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Naderian M, Hamed ME, Vaseem AA, Norland K, Dikilitas O, Teymourzadeh A, Bailey KR, Kullo IJ. Effect of disclosing a polygenic risk score for coronary heart disease on adverse cardiovascular events: 10-year follow-up of the MI-GENES randomized clinical trial. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.19.24310709. [PMID: 39072039 PMCID: PMC11275655 DOI: 10.1101/2024.07.19.24310709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Background The MI-GENES clinical trial ( NCT01936675 ), in which participants at intermediate risk of coronary heart disease (CHD) were randomized to receive a Framingham risk score (FRS g , n=103), or an integrated risk score (IRS g , n=104) that additionally included a polygenic risk score (PRS), demonstrated that after 6 months, participants randomized to IRS g had higher statin initiation and lower low-density lipoprotein cholesterol (LDL-C). Objectives In a post hoc 10-year follow-up analysis of the MI-GENES trial, we investigated whether disclosure of a PRS for CHD was associated with a reduction in adverse cardiovascular events. Methods Participants were followed from randomization beginning in October 2013 until September 2023 to ascertain adverse cardiovascular events, testing for CHD, and changes in risk factors, by blinded review of electronic health records. The primary outcome was the time from randomization to the occurrence of the first major adverse cardiovascular event (MACE), defined as cardiovascular death, non-fatal myocardial infarction, coronary revascularization, and non-fatal stroke. Statistical analyses were conducted using Cox proportional hazards regression and linear mixed-effects models. Results We followed all 203 participants who completed the MI-GENES trial, 100 in FRS g and 103 in IRS g (mean age at the end of follow-up: 68.2±5.2, 48% male). During a median follow-up of 9.5 years, 9 MACEs occurred in FRS g and 2 in IRS g (hazard ratio (HR), 0.20; 95% confidence interval (CI), 0.04 to 0.94; P =0.042). In FRS g , 47 (47%) underwent at least one test for CHD, compared to 30 (29%) in IRS g (HR, 0.51; 95% CI, 0.32 to 0.81; P =0.004). IRS g participants had a longer duration of statin therapy during the first four years post-randomization and a greater reduction in LDL-C for up to 3 years post-randomization. No significant differences between the two groups were observed for hemoglobin A1C, systolic and diastolic blood pressures, weight, and smoking cessation rate during follow-up. Conclusions The disclosure of an IRS that included a PRS to individuals at intermediate risk for CHD was associated with a lower incidence of MACE after a decade of follow-up, likely due to a higher rate of initiation and longer duration of statin therapy, leading to lower LDL-C levels.
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Kujala I, Vangipurapu J, Maaniitty T, Saraste A, Kere J, Knuuti J. Polygenic Risk Scores in Predicting Coronary Artery Disease in Symptomatic Patients. A Validation Study. J Atheroscler Thromb 2024; 31:1058-1071. [PMID: 38403640 PMCID: PMC11224689 DOI: 10.5551/jat.64623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 12/24/2023] [Indexed: 02/27/2024] Open
Abstract
AIM Clinical risk scores for coronary artery disease (CAD) are used in clinical practice to select patients for diagnostic testing and therapy. Several studies have proposed that polygenic risk scores (PRSs) can improve the prediction of CAD, but the scores need to be validated in clinical populations with accurately characterized phenotypes. We assessed the predictive power of the three most promising PRSs for the prediction of coronary atherosclerosis and obstructive CAD. METHODS This study was conducted on 943 symptomatic patients with suspected CAD for whom the phenotype was accurately characterized using anatomic and functional imaging. Previously published genome-wide polygenic scores were generated to compare a genetic model based on PRSs with a model based on clinical data. The test and PRS cohorts were predominantly Caucasian of northern European ancestry. RESULTS All three PRSs predicted coronary atherosclerosis and obstructive CAD statistically significantly. The predictive accuracy of the models combining clinical data and different PRSs varied between 0.778 and 0.805 in terms of the area under the receiver operating characteristic (AUROC), being close to the model including only clinical variables (AUROC 0.769). The difference between the clinical model and combined clinical + PRS model was not significant for PRS1 (p=0.627) and PRS3 (p=0.061). Only PRS2 slightly improved the predictive power of the model (p=0.04). The likelihood ratios showed the very weak diagnostic power of all PRSs. CONCLUSION The addition of PRSs to conventional risk factors did not clinically significantly improve the predictive accuracy for either coronary atherosclerosis or obstructive CAD, showing that current PRSs are not justified for routine clinical use in CAD.
