in gSOS increased the adjusted odds of MOF by 29% (nMOF = 256, 95% CI 1.14-1.46, p = 7 × 10-5 ) and of hip fracture by 30% (nhip fracture = 68, 95% CI 1.02-1.65, p = 0.0335). FRD users with gSOS below versus above the mean had a 54% increased adjusted odds of MOF (95% 1.19-1.99, p = 8.95 × 10-4 ) and a twofold increased adjusted odds of hip fracture (95% 1.19-3.31, p = 8.5 × 10-3 ). We therefore showed that genomically predicted heel SOS is independently associated with incident fracture among FRD users. © 2020 American Society for Bone and Mineral Research.
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Affiliation(s)
- Despoina Manousaki
- Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montréal, Canada.,Department of Human Genetics, McGill University, Montréal, Canada
| | - Vincenzo Forgetta
- Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montréal, Canada
| | - Julyan Keller-Baruch
- Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montréal, Canada.,Department of Human Genetics, McGill University, Montréal, Canada
| | - Kaiqiong Zhao
- Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montréal, Canada.,Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montréal, Canada
| | - Celia Mt Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montréal, Canada.,Department of Human Genetics, McGill University, Montréal, Canada.,Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montréal, Canada.,Gerald Bronfman Department of Oncology, McGill University, Montréal, Canada
| | - Vincent Mooser
- Department of Human Genetics, McGill University, Montréal, Canada
| | - Jh Duncan Bassett
- Molecular Endocrinology Laboratory, Department of Medicine, Imperial College London, London, UK
| | - William D Leslie
- Department of Medicine, University of Manitoba, Winnipeg, Canada
| | - J Brent Richards
- Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montréal, Canada.,Department of Human Genetics, McGill University, Montréal, Canada.,Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montréal, Canada.,Department of Medicine, McGill University, Montréal, Canada.,Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
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20
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Musunuru K, Hershberger RE, Day SM, Klinedinst NJ, Landstrom AP, Parikh VN, Prakash S, Semsarian C, Sturm AC. Genetic Testing for Inherited Cardiovascular Diseases: A Scientific Statement From the American Heart Association. CIRCULATION-GENOMIC AND PRECISION MEDICINE 2020; 13:e000067. [DOI: 10.1161/hcg.0000000000000067] [Citation(s) in RCA: 113] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Advances in human genetics are improving the understanding of a variety of inherited cardiovascular diseases, including cardiomyopathies, arrhythmic disorders, vascular disorders, and lipid disorders such as familial hypercholesterolemia. However, not all cardiovascular practitioners are fully aware of the utility and potential pitfalls of incorporating genetic test results into the care of patients and their families. This statement summarizes current best practices with respect to genetic testing and its implications for the management of inherited cardiovascular diseases.
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21
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Forgetta V, Keller-Baruch J, Forest M, Durand A, Bhatnagar S, Kemp JP, Nethander M, Evans D, Morris JA, Kiel DP, Rivadeneira F, Johansson H, Harvey NC, Mellström D, Karlsson M, Cooper C, Evans DM, Clarke R, Kanis JA, Orwoll E, McCloskey EV, Ohlsson C, Pineau J, Leslie WD, Greenwood CMT, Richards JB. Development of a polygenic risk score to improve screening for fracture risk: A genetic risk prediction study. PLoS Med 2020; 17:e1003152. [PMID: 32614825 PMCID: PMC7331983 DOI: 10.1371/journal.pmed.1003152] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 06/03/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Since screening programs identify only a small proportion of the population as eligible for an intervention, genomic prediction of heritable risk factors could decrease the number needing to be screened by removing individuals at low genetic risk. We therefore tested whether a polygenic risk score for heel quantitative ultrasound speed of sound (SOS)-a heritable risk factor for osteoporotic fracture-can identify low-risk individuals who can safely be excluded from a fracture risk screening program. METHODS AND FINDINGS A polygenic risk score for SOS was trained and selected in 2 separate subsets of UK Biobank (comprising 341,449 and 5,335 individuals). The top-performing prediction model was termed "gSOS", and its utility in fracture risk screening was tested in 5 validation cohorts using the National Osteoporosis Guideline Group clinical guidelines (N = 10,522 eligible participants). All individuals were genome-wide genotyped and had measured fracture risk factors. Across the 5 cohorts, the average age ranged from 57 to 75 years, and 54% of studied individuals were women. The main outcomes were the sensitivity and specificity to correctly identify individuals requiring treatment with and without genetic prescreening. The reference standard was a bone mineral density (BMD)-based Fracture Risk Assessment Tool (FRAX) score. The secondary outcomes were the proportions of the screened population requiring clinical-risk-factor-based FRAX (CRF-FRAX) screening and BMD-based FRAX (BMD-FRAX) screening. gSOS was strongly correlated with measured SOS (r2 = 23.2%, 95% CI 22.7% to 23.7%). Without genetic prescreening, guideline recommendations achieved a sensitivity and specificity for correct treatment assignment of 99.6% and 97.1%, respectively, in the validation cohorts. However, 81% of the population required CRF-FRAX tests, and 37% required BMD-FRAX tests to achieve this accuracy. Using gSOS in prescreening and limiting further assessment to those with a low gSOS resulted in small changes to the sensitivity and specificity (93.4% and 98.5%, respectively), but the proportions of individuals requiring CRF-FRAX tests and BMD-FRAX tests were reduced by 37% and 41%, respectively. Study limitations include a reliance on cohorts of predominantly European ethnicity and use of a proxy of fracture risk. CONCLUSIONS Our results suggest that the use of a polygenic risk score in fracture risk screening could decrease the number of individuals requiring screening tests, including BMD measurement, while maintaining a high sensitivity and specificity to identify individuals who should be recommended an intervention.
