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Sun D, Ding Y, Yu C, Sun D, Pang Y, Pei P, Yang L, Millwood IY, Walters RG, Du H, Chen X, Schmidt D, Stevens R, Chen J, Chen Z, Li L, Lv J. Joint impact of polygenic risk score and lifestyles on early- and late-onset cardiovascular diseases. Nat Hum Behav 2024:10.1038/s41562-024-01923-7. [PMID: 38987358 DOI: 10.1038/s41562-024-01923-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Accepted: 06/10/2024] [Indexed: 07/12/2024]
Abstract
Understanding the interactions between genetic risk and lifestyles on different types and age onsets of cardiovascular disease (CVD) risk can help identify individuals for whom lifestyle changes would be beneficial. Here we developed three polygenic risk scores, called MetaPRSs, for coronary artery disease, ischaemic stroke and intracerebral haemorrhage by combining PRSs for CVD and CVD-related risk factors in 96,400 participants from the prospective China Kadoorie Biobank. Genetic and lifestyle risks were categorized by the disease-specific MetaPRSs and the number of unfavourable lifestyles. High genetic risk and unfavourable lifestyles were found to be more strongly associated with early than late onset of CVD outcomes in men and women. Change from unfavourable to favourable lifestyles resulted in 14.7-, 2.5- and 2.6-fold greater reductions in incidence rates of early-onset coronary artery disease and ischaemic stroke and late-onset coronary artery disease in high than low genetic risk group. Young adults at high genetic risk may have larger benefits in preventing CVD from lifestyle improvements.
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2
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Nielsen RV, Fuster V, Bundgaard H, Fuster JJ, Johri AM, Kofoed KF, Douglas PS, Diederichsen A, Shapiro MD, Nicholls SJ, Nordestgaard BG, Lindholt JS, MacRae C, Yuan C, Newby DE, Urbina EM, Bergström G, Ridderstråle M, Budoff MJ, Bøttcher M, Raitakari OT, Hansen TH, Näslund U, Sillesen H, Eldrup N, Ibanez B. Personalized Intervention Based on Early Detection of Atherosclerosis: JACC State-of-the-Art Review. J Am Coll Cardiol 2024; 83:2112-2127. [PMID: 38777513 DOI: 10.1016/j.jacc.2024.02.053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 02/12/2024] [Accepted: 02/22/2024] [Indexed: 05/25/2024]
Abstract
Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide and challenges the capacity of health care systems globally. Atherosclerosis is the underlying pathophysiological entity in two-thirds of patients with CVD. When considering that atherosclerosis develops over decades, there is potentially great opportunity for prevention of associated events such as myocardial infarction and stroke. Subclinical atherosclerosis has been identified in its early stages in young individuals; however, there is no consensus on how to prevent progression to symptomatic disease. Given the growing burden of CVD, a paradigm shift is required-moving from late management of atherosclerotic CVD to earlier detection during the subclinical phase with the goal of potential cure or prevention of events. Studies must focus on how precision medicine using imaging and circulating biomarkers may identify atherosclerosis earlier and determine whether such a paradigm shift would lead to overall cost savings for global health.
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Affiliation(s)
- Rikke V Nielsen
- Department of Medical Science, Novo Nordisk Foundation, Hellerup, Denmark; Department of Cardiothoracic Anesthesiology, Rigshospitalet University Hospital Copenhagen, Copenhagen, Denmark.
