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Mousavi I, Suffredini J, Virani SS, Ballantyne C, Michos ED, Misra A, Saeed A, Jia X. Early Onset Atherosclerotic Cardiovascular Disease. Eur J Prev Cardiol 2024:zwae240. [PMID: 39041374 DOI: 10.1093/eurjpc/zwae240] [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: 05/09/2024] [Revised: 07/16/2024] [Accepted: 07/20/2024] [Indexed: 07/24/2024]
Abstract
Recent trends indicate a concerning increase in early-onset atherosclerotic cardiovascular disease (ASCVD) among younger individuals (age < 55 in men and <65 in women). These findings highlight the pathobiology of ASCVD as a disease process that begins early in life and underscores the need for more tailored screening methods and preventive strategies. Increasing attention has been placed on the growing burden of traditional cardiometabolic risk factors in young individuals while also recognizing unique factors that mediate risk of premature atherosclerosis in this demographic such as substance use, socioeconomic disparities, adverse pregnancy outcomes, and chronic inflammatory states that contribute to the increasing incidence of early ASCVD. Additionally, mounting evidence has pointed out significant disparities in the diagnosis and management of early ASCVD and cardiovascular outcomes based on sex and race. Moving toward a more personalized approach, emerging data and technological developments using diverse tools such as polygenic risk scores and coronary artery calcium scans have shown potential in earlier detection of ASCVD risk. Thus, we review current evidence on causal risk factors that drive the increase in early ASCVD and highlight emerging tools to improve ASCVD risk assessment in young individuals.
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Affiliation(s)
- Idine Mousavi
- Section of Cardiology, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - John Suffredini
- Section of Cardiology, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Salim S Virani
- The Aga Khan University, Karachi, Pakistan; Baylor College of Medicine and Texas Heart Institute, Houston, TX, USA
| | - Christie Ballantyne
- Section of Cardiology, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Erin D Michos
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Arunima Misra
- Section of Cardiology, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
- Section of Cardiology, Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA
| | - Anum Saeed
- Heart and Vascular Institute, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Xiaoming Jia
- Section of Cardiology, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
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2
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Vernon ST, Brentnall S, Currie DJ, Peng C, Gray MP, Botta G, Mujwara D, Nicholls SJ, Grieve SM, Redfern J, Chow C, Levesque JF, Meikle PJ, Jennings G, Ademi Z, Wilson A, Figtree GA. Health economic analysis of polygenic risk score use in primary prevention of coronary artery disease - A system dynamics model. Am J Prev Cardiol 2024; 18:100672. [PMID: 38828126 PMCID: PMC11143886 DOI: 10.1016/j.ajpc.2024.100672] [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: 02/13/2024] [Revised: 04/02/2024] [Accepted: 04/14/2024] [Indexed: 06/05/2024] Open
Abstract
Background Primary prevention programs utilising traditional risk scores fail to identify all individuals who suffer acute cardiovascular events. We aimed to model the impact and cost effectiveness of incorporating a Polygenic risk scores (PRS) into the cardiovascular disease CVD primary prevention program in Australia, using a whole-of-system model. Methods System dynamics models, encompassing acute and chronic CVD care in the Australian healthcare setting, assessing the cost-effectiveness of incorporating a CAD-PRS in the primary prevention setting. The time horizon was 10-years. Results Pragmatically incorporating a CAD-PRS in the Australian primary prevention setting in middle-aged individuals already attending a Heart Health Check (HHC) who are determined to be at low or moderate risk based on the 5-year Framingham risk score (FRS), with conservative assumptions regarding uptake of PRS, could have prevented 2, 052 deaths over 10-years, and resulted in 24, 085 QALYs gained at a cost of $19, 945 per QALY with a net benefit of $724 million. If all Australians overs the age of 35 years old had their FRS and PRS performed, and acted upon, 12, 374 deaths and 60, 284 acute coronary events would be prevented, with 183, 682 QALYs gained at a cost of $18, 531 per QALY, with a net benefit of $5, 780 million. Conclusions Incorporating a CAD-PRS in a contemporary primary prevention setting in Australia would result in substantial health and societal benefits and is cost-effective. The broader the uptake of CAD-PRS in the primary prevention setting in middle-aged Australians, the greater the impact and the more cost-effective the strategy.