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Affiliation(s)
- Vincenzo Forgetta
- Centre for Clinical Epidemiology, Department of Medicine, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada
| | | | - Marie Forest
- Centre for Clinical Epidemiology, Department of Medicine, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada
| | - Audrey Durand
- School of Computer Science, McGill University, Montréal, Québec, Canada
| | - Sahir Bhatnagar
- Centre for Clinical Epidemiology, Department of Medicine, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada
| | - John P. Kemp
- University of Queensland Diamantina Institute, University of Queensland, Woolloongabba, Queensland, Australia
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Maria Nethander
- Bioinformatics Core Facility, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute for Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Daniel Evans
- California Pacific Medical Center Research Institute, San Francisco, California, United States of America
| | - John A. Morris
- Centre for Clinical Epidemiology, Department of Medicine, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada
| | - Douglas P. Kiel
- Institute for Aging Research, Hebrew SeniorLife, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Broad Institute of MIT & Harvard University, Boston, Massachusetts, United States of America
| | | | - Helena Johansson
- Centre for Metabolic Bone Diseases, University of Sheffield, Sheffield, United Kingdom
- Australian Catholic University, Melbourne, Victoria, Australia
| | - Nicholas C. Harvey
- Medical Research Council Lifecourse Epidemiology Unit, University of Southampton, Southampton, United Kingdom
- National Institute for Health Research Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, United Kingdom
| | - Dan Mellström
- Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute for Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Magnus Karlsson
- Department of Orthopaedics and Clinical Sciences, Lund University, Skane University Hospital, Malmö, Sweden
| | - Cyrus Cooper
- Medical Research Council Lifecourse Epidemiology Unit, University of Southampton, Southampton, United Kingdom
- National Institute for Health Research Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, United Kingdom
- National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
| | - David M. Evans
- University of Queensland Diamantina Institute, University of Queensland, Woolloongabba, Queensland, Australia
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Robert Clarke
- Clinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Oxford, United Kingdom
| | - John A. Kanis
- Centre for Metabolic Bone Diseases, University of Sheffield, Sheffield, United Kingdom
- Australian Catholic University, Melbourne, Victoria, Australia
| | - Eric Orwoll
- Bone and Mineral Unit, Oregon Health & Science University, Portland, Oregon, United States of America
- Department of Medicine, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Eugene V. McCloskey
- Mellanby Centre for Bone Research, Centre for Integrated Research in Musculoskeletal Ageing, University of Sheffield and Sheffield Teaching Hospitals Foundation Trust, Sheffield, United Kingdom
| | - Claes Ohlsson
- Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute for Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Joelle Pineau
- School of Computer Science, McGill University, Montréal, Québec, Canada
| | - William D. Leslie
- Department of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Celia M. T. Greenwood
- Centre for Clinical Epidemiology, Department of Medicine, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montréal, Québec, Canada
- Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Montréal, Québec, Canada
| | - J. Brent Richards
- Centre for Clinical Epidemiology, Department of Medicine, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
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22
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Manousaki D, Kämpe A, Forgetta V, Makitie RE, Bardai G, Belisle A, Li R, Andersson S, Makitie O, Rauch F, Richards JB. Increased Burden of Common Risk Alleles in Children With a Significant Fracture History. J Bone Miner Res 2020; 35:875-882. [PMID: 31914204 DOI: 10.1002/jbmr.3956] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 12/11/2019] [Accepted: 12/14/2019] [Indexed: 12/22/2022]
Abstract
Extreme presentations of common disease in children are often presumed to be of Mendelian etiology, but their polygenic basis has not been fully explored. We tested whether children with significant fracture history and no osteogenesis imperfecta (OI) are at increased polygenic risk for fracture. A childhood significant fracture history was defined as the presence of low-trauma vertebral fractures or multiple long bone fractures. We generated a polygenic score of heel ultrasound-derived speed of sound, termed "gSOS," which predicts risk of osteoporotic fracture. We tested if individuals from three cohorts with significant childhood fracture history had lower gSOS. A Canadian cohort included 94 children with suspected Mendelian osteoporosis, of which 68 had negative OI gene panel. Two Finnish cohorts included 59 children with significant fracture history and 22 with suspected Mendelian osteoporosis, among which 18 had no OI. After excluding individuals with OI and ancestral outliers, we generated gSOS estimates and compared their mean to that of a UK Biobank subset, representing the general population. The average gSOS across all three cohorts (n = 131) was -0.47 SD lower than that in UK Biobank (n = 80,027, p = 1.1 × 10-5 ). The gSOS of 78 individuals with suspected Mendelian osteoporosis was even lower (-0.76 SD, p = 5.3 × 10-10 ). Among the 131 individuals with a significant fracture history, we observed 8 individuals with gSOS below minus 2 SD from the mean; their mean lumbar spine DXA-derived bone mineral density Z-score was -1.7 (SD 0.8). In summary, children with significant fracture history but no OI have an increased burden of common risk alleles. This suggests that a polygenic contribution to disease should be considered in children with extreme presentations of fracture. © 2020 American Society for Bone and Mineral Research.
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Affiliation(s)
- Despoina Manousaki
- Lady Davis Institute for Medical Research, Centre for Clinical Epidemiology, Jewish General Hospital, McGill University, Montreal, Canada.,Department of Human Genetics, McGill University, Montreal, Canada
| | - Anders Kämpe
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.,Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | - Vincenzo Forgetta
- Lady Davis Institute for Medical Research, Centre for Clinical Epidemiology, Jewish General Hospital, McGill University, Montreal, Canada
| | - Riikka E Makitie
- Research Program for Clinical and Molecular Metabolism, University of Helsinki, Helsinki, Finland.,Folkhälsan Institute of Genetics, Helsinki, Finland.,Molecular Endocrinology Laboratory, Department of Medicine, Hammersmith Campus, Imperial College London, London, UK
| | - Ghalib Bardai
- McGill University, Ingram School of Nursing, and Shriners Hospitals for Children, Montreal, Canada
| | | | - Rui Li
- McGill Genome Center, McGill University, Montreal, Canada
| | - Sture Andersson
- Children's Hospital, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Outi Makitie
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden.,Research Program for Clinical and Molecular Metabolism, University of Helsinki, Helsinki, Finland.,Folkhälsan Institute of Genetics, Helsinki, Finland.,Children's Hospital, Pediatric Research Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Frank Rauch
- McGill University, Ingram School of Nursing, and Shriners Hospitals for Children, Montreal, Canada
| | - J Brent Richards
- Lady Davis Institute for Medical Research, Centre for Clinical Epidemiology, Jewish General Hospital, McGill University, Montreal, Canada.,Department of Human Genetics, McGill University, Montreal, Canada.,Department of Medicine, Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Canada.,Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
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23
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Aragam KG, Natarajan P. Polygenic Scores to Assess Atherosclerotic Cardiovascular Disease Risk: Clinical Perspectives and Basic Implications. Circ Res 2020; 126:1159-1177. [PMID: 32324503 DOI: 10.1161/circresaha.120.315928] [Citation(s) in RCA: 84] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
An individual's susceptibility to atherosclerotic cardiovascular disease is influenced by numerous clinical and lifestyle factors, motivating the multifaceted approaches currently endorsed for primary and secondary cardiovascular disease prevention. With growing knowledge of the genetic basis of atherosclerotic cardiovascular disease-in particular, coronary artery disease-and its contribution to disease pathogenesis, there is increased interest in understanding the potential clinical utility of a genetic predictor that might further refine the assessment and management of atherosclerotic cardiovascular disease risk. Rapid scientific and technological advances have enabled widespread genotyping efforts and dynamic research in the field of coronary artery disease genetic risk prediction. In this review, we describe how genomic analyses of coronary artery disease have been leveraged to create polygenic risk scores. We then discuss evaluations of the clinical utility of these scores, pertinent mechanistic insights gleaned, and practical considerations relevant to the implementation of polygenic risk scores in the health care setting.