| | - Valentin Fuster
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain; Mount Sinai Fuster Heart Hospital, New York, New York, USA
| | - Henning Bundgaard
- Department of Cardiology, Rigshospitalet University Hospital Copenhagen, Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jose J Fuster
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain; CIBER en Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain
| | - Amer M Johri
- Department of Medicine Queen's University, Kingston, Ontario, Canada
| | - Klaus F Kofoed
- Department of Cardiology, Rigshospitalet University Hospital Copenhagen, Copenhagen, Denmark; Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Radiology, Rigshospitalet University Hospital Copenhagen, Copenhagen, Denmark
| | - Pamela S Douglas
- Duke University School of Medicine, Duke Clinical Research Institute, Durham, North Carolina, USA
| | - Axel Diederichsen
- Department of Cardiology, Odense University Hospital, Odense, Denmark
| | - Michael D Shapiro
- Center for Prevention of Cardiovascular Disease, Section on Cardiovascular Disease, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
| | - Stephen J Nicholls
- Victorian Heart Institute, Monash University, Melbourne, Victoria, Australia
| | - Børge G Nordestgaard
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Clinical Biochemistry and The Copenhagen General Population Study, Copenhagen University Hospital-Herlev and Gentofte, Herlev, Denmark. https://twitter.com/BNordestgaard
| | - Jes S Lindholt
- Department of Cardiothoracic and Vascular Surgery, Elite Research Centre of Individualised Treatment of Arterial Disease (CIMA), Odense University Hospital, University of Southern Denmark, Odense, Denmark
| | - Calum MacRae
- Harvard Medical School, Department of Medicine, Boston, Massachusetts, USA
| | - Chun Yuan
- Department of Radiology and Imaging Sciences, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, Utah, USA
| | - David E Newby
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, Scotland
| | - Elaine M Urbina
- Preventive Cardiology, Cincinnati Children's Hospital Medical Center and the University of Cincinnati, Cincinnati, Ohio, USA
| | - Göran Bergström
- Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg and Department of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | | | - Matthew J Budoff
- Department of Medicine, Lundquist Institute at Harbor-UCLA, Torrance, California, USA
| | - Morten Bøttcher
- University Clinic for Cardiovascular Research, Department of Cardiology, Aarhus University/Gødstrup Hospital, Aarhus, Denmark
| | - Olli T Raitakari
- Centre for Population Health Research, Research Centre of Applied and Preventive Cardiovascular Medicine, InFLAMES Research Flagship, University of Turku, Turku, Finland; Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
| | - Thomas H Hansen
- Department of Cardiology, Rigshospitalet University Hospital Copenhagen, Copenhagen, Denmark
| | - Ulf Näslund
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Henrik Sillesen
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Nikolaj Eldrup
- Department of Vascular Surgery, Rigshospitalet University Hospital Copenhagen, Copenhagen, Denmark
| | - Borja Ibanez
- Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain; CIBER en Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain; Cardiology Department, IIS-Fundación Jiménez Díaz University Hospital, Madrid, Spain.
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Curtis D. Genetic Variants Associated with Hypertension Risk: Progress and Implications. Pulse (Basel) 2024; 12:19-26. [PMID: 38404912 PMCID: PMC10890806 DOI: 10.1159/000536505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 01/19/2024] [Indexed: 02/27/2024] Open
Abstract
Background Genetic variants causing diseases with hypertension as a secondary feature have previously been identified. Studies focussing on primary hypertension have utilised common and latterly rare genetic variants in attempts to elucidate the genetic contribution to the risk of primary hypertension. Summary Using genome-wide association studies (GWASs), associations of hypertension with hundreds of common genetic variants have been reported, implicating thousands of genes. Individual variants have small effect sizes and cumulatively account for around 6% of genetic risk. The common variant signal is enriched for relevant tissues and physiological processes, while some variants are associated with traits expected to have secondary impacts on hypertension risk, such as fruit intake, BMI, or time watching television. Studies using rare variants obtained from exome sequence data have implicated a small number of genes for which impaired function has moderate effects on blood pressure and/or hypertension risk. Notably, genetic variants which impair elements of guanylate cyclase activation, stimulated by either natriuretic hormones or nitric oxide, increase hypertension risk. Conversely, variants impairing dopamine beta-hydroxylase or renin production are associated with lower blood pressure. Variants for which a definite effect can be designated remain cumulatively extremely rare and again make only a small contribution to overall genetic risk. Although these results are of interest, it is not clear that they provide radical new insights or identify drug targets which were not previously known. Nor does it seem that genetic testing could be useful in terms of quantifying disease risk or guiding treatment. Key Messages Research has increased our knowledge about the relationship between naturally occurring genetic variation and risk of hypertension. Although some results serve to confirm our understanding of underlying physiology, their value in terms of potentially leading to practical advances in the management of hypertension appears questionable.
