1
|
Nielsen VW, Bundgaard Vad O, Holgersen N, Paludan-Müller C, Meseguer Monfort L, Beyer AF, Jemec GBE, Kjærsgaard Andersen R, Egeberg A, Thyssen JP, Svendsen JH, Rosenø NAL, Hansen PR, Thomsen SF, Salling Olesen M. Genetic Susceptibility to Hidradenitis Suppurativa and Predisposition to Cardiometabolic Disease. JAMA Dermatol 2024:2824562. [PMID: 39382891 PMCID: PMC11465120 DOI: 10.1001/jamadermatol.2024.3779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2024] [Accepted: 08/06/2024] [Indexed: 10/10/2024]
Abstract
Importance Hidradenitis suppurativa (HS) is associated with an increased prevalence of cardiovascular diseases compared with the general population. Any association between polygenic risk for HS, risk of incident cardiometabolic outcomes, and the plasma proteome is unclear. Objective To investigate the genetic correlation between HS and cardiometabolic disease. Design, Setting, and Participants This cohort study used a polygenic risk score (PRS) for HS to examine the risks of coronary artery disease (CAD) and diabetes and identify changes in the plasma proteome in individuals of European ancestry from the UK Biobank. Participants were enrolled from January 1, 2006, to December 31, 2010. End of follow-up was January 1, 2023. Correlations were assessed between HS susceptibility and cardiometabolic traits using linkage disequilibrium score regression. Odds ratios were assessed in logistic regressions. The risk of incident CAD and diabetes was estimated in cause-specific survival models designed as time-to-event analyses. Exposure The PRS for HS. Main Outcomes and Measures Main outcomes were CAD and diabetes diagnosis measured by logistic regressions and incident disease measured by Cox proportional hazards regression models adjusted for sex, age, body mass index, and smoking status. Results The study included 391 481 individuals (median [IQR] age, 58 [51-64] years; 209 235 [53%] female). Genetic variants for HS correlated significantly with variants associated with CAD, diabetes, and plasma levels of high-density lipoprotein cholesterol, triglycerides, and C-reactive protein. Compared with the low-risk group, a high PRS for HS (≥75th percentile) conferred odds ratios of 1.09 (95% CI, 1.06-1.12; P < .001) for CAD and 1.13 (95% CI, 1.10-1.17; P < .001) for diabetes. Estimates remained consistent when examining only incident CAD and diabetes. The PRS for HS was significantly associated with altered expression of 58 plasma proteins. Integrating this proteomic profile and the PRS for HS in a machine learning model improved prediction of CAD and diabetes compared with a reference model based on sex, age, and body mass index. Conclusions and Relevance These findings suggest that a high genetic risk of HS is associated with increased risk of subsequent CAD and diabetes and altered composition of the plasma proteome. Additional investigation into the identified proteins and their potential roles as drug targets is warranted.
Collapse
Affiliation(s)
- Valdemar Wendelboe Nielsen
- Department of Dermato-Venereology and Wound Healing Centre, Copenhagen University Hospital–Bispebjerg and Frederiksberg, Copenhagen, Denmark
| | - Oliver Bundgaard Vad
- Department of Cardiology, The Heart Centre, Copenhagen University Hospital–Rigshospitalet, Copenhagen, Denmark
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Nikolaj Holgersen
- Department of Dermato-Venereology and Wound Healing Centre, Copenhagen University Hospital–Bispebjerg and Frederiksberg, Copenhagen, Denmark
| | - Christian Paludan-Müller
- Department of Cardiology, The Heart Centre, Copenhagen University Hospital–Rigshospitalet, Copenhagen, Denmark
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Laia Meseguer Monfort
- Department of Cardiology, The Heart Centre, Copenhagen University Hospital–Rigshospitalet, Copenhagen, Denmark
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Astrid Filt Beyer
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Gregor Borut Ernst Jemec
- Department of Dermatology, Zealand University Hospital, Roskilde, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Rune Kjærsgaard Andersen
- Department of Dermatology, Zealand University Hospital, Roskilde, Denmark
- Department of Immunology and Microbiology, Leo Foundation Skin Immunology Research Center, University of Copenhagen, Copenhagen, Denmark
| | - Alexander Egeberg
- Department of Dermato-Venereology and Wound Healing Centre, Copenhagen University Hospital–Bispebjerg and Frederiksberg, Copenhagen, Denmark
- LEO Pharma, Ballerup, Denmark
| | - Jacob P. Thyssen
- Department of Dermato-Venereology and Wound Healing Centre, Copenhagen University Hospital–Bispebjerg and Frederiksberg, Copenhagen, Denmark
- LEO Pharma, Ballerup, Denmark
| | - Jesper Hastrup Svendsen
- Department of Cardiology, The Heart Centre, Copenhagen University Hospital–Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Nana Aviaaja Lippert Rosenø
- Department of Dermato-Venereology and Wound Healing Centre, Copenhagen University Hospital–Bispebjerg and Frederiksberg, Copenhagen, Denmark
| | - Peter Riis Hansen
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Cardiology, Copenhagen University Hospital–Herlev and Gentofte, Hellerup, Denmark
| | - Simon Francis Thomsen
- Department of Dermato-Venereology and Wound Healing Centre, Copenhagen University Hospital–Bispebjerg and Frederiksberg, Copenhagen, Denmark
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Morten Salling Olesen
- Department of Biomedical Sciences, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| |
Collapse
|
2
|
Capalbo A, de Wert G, Mertes H, Klausner L, Coonen E, Spinella F, Van de Velde H, Viville S, Sermon K, Vermeulen N, Lencz T, Carmi S. Screening embryos for polygenic disease risk: a review of epidemiological, clinical, and ethical considerations. Hum Reprod Update 2024; 30:529-557. [PMID: 38805697 PMCID: PMC11369226 DOI: 10.1093/humupd/dmae012] [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: 01/10/2024] [Revised: 03/25/2024] [Indexed: 05/30/2024] Open
Abstract
BACKGROUND The genetic composition of embryos generated by in vitro fertilization (IVF) can be examined with preimplantation genetic testing (PGT). Until recently, PGT was limited to detecting single-gene, high-risk pathogenic variants, large structural variants, and aneuploidy. Recent advances have made genome-wide genotyping of IVF embryos feasible and affordable, raising the possibility of screening embryos for their risk of polygenic diseases such as breast cancer, hypertension, diabetes, or schizophrenia. Despite a heated debate around this new technology, called polygenic embryo screening (PES; also PGT-P), it is already available to IVF patients in some countries. Several articles have studied epidemiological, clinical, and ethical perspectives on PES; however, a comprehensive, principled review of this emerging field is missing. OBJECTIVE AND RATIONALE This review has four main goals. First, given the interdisciplinary nature of PES studies, we aim to provide a self-contained educational background about PES to reproductive specialists interested in the subject. Second, we provide a comprehensive and critical review of arguments for and against the introduction of PES, crystallizing and prioritizing the key issues. We also cover the attitudes of IVF patients, clinicians, and the public towards PES. Third, we distinguish between possible future groups of PES patients, highlighting the benefits and harms pertaining to each group. Finally, our review, which is supported by ESHRE, is intended to aid healthcare professionals and policymakers in decision-making regarding whether to introduce PES in the clinic, and if so, how, and to whom. SEARCH METHODS We searched for PubMed-indexed articles published between 1/1/2003 and 1/3/2024 using the terms 'polygenic embryo screening', 'polygenic preimplantation', and 'PGT-P'. We limited the review to primary research papers in English whose main focus was PES for medical conditions. We also included papers that did not appear in the search but were deemed relevant. OUTCOMES The main theoretical benefit of PES is a reduction in lifetime polygenic disease risk for children born after screening. The magnitude of the risk reduction has been predicted based on statistical modelling, simulations, and sibling pair analyses. Results based on all methods suggest that under the best-case scenario, large relative risk reductions are possible for one or more diseases. However, as these models abstract several practical limitations, the realized benefits may be smaller, particularly due to a limited number of embryos and unclear future accuracy of the risk estimates. PES may negatively impact patients and their future children, as well as society. The main personal harms are an unindicated IVF treatment, a possible reduction in IVF success rates, and patient confusion, incomplete counselling, and choice overload. The main possible societal harms include discarded embryos, an increasing demand for 'designer babies', overemphasis of the genetic determinants of disease, unequal access, and lower utility in people of non-European ancestries. Benefits and harms will vary across the main potential patient groups, comprising patients already requiring IVF, fertile people with a history of a severe polygenic disease, and fertile healthy people. In the United States, the attitudes of IVF patients and the public towards PES seem positive, while healthcare professionals are cautious, sceptical about clinical utility, and concerned about patient counselling. WIDER IMPLICATIONS The theoretical potential of PES to reduce risk across multiple polygenic diseases requires further research into its benefits and harms. Given the large number of practical limitations and possible harms, particularly unnecessary IVF treatments and discarded viable embryos, PES should be offered only within a research context before further clarity is achieved regarding its balance of benefits and harms. The gap in attitudes between healthcare professionals and the public needs to be narrowed by expanding public and patient education and providing resources for informative and unbiased genetic counselling.
Collapse
Affiliation(s)
- Antonio Capalbo
- Juno Genetics, Department of Reproductive Genetics, Rome, Italy
- Center for Advanced Studies and Technology (CAST), Department of Medical Genetics, “G. d’Annunzio” University of Chieti-Pescara, Chieti, Italy
| | - Guido de Wert
- Department of Health, Ethics & Society, CAPHRI-School for Public Health and Primary Care and GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Heidi Mertes
- Department of Philosophy and Moral Sciences, Ghent University, Ghent, Belgium
- Department of Public Health and Primary Care, Ghent University, Ghent, Belgium
| | - Liraz Klausner
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Edith Coonen
- Departments of Clinical Genetics and Reproductive Medicine, Maastricht University Medical Centre, Maastricht, The Netherlands
- School for Oncology and Developmental Biology, GROW, Maastricht University, Maastricht, The Netherlands
| | - Francesca Spinella
- Eurofins GENOMA Group Srl, Molecular Genetics Laboratories, Department of Scientific Communication, Rome, Italy
| | - Hilde Van de Velde
- Research Group Genetics Reproduction and Development (GRAD), Vrije Universiteit Brussel, Brussel, Belgium
- Brussels IVF, UZ Brussel, Brussel, Belgium
| | - Stephane Viville
- Laboratoire de Génétique Médicale LGM, Institut de Génétique Médicale d’Alsace IGMA, INSERM UMR 1112, Université de Strasbourg, France
- Laboratoire de Diagnostic Génétique, Unité de Génétique de l’infertilité (UF3472), Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Karen Sermon
- Research Group Genetics Reproduction and Development (GRAD), Vrije Universiteit Brussel, Brussel, Belgium
| | | | - Todd Lencz
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Departments of Psychiatry and Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11549, USA
| | - Shai Carmi
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| |
Collapse
|
3
|
Isgut M, Giuste F, Gloster L, Swain A, Choi K, Hornback A, Deshpande SR, Wang MD. Identifying and characterizing disease subpopulations that most benefit from polygenic risk scores. Sci Rep 2024; 14:22124. [PMID: 39333190 PMCID: PMC11436906 DOI: 10.1038/s41598-024-63705-5] [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: 03/24/2024] [Accepted: 05/31/2024] [Indexed: 09/29/2024] Open
Abstract
Polygenic risk scores (PRSs) hold promise in their potential translation into clinical settings to improve disease risk prediction. An important consideration in integrating PRSs into clinical settings is to gain an understanding of how to identify which subpopulations of individuals most benefit from PRSs for risk prediction. In this study, using the UK Biobank dataset, we trained logistic regression models to predict the 10 year incident risk of myocardial infarction, breast cancer, and schizophrenia using either just clinical features or clinical features combined with PRSs. For each disease, we identified the top 10% subgroup with the greatest magnitude of improvement in risk prediction accuracy attributed to PRSs in the multi-modal model. Using up to ~ 3.6 k demographic, lifestyle, diagnostic, lab, and physical measurement features from the UK Biobank dataset of ~ 500 k individuals, we characterized these subgroups based on various clinical, lifestyle, and demographic characteristics. The incident cases in the top 10% subgroup for each disease represent distinct phenotypes that differ from other cases and that are strongly correlated with genetic predisposition. Our findings provide insights into disease subtypes and can encourage future studies aimed at classifying these individuals to enhance the targeting of polygenic risk scoring in practice.
Collapse
Affiliation(s)
- Monica Isgut
- Department of Bioinformatics, Georgia Institute of Technology, Atlanta, GA, 30332, USA
- School of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30322, USA
| | - Felipe Giuste
- School of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30322, USA
| | - Logan Gloster
- Department of Bioinformatics, Georgia Institute of Technology, Atlanta, GA, 30332, USA
- School of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30322, USA
| | - Aniketh Swain
- School of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30322, USA
| | - Katherine Choi
- School of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30322, USA
| | - Andrew Hornback
- School of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30322, USA.
| | - Shriprasad R Deshpande
- Advanced Cardiac Therapies and Heart Transplant Program, Children's National Hospital, Washington, DC, 20010, USA
| | - May D Wang
- School of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30322, USA.
| |
Collapse
|
4
|
Samani NJ, Beeston E, Greengrass C, Riveros-McKay F, Debiec R, Lawday D, Wang Q, Budgeon CA, Braund PS, Bramley R, Kharodia S, Newton M, Marshall A, Krzeminski A, Zafar A, Chahal A, Heer A, Khunti K, Joshi N, Lakhani M, Farooqi A, Plagnol V, Donnelly P, Weale ME, Nelson CP. Polygenic risk score adds to a clinical risk score in the prediction of cardiovascular disease in a clinical setting. Eur Heart J 2024; 45:3152-3160. [PMID: 38848106 PMCID: PMC11379490 DOI: 10.1093/eurheartj/ehae342] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Revised: 04/10/2024] [Accepted: 05/16/2024] [Indexed: 09/08/2024] Open
Abstract
BACKGROUND AND AIMS A cardiovascular disease polygenic risk score (CVD-PRS) can stratify individuals into different categories of cardiovascular risk, but whether the addition of a CVD-PRS to clinical risk scores improves the identification of individuals at increased risk in a real-world clinical setting is unknown. METHODS The Genetics and the Vascular Health Check Study (GENVASC) was embedded within the UK National Health Service Health Check (NHSHC) programme which invites individuals between 40-74 years of age without known CVD to attend an assessment in a UK general practice where CVD risk factors are measured and a CVD risk score (QRISK2) is calculated. Between 2012-2020, 44,141 individuals (55.7% females, 15.8% non-white) who attended an NHSHC in 147 participating practices across two counties in England were recruited and followed. When 195 individuals (cases) had suffered a major CVD event (CVD death, myocardial infarction or acute coronary syndrome, coronary revascularisation, stroke), 396 propensity-matched controls with a similar risk profile were identified, and a nested case-control genetic study undertaken to see if the addition of a CVD-PRS to QRISK2 in the form of an integrated risk tool (IRT) combined with QRISK2 would have identified more individuals at the time of their NHSHC as at high risk (QRISK2 10-year CVD risk of ≥10%), compared with QRISK2 alone. RESULTS The distribution of the standardised CVD-PRS was significantly different in cases compared with controls (cases mean score .32; controls, -.18, P = 8.28×10-9). QRISK2 identified 61.5% (95% confidence interval [CI]: 54.3%-68.4%) of individuals who subsequently developed a major CVD event as being at high risk at their NHSHC, while the combination of QRISK2 and IRT identified 68.7% (95% CI: 61.7%-75.2%), a relative increase of 11.7% (P = 1×10-4). The odds ratio (OR) of being up-classified was 2.41 (95% CI: 1.03-5.64, P = .031) for cases compared with controls. In individuals aged 40-54 years, QRISK2 identified 26.0% (95% CI: 16.5%-37.6%) of those who developed a major CVD event, while the combination of QRISK2 and IRT identified 38.4% (95% CI: 27.2%-50.5%), indicating a stronger relative increase of 47.7% in the younger age group (P = .001). The combination of QRISK2 and IRT increased the proportion of additional cases identified similarly in women as in men, and in non-white ethnicities compared with white ethnicity. The findings were similar when the CVD-PRS was added to the atherosclerotic cardiovascular disease pooled cohort equations (ASCVD-PCE) or SCORE2 clinical scores. CONCLUSIONS In a clinical setting, the addition of genetic information to clinical risk assessment significantly improved the identification of individuals who went on to have a major CVD event as being at high risk, especially among younger individuals. The findings provide important real-world evidence of the potential value of implementing a CVD-PRS into health systems.