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Affiliation(s)
- Iida Kujala
- Turku PET Centre, Turku University Hospital and University of Turku, Turku, Finland
| | | | - Teemu Maaniitty
- Turku PET Centre, Turku University Hospital and University of Turku, Turku, Finland
- Department of clinical physiology, nuclear medicine and PET, Turku University Hospital, Turku, Finland
| | - Antti Saraste
- Turku PET Centre, Turku University Hospital and University of Turku, Turku, Finland
- Heart Center, Turku University Hospital and University of Turku, Turku, Finland
| | - Juha Kere
- Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden
- Folkhalsan Research Center, and Stem Cells and Metabolism Research Program, University of Helsinki, Helsinki, Finland
| | - Juhani Knuuti
- Turku PET Centre, Turku University Hospital and University of Turku, Turku, Finland
- Department of clinical physiology, nuclear medicine and PET, Turku University Hospital, Turku, Finland
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Martin SS, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, Baker-Smith CM, Barone Gibbs B, Beaton AZ, Boehme AK, Commodore-Mensah Y, Currie ME, Elkind MSV, Evenson KR, Generoso G, Heard DG, Hiremath S, Johansen MC, Kalani R, Kazi DS, Ko D, Liu J, Magnani JW, Michos ED, Mussolino ME, Navaneethan SD, Parikh NI, Perman SM, Poudel R, Rezk-Hanna M, Roth GA, Shah NS, St-Onge MP, Thacker EL, Tsao CW, Urbut SM, Van Spall HGC, Voeks JH, Wang NY, Wong ND, Wong SS, Yaffe K, Palaniappan LP. 2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association. Circulation 2024; 149:e347-e913. [PMID: 38264914 DOI: 10.1161/cir.0000000000001209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/25/2024]
Abstract
BACKGROUND The American Heart Association (AHA), in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, nutrition, sleep, and obesity) and health factors (cholesterol, blood pressure, glucose control, and metabolic syndrome) that contribute to cardiovascular health. The AHA Heart Disease and Stroke Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, brain health, complications of pregnancy, kidney disease, congenital heart disease, rhythm disorders, sudden cardiac arrest, subclinical atherosclerosis, coronary heart disease, cardiomyopathy, heart failure, valvular disease, venous thromboembolism, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs). METHODS The AHA, through its Epidemiology and Prevention Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States and globally to provide the most current information available in the annual Statistical Update with review of published literature through the year before writing. The 2024 AHA Statistical Update is the product of a full year's worth of effort in 2023 by dedicated volunteer clinicians and scientists, committed government professionals, and AHA staff members. The AHA strives to further understand and help heal health problems inflicted by structural racism, a public health crisis that can significantly damage physical and mental health and perpetuate disparities in access to health care, education, income, housing, and several other factors vital to healthy lives. This year's edition includes additional global data, as well as data on the monitoring and benefits of cardiovascular health in the population, with an enhanced focus on health equity across several key domains. RESULTS Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics. CONCLUSIONS The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
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Li Y, Wang H, Xiao Y, Yang H, Wang S, Liu L, Cai H, Zhang X, Tang H, Wu T, Qiu G. Lipidomics identified novel cholesterol-independent predictors for risk of incident coronary heart disease: Mediation of risk from diabetes and aggravation of risk by ambient air pollution. J Adv Res 2023:S2090-1232(23)00396-X. [PMID: 38104795 DOI: 10.1016/j.jare.2023.12.009] [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: 04/04/2023] [Revised: 09/16/2023] [Accepted: 12/10/2023] [Indexed: 12/19/2023] Open
Abstract