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Affiliation(s)
- David Curtis
- UCL Genetics Institute, University College London, London, UK
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4
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You J, Guo Y, Kang JJ, Wang HF, Yang M, Feng JF, Yu JT, Cheng W. Development of machine learning-based models to predict 10-year risk of cardiovascular disease: a prospective cohort study. Stroke Vasc Neurol 2023; 8:475-485. [PMID: 37105576 PMCID: PMC10800279 DOI: 10.1136/svn-2023-002332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 04/03/2023] [Indexed: 04/29/2023] Open
Abstract
BACKGROUND Previous prediction algorithms for cardiovascular diseases (CVD) were established using risk factors retrieved largely based on empirical clinical knowledge. This study sought to identify predictors among a comprehensive variable space, and then employ machine learning (ML) algorithms to develop a novel CVD risk prediction model. METHODS From a longitudinal population-based cohort of UK Biobank, this study included 473 611 CVD-free participants aged between 37 and 73 years old. We implemented an ML-based data-driven pipeline to identify predictors from 645 candidate variables covering a comprehensive range of health-related factors and assessed multiple ML classifiers to establish a risk prediction model on 10-year incident CVD. The model was validated through a leave-one-center-out cross-validation. RESULTS During a median follow-up of 12.2 years, 31 466 participants developed CVD within 10 years after baseline visits. A novel UK Biobank CVD risk prediction (UKCRP) model was established that comprised 10 predictors including age, sex, medication of cholesterol and blood pressure, cholesterol ratio (total/high-density lipoprotein), systolic blood pressure, previous angina or heart disease, number of medications taken, cystatin C, chest pain and pack-years of smoking. Our model obtained satisfied discriminative performance with an area under the receiver operating characteristic curve (AUC) of 0.762±0.010 that outperformed multiple existing clinical models, and it was well-calibrated with a Brier Score of 0.057±0.006. Further, the UKCRP can obtain comparable performance for myocardial infarction (AUC 0.774±0.011) and ischaemic stroke (AUC 0.730±0.020), but inferior performance for haemorrhagic stroke (AUC 0.644±0.026). CONCLUSION ML-based classification models can learn expressive representations from potential high-risked CVD participants who may benefit from earlier clinical decisions.
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Affiliation(s)
- Jia You
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Yu Guo
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Ju-Jiao Kang
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Hui-Fu Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Ming Yang
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, China
- School of Data Science, Fudan University, Shanghai, China
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Zhejiang, China
| | - Jin-Tai Yu
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Zhejiang, China
- Shanghai Medical College and Zhongshan Hospital Immunotherapy Technology Transfer Center, Fudan University, Shanghai, China
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5
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Khan SS, Coresh J, Pencina MJ, Ndumele CE, Rangaswami J, Chow SL, Palaniappan LP, Sperling LS, Virani SS, Ho JE, Neeland IJ, Tuttle KR, Rajgopal Singh R, Elkind MSV, Lloyd-Jones DM. Novel Prediction Equations for Absolute Risk Assessment of Total Cardiovascular Disease Incorporating Cardiovascular-Kidney-Metabolic Health: A Scientific Statement From the American Heart Association. Circulation 2023; 148:1982-2004. [PMID: 37947094 DOI: 10.1161/cir.0000000000001191] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
Cardiovascular-kidney-metabolic (CKM) syndrome is a novel construct recently defined by the American Heart Association in response to the high prevalence of metabolic and kidney disease. Epidemiological data demonstrate higher absolute risk of both atherosclerotic cardiovascular disease (CVD) and heart failure as an individual progresses from CKM stage 0 to stage 3, but optimal strategies for risk assessment need to be refined. Absolute risk assessment with the goal to match type and intensity of interventions with predicted risk and expected treatment benefit remains the cornerstone of primary prevention. Given the growing number of therapies in our armamentarium that simultaneously address all 3 CKM axes, novel