Collapse
Affiliation(s)
- Nilesh J Samani
- Department of Cardiovascular Sciences, University of Leicester, BHF Cardiovascular Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK
| | - Emma Beeston
- Department of Cardiovascular Sciences, University of Leicester, BHF Cardiovascular Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK
| | - Chris Greengrass
- Department of Cardiovascular Sciences, University of Leicester, BHF Cardiovascular Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK
| | | | - Radoslaw Debiec
- Department of Cardiovascular Sciences, University of Leicester, BHF Cardiovascular Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK
| | - Daniel Lawday
- Department of Cardiovascular Sciences, University of Leicester, BHF Cardiovascular Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK
| | - Qingning Wang
- Department of Cardiovascular Sciences, University of Leicester, BHF Cardiovascular Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK
| | - Charley A Budgeon
- Department of Cardiovascular Sciences, University of Leicester, BHF Cardiovascular Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK
- School of Population and Global Health, University of Western Australia, Perth WA 6009, Australia
| | - Peter S Braund
- Department of Cardiovascular Sciences, University of Leicester, BHF Cardiovascular Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK
| | - Richard Bramley
- Department of Cardiovascular Sciences, University of Leicester, BHF Cardiovascular Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK
| | - Shireen Kharodia
- Department of Cardiovascular Sciences, University of Leicester, BHF Cardiovascular Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK
| | - Michelle Newton
- Department of Cardiovascular Sciences, University of Leicester, BHF Cardiovascular Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK
| | - Andrea Marshall
- Department of Cardiovascular Sciences, University of Leicester, BHF Cardiovascular Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK
| | | | - Azhar Zafar
- Diabetes Research Centre, University of Leicester, Leicester General Hospital, Leicester LE5 4PW, UK
- Diabetes and Cardiovascular Medicine General Practice Alliance Federation Research and Training Academy, Northampton NN2 6AL, UK
| | - Anuj Chahal
- South Leicestershire Medical Group, Kibworth Beauchamp LE8 0LG, UK
| | | | - Kamlesh Khunti
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK
- Diabetes Research Centre, University of Leicester, Leicester General Hospital, Leicester LE5 4PW, UK
| | - Nitin Joshi
- Willowbrook Medical Centre, Leicester LE5 2NL, UK
| | - Mayur Lakhani
- Department of Health Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - Azhar Farooqi
- Department of Health Sciences, University of Leicester, Leicester LE1 7RH, UK
| | - Vincent Plagnol
- Genomics plc, King Charles House, Park End Street, Oxford OX1 1 JD, UK
| | - Peter Donnelly
- Genomics plc, King Charles House, Park End Street, Oxford OX1 1 JD, UK
| | - Michael E Weale
- Genomics plc, King Charles House, Park End Street, Oxford OX1 1 JD, UK
| | - Christopher P Nelson
- Department of Cardiovascular Sciences, University of Leicester, BHF Cardiovascular Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK
- NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK
| |
Collapse
|
5
|
Chen RT, Pearson V, Suebkinorn O, Bulto LN, Anderson A, Nelson AJ, Zoungas S, Nicholls SJ, Clark RA. Effectiveness of receiving genetic risk information for cardiovascular disease on health behaviors, psychological responses, and associated risk factor modification in individuals: a systematic review protocol. JBI Evid Synth 2024:02174543-990000000-00345. [PMID: 39224920 DOI: 10.11124/jbies-23-00313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
OBJECTIVE This review aims to assess the effectiveness of receiving genetic risk information for cardiovascular disease (CVD) on individual health behaviors, psychological responses, and risk factor modification. INTRODUCTION Advancements in genomics have identified strong genetic predispositions for CVD, leading to the development of CVD genetic risk information. Integrating genetic risk information into clinical practice shows promise in predicting CVD risk and facilitating multifactorial management. INCLUSION CRITERIA This review will focus on randomized controlled trials assessing individual responses to CVD genetic risk information. Participants will be adults aged 18 and older, both with and without CVD. The review will compare the effectiveness of receiving genetic risk information with receiving traditional risk information or no risk information, assessing outcomes such as health behaviors, psychological responses, and risk factor modification. METHODS Cochrane CENTRAL, MEDLINE, Embase and Emcare will be searched for relevant studies. Current or unpublished trials will be searched for in Clinical-Trials.gov and World Health Organization International Clinical Trials Registry Platform. Articles will be screened and assessed for inclusion by 2 independent reviewers. Methodological quality will be assessed using the standardized instrument from JBI. Data will be extracted and synthesized for the objectives of the study. If data are sufficient, a meta-analysis will be conducted; otherwise, the findings will be presented in narrative format, including tables and figures to aid in presentation. The certainty of evidence will be assessed using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach. REVIEW REGISTRATION PROSPERO CRD42023390876.
Collapse
Affiliation(s)
- Ruofei Trophy Chen
- Monash Victorian Heart Institute, Monash University, Melbourne, Vic, Australia
| | - Vincent Pearson
- JBI, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA, Australia
| | - Orathai Suebkinorn
- Caring Future Institute, College of Nursing and Health Sciences, Flinders University, Adelaide, SA, Australia
| | - Lemma N Bulto
- Caring Future Institute, College of Nursing and Health Sciences, Flinders University, Adelaide, SA, Australia
| | - Alice Anderson
- Monash Health Library, Monash Health, Melbourne, Vic, Australia
| | - Adam J Nelson
- Monash Victorian Heart Institute, Monash University, Melbourne, Vic, Australia
| | - Sophia Zoungas
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Vic, Australia
| | - Stephen J Nicholls
- Monash Victorian Heart Institute, Monash University, Melbourne, Vic, Australia
| | - Robyn A Clark
- Caring Future Institute, College of Nursing and Health Sciences, Flinders University, Adelaide, SA, Australia
| |
Collapse
|
6
|
Chao TH, Lin TH, Cheng CI, Wu YW, Ueng KC, Wu YJ, Lin WW, Leu HB, Cheng HM, Huang CC, Wu CC, Lin CF, Chang WT, Pan WH, Chen PR, Ting KH, Su CH, Chu CS, Chien KL, Yen HW, Wang YC, Su TC, Liu PY, Chang HY, Chen PW, Juang JMJ, Lu YW, Lin PL, Wang CP, Ko YS, Chiang CE, Hou CJY, Wang TD, Lin YH, Huang PH, Chen WJ. 2024 Guidelines of the Taiwan Society of Cardiology on the Primary Prevention of Atherosclerotic Cardiovascular Disease --- Part I. ACTA CARDIOLOGICA SINICA 2024; 40:479-543. [PMID: 39308649 PMCID: PMC11413940 DOI: 10.6515/acs.202409_40(5).20240724a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 07/24/2024] [Indexed: 09/25/2024]
Abstract
Atherosclerotic cardiovascular disease (ASCVD) is one of the leading causes of death worldwide and in Taiwan. It is highly prevalent and has a tremendous impact on global health. Therefore, the Taiwan Society of Cardiology developed these best-evidence preventive guidelines for decision-making in clinical practice involving aspects of primordial prevention including national policies, promotion of health education, primary prevention of clinical risk factors, and management and control of clinical risk factors. These guidelines cover the full spectrum of ASCVD, including chronic coronary syndrome, acute coronary syndrome, cerebrovascular disease, peripheral artery disease, and aortic aneurysm. In order to enhance medical education and health promotion not only for physicians but also for the general public, we propose a slogan (2H2L) for the primary prevention of ASCVD on the basis of the essential role of healthy dietary pattern and lifestyles: "Healthy Diet and Healthy Lifestyles to Help Your Life and Save Your Lives". We also propose an acronym of the modifiable risk factors/enhancers and relevant strategies to facilitate memory: " ABC2D2EFG-I'M2 ACE": Adiposity, Blood pressure, Cholesterol and Cigarette smoking, Diabetes mellitus and Dietary pattern, Exercise, Frailty, Gout/hyperuricemia, Inflammation/infection, Metabolic syndrome and Metabolic dysfunction-associated fatty liver disease, Atmosphere (environment), Chronic kidney disease, and Easy life (sleep well and no stress). Some imaging studies can be risk enhancers. Some risk factors/clinical conditions are deemed to be preventable, and healthy dietary pattern, physical activity, and body weight control remain the cornerstone of the preventive strategy.
Collapse
Affiliation(s)
- Ting-Hsing Chao
- Division of Cardiology, Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan
- Division of Cardiology, Department of Internal Medicine, Chung-Shan Medical University Hospital; School of Medicine, Chung Shan Medical University, Taichung
| | - Tsung-Hsien Lin
- Division of Cardiology, Department of Internal Medicine Kaohsiung Medical University Hospital
- Faculty of Medicine and Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University
| | - Cheng-I Cheng
- Division of Cardiology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung; School of Medicine, College of Medicine, Chang Gung University, Taoyuan
| | - Yen-Wen Wu
- Division of Cardiology, Cardiovascular Medical Center, Far Eastern Memorial Hospital, New Taipei City
- School of Medicine, National Yang Ming Chiao Tung University, Taipei
- Graduate Institute of Medicine, Yuan Ze University, Taoyuan
| | - Kwo-Chang Ueng
- Division of Cardiology, Department of Internal Medicine, Chung-Shan Medical University Hospital; School of Medicine, Chung Shan Medical University, Taichung
| | - Yih-Jer Wu
- Department of Medicine and Institute of Biomedical Sciences, MacKay Medical College, New Taipei City
- Cardiovascular Center, Department of Internal Medicine, MacKay Memorial Hospital, Taipei
| | - Wei-Wen Lin
- Cardiovascular Center, Taichung Veterans General Hospital, Taichung
| | - Hsing-Ban Leu
- School of Medicine, National Yang Ming Chiao Tung University, Taipei
- Cardiovascular Research Center, National Yang Ming Chiao Tung University
- Healthcare and Management Center
- Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital
| | - Hao-Min Cheng
- Ph.D. Program of Interdisciplinary Medicine (PIM), National Yang Ming Chiao Tung University College of Medicine; Division of Faculty Development; Center for Evidence-based Medicine, Taipei Veterans General Hospital; Institute of Public Health; Institute of Health and Welfare Policy, National Yang Ming Chiao Tung University College of Medicine
| | - Chin-Chou Huang
- School of Medicine, National Yang Ming Chiao Tung University, Taipei
- Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital
- Institute of Pharmacology, National Yang Ming Chiao Tung University, Taipei
| | - Chih-Cheng Wu
- Center of Quality Management, National Taiwan University Hospital Hsinchu Branch, Hsinchu; College of Medicine, National Taiwan University, Taipei; Institute of Biomedical Engineering, National Tsing-Hua University, Hsinchu; Institute of Cellular and System Medicine, National Health Research Institutes, Zhunan
| | - Chao-Feng Lin
- Department of Medicine, MacKay Medical College, New Taipei City; Department of Cardiology, MacKay Memorial Hospital, Taipei
| | - Wei-Ting Chang
- School of Medicine and Doctoral Program of Clinical and Experimental Medicine, College of Medicine and Center of Excellence for Metabolic Associated Fatty Liver Disease, National Sun Yat-sen University, Kaohsiung; Division of Cardiology, Department of Internal Medicine, Chi Mei Medical Center, Tainan
| | - Wen-Han Pan
- Institute of Biomedical Sciences, Academia Sinica, Taipei; Institute of Population Health Sciences, National Health Research Institutes, Miaoli; and Institute of Biochemistry and Biotechnology, National Taiwan University
| | - Pey-Rong Chen
- Department of Dietetics, National Taiwan University Hospital, Taipei
| | - Ke-Hsin Ting
- Division of Cardiology, Department of Internal Medicine, Yunlin Christian Hospital, Yunlin
| | - Chun-Hung Su
- Division of Cardiology, Department of Internal Medicine, Chung-Shan Medical University Hospital; School of Medicine, Chung Shan Medical University, Taichung
| | - Chih-Sheng Chu
- Division of Cardiology, Department of Internal Medicine, Kaohsiung Municipal Ta-Tung Hospital, Kaohsiung Medical University, Kaohsiung
| | - Kuo-Liong Chien
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University; Department of Internal Medicine, National Taiwan University Hospital and College of Medicine; Population Health Research Center, National Taiwan University, Taipei
| | - Hsueh-Wei Yen
- Division of Cardiology, Department of Internal Medicine Kaohsiung Medical University Hospital
| | - Yu-Chen Wang
- Division of Cardiology, Asia University Hospital; Department of Medical Laboratory Science and Biotechnology, Asia University; Division of Cardiology, China Medical University College of Medicine and Hospital, Taichung
| | - Ta-Chen Su
- Cardiovascular Center, Department of Internal Medicine, National Taiwan University Hospital
- Department of Environmental and Occupational Medicine, National Taiwan University College of Medicine
| | - Pang-Yen Liu
- Division of Cardiology, Department of Internal Medicine, Tri-Service General Hospital, National Defense Medical Center
| | - Hsien-Yuan Chang
- Division of Cardiology, Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan
| | - Po-Wei Chen
- Division of Cardiology, Department of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan
| | - Jyh-Ming Jimmy Juang
- Heart Failure Center and Division of Cardiology, Department of Internal Medicine, National Taiwan University College of Medicine, and National Taiwan University Hospital
| | - Ya-Wen Lu
- Cardiovascular Center, Taichung Veterans General Hospital, Taichung
- Cardiovascular Research Center, National Yang Ming Chiao Tung University
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei
| | - Po-Lin Lin
- Division of Cardiology, Department of Internal Medicine, Hsinchu MacKay Memorial Hospital, Hsinchu
| | - Chao-Ping Wang
- Division of Cardiology, E-Da Hospital; School of Medicine for International Students, College of Medicine, I-Shou University, Kaohsiung
| | - Yu-Shien Ko
- Cardiovascular Division, Chang Gung Memorial Hospital; College of Medicine, Chang Gung University, Taoyuan
| | - Chern-En Chiang
- General Clinical Research Center and Division of Cardiology, Taipei Veterans General Hospital and National Yang Ming Chiao Tung University
| | - Charles Jia-Yin Hou
- Cardiovascular Center, Department of Internal Medicine, MacKay Memorial Hospital, Taipei
| | - Tzung-Dau Wang
- Cardiovascular Center and Divisions of Hospital Medicine and Cardiology, Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine
| | - Yen-Hung Lin
- Division of Cardiology, Department of Internal Medicine, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei
| | - Po-Hsun Huang
- Cardiovascular Research Center, National Yang Ming Chiao Tung University
- Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital
| | - Wen-Jone Chen
- Department of Internal Medicine, Min-Sheng General Hospital, Taoyuan; Department of Internal Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| |
Collapse
|
7
|
Yang S, Sun Z, Sun D, Yu C, Guo Y, Sun D, Pang Y, Pei P, Yang L, Millwood IY, Walters RG, Chen Y, Du H, Lu Y, Burgess S, Avery D, Clarke R, Chen J, Chen Z, Li L, Lv J. Associations of polygenic risk scores with risks of stroke and its subtypes in Chinese. Stroke Vasc Neurol 2024; 9:399-406. [PMID: 37640499 PMCID: PMC7616400 DOI: 10.1136/svn-2023-002428] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 08/11/2023] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND AND PURPOSE Previous studies, mostly focusing on the European population, have reported polygenic risk scores (PRSs) might achieve risk stratification of stroke. We aimed to examine the association strengths of PRSs with risks of stroke and its subtypes in the Chinese population. METHODS Participants with genome-wide genotypic data in China Kadoorie Biobank were split into a potential training set (n=22 191) and a population-based testing set (n=72 150). Four previously developed PRSs were included, and new PRSs for stroke and its subtypes were developed. The PRSs showing the strongest association with risks of stroke or its subtypes in the training set were further evaluated in the testing set. Cox proportional hazards regression models were used to estimate the association strengths of different PRSs with risks of stroke and its subtypes (ischaemic stroke (IS), intracerebral haemorrhage (ICH) and subarachnoid haemorrhage (SAH)). RESULTS In the testing set, during 872 919 person-years of follow-up, 8514 incident stroke events were documented. The PRSs of any stroke (AS) and IS were both positively associated with risks of AS, IS and ICH (p<0.05). The HR for per SD increment (HRSD) of PRSAS was 1.10 (95% CI 1.07 to 1.12), 1.10 (95% CI 1.07 to 1.12) and 1.13 (95% CI 1.07 to 1.20) for AS, IS and ICH, respectively. The corresponding HRSD of PRSIS was 1.08 (95% CI 1.06 to 1.11), 1.08 (95% CI 1.06 to 1.11) and 1.09 (95% CI 1.03 to 1.15). PRSICH was positively associated with the risk of ICH (HRSD=1.07, 95% CI 1.01 to 1.14). PRSSAH was not associated with risks of stroke and its subtypes. The addition of current PRSs offered little to no improvement in stroke risk prediction and risk stratification. CONCLUSIONS In this Chinese population, the association strengths of current PRSs with risks of stroke and its subtypes were moderate, suggesting a limited value for improving risk prediction over traditional risk factors in the context of current genome-wide association study under-representing the East Asian population.
Collapse
Affiliation(s)
- Songchun Yang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Zhijia Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Dong Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Canqing Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Yu Guo
- Fuwai Hospital Chinese Academy of Medical Sciences, Beijing, China
| | - Dianjianyi Sun
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Yuanjie Pang
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Iona Y Millwood
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Robin G Walters
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Yan Lu
- NCDs Prevention and Control Department, Suzhou CDC, Suzhou, Jiangsu, China
| | - Sushila Burgess
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Daniel Avery
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Robert Clarke
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing, China
| | - Zhengming Chen
- Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Liming Li
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
| | - Jun Lv
- Department of Epidemiology & Biostatistics, School of Public Health, Peking University, Beijing, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing, China
- Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing, China
- State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing, China
| |
Collapse
|
8
|
Li L, Pang S, Starnecker F, Mueller-Myhsok B, Schunkert H. Integration of a polygenic score into guideline-recommended prediction of cardiovascular disease. Eur Heart J 2024; 45:1843-1852. [PMID: 38551411 PMCID: PMC11129792 DOI: 10.1093/eurheartj/ehae048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 12/20/2023] [Accepted: 01/18/2024] [Indexed: 05/29/2024] Open
Abstract
BACKGROUND AND AIMS It is not clear how a polygenic risk score (PRS) can be best combined with guideline-recommended tools for cardiovascular disease (CVD) risk prediction, e.g. SCORE2. METHODS A PRS for coronary artery disease (CAD) was calculated in participants of UK Biobank (n = 432 981). Within each tenth of the PRS distribution, the odds ratios (ORs)-referred to as PRS-factor-for CVD (i.e. CAD or stroke) were compared between the entire population and subgroups representing the spectrum of clinical risk. Replication was performed in the combined Framingham/Atherosclerosis Risk in Communities (ARIC) populations (n = 10 757). The clinical suitability of a multiplicative model 'SCORE2 × PRS-factor' was tested by risk reclassification. RESULTS In subgroups with highly different clinical risks, CVD ORs were stable within each PRS tenth. SCORE2 and PRS showed no significant interactive effects on CVD risk, which qualified them as multiplicative factors: SCORE2 × PRS-factor = total risk. In UK Biobank, the multiplicative model moved 9.55% of the intermediate (n = 145 337) to high-risk group increasing the individuals in this category by 56.6%. Incident CVD occurred in 8.08% of individuals reclassified by the PRS-factor from intermediate to high risk, which was about two-fold of those remained at intermediate risk (4.08%). Likewise, the PRS-factor shifted 8.29% of individuals from moderate to high risk in Framingham/ARIC. CONCLUSIONS This study demonstrates that absolute CVD risk, determined by a clinical risk score, and relative genetic risk, determined by a PRS, provide independent information. The two components may form a simple multiplicative model improving precision of guideline-recommended tools in predicting incident CVD.