INTRODUCTION Previous lipidomics studies have identified various lipid predictors for cardiovascular risk, however, with limited predictive increment, sometimes using too many predictor variables at the expense of practical efficiency. OBJECTIVES To search for lipid predictors of future coronary heart disease (CHD) with stronger predictive power and efficiency to guide primary intervention. METHODS We conducted a prospective nested case-control study involving 1,621 incident CHD cases and 1:1 matched controls. Lipid profiling of 161 lipid species for baseline fasting plasma was performed by liquid chromatography-mass spectrometry. RESULTS In search of CHD predictors, seven lipids were selected by elastic-net regression during over 90% of 1000 cross-validation repetitions, and the derived composite lipid score showed an adjusted odds ratio of 3.75 (95% confidence interval: 3.15, 4.46) per standard deviation increase. Addition of the lipid score into traditional risk model increased c-statistic to 0.736 by an increment of 0.077 (0.063, 0.092). From the seven lipids, we found mediation of CHD risk from baseline diabetes through sphingomyelin (SM) 41:1b with a considerable mediation proportion of 36.97% (P < 0.05). We further found that the positive associations of phosphatidylcholine (PC) 36:0a, SM 41:1b, lysophosphatidylcholine (LPC) 18:0 and LPC 20:3 were more pronounced among participants with higher exposure to fine particulate matter or its certain components, also to ozone for LPC 18:0 and LPC 20:3, while the negative association of cholesteryl ester (CE) 18:2 was attenuated with higher black carbon exposure (P < 0.05). CONCLUSION We identified seven lipid species with greatest predictive increment so-far achieved for incident CHD, and also found novel biomarkers for CHD risk stratification among individuals with diabetes or heavy air pollution exposure.
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Affiliation(s)
- Yingmei Li
- Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Hao Wang
- Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Yang Xiao
- Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Handong Yang
- Department of Cardiovascular Disease, Sinopharm Dongfeng General Hospital, Hubei University of Medicine, Shiyan, China
| | - Sihan Wang
- Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Ling Liu
- Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Hao Cai
- Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Xiaomin Zhang
- Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China
| | - Huiru Tang
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Metabonomics and Systems Biology Laboratory at Shanghai International Centre for Molecular Phenomics, Human Phenome Institute, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Tangchun Wu
- Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China.
| | - Gaokun Qiu
- Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430030, China.
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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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Morley TJ, Willimitis D, Ripperger M, Lee H, Han L, Zhou Y, Kang J, Davis LK, Smoller JW, Choi KW, Walsh CG, Ruderfer DM. Evaluating the impact of modeling choices on the performance of integrated genetic and clinical models. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.01.23297927. [PMID: 37961557 PMCID: PMC10635256 DOI: 10.1101/2023.11.01.23297927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
The value of genetic information for improving the performance of clinical risk prediction models has yielded variable conclusions. Many methodological decisions have the potential to contribute to differential results across studies. Here, we performed multiple modeling experiments integrating clinical and demographic data from electronic health records (EHR) and genetic data to understand which decision points may affect performance. Clinical data in the form of structured diagnostic codes, medications, procedural codes, and demographics were extracted from two large independent health systems and polygenic risk scores (PRS) were generated across all patients with genetic data in the corresponding biobanks. Crohn's disease was used as the model phenotype based on its substantial genetic component, established EHR-based definition, and sufficient prevalence for model training and testing. We investigated the impact of PRS integration method, as well as choices regarding training sample, model complexity, and performance metrics. Overall, our results show that including PRS resulted in higher performance by some metrics but the gain in performance was only robust when combined with demographic data alone. Improvements were inconsistent or negligible after including additional clinical information. The impact of genetic information on performance also varied by PRS integration method, with a small improvement in some cases from combining PRS with the output of a clinical model (late-fusion) compared to its inclusion an additional feature (early-fusion). The effects of other modeling decisions varied between institutions though performance increased with more compute-intensive models such as random forest. This work highlights the importance of considering methodological decision points in interpreting the impact on prediction performance when including PRS information in clinical models.