risk prediction equations are needed that incorporate predictors and outcomes relevant to the CKM context. This should also include social determinants of health, which are key upstream drivers of CVD, to more equitably estimate and address risk. This scientific statement summarizes the background, rationale, and clinical implications for the newly developed sex-specific, race-free risk equations: PREVENT (AHA Predicting Risk of CVD Events). The PREVENT equations enable 10- and 30-year risk estimates for total CVD (composite of atherosclerotic CVD and heart failure), include estimated glomerular filtration rate as a predictor, and adjust for competing risk of non-CVD death among adults 30 to 79 years of age. Additional models accommodate enhanced predictive utility with the addition of CKM factors when clinically indicated for measurement (urine albumin-to-creatinine ratio and hemoglobin A1c) or social determinants of health (social deprivation index) when available. Approaches to implement risk-based prevention using PREVENT across various settings are discussed.
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Antza C, Gallo A, Boutari C, Ershova A, Gurses KM, Lewek J, Mirmaksudov M, Silbernagel G, Sandstedt J, Lebedeva A. Prevention of cardiovascular disease in young adults: Focus on gender differences. A collaborative review from the EAS Young Fellows. Atherosclerosis 2023; 384:117272. [PMID: 37734996 DOI: 10.1016/j.atherosclerosis.2023.117272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 05/03/2023] [Accepted: 09/01/2023] [Indexed: 09/23/2023]
Abstract
A steady rise in cardiovascular morbidity and mortality has been observed in young adults within the last decades. This trend corresponds to an increasing prevalence of traditional cardiovascular risk factors such as obesity and diabetes mellitus type 2 among young adults living in developed countries. Moreover, age-specific risk factors, such as substance abuse, contraceptive medication, and pregnancy-related diseases also correlate with an increased incidence of cardiovascular diseases. In this review, we discuss the available data for young adults on the epidemiology and the rationale for the causality of traditional and newly emerging risk factors of atherosclerotic cardiovascular diseases. We focus on gender-related differences in the exposure to these risk factors, investigate the recent data regarding screening and risk stratification in the young adult population, and describe the current state of the art on lifestyle and therapeutic intervention strategies in the primary prevention setting.
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Affiliation(s)
- Christina Antza
- 3rd Department of Internal Medicine, Medical School, Aristotle University of Thessaloniki, Papageorgiou Hospital, 56429, Thessaloniki, Greece
| | - Antonio Gallo
- Sorbonne Université, INSERM UMR1166, Lipidology and Cardiovascular Prevention Unit, Department of Nutrition, APHP, Pitié-Salpètriêre Hospital, F-75013, Paris, France
| | - Chrysoula Boutari
- 2nd Propaedeutic Department of Internal Medicine, Medical School, Aristotle University of Thessaloniki, Hippokration General Hospital, 54642, Thessaloniki, Greece
| | - Alexandra Ershova
- Laboratory of Clinomics, National Medical Research Centre for Therapy and Preventive Medicine, Petroverigskiy Pereulok, 10, 101990, Moscow, Russia
| | - Kadri Murat Gurses
- Department of Cardiology, Selçuk University, School of Medicine, 42250, Selçuklu, Konya, Turkey
| | - Joanna Lewek
- Department of Preventive Cardiology and Lipidology, Chair of Nephrology and Hypertension, Medical University of Lodz, Rzgowska St. 281/289, 93-338, Lodz, Poland; Department of Cardiology and Adult Congenital Heart Diseases, Polish Mother's Memorial Hospital Research Institute (PMMHRI), Rzgowska St. 281/289, 93-338, Lodz, Poland
| | - Mirakhmadjon Mirmaksudov
- Department of Electrophysiology, Republican Specialized Scientific Practical Medical Centre of Cardiology, Osiyo St. 4, 100052, Tashkent, Uzbekistan
| | - Günther Silbernagel
- Division of Vascular Medicine, Department of Internal Medicine, Medical University of Graz, Auenbruggerpl. 2, 8036, Graz, Austria
| | - Joakim Sandstedt
- Department of Laboratory Medicine, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Sahlgrenska University Hospital, 41390, Gothenburg, Sweden; Department of Clinical Chemistry, Sahlgrenska University Hospital, 41390, Gothenburg, Sweden
| | - Anna Lebedeva
- Clinic of Internal Medicine and Cardiology, Heart Centre Dresden University Hospital, Dresden University of Technology, Fetscherst. 76, 01307, Dresden, Germany.