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Affiliation(s)
- Krishna G Aragam
- From the Cardiovascular Research Center, Massachusetts General Hospital, Boston; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA; and Department of Medicine, Harvard Medical School, Boston, MA
| | - Pradeep Natarajan
- From the Cardiovascular Research Center, Massachusetts General Hospital, Boston; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA; and Department of Medicine, Harvard Medical School, Boston, MA
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24
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Higny J, Dupont M, Guédès A. Cardiac computed tomography in asymptomatic siblings of patients with premature coronary disease: illustrations and current knowledge. Acta Cardiol 2020; 75:107-115. [PMID: 30741097 DOI: 10.1080/00015385.2018.1561350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
A strong family history of early-onset coronary artery disease (CAD) may represent a substantial predictor of enhanced development of subclinical atherosclerosis in a sibling population. In this population, standard cardiovascular (CV) risk assessment could be underrated with the predictive capacity of the Framingham Risk Score. At present, cardiac computed tomography (CT) provides a high diagnostic performance for the detection of coronary atherosclerosis. Nevertheless, there is a paucity of data concerning the prognostic value of this technology in apparently healthy relatives of patients with premature coronary events. In addition, little is known about the prevalence of CAD in the siblings of patients with premature cardiac events. However, we are convinced that the reclassification of cardiac risk in middle-aged adults at familial risk is a fundamental issue in preventive cardiology. In this manuscript, we report cardiac CT findings in three subjects apparently free of CV disease from families with early-onset CAD. Afterwards, we provide a summary of the current knowledge and discuss the potential usefulness of this non-invasive imaging technique in susceptible individuals. Finally, we hope that this article will help to increase awareness for the management of middle-aged adults from high-risk families.
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Affiliation(s)
- Julien Higny
- Department of Cardiology, CHU UCL Namur, site Godinne, Yvoir, Belgium
| | - Michaël Dupont
- Department of Radiology, CHU UCL Namur, site Godinne, Yvoir, Belgium
| | - Antoine Guédès
- Department of Cardiology, CHU UCL Namur, site Godinne, Yvoir, Belgium
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25
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Pinese M, Lacaze P, Rath EM, Stone A, Brion MJ, Ameur A, Nagpal S, Puttick C, Husson S, Degrave D, Cristina TN, Kahl VFS, Statham AL, Woods RL, McNeil JJ, Riaz M, Barr M, Nelson MR, Reid CM, Murray AM, Shah RC, Wolfe R, Atkins JR, Fitzsimmons C, Cairns HM, Green MJ, Carr VJ, Cowley MJ, Pickett HA, James PA, Powell JE, Kaplan W, Gibson G, Gyllensten U, Cairns MJ, McNamara M, Dinger ME, Thomas DM. The Medical Genome Reference Bank contains whole genome and phenotype data of 2570 healthy elderly. Nat Commun 2020; 11:435. [PMID: 31974348 PMCID: PMC6978518 DOI: 10.1038/s41467-019-14079-0] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2019] [Accepted: 12/13/2019] [Indexed: 01/24/2023] Open
Abstract
Population health research is increasingly focused on the genetic determinants of healthy ageing, but there is no public resource of whole genome sequences and phenotype data from healthy elderly individuals. Here we describe the first release of the Medical Genome Reference Bank (MGRB), comprising whole genome sequence and phenotype of 2570 elderly Australians depleted for cancer, cardiovascular disease, and dementia. We analyse the MGRB for single-nucleotide, indel and structural variation in the nuclear and mitochondrial genomes. MGRB individuals have fewer disease-associated common and rare germline variants, relative to both cancer cases and the gnomAD and UK Biobank cohorts, consistent with risk depletion. Age-related somatic changes are correlated with grip strength in men, suggesting blood-derived whole genomes may also provide a biologic measure of age-related functional deterioration. The MGRB provides a broadly applicable reference cohort for clinical genetics and genomic association studies, and for understanding the genetics of healthy ageing. Healthspan and healthy aging are areas of research with potential socioeconomic impact. Here, the authors present the Medical Genome Reference Bank (MGRB) which consist of over 4,000 individuals aged 70 years and older without a history of the major age-related diseases and report on results from whole-genome sequencing and association analyses.