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Affiliation(s)
- Theodore J. Morley
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville TN
- Center for Digital Genomic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville TN
| | - Drew Willimitis
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville TN
| | - Michael Ripperger
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville TN
| | - Hyunjoon Lee
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston MA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston MA
| | - Lide Han
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville TN
- Center for Digital Genomic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville TN
| | - Yu Zhou
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston MA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston MA
| | - Jooeun Kang
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville TN
| | - Lea K. Davis
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville TN
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville TN
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN
| | - Jordan W. Smoller
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston MA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston MA
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA
| | - Karmel W. Choi
- Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston MA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston MA
| | - Colin G. Walsh
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville TN
- Center for Digital Genomic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville TN
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville TN
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN
| | - Douglas M. Ruderfer
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville TN
- Center for Digital Genomic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville TN
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville TN
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN
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Hingorani AD, Gratton J, Finan C, Schmidt AF, Patel R, Sofat R, Kuan V, Langenberg C, Hemingway H, Morris JK, Wald NJ. Performance of polygenic risk scores in screening, prediction, and risk stratification: secondary analysis of data in the Polygenic Score Catalog. BMJ MEDICINE 2023; 2:e000554. [PMID: 37859783 PMCID: PMC10582890 DOI: 10.1136/bmjmed-2023-000554] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 08/31/2023] [Indexed: 10/21/2023]
Abstract
Objective To clarify the performance of polygenic risk scores in population screening, individual risk prediction, and population risk stratification. Design Secondary analysis of data in the Polygenic Score Catalog. Setting Polygenic Score Catalog, April 2022. Secondary analysis of 3915 performance metric estimates for 926 polygenic risk scores for 310 diseases to generate estimates of performance in population screening, individual risk, and population risk stratification. Participants Individuals contributing to the published studies in the Polygenic Score Catalog. Main outcome measures Detection rate for a 5% false positive rate (DR5) and the population odds of becoming affected given a positive result; individual odds of becoming affected for a person with a particular polygenic score; and odds of becoming affected for groups of individuals in different portions of a polygenic risk score distribution. Coronary artery disease and breast cancer were used as illustrative examples. Results For performance in population screening, median DR5 for all polygenic risk scores and all diseases studied was 11% (interquartile range 8-18%). Median DR5 was 12% (9-19%) for polygenic risk scores for coronary artery disease and 10% (9-12%) for breast cancer. The population odds of becoming affected given a positive results were 1:8 for coronary artery disease and 1:21 for breast cancer, with background 10 year odds of 1:19 and 1:41, respectively, which are typical for these diseases at age 50. For individual risk prediction, the corresponding 10 year odds of becoming affected for individuals aged 50 with a polygenic risk score at the 2.5th, 25th, 75th, and 97.5th centiles were 1:54, 1:29, 1:15, and 1:8 for coronary artery disease and 1:91, 1:56, 1:34, and 1:21 for breast cancer. In terms of population risk stratification, at age 50, the risk of coronary artery disease was divided into five groups, with 10 year odds of 1:41 and 1:11 for the lowest and highest quintile groups, respectively. The 10 year odds was 1:7 for the upper 2.5% of the polygenic risk score distribution for coronary artery disease, a group that contributed 7% of cases. The corresponding estimates for breast cancer were 1:72 and 1:26 for the lowest and highest quintile groups, and 1:19 for the upper 2.5% of the distribution, which contributed 6% of cases. Conclusion Polygenic risk scores performed poorly in population screening, individual risk prediction, and population risk stratification. Strong claims about the effect of polygenic risk scores on healthcare seem to be disproportionate to their performance.
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Affiliation(s)
- Aroon D Hingorani
- Institute of Cardiovascular Science, University College London, London, UK
- British Heart Foundation Research Accelerator, University College London, London, UK
- National Institute of Health Research Biomedical Research Centre, University College London Hospitals, London, UK
- Health Data Research UK, London, UK
| | - Jasmine Gratton
- Institute of Cardiovascular Science, University College London, London, UK
- British Heart Foundation Research Accelerator, University College London, London, UK
| | - Chris Finan
- Institute of Cardiovascular Science, University College London, London, UK
- British Heart Foundation Research Accelerator, University College London, London, UK
- National Institute of Health Research Biomedical Research Centre, University College London Hospitals, London, UK
- Health Data Research UK, London, UK
| | - A Floriaan Schmidt
- Institute of Cardiovascular Science, University College London, London, UK
- National Institute of Health Research Biomedical Research Centre, University College London Hospitals, London, UK
- Health Data Research UK, London, UK