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Affiliation(s)
- Stephen T. Vernon
- Cardiovascular Discovery Group, Kolling Institute of Medical Research, University of Sydney, Australia
- Department of Cardiology, Royal North Shore Hospital, Australia
- Northern Clinical School, Faculty of Medicine and Health, University of Sydney, Australia
| | | | | | - Cindy Peng
- Decision Analytics, The SAX Institute, Sydney, Australia
| | - Michael P. Gray
- Cardiovascular Discovery Group, Kolling Institute of Medical Research, University of Sydney, Australia
- Northern Clinical School, Faculty of Medicine and Health, University of Sydney, Australia
| | | | | | - Stephen J. Nicholls
- Monash Cardiovascular Research Centre, Monash University, Melbourne, Victoria, Australia
| | - Stuart M. Grieve
- Imaging and Phenotyping Laboratory, Charles Perkins Centre, University of Sydney, Sydney, NSW, Australia
- Sydney Medical School and School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Julie Redfern
- School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
| | - Clara Chow
- Westmead Applied Research Centre (C.K.C.), University of Sydney, Australia
| | - Jean-Frederic Levesque
- NSW Health, Sydney, NSW, Australia
- Centre for Primary Health Care and Equity, University of New South Wales, Sydney, NSW, Australia
| | - Peter J. Meikle
- Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- Monash University, Melbourne, VIC, 3800, Australia
| | | | - Zanfina Ademi
- Centre for Medicine Use and Safety, Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Melbourne, Australia
- School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia
| | - Andrew Wilson
- Menzies Centre for Health Policy and Economics, Faculty of Medicine and Health, School of Public Health, The University of Sydney, Sydney, Australia
| | - Gemma A. Figtree
- Cardiovascular Discovery Group, Kolling Institute of Medical Research, University of Sydney, Australia
- Department of Cardiology, Royal North Shore Hospital, Australia
- Northern Clinical School, Faculty of Medicine and Health, University of Sydney, Australia
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3
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Evans W, Meslin EM, Kai J, Qureshi N. Precision Medicine-Are We There Yet? A Narrative Review of Precision Medicine's Applicability in Primary Care. J Pers Med 2024; 14:418. [PMID: 38673045 PMCID: PMC11051552 DOI: 10.3390/jpm14040418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 03/27/2024] [Accepted: 04/06/2024] [Indexed: 04/28/2024] Open
Abstract
Precision medicine (PM), also termed stratified, individualised, targeted, or personalised medicine, embraces a rapidly expanding area of research, knowledge, and practice. It brings together two emerging health technologies to deliver better individualised care: the many "-omics" arising from increased capacity to understand the human genome and "big data" and data analytics, including artificial intelligence (AI). PM has the potential to transform an individual's health, moving from population-based disease prevention to more personalised management. There is however a tension between the two, with a real risk that this will exacerbate health inequalities and divert funds and attention from basic healthcare requirements leading to worse health outcomes for many. All areas of medicine should consider how this will affect their practice, with PM now strongly encouraged and supported by government initiatives and research funding. In this review, we discuss examples of PM in current practice and its emerging applications in primary care, such as clinical prediction tools that incorporate genomic markers and pharmacogenomic testing. We look towards potential future applications and consider some key questions for PM, including evidence of its real-world impact, its affordability, the risk of exacerbating health inequalities, and the computational and storage challenges of applying PM technologies at scale.
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Affiliation(s)
- William Evans
- Primary Care Stratified Medicine (PRISM), Division of Primary Care, University of Nottingham, Nottingham NG7 2RD, UK; (J.K.); (N.Q.)
| | - Eric M. Meslin
- PHG Foundation, Cambridge University, Cambridge CB1 8RN, UK;
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Joe Kai
- Primary Care Stratified Medicine (PRISM), Division of Primary Care, University of Nottingham, Nottingham NG7 2RD, UK; (J.K.); (N.Q.)
| | - Nadeem Qureshi
- Primary Care Stratified Medicine (PRISM), Division of Primary Care, University of Nottingham, Nottingham NG7 2RD, UK; (J.K.); (N.Q.)
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4
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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.
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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
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5
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Bhalala OG, Watson R, Yassi N. Multi-Omic Blood Biomarkers as Dynamic Risk Predictors in Late-Onset Alzheimer's Disease. Int J Mol Sci 2024; 25:1231. [PMID: 38279230 PMCID: PMC10816901 DOI: 10.3390/ijms25021231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/17/2024] [Accepted: 01/18/2024] [Indexed: 01/28/2024] Open
Abstract
Late-onset Alzheimer's disease is the leading cause of dementia worldwide, accounting for a growing burden of morbidity and mortality. Diagnosing Alzheimer's disease before symptoms are established is clinically challenging, but would provide therapeutic windows for disease-modifying interventions. Blood biomarkers, including genetics, proteins and metabolites, are emerging as powerful predictors of Alzheimer's disease at various timepoints within the disease course, including at the preclinical stage. In this review, we discuss recent advances in such blood biomarkers for determining disease risk. We highlight how leveraging polygenic risk scores, based on genome-wide association studies, can help stratify individuals along their risk profile. We summarize studies analyzing protein biomarkers, as well as report on recent proteomic- and metabolomic-based prediction models. Finally, we discuss how a combination of multi-omic blood biomarkers can potentially be used in memory clinics for diagnosis and to assess the dynamic risk an individual has for developing Alzheimer's disease dementia.