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Affiliation(s)
- Mark Pinese
- Garvan Institute of Medical Research, Sydney, NSW, Australia.,Children's Cancer Institute, University of New South Wales, Sydney, NSW, Australia.,School of Women's and Children's Health, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
| | - Paul Lacaze
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Emma M Rath
- Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - Andrew Stone
- Garvan Institute of Medical Research, Sydney, NSW, Australia.,Children's Cancer Institute, University of New South Wales, Sydney, NSW, Australia.,School of Women's and Children's Health, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.,St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
| | - Marie-Jo Brion
- Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - Adam Ameur
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia.,Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Sini Nagpal
- Center for Integrative Genomics, Georgia Institute of Technology, Atlanta, GA, USA
| | - Clare Puttick
- Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - Shane Husson
- Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - Dmitry Degrave
- Garvan Institute of Medical Research, Sydney, NSW, Australia
| | | | - Vivian F S Kahl
- Children's Medical Research Institute, Faculty of Medicine and Health, University of Sydney, Westmead, NSW, Australia
| | - Aaron L Statham
- Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - Robyn L Woods
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - John J McNeil
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Moeen Riaz
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Margo Barr
- Centre for Primary Health Care and Equity, University of New South Wales, Sydney, NSW, Australia
| | - Mark R Nelson
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia.,Menzies Institute for Medical Research, University of Tasmania, Hobart, TAS, Australia
| | - Christopher M Reid
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia.,School of Public Health, Curtin University, Perth, WA, Australia
| | - Anne M Murray
- Berman Center for Outcomes and Clinical Research, Hennepin Healthcare Research Institute, Hennepin Healthcare, Minneapolis, MN, USA.,Division of Geriatrics, Department of Medicine, Hennepin County Medical Center and University of Minnesota, Minneapolis, MN, USA
| | - Raj C Shah
- Department of Family Medicine and Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Rory Wolfe
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Joshua R Atkins
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia.,Centre for Brain and Mental Health Research, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Chantel Fitzsimmons
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia.,Centre for Brain and Mental Health Research, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Heath M Cairns
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia.,Centre for Brain and Mental Health Research, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Melissa J Green
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.,Neuroscience Research Australia, Sydney, NSW, Australia
| | - Vaughan J Carr
- School of Psychiatry, University of New South Wales, Sydney, NSW, Australia.,Neuroscience Research Australia, Sydney, NSW, Australia.,Department of Psychiatry, School of Clinical Sciences, Monash University, Melbourne, VIC, Australia
| | - Mark J Cowley
- Garvan Institute of Medical Research, Sydney, NSW, Australia.,Children's Cancer Institute, University of New South Wales, Sydney, NSW, Australia.,School of Women's and Children's Health, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
| | - Hilda A Pickett
- Children's Medical Research Institute, Faculty of Medicine and Health, University of Sydney, Westmead, NSW, Australia
| | - Paul A James
- Parkville Familial Cancer Centre, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Joseph E Powell
- UNSW Cellular Genomics Futures Institute, School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia.,Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - Warren Kaplan
- Garvan Institute of Medical Research, Sydney, NSW, Australia.,St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
| | - Greg Gibson
- Center for Integrative Genomics, Georgia Institute of Technology, Atlanta, GA, USA
| | - Ulf Gyllensten
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW, Australia.,Centre for Brain and Mental Health Research, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | | | - Marcel E Dinger
- Garvan Institute of Medical Research, Sydney, NSW, Australia.,School of Biotechnology and Biomolecular Sciences, Faculty of Science, University of New South Wales, Sydney, NSW, Australia
| | - David M Thomas
- Garvan Institute of Medical Research, Sydney, NSW, Australia. .,St Vincent's Clinical School, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
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26
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Tam V, Patel N, Turcotte M, Bossé Y, Paré G, Meyre D. Benefits and limitations of genome-wide association studies. Nat Rev Genet 2019; 20:467-484. [PMID: 31068683 DOI: 10.1038/s41576-019-0127-1] [Citation(s) in RCA: 888] [Impact Index Per Article: 177.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Genome-wide association studies (GWAS) involve testing genetic variants across the genomes of many individuals to identify genotype-phenotype associations. GWAS have revolutionized the field of complex disease genetics over the past decade, providing numerous compelling associations for human complex traits and diseases. Despite clear successes in identifying novel disease susceptibility genes and biological pathways and in translating these findings into clinical care, GWAS have not been without controversy. Prominent criticisms include concerns that GWAS will eventually implicate the entire genome in disease predisposition and that most association signals reflect variants and genes with no direct biological relevance to disease. In this Review, we comprehensively assess the benefits and limitations of GWAS in human populations and discuss the relevance of performing more GWAS.
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Affiliation(s)
- Vivian Tam
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Nikunj Patel
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Michelle Turcotte
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
| | - Yohan Bossé
- Institut Universitaire de Cardiologie et de Pneumologie de Québec-Université Laval, Québec City, Québec, Canada.,Department of Molecular Medicine, Laval University, Québec City, Quebec, Canada
| | - Guillaume Paré
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada.,Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
| | - David Meyre
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada. .,Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada. .,Inserm UMRS 954 N-GERE (Nutrition-Genetics-Environmental Risks), University of Lorraine, Faculty of Medicine, Nancy, France.
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Prediction and management of CAD risk based on genetic stratification. Trends Cardiovasc Med 2019; 30:328-334. [PMID: 31543237 DOI: 10.1016/j.tcm.2019.08.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Revised: 08/01/2019] [Accepted: 08/20/2019] [Indexed: 12/24/2022]
Abstract
Discovery of genetic risk variants for CAD and their assembly on a computerized microarray enables a genetic risk score (GRS) to be expressed as a single number. Utilizing this array, genetic risk stratification has been performed in over 1 million cases and controls. The genetic score based on one's DNA can be determined anytime from birth on and is independent of age and conventional risk factors. Utilizing the GRS, one can select those at highest risk and would benefit most from primary prevention. Clinical trials have shown that modifying lifestyle or using statin therapy reduces the risk for CAD by approximately 50%. The use of the GRS for primary prevention will have a transformative effect on preventing the spread of CAD.
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Natarajan P. Polygenic Risk Scoring for Coronary Heart Disease: The First Risk Factor. J Am Coll Cardiol 2019; 72:1894-1897. [PMID: 30309465 DOI: 10.1016/j.jacc.2018.08.1041] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Revised: 08/10/2018] [Accepted: 08/14/2018] [Indexed: 12/30/2022]
Affiliation(s)
- Pradeep Natarajan
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts; Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts; and the Department of Medicine, Harvard Medical School, Boston, Massachusetts.