- University Medical Centre Utrecht, Utrecht, Netherlands
| | - Riyaz Patel
- Institute of Cardiovascular Science, University College London, London, UK
- British Heart Foundation Research Accelerator, University College London, London, UK
- National Institute of Health Research Biomedical Research Centre, University College London Hospitals, London, UK
- Health Data Research UK, London, UK
| | - Reecha Sofat
- Health Data Research UK, London, UK
- Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
| | - Valerie Kuan
- Institute of Cardiovascular Science, University College London, London, UK
- British Heart Foundation Research Accelerator, University College London, London, UK
- National Institute of Health Research Biomedical Research Centre, University College London Hospitals, London, UK
| | - Claudia Langenberg
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
- Computational Medicine, Berlin Institute of Health at Charite Universitatzmedizin, Berlin, Germany
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Harry Hemingway
- British Heart Foundation Research Accelerator, University College London, London, UK
- National Institute of Health Research Biomedical Research Centre, University College London Hospitals, London, UK
- Health Data Research UK, London, UK
- Institute of Health Informatics, University College London, London, UK
| | - Joan K Morris
- Population Health Research Institute, St George's University of London, London, UK
| | - Nicholas J Wald
- Institute of Health Informatics, University College London, London, UK
- Population Health Research Institute, St George's University of London, London, UK
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Qi L, Heianza Y, Li X, Sacks FM, Bray GA. Toward Precision Weight-Loss Dietary Interventions: Findings from the POUNDS Lost Trial. Nutrients 2023; 15:3665. [PMID: 37630855 PMCID: PMC10458797 DOI: 10.3390/nu15163665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 08/13/2023] [Accepted: 08/18/2023] [Indexed: 08/27/2023] Open
Abstract
The POUNDS Lost trial is a 2-year clinical trial testing the effects of dietary interventions on weight loss. This study included 811 adults with overweight or obesity who were randomized to one of four diets that contained either 15% or 25% protein and 20% or 40% fat in a 2 × 2 factorial design. By 2 years, participants on average lost from 2.9 to 3.6 kg in body weight in the four intervention arms, while no significant difference was observed across the intervention arms. In POUNDS Lost, we performed a series of ancillary studies to detect intrinsic factors particular to genomic, epigenomic, and metabolomic markers that may modulate changes in weight and other cardiometabolic traits in response to the weight-loss dietary interventions. Genomic variants identified from genome-wide association studies (GWASs) on obesity, type 2 diabetes, glucose and lipid metabolisms, gut microbiome, and dietary intakes have been found to interact with dietary macronutrients (fat, protein, and carbohydrates) in relation to weight loss and changes of body composition and cardiometabolic traits. In addition, we recently investigated epigenomic modifications, particularly blood DNA methylation and circulating microRNAs (miRNAs). We reported DNA methylation levels at NFATC2IP, CPT1A, TXNIP, and LINC00319 were related to weight loss or changes of glucose, lipids, and blood pressure; we also reported thrifty miRNA expression as a significant epigenomic marker related to changes in insulin sensitivity and adiposity. Our studies have also highlighted the importance of temporal changes in novel metabolomic signatures for gut microbiota, bile acids, and amino acids as predictors for achievement of successful weight loss outcomes. Moreover, our studies indicate that biochemical, behavioral, and psychosocial factors such as physical activity, sleep disturbance, and appetite may also modulate metabolic changes during dietary interventions. This review summarized our major findings in the POUNDS Lost trial, which provided preliminary evidence supporting the development of precision diet interventions for obesity management.
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Affiliation(s)
- Lu Qi
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70118, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Yoriko Heianza
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70118, USA
| | - Xiang Li
- Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA 70118, USA
| | - Frank M. Sacks
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - George A. Bray
- Department of Clinical Obesity, Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, LA 70808, USA
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Xie J, Feng Y, Newby D, Zheng B, Feng Q, Prats-Uribe A, Li C, Wareham NJ, Paredes R, Prieto-Alhambra D. Genetic risk, adherence to healthy lifestyle and acute cardiovascular and thromboembolic complications following SARS-COV-2 infection. Nat Commun 2023; 14:4659. [PMID: 37537214 PMCID: PMC10400557 DOI: 10.1038/s41467-023-40310-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 07/19/2023] [Indexed: 08/05/2023] Open
Abstract
Current understanding of determinants for COVID-19-related cardiovascular and thromboembolic (CVE) complications primarily covers clinical aspects with limited knowledge on genetics and lifestyles. Here, we analysed a prospective cohort of 106,005 participants from UK Biobank with confirmed SARS-CoV-2 infection. We show that higher polygenic risk scores, indicating individual's hereditary risk, were linearly associated with increased risks of post-COVID-19 atrial fibrillation (adjusted HR 1.52 [95% CI 1.44 to 1.60] per standard deviation increase), coronary artery disease (1.57 [1.46 to 1.69]), venous thromboembolism (1.33 [1.18 to 1.50]), and ischaemic stroke (1.27 [1.05 to 1.55]). These genetic associations are robust across genders, key clinical subgroups, and during Omicron waves. However, a prior composite healthier lifestyle was consistently associated with a reduction in all outcomes. Our findings highlight that host genetics and lifestyle independently affect the occurrence of CVE complications in the acute infection phrase, which can guide tailored management of COVID-19 patients and inform population lifestyle interventions to offset the elevated cardiovascular burden post-pandemic.