Collapse
Affiliation(s)
- Ling Li
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Lazarettstr. 36, Munich 80636, Germany
- Deutsches Zentrum für Herz- und Kreislauferkrankungen (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
- School of Computation, Information and Technology, Technische Universität München, Munich, Germany
| | - Shichao Pang
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Lazarettstr. 36, Munich 80636, Germany
| | - Fabian Starnecker
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Lazarettstr. 36, Munich 80636, Germany
- Deutsches Zentrum für Herz- und Kreislauferkrankungen (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Bertram Mueller-Myhsok
- Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany
- Institute of Translational Medicine, University of Liverpool, Liverpool, UK
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Heribert Schunkert
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Lazarettstr. 36, Munich 80636, Germany
- Deutsches Zentrum für Herz- und Kreislauferkrankungen (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| |
Collapse
|
9
|
Christoffersen M, Stender S, Tybjærg-Hansen A. Polygenic risk scores for cardiovascular risk prediction: moving towards implementation into clinical practice? Eur Heart J 2024; 45:1853-1855. [PMID: 38606847 DOI: 10.1093/eurheartj/ehae125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/13/2024] Open
Affiliation(s)
- Mette Christoffersen
- Department of Clinical Biochemistry, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
| | - Stefan Stender
- Department of Clinical Biochemistry, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Anne Tybjærg-Hansen
- Department of Clinical Biochemistry, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| |
Collapse
|
10
|
Rout M, Tung GK, Singh JR, Mehra NK, Wander GS, Ralhan S, Sanghera DK. Polygenic Risk Score Assessment for Coronary Artery Disease in Asian Indians. J Cardiovasc Transl Res 2024:10.1007/s12265-024-10511-z. [PMID: 38658478 DOI: 10.1007/s12265-024-10511-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 04/11/2024] [Indexed: 04/26/2024]
Abstract
We evaluated the performance of various polygenic risk score (PRS) models derived from European (EU), South Asian (SA), and Punjabi Asian Indians (AI) studies on 13,974 subjects from AI ancestry. While all models successfully predicted Coronary artery disease (CAD) risk, the AI, SA, and EU + AI were superior predictors and more transportable than the EU model; the predictive performance in training and test sets was 18% and 22% higher in AI and EU + AI models, respectively than in EU. Comparing individuals with extreme PRS quartiles, the AI and EU + AI captured individuals with high CAD risk showed 2.6 to 4.6 times higher efficiency than the EU. Interestingly, including the clinical risk score did not significantly change the performance of any genetic model. The enrichment of diversity variants in EU PRS improves risk prediction and transportability. Establishing population-specific normative and risk factors and inclusion into genetic models would refine the risk stratification and improve the clinical utility of CAD PRS.
Collapse
Affiliation(s)
- Madhusmita Rout
- Department of Pediatrics, Section of Genetics, College of Medicine, University of Oklahoma Health Sciences Center, 940 Stanton L. Young Blvd., Rm 317 BMSB, Oklahoma City, OK, 73104, USA
| | - Gurleen Kaur Tung
- Department of Pediatrics, Section of Genetics, College of Medicine, University of Oklahoma Health Sciences Center, 940 Stanton L. Young Blvd., Rm 317 BMSB, Oklahoma City, OK, 73104, USA
| | | | | | | | - Sarju Ralhan
- Hero DMC Heart Institute, Ludhiana, Punjab, India
| | - Dharambir K Sanghera
- Department of Pediatrics, Section of Genetics, College of Medicine, University of Oklahoma Health Sciences Center, 940 Stanton L. Young Blvd., Rm 317 BMSB, Oklahoma City, OK, 73104, USA.
- Department of Pharmaceutical Sciences, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
- Department of Physiology, College of Medicine, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
- Oklahoma Center for Neuroscience, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
- Harold Hamm Diabetes Center, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA.
| |
Collapse
|
11
|
Weon B, Jang Y, Jo J, Jin W, Ha S, Ko A, Oh YK, Lim CS, Lee JP, Won S, Lee J. Association between dyslipidemia and the risk of incident chronic kidney disease affected by genetic susceptibility: Polygenic risk score analysis. PLoS One 2024; 19:e0299605. [PMID: 38626061 PMCID: PMC11020804 DOI: 10.1371/journal.pone.0299605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 02/13/2024] [Indexed: 04/18/2024] Open
Abstract
BACKGROUND The effect of dyslipidemia on kidney disease outcomes has been inconclusive, and it requires further clarification. Therefore, we aimed to investigate the effects of genetic factors on the association between dyslipidemia and the risk of chronic kidney disease (CKD) using polygenic risk score (PRS). METHODS We analyzed data from 373,523 participants from the UK Biobank aged 40-69 years with no history of CKD. Baseline data included plasma levels of total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride, as well as genome-wide genotype data for PRS. Our primary outcome, incident CKD, was defined as a composite of estimated glomerular filtration rate < 60 ml/min/1.73 m2 and CKD diagnosis according to International Classification of Disease-10 codes. The effects of the association between lipid levels and PRS on incident CKD were assessed using the Cox proportional hazards model. To investigate the effect of this association, we introduced multiplicative interaction terms into a multivariate analysis model and performed subgroup analysis stratified by PRS tertiles. RESULTS In total, 4,424 participants developed CKD. In the multivariable analysis, PRS was significantly predictive of the risk of incident CKD as both a continuous variable and a categorized variable. In addition, lower total cholesterol, LDL-C, HDL-C, and higher triglyceride levels were significantly associated with the risk of incident CKD. There were interactions between triglycerides and intermediate and high PRS, and the interactions were inversely associated with the risk of incident CKD. CONCLUSIONS This study showed that PRS presented significant predictive power for incident CKD and individuals in the low-PRS group had a higher risk of triglyceride-related incident CKD.
Collapse
Affiliation(s)
- Boram Weon
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea
| | | | - Jinyeon Jo
- Department of Public Health Sciences, Institute of Health & Environment, School of Public Health, Seoul National University, Seoul, Republic of Korea
| | - Wencheng Jin
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Seounguk Ha
- Korea Medical Institute, Seoul, Republic of Korea
| | - Ara Ko
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Yun Kyu Oh
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Chun Soo Lim
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jung Pyo Lee
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Sungho Won
- Rexsoft Corporation, Seoul, Republic of Korea
- Department of Public Health Sciences, Institute of Health & Environment, School of Public Health, Seoul National University, Seoul, Republic of Korea
| | - Jeonghwan Lee
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| |
Collapse
|
12
|
Muse ED, Topol EJ. Transforming the cardiometabolic disease landscape: Multimodal AI-powered approaches in prevention and management. Cell Metab 2024; 36:670-683. [PMID: 38428435 PMCID: PMC10990799 DOI: 10.1016/j.cmet.2024.02.002] [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: 10/26/2023] [Revised: 01/25/2024] [Accepted: 02/06/2024] [Indexed: 03/03/2024]
Abstract
The rise of artificial intelligence (AI) has revolutionized various scientific fields, particularly in medicine, where it has enabled the modeling of complex relationships from massive datasets. Initially, AI algorithms focused on improved interpretation of diagnostic studies such as chest X-rays and electrocardiograms in addition to predicting patient outcomes and future disease onset. However, AI has evolved with the introduction of transformer models, allowing analysis of the diverse, multimodal data sources existing in medicine today. Multimodal AI holds great promise in more accurate disease risk assessment and stratification as well as optimizing the key driving factors in cardiometabolic disease: blood pressure, sleep, stress, glucose control, weight, nutrition, and physical activity. In this article we outline the current state of medical AI in cardiometabolic disease, highlighting the potential of multimodal AI to augment personalized prevention and treatment strategies in cardiometabolic disease.
Collapse
Affiliation(s)
- Evan D Muse
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA 92037, USA; Division of Cardiovascular Diseases, Scripps Clinic, La Jolla, CA 92037, USA
| | - Eric J Topol
- Scripps Research Translational Institute, Scripps Research, La Jolla, CA 92037, USA; Division of Cardiovascular Diseases, Scripps Clinic, La Jolla, CA 92037, USA.
| |
Collapse
|
13
|
Alkis T, Luo X, Wall K, Brody J, Bartz T, Chang PP, Norby FL, Hoogeveen RC, Morrison AC, Ballantyne CM, Coresh J, Boerwinkle E, Psaty BM, Shah AM, Yu B. A polygenic risk score of atrial fibrillation improves prediction of lifetime risk for heart failure. ESC Heart Fail 2024; 11:1086-1096. [PMID: 38258344 PMCID: PMC10966276 DOI: 10.1002/ehf2.14665] [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: 07/28/2023] [Revised: 11/01/2023] [Accepted: 12/18/2023] [Indexed: 01/24/2024] Open
Abstract
AIMS Heart failure (HF) has shared genetic architecture with its risk factors: atrial fibrillation (AF), body mass index (BMI), coronary heart disease (CHD), systolic blood pressure (SBP), and type 2 diabetes (T2D). We aim to assess the association and risk prediction performance of risk-factor polygenic risk scores (PRSs) for incident HF and its subtypes in bi-racial populations. METHODS AND RESULTS Five PRSs were constructed for AF, BMI, CHD, SBP, and T2D in White participants of the Atherosclerosis Risk in Communities (ARIC) study. The associations between PRSs and incident HF and its subtypes were assessed using Cox models, and the risk prediction performance of PRSs was assessed using C statistics. Replication was performed in the ARIC study Black and Cardiovascular Health Study (CHS) White participants. In 8624 ARIC study Whites, 1922 (31% cumulative incidence) HF cases developed over 30 years of follow-up. PRSs of AF, BMI, and CHD were associated with incident HF (P < 0.001), where PRSAF showed the strongest association [hazard ratio (HR): 1.47, 95% confidence interval (CI): 1.41-1.53]. Only the addition of PRSAF to the ARIC study HF risk equation improved C statistics for 10 year risk prediction from 0.812 to 0.829 (∆C: 0.017, 95% CI: 0.009-0.026). The PRSAF was associated with both incident HF with reduced ejection fraction (HR: 1.43, 95% CI: 1.27-1.60) and incident HF with preserved ejection fraction (HR: 1.46, 95% CI: 1.33-1.62). The associations between PRSAF and incident HF and its subtypes, as well as the improved risk prediction, were replicated in the ARIC study Blacks and the CHS Whites (P < 0.050). Protein analyses revealed that N-terminal pro-brain natriuretic peptide and other 98 proteins were associated with PRSAF. CONCLUSIONS The PRSAF was associated with incident HF and its subtypes and had significant incremental value over an established HF risk prediction equation.
Collapse
Affiliation(s)
- Taryn Alkis
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public HealthUniversity of Texas Health Science Center at HoustonHoustonTXUSA
| | - Xi Luo
- Department of Biostatistics and Data Science, School of Public HealthUniversity of Texas Health Science Center at HoustonHoustonTXUSA
| | - Katherine Wall
- Department of Biostatistics and Data Science, School of Public HealthUniversity of Texas Health Science Center at HoustonHoustonTXUSA
| | - Jennifer Brody
- Cardiovascular Health Research UnitUniversity of WashingtonSeattleWAUSA
- Department of MedicineUniversity of WashingtonSeattleWAUSA
| | - Traci Bartz
- Cardiovascular Health Research Unit, Departments of Medicine and BiostatisticsUniversity of WashingtonSeattleWAUSA
| | - Patricia P. Chang
- Division of CardiologyUniversity of North Carolina School of MedicineChapel HillNCUSA
| | - Faye L. Norby
- Division of Epidemiology and Community HealthUniversity of MinnesotaMinneapolisMNUSA
| | | | - Alanna C. Morrison
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public HealthUniversity of Texas Health Science Center at HoustonHoustonTXUSA
| | | | - Josef Coresh
- Department of EpidemiologyJohns Hopkins Bloomberg School of Public HealthBaltimoreMDUSA
| | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public HealthUniversity of Texas Health Science Center at HoustonHoustonTXUSA
- Human Genome Sequencing CenterBaylor College of MedicineHoustonTXUSA
| | - Bruce M. Psaty
- Cardiovascular Health Research UnitUniversity of WashingtonSeattleWAUSA
- Department of MedicineUniversity of WashingtonSeattleWAUSA
- Department of EpidemiologyUniversity of WashingtonSeattleWAUSA
- Department of Health Systems and Population HealthUniversity of WashingtonSeattleWAUSA
| | - Amil M. Shah
- Department of Internal MedicineUT Southwestern Medical CenterDallasTXUSA
| | - Bing Yu
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public HealthUniversity of Texas Health Science Center at HoustonHoustonTXUSA
| |
Collapse
|
14
|
Liu Y, Ritchie SC, Teo SM, Ruuskanen MO, Kambur O, Zhu Q, Sanders J, Vázquez-Baeza Y, Verspoor K, Jousilahti P, Lahti L, Niiranen T, Salomaa V, Havulinna AS, Knight R, Méric G, Inouye M. Integration of polygenic and gut metagenomic risk prediction for common diseases. NATURE AGING 2024; 4:584-594. [PMID: 38528230 PMCID: PMC11031402 DOI: 10.1038/s43587-024-00590-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 02/13/2024] [Indexed: 03/27/2024]
Abstract
Multiomics has shown promise in noninvasive risk profiling and early detection of various common diseases. In the present study, in a prospective population-based cohort with ~18 years of e-health record follow-up, we investigated the incremental and combined value of genomic and gut metagenomic risk assessment compared with conventional risk factors for predicting incident coronary artery disease (CAD), type 2 diabetes (T2D), Alzheimer disease and prostate cancer. We found that polygenic risk scores (PRSs) improved prediction over conventional risk factors for all diseases. Gut microbiome scores improved predictive capacity over baseline age for CAD, T2D and prostate cancer. Integrated risk models of PRSs, gut microbiome scores and conventional risk factors achieved the highest predictive performance for all diseases studied compared with models based on conventional risk factors alone. The present study demonstrates that integrated PRSs and gut metagenomic risk models improve the predictive value over conventional risk factors for common chronic diseases.
Collapse
Affiliation(s)
- Yang Liu
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.
- Department of Clinical Pathology, Melbourne Medical School, University of Melbourne, Melbourne, Victoria, Australia.
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
| | - Scott C Ritchie
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cambridge Centre of Research Excellence, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Shu Mei Teo
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Centre for Youth Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Matti O Ruuskanen
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Department of Computing, University of Turku, Turku, Finland
| | - Oleg Kambur
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Qiyun Zhu
- School of Life Sciences, Arizona State University, Tempe, AZ, USA
- Biodesign Center for Fundamental and Applied Microbiomics, Arizona State University, Tempe, AZ, USA
| | - Jon Sanders
- Department of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, USA
| | - Yoshiki Vázquez-Baeza
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
| | - Karin Verspoor
- School of Computing Technologies, RMIT University, Melbourne, Victoria, Australia
- School of Computing and Information Systems, University of Melbourne, Melbourne, Victoria, Australia
| | - Pekka Jousilahti
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Leo Lahti
- Department of Computing, University of Turku, Turku, Finland
| | - Teemu Niiranen
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Division of Medicine, Turku University Hospital and University of Turku, Turku, Finland
| | - Veikko Salomaa
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Aki S Havulinna
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
- Institute for Molecular Medicine Finland, FIMM-HiLIFE, University of Helsinki, Helsinki, Finland
| | - Rob Knight
- Center for Microbiome Innovation, Jacobs School of Engineering, University of California San Diego, La Jolla, CA, USA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, School of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Guillaume Méric
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia
- Central Clinical School, Monash University, Melbourne, Victoria, Australia
- Department of Cardiometabolic Health, University of Melbourne, Melbourne, Victoria, Australia
- Department of Cardiovascular Research, Translation and Implementation, La Trobe University, Melbourne, Victoria, Australia
- Department of Medical Sciences, Molecular Epidemiology, Uppsala University, Uppsala, Sweden
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.
- Department of Clinical Pathology, Melbourne Medical School, University of Melbourne, Melbourne, Victoria, Australia.
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- British Heart Foundation Cambridge Centre of Research Excellence, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
- The Alan Turing Institute, London, UK.
| |
Collapse
|
15
|
Xiang R, Kelemen M, Xu Y, Harris LW, Parkinson H, Inouye M, Lambert SA. Recent advances in polygenic scores: translation, equitability, methods and FAIR tools. Genome Med 2024; 16:33. [PMID: 38373998 PMCID: PMC10875792 DOI: 10.1186/s13073-024-01304-9] [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: 06/09/2023] [Accepted: 02/07/2024] [Indexed: 02/21/2024] Open
Abstract
Polygenic scores (PGS) can be used for risk stratification by quantifying individuals' genetic predisposition to disease, and many potentially clinically useful applications have been proposed. Here, we review the latest potential benefits of PGS in the clinic and challenges to implementation. PGS could augment risk stratification through combined use with traditional risk factors (demographics, disease-specific risk factors, family history, etc.), to support diagnostic pathways, to predict groups with therapeutic benefits, and to increase the efficiency of clinical trials. However, there exist challenges to maximizing the clinical utility of PGS, including FAIR (Findable, Accessible, Interoperable, and Reusable) use and standardized sharing of the genomic data needed to develop and recalculate PGS, the equitable performance of PGS across populations and ancestries, the generation of robust and reproducible PGS calculations, and the responsible communication and interpretation of results. We outline how these challenges may be overcome analytically and with more diverse data as well as highlight sustained community efforts to achieve equitable, impactful, and responsible use of PGS in healthcare.
Collapse
Affiliation(s)
- Ruidong Xiang
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Martin Kelemen
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | - Yu Xu
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Laura W Harris
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Helen Parkinson
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK.
- British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK.
| | - Samuel A Lambert
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| |
Collapse
|
16
|
Li C, Liu X, Shen P, Sun Y, Zhou T, Chen W, Chen Q, Lin H, Tang X, Gao P. Improving cardiovascular risk prediction through machine learning modelling of irregularly repeated electronic health records. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2024; 5:30-40. [PMID: 38264696 PMCID: PMC10802828 DOI: 10.1093/ehjdh/ztad058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Revised: 08/03/2023] [Accepted: 09/19/2023] [Indexed: 01/25/2024]
Abstract
Aims Existing electronic health records (EHRs) often consist of abundant but irregular longitudinal measurements of risk factors. In this study, we aim to leverage such data to improve the risk prediction of atherosclerotic cardiovascular disease (ASCVD) by applying machine learning (ML) algorithms, which can allow automatic screening of the population. Methods and results A total of 215 744 Chinese adults aged between 40 and 79 without a history of cardiovascular disease were included (6081 cases) from an EHR-based longitudinal cohort study. To allow interpretability of the model, the predictors of demographic characteristics, medication treatment, and repeatedly measured records of lipids, glycaemia, obesity, blood pressure, and renal function were used. The primary outcome was ASCVD, defined as non-fatal acute myocardial infarction, coronary heart disease death, or fatal and non-fatal stroke. The eXtreme Gradient boosting (XGBoost) algorithm and Least Absolute Shrinkage and Selection Operator (LASSO) regression models were derived to predict the 5-year ASCVD risk. In the validation set, compared with the refitted Chinese guideline-recommended Cox model (i.e. the China-PAR), the XGBoost model had a significantly higher C-statistic of 0.792, (the differences in the C-statistics: 0.011, 0.006-0.017, P < 0.001), with similar results reported for LASSO regression (the differences in the C-statistics: 0.008, 0.005-0.011, P < 0.001). The XGBoost model demonstrated the best calibration performance (men: Dx = 0.598, P = 0.75; women: Dx = 1.867, P = 0.08). Moreover, the risk distribution of the ML algorithms differed from that of the conventional model. The net reclassification improvement rates of XGBoost and LASSO over the Cox model were 3.9% (1.4-6.4%) and 2.8% (0.7-4.9%), respectively. Conclusion Machine learning algorithms with irregular, repeated real-world data could improve cardiovascular risk prediction. They demonstrated significantly better performance for reclassification to identify the high-risk population correctly.