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Saadatagah S, Naderian M, Dikilitas O, Hamed ME, Bangash H, Kullo IJ. Polygenic Risk, Rare Variants, and Family History: Independent and Additive Effects on Coronary Heart Disease. JACC. ADVANCES 2023; 2:100567. [PMID: 38939477 PMCID: PMC11198423 DOI: 10.1016/j.jacadv.2023.100567] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 05/30/2023] [Accepted: 06/09/2023] [Indexed: 06/29/2024]
Abstract
Background Genetic factors are not included in prediction models for coronary heart disease (CHD). Objectives The authors assessed the predictive utility of a polygenic risk score (PRS) for CHD (defined as myocardial infarction, coronary revascularization, or cardiovascular death) and whether the risks due to monogenic familial hypercholesterolemia (FH) and family history (FamHx) are independent of and additive to the PRS. Methods In UK-biobank participants, PRSCHD was calculated using metaGRS, and 10-year risk for incident CHD was estimated using the pooled cohort equations (PCE). The area under the curve (AUC) of the receiver operator curve and net reclassification improvement (NRI) were assessed. FH was defined as the presence of a pathogenic or likely pathogenic variant in LDLR, APOB, or PCSK9. FamHx was defined as a diagnosis of CHD in first-degree relatives. Independent and additive effects of PRSCHD, FH, and FamHx were evaluated in stratified analyses. Results In 323,373 participants with genotype data, the addition of PRSCHD to PCE increased the AUC from 0.759 (95% CI: 0.755-0.763) to 0.773 (95% CI: 0.769-0.777). The AUC and NRIEvent for PRSCHD were higher before the age of 55 years. Of 199,997 participants with exome sequence data, 10,000 had a PRSCHD ≥95th percentile (PRSP95), 673 had FH, and 46,163 had FamHx. The CHD risk associated with PRSP95 was independent of FH and FamHx. The risks associated with combinations of PRSCHD, FH, and FamHx were additive and comprehensive estimates could be obtained by multiplying the risk from each genetic factor. Conclusions Incorporating PRSCHD into the PCE improves risk prediction for CHD, especially at younger ages. The associations of PRSCHD, FH, and FamHx with CHD were independent and additive.