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Affiliation(s)
- Junqing Xie
- Centre for Statistics in Medicine and NIHR Biomedical Research Centre Oxford, NDORMS, University of Oxford, Oxford, UK
| | - Yuliang Feng
- Botnar Research Centre, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
- Department of Pharmacology, School of Medicine, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Danielle Newby
- Centre for Statistics in Medicine and NIHR Biomedical Research Centre Oxford, NDORMS, University of Oxford, Oxford, UK
| | - Bang Zheng
- Department Non-communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
| | - Qi Feng
- Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Albert Prats-Uribe
- Centre for Statistics in Medicine and NIHR Biomedical Research Centre Oxford, NDORMS, University of Oxford, Oxford, UK
| | - Chunxiao Li
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Nicholas J Wareham
- Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - R Paredes
- Department of Infectious Diseases Department & irsiCaixa AIDS Research Institute, Hospital Universitari Germans 13 Trias i Pujol, Catalonia, Spain
- Center for Global Health and Diseases, Department of Pathology, Case Western Reserve University School of Medicine, Cleveland, OH, US
| | - Daniel Prieto-Alhambra
- Centre for Statistics in Medicine and NIHR Biomedical Research Centre Oxford, NDORMS, University of Oxford, Oxford, UK.
- Department of Medical Informatics, Erasmus Medical Center University, Rotterdam, Netherlands.
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10
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Bellino M, Galasso G, Silverio A, Tedeschi M, Formisano C, Romei S, Esposito L, Cancro FP, Vassallo MG, Accarino G, Verdoia M, Di Muro FM, Vecchione C, De Luca G. Soluble PCSK9 Inhibition: Indications, Clinical Impact, New Molecular Insights and Practical Approach-Where Do We Stand? J Clin Med 2023; 12:jcm12082922. [PMID: 37109259 PMCID: PMC10146045 DOI: 10.3390/jcm12082922] [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: 03/22/2023] [Revised: 04/12/2023] [Accepted: 04/14/2023] [Indexed: 04/29/2023] Open
Abstract
Current research on cardiovascular prevention predominantly focuses on risk-stratification and management of patients with coronary artery disease (CAD) to optimize their prognosis. Several basic, translational and clinical research efforts aim to determine the etiological mechanisms underlying CAD pathogenesis and to identify lifestyle-dependent metabolic risk factors or genetic and epigenetic parameters responsible for CAD occurrence and/or progression. A log-linear association between the absolute exposure of LDL cholesterol (LDL-C) and the risk of atherosclerotic cardio-vascular disease (ASCVD) was well documented over the year. LDL-C was identified as the principal enemy to fight against, and soluble proprotein convertase subtilisin kexin type 9 (PCSK9) was attributed the role of a powerful regulator of blood LDL-C levels. The two currently available antibodies (alirocumab and evolocumab) against PCSK9 are fully human engineered IgG that bind to soluble PCSK9 and avoid its interaction with the LDLR. As documented by modern and dedicated "game-changer" trials, antibodies against soluble PCSK9 reduce LDL-C levels by at least 60 percent when used alone and up to 85 percent when used in combination with high-intensity statins and/or other hypolipidemic therapies, including ezetimibe. Their clinical indications are well established, but new areas of use are advocated. Several clues suggest that regulation of PCSK9 represents a cornerstone of cardiovascular prevention, partly because of some pleiotropic effects attributed to these newly developed drugs. New mechanisms of PCSK9 regulation are being explored, and further efforts need to be put in place to reach patients with these new therapies. The aim of this manuscript is to perform a narrative review of the literature on soluble PCSK9 inhibitor drugs, with a focus on their indications and clinical impact.