Collapse
Affiliation(s)
- Chaiquan Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, No. 38 Xueyuan Road, Haidian District, 100191 Beijing, China
| | - Xiaofei Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, No. 38 Xueyuan Road, Haidian District, 100191 Beijing, China
| | - Peng Shen
- Yinzhou District Center for Disease Control and Prevention, No. 1221 Xueshi Road, Yinzhou District, 315199 Ningbo, China
| | - Yexiang Sun
- Yinzhou District Center for Disease Control and Prevention, No. 1221 Xueshi Road, Yinzhou District, 315199 Ningbo, China
| | - Tianjing Zhou
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, No. 38 Xueyuan Road, Haidian District, 100191 Beijing, China
| | - Weiye Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, No. 38 Xueyuan Road, Haidian District, 100191 Beijing, China
| | - Qi Chen
- Yinzhou District Center for Disease Control and Prevention, No. 1221 Xueshi Road, Yinzhou District, 315199 Ningbo, China
| | - Hongbo Lin
- Yinzhou District Center for Disease Control and Prevention, No. 1221 Xueshi Road, Yinzhou District, 315199 Ningbo, China
| | - Xun Tang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, No. 38 Xueyuan Road, Haidian District, 100191 Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, No. 38 Xueyuan Road, Haidian District, 100191 Beijing, China
| | - Pei Gao
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, No. 38 Xueyuan Road, Haidian District, 100191 Beijing, China
- Key Laboratory of Epidemiology of Major Diseases, Peking University, Ministry of Education, No. 38 Xueyuan Road, Haidian District, 100191 Beijing, China
- Center for Real-world Evidence Evaluation, Peking University Clinical Research Institute, No. 38 Xueyuan Road, Haidian District, 100191 Beijing, China
| |
Collapse
|
17
|
Charchar FJ, Prestes PR, Mills C, Ching SM, Neupane D, Marques FZ, Sharman JE, Vogt L, Burrell LM, Korostovtseva L, Zec M, Patil M, Schultz MG, Wallen MP, Renna NF, Islam SMS, Hiremath S, Gyeltshen T, Chia YC, Gupta A, Schutte AE, Klein B, Borghi C, Browning CJ, Czesnikiewicz-Guzik M, Lee HY, Itoh H, Miura K, Brunström M, Campbell NR, Akinnibossun OA, Veerabhadrappa P, Wainford RD, Kruger R, Thomas SA, Komori T, Ralapanawa U, Cornelissen VA, Kapil V, Li Y, Zhang Y, Jafar TH, Khan N, Williams B, Stergiou G, Tomaszewski M. Lifestyle management of hypertension: International Society of Hypertension position paper endorsed by the World Hypertension League and European Society of Hypertension. J Hypertens 2024; 42:23-49. [PMID: 37712135 PMCID: PMC10713007 DOI: 10.1097/hjh.0000000000003563] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 08/12/2023] [Accepted: 08/22/2023] [Indexed: 09/16/2023]
Abstract
Hypertension, defined as persistently elevated systolic blood pressure (SBP) >140 mmHg and/or diastolic blood pressure (DBP) at least 90 mmHg (International Society of Hypertension guidelines), affects over 1.5 billion people worldwide. Hypertension is associated with increased risk of cardiovascular disease (CVD) events (e.g. coronary heart disease, heart failure and stroke) and death. An international panel of experts convened by the International Society of Hypertension College of Experts compiled lifestyle management recommendations as first-line strategy to prevent and control hypertension in adulthood. We also recommend that lifestyle changes be continued even when blood pressure-lowering medications are prescribed. Specific recommendations based on literature evidence are summarized with advice to start these measures early in life, including maintaining a healthy body weight, increased levels of different types of physical activity, healthy eating and drinking, avoidance and cessation of smoking and alcohol use, management of stress and sleep levels. We also discuss the relevance of specific approaches including consumption of sodium, potassium, sugar, fibre, coffee, tea, intermittent fasting as well as integrated strategies to implement these recommendations using, for example, behaviour change-related technologies and digital tools.
Collapse
Affiliation(s)
- Fadi J. Charchar
- Health Innovation and Transformation Centre, Federation University Australia, Ballarat, Australia
- Department of Physiology, University of Melbourne, Melbourne, Australia
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK
| | - Priscilla R. Prestes
- Health Innovation and Transformation Centre, Federation University Australia, Ballarat, Australia
| | - Charlotte Mills
- Department of Food and Nutritional Sciences, University of Reading, Reading, UK
| | - Siew Mooi Ching
- Department of Family Medicine, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang
- Department of Medical Sciences, School of Medical and Live Sciences, Sunway University, Bandar Sunway, Selangor, Malaysia
| | - Dinesh Neupane
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, USA
| | - Francine Z. Marques
- Hypertension Research Laboratory, School of Biological Sciences, Monash University
- Heart Failure Research Group, Baker Heart and Diabetes Institute, Melbourne
| | - James E. Sharman
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia
| | - Liffert Vogt
- Department of Internal Medicine, Section Nephrology, Amsterdam UMC, University of Amsterdam, Amsterdam Cardiovascular Sciences, Microcirculation, Amsterdam, The Netherlands
| | - Louise M. Burrell
- Department of Medicine, University of Melbourne, Austin Health, Melbourne, Australia
| | - Lyudmila Korostovtseva
- Department of Hypertension, Almazov National Medical Research Centre, St Petersburg, Russia
| | - Manja Zec
- School of Nutritional Sciences and Wellness, University of Arizona, Tucson, USA
- Colorado Program for Musculoskeletal Research, Department of Orthopedics, University of Colorado Anschutz Medical Campus, Aurora, USA
| | - Mansi Patil
- Department of Nutrition and Dietetics, Asha Kiran JHC Hospital, Chinchwad
- Hypertension and Nutrition, Core Group of IAPEN India, India
| | - Martin G. Schultz
- Department of Internal Medicine, Section Nephrology, Amsterdam UMC, University of Amsterdam, Amsterdam Cardiovascular Sciences, Microcirculation, Amsterdam, The Netherlands
| | | | - Nicolás F. Renna
- Unit of Hypertension, Hospital Español de Mendoza, School of Medicine, National University of Cuyo, IMBECU-CONICET, Mendoza, Argentina
| | | | - Swapnil Hiremath
- Department of Medicine, University of Ottawa and the Ottawa Hospital, Ottawa, Canada
| | - Tshewang Gyeltshen
- Graduate School of Public Health, St. Luke's International University, Tokyo, Japan
| | - Yook-Chin Chia
- Department of Medical Sciences, School of Medical and Life Sciences, Sunway University, Selangor
- Department of Primary Care Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
| | - Abhinav Gupta
- Department of Medicine, Acharya Shri Chander College of Medical Sciences and Hospital, Jammu, India
| | - Aletta E. Schutte
- School of Population Health, University of New South Wales, The George Institute for Global Health, Sydney, New South Wales, Australia
- Hypertension in Africa Research Team, SAMRC Unit for Hypertension and Cardiovascular Disease, North-West University
- SAMRC Developmental Pathways for Health Research Unit, School of Clinical Medicine, University of the Witwatersrand, Johannesburg, South Africa
| | - Britt Klein
- Health Innovation and Transformation Centre, Federation University Australia, Ballarat, Australia
| | - Claudio Borghi
- Department of Medical and Surgical Sciences, Faculty of Medicine, University of Bologna, Bologna, Italy
| | - Colette J. Browning
- Health Innovation and Transformation Centre, Federation University Australia, Ballarat, Australia
| | - Marta Czesnikiewicz-Guzik
- School of Medicine, Dentistry and Nursing-Dental School, University of Glasgow, UK
- Department of Periodontology, Prophylaxis and Oral Medicine; Jagiellonian University, Krakow, Poland
| | - Hae-Young Lee
- Division of Cardiology, Department of Internal Medicine, Seoul National University Hospital, Seoul, Korea
| | - Hiroshi Itoh
- Department of Internal Medicine (Nephrology, Endocrinology and Metabolism), Keio University, Tokyo
| | - Katsuyuki Miura
- NCD Epidemiology Research Center, Shiga University of Medical Science, Otsu, Japan
| | - Mattias Brunström
- Department of Public Health and Clinical Medicine, Umeå University, Umeå, Sweden
| | - Norm R.C. Campbell
- Libin Cardiovascular Institute, Department of Medicine, University of Calgary, Calgary, Canada
| | | | - Praveen Veerabhadrappa
- Kinesiology, Division of Science, The Pennsylvania State University, Reading, Pennsylvania
| | - Richard D. Wainford
- Department of Pharmacology and Experimental Therapeutics, The Whitaker Cardiovascular Institute, Boston University School of Medicine, Boston
- Division of Cardiology, Emory University, Atlanta, USA
| | - Ruan Kruger
- Hypertension in Africa Research Team (HART), North-West University, Potchefstroom
- MRC Research Unit for Hypertension and Cardiovascular Disease, North-West University, Potchefstroom, South Africa
| | - Shane A. Thomas
- Health Innovation and Transformation Centre, Federation University Australia, Ballarat, Australia
| | - Takahiro Komori
- Division of Cardiovascular Medicine, Department of Medicine, Jichi Medical University School of Medicine, Tochigi, Japan
| | - Udaya Ralapanawa
- Faculty of Medicine, University of Peradeniya, Peradeniya, Sri Lanka
| | | | - Vikas Kapil
- William Harvey Research Institute, Centre for Cardiovascular Medicine and Devices, NIHR Barts Biomedical Research Centre, BRC, Faculty of Medicine and Dentistry, Queen Mary University London
- Barts BP Centre of Excellence, Barts Heart Centre, Barts Health NHS Trust, London, UK
| | - Yan Li
- Department of Cardiovascular Medicine, Shanghai Institute of Hypertension, Ruijin Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai
| | - Yuqing Zhang
- Department of Cardiology, Fu Wai Hospital, Chinese Academy of Medical Sciences, Chinese Hypertension League, Beijing, China
| | - Tazeen H. Jafar
- Program in Health Services and Systems Research, Duke-NUS Medical School, Singapore
- Duke Global Health Institute, Duke University, Durham, North Carolina, USA
| | - Nadia Khan
- Department of Medicine, University of British Columbia, Vancouver, Canada
| | - Bryan Williams
- University College London (UCL), Institute of Cardiovascular Science, National Institute for Health Research (NIHR), UCL Hospitals Biomedical Research Centre, London, UK
| | - George Stergiou
- Hypertension Centre STRIDE-7, School of Medicine, Third Department of Medicine, Sotiria Hospital, National and Kapodistrian University of Athens, Athens, Greece
| | - Maciej Tomaszewski
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and Health, University of Manchester
- Manchester Academic Health Science Centre, Manchester Royal Infirmary, Manchester University NHS Foundation Trust, Manchester, UK
| |
Collapse
|
18
|
Torkamani A, Chen SF, Lee SE, Sadaei H, Park JB, Khattab A, Henegar C, Wineinger N, Muse E. Meta-Prediction of Coronary Artery Disease Risk. RESEARCH SQUARE 2023:rs.3.rs-3694374. [PMID: 38196609 PMCID: PMC10775391 DOI: 10.21203/rs.3.rs-3694374/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
Coronary artery disease (CAD) remains the leading cause of mortality and morbidity worldwide. Recent advances in large-scale genome-wide association studies have highlighted the potential of genetic risk, captured as polygenic risk scores (PRS), in clinical prevention. However, the current clinical utility of PRS models is limited to identifying high-risk populations based on the top percentiles of genetic susceptibility. While some studies have attempted integrative prediction using genetic and non-genetic factors, many of these studies have been cross-sectional and focused solely on risk stratification. Our primary objective in this study was to integrate unmodifiable (age / genetics) and modifiable (clinical / biometric) risk factors into a prospective prediction framework which also produces actionable and personalized risk estimates for the purpose of CAD prevention in a heterogenous adult population. Thus, we present an integrative, omnigenic, meta-prediction framework that effectively captures CAD risk subgroups, primarily distinguished by degree and nature of genetic risk, with distinct risk reduction profiles predicted from standard clinical interventions. Initial model development considered ~ 2,000 predictive features, including demographic data, lifestyle factors, physical measurements, laboratory tests, medication usage, diagnoses, and genetics. To power our meta-prediction approach, we stratified the UK Biobank into two primary cohorts: 1) a prevalent CAD cohort used to train baseline and prospective predictive models for contributing risk factors and diagnoses, and 2) an incident CAD cohort used to train the final CAD incident risk prediction model. The resultant 10-year incident CAD risk model is composed of 35 derived meta-features from models trained on the prevalent risk cohort, most of which are predicted baseline diagnoses with multiple embedded PRSs. This model achieved an AUC of 0.81 and macro-averaged F1-score of 0.65, outperforming standard clinical scores and prior integrative models. We further demonstrate that individualized risk reduction profiles can be derived from this model, with genetic risk mediating the degree of risk reduction achieved by standard clinical interventions.
Collapse
Affiliation(s)
- Ali Torkamani
- Scripps Research & Scripps Research Translational Institute
| | - Shang-Fu Chen
- Scripps Research & Scripps Research Translational Institute
| | - Sang Eun Lee
- Asan Medical Center, University of Ulsan College of Medicine
| | - Hossein Sadaei
- Scripps Research & Scripps Research Translational Institute
| | | | - Ahmed Khattab
- Scripps Research & Scripps Research Translational Institute
| | | | | | - Evan Muse
- Scripps Translational Science Institute, The Scripps Research Institute, Scripps Health
| |
Collapse
|
19
|
Benincasa G, Suades R, Padró T, Badimon L, Napoli C. Bioinformatic platforms for clinical stratification of natural history of atherosclerotic cardiovascular diseases. EUROPEAN HEART JOURNAL. CARDIOVASCULAR PHARMACOTHERAPY 2023; 9:758-769. [PMID: 37562936 DOI: 10.1093/ehjcvp/pvad059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 07/19/2023] [Accepted: 08/09/2023] [Indexed: 08/12/2023]
Abstract
Although bioinformatic methods gained a lot of attention in the latest years, their use in real-world studies for primary and secondary prevention of atherosclerotic cardiovascular diseases (ASCVD) is still lacking. Bioinformatic resources have been applied to thousands of individuals from the Framingham Heart Study as well as health care-associated biobanks such as the UK Biobank, the Million Veteran Program, and the CARDIoGRAMplusC4D Consortium and randomized controlled trials (i.e. ODYSSEY, FOURIER, ASPREE, and PREDIMED). These studies contributed to the development of polygenic risk scores (PRS), which emerged as novel potent genetic-oriented tools, able to calculate the individual risk of ASCVD and to predict the individual response to therapies such as statins and proprotein convertase subtilisin/kexin type 9 inhibitor. ASCVD are the first cause of death around the world including coronary heart disease (CHD), peripheral artery disease, and stroke. To achieve the goal of precision medicine and personalized therapy, advanced bioinformatic platforms are set to link clinically useful indices to heterogeneous molecular data, mainly epigenomics, transcriptomics, metabolomics, and proteomics. The DIANA study found that differential methylation of ABCA1, TCF7, PDGFA, and PRKCZ significantly discriminated patients with acute coronary syndrome from healthy subjects and their expression levels positively associated with CK-MB serum concentrations. The ARIC Study revealed several plasma proteins, acting or not in lipid metabolism, with a potential role in determining the different pleiotropic effects of statins in each subject. The implementation of molecular high-throughput studies and bioinformatic techniques into traditional cardiovascular risk prediction scores is emerging as a more accurate practice to stratify patients earlier in life and to favour timely and tailored risk reduction strategies. Of note, radiogenomics aims to combine imaging features extracted for instance by coronary computed tomography angiography and molecular biomarkers to create CHD diagnostic algorithms useful to characterize atherosclerotic lesions and myocardial abnormalities. The current view is that such platforms could be of clinical value for prevention, risk stratification, and treatment of ASCVD.