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Affiliation(s)
| | | | - Ozan Dikilitas
- Departments of Internal Medicine and Cardiovascular Medicine, and Mayo Clinician-Investigator Training Program, Mayo Clinic, Rochester, Minnesota, USA
| | - Marwan E. Hamed
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Hana Bangash
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Iftikhar J. Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
- Gonda Vascular Center, Mayo Clinic, Rochester, Minnesota, USA
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8
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Aday AW, Bagheri M, Vaitinadin NS, Mosley JD, Wang TJ. Polygenic risk score in comparison with C-reactive protein for predicting incident coronary heart disease. Atherosclerosis 2023; 379:117194. [PMID: 37536150 PMCID: PMC10529589 DOI: 10.1016/j.atherosclerosis.2023.117194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 07/11/2023] [Accepted: 07/26/2023] [Indexed: 08/05/2023]
Abstract
BACKGROUND AND AIMS Despite interest in the use of polygenic risk scores (PRS) for predicting coronary heart disease (CHD) risk, the clinical utility of PRS compared to conventional risk factors has not been demonstrated. We compared the performance of PRS with that of high-sensitivity C-reactive protein (hsCRP) in two well-established cohorts. METHODS The study population included individuals of European ancestry free of baseline CHD from ARIC (N = 13,113) and the Framingham Offspring Study (FHS) (N = 2,696). The primary predictors included a validated PRS consisting of >6.6 million single nucleotide polymorphisms and hsCRP. The outcome was incident CHD, defined as non-fatal or fatal myocardial infarction. We compared the performance of both predictors after adjusting for the Pooled Cohort Equations in multivariable-adjusted Cox regression models. We assessed discrimination and reclassification using c-statistics and net reclassification improvement. RESULTS Incident CHD occurred in 565 ARIC and 153 FHS participants. In multivariable-adjusted models, both PRS and hsCRP were associated with incident CHD (p < 0.05 in both cohorts). In models incorporating both predictors, strengths of association were similar. For instance, in ARIC, the hazard ratio per SD increment was 1.38 (95% CI, 1.27-1.50, p = 2.94 × 10-14) for PRS and 1.41 (1.30-1.55, p = 3.10 × 10-15) for hsCRP. Neither predictor significantly increased model discrimination or net reclassification when compared with models containing the Pooled Cohort Equations alone. CONCLUSIONS In two independent cohorts, PRS performed similarly to hsCRP for the prediction of CHD risk. These findings suggest PRS does not have unique clinical utility beyond this widely-available, inexpensive measure of risk in unselected middle-aged populations.
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Affiliation(s)
- Aaron W Aday
- Vanderbilt Translational and Clinical Cardiovascular Research Center, Vanderbilt University Medical Center, Nashville, TN, USA; Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
| | - Minoo Bagheri
- Division of Cardiovascular Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Nataraja Sarma Vaitinadin
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jonathan D Mosley
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Thomas J Wang
- Department of Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
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9
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Abstract
Cardiometabolic diseases, including cardiovascular disease and diabetes, are major causes of morbidity and mortality worldwide. Despite progress in prevention and treatment, recent trends show a stalling in the reduction of cardiovascular disease morbidity and mortality, paralleled by increasing rates of cardiometabolic disease risk factors in young adults, underscoring the importance of risk assessments in this population. This review highlights the evidence for molecular biomarkers for early risk assessment in young individuals. We examine the utility of traditional biomarkers in young individuals and discuss novel, nontraditional biomarkers specific to pathways contributing to early cardiometabolic disease risk. Additionally, we explore emerging omic technologies and analytical approaches that could enhance risk assessment for cardiometabolic disease.