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Affiliation(s)
- Michele Bellino
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Gennaro Galasso
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Angelo Silverio
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Michele Tedeschi
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Ciro Formisano
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Stefano Romei
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Luca Esposito
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Francesco Paolo Cancro
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Maria Giovanna Vassallo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Giulio Accarino
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
| | - Monica Verdoia
- Division of Cardiology, Ospedale Degli Infermi, ASL Biella, 13900 Biella, Italy
| | - Francesca Maria Di Muro
- Structural Interventional Cardiology, Department of Clinical and Experimental Medicine, Clinica Medica, Careggi University Hospital, 50139 Florence, Italy
| | - Carmine Vecchione
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84081 Baronissi, Italy
- Vascular Physiopathology Unit, IRCCS Neuromed Mediterranean Neurological Institute, 86077 Pozzilli, Italy
| | - Giuseppe De Luca
- Division of Cardiology, AOU "Policlinico G. Martino", Department of Clinical and Experimental Medicine, University of Messina, 98166 Messina, Italy
- Division of Cardiology, IRCCS Hospital Galeazzi-Sant'Ambrogio, 20161 Milan, Italy
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11
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Wald NJ, Duffy SW, Hackshaw A. The Risk-Screening Converter. J Med Screen 2023; 30:1-2. [PMID: 36617846 DOI: 10.1177/09691413221149640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Affiliation(s)
- Nicholas J Wald
- Institute of Health Informatics, University College London, London, UK
| | - Stephen W Duffy
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK
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12
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de Vries PS. Polygenic risk, lifestyle and the lifetime risk of coronary artery disease. HEART (BRITISH CARDIAC SOCIETY) 2023; 109:730-731. [PMID: 36759176 DOI: 10.1136/heartjnl-2022-322057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Affiliation(s)
- Paul S de Vries
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA
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13
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Poli A, Catapano AL, Corsini A, Manzato E, Werba JP, Catena G, Cetin I, Cicero AFG, Cignarella A, Colivicchi F, Consoli A, Landi F, Lucarelli M, Manfellotto D, Marrocco W, Parretti D, Perrone Filardi P, Pirillo A, Sesti G, Volpe M, Marangoni F. LDL-cholesterol control in the primary prevention of cardiovascular diseases: An expert opinion for clinicians and health professionals. Nutr Metab Cardiovasc Dis 2023; 33:245-257. [PMID: 36566123 DOI: 10.1016/j.numecd.2022.10.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 10/04/2022] [Indexed: 11/29/2022]
Abstract
AIMS Although adequate clinical management of patients with hypercholesterolemia without a history of known cardiovascular disease is essential for prevention, these subjects are often disregarded. Furthermore, the scientific literature on primary cardiovascular prevention is not as rich as that on secondary prevention; finally, physicians often lack adequate tools for the effective management of subjects in primary prevention and have to face some unsolved relevant issues. This document aims to discuss and review the evidence available on this topic and provide practical guidance. DATA SYNTHESIS Available algorithms and risk charts represent the main tool for the assessment of cardiovascular risk in patients in primary prevention. The accuracy of such an estimate can be substantially improved considering the potential contribution of some additional risk factors (C-reactive protein, lipoprotein(a), family history of cardiovascular disease) and conditions (environmental pollution, sleep quality, socioeconomic status, educational level) whose impact on the cardiovascular risk has been better understood in recent years. The availability of non-invasive procedures to evaluate subclinical atherosclerosis may help to identify subjects needing an earlier intervention. Unveiling the presence of these conditions will improve cardiovascular risk estimation, granting a more appropriate intervention. CONCLUSIONS The accurate assessment of cardiovascular risk in subjects in primary prevention with the use of algorithms and risk charts together with the evaluation of additional factors will allow physicians to approach each patient with personalized strategies, which should translate into an increased adherence to therapy and, as a consequence, a reduced cardiovascular risk.