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Affiliation(s)
- Oneil G. Bhalala
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville 3052, Australia; (R.W.); (N.Y.)
- Department of Neurology, Melbourne Brain Centre at The Royal Melbourne Hospital, University of Melbourne, Parkville 3050, Australia
| | - Rosie Watson
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville 3052, Australia; (R.W.); (N.Y.)
- Department of Medicine, The Royal Melbourne Hospital, University of Melbourne, Parkville 3050, Australia
| | - Nawaf Yassi
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville 3052, Australia; (R.W.); (N.Y.)
- Department of Neurology, Melbourne Brain Centre at The Royal Melbourne Hospital, University of Melbourne, Parkville 3050, Australia
- Department of Medicine, The Royal Melbourne Hospital, University of Melbourne, Parkville 3050, Australia
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6
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Busby GB, Kulm S, Bolli A, Kintzle J, Domenico PD, Bottà G. Ancestry-specific polygenic risk scores are risk enhancers for clinical cardiovascular disease assessments. Nat Commun 2023; 14:7105. [PMID: 37925478 PMCID: PMC10625612 DOI: 10.1038/s41467-023-42897-w] [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: 04/26/2023] [Accepted: 10/25/2023] [Indexed: 11/06/2023] Open
Abstract
Clinical implementation of new prediction models requires evaluation of their utility in a broad range of intended use populations. Here we develop and validate ancestry-specific Polygenic Risk Scores (PRSs) for Coronary Artery Disease (CAD) using 29,389 individuals from diverse cohorts and genetic ancestry groups. The CAD PRSs outperform published scores with an average Odds Ratio per Standard Deviation of 1.57 (SD = 0.14) and identify between 12% and 24% of individuals with high genetic risk. Using this risk factor to reclassify borderline or intermediate 10 year Atherosclerotic Cardiovascular Disease (ASCVD) risk improves assessments for both CAD (Net Reclassification Improvement (NRI) = 13.14% (95% CI 9.23-17.06%)) and ASCVD (NRI = 10.70 (95% CI 7.35-14.05)) in an independent cohort of 9,691 individuals. Our analyses demonstrate that using PRSs as Risk Enhancers improves ASCVD risk assessments outlining an approach for guiding ASCVD prevention with genetic information.
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Affiliation(s)
| | - Scott Kulm
- Allelica Inc, 447 Broadway, New York, NY, 10013, USA
| | | | - Jen Kintzle
- Allelica Inc, 447 Broadway, New York, NY, 10013, USA
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7
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Misra S, Aguilar-Salinas CA, Chikowore T, Konradsen F, Ma RCW, Mbau L, Mohan V, Morton RW, Nyirenda MJ, Tapela N, Franks PW. The case for precision medicine in the prevention, diagnosis, and treatment of cardiometabolic diseases in low-income and middle-income countries. Lancet Diabetes Endocrinol 2023; 11:836-847. [PMID: 37804857 DOI: 10.1016/s2213-8587(23)00164-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 05/08/2023] [Accepted: 06/01/2023] [Indexed: 10/09/2023]
Abstract
Cardiometabolic diseases are the leading preventable causes of death in most geographies. The causes, clinical presentations, and pathogenesis of cardiometabolic diseases vary greatly worldwide, as do the resources and strategies needed to prevent and treat them. Therefore, there is no single solution and health care should be optimised, if not to the individual (ie, personalised health care), then at least to population subgroups (ie, precision medicine). This optimisation should involve tailoring health care to individual disease characteristics according to ethnicity, biology, behaviour, environment, and subjective person-level characteristics. The capacity and availability of local resources and infrastructures should also be considered. Evidence needed for equitable precision medicine cannot be generated without adequate data from all target populations, and the idea that research done in high-income countries will transfer adequately to low-income and middle-income countries (LMICs) is problematic, as many migration studies and transethnic comparisons have shown. However, most data for precision medicine research are derived from people of European ancestry living in high-income countries. In this Series paper, we discuss the case for precision medicine for cardiometabolic diseases in LMICs, the barriers and enablers, and key considerations for implementation. We focus on three propositions: first, failure to explore and implement precision medicine for cardiometabolic disease in LMICs will enhance global health disparities. Second, some LMICs might already be placed to implement cardiometabolic precision medicine under appropriate circumstances, owing to progress made in treating infectious diseases. Third, improvements in population health from precision medicine are most probably asymptotic; the greatest gains are more likely to be obtained in countries where health-care systems are less developed. We outline key recommendations for implementation of precision medicine approaches in LMICs.