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Genetics of Common, Complex Coronary Artery Disease. Cell 2019; 177:132-145. [DOI: 10.1016/j.cell.2019.02.015] [Citation(s) in RCA: 116] [Impact Index Per Article: 23.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Revised: 02/11/2019] [Accepted: 02/11/2019] [Indexed: 01/08/2023]
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Pulit SL, Weng LC, McArdle PF, Trinquart L, Choi SH, Mitchell BD, Rosand J, de Bakker PIW, Benjamin EJ, Ellinor PT, Kittner SJ, Lubitz SA, Anderson CD. Atrial fibrillation genetic risk differentiates cardioembolic stroke from other stroke subtypes. Neurol Genet 2018; 4:e293. [PMID: 30584597 PMCID: PMC6283455 DOI: 10.1212/nxg.0000000000000293] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2018] [Accepted: 09/09/2018] [Indexed: 12/24/2022]
Abstract
OBJECTIVE We sought to assess whether genetic risk factors for atrial fibrillation (AF) can explain cardioembolic stroke risk. METHODS We evaluated genetic correlations between a previous genetic study of AF and AF in the presence of cardioembolic stroke using genome-wide genotypes from the Stroke Genetics Network (N = 3,190 AF cases, 3,000 cardioembolic stroke cases, and 28,026 referents). We tested whether a previously validated AF polygenic risk score (PRS) associated with cardioembolic and other stroke subtypes after accounting for AF clinical risk factors. RESULTS We observed a strong correlation between previously reported genetic risk for AF, AF in the presence of stroke, and cardioembolic stroke (Pearson r = 0.77 and 0.76, respectively, across SNPs with p < 4.4 × 10-4 in the previous AF meta-analysis). An AF PRS, adjusted for clinical AF risk factors, was associated with cardioembolic stroke (odds ratio [OR] per SD = 1.40, p = 1.45 × 10-48), explaining ∼20% of the heritable component of cardioembolic stroke risk. The AF PRS was also associated with stroke of undetermined cause (OR per SD = 1.07, p = 0.004), but no other primary stroke subtypes (all p > 0.1). CONCLUSIONS Genetic risk of AF is associated with cardioembolic stroke, independent of clinical risk factors. Studies are warranted to determine whether AF genetic risk can serve as a biomarker for strokes caused by AF.
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Affiliation(s)
- Sara L Pulit
- Department of Genetics (S.L.P., P.I.W.d. B.), University Medical Center Utrecht, Utrecht University, The Netherlands; P.I.W.d.B. is now with Computational Genomics, Vertex Pharmaceuticals, Boston, MA; Li Ka Shing Centre for Health Information and Discovery (S.L.P.), The Big Data Institute, University of Oxford, United Kingdom; Program in Medical and Population Genetics (S.L.P., L.-C.W., S.H.C., J.R., P.T.E., S.A.L., C.D.A.), Broad Institute, Cambridge, MA; Cardiovascular Research Center (L.-C.W., P.T.E., S.A.L.), Center for Genomic Medicine (J.R., C.D.A.), J.P. Kistler Stroke Research Center (J.R., C.D.A.), and Cardiac Arrhythmia Service (P.T.E., S.A.L.), Massachusetts General Hospital, Boston; Department of Medicine (P.F.M., B.D.M.), Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore; National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study (L.T., E.J.B.); Department of Biostatistics (L.T.) and Department of Epidemiology (E.J.B.), Boston University School of Public Health, MA; Geriatrics Research and Education Clinical Center (B.D.M.), Baltimore Veterans Administration Medical Center, MD; Cardiology Preventive Medicine Sections (E.J.B.), Evans Department of Medicine, Boston University School of Medicine; Department of Neurology (S.J.K.), University of Maryland School of Medicine; and Department of Neurology (S.J.K.), Veterans Affairs Medical Center, Baltimore, MD
| | - Lu-Chen Weng
- Department of Genetics (S.L.P., P.I.W.d. B.), University Medical Center Utrecht, Utrecht University, The Netherlands; P.I.W.d.B. is now with Computational Genomics, Vertex Pharmaceuticals, Boston, MA; Li Ka Shing Centre for Health Information and Discovery (S.L.P.), The Big Data Institute, University of Oxford, United Kingdom; Program in Medical and Population Genetics (S.L.P., L.-C.W., S.H.C., J.R., P.T.E., S.A.L., C.D.A.), Broad Institute, Cambridge, MA; Cardiovascular Research Center (L.-C.W., P.T.E., S.A.L.), Center for Genomic Medicine (J.R., C.D.A.), J.P. Kistler Stroke Research Center (J.R., C.D.A.), and Cardiac Arrhythmia Service (P.T.E., S.A.L.), Massachusetts General Hospital, Boston; Department of Medicine (P.F.M., B.D.M.), Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore; National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study (L.T., E.J.B.); Department of Biostatistics (L.T.) and Department of Epidemiology (E.J.B.), Boston University School of Public Health, MA; Geriatrics Research and Education Clinical Center (B.D.M.), Baltimore Veterans Administration Medical Center, MD; Cardiology Preventive Medicine Sections (E.J.B.), Evans Department of Medicine, Boston University School of Medicine; Department of Neurology (S.J.K.), University of Maryland School of Medicine; and Department of Neurology (S.J.K.), Veterans Affairs Medical Center, Baltimore, MD
| | - Patrick F McArdle
- Department of Genetics (S.L.P., P.I.W.d. B.), University Medical Center Utrecht, Utrecht University, The Netherlands; P.I.W.d.B. is now with Computational Genomics, Vertex Pharmaceuticals, Boston, MA; Li Ka Shing Centre for Health Information and Discovery (S.L.P.), The Big Data Institute, University of Oxford, United Kingdom; Program in Medical and Population Genetics (S.L.P., L.-C.W., S.H.C., J.R., P.T.E., S.A.L., C.D.A.), Broad Institute, Cambridge, MA; Cardiovascular Research Center (L.-C.W., P.T.E., S.A.L.), Center for Genomic Medicine (J.R., C.D.A.), J.P. Kistler Stroke Research Center (J.R., C.D.A.), and Cardiac Arrhythmia Service (P.T.E., S.A.L.), Massachusetts General Hospital, Boston; Department of Medicine (P.F.M., B.D.M.), Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore; National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study (L.T., E.J.B.); Department of Biostatistics (L.T.) and Department of Epidemiology (E.J.B.), Boston University School of Public Health, MA; Geriatrics Research and Education Clinical Center (B.D.M.), Baltimore Veterans Administration Medical Center, MD; Cardiology Preventive Medicine Sections (E.J.B.), Evans Department of Medicine, Boston University School of Medicine; Department of Neurology (S.J.K.), University of Maryland School of Medicine; and Department of Neurology (S.J.K.), Veterans Affairs Medical Center, Baltimore, MD