Collapse
Affiliation(s)
- Giuditta Benincasa
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania 'Luigi Vanvitelli', 80138 Naples, Italy
- Cardiovascular Program ICCC, Research Institute of Hospital Santa Creu i Sant Pau, IIB Sant Pau, Avinguda Sant Antoni Maria Claret 167, Pavelló 11 (Antic Convent), 08049 Barcelona, Spain
| | - Rosa Suades
- Cardiovascular Program ICCC, Research Institute of Hospital Santa Creu i Sant Pau, IIB Sant Pau, Avinguda Sant Antoni Maria Claret 167, Pavelló 11 (Antic Convent), 08049 Barcelona, Spain
- Centro de Investigación Biomédica en Red Cardiovascular (CIBERCV) Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Teresa Padró
- Cardiovascular Program ICCC, Research Institute of Hospital Santa Creu i Sant Pau, IIB Sant Pau, Avinguda Sant Antoni Maria Claret 167, Pavelló 11 (Antic Convent), 08049 Barcelona, Spain
- Centro de Investigación Biomédica en Red Cardiovascular (CIBERCV) Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Lina Badimon
- Cardiovascular Program ICCC, Research Institute of Hospital Santa Creu i Sant Pau, IIB Sant Pau, Avinguda Sant Antoni Maria Claret 167, Pavelló 11 (Antic Convent), 08049 Barcelona, Spain
- Centro de Investigación Biomédica en Red Cardiovascular (CIBERCV) Instituto de Salud Carlos III, 28029 Madrid, Spain
- Cardiovascular Research Chair, Universitat Autònoma de Barcelona (UAB), 08193 Barcelona, Spain
| | - Claudio Napoli
- Department of Advanced Medical and Surgical Sciences (DAMSS), University of Campania 'Luigi Vanvitelli', 80138 Naples, Italy
| |
Collapse
|
20
|
Chang H, Zhang H, Shi G, Guo J, Chu X, Wang Z, Yao Y, Wang X. Ischemic stroke prediction using machine learning in elderly Chinese population: The Rugao Longitudinal Ageing Study. Brain Behav 2023; 13:e3307. [PMID: 37934082 PMCID: PMC10726889 DOI: 10.1002/brb3.3307] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 09/22/2023] [Accepted: 10/18/2023] [Indexed: 11/08/2023] Open
Abstract
OBJECTIVE Compared logistic regression (LR) with machine learning (ML) models, to predict the risk of ischemic stroke in an elderly population in China. METHODS We applied 2208 records from the Rugao Longitudinal Ageing Study (RLAS) for ischemic stroke risk prediction assessment. Input variables included 103 phenotypes. For 3-year ischemic stroke risk prediction, we compared the discrimination and calibration of LR model and ML methods, where ML methods include Random Forest (RF), Gaussian kernel Support Vector Machines (SVM), Multilayer perceptron (MLP), K-Nearest Neighbors Algorithm (KNN), and Gradient Boosting Decision Tree (GBDT) to develop an ischemic stroke risk prediction model. RESULTS Age, pulse, waist circumference, education level, β2-microglobulin, homocysteine, cystatin C, folate, free triiodothyronine, platelet distribution width, QT interval, and QTc interval were significant induced predictors of ischemic stroke. For ischemic stroke prediction, the ML approach was able to tap more biochemical and ECG-related multidimensional phenotypic indicators compared to the LR model, which placed more importance on general demographic indicators. Compared to the LR model, SVM provided the best discrimination and calibration (C-index: 0.79 vs. 0.71, 11.27% improvement in model utility), with the best performance in both validation and test data. CONCLUSION In a comparison of LR with five ML models, the accuracy of ischemic stroke prediction was higher by combining ML with multiple phenotypes. Combined with other studies based on elderly populations in China, ML techniques, especially SVM, have shown good long-term predictive performance, inspiring the potential value of ML use in clinical practice.
Collapse
Affiliation(s)
- Huai‐Wen Chang
- Department of Computational Biology, School of Life SciencesFudan UniversityShanghaiChina
| | - Hui Zhang
- Department of Cardiovascular Disease Aging ResearchFudan University–the People's Hospital of Rugao Joint Research Institute of Longevity and AgingRugaoJiangsuChina
- Zhangjiang Fudan International Innovation Center, Human Phenome InstituteFudan UniversityShanghaiChina
| | - Guo‐Ping Shi
- Department of Cardiovascular Disease Aging ResearchFudan University–the People's Hospital of Rugao Joint Research Institute of Longevity and AgingRugaoJiangsuChina
- The People's Hospital of RugaoRugaoJiangsuChina
| | - Jiang‐Hong Guo
- Department of Cardiovascular Disease Aging ResearchFudan University–the People's Hospital of Rugao Joint Research Institute of Longevity and AgingRugaoJiangsuChina
- The People's Hospital of RugaoRugaoJiangsuChina
| | - Xue‐Feng Chu
- Department of Cardiovascular Disease Aging ResearchFudan University–the People's Hospital of Rugao Joint Research Institute of Longevity and AgingRugaoJiangsuChina
- The People's Hospital of RugaoRugaoJiangsuChina
| | - Zheng‐Dong Wang
- Department of Cardiovascular Disease Aging ResearchFudan University–the People's Hospital of Rugao Joint Research Institute of Longevity and AgingRugaoJiangsuChina
- The People's Hospital of RugaoRugaoJiangsuChina
| | - Yin Yao
- Department of Computational Biology, School of Life SciencesFudan UniversityShanghaiChina
- Department of Cardiovascular Disease Aging ResearchFudan University–the People's Hospital of Rugao Joint Research Institute of Longevity and AgingRugaoJiangsuChina
| | - Xiao‐Feng Wang
- Department of Cardiovascular Disease Aging ResearchFudan University–the People's Hospital of Rugao Joint Research Institute of Longevity and AgingRugaoJiangsuChina
- Zhangjiang Fudan International Innovation Center, Human Phenome InstituteFudan UniversityShanghaiChina
| |
Collapse
|
21
|
Long L, He H, Shen Q, Peng H, Zhou X, Wang H, Zhang S, Qin S, Lu Z, Zhu Y, Tian J, Chang J, Miao X, Shen N, Zhong R. Birthweight, genetic risk, and gastrointestinal cancer incidence: a prospective cohort study. Ann Med 2023; 55:62-71. [PMID: 36503347 PMCID: PMC9754019 DOI: 10.1080/07853890.2022.2146743] [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: 12/14/2022] Open
Abstract
BACKGROUND The epidemiologic studies investigating the association of birthweight and genetic factors with gastrointestinal cancer remain scarce. The study aimed to prospectively assess the interactions and joint effects of birthweight and genetic risk levels on gastrointestinal cancer incidence in adulthood. METHODS A total of 254,997 participants were included in the UK Biobank study. We used multivariate restricted cubic splines and Cox regression models to estimate the hazard ratios (HRs) and 95% confidential intervals (CI) for the association between birthweight and gastrointestinal cancer risk, then constructed a polygenic risk score (PRS) to assess its interaction and joint effect with birthweight on the development of gastrointestinal cancer. RESULTS We documented 2512 incident cases during a median follow-up of 8.88 years. Compare with participants reporting a normal birthweight (2.5-4.5 kg), multivariable-adjusted HR of gastrointestinal cancer incidence for participants with high birthweight (≥4.5 kg) was 1.17 (95%CI: 1.01-1.36). Such association was remarkably observed in pancreatic cancer, with an HR of 1.82 (95%CI: 1.26-2.64). No statistically significant association was observed between low birth weight and gastrointestinal cancers. Participants with high birthweight and high PRS had the highest risk of gastrointestinal cancer (HR: 2.95, 95%CI: 2.19-3.96). CONCLUSION Our findings highlight that high birthweight is associated with a higher incidence of gastrointestinal cancer, especially for pancreatic cancer. Benefits would be obtained from birthweight control, particularly for individuals with a high genetic risk.KEY MESSAGESThe epidemiologic studies investigating the association of birthweight and genetic factors with gastrointestinal cancer remain scarce.This cohort study of 254,997 adults in the United Kingdom found an association of high birthweight with the incidence of gastrointestinal cancer, especially for pancreatic cancer, and also found that participants with high birthweight and high polygenic risk score had the highest risk of gastrointestinal cancer.Our data suggests a possible effect of in utero or early life exposures on adulthood gastrointestinal cancer, especially for those with a high genetic risk.
Collapse
Affiliation(s)
- Lu Long
- Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Heng He
- Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, China
| | - Qian Shen
- Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hongxia Peng
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xiaorui Zhou
- Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Haoxue Wang
- Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shanshan Zhang
- Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shifan Qin
- Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zequn Lu
- Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ying Zhu
- School of Public Health, Wuhan University, Wuhan, China
| | - Jianbo Tian
- School of Public Health, Wuhan University, Wuhan, China
| | - Jiang Chang
- Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaoping Miao
- School of Public Health, Wuhan University, Wuhan, China
| | - Na Shen
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, HUST, Wuhan, China
- Na Shen Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, HUST, Wuhan, 430030, China
| | - Rong Zhong
- Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- CONTACT Rong Zhong Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| |
Collapse
|
22
|
Prone-Olazabal D, Davies I, González-Galarza FF. Metabolic Syndrome: An Overview on Its Genetic Associations and Gene-Diet Interactions. Metab Syndr Relat Disord 2023; 21:545-560. [PMID: 37816229 DOI: 10.1089/met.2023.0125] [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] [Indexed: 10/12/2023] Open
Abstract
Metabolic syndrome (MetS) is a cluster of cardiometabolic risk factors that includes central obesity, hyperglycemia, hypertension, and dyslipidemias and whose inter-related occurrence may increase the odds of developing type 2 diabetes and cardiovascular diseases. MetS has become one of the most studied conditions, nevertheless, due to its complex etiology, this has not been fully elucidated. Recent evidence describes that both genetic and environmental factors play an important role on its development. With the advent of genomic-wide association studies, single nucleotide polymorphisms (SNPs) have gained special importance. In this review, we present an update of the genetics surrounding MetS as a single entity as well as its corresponding risk factors, considering SNPs and gene-diet interactions related to cardiometabolic markers. In this study, we focus on the conceptual aspects, diagnostic criteria, as well as the role of genetics, particularly on SNPs and polygenic risk scores (PRS) for interindividual analysis. In addition, this review highlights future perspectives of personalized nutrition with regard to the approach of MetS and how individualized multiomics approaches could improve the current outlook.
Collapse
Affiliation(s)
- Denisse Prone-Olazabal
- Postgraduate Department, Faculty of Medicine, Autonomous University of Coahuila, Torreon, Mexico
| | - Ian Davies
- Research Institute of Sport and Exercise Science, The Institute for Health Research, Liverpool John Moores University, Liverpool, United Kingdom
| | | |
Collapse
|
23
|
Verdier H, Thomas P, Batista J, Kempster C, McKinney H, Gleadall N, Danesh J, Mumford A, Heemskerk JWM, Ouwehand WH, Downes K, Astle WJ, Turro E. A signature of platelet reactivity in CBC scattergrams reveals genetic predictors of thrombotic disease risk. Blood 2023; 142:1895-1908. [PMID: 37647652 PMCID: PMC10733829 DOI: 10.1182/blood.2023021100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 07/27/2023] [Accepted: 08/18/2023] [Indexed: 09/01/2023] Open
Abstract
Genetic studies of platelet reactivity (PR) phenotypes may identify novel antiplatelet drug targets. However, such studies have been limited by small sample sizes (n < 5000) because of the complexity of measuring PR. We trained a model to predict PR from complete blood count (CBC) scattergrams. A genome-wide association study of this phenotype in 29 806 blood donors identified 21 distinct associations implicating 20 genes, of which 6 have been identified previously. The effect size estimates were significantly correlated with estimates from a study of flow cytometry-measured PR and a study of a phenotype of in vitro thrombus formation. A genetic score of PR built from the 21 variants was associated with the incidence rates of myocardial infarction and pulmonary embolism. Mendelian randomization analyses showed that PR was causally associated with the risks of coronary artery disease, stroke, and venous thromboembolism. Our approach provides a blueprint for using phenotype imputation to study the determinants of hard-to-measure but biologically important hematological traits.
Collapse
Affiliation(s)
- Hippolyte Verdier
- Institut Pasteur, CNRS UMR 3751, Decision and Bayesian Computation, Université Paris Cité, Paris, France
| | - Patrick Thomas
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Joana Batista
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Carly Kempster
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Institute for Cardiovascular and Metabolic Research, School of Biological Sciences, University of Reading, Reading, United Kingdom
| | - Harriet McKinney
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Nicholas Gleadall
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- National Health Service Blood and Transplant, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - John Danesh
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom
- Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, United Kingdom
- British Heart Foundation Centre of Research Excellence, School of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, United Kingdom
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom
| | - Andrew Mumford
- School of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom
- South West National Health Service Genomic Medicine Service Alliance, Bristol, United Kingdom
| | | | - Willem H. Ouwehand
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- National Health Service Blood and Transplant, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - Kate Downes
- Cambridge Genomics Laboratory, Cambridge University Hospitals NHS Foundation Trust, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - William J. Astle
- National Health Service Blood and Transplant, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Medical Research Council Biostatistics Unit, Cambridge Biomedical Campus, University of Cambridge, Cambridge, United Kingdom
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, United Kingdom
| | - Ernest Turro
- Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| |
Collapse
|
24
|
Yang S, Sun D, Sun Z, Yu C, Guo Y, Si J, Sun D, Pang Y, Pei P, Yang L, Millwood IY, Walters RG, Chen Y, Du H, Pang Z, Schmidt D, Stevens R, Clarke R, Chen J, Chen Z, Lv J, Li L. Minimal improvement in coronary artery disease risk prediction in Chinese population using polygenic risk scores: evidence from the China Kadoorie Biobank. Chin Med J (Engl) 2023; 136:2476-2483. [PMID: 37200020 PMCID: PMC10586831 DOI: 10.1097/cm9.0000000000002694] [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: 09/03/2022] [Indexed: 05/19/2023] Open
Abstract
BACKGROUND Several studies have reported that polygenic risk scores (PRSs) can enhance risk prediction of coronary artery disease (CAD) in European populations. However, research on this topic is far from sufficient in non-European countries, including China. We aimed to evaluate the potential of PRS for predicting CAD for primary prevention in the Chinese population. METHODS Participants with genome-wide genotypic data from the China Kadoorie Biobank were divided into training ( n = 28,490) and testing sets ( n = 72,150). Ten previously developed PRSs were evaluated, and new ones were developed using clumping and thresholding or LDpred method. The PRS showing the strongest association with CAD in the training set was selected to further evaluate its effects on improving the traditional CAD risk-prediction model in the testing set. Genetic risk was computed by summing the product of the weights and allele dosages across genome-wide single-nucleotide polymorphisms. Prediction of the 10-year first CAD events was assessed using hazard ratios (HRs) and measures of model discrimination, calibration, and net reclassification improvement (NRI). Hard CAD (nonfatal I21-I23 and fatal I20-I25) and soft CAD (all fatal or nonfatal I20-I25) were analyzed separately. RESULTS In the testing set, 1214 hard and 7201 soft CAD cases were documented during a mean follow-up of 11.2 years. The HR per standard deviation of the optimal PRS was 1.26 (95% CI:1.19-1.33) for hard CAD. Based on a traditional CAD risk prediction model containing only non-laboratory-based information, the addition of PRS for hard CAD increased Harrell's C index by 0.001 (-0.001 to 0.003) in women and 0.003 (0.001 to 0.005) in men. Among the different high-risk thresholds ranging from 1% to 10%, the highest categorical NRI was 3.2% (95% CI: 0.4-6.0%) at a high-risk threshold of 10.0% in women. The association of the PRS with soft CAD was much weaker than with hard CAD, leading to minimal or no improvement in the soft CAD model. CONCLUSIONS In this Chinese population sample, the current PRSs minimally changed risk discrimination and offered little improvement in risk stratification for soft CAD. Therefore, this may not be suitable for promoting genetic screening in the general Chinese population to improve CAD risk prediction.
Collapse
Affiliation(s)
- Songchun Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, Hunan 410008, China
| | - Dong Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - Zhijia Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - Canqing Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
| | - Yu Guo
- Fuwai Hospital Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Jiahui Si
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - Dianjianyi Sun
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
| | - Yuanjie Pang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
| | - Pei Pei
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
| | - Ling Yang
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford OX3 7LF, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Iona Y. Millwood
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford OX3 7LF, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Robin G. Walters
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford OX3 7LF, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Yiping Chen
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford OX3 7LF, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Huaidong Du
- Medical Research Council Population Health Research Unit at the University of Oxford, Oxford OX3 7LF, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Zengchang Pang
- Qingdao Center of Disease Control and Prevention, Qingdao, Shandong 266033, China
| | - Dan Schmidt
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Rebecca Stevens
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Robert Clarke
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Junshi Chen
- China National Center for Food Safety Risk Assessment, Beijing 100738, China
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford OX3 7LF, UK
| | - Jun Lv
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
| | - Liming Li
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China
- Peking University Center for Public Health and Epidemic Preparedness & Response, Beijing 100191, China
| |
Collapse
|
25
|
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: 16] [Impact Index Per Article: 16.0] [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.
Collapse
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
| |
Collapse
|
26
|
Mason AM, Obi I, Ayodele O, Lambert SA, Fahle S. What makes a good life: using theatrical performance to enhance communication about polygenic risk scores research in patient and public involvement. J Community Genet 2023; 14:453-458. [PMID: 36763324 PMCID: PMC10576689 DOI: 10.1007/s12687-023-00635-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 01/17/2023] [Indexed: 02/11/2023] Open
Abstract
The aim of this patient and public involvement and engagement (PPIE) work was to explore improvised theatre as a tool for facilitating bi-directional dialogue between researchers and patients/members of the public on the topic of polygenic risk scores (PRS) use within primary or secondary care. PRS are a tool to quantify genetic risk for a heritable disease or trait and may be used to predict future health outcomes. In the United Kingdom (UK), they are often cited as a next-in-line public health tool to be implemented, and their use in consumer genetic testing as well as patient-facing settings is increasing. Despite their potential clinical utility, broader themes about how they might influence an individual's perception of disease risk and decision-making are an active area of research; however, this has mostly been in the setting of return of results to patients. We worked with a youth theatre group and patients involved in a PPIE group to develop two short plays about public perceptions of genetic risk information that could be captured by PRS. These plays were shared in a workshop with patients/members of the public to facilitate discussions about PRS and their perceived benefits, concerns and emotional reactions. Discussions with both performers and patients/public raised three key questions: (1) can the data be trusted?; (2) does knowing genetic risk actually help the patient?; and (3) what makes a life worthwhile? Creating and watching fictional narratives helped all participants explore the potential use of PRS in a clinical setting, informing future research considerations and improving communication between the researchers and lay members of the PPIE group.
Collapse
Affiliation(s)
- Amy M Mason
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Medical Research Council Biostatistics Unit, Cambridge Institute of Public Health, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
| | | | | | - Samuel A Lambert
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Sarah Fahle
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Heart and Lung Research Institute, University of Cambridge, Cambridge, UK.