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Affiliation(s)
- Shivani Misra
- Division of Metabolism, Digestion and Reproduction, Imperial College London, London, UK; Department of Diabetes and Endocrinology, St Mary's Hospital, Imperial College Healthcare NHS Trust, London, UK
| | - Carlos A Aguilar-Salinas
- Dirección de Nutricion, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico; Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, México
| | - Tinashe Chikowore
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; MRC/Wits Developmental Pathways for Health Research Unit, Department of Paediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Flemming Konradsen
- Novo Nordisk Foundation, Copenhagen, Denmark; Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Ronald C W Ma
- Department of Medicine and Therapeutics, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Li Ka Shing Institute of Health Sciences, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China; Chinese University of Hong Kong-Shanghai Jiao Tong University Joint Research Centre in Diabetes Genomics and Precision Medicine, Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong Special Administrative Region, China
| | | | - Viswanathan Mohan
- Madras Diabetes Research Foundation, ICMR Centre for Advanced Research in Diabetes, Chennai, India; Dr Mohan's Diabetes Specialties Centre, IDF Centre of Excellence in Diabetes Care, Chennai, India
| | | | - Moffat J Nyirenda
- MRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Uganda; London School of Hygiene and Tropical Medicine, London, UK
| | - Neo Tapela
- Botswana Harvard AIDS Institute Partnership, Gaborone, Botswana; International Consortium for Health Outcomes Measurement, Oxford, UK
| | - Paul W Franks
- Novo Nordisk Foundation, Copenhagen, Denmark; Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmö, Sweden; Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK; Harvard T H Chan School of Public Health, Boston, MA, USA.
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8
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Vassy JL, Kerman BJ, Harris EJ, Lemke AA, Clayman ML, Antwi AA, MacIsaac K, Yi T, Brunette CA. Perceived benefits and barriers to implementing precision preventive care: Results of a national physician survey. Eur J Hum Genet 2023; 31:1309-1316. [PMID: 36807341 PMCID: PMC10620193 DOI: 10.1038/s41431-023-01318-8] [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: 11/19/2022] [Revised: 01/25/2023] [Accepted: 02/06/2023] [Indexed: 02/22/2023] Open
Abstract
Polygenic risk scores (PRS) may improve risk-stratification in preventive care. Their clinical implementation will depend on primary care physicians' (PCPs) uptake. We surveyed PCPs in a national physician database about the perceived clinical utility, benefits, and barriers to the use of PRS in preventive care. Among 367 respondents (participation rate 96.3%), mean (SD) age was 54.9 (12.9) years, 137 (37.3%) were female, and mean (SD) time since medical school graduation was 27.2 (13.3) years. Respondents reported greater perceived utility for more clinical action (e.g., earlier or more intensive screening, preventive medications, or lifestyle modification) for patients with high-risk PRS than for delayed or discontinued prevention actions for low-risk patients (p < 0.001). Respondents most often chose out-of-pocket costs (48%), lack of clinical guidelines (24%), and insurance discrimination concerns (22%) as extreme barriers. Latent class analysis identified 3 subclasses of respondents. Skeptics (n = 83, 22.6%) endorsed less agreement with individual clinical utilities, saw patient anxiety and insurance discrimination as significant barriers, and agreed less often that PRS could help patients make better health decisions. Learners (n = 134, 36.5%) and enthusiasts (n = 150, 40.9%) expressed similar levels of agreement that PRS had utility for preventive actions and that PRS could be useful for patient decision-making. Compared with enthusiasts, however, learners perceived greater barriers to the clinical use of PRS. Overall results suggest that PCPs generally endorse using PRS to guide medical decision-making about preventive care, and barriers identified suggest interventions to address their needs and concerns.
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Affiliation(s)
- Jason L Vassy
- Harvard Medical School, Boston, MA, USA.
- Veterans Affairs Boston Healthcare System, Boston, MA, USA.