| | - Ludovic Trinquart
- Department of Genetics (S.L.P., P.I.W.d. B.), University Medical Center Utrecht, Utrecht University, The Netherlands; P.I.W.d.B. is now with Computational Genomics, Vertex Pharmaceuticals, Boston, MA; Li Ka Shing Centre for Health Information and Discovery (S.L.P.), The Big Data Institute, University of Oxford, United Kingdom; Program in Medical and Population Genetics (S.L.P., L.-C.W., S.H.C., J.R., P.T.E., S.A.L., C.D.A.), Broad Institute, Cambridge, MA; Cardiovascular Research Center (L.-C.W., P.T.E., S.A.L.), Center for Genomic Medicine (J.R., C.D.A.), J.P. Kistler Stroke Research Center (J.R., C.D.A.), and Cardiac Arrhythmia Service (P.T.E., S.A.L.), Massachusetts General Hospital, Boston; Department of Medicine (P.F.M., B.D.M.), Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore; National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study (L.T., E.J.B.); Department of Biostatistics (L.T.) and Department of Epidemiology (E.J.B.), Boston University School of Public Health, MA; Geriatrics Research and Education Clinical Center (B.D.M.), Baltimore Veterans Administration Medical Center, MD; Cardiology Preventive Medicine Sections (E.J.B.), Evans Department of Medicine, Boston University School of Medicine; Department of Neurology (S.J.K.), University of Maryland School of Medicine; and Department of Neurology (S.J.K.), Veterans Affairs Medical Center, Baltimore, MD
| | - Seung Hoan Choi
- Department of Genetics (S.L.P., P.I.W.d. B.), University Medical Center Utrecht, Utrecht University, The Netherlands; P.I.W.d.B. is now with Computational Genomics, Vertex Pharmaceuticals, Boston, MA; Li Ka Shing Centre for Health Information and Discovery (S.L.P.), The Big Data Institute, University of Oxford, United Kingdom; Program in Medical and Population Genetics (S.L.P., L.-C.W., S.H.C., J.R., P.T.E., S.A.L., C.D.A.), Broad Institute, Cambridge, MA; Cardiovascular Research Center (L.-C.W., P.T.E., S.A.L.), Center for Genomic Medicine (J.R., C.D.A.), J.P. Kistler Stroke Research Center (J.R., C.D.A.), and Cardiac Arrhythmia Service (P.T.E., S.A.L.), Massachusetts General Hospital, Boston; Department of Medicine (P.F.M., B.D.M.), Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore; National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study (L.T., E.J.B.); Department of Biostatistics (L.T.) and Department of Epidemiology (E.J.B.), Boston University School of Public Health, MA; Geriatrics Research and Education Clinical Center (B.D.M.), Baltimore Veterans Administration Medical Center, MD; Cardiology Preventive Medicine Sections (E.J.B.), Evans Department of Medicine, Boston University School of Medicine; Department of Neurology (S.J.K.), University of Maryland School of Medicine; and Department of Neurology (S.J.K.), Veterans Affairs Medical Center, Baltimore, MD
| | - Braxton D Mitchell
- Department of Genetics (S.L.P., P.I.W.d. B.), University Medical Center Utrecht, Utrecht University, The Netherlands; P.I.W.d.B. is now with Computational Genomics, Vertex Pharmaceuticals, Boston, MA; Li Ka Shing Centre for Health Information and Discovery (S.L.P.), The Big Data Institute, University of Oxford, United Kingdom; Program in Medical and Population Genetics (S.L.P., L.-C.W., S.H.C., J.R., P.T.E., S.A.L., C.D.A.), Broad Institute, Cambridge, MA; Cardiovascular Research Center (L.-C.W., P.T.E., S.A.L.), Center for Genomic Medicine (J.R., C.D.A.), J.P. Kistler Stroke Research Center (J.R., C.D.A.), and Cardiac Arrhythmia Service (P.T.E., S.A.L.), Massachusetts General Hospital, Boston; Department of Medicine (P.F.M., B.D.M.), Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore; National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study (L.T., E.J.B.); Department of Biostatistics (L.T.) and Department of Epidemiology (E.J.B.), Boston University School of Public Health, MA; Geriatrics Research and Education Clinical Center (B.D.M.), Baltimore Veterans Administration Medical Center, MD; Cardiology Preventive Medicine Sections (E.J.B.), Evans Department of Medicine, Boston University School of Medicine; Department of Neurology (S.J.K.), University of Maryland School of Medicine; and Department of Neurology (S.J.K.), Veterans Affairs Medical Center, Baltimore, MD
| | - Jonathan Rosand
- Department of Genetics (S.L.P., P.I.W.d. B.), University Medical Center Utrecht, Utrecht University, The Netherlands; P.I.W.d.B. is now with Computational Genomics, Vertex Pharmaceuticals, Boston, MA; Li Ka Shing Centre for Health Information and Discovery (S.L.P.), The Big Data Institute, University of Oxford, United Kingdom; Program in Medical and Population Genetics (S.L.P., L.-C.W., S.H.C., J.R., P.T.E., S.A.L., C.D.A.), Broad Institute, Cambridge, MA; Cardiovascular Research Center (L.-C.W., P.T.E., S.A.L.), Center for Genomic Medicine (J.R., C.D.A.), J.P. Kistler Stroke Research Center (J.R., C.D.A.), and Cardiac Arrhythmia Service (P.T.E., S.A.L.), Massachusetts General Hospital, Boston; Department of Medicine (P.F.M., B.D.M.), Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore; National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study (L.T., E.J.B.); Department of Biostatistics (L.T.) and Department of Epidemiology (E.J.B.), Boston University School of Public Health, MA; Geriatrics Research and Education Clinical Center (B.D.M.), Baltimore Veterans Administration Medical Center, MD; Cardiology Preventive Medicine Sections (E.J.B.), Evans Department of Medicine, Boston University School of Medicine; Department of Neurology (S.J.K.), University of Maryland School of Medicine; and Department of Neurology (S.J.K.), Veterans Affairs Medical Center, Baltimore, MD