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK.
| |
Collapse
|
27
|
Wang W, Jia T, Liu Y, Deng H, Chen Z, Wang J, Geng Z, Wei R, Qiao J, Ma Y, Jiang X, Xu W, Shao J, Zhou K, Li Y, Pan Q, Yang W, Weng J, Guo L. Data-driven subgroups of newly diagnosed type 2 diabetes and the relationship with cardiovascular diseases at genetic and clinical levels in Chinese adults. Diabetes Metab Syndr 2023; 17:102850. [PMID: 37683311 DOI: 10.1016/j.dsx.2023.102850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 08/20/2023] [Accepted: 08/29/2023] [Indexed: 09/10/2023]
Abstract
BACKGROUND To subgroup Chinese patients with newly diagnosed type 2 diabetes (T2D) by K-means cluster analysis on clinical indicators, and to explore whether these subgroups represent different genetic features and calculated cardiovascular risks. METHODS The K-means clustering analysis was performed on two cohorts (n = 590 and 392), both consisting of Chinese participants with newly diagnosed T2D. To assess genetic risks, multiple polygenic risk scores (PRSs) and mitochondrial DNA copy numbers (mtDNA-CN) were calculated for all participants. Furthermore, Framingham risk scores (FRS) of cardiovascular diseases in two cohorts were also calculated to verify the genetic risks. RESULTS Four clusters were identified including the mild age-related diabetes (MARD)(35.08%), mild obesity-related diabetes (MOD) (34.41%), severe autoimmune diabetes (SAID) 19.15%, and severe insulin-resistant diabetes (SIRD) 11.36% subgroups in the MARCH (metformin, and acarbose in Chinese patients as the initial hypoglycemic treatment) cohort. There was a significant difference in PRS for cardiovascular diseases (CVD) across four subgroups in the MARCH cohort (p < 0.05). Compared with the SIDD and SIRD subgroups, patients in the MOD subgroup had a relatively lower PRS for CVD (p < 0.05) in the MARCH cohort. Females had a higher PRS compared to males, with no significant difference in FRS across the four clusters. The MOD subgroup had a significantly lower FRS which was consistent with the results of PRS. Similar results of PRS and FRS were also replicated in the CONFIDENCE (comparison of glycemic control and b-cell function among newly diagnosed patients with type 2 diabetes treated with exenatide, insulin or pioglitazone) cohort. CONCLUSION There are different CVD risks in diabetic subgroups based on clinical and genetic evidence which may promote precision medicine.
Collapse
Affiliation(s)
- Weihao Wang
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Tong Jia
- Institute for Artificial Intelligence, Peking University, Beijing, China
| | - Yiying Liu
- Institute of Biophysics, Chinese Academy of Sciences, Beijing, China; College of Life Sciences, University of Chinese Academy of Sciences, China
| | - Hongrong Deng
- Department of Endocrinology and Metabolism, Guangdong Provincial Key Laboratory of Diabetology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Zihao Chen
- School of Software and Microelectronics, Peking University, Beijing, China
| | - Jing Wang
- Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Zhaoxu Geng
- Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Ran Wei
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Jingtao Qiao
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Yanhua Ma
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Xun Jiang
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China
| | - Wen Xu
- Department of Endocrinology and Metabolism, Guangdong Provincial Key Laboratory of Diabetology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jian Shao
- No. 9 XingDaoHuanBei Road, Guangzhou International Bio Island, Guangzhou, 510005, Guangdong Province, China
| | - Kaixin Zhou
- The Fifth People's Hospital of Chongqing, Chongqing, China
| | - Ying Li
- School of Software and Microelectronics, Peking University, Beijing, China
| | - Qi Pan
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China.
| | - Wenying Yang
- China-Japan Friendship Hospital, Beijing, China.
| | - Jianping Weng
- Department of Endocrinology and Metabolism, Guangdong Provincial Key Laboratory of Diabetology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
| | - Lixin Guo
- Department of Endocrinology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, PR China.
| |
Collapse
|
28
|
Gupta R. Genetics-based risk scores for prediction of premature coronary artery disease. Indian Heart J 2023; 75:327-334. [PMID: 37633460 PMCID: PMC10568063 DOI: 10.1016/j.ihj.2023.08.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 07/24/2023] [Accepted: 08/20/2023] [Indexed: 08/28/2023] Open
Abstract
Premature coronary artery disease (CAD) is endemic in India. Global Burden of Diseases study has reported that it led to 286,000 deaths in 2019 in India. Many of these patients have standard risk factors but a third have none. Clinical risk algorithms and imaging provide limited risk information in premature CAD. CAD is multifactorial and studies have now focused on the predictive capability of clusters of genes and single nucleotide polymorphisms (SNPs) using gene risk score (GRS). Older studies combined data from 10 to 12 genes and 100-500 SNPs to calculate GRS, however, following the advent of genome-wide association studies (GWAS), millions of SNPs have been incorporated. Studies have reported that GWAS-based GRS may be more discriminative than conventional tools. Recent studies, especially among South Asians, have reported that GRS improves net reclassification by 15% (12-19%) for younger individuals. Aggressive lifestyle interventions and lipid-lowering therapies can ameliorate risk in high-GRS individuals and potentially prevent premature CAD.
Collapse
Affiliation(s)
- Rajeev Gupta
- Department of Preventive Cardiology & Medicine, Eternal Heart Care Centre & Research Institute, Jaipur, India.
| |
Collapse
|
29
|
Lo Faro V, Johansson T, Höglund J, Hadizadeh F, Johansson Å. Polygenic risk scores and risk stratification in deep vein thrombosis. Thromb Res 2023; 228:151-162. [PMID: 37331118 DOI: 10.1016/j.thromres.2023.06.011] [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: 01/03/2023] [Revised: 05/18/2023] [Accepted: 06/09/2023] [Indexed: 06/20/2023]
Abstract
INTRODUCTION Deep vein thrombosis (DVT) is a complex disease, where 60 % of risk is due to genetic factors, such as the Factor V Leiden (FVL) variant. DVT is either asymptomatic or manifests with unspecific symptoms and, if left untreated, DVT leads to severe complications. The impact is dramatic and currently, there is still a research gap in DVT prevention. We characterized the genetic contribution and stratified individuals based on genetic makeup to evaluate if it favorably impacts risk prediction. METHODS In the UK Biobank (UKB), we performed gene-based association tests using exome sequencing data, as well as a genome-wide association study. We also constructed polygenic risk scores (PRS) in a subset of the cohort (Number of cases = 8231; Number of controls = 276,360) and calculated the impact on the prediction capacity of the PRS in a non-overlapping part of the cohort (Number of cases = 4342; Number of controls = 142,822). We generated additional PRSs that excluded the known causative variants. RESULTS We discovered and replicated a novel common variant (rs11604583) near the region where are located the TRIM51 and LRRC55 genes and identified a novel rare variant (rs187725533) located near the CREB3L1 gene, associated with 2.5-fold higher risk of DVT. In one of the PRS models constructed, the top decile of risk is associated with 3.4-fold increased risk, an effect that is 2.3-fold when excluding FVL carriers. In the top PRS decile, the cumulative risk of DVT at the age of 80 years is 10 % for FVL carriers, contraposed to 5 % for non-carriers. The population attributable fractions of having a high polygenic risk on the rate of DVT was estimated to be around 20 % in our cohort. CONCLUSION Individuals with a high polygenic risk of DVT, and not only carriers of well-studied variants such as FVL, may benefit from prevention strategies.
Collapse
Affiliation(s)
- Valeria Lo Faro
- Department of Immunology, Genetics and Pathology, Genomics and Neurobiology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
| | - Therese Johansson
- Department of Immunology, Genetics and Pathology, Genomics and Neurobiology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden; Centre for Women's Mental Health during the Reproductive Lifespan - Womher, Uppsala University, Uppsala, Sweden
| | - Julia Höglund
- Department of Immunology, Genetics and Pathology, Genomics and Neurobiology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Fatemeh Hadizadeh
- Department of Immunology, Genetics and Pathology, Genomics and Neurobiology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Åsa Johansson
- Department of Immunology, Genetics and Pathology, Genomics and Neurobiology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| |
Collapse
|
30
|
Chung R, Xu Z, Arnold M, Ip S, Harrison H, Barrett J, Pennells L, Kim LG, Di Angelantonio E, Paige E, Ritchie SC, Inouye M, Usher‐Smith JA, Wood AM. Using Polygenic Risk Scores for Prioritizing Individuals at Greatest Need of a Cardiovascular Disease Risk Assessment. J Am Heart Assoc 2023; 12:e029296. [PMID: 37489768 PMCID: PMC7614905 DOI: 10.1161/jaha.122.029296] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Accepted: 06/28/2023] [Indexed: 07/26/2023]
Abstract
Background The aim of this study was to provide quantitative evidence of the use of polygenic risk scores for systematically identifying individuals for invitation for full formal cardiovascular disease (CVD) risk assessment. Methods and Results A total of 108 685 participants aged 40 to 69 years, with measured biomarkers, linked primary care records, and genetic data in UK Biobank were used for model derivation and population health modeling. Prioritization tools using age, polygenic risk scores for coronary artery disease and stroke, and conventional risk factors for CVD available within longitudinal primary care records were derived using sex-specific Cox models. We modeled the implications of initiating guideline-recommended statin therapy after prioritizing individuals for invitation to a formal CVD risk assessment. If primary care records were used to prioritize individuals for formal risk assessment using age- and sex-specific thresholds corresponding to 5% false-negative rates, then the numbers of men and women needed to be screened to prevent 1 CVD event are 149 and 280, respectively. In contrast, adding polygenic risk scores to both prioritization and formal assessments, and selecting thresholds to capture the same number of events, resulted in a number needed to screen of 116 for men and 180 for women. Conclusions Using both polygenic risk scores and primary care records to prioritize individuals at highest risk of a CVD event for a formal CVD risk assessment can efficiently prioritize those who need interventions the most than using primary care records alone. This could lead to better allocation of resources by reducing the number of risk assessments in primary care while still preventing the same number of CVD events.
Collapse
Affiliation(s)
- Ryan Chung
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary CareUniversity of CambridgeUnited Kingdom
- Heart and Lung Research InstituteUniversity of CambridgeUnited Kingdom
| | - Zhe Xu
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary CareUniversity of CambridgeUnited Kingdom
- Heart and Lung Research InstituteUniversity of CambridgeUnited Kingdom
| | - Matthew Arnold
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary CareUniversity of CambridgeUnited Kingdom
- Heart and Lung Research InstituteUniversity of CambridgeUnited Kingdom
| | - Samantha Ip
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary CareUniversity of CambridgeUnited Kingdom
- Heart and Lung Research InstituteUniversity of CambridgeUnited Kingdom
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary CareUniversity of CambridgeUnited Kingdom
| | - Hannah Harrison
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary CareUniversity of CambridgeUnited Kingdom
| | - Jessica Barrett
- Medical Research Council Biostatistics UnitUniversity of CambridgeUnited Kingdom
| | - Lisa Pennells
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary CareUniversity of CambridgeUnited Kingdom
- Heart and Lung Research InstituteUniversity of CambridgeUnited Kingdom
| | - Lois G. Kim
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary CareUniversity of CambridgeUnited Kingdom
- Heart and Lung Research InstituteUniversity of CambridgeUnited Kingdom
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and BehaviourUniversity of CambridgeUnited Kingdom
| | - Emanuele Di Angelantonio
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary CareUniversity of CambridgeUnited Kingdom
- Heart and Lung Research InstituteUniversity of CambridgeUnited Kingdom
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and BehaviourUniversity of CambridgeUnited Kingdom
- British Heart Foundation Centre of Research ExcellenceUniversity of CambridgeUnited Kingdom
- Health Data Research UK CambridgeWellcome Genome Campus and University of CambridgeUnited Kingdom
- Health Data Science Research CentreHuman TechnopoleMilanItaly
| | - Ellie Paige
- National Centre for Epidemiology and Population HealthAustralian National UniversityCanberraAustralia
- The George Institute for Global HealthUNSW SydneyAustralia
| | - Scott C. Ritchie
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary CareUniversity of CambridgeUnited Kingdom
- Heart and Lung Research InstituteUniversity of CambridgeUnited Kingdom
- British Heart Foundation Centre of Research ExcellenceUniversity of CambridgeUnited Kingdom
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary CareUniversity of CambridgeUnited Kingdom
| | - Michael Inouye
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary CareUniversity of CambridgeUnited Kingdom
- Heart and Lung Research InstituteUniversity of CambridgeUnited Kingdom
- British Heart Foundation Centre of Research ExcellenceUniversity of CambridgeUnited Kingdom
- Health Data Research UK CambridgeWellcome Genome Campus and University of CambridgeUnited Kingdom
- The George Institute for Global HealthUNSW SydneyAustralia
- Cambridge Baker Systems Genomics InitiativeBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
| | - Juliet A. Usher‐Smith
- Primary Care Unit, Department of Public Health and Primary CareUniversity of CambridgeUnited Kingdom
| | - Angela M. Wood
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary CareUniversity of CambridgeUnited Kingdom
- Heart and Lung Research InstituteUniversity of CambridgeUnited Kingdom
- National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and BehaviourUniversity of CambridgeUnited Kingdom
- British Heart Foundation Centre of Research ExcellenceUniversity of CambridgeUnited Kingdom
- Health Data Research UK CambridgeWellcome Genome Campus and University of CambridgeUnited Kingdom
- Cambridge Centre of Artificial Intelligence in MedicineUniversity of CambridgeUnited Kingdom
| |
Collapse
|
31
|
Patel AP, Wang M, Ruan Y, Koyama S, Clarke SL, Yang X, Tcheandjieu C, Agrawal S, Fahed AC, Ellinor PT, Tsao PS, Sun YV, Cho K, Wilson PWF, Assimes TL, van Heel DA, Butterworth AS, Aragam KG, Natarajan P, Khera AV. A multi-ancestry polygenic risk score improves risk prediction for coronary artery disease. Nat Med 2023; 29:1793-1803. [PMID: 37414900 PMCID: PMC10353935 DOI: 10.1038/s41591-023-02429-x] [Citation(s) in RCA: 63] [Impact Index Per Article: 63.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 05/30/2023] [Indexed: 07/08/2023]
Abstract
Identification of individuals at highest risk of coronary artery disease (CAD)-ideally before onset-remains an important public health need. Prior studies have developed genome-wide polygenic scores to enable risk stratification, reflecting the substantial inherited component to CAD risk. Here we develop a new and significantly improved polygenic score for CAD, termed GPSMult, that incorporates genome-wide association data across five ancestries for CAD (>269,000 cases and >1,178,000 controls) and ten CAD risk factors. GPSMult strongly associated with prevalent CAD (odds ratio per standard deviation 2.14, 95% confidence interval 2.10-2.19, P < 0.001) in UK Biobank participants of European ancestry, identifying 20.0% of the population with 3-fold increased risk and conversely 13.9% with 3-fold decreased risk as compared with those in the middle quintile. GPSMult was also associated with incident CAD events (hazard ratio per standard deviation 1.73, 95% confidence interval 1.70-1.76, P < 0.001), identifying 3% of healthy individuals with risk of future CAD events equivalent to those with existing disease and significantly improving risk discrimination and reclassification. Across multiethnic, external validation datasets inclusive of 33,096, 124,467, 16,433 and 16,874 participants of African, European, Hispanic and South Asian ancestry, respectively, GPSMult demonstrated increased strength of associations across all ancestries and outperformed all available previously published CAD polygenic scores. These data contribute a new GPSMult for CAD to the field and provide a generalizable framework for how large-scale integration of genetic association data for CAD and related traits from diverse populations can meaningfully improve polygenic risk prediction.
Collapse
Affiliation(s)
- Aniruddh P Patel
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Minxian Wang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China.
| | - Yunfeng Ruan
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Satoshi Koyama
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Veteran Affairs Boston Healthcare System, Boston, MA, USA
| | - Shoa L Clarke
- Stanford University School of Medicine, Palo Alto, CA, USA
- Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA, USA
| | - Xiong Yang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, China
| | | | - Saaket Agrawal
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Akl C Fahed
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Patrick T Ellinor
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Philip S Tsao
- Stanford University School of Medicine, Palo Alto, CA, USA
- Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA, USA
| | - Yan V Sun
- Veteran Affairs Atlanta Healthcare System, Decatur, GA, USA
| | - Kelly Cho
- Veteran Affairs Boston Healthcare System, Boston, MA, USA
| | | | - Themistocles L Assimes
- Stanford University School of Medicine, Palo Alto, CA, USA
- Veterans Affairs Palo Alto Healthcare System, Palo Alto, CA, USA
| | - David A van Heel
- Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, and Centre of Research Excellence, University of Cambridge, Cambridge, UK
| | - Krishna G Aragam
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Pradeep Natarajan
- Division of Cardiology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Amit V Khera
- Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
- Verve Therapeutics, Boston, MA, USA.
| |
Collapse
|
32
|
Farooqi R, Kooner JS, Zhang W. Associations between polygenic risk score and covid-19 susceptibility and severity across ethnic groups: UK Biobank analysis. BMC Med Genomics 2023; 16:150. [PMID: 37386504 PMCID: PMC10311902 DOI: 10.1186/s12920-023-01584-x] [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: 11/30/2022] [Accepted: 06/16/2023] [Indexed: 07/01/2023] Open
Abstract
BACKGROUND COVID-19 manifests with huge heterogeneity in susceptibility and severity outcomes. UK Black Asian and Minority Ethnic (BAME) groups have demonstrated disproportionate burdens. Some variability remains unexplained, suggesting potential genetic contribution. Polygenic Risk Scores (PRS) can determine genetic predisposition to disease based on Single Nucleotide Polymorphisms (SNPs) within the genome. COVID-19 PRS analyses within non-European samples are extremely limited. We applied a multi-ethnic PRS to a UK-based cohort to understand genetic contribution to COVID-19 variability. METHODS We constructed two PRS for susceptibility and severity outcomes based on leading risk-variants from the COVID-19 Host Genetics Initiative. Scores were applied to 447,382 participants from the UK-Biobank. Associations with COVID-19 outcomes were assessed using binary logistic regression and discriminative power was validated using incremental area under receiver operating curve (ΔAUC). Variance explained was compared between ethnic groups via incremental pseudo-R2 (ΔR2). RESULTS Compared to those at low genetic risk, those at high risk had a significantly greater risk of severe COVID-19 for White (odds ratio [OR] 1.57, 95% confidence interval [CI] 1.42-1.74), Asian (OR 2.88, 95% CI 1.63-5.09) and Black (OR 1.98, 95% CI 1.11-3.53) ethnic groups. Severity PRS performed best within Asian (ΔAUC 0.9%, ΔR2 0.98%) and Black (ΔAUC 0.6%, ΔR2 0.61%) cohorts. For susceptibility, higher genetic risk was significantly associated with COVID-19 infection risk for the White cohort (OR 1.31, 95% CI 1.26-1.36), but not for Black or Asian groups. CONCLUSIONS Significant associations between PRS and COVID-19 outcomes were elicited, establishing a genetic basis for variability in COVID-19. PRS showed utility in identifying high-risk individuals. The multi-ethnic approach allowed applicability of PRS to diverse populations, with the severity model performing well within Black and Asian cohorts. Further studies with larger sample sizes of non-White samples are required to increase statistical power and better assess impacts within BAME populations.