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
- Precision Population Health, Ariadne Labs, Boston, MA, USA.
| | - Benjamin J Kerman
- Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Elizabeth J Harris
- Harvard Medical School, Boston, MA, USA
- Veterans Affairs Boston Healthcare System, Boston, MA, USA
| | - Amy A Lemke
- Norton Children's Research Institute, Affiliated with the University of Louisville School of Medicine, Louisville, KY, USA
| | - Marla L Clayman
- UMass Chan Medical School, Department of Population and Quantitative Health Sciences, Worcester, MA, USA
- Edith Nourse Rogers Memorial Veterans' Hospital, Bedford, MA, USA
| | - Ashley A Antwi
- Veterans Affairs Boston Healthcare System, Boston, MA, USA
| | - Katharine MacIsaac
- Harvard Medical School, Boston, MA, USA
- Veterans Affairs Boston Healthcare System, Boston, MA, USA
| | - Thomas Yi
- Veterans Affairs Boston Healthcare System, Boston, MA, USA
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Lee J, Jukarainen S, Karvanen A, Dixon P, Davies NM, Smith GD, Natarajan P, Ganna A. Quantifying the causal impact of biological risk factors on healthcare costs. Nat Commun 2023; 14:5672. [PMID: 37704630 PMCID: PMC10499912 DOI: 10.1038/s41467-023-41394-4] [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: 11/11/2022] [Accepted: 08/29/2023] [Indexed: 09/15/2023] Open
Abstract
Understanding the causal impact that clinical risk factors have on healthcare-related costs is critical to evaluate healthcare interventions. Here, we used a genetically-informed design, Mendelian Randomization (MR), to infer the causal impact of 15 risk factors on annual total healthcare costs. We calculated healthcare costs for 373,160 participants from the FinnGen Study and replicated our results in 323,774 individuals from the United Kingdom and Netherlands. Robust causal effects were observed for waist circumference (WC), adult body mass index, and systolic blood pressure, in which a standard deviation increase corresponded to 22.78% [95% CI: 18.75-26.95], 13.64% [10.26-17.12], and 13.08% [8.84-17.48] increased healthcare costs, respectively. A lack of causal effects was observed for certain clinically relevant biomarkers, such as albumin, C-reactive protein, and vitamin D. Our results indicated that increased WC is a major contributor to annual total healthcare costs and more attention may be given to WC screening, surveillance, and mitigation.
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Affiliation(s)
- Jiwoo Lee
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Sakari Jukarainen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Antti Karvanen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Padraig Dixon
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Neil M Davies
- Division of Psychiatry, University College London, Maple House, 149 Tottenham Court Rd, London, W1T 7NF, UK
- Department of Statistical Sciences, University College London, London, WC1E 6BT, UK
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, University of Bristol, BS8 2BN, Bristol, UK
| | - Pradeep Natarajan
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Andrea Ganna
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
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10
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Sud A, Horton RH, Hingorani AD, Tzoulaki I, Turnbull C, Houlston RS, Lucassen A. Realistic expectations are key to realising the benefits of polygenic scores. BMJ 2023; 380:e073149. [PMID: 36854461 PMCID: PMC9973128 DOI: 10.1136/bmj-2022-073149] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/02/2023]
Affiliation(s)
- Amit Sud
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
- Haemato-oncology Unit, Royal Marsden Hospital NHS Foundation Trust, Sutton, UK
| | - Rachel H Horton
- Wessex Clinical Genetics Service, Princess Anne Hospital, Southampton, UK
- Clinical Ethics, Law, and Society Group, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Centre for Personalised Medicine, University of Oxford, Oxford, UK
| | - Aroon D Hingorani
- Institute of Cardiovascular Science, Faculty of Population Health, University College London, UK
- University College London British Heart Foundation Research Accelerator Centre, London, UK
- Health Data Research UK, London, UK
- University College London, National Institute of Health Research Biomedical Research Centre, London, UK
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, UK
- National Institute for Health Research, Imperial Biomedical Research Centre, Imperial College London, UK
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece
| | - Clare Turnbull
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
- Cancer Genetics Unit, Royal Marsden NHS Foundation Trust, London, UK
| | - Richard S Houlston
- Division of Genetics and Epidemiology, Institute of Cancer Research, London, UK
| | - Anneke Lucassen
- Clinical Ethics, Law, and Society Group, Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Centre for Personalised Medicine, University of Oxford, Oxford, UK
- Data Health and Society, NIHR Biomedical Research Centre, Southampton, UK
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