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Affiliation(s)
- Usman A Tahir
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School
| | - Robert E Gerszten
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School
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10
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Vassy JL, Posner DC, Ho YL, Gagnon DR, Galloway A, Tanukonda V, Houghton SC, Madduri RK, McMahon BH, Tsao PS, Damrauer SM, O’Donnell CJ, Assimes TL, Casas JP, Gaziano JM, Pencina MJ, Sun YV, Cho K, Wilson PW. Cardiovascular Disease Risk Assessment Using Traditional Risk Factors and Polygenic Risk Scores in the Million Veteran Program. JAMA Cardiol 2023; 8:564-574. [PMID: 37133828 PMCID: PMC10157509 DOI: 10.1001/jamacardio.2023.0857] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 03/09/2023] [Indexed: 05/04/2023]
Abstract
Importance Primary prevention of atherosclerotic cardiovascular disease (ASCVD) relies on risk stratification. Genome-wide polygenic risk scores (PRSs) are proposed to improve ASCVD risk estimation. Objective To determine whether genome-wide PRSs for coronary artery disease (CAD) and acute ischemic stroke improve ASCVD risk estimation with traditional clinical risk factors in an ancestrally diverse midlife population. Design, Setting, and Participants This was a prognostic analysis of incident events in a retrospectively defined longitudinal cohort conducted from January 1, 2011, to December 31, 2018. Included in the study were adults free of ASCVD and statin naive at baseline from the Million Veteran Program (MVP), a mega biobank with genetic, survey, and electronic health record data from a large US health care system. Data were analyzed from March 15, 2021, to January 5, 2023. Exposures PRSs for CAD and ischemic stroke derived from cohorts of largely European descent and risk factors, including age, sex, systolic blood pressure, total cholesterol, high-density lipoprotein (HDL) cholesterol, smoking, and diabetes status. Main Outcomes and Measures Incident nonfatal myocardial infarction (MI), ischemic stroke, ASCVD death, and composite ASCVD events. Results A total of 79 151 participants (mean [SD] age, 57.8 [13.7] years; 68 503 male [86.5%]) were included in the study. The cohort included participants from the following harmonized genetic ancestry and race and ethnicity categories: 18 505 non-Hispanic Black (23.4%), 6785 Hispanic (8.6%), and 53 861 non-Hispanic White (68.0%) with a median (5th-95th percentile) follow-up of 4.3 (0.7-6.9) years. From 2011 to 2018, 3186 MIs (4.0%), 1933 ischemic strokes (2.4%), 867 ASCVD deaths (1.1%), and 5485 composite ASCVD events (6.9%) were observed. CAD PRS was associated with incident MI in non-Hispanic Black (hazard ratio [HR], 1.10; 95% CI, 1.02-1.19), Hispanic (HR, 1.26; 95% CI, 1.09-1.46), and non-Hispanic White (HR, 1.23; 95% CI, 1.18-1.29) participants. Stroke PRS was associated with incident stroke in non-Hispanic White participants (HR, 1.15; 95% CI, 1.08-1.21). A combined CAD plus stroke PRS was associated with ASCVD deaths among non-Hispanic Black (HR, 1.19; 95% CI, 1.03-1.17) and non-Hispanic (HR, 1.11; 95% CI, 1.03-1.21) participants. The combined PRS was also associated with composite ASCVD across all ancestry groups but greater among non-Hispanic White (HR, 1.20; 95% CI, 1.16-1.24) than non-Hispanic Black (HR, 1.11; 95% CI, 1.05-1.17) and Hispanic (HR, 1.12; 95% CI, 1.00-1.25) participants. Net reclassification improvement from adding PRS to a traditional risk model was modest for the intermediate risk group for composite CVD among men (5-year risk >3.75%, 0.38%; 95% CI, 0.07%-0.68%), among women, (6.79%; 95% CI, 3.01%-10.58%), for age older than 55 years (0.25%; 95% CI, 0.03%-0.47%), and for ages 40 to 55 years (1.61%; 95% CI, -0.07% to 3.30%). Conclusions and Relevance Study results suggest that PRSs derived predominantly in European samples were statistically significantly associated with ASCVD in the multiancestry midlife and older-age MVP cohort. Overall, modest improvement in discrimination metrics were observed with addition of PRSs to traditional risk factors with greater magnitude in women and younger age groups.