Collapse
Affiliation(s)
- Raabia Farooqi
- Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK.
| | - Jaspal S Kooner
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, Middlesex, UB1 3HW, UK
- National Heart and Lung Institute, Imperial College London, London, W12 0NN, UK
- Imperial College Healthcare NHS Trust, London, W12 0HS, UK
- MRC-PHE Centre for Environment and Health, Imperial College London, London, W2 1PG, UK
| | - Weihua Zhang
- Department of Epidemiology and Biostatistics, Imperial College London, London, W2 1PG, UK
- Department of Cardiology, Ealing Hospital, London North West University Healthcare NHS Trust, Middlesex, UB1 3HW, UK
| |
Collapse
|
33
|
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.
Collapse
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
| |
Collapse
|
34
|
Zhou X, Chen Y, Ip FCF, Jiang Y, Cao H, Lv G, Zhong H, Chen J, Ye T, Chen Y, Zhang Y, Ma S, Lo RMN, Tong EPS, Mok VCT, Kwok TCY, Guo Q, Mok KY, Shoai M, Hardy J, Chen L, Fu AKY, Ip NY. Deep learning-based polygenic risk analysis for Alzheimer's disease prediction. COMMUNICATIONS MEDICINE 2023; 3:49. [PMID: 37024668 PMCID: PMC10079691 DOI: 10.1038/s43856-023-00269-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 03/06/2023] [Indexed: 04/08/2023] Open
Abstract
BACKGROUND The polygenic nature of Alzheimer's disease (AD) suggests that multiple variants jointly contribute to disease susceptibility. As an individual's genetic variants are constant throughout life, evaluating the combined effects of multiple disease-associated genetic risks enables reliable AD risk prediction. Because of the complexity of genomic data, current statistical analyses cannot comprehensively capture the polygenic risk of AD, resulting in unsatisfactory disease risk prediction. However, deep learning methods, which capture nonlinearity within high-dimensional genomic data, may enable more accurate disease risk prediction and improve our understanding of AD etiology. Accordingly, we developed deep learning neural network models for modeling AD polygenic risk. METHODS We constructed neural network models to model AD polygenic risk and compared them with the widely used weighted polygenic risk score and lasso models. We conducted robust linear regression analysis to investigate the relationship between the AD polygenic risk derived from deep learning methods and AD endophenotypes (i.e., plasma biomarkers and individual cognitive performance). We stratified individuals by applying unsupervised clustering to the outputs from the hidden layers of the neural network model. RESULTS The deep learning models outperform other statistical models for modeling AD risk. Moreover, the polygenic risk derived from the deep learning models enables the identification of disease-associated biological pathways and the stratification of individuals according to distinct pathological mechanisms. CONCLUSION Our results suggest that deep learning methods are effective for modeling the genetic risks of AD and other diseases, classifying disease risks, and uncovering disease mechanisms.
Collapse
Affiliation(s)
- Xiaopu Zhou
- Division of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong, China
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen, Guangdong, 518057, China
| | - Yu Chen
- Division of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen, Guangdong, 518057, China
- Chinese Academy of Sciences Key Laboratory of Brain Connectome and Manipulation, Shenzhen Key Laboratory of Translational Research for Brain Diseases, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, Guangdong, 518055, China
| | - Fanny C F Ip
- Division of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong, China
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen, Guangdong, 518057, China
| | - Yuanbing Jiang
- Division of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong, China
| | - Han Cao
- Division of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Ge Lv
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Huan Zhong
- Division of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong, China
| | - Jiahang Chen
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Tao Ye
- Division of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen, Guangdong, 518057, China
- Chinese Academy of Sciences Key Laboratory of Brain Connectome and Manipulation, Shenzhen Key Laboratory of Translational Research for Brain Diseases, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, Guangdong, 518055, China
| | - Yuewen Chen
- Division of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen, Guangdong, 518057, China
- Chinese Academy of Sciences Key Laboratory of Brain Connectome and Manipulation, Shenzhen Key Laboratory of Translational Research for Brain Diseases, The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, Guangdong, 518055, China
| | - Yulin Zhang
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen, Guangdong, 518057, China
| | - Shuangshuang Ma
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen, Guangdong, 518057, China
| | - Ronnie M N Lo
- Division of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Estella P S Tong
- Division of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Vincent C T Mok
- Gerald Choa Neuroscience Centre, Lui Che Woo Institute of Innovative Medicine, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Division of Neurology, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Timothy C Y Kwok
- Therese Pei Fong Chow Research Centre for Prevention of Dementia, Division of Geriatrics, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, Hong Kong, China
| | - Qihao Guo
- Department of Gerontology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, 200233, China
| | - Kin Y Mok
- Division of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong, China
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
| | - Maryam Shoai
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
| | - John Hardy
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong, China
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, London, UK
- HKUST Jockey Club Institute for Advanced Study, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Lei Chen
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
| | - Amy K Y Fu
- Division of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong, China
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen, Guangdong, 518057, China
| | - Nancy Y Ip
- Division of Life Science, State Key Laboratory of Molecular Neuroscience, Molecular Neuroscience Center, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China.
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong Science Park, Hong Kong, China.
- Guangdong Provincial Key Laboratory of Brain Science, Disease and Drug Development, HKUST Shenzhen Research Institute, Shenzhen-Hong Kong Institute of Brain Science, Shenzhen, Guangdong, 518057, China.
| |
Collapse
|
35
|
Jurrjens AW, Seldin MM, Giles C, Meikle PJ, Drew BG, Calkin AC. The potential of integrating human and mouse discovery platforms to advance our understanding of cardiometabolic diseases. eLife 2023; 12:e86139. [PMID: 37000167 PMCID: PMC10065800 DOI: 10.7554/elife.86139] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Accepted: 03/15/2023] [Indexed: 04/01/2023] Open
Abstract
Cardiometabolic diseases encompass a range of interrelated conditions that arise from underlying metabolic perturbations precipitated by genetic, environmental, and lifestyle factors. While obesity, dyslipidaemia, smoking, and insulin resistance are major risk factors for cardiometabolic diseases, individuals still present in the absence of such traditional risk factors, making it difficult to determine those at greatest risk of disease. Thus, it is crucial to elucidate the genetic, environmental, and molecular underpinnings to better understand, diagnose, and treat cardiometabolic diseases. Much of this information can be garnered using systems genetics, which takes population-based approaches to investigate how genetic variance contributes to complex traits. Despite the important advances made by human genome-wide association studies (GWAS) in this space, corroboration of these findings has been hampered by limitations including the inability to control environmental influence, limited access to pertinent metabolic tissues, and often, poor classification of diseases or phenotypes. A complementary approach to human GWAS is the utilisation of model systems such as genetically diverse mouse panels to study natural genetic and phenotypic variation in a controlled environment. Here, we review mouse genetic reference panels and the opportunities they provide for the study of cardiometabolic diseases and related traits. We discuss how the post-GWAS era has prompted a shift in focus from discovery of novel genetic variants to understanding gene function. Finally, we highlight key advantages and challenges of integrating complementary genetic and multi-omics data from human and mouse populations to advance biological discovery.
Collapse
Affiliation(s)
- Aaron W Jurrjens
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Central Clinical School, Monash University, Melbourne, Australia
| | - Marcus M Seldin
- Department of Biological Chemistry and Center for Epigenetics and Metabolism, University of California, Irvine, Irvine, United States
| | - Corey Giles
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Bundoora, Australia
| | - Peter J Meikle
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Central Clinical School, Monash University, Melbourne, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
- Baker Department of Cardiovascular Research Translation and Implementation, La Trobe University, Bundoora, Australia
| | - Brian G Drew
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Central Clinical School, Monash University, Melbourne, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
| | - Anna C Calkin
- Baker Heart and Diabetes Institute, Melbourne, Australia
- Central Clinical School, Monash University, Melbourne, Australia
- Baker Department of Cardiometabolic Health, University of Melbourne, Melbourne, Australia
| |
Collapse
|
36
|
Genetic Variants Determine Treatment Response in Autoimmune Hepatitis. J Pers Med 2023; 13:jpm13030540. [PMID: 36983720 PMCID: PMC10052918 DOI: 10.3390/jpm13030540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 03/14/2023] [Accepted: 03/15/2023] [Indexed: 03/19/2023] Open
Abstract
Background: Autoimmune hepatitis (AIH) is a rare entity; in addition, single-nucleotide polymorphisms (SNPs) may impact its course and outcome. We investigated liver-related SNPs regarding its activity, as well as in relation to its stage and treatment response in a Central European AIH cohort. Methods: A total of 113 AIH patients (i.e., 30 male/83 female, median 57.9 years) were identified. In 81, genotyping of PNPLA3-rs738409, MBOAT7-rs626238, TM6SF2-rs58542926, and HSD17B13-rs72613567:TA, as well as both biochemical and clinical data at baseline and follow-up, were available. Results: The median time of follow-up was 2.8 years; five patients died and one underwent liver transplantation. The PNPLA3-G/G homozygosity was linked to a worse treatment response when compared to wildtype [wt] (ALT 1.7 vs. 0.6 × ULN, p < 0.001). The MBOAT7-C/C homozygosity was linked to non-response vs. wt and heterozygosity (p = 0.022). Male gender was associated with non-response (OR 14.5, p = 0.012) and a higher prevalence of PNPLA3 (G/G vs. C/G vs. wt 41.9/40.0/15.0% males, p = 0.03). The MBOAT7 wt was linked to less histological fibrosis (p = 0.008), while no effects for other SNPs were noted. A polygenic risk score was utilized comprising all the SNPs and correlated with the treatment response (p = 0.04). Conclusions: Our data suggest that genetic risk variants impact the treatment response of AIH in a gene-dosage-dependent manner. Furthermore, MBOAT7 and PNPLA3 mediated most of the observed effects, the latter explaining, in part, the predisposition of male subjects to worse treatment responses.
Collapse
|
37
|
Integrating polygenic and clinical risks to improve stroke risk stratification in prospective Chinese cohorts. SCIENCE CHINA. LIFE SCIENCES 2023:10.1007/s11427-022-2280-3. [PMID: 36881318 DOI: 10.1007/s11427-022-2280-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 01/13/2023] [Indexed: 03/08/2023]
Abstract
The utility of the polygenic risk score (PRS) to identify individuals at higher risk of stroke beyond clinical risk remains unclear, and we clarified this using Chinese population-based prospective cohorts. Cox proportional hazards models were used to estimate the 10-year risk, and Fine and Gray's models were used for hazard ratios (HRs), their 95% confidence intervals (CIs), and the lifetime risk according to PRS and clinical risk categories. A total of 41,006 individuals aged 30-75 years with a mean follow-up of 9.0 years were included. Comparing the top versus bottom 5% of the PRS, the HR was 3.01 (95%CI 2.03-4.45) in the total population, and similar findings were observed within clinical risk strata. Marked gradients in the 10-year and lifetime risk across PRS categories were also found within clinical risk categories. Notably, among individuals with intermediate clinical risk, the 10-year risk for those in the top 5% of the PRS (7.3%, 95%CI 7.1%-7.5%) reached the threshold of high clinical risk (⩾7.0%) for initiating preventive treatment, and this effect of the PRS on refining risk stratification was evident for ischemic stroke. Even among those in the top 10% and 20% of the PRS, the 10-year risk would also exceed this level when aged ⩾50 and ⩾60 years, respectively. Overall, the combination of the PRS with the clinical risk score improved the risk stratification within clinical risk strata and distinguished actual high-risk individuals with intermediate clinical risk.
Collapse
|
38
|
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.
Collapse
|
39
|
Andreassen OA, Hindley GFL, Frei O, Smeland OB. New insights from the last decade of research in psychiatric genetics: discoveries, challenges and clinical implications. World Psychiatry 2023; 22:4-24. [PMID: 36640404 PMCID: PMC9840515 DOI: 10.1002/wps.21034] [Citation(s) in RCA: 48] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/07/2022] [Indexed: 01/15/2023] Open
Abstract
Psychiatric genetics has made substantial progress in the last decade, providing new insights into the genetic etiology of psychiatric disorders, and paving the way for precision psychiatry, in which individual genetic profiles may be used to personalize risk assessment and inform clinical decision-making. Long recognized to be heritable, recent evidence shows that psychiatric disorders are influenced by thousands of genetic variants acting together. Most of these variants are commonly occurring, meaning that every individual has a genetic risk to each psychiatric disorder, from low to high. A series of large-scale genetic studies have discovered an increasing number of common and rare genetic variants robustly associated with major psychiatric disorders. The most convincing biological interpretation of the genetic findings implicates altered synaptic function in autism spectrum disorder and schizophrenia. However, the mechanistic understanding is still incomplete. In line with their extensive clinical and epidemiological overlap, psychiatric disorders appear to exist on genetic continua and share a large degree of genetic risk with one another. This provides further support to the notion that current psychiatric diagnoses do not represent distinct pathogenic entities, which may inform ongoing attempts to reconceptualize psychiatric nosology. Psychiatric disorders also share genetic influences with a range of behavioral and somatic traits and diseases, including brain structures, cognitive function, immunological phenotypes and cardiovascular disease, suggesting shared genetic etiology of potential clinical importance. Current polygenic risk score tools, which predict individual genetic susceptibility to illness, do not yet provide clinically actionable information. However, their precision is likely to improve in the coming years, and they may eventually become part of clinical practice, stressing the need to educate clinicians and patients about their potential use and misuse. This review discusses key recent insights from psychiatric genetics and their possible clinical applications, and suggests future directions.
Collapse
Affiliation(s)
- Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Guy F L Hindley
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Oleksandr Frei
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Centre for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Olav B Smeland
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| |
Collapse
|
40
|
Surakka I, Wolford BN, Ritchie SC, Hornsby WE, Sutton NR, Gabrielsen ME, Skogholt AH, Thomas L, Inouye M, Hveem K, Willer CJ. Sex-Specific Survival Bias and Interaction Modeling in Coronary Artery Disease Risk Prediction. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2023; 16:e003542. [PMID: 36580301 PMCID: PMC10525909 DOI: 10.1161/circgen.121.003542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 09/29/2022] [Indexed: 12/30/2022]
Abstract
BACKGROUND The 10-year Atherosclerotic Cardiovascular Disease risk score is the standard approach to predict risk of incident cardiovascular events, and recently, addition of coronary artery disease (CAD) polygenic scores has been evaluated. Although age and sex strongly predict the risk of CAD, their interaction with genetic risk prediction has not been systematically examined. This study performed an extensive evaluation of age and sex effects in genetic CAD risk prediction. METHODS The population-based Norwegian HUNT2 (Trøndelag Health Study 2) cohort of 51 036 individuals was used as the primary dataset. Findings were replicated in the UK Biobank (372 410 individuals). Models for 10-year CAD risk were fitted using Cox proportional hazards, and Harrell concordance index, sensitivity, and specificity were compared. RESULTS Inclusion of age and sex interactions of CAD polygenic score to the prediction models increased the C-index and sensitivity by accounting for nonadditive effects of CAD polygenic score and likely countering the observed survival bias in the baseline. The sensitivity for females was lower than males in all models including genetic information. We identified a total of 82.6% of incident CAD cases by using a 2-step approach: (1) Atherosclerotic Cardiovascular Disease risk score (74.1%) and (2) the CAD polygenic score interaction model for those in low clinical risk (additional 8.5%). CONCLUSIONS These findings highlight the importance and complexity of genetic risk in predicting CAD. There is a need for modeling age- and sex-interaction terms with polygenic scores to optimize detection of individuals at high risk, those who warrant preventive interventions. Sex-specific studies are needed to understand and estimate CAD risk with genetic information.
Collapse
Affiliation(s)
- Ida Surakka
- Division of Cardiovascular Medicine, Dept of Internal Medicine, Univ of Michigan
| | - Brooke N. Wolford
- Dept of Biostatistics & Center for Statistical Genetics, Univ of Michigan School of Public Health, Ann Arbor, MI
- Dept of Computational Medicine & Bioinformatics, Univ of Michigan
| | - Scott C. Ritchie
- Cambridge Baker Systems Genomics Initiative, Dept of Public Health & Primary Care, Univ of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- British Heart Foundation Cardiovascular Epidemiology Unit, Dept of Public Health & Primary Care, Univ of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, Univ of Cambridge, Cambridge, UK
| | - Whitney E. Hornsby
- Division of Cardiovascular Medicine, Dept of Internal Medicine, Univ of Michigan
| | - Nadia R. Sutton
- Division of Cardiovascular Medicine, Dept of Internal Medicine, Univ of Michigan
| | - Maiken Elvenstad Gabrielsen
- K.G. Jebsen Center for Genetic Epidemiology, Dept of Public Health & Nursing, NTNU, Norwegian Univ of Science & Technology, Trondheim, Norway
| | - Anne Heidi Skogholt
- K.G. Jebsen Center for Genetic Epidemiology, Dept of Public Health & Nursing, NTNU, Norwegian Univ of Science & Technology, Trondheim, Norway
| | - Laurent Thomas
- K.G. Jebsen Center for Genetic Epidemiology, Dept of Public Health & Nursing, NTNU, Norwegian Univ of Science & Technology, Trondheim, Norway
- Dept of Clinical & Molecular Medicine, Norwegian Univ of Science & Technology, Trondheim, Norway, Norwegian Univ of Science & Technology, Trondheim, Norway
- BioCore - Bioinformatics Core Facility, Norwegian Univ of Science & Technology, Trondheim, Norway, Norwegian Univ of Science & Technology, Trondheim, Norway
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Dept of Public Health & Primary Care, Univ of Cambridge, Cambridge, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart & Diabetes Institute, Melbourne, Victoria, Australia
- British Heart Foundation Cardiovascular Epidemiology Unit, Dept of Public Health & Primary Care, Univ of Cambridge, Cambridge, UK
- British Heart Foundation Centre of Research Excellence, Univ of Cambridge, Cambridge, UK
- Health Data Research UK Cambridge, Wellcome Genome Campus & Univ of Cambridge, Cambridge, UK
- Dept of Clinical Pathology, Univ of Melbourne, Parkville, Victoria, Australia
- The Alan Turing Institute, London, UK
| | - Kristian Hveem
- K.G. Jebsen Center for Genetic Epidemiology, Dept of Public Health & Nursing, NTNU, Norwegian Univ of Science & Technology, Trondheim, Norway
- HUNT Research Centre, Dept of Public Health & Nursing, Norwegian University of Science and Technology, Levanger, Norway
| | - Cristen J. Willer
- Division of Cardiovascular Medicine, Dept of Internal Medicine, Univ of Michigan
- Dept of Computational Medicine & Bioinformatics, Univ of Michigan
- HUNT Research Centre, Dept of Public Health & Nursing, Norwegian University of Science and Technology, Levanger, Norway
- Dept of Human Genetics, Univ of Michigan
| |
Collapse
|
41
|
Pang S, Yengo L, Nelson CP, Bourier F, Zeng L, Li L, Kessler T, Erdmann J, Mägi R, Läll K, Metspalu A, Mueller-Myhsok B, Samani NJ, Visscher PM, Schunkert H. Genetic and modifiable risk factors combine multiplicatively in common disease. Clin Res Cardiol 2023; 112:247-257. [PMID: 35987817 PMCID: PMC9898372 DOI: 10.1007/s00392-022-02081-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 08/02/2022] [Indexed: 02/06/2023]
Abstract
BACKGROUND The joint contribution of genetic and environmental exposures to noncommunicable diseases is not well characterized. OBJECTIVES We modeled the cumulative effects of common risk alleles and their prevalence variations with classical risk factors. METHODS We analyzed mathematically and statistically numbers and effect sizes of established risk alleles for coronary artery disease (CAD) and other conditions. RESULTS In UK Biobank, risk alleles counts in the lowest (175.4) and highest decile (205.7) of the distribution differed by only 16.9%, which nevertheless increased CAD prevalence 3.4-fold (p < 0.01). Irrespective of the affected gene, a single risk allele multiplied the effects of all others carried by a person, resulting in a 2.9-fold stronger effect size in the top versus the bottom decile (p < 0.01) and an exponential increase in risk (R > 0.94). Classical risk factors shifted effect sizes to the steep upslope of the logarithmic function linking risk allele numbers with CAD prevalence. Similar phenomena were observed in the Estonian Biobank and for risk alleles affecting diabetes mellitus, breast and prostate cancer. CONCLUSIONS Alleles predisposing to common diseases can be carried safely in large numbers, but few additional ones lead to sharp risk increments. Here, we describe exponential functions by which risk alleles combine interchangeably but multiplicatively with each other and with modifiable risk factors to affect prevalence. Our data suggest that the biological systems underlying these diseases are modulated by hundreds of genes but become only fragile when a narrow window of total risk, irrespective of its genetic or environmental origins, has been passed.