| | - Paul I W de Bakker
- Department of Genetics (S.L.P., P.I.W.d. B.), University Medical Center Utrecht, Utrecht University, The Netherlands; P.I.W.d.B. is now with Computational Genomics, Vertex Pharmaceuticals, Boston, MA; Li Ka Shing Centre for Health Information and Discovery (S.L.P.), The Big Data Institute, University of Oxford, United Kingdom; Program in Medical and Population Genetics (S.L.P., L.-C.W., S.H.C., J.R., P.T.E., S.A.L., C.D.A.), Broad Institute, Cambridge, MA; Cardiovascular Research Center (L.-C.W., P.T.E., S.A.L.), Center for Genomic Medicine (J.R., C.D.A.), J.P. Kistler Stroke Research Center (J.R., C.D.A.), and Cardiac Arrhythmia Service (P.T.E., S.A.L.), Massachusetts General Hospital, Boston; Department of Medicine (P.F.M., B.D.M.), Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore; National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study (L.T., E.J.B.); Department of Biostatistics (L.T.) and Department of Epidemiology (E.J.B.), Boston University School of Public Health, MA; Geriatrics Research and Education Clinical Center (B.D.M.), Baltimore Veterans Administration Medical Center, MD; Cardiology Preventive Medicine Sections (E.J.B.), Evans Department of Medicine, Boston University School of Medicine; Department of Neurology (S.J.K.), University of Maryland School of Medicine; and Department of Neurology (S.J.K.), Veterans Affairs Medical Center, Baltimore, MD
| | - Emelia J Benjamin
- Department of Genetics (S.L.P., P.I.W.d. B.), University Medical Center Utrecht, Utrecht University, The Netherlands; P.I.W.d.B. is now with Computational Genomics, Vertex Pharmaceuticals, Boston, MA; Li Ka Shing Centre for Health Information and Discovery (S.L.P.), The Big Data Institute, University of Oxford, United Kingdom; Program in Medical and Population Genetics (S.L.P., L.-C.W., S.H.C., J.R., P.T.E., S.A.L., C.D.A.), Broad Institute, Cambridge, MA; Cardiovascular Research Center (L.-C.W., P.T.E., S.A.L.), Center for Genomic Medicine (J.R., C.D.A.), J.P. Kistler Stroke Research Center (J.R., C.D.A.), and Cardiac Arrhythmia Service (P.T.E., S.A.L.), Massachusetts General Hospital, Boston; Department of Medicine (P.F.M., B.D.M.), Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore; National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study (L.T., E.J.B.); Department of Biostatistics (L.T.) and Department of Epidemiology (E.J.B.), Boston University School of Public Health, MA; Geriatrics Research and Education Clinical Center (B.D.M.), Baltimore Veterans Administration Medical Center, MD; Cardiology Preventive Medicine Sections (E.J.B.), Evans Department of Medicine, Boston University School of Medicine; Department of Neurology (S.J.K.), University of Maryland School of Medicine; and Department of Neurology (S.J.K.), Veterans Affairs Medical Center, Baltimore, MD
| | - Patrick T Ellinor
- Department of Genetics (S.L.P., P.I.W.d. B.), University Medical Center Utrecht, Utrecht University, The Netherlands; P.I.W.d.B. is now with Computational Genomics, Vertex Pharmaceuticals, Boston, MA; Li Ka Shing Centre for Health Information and Discovery (S.L.P.), The Big Data Institute, University of Oxford, United Kingdom; Program in Medical and Population Genetics (S.L.P., L.-C.W., S.H.C., J.R., P.T.E., S.A.L., C.D.A.), Broad Institute, Cambridge, MA; Cardiovascular Research Center (L.-C.W., P.T.E., S.A.L.), Center for Genomic Medicine (J.R., C.D.A.), J.P. Kistler Stroke Research Center (J.R., C.D.A.), and Cardiac Arrhythmia Service (P.T.E., S.A.L.), Massachusetts General Hospital, Boston; Department of Medicine (P.F.M., B.D.M.), Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore; National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study (L.T., E.J.B.); Department of Biostatistics (L.T.) and Department of Epidemiology (E.J.B.), Boston University School of Public Health, MA; Geriatrics Research and Education Clinical Center (B.D.M.), Baltimore Veterans Administration Medical Center, MD; Cardiology Preventive Medicine Sections (E.J.B.), Evans Department of Medicine, Boston University School of Medicine; Department of Neurology (S.J.K.), University of Maryland School of Medicine; and Department of Neurology (S.J.K.), Veterans Affairs Medical Center, Baltimore, MD
| | - Steven J Kittner
- Department of Genetics (S.L.P., P.I.W.d. B.), University Medical Center Utrecht, Utrecht University, The Netherlands; P.I.W.d.B. is now with Computational Genomics, Vertex Pharmaceuticals, Boston, MA; Li Ka Shing Centre for Health Information and Discovery (S.L.P.), The Big Data Institute, University of Oxford, United Kingdom; Program in Medical and Population Genetics (S.L.P., L.-C.W., S.H.C., J.R., P.T.E., S.A.L., C.D.A.), Broad Institute, Cambridge, MA; Cardiovascular Research Center (L.-C.W., P.T.E., S.A.L.), Center for Genomic Medicine (J.R., C.D.A.), J.P. Kistler Stroke Research Center (J.R., C.D.A.), and Cardiac Arrhythmia Service (P.T.E., S.A.L.), Massachusetts General Hospital, Boston; Department of Medicine (P.F.M., B.D.M.), Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore; National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study (L.T., E.J.B.); Department of Biostatistics (L.T.) and Department of Epidemiology (E.J.B.), Boston University School of Public Health, MA; Geriatrics Research and Education Clinical Center (B.D.M.), Baltimore Veterans Administration Medical Center, MD; Cardiology Preventive Medicine Sections (E.J.B.), Evans Department of Medicine, Boston University School of Medicine; Department of Neurology (S.J.K.), University of Maryland School of Medicine; and Department of Neurology (S.J.K.), Veterans Affairs Medical Center, Baltimore, MD
| | - Steven A Lubitz