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Affiliation(s)
- Jason L. Vassy
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
- Department of Medicine, Brigham & Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - Daniel C. Posner
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
| | - Yuk-Lam Ho
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
| | - David R. Gagnon
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Ashley Galloway
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
| | | | | | - Ravi K. Madduri
- Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois
- University of Chicago Consortium for Advanced Science and Engineering, The University of Chicago, Chicago, Illinois
| | - Benjamin H. McMahon
- Theoretical Biology and Biophysics, Los Alamos National Laboratory, Los Alamos, New Mexico
| | - Philip S. Tsao
- Palo Alto VA Healthcare System, Palo Alto, California
- Stanford Cardiovascular Institute, Stanford University, Stanford, California
| | - Scott M. Damrauer
- Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania
- Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia
| | | | - Themistocles L. Assimes
- Palo Alto VA Healthcare System, Palo Alto, California
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California
- Stanford Cardiovascular Institute, Stanford University, Stanford, California
| | - Juan P. Casas
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
- Department of Medicine, Brigham & Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - J. Michael Gaziano
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
- Division of Aging, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Michael J. Pencina
- Department of Biostatistics, Duke University Medical Center, Durham, North Carolina
| | - Yan V. Sun
- Veterans Affairs Atlanta Healthcare System, Decatur, Georgia
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia
| | - Kelly Cho
- Veterans Affairs Boston Healthcare System, Boston, Massachusetts
- Department of Medicine, Brigham & Women’s Hospital and Harvard Medical School, Boston, Massachusetts
| | - Peter W.F. Wilson
- Veterans Affairs Atlanta Healthcare System, Decatur, Georgia
- Division of Cardiology, Emory University School of Medicine, Atlanta, Georgia
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia
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The necessity of incorporating non-genetic risk factors into polygenic risk score models. Sci Rep 2023; 13:1351. [PMID: 36807592 PMCID: PMC9941118 DOI: 10.1038/s41598-023-27637-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 01/05/2023] [Indexed: 02/22/2023] Open
Abstract
The growing public interest in genetic risk scores for various health conditions can be harnessed to inspire preventive health action. However, current commercially available genetic risk scores can be deceiving as they do not consider other, easily attainable risk factors, such as sex, BMI, age, smoking habits, parental disease status and physical activity. Recent scientific literature shows that adding these factors can improve PGS based predictions significantly. However, implementation of existing PGS based models that also consider these factors requires reference data based on a specific genotyping chip, which is not always available. In this paper, we offer a method naïve to the genotyping chip used. We train these models using the UK Biobank data and test these externally in the Lifelines cohort. We show improved performance at identifying the 10% most at-risk individuals for type 2 diabetes (T2D) and coronary artery disease (CAD) by including common risk factors. Incidence in the highest risk group increases from 3.0- and 4.0-fold to 5.8 for T2D, when comparing the genetics-based model, common risk factor-based model and combined model, respectively. Similarly, we observe an increase from 2.4- and 3.0-fold to 4.7-fold risk for CAD. As such, we conclude that it is paramount that these additional variables are considered when reporting risk, unlike current practice with current available genetic tests.
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12
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Abstract
Polygenic scores quantify inherited risk by integrating information from many common sites of DNA variation into a single number. Rapid increases in the scale of genetic association studies and new statistical algorithms have enabled development of polygenic scores that meaningfully measure-as early as birth-risk of coronary artery disease. These newer-generation polygenic scores identify up to 8% of the population with triple the normal risk based on genetic variation alone, and these individuals cannot be identified on the basis of family history or clinical risk factors alone. For those identified with increased genetic risk, evidence supports risk reduction with at least two interventions, adherence to a healthy lifestyle and cholesterol-lowering therapies, that can substantially reduce risk. Alongside considerable enthusiasm for the potential of polygenic risk estimation to enable a new era of preventive clinical medicine is recognition of a need for ongoing research into how best to ensure equitable performance across diverse ancestries, how and in whom to assess the scores in clinical practice, as well as randomized trials to confirm clinical utility.
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Affiliation(s)
- Aniruddh P Patel
- Division of Cardiology and Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; , .,Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.,Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Amit V Khera
- Division of Cardiology and Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA; , .,Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA.,Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA.,Verve Therapeutics, Cambridge, Massachusetts, USA
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