Collapse
Affiliation(s)
- Shichao Pang
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Lazarettstr. 36, 80636, Munich, Germany
| | - Loic Yengo
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Christopher P Nelson
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK.,NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Felix Bourier
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Lazarettstr. 36, 80636, Munich, Germany.,Deutsches Zentrum Ffür Herz- und Kreislauferkrankungen (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Lingyao Zeng
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Lazarettstr. 36, 80636, Munich, Germany
| | - Ling Li
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Lazarettstr. 36, 80636, Munich, Germany
| | - Thorsten Kessler
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Lazarettstr. 36, 80636, Munich, Germany.,Deutsches Zentrum Ffür Herz- und Kreislauferkrankungen (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Jeanette Erdmann
- Institute for Cardiogenetics, and University Heart Center, University of Lübeck, Lübeck, Germany.,DZHK (German Research Centre for Cardiovascular Research), Partner Site Hamburg/Lübeck/Kiel, Hamburg/Kiel/Lübeck, Germany
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Kristi Läll
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Andres Metspalu
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Bertram Mueller-Myhsok
- Statistical Genetics, Max Planck Institute of Psychiatry, Munich, Germany.,Institute of Translational Medicine, University of Liverpool, Liverpool, UK.,Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Nilesh J Samani
- Department of Cardiovascular Sciences, University of Leicester, Leicester, UK.,NIHR Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK
| | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, Australia
| | - Heribert Schunkert
- Department of Cardiology, Deutsches Herzzentrum München, Technische Universität München, Lazarettstr. 36, 80636, Munich, Germany. .,Deutsches Zentrum Ffür Herz- und Kreislauferkrankungen (DZHK), Partner Site Munich Heart Alliance, Munich, Germany.
| |
Collapse
|
42
|
Zhao D, Sun H, Li H, Li C, Zhou B. A prediction model for the impact of environmental and genetic factors on cardiovascular events: development in a salt substitutes population. J Transl Med 2023; 21:62. [PMID: 36717874 PMCID: PMC9887817 DOI: 10.1186/s12967-023-03899-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: 11/09/2022] [Accepted: 01/17/2023] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Cardiovascular disease (CVD) has evolved into a serious public health issue that demands the use of suitable methods to estimate the risk of the disease. As a result, in a sample of individuals who completed a 3-year low-sodium salt or conventional salt intervention in a hypertensive environment, we constructed a 13-year cardiovascular (CV) event risk prediction model with a 10-year follow-up. METHODS A Cox proportional hazards model was used to build a prediction model based on data from 306 participants who matched the inclusion criteria. Both the discriminating power and the calibration of the prediction models were assessed. The discriminative power of the prediction model was measured using the area under the curve (AUC). Brier scores and calibration plots were used to assess the prediction model's calibration. The model was internally validated using the tenfold cross-validation method. The nomogram served as a tool for visualising the model. RESULTS Among the 306 total individuals, there were 100 cases and 206 control. In the model, there were six predictors including age, smoking, LDL-C (low-density lipoprotein cholesterol), baseline SBP (systolic blood pressure), CVD (cardiovascular history), and CNV (genomic copy number variation) nsv483076. The fitted model has an AUC of 0.788, showing strong model discrimination, and a Brier score of 0.166, indicating that it was well-calibrated. According to the results of internal validation, the prediction model utilised in this study had a good level of repeatability. According to the model integrating the interaction of CNVs and baseline blood pressure, the effect of baseline SBP on CV events may be greater when nsv483076 was normal double copies than when nsv483076 was copy number variation. CONCLUSIONS The efficacy of risk prediction models for CV events that include environmental and genetic components is excellent, and they may be utilised as risk assessment tools for CV events in specific groups to offer a foundation for tailored intervention strategies.
Collapse
Affiliation(s)
- Dan Zhao
- grid.412636.40000 0004 1757 9485Department of Clinical Epidemiology and Evidence-Based Medicine, The First Hospital of China Medical University, No.155, Nanjing North Street, Heping District, Shenyang, Liaoning China ,grid.412449.e0000 0000 9678 1884School of Public Health, China Medical University, Shenyang, Liaoning China
| | - Hao Sun
- grid.412636.40000 0004 1757 9485Department of Clinical Epidemiology and Evidence-Based Medicine, The First Hospital of China Medical University, No.155, Nanjing North Street, Heping District, Shenyang, Liaoning China
| | - Huamin Li
- grid.412636.40000 0004 1757 9485Department of Clinical Epidemiology and Evidence-Based Medicine, The First Hospital of China Medical University, No.155, Nanjing North Street, Heping District, Shenyang, Liaoning China ,grid.412449.e0000 0000 9678 1884School of Public Health, China Medical University, Shenyang, Liaoning China
| | - Chaoxiu Li
- grid.412636.40000 0004 1757 9485Department of Clinical Epidemiology and Evidence-Based Medicine, The First Hospital of China Medical University, No.155, Nanjing North Street, Heping District, Shenyang, Liaoning China ,grid.412449.e0000 0000 9678 1884School of Public Health, China Medical University, Shenyang, Liaoning China
| | - Bo Zhou
- Department of Clinical Epidemiology and Evidence-Based Medicine, The First Hospital of China Medical University, No.155, Nanjing North Street, Heping District, Shenyang, Liaoning, China.
| |
Collapse
|
43
|
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.
Collapse
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
| |
Collapse
|
44
|
Affiliation(s)
- Marie Loh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore,Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom,National Skin Centre, Singapore,Correspondence: Prof Marie Loh, Lee Kong Chian School of Medicine, Nanyang Technological University, Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore. E-mail:
| | - John Campbell Chambers
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore,Department of Epidemiology and Biostatistics, Imperial College London, London, United Kingdom
| |
Collapse
|
45
|
Kappel DB, Legge SE, Hubbard L, Willcocks IR, O'Connell KS, Smith RL, Molden E, Andreassen OA, King A, Jansen J, Helthuis M, Owen MJ, O'Donovan MC, Walters JTR, Pardiñas AF. Genomic Stratification of Clozapine Prescription Patterns Using Schizophrenia Polygenic Scores. Biol Psychiatry 2023; 93:149-156. [PMID: 36244804 PMCID: PMC10804961 DOI: 10.1016/j.biopsych.2022.07.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 07/14/2022] [Accepted: 07/19/2022] [Indexed: 11/28/2022]
Abstract
BACKGROUND Treatment-resistant schizophrenia affects approximately 30% of individuals with the disorder. Clozapine is the medication of choice in treatment-resistant schizophrenia, but optimizing administration and dose titration is complex. The identification of factors influencing clozapine prescription and response, including genetics, is of interest in a precision psychiatry framework. METHODS We used linear regression models accounting for demographic, pharmacological, and clinical covariates to determine whether a polygenic risk score (PRS) for schizophrenia would be associated with the highest dose recorded during clozapine treatment. Analyses were performed across 2 independent multiancestry samples of individuals from a UK patient monitoring system, CLOZUK2 (n = 3133) and CLOZUK3 (n = 909), and a European sample from a Norwegian therapeutic drug monitoring service (n = 417). In a secondary analysis merging both UK cohorts, logistic regression models were used to estimate the relationship between schizophrenia PRSs and clozapine doses classified as low, standard, or high. RESULTS After controlling for relevant covariates, the schizophrenia PRS was correlated with the highest clozapine dose on record for each individual across all samples: CLOZUK2 (β = 12.22, SE = 3.78, p = .001), CLOZUK3 (β = 12.73, SE = 5.99, p = .034), and the Norwegian cohort (β = 46.45, SE = 18.83, p = .014). In a secondary analysis, the schizophrenia PRS was associated with taking clozapine doses >600 mg/day (odds ratio = 1.279, p = .006). CONCLUSIONS The schizophrenia PRS was associated with the highest clozapine dose prescribed for an individual in records from 3 independent samples, suggesting that the genetic liability for schizophrenia might index factors associated with therapeutic decisions in cohorts of patients with treatment-resistant schizophrenia.
Collapse
Affiliation(s)
- Djenifer B Kappel
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Sophie E Legge
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Leon Hubbard
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Isabella R Willcocks
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Kevin S O'Connell
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Robert L Smith
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway; Center for Psychopharmacology, Diakonhjemmet Hospital, Oslo, Norway
| | - Espen Molden
- Section for Pharmacology and Pharmaceutical Biosciences, Department of Pharmacy, University of Oslo, Oslo, Norway; Center for Psychopharmacology, Diakonhjemmet Hospital, Oslo, Norway
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Adrian King
- Magna Laboratories Ltd., Ross-on-Wye, United Kingdom
| | | | | | - Michael J Owen
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Michael C O'Donovan
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - James T R Walters
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - Antonio F Pardiñas
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom.
| |
Collapse
|
46
|
O'Sullivan JW, Ashley EA, Elliott PM. Polygenic risk scores for the prediction of cardiometabolic disease. Eur Heart J 2023; 44:89-99. [PMID: 36478054 DOI: 10.1093/eurheartj/ehac648] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 08/28/2022] [Accepted: 10/27/2022] [Indexed: 12/12/2022] Open
Abstract
Cardiometabolic diseases contribute more to global morbidity and mortality than any other group of disorders. Polygenic risk scores (PRSs), the weighted summation of individually small-effect genetic variants, represent an advance in our ability to predict the development and complications of cardiometabolic diseases. This article reviews the evidence supporting the use of PRS in seven common cardiometabolic diseases: coronary artery disease (CAD), stroke, hypertension, heart failure and cardiomyopathies, obesity, atrial fibrillation (AF), and type 2 diabetes mellitus (T2DM). Data suggest that PRS for CAD, AF, and T2DM consistently improves prediction when incorporated into existing clinical risk tools. In other areas such as ischaemic stroke and hypertension, clinical application appears premature but emerging evidence suggests that the study of larger and more diverse populations coupled with more granular phenotyping will propel the translation of PRS into practical clinical prediction tools.
Collapse
Affiliation(s)
- Jack W O'Sullivan
- Stanford Center for Inherited Cardiovascular Disease, Stanford University School of Medicine, Stanford, CA, USA
- Division of Cardiology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Euan A Ashley
- Stanford Center for Inherited Cardiovascular Disease, Stanford University School of Medicine, Stanford, CA, USA
- Division of Cardiology, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Perry M Elliott
- UCL Institute of Cardiovascular Science, Gower Street, London WC1E 6BT, UK
- St. Bartholomew's Hospital, W Smithfield, London EC1A 7BE, UK
| |
Collapse
|
47
|
Sutton NR, Malhotra R, Hilaire C, Aikawa E, Blumenthal RS, Gackenbach G, Goyal P, Johnson A, Nigwekar SU, Shanahan CM, Towler DA, Wolford BN, Chen Y. Molecular Mechanisms of Vascular Health: Insights From Vascular Aging and Calcification. Arterioscler Thromb Vasc Biol 2023; 43:15-29. [PMID: 36412195 PMCID: PMC9793888 DOI: 10.1161/atvbaha.122.317332] [Citation(s) in RCA: 34] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 11/11/2022] [Indexed: 11/23/2022]
Abstract
Cardiovascular disease is the most common cause of death worldwide, especially beyond the age of 65 years, with the vast majority of morbidity and mortality due to myocardial infarction and stroke. Vascular pathology stems from a combination of genetic risk, environmental factors, and the biologic changes associated with aging. The pathogenesis underlying the development of vascular aging, and vascular calcification with aging, in particular, is still not fully understood. Accumulating data suggests that genetic risk, likely compounded by epigenetic modifications, environmental factors, including diabetes and chronic kidney disease, and the plasticity of vascular smooth muscle cells to acquire an osteogenic phenotype are major determinants of age-associated vascular calcification. Understanding the molecular mechanisms underlying genetic and modifiable risk factors in regulating age-associated vascular pathology may inspire strategies to promote healthy vascular aging. This article summarizes current knowledge of concepts and mechanisms of age-associated vascular disease, with an emphasis on vascular calcification.
Collapse
Affiliation(s)
- Nadia R. Sutton
- Division of Cardiovascular Medicine, Michigan Medicine, Ann Arbor, Michigan, USA
| | - Rajeev Malhotra
- Cardiology Division, Massachusetts General Hospital and Harvard Medical School, Boston, MA USA
| | - Cynthia Hilaire
- Division of Cardiology, Departments of Medicine and Bioengineering, Pittsburgh Heart, Lung, and Blood Vascular Medicine Institute, University of Pittsburgh, 1744 BSTWR, 200 Lothrop St, Pittsburgh, PA, 15260 USA
| | - Elena Aikawa
- Cardiovascular Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA
| | - Roger S. Blumenthal
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease; Baltimore, MD
| | - Grace Gackenbach
- Division of Cardiovascular Medicine, Michigan Medicine, Ann Arbor, Michigan, USA
| | - Parag Goyal
- Department of Medicine, Weill Cornell Medicine, New York, NY
| | - Adam Johnson
- Cardiology Division, Massachusetts General Hospital and Harvard Medical School, Boston, MA USA
| | - Sagar U. Nigwekar
- Division of Nephrology, Massachusetts General Hospital and Harvard Medical School, Boston, MA USA
| | - Catherine M. Shanahan
- School of Cardiovascular and Metabolic Medicine and Sciences, King’s College London, London, UK
| | - Dwight A. Towler
- Department of Medicine | Endocrine Division and Pak Center for Mineral Metabolism Research, UT Southwestern Medical Center, Dallas, TX USA
| | - Brooke N. Wolford
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - Yabing Chen
- Department of Pathology, University of Alabama at Birmingham and Research Department, Veterans Affairs Birmingham Medical Center, Birmingham, AL, USA
| |
Collapse
|
48
|
de Geus EJ. Genetic Pathways Underlying Individual Differences in Regular Physical Activity. Exerc Sport Sci Rev 2023; 51:2-18. [PMID: 36044740 PMCID: PMC9762726 DOI: 10.1249/jes.0000000000000305] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/16/2022] [Indexed: 12/15/2022]
Abstract
Twin and family studies show a strong contribution of genetic factors to physical activity (PA) assessed by either self-report or accelerometers. PA heritability is around 43% across the lifespan. Genome-wide association studies have implied biological pathways related to exercise ability and enjoyment. A polygenic score based on genetic variants influencing PA could help improve the success of intervention programs.
Collapse
|
49
|
Masoli JAH, Pilling LC, Frayling TM. Genomics and multimorbidity. Age Ageing 2022; 51:6872694. [PMID: 36469092 DOI: 10.1093/ageing/afac285] [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: 10/17/2022] [Indexed: 12/11/2022] Open
Abstract
Multimorbidity has increased in prevalence world-wide. It is anticipated to affect over 1 in 6 of the UK population by 2035 and is now recognised as a global priority for health research. Genomic medicine has rapidly advanced over the last 20 years from the first sequencing of the human genome to integration into clinical care for rarer conditions. Genetic studies help identify new disease mechanisms as they are less susceptible to the bias and confounding that affects epidemiological studies, as genetics are assigned from conception. There is also genetic variation in the efficacy of medications and the risk of side effects, pharmacogenetics. Genomic approaches offer the potential to improve our understanding of mechanisms underpinning multiple long-term conditions/multimorbidity and guide precision approaches to risk, diagnosis and optimisation of management. In this commentary as part of the Age and Ageing 50th anniversary commentary series, we summarise genomics and the potential utility of genomics in multimorbidity.
Collapse
Affiliation(s)
- Jane A H Masoli
- Department of Clinical and Biomedical Science, University of Exeter, Exeter, Devon EX12LU, UK.,Healthcare for Older People, Royal Devon University Healthcare NHS Foundation Trust, EX25DW, UK
| | - Luke C Pilling
- Department of Clinical and Biomedical Science, University of Exeter, Exeter, Devon EX12LU, UK
| | - Timothy M Frayling
- Department of Clinical and Biomedical Science, University of Exeter, Exeter, Devon EX12LU, UK
| |
Collapse
|
50
|
Affiliation(s)
- Cathryn M Lewis
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Lewis, Vassos); Department of Medical and Molecular Genetics, Faculty of Life Sciences and Medicine, King's College London (Lewis)
| | - Evangelos Vassos
- Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology, and Neuroscience, King's College London (Lewis, Vassos); Department of Medical and Molecular Genetics, Faculty of Life Sciences and Medicine, King's College London (Lewis)
| |
Collapse
|