- Department of Genetics (S.L.P., P.I.W.d. B.), University Medical Center Utrecht, Utrecht University, The Netherlands; P.I.W.d.B. is now with Computational Genomics, Vertex Pharmaceuticals, Boston, MA; Li Ka Shing Centre for Health Information and Discovery (S.L.P.), The Big Data Institute, University of Oxford, United Kingdom; Program in Medical and Population Genetics (S.L.P., L.-C.W., S.H.C., J.R., P.T.E., S.A.L., C.D.A.), Broad Institute, Cambridge, MA; Cardiovascular Research Center (L.-C.W., P.T.E., S.A.L.), Center for Genomic Medicine (J.R., C.D.A.), J.P. Kistler Stroke Research Center (J.R., C.D.A.), and Cardiac Arrhythmia Service (P.T.E., S.A.L.), Massachusetts General Hospital, Boston; Department of Medicine (P.F.M., B.D.M.), Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore; National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study (L.T., E.J.B.); Department of Biostatistics (L.T.) and Department of Epidemiology (E.J.B.), Boston University School of Public Health, MA; Geriatrics Research and Education Clinical Center (B.D.M.), Baltimore Veterans Administration Medical Center, MD; Cardiology Preventive Medicine Sections (E.J.B.), Evans Department of Medicine, Boston University School of Medicine; Department of Neurology (S.J.K.), University of Maryland School of Medicine; and Department of Neurology (S.J.K.), Veterans Affairs Medical Center, Baltimore, MD
| | - Christopher D Anderson
- Department of Genetics (S.L.P., P.I.W.d. B.), University Medical Center Utrecht, Utrecht University, The Netherlands; P.I.W.d.B. is now with Computational Genomics, Vertex Pharmaceuticals, Boston, MA; Li Ka Shing Centre for Health Information and Discovery (S.L.P.), The Big Data Institute, University of Oxford, United Kingdom; Program in Medical and Population Genetics (S.L.P., L.-C.W., S.H.C., J.R., P.T.E., S.A.L., C.D.A.), Broad Institute, Cambridge, MA; Cardiovascular Research Center (L.-C.W., P.T.E., S.A.L.), Center for Genomic Medicine (J.R., C.D.A.), J.P. Kistler Stroke Research Center (J.R., C.D.A.), and Cardiac Arrhythmia Service (P.T.E., S.A.L.), Massachusetts General Hospital, Boston; Department of Medicine (P.F.M., B.D.M.), Program for Personalized and Genomic Medicine, University of Maryland School of Medicine, Baltimore; National Heart, Lung, and Blood Institute's and Boston University's Framingham Heart Study (L.T., E.J.B.); Department of Biostatistics (L.T.) and Department of Epidemiology (E.J.B.), Boston University School of Public Health, MA; Geriatrics Research and Education Clinical Center (B.D.M.), Baltimore Veterans Administration Medical Center, MD; Cardiology Preventive Medicine Sections (E.J.B.), Evans Department of Medicine, Boston University School of Medicine; Department of Neurology (S.J.K.), University of Maryland School of Medicine; and Department of Neurology (S.J.K.), Veterans Affairs Medical Center, Baltimore, MD
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Personalized Approach to Statin Selection in Primary Prevention: Genetic Risk Scores Vs Imaging Risk Scores. CURRENT CARDIOVASCULAR RISK REPORTS 2018. [DOI: 10.1007/s12170-018-0591-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Abstract
PURPOSE OF REVIEW To review the literature showing genetic risk variants is a reliable means of stratifying for risk of CAD for primary prevention. RECENT FINDINGS Over 90 genetic risk variants have been discovered that predispose to CAD. Results of several studies show that these risk variants effectively stratify for risk of CAD in asymptomatic individuals. SUMMARY The total individual genetic risk can be summarized into a single number referred to as the genetic risk score (GRS). The GRS unlike the Framingham Risk Score is not dependent on age and independent of conventional risk factors. As DNA does not change during one's lifetime the GRS can be calculated at birth or any time thereafter. Furthermore, the GRS has been shown to provide superior discriminatory power in selecting individuals who will benefit most from lifestyle changes or statin therapy. A prospective study showed individuals with high GRS and a favorable lifestyle was associated with significant reduction of cardiac events compared with an unfavorable lifestyle. Furthermore, the study shows inherited risk can be reduced analogous to reduction of risk form acquired and environmental factors. The use of GRS to stratify for risk of CAD in asymptomatic individuals could transform primary prevention worldwide.
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Abstract
Blood lipids are important modifiable risk factors for coronary heart disease and various drugs have been developed to target lipid fractions. Considerable efforts have been made to identify genetic variants that modulate responses to drugs in the hope of optimizing their use. Pharmacogenomics and new biotechnologies now allow for meaningful integration of human genetic findings and therapeutic development for increased efficiency and precision of lipid-lowering drugs. Polygenic predictors of disease risk are also changing how patient populations can be stratified, enabling targeted therapeutic interventions to patients more likely to derive the highest benefit, marking a shift from single variant to genomic approaches in pharmacogenomics.
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Affiliation(s)
- Marc-André Legault
- Montreal Heart Institute, Montreal, QC, H1T 1C8, Canada.,Université de Montréal, Faculté de médecine, Montreal, QC, H3T 1J4, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montreal, H1T 1C8, QC, Canada
| | - Jean-Claude Tardif
- Montreal Heart Institute, Montreal, QC, H1T 1C8, Canada.,Université de Montréal, Faculté de médecine, Montreal, QC, H3T 1J4, Canada
| | - Marie-Pierre Dubé
- Montreal Heart Institute, Montreal, QC, H1T 1C8, Canada.,Université de Montréal, Faculté de médecine, Montreal, QC, H3T 1J4, Canada.,Université de Montréal Beaulieu-Saucier Pharmacogenomics Centre, Montreal, H1T 1C8, QC, Canada
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