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Affiliation(s)
- Andrea Poli
- NFI - Nutrition Foundation of Italy, Milan, Italy.
| | - Alberico L Catapano
- Department of Pharmacological and Biomolecular Sciences, University of Milan, Milan, Italy; Center for the Study of Dyslipidaemias, IRCCS MultiMedica, Sesto S. Giovanni, Milan, Italy
| | - Alberto Corsini
- Department of Pharmacological and Biomolecular Sciences, University of Milan, Milan, Italy
| | - Enzo Manzato
- Department of Medicine, University of Padova, Padova, Italy; SISA - Italian Society for the Study of Atherosclerosis, Italy
| | - José Pablo Werba
- Unit of Atherosclerosis Prevention, Monzino Cardiology Center, IRCCS, Milan, Italy
| | | | - Irene Cetin
- Department of Woman, Mother and Neonate Hospital Buzzi, Milan, University of Milan, Italy; SIGO - Italian Society of Gynecology and Obstetrics, Italy
| | - Arrigo F G Cicero
- Hypertension and Cardiovascular Risk Research Center, Medical and Surgical Sciences Department, IRCCS AOU di Bologna, Bologna, Italy; SINut - Italian Nutraceutical Society, Italy
| | - Andrea Cignarella
- Department of Medicine, University of Padova, Padova, Italy; Italian Research Center for Gender Health and Medicine, Italy
| | - Furio Colivicchi
- Division of Clinical Cardiology, San Filippo Neri Hospital, Rome, Italy; ANMCO - Italian National Association of Hospital Cardiologists, Italy
| | - Agostino Consoli
- Department of Medicine and Aging Sciences, University G. D'Annunzio, Chieti, Italy; SID - Italian Society of Diabetology, Italy
| | - Francesco Landi
- Fondazione Policlinico Universitario A. Gemelli IRCSS, Rome, Italy; SIGG - Italian Society of Gerontology and Geriatrics, Italy
| | - Maurizio Lucarelli
- SNaMID - National Society of Medical Education in General Practice, Italy
| | - Dario Manfellotto
- Department of Internal Medicine, Fatebenefratelli Hospital, Isola Tiberina, Rome, Italy; FADOI - Federation of Associations of Hospital Internists, Italy
| | - Walter Marrocco
- SIMPeSV and FIMMG - Italian Society of Preventive and Lifestyle Medicine and Italian Federation of General Practitioners, Italy
| | | | - Pasquale Perrone Filardi
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy; SIC - Italian Society of Cardiology, Italy
| | - Angela Pirillo
- Center for the Study of Dyslipidaemias, IRCCS MultiMedica, Sesto S. Giovanni, Milan, Italy; Center for the Study of Atherosclerosis, E. Bassini Hospital, Cinisello Balsamo, Milan, Italy
| | - Giorgio Sesti
- Department of Clinical and Molecular Medicine, Sapienza University of Rome, Rome, Italy; SIMI - Italian Society of Internal Medicine, Italy
| | - Massimo Volpe
- Department of Clinical and Molecular Medicine, Sapienza University of Rome, Italy; SIPREC - Italian Society for Cardiovascular Prevention, Italy
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14
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Polygenic risk scores in coronary artery disease. Curr Opin Cardiol 2023; 38:39-46. [PMID: 36598448 DOI: 10.1097/hco.0000000000001007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
PURPOSE OF REVIEW Recent advances in genetics have facilitated the calculation of polygenic risk scores (PRSs) based on common genetic risk variants of coronary artery disease (CAD). Here, we provide an explanation of the genetic basis for PRSs and review recent literature investigating PRSs and the clinical utility for different aspects of CAD. RECENT FINDINGS CAD-based PRSs are strongly associated with atherosclerosis burden in the coronary arteries and other vascular beds. In multiple studies, PRSs have proven to be a measure of CAD risk, more powerful than most established risk factors alone, that can be used from early life to stratify individuals into varying trajectories of lifetime risk. When implemented in risk stratification models for primary prevention of cardiovascular disease, PRSs provide modest improvements in discrimination (C-index generally increasing 0-4% points) and reclassification, but yield significant clinical benefit as a risk enhancer. Additionally, data suggest possible value of PRSs for aiding decisions in other aspects of diagnostics and treatment in CAD. SUMMARY Once genotyped, the genetic information may be used to calculate an infinite number of PRSs and contribute to personalize medicine providing clinical value for risk stratification, diagnostics and treatment in CAD as well as in other diseases.
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